A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

This paper introduces a new destriping algorithm for remote sensing data. The method is based on combined Haar Stationary Wavelet transform and Fourier filtering. State-of-the-Art methods based on the discrete wavelet transform (DWT) may not always be effective and may cause different artifacts. Our contribution is three-fold: i) we propose to use the Undecimated Wavelet transform (UWT) to avoid as much as possible shortcomings of the classical DWT; ii) we combine a spectral filtering and UWT using the simplest possible wavelet, the Haar basis, for a computational efficiency; iii) we handle 2D fields with missing data, as commonly observed in ocean remote sensing data due to atmospheric conditions (e.g., cloud contamination). The performances of the proposed filter are tested and validated on the suppression of horizontal strip artifacts in cloudy L2 Sea Surface Temperature (SST) and ocean color snapshots.

This paper presents a theory on graph topological structure and graph coloring, proving that for any N-order graph structure (with a topological structure similar to ), the maximum number of colors required for coloring is less than or equal to . The Four Color Theorem is just one special case of this theory, with the maximum structure size for a four-color graph being a 4-order structure graph, hence requiring only a maximum of 4 colors for coloring.

The tumor microenvironment is known to have increased levels of cytokines and metabolites, such as glutamate, due to their release from the surrounding cells. A normal cell around the tumor that responds to the inflammatory environment is likely to be subsequently altered. We will discuss how these abnormalities will support tumor survival via the actions of gap junctions (GJs) and hemichannels (HCs) which are composed of hexamer of connexin 43 (Cx43) protein. In particular, we will discuss how GJ intercellular communication (GJIC) in glioma cells, the primary brain tumor, is a regulatory factor and its attenuation leads to tumor invasion. In contrast, the astrocytes, which are normal cells around the glioma, are "hijacked" by tumor cells, either by receiving the transmission of malignant substances from the cancer cells via GJIC, or perhaps via astrocytic HC activity through the paracrine signaling which enable the delivery of these substances to the distal astrocytes. This astrocytic signaling would promote tumor expansion in the brain. In addition, brain metastasis from peripheral tissues has also been known to be facilitated by GJs formed between cerebral vascular endothelial cells and cancer cells. Astrocytes and microglia are generally thought to eliminate cancer cells at the blood brain barrier. In contrast, some reports suggest they facilitate tumor progression as tumor cells take advantage of the normal functions of astrocytes that support the survival of the neurons by exchanging nutrients and metabolites. In summary, GJIC is essential for the normal physiological function of growth and allowing the diffusion of physiological substances. Therefore, whether GJIC is cancer promoting or suppressing may be dependent by what permeates through GJs, when it is active, and to which cells. The nature of GJs, which has been ambiguous in brain tumor progression, will need to be re-visited and understood together with new findings on Cx proteins and HC activities.

The reasons for the circular sense of human color perception generated by two sorts of color opponent neurons and three cone types are not well understood. Here we use geometrical analysis to examine the hypothesis that opponency, the recursive nature of color perception, and trichromacy arise as the most efficient ways of distinguishing spectrally different points on a plane using a minimum of color classes and receptor types.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

In the area of dental healthcare services, where patients and professionals deal with multiple auditory stimuli, this cross-sectional study reports on the intricate dynamics of sound preferences, noise obstacles, and the profound impact of music on anxiety reduction within dental office set-tings. The sample comprises of 134 dental patients, primarily females (56.7%), with varying age groups, education levels, and therapy durations in the university clinics of the department of Dentistry, National and Kapodistrian University of Athens and four private dental offices at the metropolitan area of Athens, Greece. Notably, 92.5% of the participants report no hearing loss, and 56.7% exhibit some level of noise sensitivity. The study reveals a moderate to good perceived health status (M=3.84, SD=0.95) among participants, with low perceived noise disturbance in university dental clinics. Preferences for instrumental music, foreign pop, and classical music are prominent during waiting times. Potential irritations include impersonal treatment (44%) and staff behavior (41.8%). Music player usage within the settings of the dental clinic involves radios (49.3%) and headphones (41.0%). Correlations and predictors indicate associations between health status, music preferences, and noise disturbances. More specifically, visiting the university dental clinics (β = .300, p = .005) as well as younger age (β = .300, p = .034) were significant predictors of better perceived health status. Clearer perception of external sounds while using the music player (β = .190, p = .039) was a significant predictor of better perceived health status, while poorer health was predicted by the preference of Greek folk songs (β = -.200, p = .030). Also, the use of smartphones for playing music was related to poorer health (β = -.370, p < .001). Following, younger age (β = -.270, p = .015), lower education (β = -.210, p = .035), higher noise sensitivity (β = .190, p = .027), as well as feelings of anxiety and nervousness due to ambient noise from people and machines (β = .510, p = .027) were significant predictors of noise disturbance from machines. Also, visiting private clinics (β = .240, p = .018), preferences of specific playlist (β = .310, p = .012), no music (β = .280, p = .016) and use of smartphones to play music (β = .230, p = .029) were significant predictors of noise disturbance from patients or staff. Finally, the preference of listening to a specific playlist was related to better health status (r = .190, p < .05). Findings aim to optimize eco-soundscapes in dental offices, con-tributing to enhanced patient well-being. The research underscores the potential of music inter-ventions to alleviate dental anxiety and improve the overall dental experience and quality care.

Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, the analysis covers the theoretical and practical aspects of sound attenuation in a turboengine testing stand. This paper presents a preliminary analysis and also an evaluation of a procedure for projecting a noise attenuator for industrial application and especially for turboengine test stands. While primarily focusing on the static acoustic behavior of the attenuator, considerations were also made regarding flow dynamics, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective in selecting the optimal design configuration. The study investigates different calculation methods for assessment of the noise reduction for linear and staggered baffles, applied on a scaled reduced model of attenuator. Thus, the critical parameters and projecting requirements necessary for effective noise reduction in high-performance turboengine testing environments will be evaluated in a down-scaled model. Key factors examined include the selection of design parameters and configurations from various options. Advanced computational methods, like analytic and finite element analysis (FEM) are utilized to predict the acoustic performance and identify potential design optimizations. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The findings indicate that a well-designed sound attenuator module can significantly reduce noise levels without compromising the operational performance of the turboengine inside a test cell.

In this paper, the mechanism of combined process of ion beam sputtering (IBS) and HF acid etching on the chemical structure defects of fused silica and its laser damage resistance perfor-mance were investigated. During the removal process of surface material, the sputtering effect causes lattice atoms to detach from their original positions, forming a large number of unsaturated structures on silica surface, which improves the chemical activity of Si and O atoms, accelerates the chemical reaction process between surface atoms and water molecules, increases the content of hydroxyl (OH-) groups in the shallow layer, and enhances the photothermal weak absorption intensity. However, the presence of OH- groups reduces the bonding strength of Si-O, destroys the spatial network structure of silica bulk, and reduces the mechanical strength of silica surface, resulting in a decrease in its laser damage resistance performance. The paper reveals for the first time the mechanism of IBS to change the structure characteristics of silica material, accelerate the surface hydroxylation process, and thereby reduces the laser damage resistance performance. This work provides technical guidance for effectively suppressing chemical structure defects on silica surface and improving laser damage resistance performance of optical components under high-flux laser irradiation.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

This study measures the noise levels in a baseball stadium and analyzes baseball fans’ attitude of effect of recreational noise exposure on their hearing. In the baseball stadium, noise levels were measured in four seating sections using a sound level meter during the games. The LAeq average of the 16 measures produced 91.7 dBA, showing a significantly high noise level in the red and navy sections. As a function of frequency by LZeq analysis, the noise levels were significantly higher in low frequencies than other frequencies. For the survey sample, 688 randomly selected participants completed a 16-question survey on their noise exposure during the game and on the potential risk of hearing loss. Despite the very high noise levels, 70% of the respondents preferred sitting in either the red or the navy section to be closer to the cheerleaders and to obtain a good view. Most respondents reported that they did not consider wearing earplugs, and one-third experienced hearing muffled speech after the game. We conclude that the noise levels in baseball stadiums are high enough to cause hearing damage and/or tinnitus later, but expect these results to improve public education regarding safe noise exposure during popular sports activities.

