This paper describes different variants of broadband and simple attenuator modules for beamforming applications, based on Radio Frequency Micro Electro-Mechanical Systems (RF-MEMS), framed within Coplanar Waveguide (CPW) structures. The modules proposed in the first part of this work differ in their actuation voltage, topology, and desired attenuation level. Fabricated samples of basic 1-bit attenuation modules, characterized by a moderate footprint of 690 x 1960 µm2 and aiming at attenuation levels of -2, -3 and -5 dB in the 24.25-27.5 GHz range, are presented in their variants featuring both low actuation voltages (5-7 V) as well as higher values (~ 45V), the latter ones ensuring larger mechanical restoring force (and robustness against stiction). Beyond the fabrication non-idealities that affected the described samples, the substantial agreement between simulations and measurement outcomes proved that the proposed designs could provide precise attenuation levels up to 40 GHz, ranging up to nearly -3 dB and -5 dB for the series and shunt variants, respectively. Moreover, they could be effective building blocks for future wideband and reconfigurable RF-MEMS attenuators. In fact, in the second part of this work, combinations of the discussed cells and other configurations meant for larger attenuation levels will be investigated.

Infrared sensors incorporating suspended zinc oxide (ZnO) pyroelectric films and thermally-insulated silicon substrates are fabricated using conventional MEMS-based thin-film deposition, photolithography, and etching techniques. The responsivity of the pyroelectric film is improved through annealing at 500℃ for 4 h. The voltage response of the fabricated sensors is evaluated experimentally for a substrate thickness of 1 µm over a sensing range of 30 cm. The results show that the voltage signal varies as an inverse exponential function of the distance. A positioning system based on three infrared sensors is implemented in LabVIEW. It is shown that the position estimates obtained using the proposed system are in excellent agreement with the actual locations. In general, the results presented in this study provide a useful source of reference for the further development of MEMS-based pyroelectric infrared sensors.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

A lead zirconate titanate [PZT;Pb(Zr_0.52Ti_0.48)O₃] layer embedded infrared (IR) detector decorated with wavelength-selective plasmonic crystals has been investigated for high-performance non-dispersive infrared (NDIR) spectroscopy. A plasmonic IR detector with an enhanced IR absorption band has been designed based on numerical simulations, fabricated by conventional microfabrication techniques, and characterized with a broadly tunable quantum cascade laser. The enhanced responsivity of the plasmonic IR detector at specific wavelength band has improved the performance of NDIR spectroscopy and pushed the limit of detection (LOD) by an order of magnitude. In this paper, a 13 fold enhancement in the LOD of a methane gas sensing using NDIR spectroscopy is demonstrated with the plasmonic IR detector.

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computation time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring.

In this work performance improvements are described of a low power consumption non-dispersive infrared (NDIR) methane (CH₄) gas sensor using customised optical thin film bandpass filters (BPF). BPF’s shape the spectral characteristic of the combined mid infrared III-V based light emitting diode (LED)/ photodiode (PD) light source/detector optopair, enhancing NDIR CH₄ sensor performance. The BPF, deposited using a novel microwave plasma assisted pulsed DC sputter deposition process, is deposited at room temperature directly onto the temperature sensitive PD heterostructure. BPF’s comprise germanium (Ge) and niobium pentoxide (Nb₂O₅) alternating high and low refractive index layers respectively. Two different optical filter designs are progressed; with BPF bandwidths (BWs) of 160 nm and 300 nm. Comparison of modelled and measured NDIR sensor performance is described, highlighting maximized signal to noise ratio (SNR) and minimized cross talk performance benefits. BPF spectral stability for various environmental temperature and humidity conditions is demonstrated.

Volcanic activity essentially consists of the transfer of heat from the Earth’ interior to the surface. The precise signature of this heat transfer relates directly to the processes underway at and within a particular volcano and this can be observed, at a safe distance, remotely, using infrared sensors that are present on Earth-orbiting satellites. For over 50 years, scientists have perfected this art using sensors intended for other purposes, and they are now in a position to determine the particular sort of activity that characterizes different volcanoes. This review will describe the theoretical basis of the discipline and then discuss the sensors available for the task and the history of their use. Challenges and opportunities for future development in the discipline are then discussed.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

This study describes the synthesis, and characterization of lead-free organic ferroelectric perovskite N-methyl-N’-diazabicyclo[2.2.2]octonium)-ammonium trichloride (MDABCO-NH4Cl3). The electrospinning technique was employed to obtain nanofibers embedded with this perovskite into PVC polymer for hybrid fiber production. The dielectric, piezoelectric and pyroelectric properties of these fibers were carefully examined. From measurements of the dielectric permittivity temperature and frequency dependence, together with the pyroelectric results, a transition from a high temperature paraelectric to a ferroelectric phase that remained down to room temperature, was found to occur at 438 K. A pyroelectric coefficient as high as 290 C K-1 m-2value obtained is within the same order of magnitude as that reported for MDABCO-NH4I3 and the semiorganic ferroelectric triglycine sulfate (TGS). The hybrid nanofibers exhibited good morphological characteristics and demonstrated very good piezoelectric properties. Specifically, a piezoelectric coefficient of 42 pC/N was obtained when a periodical force of 3 N was applied. The performance of these fibers is on par with materials discussed in the existing literature for fabrication of nano energy harvesting generators. Importantly, the perovskite nanocrystals within the fibers not harmed by oxidation, making them a promising environmentally friendly platform for flexible energy harvesting.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

This review delves into the growing field of flexible and stretchable strain sensors utilized in human gait analysis, focusing on determining knee bending angle and muscle activities when a human subject walks. Recent advancements have enabled these sensors to accurately capture biomechanical parameters while accommodating the body's natural movements. We examine the principles underlying these sensors' design and fabrication techniques, emphasizing their flexibility, stretchability, and biocompatibility for seamless integration into wearable systems. Through critical evaluation of existing methodologies, we assess the reliability and validity of these sensors in quantifying gait parameters. Furthermore, we discuss this technology's clinical and research implications, highlighting its potential in personalized rehabilitation and sports performance optimization. We provide an overview of flexible strain sensors and their application for analyzing human walking gait, especially for observing walking gait in rehabilitation programs. First, we present the basic working principles of various flexible strain sensors. Second, by the application of flexible strain sensors as walking gait monitoring. Finally, current challenges and future opportunities in this research area are discussed. By synthesizing current knowledge and outlining future directions, this review aims to provide insights for researchers and practitioners seeking to leverage flexible and stretchable strain sensors for comprehensive human gait analysis.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

Lead-free ferroelectric perovskite N-methyl-N'-diazabicyclo[2.2.2]octonium)–ammonium triio-dide, MDABCO-NH₄I₃, nanocrystals are embedded in three different polymer fibers fabricated by the electrospinning technique. The nanofibers, which are very flexible and have a high Young modulus, behave as active piezoelectric energy harvesting sources that produce a piezoelectric voltage coefficient up to geff = 3.6 VmN-1 and show a blue intense luminescence band at 325 nm. In this work, the pyroelectric coefficient is reported for the MDABCO-NH₄I₃ perovskite inserted in electrospun fibers. At the ferroelectric-paraelectric phase transition, the embedded nanocrystals display a pyroelectric coefficient as high as 194×10-6 Cm-2k-1, within the same order of magnitude as that reported for the state-of-the-art bulk ferroelectric triglycine sulfate (TGS). The perovskite nanocrystals embedded into the polymer fibers remain stable, and no degradation is caused by oxidation.

