Search Results (2,396)

Conventional dry electrodes often yield unstable results due to the presence of parasitic capacitance between the flat electrode surface and the non-uniform skin interface. To address this issue, a gel is typically placed between the electrodes to minimize parasitic capacitance. However, this approach has the drawbacks of being unsuitable for repeated use, limited lifetime due to gel evaporation, and the possibility of developing skin irritation. This is particularly problematic in underserved areas since, due to the cost of disposable wet electrodes, they often sterilize and reuse dry electrodes. In this study, we propose a method to neutralize the effects of parasitic capacitance by attaching high-value capacitors to the electrodes in parallel, specifically when applied to pulse wave monitoring through bioimpedance. Skin capacitance can also be mitigated due to the serial connection, enabling stable reception of arterial pulse signals through bioimpedance circuits. A high-frequency structure simulator (HFSS) was first used to simulate the capacitance when injection currents flow into the arteries through the bioimpedance circuits. We also used the simulation to investigate the effects of add-on capacitors. Lastly, we conducted preliminary comparative analyses between wet electrodes and dry electrodes in vivo with added capacitance values ranging from 100 pF to 1 μF, altering capacitance magnitudes by factors of 100. As a result, we obtained a signal-to-noise ratio (SNR) that was 8.2 dB higher than that of dry electrodes. Performance was also shown to be comparable to wet electrodes, with a reduction of only 0.4 dB using 1 μF. The comparative results demonstrate that the addition of capacitors to the electrodes has the potential to allow for performance similar to that of wet electrodes for bioimpedance pulse rate monitoring and could potentially be used for other applications of dry electrodes. Full article

Acoustic emission (AE) technology has been widely utilized to monitor the SiC wafer lapping process. The root-mean-square (RMS) of the time–domain eigenvalues of the AE signal has a linear relationship with the material removal rate (MRR). However, the existence of background noise severely reduces signal monitoring accuracy. Noise interference often leads to increased RMS deviation and signal distortion. In the study presented in this manuscript, a frequency threshold noise reduction approach was developed by combining and improving wavelet packet noise reduction and spectral subtraction noise reduction techniques. Three groups of SiC lapping experiments were conducted on a fixed abrasive pad, and the lapping acoustic signals were processed using three different noise reduction approaches: frequency threshold, wavelet packet, and spectral subtraction. The results show that the noise reduction method using the frequency threshold is the most effective, with the best coefficient of determination (R²) for the linear fit of the RMS to the MRR. Full article

In the maritime environment, particularly within tidal flats, the frequent occurrence of sea fog significantly impairs the quality of images captured by unmanned aerial vehicles (UAVs). This degradation manifests as a loss of detail, diminished contrast, and altered color profiles, which directly impact the accuracy and effectiveness of the monitoring data and result in delays in the execution and response speed of monitoring tasks. Traditional physics-based dehazing algorithms have limitations in terms of detail recovery and color restoration, while neural network algorithms are limited in their real-time application on devices with constrained resources due to their model size. To address the above challenges, in the following study, an advanced dehazing algorithm specifically designed for images captured by UAVs over tidal flats is introduced. The algorithm integrates dense convolutional blocks to enhance feature propagation while significantly reducing the number of network parameters, thereby improving the timeliness of the dehazing process. Additionally, an attention mechanism is introduced to assign variable weights to individual channels and pixels, enhancing the network’s ability to perform detail processing. Furthermore, inspired by contrastive learning, the algorithm employs a hybrid loss function that combines mean squared error loss with contrastive regularization. This function plays a crucial role in enhancing the contrast and color saturation of the dehazed images. Our experimental results indicate that, compared to existing methods, the proposed algorithm has a model parameter size of only 0.005 M and a latency of 0.523 ms. When applied to the real tidal flat image dataset, the algorithm achieved a peak signal-to-noise ratio (PSNR) improvement of 2.75 and a mean squared error (MSE) reduction of 9.72. During qualitative analysis, the algorithm generated high-quality dehazing results, characterized by a natural enhancement in color saturation and contrast. These findings confirm that the algorithm performs exceptionally well in real-time fog removal from UAV-captured tidal flat images, enabling the effective and timely monitoring of these environments. Full article