High level of noise is a disturbance to the human environment. Noise in industries is also an occupational hazard because of its attendant effects on workers’ health. Noise presents health and social problems in industrial operations, and the source is related to the machineries used in the industries. One of the unique features of the noise associated with wood machinery is the level of exposure and duration. Equipment used in a factory can be extremely loud. They can produce noise at decibels high enough to cause environmental health and safety concerns. The mechanically driven transport and handling equipment, cutting, milling, shaping and dust extractor installations in the wood industry generate noise. The sources of noise pollution have increased due to non-compliance with basic safety practices. The increased use of locally fabricated machine in the industry has increased the level of noise and vibration. The effects of industrial noise pollution as discussed include: increase in blood pressure; increased stress; fatigue; vertigo; headaches; sleep disturbance; annoyance; speech problems; dysgraphia, which means reading/learning impairment; aggression; anxiety and withdrawal. As presented in this paper, noise control techniques include; sound insulation, sound absorption, vibration damping and Vibration isolation.

Background/Objectives: It is known that balance disorders involve occupational hazards. However, the inverse relationship (between certain occupations and an increased incidence of vertigo or dizziness) has been scarcely studied. The objective of this work was to analyze the occupation of a group of patients with vertigo, compared to economically active general population, and to evaluate the prevalence of occupational noise and/or vibration-exposure in both groups. Methods: A cross-sectional, observational, case-control study was carried out including 461 patients (220: Meniere’s disease; 73: vestibular migraine; 28: vestibular neuritis; 140: BPPV) (310 women and 151 men). These patients were compared to a control group from the general population, obtained from 6th EWCS-Spain (2015). Possible differences regarding sex, age, occupation, exposure to noise, and exposure to mechanical vibrations were analyzed. Results: Differences in distribution of occupations between patients with vertigo and general population were observed (Chi-square, p=6.142 e-30). Patients with vertigo were significantly more exposed to noise (p=8.19 e-12; OR=2,127, CI95% (1.720;2.630)) and vibrations (p=1.11 e-12; OR=2.288, CI95% (1.835;2.853)) than the control group. These differences were observed both between men and women. Conclusions: A relationship between occupational exposure to noise and/or vibrations and the presence of vertigo was observed. Protective and preventive measures could help prevent the occurrence of some diseases involving vertigo.

The present paper is aimed at presenting an updated review of prediction methods for the aerodynamic noise of ducted rotor-stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and increasingly used, for both aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced, from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered as the best suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons.

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

A novel five-surface phosphor-in-glass (FS-PiG) for high illumination and excellent color uniformity in larger-scale LEDs for sensor light source application is demonstrated. YAG phosphor (Y3Al5O12:Ce3+) was uniformly mixed with ceramic and sintered at 680°C to form a phosphor wafer. A sophisticated laser engraving was employed on the phosphor wafer to form a saddle-shaped large-scale FS-PiG LEDs. The performance of the FS-PiG LEDs exhibited the illumination of 401lm, average color temperature (CCT) of 5488K±110K, and color coordinates (CIE) of (0.3179±0.003, 0.3352±0.003). In contrast to convention single-surface phosphor-in-glass (SS-PiG) LEDs, the performance exhibited the illumination of 380lm, average CCT of 5830K±758K, and CIE of (0.3083±0.07, 0.3172±0.07). These indicated that the performance of the FS-PiG LEDs was higher than the SS-PiG LEDs for illumination, CCT, and CIE of 1.7, 7, and 23 times, respectively. Furthermore, the FS-PiG LEDs demonstrate lower lumen loss of 2% and less chromaticity shift of 5.4x10-3 under accelerated aging at 350°C for 1008 hours, owing to the high ceramic melting temperature of up to 510°C. In this study, the proposed FS-PiG larger-scale LEDs having excellent optical performance and high reliability may be the promising candidates to replace the convention phosphor-in-organic silicone material used in high-power LEDs for the next generation of sensor light sources, display, and headlight applications.

Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a to a mix between noise-induced auditory lesions and energetic masking of speech signal. For years, the monitoring of conventional audiometric thresholds has been the usual way to check and preserve the auditory function. But the highlighting of supra-threshold deficits, notably speech in noise understanding difficulties, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise under-standing in order to suggest a new way of regular monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modula-tion detection thresholds, conventional and extended high frequency audiometric thresholds) variables were collected in a population of individuals presenting a relative homogeneous occu-pational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict scores of three different speech in noise tests and a self-report of speech in noise ability. The extended high frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise exposed professionals.

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of CMYK and RGB color models are applied. The interrelationships of the two colors models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change of color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. The research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

This paper presents an Edge-based color image segmentation derived from the method of Particle Motion in a Vector Image Fields (PMVIF) that could previously be applied only to monochrome images. Instead of using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields, directly computed from a color image, one parallel and another orthogonal to the edges, were used in the model to force a particle to move along the object edges. The normal compressive vector field is derived from the center-to-centroid vectors of local color distance images. Next, the edge vector field is derived by taking the normal compressive vector field, multiplied by differences of auxiliary image pixels to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012) and the Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison with those of the traditional PMVIF, Watershed, SLIC, K-means, Mean shift, and JSEG. The proposed method yields better RI, GCE, NVI, BDE, Dice coefficients, faster computation time, and noise resistance.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

Highly-doped graphene samples show the conductance reduced and the shot-noise power enhanced compared to standard ballistic systems in two-dimensional electron gas. These features can be understood within a model assuming incoherent scattering of Dirac electrons between two interfaces separating the sample and the leads. Here we find, by adopting the above-mentioned model for the edge-free (Corbino) geometry and by means of the computer simulation of quantum transport, that another graphene-specific feature should be observable when the current flow through a doped disk is blocked by high magnetic field. In case the conductance drops to zero, the Fano factor approaches the value of $F\approx{}0.56$, with a very weak dependence on the disk radii ratio. The role of finite source-drain voltages and the system behavior upon tuning the electrostatic potential barrier from a rectangular to parabolic shape are also discussed.

In recent years, there has been an increasing interest in adopting novel sensing technologies for the continuous monitoring of structural systems. In this respect, Micro-Electro-Mechanical System (MEMS) sensors are widely used in several applications, including Structural Health Monitoring (SHM). Thanks to their significantly lower cost, ease of installation on the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analysis show that MEMS can correctly detect modal frequencies, useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

A novel five-surface phosphor-in-ceramic (FS-PiC) for high illumination and excellent color uniformity in larger-scale LEDs for sensor light source application is demonstrated. YAG phosphor (Y3Al5O12:Ce3+) was uniformly mixed with ceramic and sintered at 680°C to form a phosphor wafer. A sophisticated laser engraving was employed on the phosphor wafer to form a saddle-shaped large-scale FS-PiC LEDs. The performance of the FS-PiC LEDs exhibited the illumination of 401lm, average color temperature (CCT) of 5488K±110K, and color coordinates (CIE) of (0.3179±0.003, 0.3352±0.003). In contrast to convention single-surface phosphor-in-ceramic (SS-PiC) LEDs, the performance exhibited the illumination of 380lm, average CCT of 5830K±758K, and CIE of (0.3083±0.07, 0.3172±0.07). These indicated that the performance of the FS-PiC LEDs was higher than the SS-PiC LEDs for illumination, CCT, and CIE of 1.1, 7, and 23 times, respectively. Furthermore, the FS-PiC LEDs demonstrate lower lumen loss of 2% and less chromaticity shift of 5.4x10-3 under accelerated aging at 350°C for 1008 hours, owing to the high ceramic melting temperature of up to 510°C. In this study, the proposed FS-PiC larger-scale LEDs having excellent optical performance and high reliability may be the promising candidates to replace the convention phosphor-in-organic silicone material used in high-power LEDs for the next generation of sensor light sources, display, and headlight applications.

This study aimed to evaluate the discrepancies between genetic and visual coat color assignment in the Sarcidano Horse and to elucidate potential reasons. Individual DNA from 90 Sarcidano Horses was used for genetic assignment of coat color to explore the correspondence with individual forms containing phenotypical traits. The MC1R exon 1 and ASIP exon 3 have been genotyped and sequenced to obtain a picture of the coat color distribution in this breed. Surprisingly, once we compared the genetic results with the individual forms reporting the phenotypic data for each subject, a certain degree of non-correspondence between the phenotypic and genetic data in relation to coat color emerged. From the genetic analysis, Chestnuts (n = 58) resulted the most common Sarcidano Horse (n = 58), followed by a quite large number of Blacks (n = 28) and a very small number of Bays (n = 4), whereas phenotypic distribution resulted in 38 Chestnuts, 40 Bays, only 2 Blacks, and 10 Grays (without the possibility of recognizing the true color they carried). Chestnut resulted a very representative coat color, while many horses that visually identified as Bays were genetically Blacks. This discrepancy, that could be due to a variety of individual and external factors, including age, time of year, living situation and dietary condition, suggesting the importance of accurate coat color identification to ensure adequate features registration and reliable prediction of offspring’s coat color.