This work is devoted to the study of the pyroelectric effect on the qualities of optical waveguides formed in a lithium niobate crystal by proton exchange. In the present work, we investigated the cessation effect of the radiation channeling during thermocycling of Y-splitters samples. We examined the spectral dependence of optical losses on a wavelength using an optical spectrum analyzer. The results demonstrate that in the range of 1530–1570 nm, all wavelengths are suppressed equally. The optical reflectometry method in the frequency domain shows that the increase of optical losses is observed along the entire waveguide, but not only at the Y-distribution point, as supposed earlier.

Among magnetoelectric (ME) heterostructures, ME laminates of the type Metglas-like / PVDF (magnetostrictive+piezoelectric constituents) have shown the highest induced ME voltages, usually detected at the magnetoelastic resonance of the magnetostrictive constituent. This ME coupling happens because of the high cross-correlation coupling between magnetostrictive and piezoelectric material, and is usually associated with a promising application scenario for sensors or actuators. In this work we detail the basis of the operation of such devices, as well as some arising questions (as size effects) concerning their best performance. Also, some examples of their use as very sensitive magnetic fields sensors or innovative energy harvesting devices will be presented At the end, the challenges, future perspectives and technical difficulties that will determine the success of ME composites for sensor applications are discussed.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

The adsorption of surfaces exposed to sunlight results in increased temperature that can cause physical damage and increase in energy consumption. The infrared reflective coatings that keep objects cooler have significant benefits in a wide variety of application by reflecting the infrared lights, reducing the operating costs, improving energy efficiency of buildings and vehicles (roofs, walls and windows), extend the objects’ lifespan. Our research focused on the elaboration of coatings with minimum adsorption in the infrared wavelength range. This was achieved by production of coatings that have infrared transparent and infrared reflective ability. The infrared reflection and surface warm up was investigated in the function of concentration and composition of pigments in the coatings. With investigation of different coating compositions the pigments and the binding systems were optimized. The coatings with different compositions were characterized by total solar reflection in the UV, visible and infrared wavelength range as well as by infrared reflection. Different coatings were produced in RAL7016 anthracite green color but with much better infrared reflectance, transparency as well as with heat reflectance.

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

This study presents the utilization of the Microwave InfraRed Thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite rein-forcement of concrete specimens. At first, a set of the numerical models was created, comprising of the broadband pyramidal horn antenna and the analyzed specimen. The utilization the system operating at a power of 1000W in a continuous mode, operating at frequency of 2.45 GHz was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and CFRP within the adhesive layer. In the modelling stage the study investigated three separate scenarios: a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte, to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from nu-merical modeling. In the experiments the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These con-clusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable, since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP.

In mining machines with friction discs, but also in multi-rope traction elevators, it is necessary to distribute the applied tensile load, generated by the weight of the cage and counterweight, evenly in all cross-sections of the load-bearing ropes. Hydraulic devices used for this purpose can operate on the principle of Pascal's law. The article presents a structural design, a 3D model and an implemented solution of a laboratory device capable of simulating a practical method of evenly distributing the total weight of the load into partial tensile forces of the same size acting on a selected number of load-bearing ropes. The laboratory equipment uses two pairs of three steel cables of finite length for the simulations. During the experimental measurements, tensile forces derived from the tractive force of the piston rods, pushed into the bodies of the hydraulic cylinders by the pressure of the hydraulic oil supplied through the pipeline under the pistons of the hydraulic cylinders, were detected. The resulting amount of hydraulic oil pressure in the hydraulic circuit influenced by different values of the hydraulic oil pressures in the hydraulic cylinders and by the pressure in the supply pipe was experimentally studied on the laboratory equipment. Simulations were also carried out in order to detect the hydraulic oil pressure in the hydraulic circuit caused by the change in the different magnitudes of the tensile forces in the ropes From the experiments carried out, it follows that with the appropriate choice of hydraulic elements and the design of the hydraulic circuit, the weight of the load, acting as the total pulling force in the ropes, can be evenly distributed (with a deviation of up to 5%) to all cross-sections of the load-bearing ropes. If the exact values of the hydraulic oil volumes under the pistons of all hydraulic cylinders are not known, it is not possible to calculate the pressure values in the hydraulic circuit when the valves of the hydraulic pipes are gradually opened.

It is shown that the termination of the channeling of the fundamental radiation mode in the waveguide can be observed upon heating of an optical integrated circuit based on proton exchange channel waveguides formed in a lithium niobate single crystal. This process is reversible, but restoration of waveguide performance takes tens of minutes. The effect of the waveguide disappearance is observed upon rapid heating (5 °C/min) from a low temperature (minus 40 °C). This effect can lead to a temporary failure of navigation systems using fiber optic gyroscopes with modulators based on a lithium niobate crystal

Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application in both brain and breast imaging. This paper discusses the current trends in NIRS for brain and breast imaging, exploring advances in multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, and the impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts to enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited spatial resolution and tissue heterogeneity, are discussed. The discussion extends to diffuse optical tomography and ongoing instrumentation development, clinical trials and studies validating NIRS diagnostic efficacy in breast imaging. The paper emphasizes the need for standardization, integration into routine clinical practice, and motivates for future work.

For the agricultural sector to develop sustainably in the future, progress toward more environmentally friendly technologies and methods is crucial. It is necessary to increase output while reducing the demand for energy, agrochemicals, and water resources. Although greenhouses can be utilized successfully for this purpose, significant technical advancements are required, especially when it comes to heating, to lower the use of fossil fuels and boost energy efficiency. Infrared waves and microwaves, for instance, can warm plants without having to heat the entire greenhouse volume, which takes a significant amount of energy to compensate for heat loss to the outdoor environment. In this paper, through a thorough examination of the state of the art, a general overview of novel greenhouse heating systems based on radiation is reported. First, infrared heating of greenhouses is analyzed, then the strengths and weaknesses of microwave and dielectric heating are discussed, and finally the use of microwaves for soil sterilization is examined. All outcomes suggest these irradiation-based technologies can contribute significantly to an agriculture that is energetically sustainable.

Membrane-based sensors (MePSs) exhibit remarkable precision and sensitivity in detecting pressure changes. MePSs are commonly used to monitor catalytic reactions in solution, generating gas products crucial for signal amplification in bioassays. They also allow catalyst quantification by indirectly measuring the pressure generated by the gaseous products. This is particularly interesting to detect enzymes in biofluids associated with disease onset. To enhance the performance of a MePS, various structural factors influence membrane flexibility and response time, ultimately dictating the device's pressure sensitivity. In this study, we fabricated MePSs using polydimethylsiloxane (PDMS) and investigated how structural modifications affect the Young's modulus (E) and residual stress (σ0) of the membranes. These modifications have a direct impact on the sensors' sensitivity to pressure variations, observed as a function of the volume of the chamber (Σ) or of the mechanical properties of the membrane itself (S). MePSs exhibiting the highest sensitivities were then employed to detect catalyst quantities inducing the dismutation of hydrogen peroxide, producing dioxygen as gaseous product. As a result, catalase enzyme was successfully detected using these optimized MePSs, achieving a remarkable sensitivity of (22.7 ± 1.2) µm/nM and a limit of detection (LoD) of 396 pM.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli, an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluid (MRF), magnetorheological elastomer (MRE) and magnetorheological plastomer (MRP). To easily understand the history of sensing research using the two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss on the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensor, tactile device, strain sensor, wearable bending sensor, magnetometer, display device and flux measurement sensor. After briefly describing what have been reviewed in a conclusion, several challenging future works, which should be done for practical applications as sensors or sensing devices, are remarked in terms of new technologies such as artificial intelligence neural network in which several parameters affecting the sensor signals can be precisely tuned or controlled. It is sure that this review article is very helpful to make creative sensors using not only the proposed smart materials but also several different types of smart materials including shape memory alloys and active polymers.

