Low-Noise Magnetic Coil System for Recording 3-Dimensional Eye Movements

Rail surface defect data enhancement method based on improved ACGAN

Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image quality. Substituting the generator's deconvolution layer with upsampling and convolution operations enhances computational efficiency. A gradient penalty mechanism based on regret values addresses gradient abnormality concerns. Experimental validation demonstrates superior image clarity and classification accuracy compared to ACGAN, with a 17.6% reduction in FID value. MNIST dataset experiments verify the model's generalization ability. This approach offers practical value for real-world applications.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Extending the electric vehicle battery first life: Performance beyond the current end of life threshold

Presently, Electric Vehicle batteries are considered to have reached the End of Life once their State of Health falls to 70-80%. However, this criteria is universal to all battery capacities and not based on the specific application requirements. To evaluate whether the End of Life can be extended below the current threshold, the impact of the Internal Resistance increase needs to be addressed. In this sense, this study employs a degradation aware electrothermal model to evaluate the battery performance for different use cases. The findings reveal that capacity constraints are the main cause of the End of Life, followed by power constraints. However, this is highly dependent on the battery capacity. Large capacity batteries tend to reach the End of Life for capacity constraints, whereas smaller ones show power limitations first. The temperature increase has not shown to be a restriction for any of the cases simulated. The decline in performance is for most cases (37.5% of the simulated ones) noticed below 70% State of Health, supporting that the first-life of most batteries can be extended without compromising the vehicle's performance. This is especially the case for most average drivers using large battery capacities, currently emerging on the market. The methodology proposed for the simulated cases can be extended to real time operation in the Battery Management System. Estimating the End of Life in this way can support the maximization of the first-life and only requires an appropriate use of the available data.

High sensitivity and ultra-low concentration range photoacoustic spectroscopy based on trapezoid compound ellipsoid resonant photoacoustic cell and partial least square

A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C2H2) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.

Ultrasensitive detection of vital biomolecules based on a multi-purpose BioMEMS for Point of care testing: digoxin measurement as a case study

Rapid and label-free detection of very low concentrations of biomarkers in disease diagnosis or therapeutic drug monitoring is essential to prevent disease progression in Point of Care Testing. For this purpose, we propose a multi-purpose optical Bio-Micro-Electro-Mechanical-System (BioMEMS) sensing platform which can precisely measure very small changes of biomolecules concentrations in plasma-like buffer samples. This is realized by the development of an interferometric detection method on highly sensitive MEMS transducers (cantilevers). This approach facilitates the precise analysis of the obtained results to determine the analyte type and its concentrations. Furthermore, the proposed multi-purpose platform can be used for a wide range of biological assessments in various concentration levels by the use of an appropriate bioreceptor and the control of its coating density on the cantilever surface. In this study, the present system is prepared for the identification of digoxin medication in its therapeutic window for therapeutic drug monitoring as a case study. The experimental results represent the repeatability and stability of the proposed device as well as its capability to detect the analytes in less than eight minutes for all samples. In addition, according to the experiments carried out for very low concentrations of digoxin in plasma-like buffer, the detection limit of LOD = 300 fM and the maximum sensitivity of S = 5.5 × 1012 AU/M are achieved for the implemented biosensor. The present ultrasensitive multi-purpose BioMEMS sensor can be a fully-integrated, cost-effective device to precisely analyze various biomarker concentrations for various biomedical applications.

Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation

Visualisation of cardiac fibrillation plays a very considerable role in cardiophysiological study and clinical applications. One of the ways to obtain the image of these phenomena is the registration of mechanical displacement fields reflecting the track from electrical activity. In this work, we read these fields using cross-correlation analysis from the video of open pig's epicardium at the start of fibrillation recorded with electrocardiogram. However, the quality of obtained displacement fields remains low due to the weak pixels heterogeneity of the frames. It disables to see more clearly such interesting phenomena as mechanical vortexes that underline the mechanical dysfunction of fibrillation. The applying of chemical or mechanical markers to solve this problem can affect the course of natural processes and falsify the results. Therefore, we developed a novel scheme of an unsupervised deep neural network that is based on the state-of-art positional coding technology for a multilayer perceptron. This network enables to generate a couple of frames with a more heterogeneous pixel texture, that is more suitable for cross-correlation analysis methods, from two consecutive frames. The novel network scheme was tested on synthetic pairs of images with different texture heterogeneity and frequency of displacement fields and also it was compared with different filters on our cardiac tissue image dataset. The testing showed that the displacement fields obtained with our method are closer to the ground truth than those which were computed only with the cross-correlation analysis in low contrast images case where filtering is impossible. Moreover, our model showed the best results comparing with the one of the popular filter CLAHE on our dataset. As a result, using our approach, it was possible to register more clearly a mechanical vortex on the epicardium at the start of fibrillation continuously for several milliseconds for the first time.