The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. This study proposes a novel method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kernel principal component analysis (KPCA), and a support vector machine (SVM) to enhance drum load recognition. The method consists of three principal steps: the initial experiments with ETDPHs to identify the critical factors affecting drum load and to formulate classification criteria; the development of a CEEMDAN-KPCA-SVM model for ETDPH drum load recognition, where drum spindle torque signals are processed by CEEMDAN for decomposition and reconstruction, followed by feature extraction and dimensionality reduction via KPCA to refine the model’s accuracy and training efficiency; and evaluation of the model’s performance on real datasets, highlighting the improvements brought by CEEMDAN and KPCA, as well as comparative analysis with other machine learning models. The results describe four load conditions—no load (mass of pepper intake (MOPI) = 0 kg/s), low load (0 < MOPI ≤ 0.658 kg/s), normal load (0.658 < MOPI ≤ 1.725 kg/s), and high load (MOPI > 1.725 kg/s)—with the CEEMDAN-KPCA-SVM model achieving 100% accuracy on both training and test sets, outperforming the standalone SVM by 6% and 12.5%, respectively. Additionally, it reduced the training time to 2.88 s, a 10.9% decrease, and reduced the prediction time to 0.0001 s, a 63.6% decrease. Comparative evaluations confirmed the superiority of the CEEMDAN-KPCA-SVM model over random forest (RF) and gradient boosting machine (GBM) in classification tasks. The synergistic application of CEEMDAN and KPCA significantly improved the accuracy and operational efficiency of the SVM model, providing valuable insights for load recognition and adaptive control of ETDPH drum parameters. Full article

Objectives: Investigate factors contributing to the effective management of age-related hearing loss (ARHL) rehabilitation. Methods: A systematic review was conducted following PRISMA guidelines. The protocol was registered in PROSPERO (CRD42022374811). Articles were identified through systematic searches in the Scopus, PubMed, Web of Science, and Cochrane databases in May 2024. Only articles published between January 2005 and May 2024 were included. Studies were assessed for eligibility by two independent researchers and evaluated using the Crowe Critical Appraisal Tool v1.4 (CCAT). Results: Of the 278 articles identified, 54 were included. Three factors explain effective HA use. First, hearing aid signal processing, with directional microphones and noise reduction, improves user comfort and understanding regarding noise. Second, there is hearing aid fitting, with the NAL prescription rules as the gold standard, and bilateral, high-level HA performance for spatial localization and noise comprehension. Third, there is a patient-centered approach, using patient-related outcome measures (PROMs), questionnaires, counseling, and regular follow-up to involve patients in their therapeutic rehabilitation. Conclusions: Reaching a consensus on acoustic parameters is challenging due to variability in audiological results. Involving patients in their rehabilitation, addressing their needs and expectations, and offering individualized care are crucial. Full article

Environmental and occupational noise has the potential to result in health risks. The presence of high noise levels aboard ships can cause substantial hazards that affect the well-being of those employed in the maritime industry. The study and implementation of occupational noise reduction aboard ships are of the highest priority for ensuring the well-being of marine workers, compliance with regulatory standards, protection of the environment, and improvement of overall operational efficiency and safety within the maritime sector. A scoping study was conducted to collect and summarize the existing scientific literature about approaches to preventing occupational noise in vessel operations. We searched electronic databases for papers published up to June 2024. Initially, 94 articles were identified for screening, and the present research produced 16 studies, which were finally analyzed. Resultantly, noise control may begin with elimination, substitution, engineering, administrative, and hearing protection (ear plugs or muffs). Noise control innovation would be started with engineering techniques. Hearing protection devices (HPDs) could be used to reduce noise and as an instrument of communication between sailors. More research needs to be carried out in order to find the best ways for maritime vessels to reduce noise at work and to see how well they work in lowering the risks that come with noise for workers on board. Full article

Advances and cost reductions in Light Detection and Ranging (LiDAR) sensor technology have allowed for their implementation in detecting vehicles, cyclists, and pedestrians at signalized intersections. Most LiDAR use cases have focused on safety analyses using its high-fidelity tracking capabilities. This study presents a methodology to transform LiDAR data into localized, verified, and linear-referenced trajectories to derive Purdue Probe Diagrams (PPDs). The following four performance measures are then derived from the PPDs: arrivals on green (AOG), split failures (SF), downstream blockage (DSB), and control delay level of service (LOS). Noise is filtered for each detected vehicle by iteratively projecting each sample’s future location and keeping the subsequent sample that is close enough to the estimated destination. Then, a far side is defined for the analyzed intersection’s movement to linear reference sampled trajectories and to remove those that do not cross through that point. The technique is demonstrated by using over one hour of LiDAR data at an intersection in Utah to derive PPDs. Signal performance is then estimated from these PPDs. The results are compared to those obtained from comparable PPDs derived from connected vehicle (CV) trajectory data. The generated PPDs from both data sources are similar, with relatively modest differences of 1% AOG and a 1.39 s/veh control delay. Practitioners can use the presented methodology to estimate trajectory-based traffic signal performance measures from their deployed LiDAR sensors. The paper concludes by recommending that unfiltered LiDAR data are used for deriving PPDs and extending the detection zones to cover the largest observed queues to improve performance estimation reliability. Full article