Tengchongite is a uranyl molybdate uranium mineral and it was found and named by Chen 1985.No more scholars studied on tengchongite after Chen’s work, The identification of this mineral has only been confirmed by single crystal X-ray diffraction. In the paper, micro laser Raman spectroscopy and infrared spectroscopy are used to identify the spectroscopy properties of tengchongite. The studies fill in the gaps of more than 30 years in terms of the molecular spectroscopy research of tengchongite. The mineral has an ideal model of Ca(UO₂)₆(MoO₄)₂O₅•12H₂O its bands attributed to the vibrating units are clearly identified in the Raman spectrum. Symmetric stretching modes at 812 cm^-1 and 839 cm^-1 are assigned to ν1 (UO₂)²⁺ ,The ν3 antisymmetric stretching modes of (UO₂)²⁺ are observed at 896 cm^-1, Symmetric stretching modes at 419^-1 and 440 cm^-1 are assigned to ν2 (UO₂)²⁺ .Symmetric stretching modes at 919cm-1 are assigned to ν1(MoO₄)^2-,The ν3 antisymmetric stretching modes of (MoO₄)^2- are observed at 752 cm^-1, the in-plane ν2(MoO₄)^2-and the out-of-plane ν4(MoO₄)^2- bending modes are at 169 cm^-1 and 254 cm^-1. IR spectrum of tengchongite shows the major uranyl band at 858 cm^-1 and 693 cm^-1, Mo-O bonds are observed at about 985 cm^-1 and 780 cm^-1, and H₂O groups are present with a wide range peaks from 3100 cm^-1 to 3500 cm^-1 and 1647 cm^-1, and the band at 1432.4 cm^-1 is probably due to the stretching vibration hydroxyl (OH^-1), therefore, tengchongite contains may include hydroxyl and its chemical formula needs to be modified .

Modeling and design of fiber lasers facilitate the process of their practical realization. Of particular interest during the last few years is the development of lanthanide ion-doped fiber lasers which operate at wavelengths exceeding 2000 nm. There are two main host glass materials considered for this purpose, namely fluoride and chalcogenide glasses. Therefore, this study concerns comparative modeling of fiber lasers operating within the infrared wavelength region beyond 2000 nm. In particular, the convergence properties of selected algorithms, implemented within various software environments, are studied with a specific focus on the central processing unit (CPU) time and calculation residual. Two representative fiber laser cavities are considered: one is based on a chalcogenide-selenide glass step-index fiber doped with trivalent dysprosium ions whilst the other is a fluoride step-index fiber doped with trivalent erbium ions. The practical calculation accuracy is also assessed by comparing directly the results obtained from the different models.

Sensors and actuators are fundamental units in Cyber-Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), that are patterns that describe abstract conceptual entities. For concreteness, we study them here in the context of autonomous cars. An autonomous car is a complex system because, in ad-dition to its own complex design, it interacts with other vehicles and with the surrounding in-frastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber-Physical System, where some of its func-tions are performed by Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appro-priate physical actions. From AEPs we can derive concrete patterns, a structure combining re-lated AEPs is an Entity Solution Frame (ESF). Both sensors and actuators are susceptible to mali-cious attacks due to the large attack surface of the system where they are used. Our work is in-tended to make autonomous cars more secure, which also increases their safety. Our final objec-tive is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups to-gether and organizes related security patterns. This article is the first stage of a secure unit that can be used to design not only secure autonomous cars but also any system where sensors and actuators are used. This paper concentrates on AEPs and ESFs for sensors and actuators; that is, on the functional aspects of these devices.

Magnetic sensors are widely used in health management systems for turbomachinery, but their applications in the hot zone are limited due to the loss of magnetic properties by permanent magnets with increasing temperature. The paper presents and verifies models and design solutions aimed at improving the performance of an inductive sensor for measuring the motion of rotating objects operating at elevated temperatures (200-1000C), such as compressor and turbine blades. Physical, analog and mathematical models of the interaction of blades with the sensor were developed. A prototype of the sensor was made and its tests were carried out on the RK-4 rotor rig for the speed of 7000 rpm, in which the temperature of the sensor head was gradually increased to 1100C. The sensor signal level was compared to that of an identical sensor operating at room temperature. The heated sensor works continuously producing the output signal whose level does not change significantly. What is more, a set of six probes passed an initial engine test in an SO-3 turbojet. It was confirmed that the proposed design of the inductive sensor is suitable for blade health monitoring of the last stages of compressors, steam turbines as well as previous generation gas turbines operating below 1000C, even without a dedicated cooling system. In real-engine applications, sensor performance will depend on how the sensor is installed and the available heat dissipation capability

Plasmonic sensors exploiting the Localized Surface Plasmon Resonance (LSPR) of noble metal nanoparticles are common in the visual spectrum. However, for bio-sensors the near infra-red (NIR) windows (600 nm – 900 nm and 1000 nm -1400 nm) are of interest, as it is a region where the absorption coefficient of water, melaninm deoxy- and hemoglobin are all low. The first part of this paper reviews the work that has been undertaken on using gold (Au) and silver (Ag) particles in Metal Enhanced Fluorescence (MEF) in the NIR. Despite this success there are limitations, as there is only a narrow band in the visual and NIR where losses are low for traditional plasmonic materials. Further, noble metals are not compatible with standard silicon manufacturing processes, making it challenging to produce on-chip integrated plasmonic sensors with Au or Ag. Therefore, it is desirable to use different materials for plasmonic chemical and biological sensing, that are foundry-compatible with silicon (Si) and germanium (Ge). One material that has received significant attention is highly doped Ge which starts to exhibit metallic properties at a wavelength as short as 6 μm. This is discussed in the second part of the paper and the results of recent analysis are included.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.

Propolis is a bee product widely used as a dietary supplement and included in sweets or baby foods due to its well-known antioxidant and nutritional properties that are directly correlated with its phenolic composition. For this reason, this study analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols, and the antioxidant capacity by using the methods of ABTS and linoleic acid/β-carotene in 99 samples of propolis from Spain and Chile. A rapid method was developed for quantifying these parameters in raw propolis using near infrared (NIR) spectroscopy with an optical fibre probe of remote reflectance applied directly to the ground up sample. The models developed allow the determination of the total of flavones and flavonols (0-183 mg rutin/ g propolis), of the total of flavanones and dihydroflavonols (9-109 mg pinocembrin/ g propolis extract), and the antioxidant capacity by the ABTS method (0-3212 nmolesTrolox/ mg of propolis) and of linoleic acid/β-carotene (22-86% inhibition). The NIR spectroscopy models were applied in external validation to different samples of the calibration group, which led to the conclusion that the methods developed provide significantly identical data to the initial chemical data of reference.

The world is facing a great technological transformation towards full autonomous vehicles, where optimists predict that by 2030, autonomous vehicles will be sufficiently reliable, affordable and common to displace most human driving. To cope with these trends, reliable perception systems must enable vehicles to hear and see all the surroundings, being light detection and ranging (LiDAR) sensors a key instrument for recreating a 3D visualization of the world in real time. However, perception systems must rely in accurate measurements of the environment. Thus, sensors must be calibrated and benchmarked before being placed on the market or assembled in a car. This article presents an Evaluation and Testing Platform for Automotive LiDAR sensors with the main goal of testing not only commercially available sensors, but also sensor prototypes currently under development in Bosch Automotive Electronics division. The testing system can benchmark any LiDAR sensor under different conditions, recreating the expected driving environment to which such devices are normally subjected. To characterize and validate the sensor under test, the platform evaluates several parameters such as the field of view (FoV), angular resolution, sensor’s range, etc. This project results from a partnership between the University of Minho and Bosch Car Multimedia Portugal, S.A.

