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This work describes a new type of sensor for growth process monitoring named broadband cavity-enhanced optical flux monitoring sensor (BBCE-OFM). Like existing optical flux monitoring (OFM) solutions, it relies on absorption spectroscopy. However, the implementation of an optical cavity reduces the measurement uncertainty, enabling efficient operation even at very low growth rates. Using the BBCE-OFM sensor mounted in our solid-source oxide molecular beam epitaxy reactor, we achieved an uncertainty of ±2% on the measurement of Sr and Ti growth rates in SrTiO3 at around 1 Ml/min, to be compared to the ±16% obtained in the same conditions using a conventional OFM setup. Furthermore, our sensor architecture, based on an echelle monochromator and LEDs replacing the hollow cathode lamps used in standard OFM sensors, is more robust against drift. [ABSTRACT FROM AUTHOR]

We report on the construction of the SiRO—SiPM ReadOut muon detector, a detection system based on plastic scintillator bars designed for muography applications. Using six 1 m 2 layers of active medium, grouped two by two into three rectangular matrices of pixels, each separated by a variable distance, the spatial coordinates of the muon's impact point on every matrice are obtained and used for trajectory reconstruction. Validation studies have been performed using Monte Carlo simulations and later confirmed by preliminary measurements in our laboratory and in underground, in the Slănic Prahova salt mine, in Romania. [ABSTRACT FROM AUTHOR]

Transition-Edge Sensors (TESs) constitute highly sensitive particle and radiation detectors, widely used in many applications. Each of these requires optimization of TES performances and designs, including sizes and geometries. These may have implications on the superconducting transition mechanisms and, therefore, on TESs performances and stability, through the specific shape of the resistance vs temperature and current R(T,I) and the nature of noise. In this study, we investigate the dependence of the superconducting transition, characterized by R(T,I), on TES size and bias current density. Through analyses of R(T,I) in bare Mo/Au TESs with Tc tuned for this study, we observe how the weak link behavior induced by the superconducting leads weakens and disappears as TES length or driving current increase, being substituted by another dominant transition mechanism, which might be related to a Berezinskii–Kosterlitz–Thouless transition. We also observe a significant broadening of the transition's upper part, attributed to the longitudinal proximity effect induced by the pads; for the shorter devices, this effect is observed for R > 70% Rn and results in TES resistances considerably lower than Rn up to temperatures well above the TES transition: R < Rn up to 3 K for a 8 μm-long device. [ABSTRACT FROM AUTHOR]

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance. [ABSTRACT FROM AUTHOR]

A highly sensitive terahertz stereo metasurface sensor, characterized by a high quality factor (Q-factor) and based on dual toroidal dipole (TD) resonance, has been proposed. The optimal structural parameters are ascertained by comparing the pertinent parameters of the stereo and planar structures in relation to TD modal excitation. The effective excitation of the TD mode is demonstrated using the calculations of multipole scattered power, reflection spectra, surface currents, electric fields, and magnetic field distributions. It is crucial that the stereo metasurface exhibits simplicity and that the dual TD resonance can be readily excited through simple adjustments in the distance and height of the intermediate gap. It also demonstrates exceptionally high sensitivity and Q-factor, both of which are essential for sensing applications. Moreover, the proposed stereo terahertz metasurface sensor still shows excellent sensing performance in a wide range of incidence angles (±40°), which is of great significance for practical applications. In conclusion, this structure offers a novel design framework for high-performance terahertz sensors based on the TD mode. [ABSTRACT FROM AUTHOR]

This article presents the possibility of controlling/managing the frequency selection ability of membrane-based broadband frequency sensors (MB2FS), exploiting systematic selection of membrane stiffness. MB2FS is a bio-inspired system mimicking the geometry and functionality of the basilar membrane (BM) in the mammalian cochlea. The actual BM is tapered in geometry (both length wise and thickness wise), which makes the stiffness of the membrane uniformly varied over its length. Because of varied stiffness, different locations of the BM show resonance deflection at different frequency inputs, which allow the BM to select/sense the entire sonic frequency band within its 35 mm length. While actual BM and conventional MB2FS possess homogeneous stiffness over the domain length, in this article, a comprehensive insight is provided of how the frequency selection ability of the sensors can be manipulated and controlled, predictively, using functionally graded structural stiffness. Therefore, this work is not intended to develop an artificial BM, rather is focused on developing frequency sensors inspired by the membrane stiffness of BM, which plays a vital role in the spatial selection of acoustic frequencies. The study is performed using a numerically validated predictive model developed in a semi-analytical interface to explain the effects of MB2FS stiffness variations. Based on biological occurrence of stiffness in natural BM, three functions (logarithmic, linear, and exponential) are assumed to predict the FSP. While a random sonic frequency band of 10–12 kHz is targeted in this study to demonstrate the stiffness grading principle of MB2FS, a similar process (e.g., choosing the appropriate stiffness distribution of the beam) can be used to develop both sonic and ultrasonic frequency sensors. This study presents a detailed framework of how the sensing parameters of a specific frequency band (e.g., sensing location of band start and end frequencies and membrane segment width necessary to sense the entire frequency band) are dependent on the function coefficients. Finally, a comprehensive guideline is provided to predictively determine the function coefficients for user-defined frequency selection parameters. While existing state-of-the-art only allows designing MB2FS for a specific frequency band, the work presented in this study will open the opportunity to select multiple frequency bands of an MB2FS without altering its geometric configuration. [ABSTRACT FROM AUTHOR]