Color plays an important part in human activities, affects circadian cycle and decision making process. Therefore, it is important to investigate human judgment under different color of illumination, especially, because color perception is subjective. In this study, we developed required instrumentation to control the red (R), green (G), Blue (B) and Amber (A) colored light emitting diode (LED) lamp for carrying out Magneto Encephalography (MEG) brain scan. We developed a software to generate all the colors in the visual spectrum, predefined white light combinations and saturation of an illuminated objects by using RGBA color pallet. The lamp is required to control from outside of the MEG electromagnetically shielded room with the LED lamp located inside the MEG room. Hence, USB is used for the communication mode. The feasibility of using LED lamp with MEG brain scanner has been validated for bio-medical and psychological MEG experiments.

Vertebrate color vision is evolutionarily ancient. Jawless fish evolved four main spectral types of cone photoreceptor, almost certainly complemented by retinal circuits to process chromatic opponent signals. Subsequent evolution of photoreceptors and visual pigments are now documented for many vertebrate lineages and species, giving insight into evolutionary variation and ecological adaptation of color vision. We look at organization of the photoreceptor mosaic and the functions different types of cone in teleost fish, primates, and birds and reptiles. By comparison less is known about the underlying neural processing. Here we outline the diversity of vertebrate color vision and summarize our understanding of how spectral information picked up by animal photoreceptor arrays is adapted to natural signals. We then turn to the question of how spectral information is processed in the retina. Here, the quite well known and comparatively ‘simple’ system of mammals such as mice and primates reveals some evolutionarily conserved features such as the mammalian Blue^ON system which compares short and long wavelength receptors signals. We then survey our current understanding of the more complex circuits of fish, amphibians, birds and reptiles. Together, these clades make up more than 90% of vertebrate species, yet we know disturbingly little about their neural circuits for colour vision beyond the photoreceptors. Here, long-standing work on goldfish, freshwater turtles and other species is being complemented by new insights gained from the experimentally amendable retina of zebrafish. From this body of work, one thing is clear: The retinal basis of colour vision in non-mammalian vertebrates is substantially richer compared to mammals: Diverse and complex spectral tunings are established at the level of the cone output via horizontal cell feedforward circuits. From here, zebrafish use cone-selective wiring in bipolar cells to set-up color opponent synaptic layers in the inner retina, which in turn lead a large diversity of color-opponent channels for transmission to the brain. However, while we are starting to build an understanding of the richness of spectral properties in some of these species’ retinal neurons, little is known about inner retinal connectivity and cell-type identify. To gain an understanding of their actual circuits, and thus to build a more generalised understanding of the vertebrate retinal basis of color vision, it will be paramount to expand ongoing efforts in deciphering the retinal circuits of non-mammalian models.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Noise reduction is the major issue in the loudspeaker for the application of the musical instruments and related areas. In this paper, a noise disturbance control of a loudspeaker with optimal and robust controllers has been done successfully. The noise of the loudspeaker has been analyzed by simply track a reference cone displacement with the actual cone displacement. Static output feedback and H infinity optimal loop shaping controllers have been used to compare the actual and reference cone displacements by using a sine wave and random cone displacement signals and a promising results have been analyzed.

The main sources of intrinsic noise in excitable cells at the microcircuit and network levels are the stochastic characteristics of ion channel gating and activation of the synaptic conductance. Studies using in vivo, in vitro, and in silico methods to examine the effects of synaptic background activity were not adequately investigated in non-neuronal excitatory cells, where neurotransmitter-based innervation also occurs. We created a mathematical model to replicate the background synaptic noise dynamics in a non-neuronal cell. We utilized the stochastic Ornstein-Uhlenbeck process to represent excitatory synaptic conductance, which was incorporated into a whole-cell model to produce spontaneous and evoked cellular electrical activities. The single-cell model includes many biophysically detailed ion channels represented by a set of ordinary differential equations in Hodgkin-Huxley and Markov formalisms. This paradigm effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potential (sAP) resembling in vitro-like electrical activity in the cells. The input resistance decreased by multiple factors, and the spontaneous action potential firing rate elevated. The potential to reach the action potential threshold is altered. Background synaptic activity can alter the input/output characteristics of non-neuronal excitatory cells. Suppressing these baseline activities would facilitate the discovery of new pharmaceutical targets for different clinical diseases.

In this study, the degradation characteristics of radio-frequency (RF) low-noise amplifier (LNA) based on silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) due to total ionizing dose (TID) are investigated. The small-signal equivalent model of a SiGe HBT is utilized to analyze circuit-level performance including the input and the output matching and noise figure (NF). As a target circuit, an LNA with a cascode common-emitter stage with emitter degeneration is studied and the equation of each performance parameter is derived. The RF LNA fabricated using commercial 350 nm SiGe technology was exposed to X-ray irradiation with the total dose up to 3 Mrad (SiO2). The experimental results exhibit that modeled device parameters estimate the degraded circuit performance. In addition, the relative impact of each parameter on the circuit metrics is revealed, which is expected from the derived design equations. The key device parameters for modeling TID-induced circuit degradations include the base resistance, the transconductance, and the base-to-emitter capacitance.

Introduction Color vision deficiency (CVD) constitutes one of the frequently observed eye disorders in all human populations. Color is a prominent sign utilized in the medical profession to study and identify histopathological specimens, lab instruments, and patient examination. Color deficiency affects the medical skills of students resulting in poor clinical examination and color appreciation. There is no effective screening of CVD at any level of the medical profession. Hence, this study was aimed to determine the prevalence of CVD among medical students. Materials and methods This was a cross-sectional study conducted from September 2019 to February 2020 over a period of six months in Karachi, Pakistan. All medical students aged 18-21 years of either gender enrolled in the first and second years of medical college were included in this study. The examination was performed during daylight. Ishihara plates were placed at a distance of 75 cm from the subject and tilted so that the plane of the paper lies perpendicular to the line of vision. Students were given five seconds to read the plate and one examiner was instructed to mark the checklist. A score of less than 12 out of 14 red/green test plates (not including the demonstration plate) was considered as a CVD. All statistical analysis was performed using Statistical Package for Social Sciences version 20.0 (Armonk, NY: IBM Corp). Results The mean age of the medical students was 19.61± 1.22 years. There were (n=123) 53.0% females and (n=111) 47.0% males. Most of the medical students (n=131, 56.0%) belonged to the upper-middle-class socioeconomic group. CVD was observed in (n=13) 6.0%of medical students. Age (p=0.001) and socioeconomic status (p=0.001) were the only demographic factors significantly associated with color deficiency. Conclusions Color deficiency, although an unnoticed concern, is fairly common among medical students. Medical students must be screened for CVD as this will enable them to be aware of their limitations in their future observational skills as a doctor and devise ways of overcoming them in clinical practice.

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.

Color change is the most obvious characteristic of tomato ripening stage and an important indicator of tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit in mature stage, a gated recurrent unit network with an encoder-decoder structure that dynamically simulates tomato growth and development with time-dependent lines using tomato color and shape as real-time data was proposed in this paper. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, and the tomato growth and development data set was successfully constructed. Then, a network of gated recurrent units with encoder-decoder structure was constructed to predict the future growth trend of tomato under different greenhouse temperatures. The experimental results showed that for the gated recurrent unit network of encoder-decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence would be realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93℃, the average soil temperature was 24.06℃, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, thus to understand the growth status of tomato in time. The statement provided a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity. And it offered recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.

We present a high-throughput nanoindentation study of in-situ bending effects on incipient plastic deformation behavior of polycrystalline and single-crystalline pure aluminum and pure copper at ultra-nano depths (<200nm). We find that hardness displays a statistically inverse dependence on in-plane stress for indentation depths smaller than 10nm, and the dependence disappears for larger indentation depths. In addition, plastic noise in the nanoindentation force and displacement displays statistically robust noise features, independently of applied stresses. Our experimental results suggest the existence of a regime in FCC crystals where ultra-nano hardness is sensitive to residual applied stresses, but plasticity pop-in noise is insensitive to it.