This paper present a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source [1], is designed for researchers, students and makers for a broad range of their exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system are illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperatures in a lake.

Since the systematic evolution of ligands by exponential enrichment (SELEX) method was developed, aptamers have made significant contributions as bio-recognition sensors. Microdevice systems allow for low reagent consumption, high-throughput of samples, and disposability. Due to these advantages, there has been an increasing demand to develop microfluidic-based aptasensors for analytical technique applications. This review introduces the principal concepts of aptasensors and then presents some advanced applications of microdevice-based aptasensors on several platforms. Highly sensitive detection techniques such as electrochemical and optical detection have been integrated into lab-on-a-chip devices and researchers have moved towards the goal of establishing point-of-care diagnoses for target analyses.

The purpose of this study was to explore and develop high-resolution infrared thermal (HRIT) imaging for screening toddler’s fracture. A Toddler’s fracture is a common tibial fracture of children younger than 6 years old. Initial x-ray radiographs may not show the fracture and another x-ray during a follow-on visit is usually required. The study included 39 participants admitted to an emergency department with a suspected toddler’s fracture. X-ray confirmed 8 cases with a toddler’s fracture (20.5%). Infrared images of participants were recorded on their index visit, focusing on region-of-interests on the injured and the contralateral (uninjured) legs. The uninjured leg acted as a thermal reference. Nine statistical measures were extracted from the images: maximum, mini-mum, mean, standard deviation, median, mode, interquartile range, skewness, and kurtosis. Wilcoxon rank sum test indicated that maximum, mean, standard deviation, median, and inter quartile range temperature measures were significantly different (p<0.05) when comparing fractured and non-fractured legs. Principal component analysis of these measures highlighted distinct separation of toddler’s fracture and non-fracture cases. Similarly, plots of the statistical measures further confirmed clustering of toddler’s fracture cases. The study demonstrated that HRIT imaging can be a valuable tool for screening for toddler’s fracture.

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scale. However, this increase in resolution creates challenges with sample characteristics such as grain size, surface roughness and porosity that can influence the spectral signature. This research explores the effect of rock sample surface preparation on the TIR spectral signatures. We applied three surface preparation methods (split, saw and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.

There is a need to develop tools for mastitis management in goats and to measure the effects of milking machines on teats. Infrared thermography (IRT), as shown in cows, was a good candidate for early detection of mastitis and focusing on milking equipment and settings implicated in potential problems. The aim of this study was to test IRT to detect udder inflammation and the effects of mechanical milking on teats in relation to inflammation status, udder balance and teat shape in Alpine goats. IRT spectra were compared before and after milking in 551 goats from three commercial herds compared to their individual SCC (somatic cell count). We found no regression or trend between logSCC and IRT measurement or response to milking, even in highly inflamed goat udders. The effect of milking was significant (P

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

Infrared and visible image fusion technologies are used to characterize the same scene by diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to the source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. And the visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.

Electrochemical sensors have been used for many decades. Yet, modeling of such sensors used in electrolysis mode is poorly documented, especially in the case parallel action of multiple gases. Those ones are of great interest since they constitute the first brick to bring information on natures and concentrations of a gaseous mixture composition thanks to grey box modeling of sensors array for example. Based on Butler Volmer’s equations, a model assuming parallel reactions at gold cathode has been introduced in this article and confronted to experimental results. Establishment of the model is based on the extraction of 3 variables: the charge transfer coefficient “α”, the reaction order γ, and the reaction constant rate k0. Tests performed without pollutants and with different concentrations of oxygen could be nicely fitted by the model. Then, the influence of polarization current on the 3 variables of the model has been evaluated showing a clear influence on the constant rate and the reaction order. Also, increasing the polarization current enabled us to get selectivity to oxidant gases. Similarly, the effect of the oxygen concentration was evaluated. Results showed that, in this case, the charge transfer coefficients “α” obtained for oxidant gases is quite different that the ones obtained in the polarization current varying conditions. Therefore, the model will be interesting in situations where polarization current and oxygen content are not varied together. Variation of polarization current can be quite interesting to get increased information for multivariate analysis purpose in constant oxygen content situations. Besides, other parameters have are required for applications in which the oxygen content is bound to change like exhaust gases or combustion.

This study investigates novel ferrocene-based electrochemical sensors for metal cation detection through the design, synthesis and characterisation of ferrocene derivatives. Specifically, the re-search determines the redox potentials of ferrocene versus decamethylferrocene to provide in-sight into the redox potential variations between them. The investigation also examines how electrochemical oxidation of the ferrocene moiety can modulate host affinity for transition metals cations via effects such as electrostatic interactions and changes to coordination chemistry. Metal ion coordination to receptors containing functional groups like imine and quinoline is explored to elucidate selectivity mechanisms. These findings advance the fundamental understanding of ferrocene electrochemistry and host-guest interactions, supporting the development of improved cation sensors with optimised recognition properties, sensitivity, and selectivity. Overall, the work lays the necessary groundwork for applications in analytical chemistry and sensor technologies through customised ferrocene-derived materials.

Light-addressable potentiometric sensor (LAPS) is an electrochemical sensor based on the field-effect principle of semiconductor. It is able to sense the change of Nernst potential on the sensor surface, and the measuring area can be controlled by the illumination. Due to the unique light-addressable ability of LAPS, the chemical imaging system constructed with LAPS can realize the two-dimensional image distribution detection of chemical/biomass. In this paper, the advantages of LAPS as sensing unit of microelectrochemical analysis system are summarized. Then, the greatest development of LAPS analysis system is explained and discussed. Especially, this paper focused on the research of ion diffusion, enzymatic reaction, microbial metabolism and droplet microfluidics by using LAPS analysis system. Finally, the development trends and prospects of LAPS analysis system are illustrated.

Current smartphones incorporate different types of sensors that allow us to know our spatial position, they give us information about pressure, speed, acceleration, time, acoustic level, and other different physical magnitudes. These smartphones measure each component of the magnetic field, bearing in mind that any current perpendicular to a magnetic field produces a small potential difference, transversal to the said current, being this voltage easily measurable by Hall sensors. With the implementation of three Hall sensors, and an appropriate app, we can measure the three components of the magnetic field vector, and with this we can obtain information and deduce properties of the physical systems considered. In this paper we are exploring the use of smartphones in a physics laboratory for freshman students. To do this, we have measured, using Hall sensors, the magnetic field created by a linear quadrature, and we have obtained, first of all, its dependence on the distance between the quadrupole and the magnetic sensor. The second purpose of this work is to show that the laboratory is a powerful tool that increases the significant learning of freshman students through advanced technological tools.

Flow sensing in hostile environment is of increasing interest for applications in automotive, aerospace, and chemical and resource industries. Compared to their counterparts, thermal flow sensors are attractive candidates due to the ease of fabrication, lack of moving parts and higher sensitivity. Recently, a number of thermal flow sensor prototypes have been reported in the literature demonstrating the measurement of fluid flows under hostile conditions. This paper summarizes the concept of thermal flow sensing, operational modes and transduction mechanisms. Then, the choice of materials and their corresponding properties are presented in details. The paper also reports recent progress in the development of thermal flow sensors for harsh environment. In addition, the issues and considerations in packaging are reviewed. Finally, we conclude the review with the future prospects.