Anomalous Hall effect (AHE) sensor has attracted significant attention in recent years due to its simpler structure and better sensitivity as compared to semiconductor-based Hall sensors. However, up to now, most of the work has been focused on its basic functionalities, with very little attention given to optimizing its performance for practical applications. In this work, we systematically investigated how the lateral dimension affects the performance of CoFeB-based AHE sensors. By adjusting the sensor width while keeping the length and other parameters constant, it is found that although the output signal amplitude decreases with increasing the sensor width, the noise and power consumption also decrease simultaneously, leading to an overall enhancement of the sensor performances with a sensitivity, detectivity, and power efficiency of 8960 Ω/T, 205 nT / Hz @ 1 Hz , and 2009 V/WT, respectively. To illustrate the potential applications of AHE sensors, we demonstrate a proof-of-concept experiment to showcase the potential AHE sensor in pulse detection. [ABSTRACT FROM AUTHOR]

Four types of barrier detectors based on a type II InAs/InAsSb superlattice with a wide-gap barrier made of a solid AlInAsSb lattice matched to the GaSb buffer were compared. The tested detectors differed in the type of doping of the active layer and the level and type of doping of the contact layer at the barrier. The epitaxial layers were deposited on GaAs (100) substrates using the molecular beam epitaxy method. The spectral and current–voltage characteristics of the analyzed detectors were compared. The highest current responsivities were observed in the structure with a p-type absorber (p+BpN+). Detectors with an n-type absorber (p+Bnn+, n+Bnn+, and nBnn+) show an increase in the current responsivity with an increase in the reverse bias voltage due to the reduction in the undesirable barrier in the valence band. Arrhenius characteristics for the dark current show that only in nBnn+ detectors, it was possible to limit the generation–recombination current. These detectors at 150 K were characterized by the highest normalized detectivity of approximately 3 × 1011 cm · Hz1/2/W. The obtained results were compared with literature data, showing that the parameters of type II superlattice photodetectors are close to those of HgCdTe photodiodes according to the "Rule 07" and "Rule 22" principles. [ABSTRACT FROM AUTHOR]

Transition-edge sensors (TESs) have the potential to perform electron spectroscopic measurements with far greater measurement rates and efficiencies than can be achieved using existing electron spectrometers. Existing spectrometers filter electrons by energy before detecting a narrow energy band at a time, discarding the vast majority of electrons available for measurement. In contrast, TESs have intrinsic energy sensitivity and so do not require prior filtering to perform energy-resolved measurements. Despite this fundamental advantage, TES electron spectroscopy has not, to our knowledge, previously been reported in the literature. We present the results of a set of proof-of-principle experiments demonstrating TES electron spectroscopy experiments using Mo/Au TESs repurposed for electron calorimetry. Using these detectors, we successfully measured the electron spectrum generated by an electron beam striking a graphite target with energies between 750 and 2000 eV, at a noise-limited energy resolution of 4 eV. Based on the findings of these experiments, we suggest improvements that could be made to TES design to enhance their electron detection capabilities through the use of a dedicated electron absorber in the device with integrated electron optics. [ABSTRACT FROM AUTHOR]

Dual-energy imaging represents a versatile and evolving technology with wide-ranging applications in medicine and beyond. Recent technological developments increased the potential for improved diagnostic accuracy and expanded imaging capabilities across various fields. The purpose of this work is to design and develop an energy-integrating multilayer detector, known as a sandwich detector, aimed at single-shot dual-energy imaging tasks such as material discrimination and contrast cancellation. The sandwich detector uses two complementary metal oxide semiconductor advanced pixel sensors of 50 μm pixel size. The top and bottom sensors detect low-energy (LE) and high-energy (HE) photons, with sensors coupled with 250 and 600 μm scintillators, respectively. For better spectral separation between layers without excessively affecting the detected statistic in the bottom layer, the insertion of a 0.25-mm Cu filter between the layers was found to be the optimal choice, from among the tested 0-, 0.25-, and 0.5-mm filter options. The thickness selection for scintillator and intermediate Cu filter was carried out through a dual-energy simulation model. The experiments confirmed the model's reliability in selecting the optimal thicknesses of the intermediate Cu filter, thereby providing reassurance also on the choice of the top scintillator. [ABSTRACT FROM AUTHOR]

Miniaturized photoionization detectors (PIDs) are used in conjunction with gas chromatography systems to detect volatile compounds in gases by collecting the current from the photoionized gas analytes. PIDs should be inexpensive and compatible with a wide range of analyte species. One such PID is based on the formation of a He plasma in a dielectric barrier discharge (DBD), which generates vacuum UV (VUV) photons from excited states of He to photoionize gas analytes. There are several design parameters that can be leveraged to increase the ionizing photon flux to gas analytes to increase the sensitivity of the PID. To that end, the methods to maximize the photon flux from a pulsed He plasma in a DBD-PID were investigated using a two-dimensional plasma hydrodynamics model. The ionizing photon flux originated from the resonance states of helium, He(3P) and He(21P), and from the dimer excimer He2*. While the photon flux from the resonant states was modulated over the voltage pulse, the photon flux from He2* persisted long after the voltage pulse passed. Several geometrical optimizations were investigated, such as using an array of pointed electrodes. However, increasing the capacitance of the dielectric enclosing the plasma chamber had the largest effect on increasing the VUV photon fluence to gas analytes. [ABSTRACT FROM AUTHOR]