This study investigates lightweight and efficient candidates for sound absorption to address the growing demand for sustainable and eco-friendly materials in noise attenuation. Juncus effusus (JE) is a natural fiber known for its unique three-dimensional network, providing a viable and sustainable filler for enhanced sound absorption in honeycomb panels. Microperforated-panel (MPP) honeycomb absorbers incorporating JE fillers were fabricated and designed, focusing on optimizing the absorber designs by varying JE filler densities, geometrical arrangements, and MPP parameters. At optimal filling densities, the MPP-type honeycomb structures filled with JE fibers achieved high noise reduction coefficients (NRC) of 0.5 and 0.7 at 20 mm and 50 mm thicknesses, respectively. Using an analytical model and an artificial neural network (ANN) model, the sound absorption characteristics of these absorbers were successfully predicted. This study demonstrates the potential of JE fibers in improving noise mitigation strategies across different industries, offering more sustainable and efficient solutions for construction and transportation. Full article

The switched reluctance motor (SRM) has many merits, such as robustness, a simple construction, low cost, and no permanent magnets. However, its deployment in servo applications is restrained due to acoustic noise and torque ripple (TR). This paper presents a new torque control approach for TR reduction in switched reluctance drives. The approach is based on the maximum utilisation of the available dc-link voltage, hence extending the zero torque-ripple speed range. The approach is suitable for an SRM with any number of phases and stator/rotor poles. Soft switching control is deployed, which reduces switching losses. At any instant (regardless of the number of phases being conducted simultaneously), only one phase current is controlled. The well-established torque-sharing function concept is adapted and generalised to cater for more than two phases conducting simultaneously. MATLAB/Simulink confirmation simulations are based on the widely studied four-phase 8/6, 4 kW, 1500 rpm SRM. Full article

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy. Full article

This paper presents an analytical study of signal clipping that leads to the noise/distortion in the waveform of DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM)-based visible light communication (VLC) systems. The pilot-assisted (PA) technique is used to reduce the high peak-to-average power ratio (PAPR) of the time-domain waveform of the DCO-OFDM system. The bit error rate (BER) performance of the PA DCO-OFDM system is investigated analytically at three different clipping levels as well as without any clipping. The analytical BER performance is verified through simulation and then compared to that of the conventional DCO-OFDM without PAPR reduction at the selected clipping levels. The PA DCO-OFDM system shows improved BER performance at all three clipping levels. Full article

Medical image denoising plays a critical role in enhancing the quality of diagnostic imaging, where noise reduction without compromising image details is paramount. In this paper, we propose a novel hybrid approach aimed at improving the denoising efficacy for medical images. Initially, we employ an adaptive Kalman filter to attenuate noise, leveraging its proficiency in state estimation from noisy measurements. Unlike conventional Kalman filters with fixed parameters, our adaptive Kalman filter dynamically adjusts its parameters based on the noise characteristics of the input image, thus offering enhanced accuracy in estimating the underlying true state of the system represented by the medical image. Subsequently, both a non-local means (NLM) method and a median filter are introduced as post-processing steps to further refine the denoised image. The NLM method leverages the similarities between image patches to effectively reduce noise, while the median filter further enhances the denoised image by suppressing residual noise and preserving image details. However, the effectiveness of NLM and the median filter is highly dependent on carefully chosen parameters, which traditionally necessitates extensive computational resources for optimization. To address this challenge, we introduce the innovative use of Latin square optimization, a structured experimental design technique, to efficiently determine optimal parameters for NLM. By systematically exploring parameter combinations using Latin square optimization, we mitigate the complexity of experiments while enhancing denoising performance. The experimental results on medical images demonstrate the effectiveness of our proposed approach, showcasing significant improvements in noise reduction and the preservation of image features compared to conventional methods. Our hybrid approach not only advances the state-of-the-art in medical image denoising but also presents a practical solution for optimizing parameter selection in NLM, thereby facilitating their broader adoption in medical imaging applications. Full article

Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method’s applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The “Adaptive Howland Current Source” (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer. Full article

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs. Full article

Particle damping technology is applied in vibration and noise reduction because of its good broadband vibration reduction effect. The energy transfer and loss between particles are keys to the role of damping. This paper investigates the relationship between the thermal energy dissipation caused by the collision of particles and the input energy. The temperature rise characteristics under different vibration states are studied. The results show significant differences in the thermal dissipation characteristics of granular assemblies for different vibration states. Under equivalent excitation amplitudes, the frequency increases, and the thermal loss of the particles increases. At the same frequency, the excitation amplitudes increase, and the thermal loss of the particles decreases. Granular assemblies in strong vibrational states, such as a gas-like state, have intense vibrations and apparent temperature-increasing effects. However, in this vibration state, the input energy to the particles is considerable, and the thermal loss accounts for a small proportion of the total energy. In states such as solid-like states, micro-vibrational states, and intermediate vibrational states, the particles interact closely, and the input energy for the particles is small. Additionally, the movement of the particles is not intense, and the thermal loss accounts for a large proportion of the total energy. The thermal loss of the particles also shows a frequency variation characteristic. According to the different temperature rise characteristics of the particles, the proportion of thermal energy consumption is analyzed. The research shows that the proportion of thermal energy consumption is not more than 70%, so there are other forms of energy consumption in the vibration reduction and energy consumption of the particles. Full article