We report on the phosphorescence of singlet oxygen photogenerated through a stimulated Raman process. Nanosecond radiation in the green spectral region focused on hexane induces a Raman transition of the dissolved in the solvent oxygen molecules toward the singlet oxygen state producing a Stokes signal in the near infrared. The excited oxygen relaxes to ground emitting an infrared photon at 1269 nm. While the Stokes signal’s wavelength changes with the pumping light’s wavelength, the wavelength of the phosphorescent photon remains unaltered. The result confirms previously reports on stimulated Raman excitation of singlet oxygen.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

A focal plane array (FPA) detector was used for hyperspectral imaging in the infrared (IR) spectral region using thermal and synchrotron light sources. FPA Fourier-transform IR (FTIR) imaging microspectroscopy will be able to monitor real time changes at specific absorption bands when combined with high brightness synchrotron source. In this study, several types of samples with unique structural motifs were selected and used for assessing the capability of the FPA-FTIR imaging technique. It was shown that the time required for polariscopy at IR wavelengths can be substantially reduced by the FPA-FTIR imaging approach. By using natural and laser fabricated polymers with sub-wavelength features, alignment of absorbing molecular dipoles was revealed as well as higher order patterns (laser fabricated structures). Micro-spectroscopy of absorber orientation reveals alignment patterns even when they are not spatially resolved.

This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.

Due to Motor Neurone Diseases, a large population remains disabled worldwide, negatively impacting their independence and quality of life. This typically involves a weakness in the hand and forearm muscles, making it difficult to perform fine motor tasks such as writing, buttoning a shirt, or gripping objects. This project presents a vision-enabled rehabilitation hand exoskeleton to assist disabled persons in their hand movements. The design goal was to create an accessible tool to help with a simple interface requiring no training. This prototype is built on a commercially available glove where a camera and embedded processor were integrated to help open and close the hand, using air pressure, thus grabbing an object. An accelerometer is also implemented to detect the characteristic hand gesture to release the object when desired. This passive vision-based control differs from active EMG-based designs as it does not require individualized training. Continuing the research will reduce the cost, weight, and power consumption to facilitate mass implementation.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

In recent years, the integration of sensors in agriculture has emerged as a pivotal catalyst for transforming conventional farming practices into precision agriculture. Sensor application is impacting the everyday objects that enhance human life. In this special issue, the main objective was to address recent advantages of sensor application in agriculture covering a wide range of topics in this field. This provides overview of diverse application of sensors in agriculture, highlighting their role in enhancing productivity, sustainability, and resource management. sensors are instrumental in monitoring various agriculture parameter such as soil condition, weather, crop health and livestock wellbeing. By collecting real-time data and providing valuable insight, sensors enable precision farming, optimize resource allocation, and minimize environmental impacts. we have to explore the key sensor types used in agriculture, their applications, benefits, and future prospects, emphasizing their crucial roles in the modernization of agriculture and global quest for food security and crop production.

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.

Polycyclic aromatic hydrocarbons (PAHs) have emerged as promising materials for organic electronics, including organic photovoltaics (OPVs), organic field-effect transistors (OFETs), and organic light-emitting diodes (OLEDs). Particularly, non-hexagonal ring-fused PAHs are highly desirable due to their unique optoelectronic properties. Herein, a new redox-active azulene-perylene diimide triad 1 and its ring-fused counterpart, diazulenocoronene diimide 2, were synthesized and fully characterized by a combination of NMR, cyclic voltammetry, spectroelectrochemistry, and UV-visible absorption spectroscopy. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were performed to gain further insight into various electronic transitions. Interestingly, we found that the adaptive response to acids and bases manifests itself in a reversible two-color-change that can be attributed to changes in the chemical structures.

Novel tetragonal matrix Ba0.5Gd0.5-xNaxF2.5-x, where x = 0.05, doped by Yb3+, Ho3+, Er3+, was synthesized by molten salt synthesis (MSS) from nitrate flux. XRD data show that tetragonal phase with a = 4.122(1) Å, c = 17.672(1) Å is stable in argon atmosphere up to 960 °C. Luminescence spectra recorded in 500-900 nm and 1050-1700 nm upon 974 nm pumping demonstrated the characteristic luminescence at 1550 nm (4I13/2 → 4I15/2) for Er3+ and 1150 nm (5I6 → 5I8) for Ho3+. The relative thermal sensitivity (Sr) at 296-316 K were 0.3 %*K-1 and 5.5%*К-1 in shortwave infrared (SWIR) and visible range, respectively. Synthesized luminophores can be used as dual-range optical temperature sensors, which simultaneously operating in visible and SWIR ranges.

Sensing Technology (ST) plays a key role in Structural Health Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems or smart materials that monitor a wide variety of materials properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and allowing continuous and permanent measurements of the structural integrity. The integration of ESs is limited to the processing technology to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material composition, either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM employing ESs, focusing on the actual developments and innovation of these, as well as analysing the challenges that these technologies present, to build a path that allows a connected world through distributed measurement systems.

: This paper presents a couple of meal monitoring systems for senile dementia patients by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their families or nurses in charge. Thereby, the nurses can easily spot the most desperate patient to take care of while the families can have relief to see the crucial information for survival of their parents at least three times a day. Meanwhile, the senile dementia patients tend to suffer the burn of their tongues because they can hardly recognize the temperature of hot meals served and therefore avoid the burn of tongues. This phenomenon can be discarded by utilizing the meal temperature monitoring system which displays alarm to the patients when the meal temperature is above the reference. These meal monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino UNO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief not only for the professional nursing nurses working in elder-care hospitals and nursing homes, but also for the families of the dementia patients.

The protection of electronics against environmental influences and mechanical loads is important for integration of conventional electronics in textile conductive tapes. For this purpose, the sensors on the tapes are molded with plastic locally. This process step is recognized in the injection molding process. The molding compound is then later selected depending on: The field of application, the parameters of the manufacturing process and the textile tape properties. We have designed a mould for liquid silicone (LSR) as well as for the textile and electronic insertions. The cavities are sealed by a local compression of the textile and the two inserts are positioned with position pins representing the main aspect of the mould design. The sampling tool and the process parameter optimization are mainly based on the material properties of the silicone and the mechanical sensitivity of the inserts. To reduce the deformation of the circuit boards by the melt front and ensuring the functionality of the electronics a low-pressure process is used.

Despite the increasing popularity of liquid chromatography-mass spectrometry (LC-MS)-based lipidomics, there is a lack of accepted and validated method for lipid extract quality and quantity assessment prior to LC-MS. Fourier-Transform Infrared Spectroscopy (FTIR) has been reported for quantification of pure lipids, however, the sample complexity and purity complex lipid extract quantification in lipidomics experiments could be impact quantification accuracy. Here, we report comprehensive assessment of the sample matrix on the accuracy of lipid quantification using Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR). Pure lipids are characterized by CH-and C=O-stretching vibrations on FTIR, with quantitative range of 40–3000 ng and a limit of detection of 12 ng. Sample extraction method and local baseline subtraction during FTIR spectral processing significantly impact lipid quantification by CH-stretching. To facilitate sample quality screening, we developed the Lipid Quality (LiQ) score from a spectral library of common contaminants, using a ratio of peak heights between CH-stretching vibrations maxima and the collective vibrations from amide/amine, CH-stretching minima and sugar moieties. We evaluated LiQ score as a rapid sample quality control method by comparing to total LC-MS intensity of targeted lipidomics of 107 human plasma lipid extracts. Exclusion of poor-quality samples based on LiQ score improved the correlation between FTIR and LC-MS quantification. Finally, the uncertainty of absolute quantification by FTIR was estimated using a 795 ng SPLASH LipidoMix standard to be &lt;10%. In summary, this study identified key parameters for accurate FTIR-based quantification of complex lipid mixture, and developed a rapid workflow requiring only 1 µL of MS-ready sample and &lt; 5 minutes for routine lipidomics sample quality and quantity assessment.