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

A novel mode-localized acceleration sensor employing an electrostatically coupled resonator and integrating a lever with proof mass is micromachined using standard silicon on insulator (SOI) technology. In order to determine the linear dynamic range of the sensor, a reduced order model is developed while assuming that the resonators vibrate below the critical amplitude. Then, open-loop and closed-loop testing platforms are established to measure the performance of the linearly operating accelerometer in a vacuum environment (less than 5 Pa). Moreover, the corresponding amplifier circuit based on the capacitive detection principle is designed in order to extract and amplify the current signal from the resonators. The obtained results show that the accelerometer sensitivity can be increased by three orders of magnitude when using the relative shift of amplitude ratio as the output metric instead of the relative shift of frequency, and the experimental measurements are consistent with the theoretical predictions. Remarkably, the Allan standard deviation of the mode-localized acceleration sensor obtained from the closed-loop testing circuit is around 5.03 μg. [ABSTRACT FROM AUTHOR]

Introduction: To perform energy-dispersive X-ray imaging, we constructed a photon-counting X-ray computed tomography (CT) scanner to perform enhanced K-edge CT. Methods: X-ray photons penetrating through an object were detected using a cadmium telluride flat panel detector (FPD) with pixel dimensions of 100 x 100 mm[sup.2], and 720 radiograms from the FPD were sent to the personal computer to reconstruct tomograms. Gadolinium (Gd) K-edge energy is 50.2 keV, and Gd-Kedge CT was carried out using photons with an energy range of 50-100 keV. Results: Compared with low-energy CT of 15-50 keV, the gray density of muscle and bone substantially decreased, and the image contrast of Gd media was improved utilizing Gd-K-edge CT. Conclusion: Using the cone beam, the effective pixel dimensions were 80 x 80 μm[sup.2], and blood vessels were observed at a high contrast using Gd-Kedge CT. Keywords: Cadmium telluride flat panel detector, dual-energy selection, gadolinium-K-edge computed tomography, high spatial resolution, photon-counting computed tomography, threshold energy, Author(s): Jiro Sato [1]; Eiichi Sato (corresponding author) [2]; Kazuki Ito [1]; Hodaka Moriyama [1]; Osahiko Hagiwara [1]; Toshiyuki Enomoto [1]; Manabu Watanabe [1]; Sohei Yoshida [3]; Kunihiro Yoshioka [3]; [...]

In this work, we studied the free-carrier transport properties and space-charge formation/polarization in a CdZnTe bar-shaped radiation detector using the Laser-Induced Transient Current technique. We found out that a steady-state space charge ranging from 8 × 108 to 2.1 × 109 cm−3 is formed throughout the detector at about 0.5 s following biasing. The measured current waveforms were modeled by Monte Carlo simulations after taking into account the electric and weighting fields inside the detector and shielding box. Modeling of the unpolarized waveforms revealed an exceptionally high electron mobility-lifetime product μ e τ e ≥ 0.095 c m 2 V − 1 . The observed formation of positive space charge in the biased detector and linear scaling of the current waveform shapes on applied bias revealed that the detector polarization is attributed to carrier injection at the anode. Measurements in pulsed and DC bias in modified electrode geometry proved the surface-charge formation. [ABSTRACT FROM AUTHOR]

Millimeter-thick methylammonium lead tribromide (MAPbBr3) single crystal x-ray detectors have recently raised attention due to their high x-ray attenuation efficiency and good charge transport properties. However, an intriguing feature of the photocurrent response of MAPbBr3 detectors has been largely overlooked in the literature. After biasing, transient sensitivity is measured under x rays at short-circuit (bias = 0 V), thus revealing a large remnant electric field that builds up under bias. Here, we exploit the x-ray sensitivity of MAPbBr3 detectors at zero-bias in order to probe the internal built-in field, as well as to investigate the charge transport properties of the perovskite material. Our model derived from the Hecht equation is able to fully rationalize the response of the detectors both at short-circuit and under moderate applied bias. Moreover, we provide a method for the estimation of the internal electric field, and for the sum of the electrons and holes mobility–lifetime products μ e τ e + μ h τ h. This general method could extend to any perovskite-based x-ray detector exhibiting transient sensitivity at zero-bias. [ABSTRACT FROM AUTHOR]

Sweep cover is a basic issue in wireless sensor networks, which uses mobile sensors to inspect points of interest (PoIs) periodically. There are many studies on the problem assuming that all mobile sensors have a uniform velocity, while some literature shows that using heterogeneous velocities may greatly increase the difficulty of research. In this paper, we study a minimum energy partial sweep cover (MinEPSC) problem on a line, the goal of which is to minimize energy consumption to sweep-cover at least K PoIs, where the energy consumption of a mobile sensor is related with its velocity in somewhat arbitrary but monotone manner. We design algorithms with theoretically guaranteed approximation ratios under different assumptions on the candidate velocity sets. [ABSTRACT FROM AUTHOR]