Integrated near-infrared fiber-optic photoacoustic sensing demodulator for ultra-high sensitivity gas detection

An integrated near-infrared fiber-optic photoacoustic sensing demodulator was established for ultra-high sensitivity gas detection. The demodulator has capacities of interference spectrum acquisition and calculation, laser modulation control as well as digital lock-in amplification. FPGA was utilized to realize all the control and signal processing functions, which immensely improved the integration and stability of the system. The photoacoustic signal detection based on fiber-optic Fabry-Perot (F-P) acoustic sensor was realized by applying ultra-high resolution spectral demodulation technique. The detectable frequency of photoacoustic signal achieved 10 kHz. The system integrated lock-in amplification technology, which made the noise sound pressure and dynamic response range of sound pressure detection reached 3.7 μPa/√Hz @1 kHz and 142 dB, respectively. The trace C2H2 gas was tested with a multi-pass resonant photoacoustic cell. Ultra-high sensitivity gas detection was accomplished, which was based on high acoustic detection sensitivity and the matching digital lock-in amplification. The system detection limit and normalized noise equivalent absorption (NNEA) coefficient were reached 3.5 ppb and 6.7 × 10-10 cm-1WHz-1/2, respectively. The devised demodulator can be applied for long-distance gas measurement, which depends on the fact that both the near-infrared photoacoustic excitation light and the probe light employ optical fiber as transmission medium.

Early prediction of remaining useful life for lithium-ion batteries based on CEEMDAN-transformer-DNN hybrid model

A reliable and safe energy storage system utilizing lithium-ion batteries relies on the early prediction of remaining useful life (RUL). Despite this, accurate capacity prediction can be challenging if little historical capacity data is available due to the capacity regeneration and the complexity of capacity degradation over multiple time scales. In this study, data decomposition, transformers, and deep neural networks (DNNs) are combined to develop a model of RUL prediction for lithium-ion batteries. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used for battery capacity sequential data to account for the capacity regeneration effect. The transformer networks are leveraged to predict each component of capacity regeneration thus improving the model's ability to handle long sequences while reducing the amount of data. The global degradation trend is predicted using a deep neural network. We validated the early prediction performance of the model using two publicly available battery datasets. Results show that the prediction model only uses 25%-30% data to achieve high accuracy. In the two public data sets, the RMSE errors were 0.0208 and 0.0337, respectively. A high level of accuracy is achieved with the model proposed in this study, which is based on fewer capacity data.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

Directed searching optimized texture based adaptive gamma correction (DSOTAGC) technique for medical image enhancement

Because of complexity and low contrast in medical images, few enhancement techniques result unwanted artifacts and information loss by affecting the structure similarity and peak signal to noise ratio. To meet these challenges, a Directed searching optimized texture-based adaptive gamma correction technique is proposed in this article. This proposed technique utilizes the textured regions of the image and suppresses the effect of non-textured regions for eliminating the artifacts. An adaptive clipping threshold is used in the textured image to control the enhancement rate. For improving the contrast, the transfer function of the enhanced image is evaluated using the modified weighted probability density function and adaptive gamma parameter. To make the algorithm more adaptive, parameters like clipped threshold, gamma parameter, and textural threshold are to be optimized using directed searching optimization algorithm. For improving the information contents and noise suppression capability, the proposed technique incorporated a fitness function which is a combination of entropy and peak signal to noise ratio. Equal weightage has been given to each parameter in the fitness function for obtaining a balanced optimal result. Then, the performance of the proposed technique is evaluated in terms of visual quality, information contents, average mean brightness error, noise suppression, and structural similarity. Experimental results show the proposed technique results in better visual effects without information loss. It effectively suppresses the effect of artifacts and significantly improves the contrast by making edges clearer and textures richer over other algorithms.