We derive the thermal noise spectrum of the Fourier transform of the electric field operator of a given wave vector starting from the quantum-statistical definitions and relate it to the complex frequency and wave vector dependent complex conductivity in a homogeneous, isotropic system of electromagnetic interacting electrons. We analyze separately the longitudinal and transverse case with their peculiarities. The Nyquist formula for vanishing frequency and wave vector, as well as its modification for non-vanishing frequencies and wave vectors follow immediately. Furthermore we discuss also the noise of the photon occupation numbers. It is important to stress that no additional assumptions at all were used in this straightforward proof.

Considerable amounts of agro-industrial by-products are discarded or burnt as fuel every year. However, they can be utilized as a valuable source of fiber, phenolics and other useful compounds such as antioxidants. The profile of beneficial compounds can be affected by genotype, locality, orchard management and productive season, adding or decreasing the bioactive potential of by-products. In this study, hazelnut (Corylus avellana L.) and walnut (Juglans regia L.) nutshells were investigated for their fiber content and antioxidant capacity as valorized by-products for the industry. Determinations (ORAC, TPC and color asses) were performed in hazelnut and walnut orchards located in Southern Chile during three consecutive seasons (2020/21; 2021/22 and 2022/23). Analyses of oxygen radical absorbance capacity (ORAC) in nutshells showed the highest ORAC from both species in the season 2020/21 (3217 µmol TE g1DW for hazelnut shells and 4663 µmol TE g DW1 for walnut shells), while variability in the consecutive seasons was lower for hazelnut than in walnut. Total phenolic content in hazelnut shells was positively correlated with L* (R: 0.883) and E (R: 0.924) during 2020/21 season, also with L* for 2022/23 (R: 0.907). While ORAC was negatively correlated with L* (R: 0.922) in 2021/22. In addition, morphological and structural features of both nutshells examined by Scavenging Electron Microscopy (VP-SEM) and Confocal Scavenging Laser Microscopy (CSLM) revealed differential tissue distribution and accumulation patterns of both cellulose and lignin. On the other hand, photo-colorimetric values were determined for both shells and corresponding seasons, and non-significative differences were found both shells and among seasons Finally, these results provide insight into the physicochemical characteristics of two shell types which should be considered together with antioxidant capacity in their valorization as a residual material.

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.

The correlations of seismic noise, recorded during 1997-2022 at 229 stations around the world are investigated. The property of waveforms, known as the Donoho-Johnston threshold, is analyzed, which separates the absolute values of the orthogonal wavelet coefficients into "small" and "large". Network of 50 reference points is considered. For each reference point, a time series with a time step of 1 day is calculated as the median of the values of the DJ index from the 5 nearest stations. For each reference point, the correlations between the values of the DJ index at other reference points are calculated in a sliding time window of 365 days. Average values of the DJ index decrease, which is interpreted as indicators of global seismic hazard increasing. The maximum distances between reference points with strong correlations show an explosive increase after 2011. Bursts in the mean coherences between the length of day (LOD) and the values of the DJ index at the reference points precede the release of seismic energy with a delay of about 530 days. High amplitude of the DJ index response to LOD for the times 2020-2022 was identified, which may forecast a strong earthquake in the second half of 2023.

This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups—0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R2 and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model’s polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R2 and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.

This document introduces a new cryptosystem mixing two improvement standards generally used for text encryption, in order to give birth a new color image encryption algorithm capable of dealing with known attacks. Firstly, two substitution matrixes attached to a strong replacement function will be generated for advanced Vigenere technique application. At the end of this first round, the output vector is subdivided into size blocks according to the used chaotic map, for acting a single enhanced Hill circuit insured by a large inversible matrix. A detailed description of such a large involutive matrix constructed using Kronecker products will be given. accompanied by a dynamic translation vector to eliminate any linearity. A solid chaining is established between the encrypted block and the next clear block to avoid any differential attack. Simulations carried out on a large volume of images of different sizes and formats ensure that our approach is not exposed to any known attacks.

In this paper, we found new classes of exact models to the Einstein-Maxwell system of equations which describe the internal structure of a compact star made of strange matter considering the equation of state proposed by Rocha, Bernardo, de Avellar and Horvath in 2019. It has been assumed that this matter is composed of equal number of up, down and strange quarks and a small amount of electrons required to reaching the charge neutrality. If this hypothesis is correct, the neutron stars could be strange stars or hybrid stars with a thin crust of nuclei where the temperatures and pressures are capable of converting hadronic matter into this new stable phase of quarks. We have chosen a particular form of gravitational potential Z(x) that depends on an adjustable parameter related to degree of anisotropy of the models and the new solutions can be written in terms of elementary and polynomial functions. The obtained models satisfy all physical features expected in a realistic star and the expressions for mass, density and stellar radius are comparable with the experimental results.

The limits for recording weak signals for electrochemical seismic sensors are determined by their self-noise. At the same time, the noise nature is complex and not fully understood. In this work, for the first time, in addition to the frequency dependences of the noise amplitude, the difference in the phases of the noise currents of the cathodes of an electrochemical sensor has been studied. It has been established that at low frequencies the cathode currents change in an-tiphase. As the frequency increases, the phase difference becomes random. At the same time, the frequency at which the phase difference changes depend on the interelectrode distance; the clos-er the electrodes are located to each other, the greater it is. Physically, this effect is explained by the transition from noise caused by the integral flow of the working fluid to noise associated with vortex flows, the size of which depends on frequency. Thus, a new way of studying noise makes it possible to identify its physical nature and develop new methods of reduction.

Analyzing the records of Advanced LIGO and Virgo gravitational observatories, we found a specific time-domain asymmetry inherent only to the signals of their gravitational detectors. Experiments with different periodic signals, Gaussian and non-Gaussian noises, made it possible to conclude that the noise of gravitational detectors is an unusual mixture of signals. We also developed a specialized Pearson correlation analyzer to recognize the gravitational-wave events. It turned out that the LIGO detectors’ output signals include a significant (– 6 dB) component, which has the properties of records of reliably recognized gravitational waves. It allows us to argue that the gravitational background noise is largely due to the processes of merging astronomical objects. Since the specific signal is registered by the detectors continuously, we can consider the sub-kilohertz band gravitational oscillations field as detected. Our analysis method also allows us to estimate the contribution of the gravitational background component to the total signal energy. With its help, it will be possible not only to provide the radio-frequency estimation of the magnitude of gravitational disturbances but also, possibly, to construct a map of the gravitational noise of the sky.

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Whiplash compensation is additionally implemented for comparison. The control method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

This study was undertaken to investigate and analyze noise pollution in a large Chinese governmental hospital’s medical intensive care unit and compare to the WHO guidelines.This cross-sectional study was conducted in a MICU at a public governmental teaching hospital in Fujian province between July and August of 2017. A WENSN® WS1361 Integrated Sound Level Meter (China) was used for continuous every five seconds one week noise levels recording. After this measurement, the decibel meter was used for recording different location of isolation rooms and open bays, including occupied and unoccupied patient, and recording sound events occurs in the ICU to identify sources of noise. Peak and average noise levels were obtained from the meter, and data were downloaded from the WS1361 into a laptop computer. The measured mean equivalent sound pressure levels (L) and standard Aeq deviation over one week period were 66.64±7.57 dB(A), with acute spikes reaching 119.7 dB(A), the average sound level for a 24 hour period in a work day was 68.03±5.07 dB(A). These are higher than the current daytime environmental noise limit of 40-45 decibels in China and WHO. Mean work day noise was significantly louder than weekend time, there was a significant difference in work days and weekend (t=16.85;P=0.000).There was a statistical difference between the day time and night time shifts (t=34.67;P=0.000). The isolation rooms were significantly quieter than the open-bay rooms(t=46.15; p=0.00), sound levels in the occupied and unoccupied rooms also had significant difference(t=17.26; P=0.000).Two types of noise resources, including twenty kinds sources were identified and measured, mean noise levels ranged from of 61.33 to 79.21 dB(A). This study shows noise levels in intensive care units were exceeded the recommended. The study of the influence of noise on patient and staff is needed, and noise reduction strategies must be conduct in ICU.