Current generation witnesses a huge interest in optical waveguide due to their salient feature such as low cost, immune to electromagnetic interference, easy to multiplex, compact size, etc. These features of the optical fibers makes it an useful tool for various sensing applications including medicine, automotive, biotechnology, food quality control, aerospace, physical and chemical monitoring etc. Among all the reported application, the device has been widely exploited to measure the physical and chemical variation in surrounding environment. Optical fiber based temperature sensor plays a crucial role in this decade to detect high fever and tackle COVID like pandemic. Recognizing the major developments in the field of the optical fibers, this article aims to provide recent progress in temperature sensor utilizing several sensing configuration including standard fiber, photonic crystal fiber, and Bragg grating fibers. Additionally, the article also highlights the advantages, limitations, and future possibilities in this area.

It is since long known, that SAW devices, resonators as well as delay lines, can be used as passive wireless sensors for physical quantities like temperature and pressure as well as gas sensors or ID-Tags. The sensors are robust, work passively without battery, can be applied at high temperatures and provide a high resolution. Nevertheless, if the devices should be readout wirelessly in an industrial environment, several constraints have to be taken into account, especially when more than one quantity or device needs to be measured at the same time. The paper addresses the challenges that have to be tackled when establishing multi-sensor-wireless-readout for industrial applications. Major issues here are the legal ISM-band regulations, as well as sampling time and costs, which impose severe restrictions to any system design. We describe several design approaches and their constraints. We have successfully designed sensors based on reflective delay lines that allow the parallel readout of four independent temperature sensors in the 2.45 GHz ISM-band. These devices have been fabricated, positively tested and demonstrate the applicability of SAW sensors for industrial applications.

Carbon nanotubes have been attracting considerable interest among material scientists, physicists, chemists and engineers for almost 30 years. Owing to their high aspect ratio, coupled with remarkable mechanical, electronic and thermal properties, carbon nanotubes have found application in diverse fields. In this review, we will cover the work on carbon nanotubes used for sensing applications. In particular, we will see examples where carbon nanotubes act as main players in devices sensing biomolecules, gas, light or pressure changes. Furthermore, we will discuss how to improve the performance of carbon nanotube-based sensors after proper modification.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

Group-IV GeSn photodetectors (PDs) compatible with standard complementary metal-oxide-semiconductor (CMOS) processing have emerged as a new and non-toxic infrared detection technology to enable a wide range of infrared applications. The performance of GeSn PDs is highly dependent on the Sn composition and operation temperature. Here, we develop theoretical models to establish a simple rule of thumb, namely “GeSn-rule 23”, to describe GeSn PDs’ dark current density in terms of operation temperature, cutoff wavelength, and Sn composition. In addition, analysis of GeSn PDs’ performance shows that the responsivity, detectivity, and bandwidth are highly dependent on operation temperature. This rule provides a simple and convenient indicator for device developers to estimate the device performance at various conditions for practical applications.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, [email protected] achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, [email protected] was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

The near-infrared (NIR) organic dyes with strong ultrafast nonlinear optical (NLO) activities are of importance for various applications. However, such kinds of dyes are still scarce. In this work, we have compared the NLO properties of two NIR Aza-BODIPY derivatives, in which the strong electron-donating groups, namely 4-(N, N-dimethylamino) phenyl and 1-ethyl-1,2,3,4-tetrahydroquinoline groups, are connected with the cores of Aza-BODIPY. Z-Scan experimental results show that two Aza-BODIPY derivatives exhibit strong saturation absorption and large modulation depth under the excitation of femtosecond pulses at 800 nm. Under 1300 nm excitation, two derivatives exhibit strong nonlinear refraction. In addition, the Aza-BODIPY derivatives also display effective two-photon excited fluorescence emission in the wavelength range of 1200-1600 nm. Based on the experimental results, it is found that 1-ethyl-1,2,3,4-tetrahydroquinoline group can more effectively enhance the NLO properties of Aza-BODIPY derivatives compared with the 4-(N, N-dimethylamino) phenyl group, thus providing new possibilities for the design and development of NIR NLO materials.

Strained layer superlattice (SLS) material system is being considered for large format, small-pitch and low-cost focal plane arrays for different applications. To address difficulties associated to focal plane arrays fabrication arising from mesa-isolation etching or complex surface treatment/ passivation process, planar structures have been considered. In this work, the recent progress on planar SLS photodetector using ion-implantation for device isolation is presented. The devices are presented here are nBn and pBn heterostructure InAs/InAsSb SLS photodetector, where Zn and Si were chosen as the ion implants, respectively. The electrical and optical performance of the devices were compared along with comparison with mesa-etched device, to give deeper view of the device performance.

Polarisation analysis in the mid-infrared fingerprint region was carried out on thin (∼1μm) Si and SiO2 films evaporated via glancing angle deposition (GLAD) method at 70∘ to the normal. Synchrotron-based infrared microspectroscopic measurements were carried out on the Infrared Microspectroscopy (IRM) beamline at Australian Synchrotron. Specific absorption bands, particularly Si-O-Si stretching vibration, was found to follow the angular dependence of ∼cos2θ, consistent with the absorption anisotropy. This unexpected anisotropy stems from the enhanced absorption in nano-crevices, which have orientation following the cos2θ angular dependence as revealed by Fourier transforming the image of the surface of 3D columnar films and numerical modeling of light field enhancement by sub-wavelength nano-crevices.

Earth-abundant transition metals like iron, nickel, copper, molybdenum, and vanadium have been identified as essential constituents of the cellular gas metabolism in all kingdoms of life. Associated with biological macromolecules, gas-processing metalloenzymes (GPMs) are formed that catalyse a variety of redox reactions. This includes the reduction of O₂ to water by cytochrome c oxidase (‘complex IV’), the reduction of N₂ to NH₄ by nitrogenase, as well as the reduction of protons to H₂ (and oxidation of the later) by hydrogenase. GPMs perform at ambient temperature and pressure, in the presence of water, and often extremely low educt concentrations, thus serving as natural examples for efficient catalysis. Facilitating the design of biomimetic catalysts, biophysicist thrive to understand the reaction principles of GPMs making use of various techniques. In this perspective, I will introduce Fourier-transform infrared spectroscopy in attenuated total reflection configuration (ATR FTIR) for the analysis of GPMs like cytochrome c oxidase, nitrogenase, and hydrogenase. Infrared spectroscopy provides information about the geometry and redox state of the catalytic cofactors, the protonation state of amino acid residues, the hydrogen-bonding network, and protein structural changes. I developed an approach to probe and trigger the reaction of GPMs by gas exchange experiments, exploring the reactivity of these enzymes with their natural reactants. This allows recording sensitive ATR FTIR difference spectra with seconds time resolution. Finally yet importantly, infrared spectroscopy is an electronically non-invasive technique that allows investigating protein samples under biologically relevant conditions, i.e., at ambient temperature and pressure, and in the presence of water.