Binocular 3-D otolith-ocular reflexes: responses of chinchillas to natural and prosthetic stimulation after ototoxic injury and vestibular implantation

The otolith end organs inform the brain about gravitational and linear accelerations, driving the otolith-ocular reflex (OOR) to stabilize the eyes during translational motion (e.g., moving forward without rotating) and head tilt with respect to gravity. We previously characterized OOR responses of normal chinchillas to whole body tilt and translation and to prosthetic electrical stimulation targeting the utricle and saccule via electrodes implanted in otherwise normal ears. Here we extend that work to examine OOR responses to tilt and translation stimuli after unilateral intratympanic gentamicin injection and to natural/mechanical and prosthetic/electrical stimulation delivered separately or in combination to animals with bilateral vestibular hypofunction after right ear intratympanic gentamicin injection followed by surgical disruption of the left labyrinth at the time of electrode implantation. Unilateral intratympanic gentamicin injection decreased natural OOR response magnitude to about half of normal, without markedly changing OOR response direction or symmetry. Subsequent surgical disruption of the contralateral labyrinth at the time of electrode implantation surgery further decreased OOR magnitude during natural stimulation, consistent with bimodal-bilateral otolith end organ hypofunction (ototoxic on the right ear, surgical on the left ear). Delivery of pulse frequency- or pulse amplitude-modulated prosthetic/electrical stimulation targeting the left utricle and saccule in phase with whole body tilt and translation motion stimuli yielded responses closer to normal than the deficient OOR responses of those same animals in response to head tilt and translation alone.NEW & NOTEWORTHY Previous studies to expand the scope of prosthetic stimulation of the otolith end organs showed that selective stimulation of the utricle and saccule is possible. This article further defines those possibilities by characterizing a diseased animal model and subsequently studying its responses to electrical stimulation alone and in combination with mechanical motion. We show that we can partially restore responses to tilt and translation in animals with unilateral gentamicin ototoxic injury and contralateral surgical disruption.

A novel attention-based enhancement framework for face mask detection in complicated scenarios

In the context of COVID-19 pandemic prevention and control, it is of vital significance to realize accurate face mask detection via computer vision technique. In this paper, a novel attention improved Yolo (AI-Yolo) model is proposed, which can handle existing challenges in the complicated real-world scenarios with dense distribution, small-size object detection and interference of similar occlusions. In particular, a selective kernel (SK) module is set to achieve convolution domain soft attention mechanism with split, fusion and selection operations; a spatial pyramid pooling (SPP) module is applied to enhance the expression of local and global features, which enriches the receptive field information; and a feature fusion (FF) module is utilized to promote sufficient fusions of multi-scale features from each resolution branch, which adopts basic convolution operators without excessive computational complexity. In addition, the complete intersection over union (CIoU) loss function is adopted in the training stage for accurate positioning. Experiments are carried out on two challenging public face mask detection datasets, and the results demonstrate the superiority of the proposed AI-Yolo against other seven state-of-the-art object detection algorithms, which achieves the best results in terms of mean average precision and F1 score on both datasets. Furthermore, effectiveness of the meticulously designed modules in AI-Yolo is validated through extensive ablation studies. In a word, the proposed AI-Yolo is competent to accomplish face mask detection tasks under extremely complex situations with precise localization and accurate classification.

Highly sensitive photoacoustic acetylene detection based on differential photoacoustic cell with retro-reflection-cavity

In this paper, a highly sensitive photoacoustic spectroscopy (PAS) sensor based on retro-reflection-cavity-enhanced differential photoacoustic cell (DPAC) is demonstrated for the first time. Acetylene (C₂H₂) was selected as the analyte. The DPAC was designed to effectively suppress noise and increase signal level. The retro-reflection-cavity consisted of two right-angle prisms was designed to reflect the incident light to realize four passes. The photoacoustic response of the DPAC was simulated and investigated based on the finite element method. Wavelength modulation and second harmonic demodulation technologies were applied for sensitive trace gas detection. The first-order resonant frequency of the DPAC was found to be 1310 Hz. The differential characteristics were investigated and the 2f signal amplitude for this C₂H₂-PAS sensor based on retro-reflection-cavity-enhanced DPAC had a 3.55 times improvement compared to the system without the retro-reflection-cavity. An Allan deviation analysis was performed to investigate the long-term stability of the system. The minimum detection limit (MDL) was measured to be 15.81 ppb with an integration time of 100 s.

Portable Device for Multipurpose Research on Dendritic Yanson Point Contacts and Quantum Sensing

Quantum structures are ideal objects by which to discover and study new sensor mechanisms and implement advanced approaches in sensor analysis to develop innovative sensor devices. Among them, one of the most interesting representatives is the Yanson point contact. It allows the implementation of a simple technological chain to activate the quantum mechanisms of selective detection in gaseous and liquid media. In this work, a portable device for multipurpose research on dendritic Yanson point contacts and quantum sensing was developed and manufactured. The device allows one to create dendritic Yanson point contacts and study their quantum properties, which are clearly manifested in the process of the electrochemical cyclic switchover effect. The device tests demonstrated that it was possible to gather data on the compositions and characteristics of the synthesized substances, and on the electrochemical processes that influence the production of dendritic Yanson point contacts, as well as on the electrophysical processes that provide information on the quantum nature of the electrical conductance of dendritic Yanson point contacts. The small size of the device makes it simple to integrate into a micro-Raman spectrometer setup. The developed device may be used as a prototype for designing a quantum sensor that will serve as the foundation for cutting-edge sensor technologies, as well as be applied to research into atomic-scale junctions, single-atom transistors, and any relative subjects.