Currently, narrowband Power line communication (PLC) is considered as an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and \(\alpha\) stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by \(\alpha\) stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by MCA model.

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

Oriented strand board (OSB) panels are widely marketed for several applications, from building to packaging. The manufacturing of poplar OSB started recently in Northern Italy representing a relevant innovation in the sector since this product is usually made of coniferous wood. The thermal treatment is widely used for reducing the hygroscopicity of wood-based products, nonetheless it influences the mechanical behavior and degrades the main components of wood, which can affect their finishing. Therefore, it is important to know the properties of the treated surfaces. To this purpose, in this study a lot of OSB panels, made of poplar wood, 15 mm thick and with a density of 590 kg/m³, were thermally treated under vacuum conditions at 190 °C for 2 h and then subjected to accelerated weathering. The changes in color and in wettability due to treatment and accelerated weathering were studied. The thermal treatment determined a significant darkening of the color; the accelerated weathering darkened the untreated surfaces and, on the opposite, lightened the thermo-treated surfaces. The wettability decreased after thermal treatment and increased after weathering, more evidently in treated panels. Overall, this study improves the knowledge about the behavior of the surface of thermo-treated poplar OSB, which is relevant for the industrial coating of this product.

The need for miniaturized shear force sensors is expanding, particularly for biomedical applications. Examples include measuring interfacial shear stresses between a human and an external device (e.g., footwear or a prosthesis). However, there are considerable challenges in designing a shear sensor for these applications due to the need for a small package, low power requirements, and resistance to interference from motion artifact and electromagnetic fields. This paper presents the design, fabrication, and characterization sensor that measures two-axis shear force by detecting displacement between a color panel and a red, green, and blue light-sensing photodiode. The sensor response to applied displacements and forces was characterized under benchtop testing conditions. We also present the design of a prototype wireless version of the sensor for integration into footwear. The sensor exhibited strong agreement with gold standard measurements for two axis shear displacements (R2>0.99, RMSE≤5.0 µm) and forces (R2>0.99, RMSE≤0.94 N). This performance, along with the sensor’s scalability, miniaturized form, and low power requirements make it well-suited a variety of biomedical applications.

Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to induce synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. The spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience is reproduced by the model. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here begins to address this need.

Wolfenden and his coworkers observed the astronomical numbers for the catalytic proficiency of some enzymes. We connected that pinnacle of biological evolution to the universal thermodynamic evolution. We added or multiplied a random noise with chosen rate constants to explore the correlation between dissipation and enzyme efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. The turnover number kcat and catalytic efficiency kcat/KM are proportional to the overall entropy production – the main parameter from irreversible thermodynamics. For most enzymes with the Michaelis-Menten type cycle kinetics, the best increase in the forward kcat/KM follows after increasing the equilibrium constant of substrate-enzyme association. The Discussion section emphasizes the role of biological evolution in harvesting order (high enzyme efficiency) from disorder (high noise and dissipation). It also connects the applications of the maximum partial entropy production theorem in optimizing enzyme kinetics (D. Juretić "Bioenergetics - A Bridge across Life and Universe") with the present total entropy production role analysis. De novo enzyme design and various attempts to speed up the rate-limiting catalytic steps may profit from our theoretical insights.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

This study aims to assess using a smartphone app (DecibelX), as a noise measuring alternative to the more costly traditional use of measuring noise levels with a Sound Level Meter (SLM). The study compares the accuracy of the app to readings taken with a SLM and dosimeter, and also evaluates the app’s performance for pure tone and narrow band noise. And a usability study identifies strengths and weaknesses related to usability of the app.

The effect of fungicides on fermentation is of paramount importance to control the quality and safety of wines. In this work, the quality (oenological parameters, color, phenolic content, antioxidant activity, and fungicide residues) of wines from Monastrell grapes fortified with iprovalicarb, mepanipyrim and tetraconazole fungicides was evaluated. Along of the winemaking process, initial residues of mepanipyrim and tetraconazole were removed in more than a 90 % while dissipation of iprovalicarb was around 73 %. Significant statistical differences were found in presence of iprovalicarb and mepanipyrim residues especially at the highest concentration assayed. For both fungicides, an increase of the volatile acidity (between 4 and 8.6 times), the lactic acid content (between 8.6 and 20.5 times), the percentage of polymeric anthocyanins (between 1.3 and 1.7 times) and also a slight increase of the total phenolic index and the total anthocyanins content determined by spectrophotometry was observed. On the contrary, the total monomeric anthocyanins content decreased about 16.3 and 28.6 % in presence of iprovalicarb and mepanipyrim, respectively. These results could be related with the addition of SO2 to the grape must and a higher development of acetic acid or lactic bacteria in presence of these fungicides. The color of the final wines was also different in comparison with the control, with a higher yellow component, color intensity, tonality and hue angle, because of pH changes in the medium. Tetraconazole fermentations had a more similar trend to the control wine, probably due to the lower concentration of this fungicide in the grape must at the initial time. No effects on the antioxidant activity was observed for anyone of the target fungicides. A multivariate statistical analysis was done to view interrelationships between different variables (color and anthocyanins profile). The obtained model allowed to separate wines according to the fungicide treatment applied.

This in vitro study aimed to analyze the surface roughness (Ra) and color stability (ΔEab, ΔE00) following simulated mechanical brushing, and to evaluate the microtensile (μTBS) to dentin of Self Adhering Resin Flowable (SARF). The selected materials were Constic, Yflow AS, and Tetric N flow (TNF/control). Thirty composite resin cylinders were fabricated for surface property evaluation. Ra and color were assessed both before and after simulated brushing. The thresholds of 50:50% perceptibility and acceptability of color difference of the evaluated resins were assessed. For μTBS analysis, fifteen molars were selected, sectioned to expose flat dentin surfaces, and restored according to manufacturers' instructions. Subsequently, they were sectioned into sticks for microtensile testing. Statistically significant differences in Ra among the groups, with Constic exhibiting the highest Ra value (p < 0.05), while Yflow AS showed the lowest Ra value. No statistically significant difference in color was observed among the groups (p > 0.05). The 50:50% perceptibility and acceptability thresholds set at 1.2 and 2.7 for ΔEab and 0.8 and 1.8 for ΔE 00. All the results fell within the acceptable limits. The mean μTBS values of Constic, Yflow AS, and TNF were 10.649 MPa, 8.170 MPa, and 33.669 MPa, respectively. The study revealed increased Ra and comparable color stability among all tested composite resins after abrasion. However, SARF exhibited lower μTBS compared to conventional using an adhesive system.

This study investigates the effects of ambient illumination and negatively polarized text color on visual fatigue, exploring the issue of visual fatigue when using visual display terminals in low-illumination environments. The research methodology utilized an experimental design to collect data on changes in pupil accommodation and blink rate through an eye tracker. Partici-pants completed a reading task while exposed to various text colors and ambient light conditions to evaluate visual fatigue and cognitive performance. The study's findings suggest that text color significantly affects visual fatigue, with red text causing the highest level of visual fatigue and yellow text causing the lowest level of visual fatigue. Improvements in ambient lighting reduce visual fatigue, but the degree of improvement varies depending on the text color. Additionally, cognitive performance is better when using yellow and white text but worse when using red text. Yellow text is the most effective choice for reducing visual fatigue under negative polarity. In-creasing ambient lighting can also improve visual fatigue in low-illumination conditions. These findings will offer valuable guidance for designing visual terminal device interfaces, especially for low-illumination or night environments, to minimize visual fatigue and improve the user ex-perience.

This research assesses the classification of Arabic coffee into three primary variations (light, medium, and dark) using simulated data based on actual measurements of color information, antioxidant laboratory tests, and chemical composition tests. Two types of simulated data were generated, with the standard deviation of the measures varied. Multiple classifiers were used for classification. The findings demonstrate the use of color information in accurately distinguishing Arabic coffee types. The different CIE color values, as well as the excellent connection between color information and coffee classes, lead to flawless classification results. However, depending primarily on antioxidant information results in lower classification performance, with variability amongst classifiers due to increasing data complexity. Chemical composition information, on the other hand, possesses outstanding discriminatory strength, allowing faultless classification on its own. Certain characteristics, such as crude protein and crude fiber, show strong correlations and are critical in the classification of coffee types. Based on the findings, it is recommended to create a mobile application that uses image recognition to analyze coffee color while providing chemical composition information. End users, such as consumers, will be able to make informed judgments regarding their coffee preferences owing to this application.