Aquaculture is an important economic and food source in many countries. Due to environmental restrictions and the effects of aquatic diseases, aquaculture requires a lot of labor and expensive materials, and it relies on the expertise of aquaculture experts. The quality of water is essential for aquaculture development. Therefore, in this paper, a novel aquaculture water quality monitoring using autonomous sensors (AquaSense) has been proposed which uses autonomous sensors for efficient monitoring of water in the aquaculture environment. In the AquaSense framework, temperature, pH, dissolved oxygen, and salinity measurements are recorded and collected using a variety of autonomous sensors. Based on the information gathered, users can assess the condition of their farm through the Internet. The MATLAB R2012b platform is employed to verify the effectiveness of the suggested water quality (WQ) monitoring technique and analyze the data. Several criteria including accuracy, precision, recall, specificity, and f1-score have been used to assess the effectiveness of the suggested strategy. AquaSense achieves the high accuracy ranges of 96.98%, and existing techniques ISAS, AquaStat, and IoT-WQI FIS achieve 91.24%, 93.39%, and 88.92%, respectively. [ABSTRACT FROM AUTHOR]

The dilemma zone (DZ) has been physically characterized based on two divergent definitions: Type I and Type II. However, treating DZ differently based on these definitions may lead to inaccurate results of DZ boundary and subsequent safety analyses. Moreover, an integrated empirical assessment of Type I and Type II definitions for consistency in boundary quantification is not yet well-addressed. To this end, we empirically analyzed the two DZ definitions by comparing their boundary dynamics with approach velocity and time of day. First, we proposed a rule-based matching methodology with 92% accuracy to match actuation events between the advance and stop-bar detectors. This methodology was then applied to process two months of high-resolution event data from an intersection approach, yielding 28,700 vehicle arrivals on yellow. Results showed that 13.2% of approaching vehicles fall into an indecision zone or make Type I-contrary stop/run decisions at the yellow onset. The Type I and Type II DZ boundaries were temporally segregated and did not significantly overlap. Our novel findings indicate a lack of consistency in quantifying DZ and emphasize a need for data-driven quantification of the DZ boundary and its dynamics. [ABSTRACT FROM AUTHOR]

The ground conductor is prone to corrosion, which affects its ability to diverge current and the normal operation of the power system. The traditional detection method can't detect its corrosion degree intuitively. This paper proposes a novel method based on Electromechanical impedance (EMI) technique to evaluate the corrosion degree of the grounding conductor. Firstly, based on the analysis of corrosion of metals. The change of ground conductor radius Δr is used as the corrosion parameter. A linear model of peak frequency Δfm and corrosion parameter Δr in asymmetric layout is established by analysing EMI and conductor mechanical admittance. Secondly, the EMI coupling models with different sensor layouts were built on the finite element software. It is verified that the asymmetric sensor layout effectively improves the sensitivity to corrosion. The linear model greatly improves the assessment accuracy compared with the Mean Absolute Percentage Deviation (MAPD) and Root Mean Square Deviation (RMSD) damage index. Thirdly, the proposed method is verified in a real grounding conductor corrosion experiment. The method based on the EMI technique with asymmetric sensor layout can evaluate the corrosion of the grounding conductor well. [ABSTRACT FROM AUTHOR]

With the rapid development of remote sensing technology and the widespread application of remote sensing images, remote sensing object detection has become a hot research direction. However, we observe three primary challenges in remote sensing object detection: scale variations, small objects, and complex backgrounds. To address these challenges, we propose a novel detector, he Multi-Scale Context-Aware Network (MSCANet). First, we introduce a Multi-Scale Fusion Module (MSFM) that provides various scales of receptive fields to extract contextual information of objects at different scales adequately. Second, the Multi-Scale Guidance Module (MSGM) is proposed, which fuses deep and shallow feature maps from multiple scales, reducing the loss of feature information in small objects. Finally, we introduce the Context-Aware DownSampling Module (CADM). It dynamically adjusts context information weights at different scales, effectively reducing interference from complex backgrounds. Experimental results demonstrate that the proposed MSCANet achieves superior performance results with mean average precision (mAP) of 97.1% and 73.4% on the challenging RSOD and DIOR datasets, respectively, which indicates that the proposed network is suitable for remote sensing object detection and is of a great reference value. [ABSTRACT FROM AUTHOR]

Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders. [ABSTRACT FROM AUTHOR]

Data races represent a class of concurrency errors when two threads access a shared memory location without proper synchronization. Data races are hard to reveal and debug. This paper presents RaceHunter, a dynamic data race detection technique, which monitors executions of shared-memory concurrent programs, discovers pairs of conflicting memory accesses, and systematically checks them for data races. RaceHunter does not report false data races when the target software exploits nonstandard synchronization primitives or unknown synchronization protocols, and it can also detect data races missed by other approaches. Dynamic data race detectors can monitor continuous, e.g., real-life, program executions or they can verify relatively short program executions, e.g., organized by system tests. The latter is the primary use case scenario for RaceHunter. [ABSTRACT FROM AUTHOR]