The realization of a perfect absorber A = 1 with transmittance and reflectance T=R=0 by a thin metasurface is one of the hot topics in recent nanophotonics prompted by energy harvesting and sensor applications (A + R + T =1 is the energy conservation). Here we tested optical properties of over 400 structures of metal-insulator-metal (MIM) metasurfaces for a range of variation in thickness of insulator, the diameter of a disc and intra-disc distance experimentally and numerically. Conditions of a near-perfect absorption A > 95% with simultaneously occurring anti-reflection property (R < 5%) were experimentally determined. Differences between the bulk vs. nano-thin film properties at mid-IR of the used materials can be of interest for plasmonic multi-metal alloys and high entropy metals.

NIRS uses the relative absorption of light at 850nm and 760nm, to determine skeletal muscle oxygen saturation. Previous studies have used the ratio of both signals to report muscle oxygen saturation. Purpose: To evaluate the different approaches used to represent muscle oxygen saturation, and to evaluate the pulsations of the O₂heme and Heme signal. Method: Twelve participants, ages 20-29years were tested on the forearm flexor muscles using continuous wave NIRS at rest. Measurements were taken during 2-3mins rest, during physiological calibration (5-minuts Ischemia) and during reperfusion. Results: There was a significant difference in pulse size between O₂heme and Heme signal at the three locations (p < 0.05). Resting oxygen saturation was 58.8+9.2%, 69.6+3.9%, and 89.2+6.9% when calibrated using O₂heme, TSI, and Heme, respectively. Conclusion: The difference in magnitude of O₂heme and Heme pulse with each heartbeat might suggest different anatomical locations of these signals, which propose calibrating with just one of the signals instead of the ratio of both. Calculations of physiological calibration must account for increased blood volume in the tissue, because of the changes in blood volume which appear to be primarily from the O₂heme signal. Resting oxygen levels calibrated with Heme agrees with theoretical oxygen saturation.

Chloroquine was among the first of several effective drug treatments against malaria until the onset of chloroquine resistance. In light of diminished clinical efficacy of chloroquine as an antimalarial therapeutic, there is potential in efforts to adapt chloroquine for other clinical applications, such as in combination therapies and in diagnostics. In this context, we designed and synthesized a novel asymmetrical squaraine dye coupled with chloroquine (SQR1-CQ). In this study, SQR1-CQ was used to label live Plasmodium falciparum (P. falciparum) parasite cultures of varying sensitivities towards chloroquine. SQR1-CQ positively stained ring, mature trophozoite and schizont stages of both chloroquine–sensitive and chloroquine–resistant P. falciparum strains. In addition, SQR1-CQ exhibited significantly higher fluorescence, when compared to a chloroquine-BODIPY (borondipyrromethene) conjugate. We also achieved successful SQR1-CQ labelling of P. falciparum directly on thin blood smear preparations. Drug efficacy experiments measuring half-maximal inhibitory concentration (IC50) showed lower concentration of effective inhibition against resistant strain K1 by SQR1-CQ compared to conventional chloroquine. Taken together, the versatile and highly fluorescent labelling capability of SQR1-CQ and promising preliminary IC50 findings potentiates it to be further developed as a promising diagnostic bioimaging tool with drug efficacy against chloroquine-resistant P. falciparum.

This paper presents the design, fabrication and characterization of Schottky erbium/silicon photodetectors working at 1.55 µm. These erbium/silicon junctions are carefully characterized using both electric and optical measurements at room temperature. A Schottky barrier ΦB of ~673 meV is extrapolated; the photodetectors show external responsivity of 0.55 mA/W at room temperature under a 8 V of reverse bias applied. In addition, the device performance is discussed in terms of normalized noise and noise equivalent power. To the best of our knowledge, these are the first Er/Si photodetectors designed for operation in free space at 1.55 µm. The proposed devices will pave the way towards development of Er-based photodetectors and light sources to be monolithically integrated in the same silicon substrate and both operating at 1.55 µm.

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. Companies in the manufacturing sector are heavily integrating Industry 4.0 technologies, including cloud compu-ting, cyber-physical systems (CPS), big data analytics, horizontal and vertical system integration, the internet of things (IoT), and additive manufacturing (3d printing). The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations, allowing critical data collection on products, equipment performance, and maintenance. The sensors connect systems and machines, allowing them to communicate and track operations for enhanced operations. While numerous research has been conducted to show the role of sensors in industry 4.0, limited studies show how these innovations can leverage product design to maximize benefits and opportunities. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. This research paper employs a systematic bibliometric literature review (LRSB) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.

Cratylia argentea is a leguminous shrub with a great potential for livestock feeding in tropical areas. However, time consuming and labor-intensive methods of chemical analysis limits the understanding of its nutritive value. Near infrared spectroscopy (NIRS) is a low-cost technology that has been widely used in forage crops to fasten the assessment of its chemical composition. The objective of this study was to develop prediction models to assess crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and dry matter (DM) of Cratylia based on NIRS and partial least square analysis. A total of 155 samples were harvested at different maturity levels and used for model development, whereas 107 were used for calibration and 48 for external validation. The cross-validation presented Root Mean Square Error of Prediction of 0.77, 2.56, 3.43, and 0.42; a Ratio of Performance to Deviation of 4.8, 4.0, 3.8, and 3.4; an R2 of 0.92, 0.92, 0.87, and 0.84 for CP, NDF, ADF, and DM, respectively. Based on the obtained results, we conclude the ability to predict chemical parameters of Cratylia with the current model was accurate. This way, livestock producers and researchers may use it to fasten the assessment of Cratylia’s nutritive value.

The monitoring of sick newborns is a challenging task that health care providers in Neonatal Intensive Care Units (NICU) must contend with each day. Conventionally, newborns are monitored via probes that are affixed to their skin and attached to processing monitors (Fig.1). However, an alternative exists in contactless imaging to record such physiological signals (Physio-Markers), surface changes and internal structures which can be used independently of, or in conjunction with conventional monitors. Advantages of contactless monitoring methods include: i) quick data generation; ii) lack of contact with skin, which reduces skin breakdown and decreases risk of infection; and iii) minimizing the number of probes and monitors affixed to the skin, which allows greater body surface-area for other care. This paper is an attempt to build a foundation for and to provide a vision of the potential neonatal clinical applications of technologies that use non-contact modalities such as Visible Light Imaging (VLI), Near InfraRed Spectrum (NIRS), and Thermal Imaging (TI) using InfraRed Spectrum (IRS).

Today, there are commercially available high-performance electric wheelchairs, capa-ble of responding to strong limitations. However, despite these advancements, there is a significant portion of the population with severe disabilities who cannot access this mobility solution due to safety reasons. Indeed, either due to motor issues preventing reliable control of the wheelchair's commands, or associated visual or cognitive pathologies, the use of an electric wheelchair by these individuals would put them at risk and could also constitute a danger to those around them. This is why our research team is working on the development of augmented wheelchairs that allow a greater autonomy. We are mainly developing a system compatible with all electric wheel-chairs on the market, capable of making them anti-collision and anti-fall on uneven ground (sidewalks, stairs, etc.). In this article, we present our solution regarding the anti-collision function, in particular the sensors used to do this, as well as the strategy. We will focus on the shortcomings and weaknesses of various types of sensors and demonstrate how, by combining them in a certain way, we can develop a collision avoidance device capable of managing the diversity of everyday life situations. The two types of sensors that will be combined here are an in-house developed special near infrared sensor from the laboratory and a commercially available ultrasonic sensor. We will present the internal operation of the sensors, their limitations, experimental tests involving integration with the wheelchair, and the structure of the complete device.