In this study, we propose a solution process for realizing color glass for building integrated pho-tovoltaic (BIPV) systems by spin coating a color solution composed of a pearlescent pigment mixed in a norland optical adhesive (NOA) matrix. Color solutions are made from mixing pearlescent pigments in NOA63. Compared to a physical vapor deposition process, color coatings are achieved by spin coating in a relatively simple and inexpensive process at room temperature. The optical properties can be easily controlled by adjusting the spin coating speed and the concentra-tion of the pearlescent pigments. The produced color glasses achieved a high transmittance of 85% or more in the visible wavelength range, except the wavelength spectrum exhibiting the maxi-mum reflectance. In addition, we propose a one-step lamination process of color glass on a solar cell by leveraging on the adhesive property of the NOA matrix. This eliminates the cost and pro-cess of additional ethylene vinyl acetate (EVA) layer or other materials used in the conventional lamination process. The color glass produced through this study has stability that does not change its properties over time. Therefore, it is expected to be applied to the BIPV solar module market where aesthetics and energy efficiency are required.

Color can direct visual attention to specific locations through bottom-up and top-down mechanisms. Using Continuous Flash Suppression (CFS) as way to investigate the factors that gate access to consciousness, the current study investigated whether color also directly affected the timing of conscious perception. Low or high spatial frequency (SF) gratings with different orientations were shown as targets to the non-dominant eye of human participants. CFS patterns were presented at a rate of 10Hz to the dominant eye to delay conscious perception of the targets, and participants had the task to report the target’s orientation as soon as they could see it. With low-SF targets, two types of color-based effects became evident. First, when the targets and the CFS patterns had different colors, the targets entered consciousness faster than in trials where the targets and CFS patterns had the same color. Second, when participants searched for a specific target color, targets that matched these search settings entered consciousness faster compared to conditions where the target color was irrelevant and could vary from trial to trial. Thus, the current study demonstrates that color is a central feature of human perception and leads to faster conscious perception of visual stimuli through bottom-up and top-down attentional mechanisms.

Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. Spiking Neural Networks (SNNs) can utilize biological temporal coding features, such as noise-induced stochastic resonance and dynamical synapses to increase a model’s performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified Convolutional Neural Network analog, this paper demonstrates two key results: (1) SNNs solve the problem linearly inseparable in ANN with over 10% improvement in the optimal setting, and (2) in SNNs, the synaptic noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results.

A crucial component for developing an automated speaker or speech recognition system is voice preprocessing, which filters unwanted noise and detects the speech part. This study aimed to develop a computerized model to remove background noise and improve signal quality for future applications. This model was developed on a large longitudinal dataset containing varying real-world noise and validated on both a public dataset and a locally collected test dataset in different environments. An overall voice pre-processing pipeline is presented in this study, including denoising, segmentation, feature extraction, and ease of storage. The backbone of the denoising model is a Kalman filter, where the parameters were obtained from a grid search method; the signal-to-noise ratio (SNR) was used as the performance metric. Also, the segmentation was done to remove the pauses from the audio signal before the feature extraction. Finally, the data was stored as a template with the most common features. The SNR result suggested that the Kalman filter-based proposed method performed successfully across diverse datasets. Thus, this model provides a robust and adaptable solution for real-world scenarios and also ensures the data storing quality for future applications.

Wind energy in Europe is aimed to grow at a steady, high pace, but opposition from residents to local wind farm plans is one of the obstacles to further growth. A large body of evidence shows that local populations want to be involved and respected for their concerns, but in practice this is a complex process that cannot be solved with simple measures such as financial compensation. The visual presence and the acoustic impact of a wind farm is an important concern for residents. Generally environmental noise management aims to reduce the exposure of the population, usually based on acoustics and restricted to a limited number of sources (such as transportation or industry) and sound descriptors (such as Lden). Individual perceptions are taken into account only at an aggregate, statistical level (such as percentage of exposed, annoyed or sleep-disturbed persons in the population). Individual perceptions and reactions to sound vary in intensity and over different dimensions (such as pleasure/fear or distraction). Sound level is in fact a weak predictor of the perceived health effects of sound. The positive or negative perception of and attitude to the source of the sound is a better predictor of its effects. This article aims to show how the two perspectives (based on acoustics and on perception) can lead to a combined approach in the management of a wind farm aimed to reduce annoyance, not primarily of sound level. An important aspect in this approach is what the sound means to people: is it associated with the experience of having no say in plans, does it lead to anxiety or worry, is it appropriate? The available knowledge will be applied to wind farm management: planning as well as operation.

Chronic exposure to noise can cause several extraordinary effects and involve all the systems of the human organism. In addition to cardiovascular, gastrointestinal and immune effects, the data in the literature show alterations in behavioral disturbances, in memory capacity and cognitive performance. Through this systematic review, the authors try to find out which are the main neurobehavioral alterations, in case of occupational exposure to noise. Literature review included articles published in the major databases (PubMed, Cochrane Library, Scopus), using a combina-tion of some relevant keywords. This online search yielded 4434 references; after selection, the authors analyzed 41 articles (4 narrative reviews and 37 original articles). From this analysis, it appears that main symptoms are related to psychological distress, annoyance, sleep disturbances, cognitive performance. Regarding tasks, the most frequent employments concern school staff, followed by employees from various industrial sectors and office workers. Although the causes are still widely debated, it is essential to protect these workers against chronic exposure to noise. In fact, in addition to a hearing loss, they can manifest many other related discomforts over time and compromise their full working capacity, as well as expose them to a greater risk of accidents or absences from work.

Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).

This paper exposes the initial part of a larger research on the impact of the automotive on human health and the quality of life and environment. After a brief introduction on the need for responsible and sustainable approaches and few questions on the subject, some considerations and statistical data are presented, based on a study of the literature on the impact of road traffic and vehicles noise and vibration on people's comfort and health and their quality of life. The research results show, based on official statistics, the significant harmful impact of noise and vibration from motor vehicles and road transport on people's health and quality of life, especially in urban areas during the day. Also, the significant increase in the number of electric and hybrid vehicles from one year to another, a reality and a necessity nowadays, and the awareness that electric vehicles are not perfectly quiet and comfortable, open new research opportunities and require the development of new standards, materials, tools, equipment and test methods in the field of NVH, in a sustained synergistic approach from all stakeholders, to meet the needs and demands of today's consumers, and to comply with existing regulations and standards on environmental protection and sustainable development.

In forensic acoustics, a possible area of analysis is represented by unwanted sound that 1 is perceived as a source of intrusion or disturbance within a certain auditory context. This context 2 is defined as the "auditory scene" and refers to the set of sounds present in a specific environment. 3 The presence of unwanted sounds in the auditory scene can cause a wide range of negative effects, 4 including disturbance, discomfort, moral or immoral harm, and other types of negative impact on the 5 health and well-being of individuals exposed to noise. In 2022, the technical specification UNI/TS 6 11844:2022 dedicated to the measurement and analysis of intrusive noise was published. The standard 7 introduces the concept of intrusive noise and defines its calculation methods based on environmental 8 measurements. The purposes of this technical specification is to provide an objective support to 9 methods already in used in acoustic disputes, where the assessment of the annoyance of a noise is 10 often a subjective evaluation of the technician. This work delves into the application to some real 11 cases, identifying the potentiality and the limits of the standardized method.

This study develops a vitamin C controlled-release system trackable via color changes as a function of vitamin C release. The system is composed of coaxial microfibers prepared via coaxial electrospinning, with a core of poly(ethylene oxide) (PEO) incorporating vitamin C, and a shell composed of polycaprolactone (PCL) containing polydiacetylene (PDA) as the color-changing material. The shell thickness was controlled by adjusting the amount of PCL ejected during electrospinning, allowing the regulation of the release rate of vitamin C. When vitamin C added to PEO penetrated the PCL layer, the color of PDA changed from blue to red, indicating a color change. The results of this study can be applied to devices that require immediate detection of vitamin C release levels.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired in the visual cortex, are characterized by their hierarchical learning structure, which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable.