Existing Out-of-Distribution (OoD) detection methods address to detect OoD samples from In-Distribution (InD) data mainly by exploring differences in features, logits and gradients in Deep Neural Networks (DNNs). We in this work propose a new perspective upon loss landscape and mode ensemble to investigate OoD detection. In the optimization of DNNs, there exist many local optima in the parameter space, or namely modes. Interestingly, we observe that these independent modes, which all reach low-loss regions with InD data (training and test data), yet yield significantly different loss landscapes with OoD data. Such an observation provides a novel view to investigate the OoD detection from the loss landscape, and further suggests significantly fluctuating OoD detection performance across these modes. For instance, FPR values of the RankFeat (Song et al. in Advances in Neural Information Processing Systems 35:17885–17898, 2022) method can range from 46.58% to 84.70% among 5 modes, showing uncertain detection performance evaluations across independent modes. Motivated by such diversities on OoD loss landscape across modes, we revisit the deep ensemble method for OoD detection through mode ensemble, leading to improved performance and benefiting the OoD detector with reduced variances. Extensive experiments covering varied OoD detectors and network structures illustrate high variances across modes and validate the superiority of mode ensemble in boosting OoD detection. We hope this work could attract attention in the view of independent modes in the loss landscape of OoD data and more reliable evaluations on OoD detectors. [ABSTRACT FROM AUTHOR]

Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios. [ABSTRACT FROM AUTHOR]

Hydrogen peroxide (H2O2) has an important role in living organisms, and its detection is of great importance in medical, chemical, and food safety applications. This review provides a comparison of different types of Surface-enhanced Raman scattering (SERS) sensors for H2O2 and related substances with respect to their detection limits, which are of interest due to high sensitivity compared to conventional sensors. According to the latest research report, this review focuses on the sensing mechanism of different sensors and summarizes the linear range, detection limits, and cellular applications of new SERS sensors, and discusses the limitations in vivo and future prospects of SERS technology for the detection of H2O2. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a method for capacitance health monitoring (CHM) of submodule capacitors in modular multilevel converters (MMCs) that uses a reduced number of voltage sensors. In the proposed method, the MMC arms are divided into some groups of submodules and one voltage sensor is used for each group. In addition, a capacitor voltage estimation technique with minimal computational complexity is used to determine the capacitor voltage. The ratio of the estimated to measured capacitor voltage during positive arm current is also used to demonstrate a simple and efficient capacitance monitoring method. Moreover, contrary to the limitations associated with traditional CHM methods, the proposed method reduces the required voltage sensors and minimizes data exchange between the power system and the central processor. The effectiveness of the proposed method is validated through simulation and experimental results under various operating conditions. [ABSTRACT FROM AUTHOR]

Sensors utilizing mesoporous materials have garnered significant interest; however, they are primarily designed for detecting a single analyte. In this study, we successfully synthesized a three-dimensional (3D) hollow mesoporous α-Fe 2 O 3 using a nanocasting process for the purpose of sensing LPG and humidity. The α-Fe 2 O 3 obtained in this study maintained the cubic framework structure of MCM-48, demonstrating a mesoporous and hollow architecture. The Liquified Petroleum Gas (LPG) sensing characteristics of mesoporous α-Fe 2 O 3 were examined at different operating temperatures (170–210 °C). The gas sensing response was optimized 99.3 % at 190 °C and the response/recovery time comes out to be 11.79 s & 0.84 s respectively. The humidity sensor's traits were evaluated at room temperature, with relative humidity (RH%) ranging from 11 % to 98 %, using mesoporous α- Fe 2 O 3. The response/recovery time of mesoporous α-Fe 2 O 3 as a humidity sensor comes out to be 6.3 s & 7.2 s respectively. The highly sensitive nature of the sensor along with negligible hysteresis, excellent repeatability and considerable stability for 30 days makes it a promisable candidate for real-time subtle respiration monitoring. Meanwhile, mesoporous α-Fe 2 O 3 humidity sensor is also utilized for evaluation of sleep hypopnea-apnea syndrome. [ABSTRACT FROM AUTHOR]

In this paper, a whole new multistage refractive index sensor is proposed, which is based on the horizontal displacement of the photonic spin Hall effect. It is found that the angle of displacement is very sensitive to the diversification in the refractive index of the measured analyte, and the angle of horizontal displacement can vary by 12.5 degrees when the refractive index changes by 1. In addition, to further expand the function of this sensor, the multistage refractive index measurement by adjusting the Fermi level of the graphene is realized. Specifically speaking, when the Fermi level is 0.2 eV, the refractive index measurement range of this sensor is 2.2-3.2, and the sensitivity can be up to 7.5 degrees/RIU at this time. Eventually, the transformation of temperature and thicknesses of both the graphene layer and the layer to be measured in the two cases are respectively discussed, and the result is that this sensor has good temperature resistance and the stability of thickness errors in the engineering process. It can be said that the emergence of this new type of sensor will likely have broad application prospects in the measurement field, especially for some special cases of single frequency point work. [ABSTRACT FROM AUTHOR]

A hybrid film that exhibits reversible fluorescence (FL) is prepared by embedding a conjugated polymer, having an intramolecular stacking structure of the side phenyl rings, in a highly dispersed state into a silicone elastomer. The hybrid film experiences significant FL quenching during stretching and restores its original FL when the external stress is removed. In particular, the hybrid film manufactured using a shadow-masked, plasma-treated silicone elastomer provides a high-resolution FL image, which disappears when the film is stretched and reappears when the external stress is removed. The hybrid film can be applied as a strain-gauge sensor or a security film with embedded images that can be mechanically erased and regenerated. [ABSTRACT FROM AUTHOR]