This paper provides a data set collected with an underground shaft mobile mapping system equipped with rotated Velodyne VLP-16, Velodyne Ultra Puck VLP-32c, Livox Tele-15, and IMU Xsens MTi-30. The ground truth data was acquired with a geodetic survey including 15 ground control points and 6 Faro Focus 3D Terrestrial Laser Scanner stations of a total 273,784,932 of 3D measurement points. The aim of the paper is to describe the data set of a mining shaft. Obtained mobile mapping data can be the basis for detailed analyzes of the technical condition of the shaft. This data set provides an end-user case study of realistic applications in mobile mapping technology. It provides ground truth data, mobile mapping data and software tools for manipulation and visualization. The project is released and maintained at https://michalpelka.github.io/mine-mapping-dataset/.

Vehicle safety promises to be one of the Advanced Driver Assistance System (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, Lidar and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. As most of the current study is based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. The study developed a novel method for evaluating the freshness of citrus fruits by integrating near-infrared spectroscopy with the nonlinear data processing capabilities of a BP neural network. This approach utilizes specific wavelength analysis to distinguish between fresh and non-fresh fruits effectively. Advanced preprocessing techniques are employed to remove spectral anomalies, enhancing the network's ability to accurately identify crucial quality indicators like sugar content. Concurrently, an experiment utilising a MATLAB-based BP neural network optimised the number of hidden layer nodes, identifying 61 as optimal. This configuration achieved impressive metrics, including a mean squared error of 0.0025665 and a root mean squared error of 49.8214, over 1000 training iterations with an 80% learning rate. The model demonstrated a high accuracy rate of 97.6275%, confirming its precision and reliability in assessing citrus freshness. This synergy between advanced neural network processing and spectroscopic techniques marks a significant advancement in agricultural quality assessment, setting new standards for speed and efficiency in data processing.

We report a novel dual-band barrier infrared detector (DBIRD) design using type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a "blue channel" (BC) diode which has an nBp architecture, an n-type layer of larger bandgap for absorbing blue band infrared/barrier/p-type layer, and a "red channel" (RC) diode which has an pBn architecture, a p-type layer of smaller bandgap for absorbing red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage, as is preferable for high-speed thermal imaging. Device modeling of DBIRDs and simulation results of the current-voltage characteristics under dark and illuminated conditions are also presented. They predict that dual-band operation of the DBIRD will produce low dark currents and 27-33% quantum efficiencies for the in-band photons in the BC with λc=5.58 μm, and a nearly-constant 33% in the RC with λc=8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 20% over the range of λ=3 − 4.2 μm, while that of the RC has a peak of 42% at 5.7 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors.

Background: Glioblastoma remains a challenging brain tumor to treat due to its infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful tumor resection. This study introduces an innovative intraoperative visualization method utilizing a surgical fluorescence microscope to precisely locate tumor cells. The focus of the study is on IR-Glint, a novel preparation comprising Cy7.5-labeled aptamers Gli-233nt and Gli-55_3L specific to human glioblastoma. Methods: The aptamers were modified using molecular modeling and quantum chemical techniques. The effectiveness of the preparation was assessed using flow cytometry and microscopy on primary cultures of human glioblastoma. In vivo studies were conducted on mouse and rabbit models, employing orthotopic xenotransplantation of human brain glioblastoma with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. Results: The experiments validated the potential of IR-Glint for intraoperative visualization of glioblastoma using infrared imaging. Surface application of the aptamer-based formulation on the brain allowed clear visualization of the tumor, aiding surgeons in tumor resection. This approach also reduces the dosage required and mitigates potential toxic effects on the patient. Conclusions: This study demonstrates the promising potential of using in-frared dye-labeled aptamers for intraoperative visualization of glioblastoma. By improving surgical treatment outcomes in neurosurgery, this novel approach may pave the way for future advancements in the field.

Amorphous germanium films on non-refractory glass substrates were annealed by ultrashort near-infrared (1030 nm, 1.4 ps) and mid-infrared (1500 nm, 70 fs) laser pulses. Crystallization of germanium irradiated at a laser energy density (fluence) range from 25 to 400 mJ/cm2 under single-shot and multi-shot conditions was investigated using Raman spectroscopy. The dependence of the fraction of the crystalline phase on the fluence was obtained for picosecond and femtosecond laser annealings. The regimes of almost complete crystallization of germanium films over the entire thickness are obtained (from the analysis of Raman spectra with excitation of 785 nm laser). The possibility of scanning laser processing is shown, which can be used to create films of micro and nanocrystalline germanium on flexible substrates.

Emergence of zoonotic-human pathogens is proven to be a lethal threat to public health, and RNA virus including influenza viruses, severe acute respiratory syndrome coronavirus, middle east respiratory syndrome coronavirus, Wuhan coronavirus (COVID-19), plays a pivotal role. As those viruses as airborne microorganisms spread mainly by tiny airborne particles, it is important to de-active those airborne particles before their entry into human bodies. In this study, we investigated the effect of far infrared (FIR) radiation on inhibition of airborne microorganisms. The result confirmed that double stand DNA from airborne microorganisms were stable under mild FIR radiation. However, single strand RNA from them was found to be sensitive to FIR radiation, indicating that RNA virus in airborne particles is instable under FIR radiation. Based on this observation, two models on usage of FIR radiation to prevent RNA virus transmission and cure RNA virus infection were proposed, implying that FIR radiation might be a cheap, convenient, and efficient method in clinic to treat RNA virus.

Near-Infrared Spectroscopy (NIRS) has been used to measure muscle mitochondrial capacity. The current method requires as many as 22 short ischemic occlusions to generate a recovery curve for mitochondrial capacity. PURPOSE: To determine the effectiveness of using a 6-occlusion analysis protocol to study muscle mitochondrial capacity. METHOD: Two independent, unidentified data sets were analyzed (bicep n=48, forearm n=41) from previous studies using a NIRS device (Artinis, Ltd.). Both data sets had two recovery tests that included 22 ischemic occlusions. A recovery rate used to indicate mitochondrial capacity was calculated two different ways (simultaneously). Each sample was analyzed with a MATLAB program; with a curve-fit for the 22 ischemic occlusions and curve matching for the first six ischemic cuffs and an end resting value. The two resulting rate constants were compared using correlations, both for the two data sets, good and bad fitting data, using the best 5 of 6 points for the 6 cuff approach. RESULTS: The rate constants were not significantly different between the 22 cmuff and 6 cuff for the total data sets: bicep (1.43+0.32min^-1, 1.44+0.35min^-1, p=0.56), forearm (1.94+0.42min^-1, 1.95+0.44min^-1, p=0.76). The average bicep rate constants, when compared to each other, had an equation of y=1.07x-0.09, R²=0.90. The average forearm rate constants, when compared to each other, had an equation of 0.98x+0.02, R²=0.93. CONCLUSIONS: The 6-Cuff analysis provided the same results as the longer 22-cuff. The 6-cuff approach is both shorter in time and uses less ischemic occlusion periods, increasing the practicality of the NIRS mitochondrial capacity test.

We apply deep convolutional neural networks (CNNs) to estimate wave breaking type from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. Whereas for a plunging breaker, the crest and back face of the wave are most important. This suggests that CNN-based models utilize the distinctive `streak' temperature patterns observed on the back face of plunging breakers for classification.

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. Maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors which operate above 10 MHz and low-frequency sensors. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. Thus, identifying emerging trends and gaps in knowledge, it encourages further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

Palpation is a simple but effective method to differentiate tumors from healthy tissues. Development of miniaturized tactile sensors embedded on endoscopic or robotic devices are the key toward precise palpation diagnosis and the subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. Utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration combining pneumatic sensing and hydraulic actuating also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibility in early detection of solid tumor as well as in the development of all in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.