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex &amp; Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

Oenothera flower petals change color during senescence. When in full bloom, the flowers of O. tetraptera are white and those of O. laciniata and O. stricta are yellow; however, the colors change to pink and orange, respectively, when the petals fade. We analyzed the flavonoid components in these petals as a function of senescence using HPLC-DAD and LC-MS. In all three species, cyanidin 3-glucoside (Cy3G) was found in faded petals, and the content of Cy3G increased in senescence. In full bloom (0 h), no Cy3G was detected in any of the petals, but after 12 h, the content of Cy3G in O. tetraptera was 0.97 μmol/gFW and that in O. laciniata was 1.82 μmol/gFW. Together with anthocyanins, major flavonoid components in petals were identified. Quercitrin was detected in the petals of O. tetraptera, and isosalipurposide was found in the petals of O. laciniata and O. stricta. The content of quercitrin did not change during senescence, but that of isosalipurposide in O. laciniata increased from 3.4 μmol/gFW at 0 h to 4.8 μmol/gFW at 12 h. The color change in all the three Oenothera flowers was confirmed to be due to the de novo biosynthesis of Cy3G.

Interleaved power factor correction (PFC) is widely used circuit topology due to good efficiency and power density for single-switch boost PFC. As the differential mode (DM) electromagnetic interference (EMI) noise magnitude depends upon the input current ripple, this research details a comprehensive study of DM EMI filter design for interleaved boost PFC with the aim of minimizing the component size. It is also demonstrated that the different numbers of interleaved stages and switching frequency influence the filter attenuation requirement and, thus, the EMI filter size. First, an analytical model is derived on the basis of the Norton equivalent circuit model for the differential mode noises of interleaved boost PFC within the frequency range of 9-500 kHz. The derived model can help identify the optimal phase shifting among the interleaved boost converters in order to minimize the considered differential mode noises at the filter design frequency. So, a novel phase-shift method is developed to get a minimized attenuation required by a filter in Band B. Further, a volume optimization of the required DM filter was introduced based on the calculated filter attenuation and volumetric component parameters. Based on the obtained results, unconventional and conventional phase shifts have demonstrated a good performance in decreasing the EMI filter volume in Band B and Band A, respectively. A 2-kW interleaved PFC case study is presented to verify the theoretical analyses and the impact of phase-shifting on EMI filter size.

Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

Although the effect of thermal modification (TM) on teak wood color is well documented, a few studies were carried out in closed system processes, as well as it remains unclear the effect of different processes on the same material. This work aimed to verify the effect of closed and open system processes of TM on the color of fast-growing teak wood. Thermally modified wood was evaluated in a closed system at 160 °C (CS160) and in an open system at 185 °C and 210 °C (OS185 and OS210). We measured the moisture content (initial and final) of wood, and the corrected mass loss (CML). The chemical analyses encompassed the contents of alpha-cellulose, hemicelluloses, lignin, and extractives (total, in acetone and dichloromethane). Wood color was measured before and after TM according to the CIEL*a*b* color space. It was possible to achieve the same color using different processes of thermal modification (CS160 and OS210). TM reduced wood lightness (L*), red-green chromaticity coordinate (a*), and yellow-blue chromaticity coordinate (b*). L* and a* had the bigger and lower variations, respectively. The color of thermally modified wood was significantly changed, even at the mildest condition tested (OS185, 0.33% CML).

The valley pseudospin properties of electrons in two–dimensional hexagonal materials result in lots of fascinating physical phenomena, which opens up the new field of valleytronics. The valley-contrasting physics aims at distinguishing the valley degree of freedom based on valley–dependent effects. Here, we theoretically demonstrate that both of the valley–selective high harmonic generation and valley–selective electronic excitation can be achieved by using an orthogonal two–color (OTC) laser field in gapped graphene. It is shown that the asymmetry degrees of harmonic yields in the plateaus, cutoff energies of generated harmonics and electron populations from two different valleys can be precisely controlled by the relative phase of the OTC laser field. Thus the selectivity of the dominant valley for the harmonic radiation and electronic polarization can be switched by adjusting the relative phase of the OTC laser field. Our work offers an all–optical route to produce the valley–resolved high harmonic emissions and manipulate the ultrafast valley polarization on a femtosecond timescale in condensed matter.

Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease damages grain quality and reduces yield. The detection of this disease in a high throughput way is crucial to planters and breeders. Our study focused on developing a method for processing wheat color images and accurately detecting disease areas using deep learning and image processing techniques. The color images of wheat at the milky stage were collected and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model can detect spikes, and the coefficient of determination of the number of spikes between the manual count and the detection was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results of spike detection, a new color feature was applied to obtain the gray image of each spike. Then, a modified region growing algorithm was implemented to segment and detect the diseased areas of each spike. Results show that the region growing algorithm performs better than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates that deep learning techniques enable the accurate detection of FHB in wheat using color images, and the proposed method can effectively detect spikes and diseased areas, thereby improving the efficiency of FHB detection.

The detection of temporary color steel buildings and the analysis of their spatiotemporal patterns and characteristics are of great importance in many developing cities, because areas densely distributed with color steel buildings are usually problematic regions with high population density, sustainable development and risk level, which are challenging for local governments. Using the high-resolution satellite images of the Anning District, Lanzhou City, China as the case study, this paper first extracts the information of the color steel buildings for two different periods. Then the spatiotemporal distributional characteristics of the color steel buildings are analyzed, both at small scale (temporary buildings) and large scale (planthouses, warehouses, etc.). Finally, this paper utilizes kernel density estimation, Delaunay triangulation and Voronoi diagrams to examine the spatial distribution, aggregation and proximity characteristics of the color steel buildings. Our experiments show that the temporal and spatial differences of urban development, the phased characteristics and urban space are represented by various types of color steel buildings. Thus, there are some robust coupling relationship between color steel buildings, urban spatial form and urban development.

Assessing observation noise is a critical step in the development of a stochastic model for GNSS navigation and positioning. This process ensures that the statistical properties of the observational data are accurately characterized, leading to more reliable and precise positioning results. Traditionally, the one sigma values for observation noise, 0.3 m for code and 3 mm for carrier phase, alongside elevation-dependent weighting schemes, have been standard. However, these may no longer be suitable due to significant advancements in GNSS systems, receivers, and antennas. Previous research predominantly focused on code type and PPP techniques, often limited by the inability to separately assess observation types across different frequency bands due to ionospheric delay. Furthermore, these studies were generally constrained by limited experimental data. This research advocates for the use of ultra-short baselines to eliminate atmospheric delay and other error sources, providing a detailed assessment of observation noise for various frequency bands, GNSS systems, and receiver & antenna types over an extensive period and across multiple baselines. The findings suggest a need to reconsider traditional one sigma values and elevation-dependent weighting schemes for current applications.

This study aims to tackle the challenge of high noise levels on balconies while preserving natural ventilation. Eight innovative balcony designs, incorporating elements like diffuser edges, undulating ceilings, Helmholtz resonators, grooves, or sound traps, were evaluated via finite element (FE) modeling. The extensive FE studies involved about 10 million elements per analysis case. Results showed that a standard balcony with a front parapet wall could achieve a noise reduction of around 10 dBA, with the jagged ledge design providing an additional 2 dBA improvement. Adding protruded ledge designs led to higher insertion loss (IL) by up to 9.4 dBA, especially at higher stories. Having wall-embedded Helmholtz resonators improved IL by up to 1.5 dBA. The jagged ledge design consistently exhibited positive IL across a broad frequency range from 200 Hz to 2 kHz. Combining beneficial balcony ceilings and wall ledges significantly reduced noise across lower and higher frequency ranges. Front jagged and full wavy ceilings offered positive IL throughout different storeys, proving their effectiveness against human vocal range. This study found that jagged ledge and front jagged ceiling designs, effective across various balcony heights, are viable options for improving balcony noise mitigation, providing valuable insights for architects and designers seeking practical solutions for outdoor noise reduction.

This article investigates the source of booming noise emanating from the tailgates of electric vehicles, along with proposed strategies to mitigate it. This study involved the measurement of booming noises during on-road vehicle tests to pinpoint their origins. Additionally, operational deflection shapes (ODS) were extracted from the tailgate vibration signals to gain insight into its dynamic behavior. Modal tests were conducted on the tailgate to determine its dynamic characteristics and compared with driving test results to reveal the mechanism responsible for tailgate-induced booming noise. It was established that such noise is primarily due to the tailgate modes resulting from a combination of rigid body motion in the fore-aft direction and deformation in the central section of the panel. An analytical model of the tailgate was developed using commercial finite element analysis software to propose measures for reducing booming noise. Experimental findings validated the model accuracy. Structural enhancements were implemented to enhance the panel stiffness and improve the connection between the vehicle and tailgate via bushings to dampen the booming noise resulting from tailgate motion. Under random force inputs, analytical results demonstrated a 13.8% reduction in maximum deformation in the tailgate model in the improved structural configuration with increased panel stiffness.