QCM-based alcohol vapor sensors have been widely researched, and various materials have been deposited onto the sensor surface to serve as a functional layer. However, non-conductive polymers such as SBS as a functional layer for alcohol sensors are still minimally reported. In this study, the SBS was deposited on 1 side of the QCM using the spin coating technique. After deposition, the layer was annealed for 1 h. The annealing temperatures are 100, 150 and 200 °C. The concentrations of exposed alcohol vapor were 5, 10, 15, 20, 25, 50, 75 and 100 ppm. This study demonstrated that the SBS layer has the potential to be a functional layer of alcohol sensors. SBS 200 °C is an optimum functional layer for alcohol vapor sensors due to its high Δf and sensitivity. Meanwhile, SBS 100 °C has poor Δf and sensitivity. This is due to the morphology of 200 °C SBS, which consists of more valleys that perform as interaction sites for SBS and alcohol molecules. However, one of the limitations of SBS as a functional sensor layer for alcohol sensors is its low selectivity, which is a characteristic of rubbery polymers. [ABSTRACT FROM AUTHOR]

Efficient detection of toxic heavy metal anions Cr2O72− residue in water media is of great significance due to its severe damage to ecosystem and human health. Herein, a new 2D (two-dimensional) luminescent metal–organic frameworks (MOFs) {[Zn2(bmida)(phen)]·H2O}n (abbr.Zn-MOF) (1,10-phen = 1,10-phenanthroline, H4bmida = N-(phosphonomethyl)iminodiacetic acid) was constructed and structurally characterized. Notably, this Zn-MOF as an efficient luminescent sensor can detect Cr2O72− both in water media and HEPES biological buffer solution with high selectivity and sensitivity, and good cyclic stability. The corresponding detection limit (LOD) of Zn-MOF towards Cr2O72− is as low as 1.21 and 5.46 µM with large quenching constant (Ksv), respectively. The LOD in H2O solution is lower than the benchmark of drinking water recommended by United States Environmental Protection Agency (USEPA, 1.92 µM). Moreover, a possible competitive energy absorption mechanism is suggested by multiple experiments. [ABSTRACT FROM AUTHOR]

In digital radiography, moir0 artifacts are interference patterns that result from the interaction between a digital detector's pixel matrix and an anti-scatter grid. These artifacts can potentially cause diagnostic errors and increased radiation exposure by blurring important diagnostic information and degrading image quality. Several factors contribute to their presence, including detector under-sampling, incorrect alignment, and mismatches in grid frequencies. Implementing high-frequency gridsand ensuring correct alignmentare effective methods to mitigate moir0 artifacts. It is crucial to understand and manage these artifacts to maintain the accuracy and reliability of radiographic imaging in clinical practice. [ABSTRACT FROM AUTHOR]

Vision-based driver monitoring, a non-invasive method designed to identify potentially dangerous operations, has attracted increasing attention in recent years. In this study, a head pitch angle detection method was established to evaluate the driver's drowsiness. Rather than employing the front facial landmarks to estimate head pitch angle, the proposed method measure this angel directly from driver's profile face. To meet the requirement of real-time detection, the method applies the YOLOv8 network of single-stage detection and utilizes MobileNetV3 and FasterNet for lightweight improvement. The detector is trained with re-labeled CFP datasets, and real-time speed tests have been performed. Results demonstrate that the non-improved detector can achieve an mAP50 of 97.3% of the keypoints in a single frame, meanwhile realizing the frame rate of 30.41 FPS. After improvement, parameters of the model have been reduced by 21.3% and 40.9% respectively, while the frame rate can be increased to 37.13 FPS and 52.70 FPS, and the mAP50 of keypoints is increased by 0.41% and 0.51%. The results during the in-car experiment have proved that the developed detection method can effectively evaluate the head pitch angle, thus detect the driver's drowsiness. We provide open-access to the annotated data and pre-trained models in this study. [ABSTRACT FROM AUTHOR]

Capacitive sensor is best suitable sensor for the fabrication. In this study, we used Finite Element Method (FEM) tool to design and simulate capacitive pressure sensor based on the nob-linearity and sensitivity aspects. The sensor consists of parallel plates which is electrodes made up by optimizing two sensing devices that is microbridge and micro-cantilever with three different materials aluminium, gold and platinum. Among those aluminium is better due to its non-linearity and cost. The suitable sensing element is integrated into pressure sensor in order to measure the variation in glucose concentration levels. [ABSTRACT FROM AUTHOR]

Objective: The study aimed to enhance transparency in autonomous systems by automatically generating and visualizing confidence and explanations and assessing their impacts on performance, trust, preference, and eye-tracking behaviors in human–automation interaction. Background: System transparency is vital to maintaining appropriate levels of trust and mission success. Previous studies presented mixed results regarding the impact of displaying likelihood information and explanations, and often relied on hand-created information, limiting scalability and failing to address real-world dynamics. Method: We conducted a dual-task experiment involving 42 university students who operated a simulated surveillance testbed with assistance from intelligent detectors. The study used a 2 (confidence visualization: yes vs. no) × 3 (visual explanations: none, bounding boxes, bounding boxes and keypoints) mixed design. Task performance, human trust, preference for intelligent detectors, and eye-tracking behaviors were evaluated. Results: Visual explanations using bounding boxes and keypoints improved detection task performance when confidence was not displayed. Meanwhile, visual explanations enhanced trust and preference for the intelligent detector, regardless of the explanation type. Confidence visualization did not influence human trust in and preference for the intelligent detector. Moreover, both visual information slowed saccade velocities. Conclusion: The study demonstrated that visual explanations could improve performance, trust, and preference in human–automation interaction without confidence visualization partially by changing the search strategies. However, excessive information might cause adverse effects. Application: These findings provide guidance for the design of transparent automation, emphasizing the importance of context-appropriate and user-centered explanations to foster effective human–machine collaboration. [ABSTRACT FROM AUTHOR]