We discuss the optimal control formulation for enhancement and denoising of satellite multiband images and propose to take it in the form of an L1-control problem for quasi-linear parabolic equation with non-local p[u]-Laplacian and with a cost functional of a tracking type. The main characteristic feature of the considered class of parabolic problems is the fact that the variable exponent p(t,x) and the anisotropic diffusion tensor D(t,x) are not well predefined a priori, but instead these characteristic non-locally depend on a solution of this problem, i.e., pu=p(t,x,u) and Du=D(t,x,u). We prove the existence of optimal pairs with sparse L1-controls using for that the indirect approach and a special family of approximation problems.

In underwater optical wireless communications (UOWC), scattering of the propagating light beam results in both intensity and phase variations, which limit the transmission link range and channel bandwidth, respectively. Scattering of photons while propagating through the channel is a random process, which results in the channel-dependent scattering noise. In this work, we introduce for the first time an analytical model for this noise and investigate its effect on the bit error rate performance of the UOWC system for three types of waters and a range of transmission link spans. We show that, for a short range of un-clear water or a longer range of clear water, the number of photons experiencing scattering is high, thus leading to the increased scattering noise.

It has remained a hard nut for years to segment sonar images, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds to be calculated. To realize this goal, it follows the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, segment the region into the background region, buffer region and target regions. After filtering, reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts. Finally, remove the remaining noise from images by means of morphological image processing. The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar image quickly and effectively, with no problem of incomplete image target shape. Thus, the stable and feasible method is testified.

Purpose: The study is aimed to quantify the effects of social noise (personal music players (PMP), high-intensity noise exposure events) and road traffic noise exposures in the sample of Slovak university students living and studying in Bratislava. Methods: There were 1,003 university students (306 males and 697 females, average age 23.13±2) enrolled in the study; 347 lived in the student housing facility exposed to road traffic noise (LAeq =67.6 dB) and 656 in the control one (LAeq =53.4 dB). Respondents completed a validated ICBEN 5-grade scale “Noise annoyance questionnaire”. The exposure to PMP was objectified by the conversion of the subjective evaluation of the volume setting and duration. With the cooperation of the ENT specialist, we arranged audiometric examinations on the pilot sample of 41 volunteers. Results: From the total sample of 1,003 students, 794 (79.16 %) of them reported the use of PMP in the course of the last week; average time of 285 minutes. There was a significant difference in PMP use between the exposed (85.59 %) and the control group (75.76 %) (p=0.01). Among PMP users 30.7 % exceeded the LAV (lower action value for industry LAeq,8h = 80 dB). On a pilot sample of volunteers (n=41) audiometry testing was performed indicating a hearing threshold shift at higher frequencies in 22% of subjects. Conclusions: The results of the study on a sample of young healthy individuals showed the importance of exposure to environmental noise from different sources (transportation, neighborhood, construction, entertainment facilities, etc.) as well as social noise and the need for prevention and intervention.

Optical motion capture systems are state-of-the-art in motion acquisition, however as any measurement systems they are not error free -- noise is their intrinsic feature. The works so far mostly employ simple noise model, expressing the uncertainty as a simple variance. In the work we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. For the automated readout of the noise coefficients we solve the multidimensional regression problem using sophisticated metaheuristics in exploration-exploitation scheme. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion in our facility. We had also opportunity to observe the influence of camera failure to the overall performance.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

The Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) is a robust data processing platform that supports a large array of multi-spectral and hyper-spectral sensors. It provides accurate aerosol optical depths and remote sensing reflectances (Rrs estimates) that can be used to generate products such as absorption coefficients due to phytoplankton and detritus/Gelbstoff as well as backscattering coefficients due to particulate matter. The OC-SMART platform yields improved performance in complex environments by utilizing scientific machine learning (SciML) in conjunction with comprehensive radiative transfer computations. This paper expands the capability of OC-SMART by quantifying uncertainties in ocean color retrievals. Bayesian inversion is used to relate measured top of atmosphere radiances and a priori data to estimate posterior probability density functions and associated uncertainties. A framework of the methodology and implementation strategy is presented and uncertainty estimates for Rrs retrievals are provided to demonstrate the approach by applying it to MODIS, OLCI Sentinel-3, and VIIRS sensor data.

The largest part of the image encryption algorithms operates on the pixel as a central element by implementing diffusion confusion and eventually a permutation. On the other side, a permutation applied at the bit level changes not only the pixel value, but also its location within the image. In this work, we will propose a new technology of medical and color image encryption, based on chaotic permutations acting at the bit level, and a diffusion confusion ensured by an application of the method of Vigenere largely improved and adapted to the subject. Simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.

We investigated layered titanium nitride (TiN) and aluminum nitride (AlN) for color glasses in Building Integrated Photovoltaic (BIPV) systems. AlN and TiN are among suitable and cost-effective optical materials to be used as thin multilayer films, owing to the significant difference in their refractive index. To fabricate the structure, we used radio frequency magnetron deposition method to achieve the target thickness uniformly. A simple, fast, and cheap fabrication method is achieved by depositing the multilayer films in a single sputtering chamber. It is demonstrated that a multilayer stack that allows light to move from a low refractive index layer to a high refractive index layer or vice-versa can effectively create various distinct color reflections for different film thicknesses and multilayer structures. It is investigated from simulation based on wave optics that, TiN/AlN multilayer offers better color design freedom and cheaper fabrication process as compared to AlN/TiN multilayer films. Blue, green, and yellow color glasses with optical transmittance of more than 80% was achieved by ITO coated glass/TiN/AlN multilayer films. This technology exhibits good potential in commercial BIPV system applications.

Victor Vasarely’s (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a large-scale pictorial plane. Current perception science explains such effects by invoking brain mechanisms for the processing of monocular (2D) depth cues. Here in this study, we illustrate and explain the local effects of 2D color and contrast cues on the perceptual organization in terms of figure-ground assignments, i.e. which local surfaces are likely to be seen as “nearer” or “bigger” in the image plane. Paired configurations are embedded in a larger, structurally ambivalent pictorial context inspired by some of Vasarely’s creations. The figure-ground effects these configurations produce reveal a significant correlation between perceptual solutions for “nearer” and “bigger” when no other monocular depth cues are given in the image. In consistency with previous findings on similar, albeit simpler visual displays, a specific color may compete with luminance contrast in resolving the planar ambiguity of a complex pattern context. Vasarely intuitively understood, and successfully exploited, this kind of subtle context effect in his art, well before empirical investigations had set out to study and explain their genesis in terms of information processing by the visual brain.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

In order to realize rapid and nondestructive monitoring of wheat biomass in field, field experiments based on different densities, nitrogen fertilizer and variety treatments were studied. RGB images of wheat in the main growth stage were obtained by UAV, and wheat color and texture feature indices were obtained by image processing, and wheat biomass was obtained by field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the color and texture feature index suitable for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, and most of them reached a very significant correlation level. However, the correlation between image texture feature index and wheat biomass was poor, only a few indexes reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture feature in different growth stage were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the significant level, RMSE were smaller. This indicated that the estimated results by the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV image were better than the single color index models. The results would provide a new method for real-time monitoring of wheat field growth and biomass estimation.

With continued technological innovation in the fields of mixed reality (MR), wearable-type MR devices, such as helmets, have been released and are frequently used in various fields, such as entertainment, training, and education. However, because each product has different parts and specifications in terms of the design and manufacturing process, users feel that the virtual objects overlaying real environments in MR are visualized differently depending on the scale and color used by the MR device. In this paper, we compare the effect of scale and color parameters on users’ perception in using different types of MR devices to improve MR experience. We conducted two experiments (scale and color), and our experimental study showed that the subjects who participated in the scale perception experiment clearly tended to underestimate virtual objects, compared with real objects, and overestimate color in MR environments. [MM1]Please confirm meaning.