Visual object tracking has been a fundamental topic of machine vision in recent years. Most trackers can hardly top the performance and work in real time. This paper presents a tracking framework based on the SiamFC network, which can be taught online from the beginning of tracking and is real time. SiamFC network has a high tracking speed but cannot be trained online. This limitation made it unable to track the target for a long time. Hybrid-Siam can be trained online to distinguish target and background by switching traditional tracking and deep learning methods. Using the traditional tracking method and a target detector based on saliency detection has led to long-term tracking. Our method runs at more than 60 frame per second during test time and achieves state-of-the-art performance on tracking benchmarks, while robust results for long-term tracking. Hybrid-Siam improves SiamFC and achieves AUC score 81.7% on LaSOT, 72.3% on OTB100, and average overlap of 66.2% on GOT-10 k. [ABSTRACT FROM AUTHOR]

Traditional object detection algorithms operate within a closed set, where the training data may not cover all real-world objects. Therefore, the issue of open-world object detection has attracted significant attention. Open-world object detection faces two major challenges: "neglecting unknown objects" and "misclassifying unknown objects as known ones." In our study, we address these challenges by utilizing the Region Proposal Network (RPN) outputs to identify potential unknown objects with high object scores that do not overlap with ground truth annotations. We introduce the reselection mechanism, which separates unknown objects from the background. Subsequently, we employ the simulated annealing algorithm to disentangle features of unknown and known classes, guiding the detector's learning process. Our method has improved on multiple evaluation metrics such as U-mAP, U-recall, and UDP, greatly alleviating the challenges faced by open world object detection. [ABSTRACT FROM AUTHOR]

Human pose estimation aims to locate and predict the key points of the human body in images or videos. Due to the challenges of capturing complex spatial relationships and handling different body scales, accurate estimation of human pose remains challenging. Our work proposes a real-time human pose estimation method based on the anchor-assisted YOLOv7 framework, named MDA-YOLO Person. In this study, we propose the Keypoint Augmentation Strategies (KAS) to overcome the challenges faced in human pose estimation and improve the model's ability to accurately predict keypoints. Furthermore, we introduce the Anchor Adjustment Module (AAM) as a replacement for the original YOLOv7's detection head. By adjusting the parameters associated with the detector's anchors, we achieve an increased recall rate and enhance the completeness of the pose estimation. Additionally, we incorporate the Multi-Scale Dual-Head Attention (MDA) module, which effectively models the weights of both channel and spatial dimensions at multiple scales, enabling the model to focus on more salient feature information. As a result, our approach outperforms other methods, as demonstrated by the promising results obtained on two large-scale public datasets. MDA-YOLO Person outperforms the baseline model YOLOv7-pose on both MS COCO 2017 and CrowdPose datasets, with improvements of 2.2% and 3.7% in precision and recall on MS COCO 2017, and 1.9% and 3.5% on CrowdPose, respectively. [ABSTRACT FROM AUTHOR]

The RPL routing protocol is susceptible to Selective Forwarding Attack (SFA), wherein malicious nodes selectively forward certain packets while discarding others and isolating a specific node or group of nodes from the network topology. Despite the countermeasures implemented by traditional IDS, the expansion of the IoT domain towards low-power and lossy networks requires innovative and suitable solutions to meet the distinct needs of resource-constraint devices. Dissimilarity and Adaptive Threshold-based Intrusion Detection System (DSAT-IDS), designed to mitigate SFAs targeting the routing mechanisms of RPL-based 6LoWPAN. DSAT-IDS works on the principle that the presence of an SFA node can be detected by monitoring the packet forwarding behaviour of groups of nodes instead of the individual nodes; once a group of nodes is identified with reduced packet forwarding behaviour, the individual nodes of the group can be examined to identify the SFA node, which reduces the network and detection overhead. DSAT-IDS consists of four main modules, each with sub-modules, namely, pre-detection module, sensor module, analyser module and attack handler module. DSAT-IDS optimizes network performance by reducing network overhead, employs sub-tree-based monitoring, dynamically computes threshold value based on the network environment, monitors packet forwarding behaviour and identifies the dissimilarities (θ, β, δ) in packet forwarding behaviour. The performance evaluation demonstrates that DSAT-IDS outperforms other IDSs in countering SFA, achieving TPR ranging from 99.12% to 99.99%, PDR ranging from 99.52% to 99.98%, throughput ranging from 99.1% to 99.4%, maintaining a low FPR of 1.0558%, and exhibiting an FNR spanning approximately 1.92% to 2.16%. Furthermore, the AEC ranges from 19,000 mJ to 22,300 mJ, shows notable energy efficiency and achieves high accuracy levels, ranging from 99.69% to 99.9% across diverse scenarios. [ABSTRACT FROM AUTHOR]

Hexa-peri-hexabenzocoronene (HBC) modified glassy carbon electrodes (HBC/GCEs) was fabricated by an electrochemical synthesis, solving the difficulty of the preparation of HBC films because of its poor solubility. HBC/GCE was used as a sensing electrode for the voltammetric investigation of imidacloprid (IDP). The presence of HBC increased the conductivity of GCE, and showed good electrocatalytic activity toward the reaction of IDP. Therefore, the HBC enhanced the electrochemical signal of IDP, which increased the sensitivity of the sensor. The best performance was obtained by optimizing the electrochemical synthesis time, pH and enrichment time. The linear response range and the limit of detection were 5 to 750 μmol·L−1 and 1.5 μmol·L−1, respectively. Moreover, the proposed sensor also exhibited good repeatability, selectivity and stability, and promising applications for real sample. [ABSTRACT FROM AUTHOR]

Target tracking is a key component of control systems with applications in various domains. Several examples of commercial applications may be given, which benefit from this technology: the development of car collision avoidance systems which must detect and track potential obstacles and hazards, or the development of UAVs that can track and record the evolution of athletes. For the purpose of our research, this technology was developed and tested as part of a less complex application. The current work describes a simple yet practical implementation of vision-based control for a mobile robot system. In the experiment, we used a mobile robot as target and a similar robot as follower. To achieve the tracking task, the used strategy involves the detection of a specific visual object mounted on the target, by extracting its features which are then used in issuing control commands within a remotely-closed loop. A Deep Learning approach is used for object detection, incorporating a detector model into the strategy while preserving an explicit controller in the overall scheme. The carried experiment has proven that this new approach provides the expected results, which make it a suitable tool for development of larger scale applications of control systems. [ABSTRACT FROM AUTHOR]

In this study, we explore the integration of eye-gaze (EG) information obtained via eyetracking sensors to enhance medical image segmentation. EG is additional information on an image, and to incorporate EG information in medical image segmentation, we apply Gaussian blurring masked by the collected attention maps generated by eye tracking. The variance of distribution on each pixel is adjusted in a certain way by EG information. After applying Gaussian blurring, classic models including UNet, FCN, and other models were trained and compared. The results indicate that incorporating EG information in addition to preprocessing data yields superior performance on certain metrics, demonstrating notable advantages in accurately identifying isolated polyps that grow on the surface of a human tissue. This innovative approach highlights the potential of combining sensor-derived data with advanced image processing techniques to improve medical diagnostics. [ABSTRACT FROM AUTHOR]

With the rapid development of sensors and sensor technology, facial expression recognition (FER) systems can be developed and applied to real-world scenarios. Vision scan sensors and ambient light sensors capture clear and noise-free images of faces. However, in the real world, annotating large facial expressions is challenging owing to inconsistent labels, which are caused by the annotators' subjectivity and the facial expressions' ambiguity. Moreover, current studies present limitations when addressing facial expression differences due to the gender gap. We not only rely on visual sensors for FER but also utilize nonvisual sensors. Therefore, in this paper, we propose a self-cure dual-branch network (SC-DBN) for FER, which automatically prevents deep networks from overfitting ambiguous samples. First, on the basis of SC-DBN, a two-branch training method is designed, taking full advantage of the gender information. Furthermore, a self-attention mechanism highlights the essential samples and weights, each with a regular weighting. Finally, a relabeling module is used to modify the labels of these samples in inconsistent labels. Many experiments on public datasets show that SC-DBN can effectively integrate gendered information and self-cure networks to improve performance. [ABSTRACT FROM AUTHOR]

In bearing fault diagnosis, traditional deep learning methods often fall short of achieving satisfactory diagnostic accuracy under variable operating conditions. A critical phase in this process is data acquisition, which heavily relies on high-precision sensors to accurately capture the real-time operational state of the bearing ring. To address this, a diversified sensor fusion strategy has been proposed, encompassing various sensor types such as temperature, and acoustic sensors. The strategy allows for comprehensive monitoring of the bearing's state from multiple dimensions. Vibration sensors are responsible for detecting minute vibrations and abnormal vibration patterns during the bearing's operation. Temperature sensors monitor changes in the bearing ring's temperature to identify potential overheating issues, whereas acoustic sensors capture unusual noises that may indicate faults. From the collective data gathered by these sensors, a comprehensive view of the bearing's operational state can be obtained, significantly enhancing the accuracy of fault diagnosis. To tackle the issue of low diagnostic accuracy under variable working conditions, an algorithm combining the advantages of data reconstruction and discriminative space optimization, data deconstruction and metalearning discriminative space optimization (DR-MLDSO), has been utilized. Additionally, by integrating a hybrid dual-channel attention mechanism into the feature extraction network, challenges arising from variable application scenarios and data quality issues have been effectively addressed. Faced with the challenge of insufficient sample size, a similarity-based meta-learning algorithm was employed to train the encoder. Furthermore, the introduction of new constraints in the loss function leads to an improved sparse denoising autoencoder that optimizes data reconstruction, effectively reducing noise while preserving key features. Finally, incorporating a self-attention mechanism enhances the model's diagnostic capabilities in noisy environments, achieving superior diagnostic performance under variable working conditions, even with small sample sizes. [ABSTRACT FROM AUTHOR]

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