BIMMS: A versatile and portable system for biological tissue and electrode-tissue interface electrical characterization

The presented design is a low-cost, compact, and open-source USB-controlled platform for biological tissue and electrode-tissue interface electrical measurements, capable of potentiostatic and galvanostatic electrical impedance spectroscopy up to 10 MHz and cyclic voltammetry with voltage compliance of +-8 V and up to 2.4 mA while ensuring tissue-safety conditions. The data acquisition and generation are based on an Analog Discovery 2 platform (Digilent, USA). We provide accuracy analysis and comparisons with a commercially available calibrated impedance analyzer. Impedance measurements are demonstrated on implanted electrodes for neural stimulation and on an isolated ex-vivo calf brain as an example use case of the presented design.

Time-domain transmission sensor system for on-site dielectric permittivity measurements in soil: A compact, low-cost and stand-alone solution

Dielectric-based measurement techniques have been shown to be very effective in determining the properties of various materials. These techniques have been widely used in a variety of fields and applications. Time Domain Transmission (TDT) techniques have grown in popularity because they are practical, non-destructive, provide measurements in real time and produce accurate measurements that are independent of multiple reflections. TDT techniques, on the other hand, are mostly performed with specialized bulky laboratory equipment, such as a Vector Network Analyzer (VNA) which makes TDT measurements prohibitively costly and unpractical. In fact, few works in the literature have reported portable on-site TDT systems. The aim of this paper is to design and implement a dedicated, compact, and low-cost microwave Time Domain Transmission (TDT) sensor for measuring superficial soil dielectric properties on-site. Our sensor uses a time-delay measurement technique over a microstrip transmission line to estimate the dielectric properties of the soil under test. Measurement results show that the computed mean absolute error (MAE) is less than 1.2 when compared to a calibrated dielectric assessment kit (DAK) with soils containing less than 20 % of water ( ε ' r < 5.0 ), implying that our TDT sensor system can obtain on-site measurements in relatively dry soils with acceptable accuracy.

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning

Blood Oxygen ( SpO 2 ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower SpO 2 before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring SpO 2 by face videos, this paper proposes a novel multi-model fusion method based on deep learning for SpO 2 estimation. The method includes the feature extraction network named Residuals and Coordinate Attention (RCA) and the multi-model fusion SpO 2 estimation module. The RCA network uses the residual block cascade and coordinate attention mechanism to focus on the correlation between feature channels and the location information of feature space. The multi-model fusion module includes the Color Channel Model (CCM) and the Network-Based Model(NBM). To fully use the color feature information in face videos, an image generator is constructed in the CCM to calculate SpO 2 by reconstructing the red and blue channel signals. Besides, to reduce the disturbance of other physiological signals, a novel two-part loss function is designed in the NBM. Given the complementarity of the features and models that CCM and NBM focus on, a Multi-Model Fusion Model(MMFM) is constructed. The experimental results on the PURE and VIPL-HR datasets show that three models meet the clinical requirement(the mean absolute error ⩽ 2%) and demonstrate that the multi-model fusion can fully exploit the SpO 2 features of face videos and improve the SpO 2 estimation performance. Our research achievements will facilitate applications in remote medicine and home health.

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold

The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.

Study of Nonlinear Excitation Circuits for Fluxgate Magnetometer

This paper presents the common methods and corresponding drawbacks concerning nonlinear analysis of fluxgate excitation circuits and emphasizes the importance of nonlinear analysis for these circuits. With regard to the nonlinearity of the excitation circuit, this paper proposes the use of the core-measured hysteresis curve for mathematical analysis and the use of a nonlinear model that considers the coupling effect of the core and winding and influence of the historical magnetic field on the core for simulation analysis. The feasibility of mathematical calculations and simulation for the nonlinear study of fluxgate excitation circuit is verified via experiments. The results demonstrate that, in this regard, the simulation is four times better than a mathematical calculation. The simulation and experimental results of the excitation current and voltage waveforms under different excitation circuit parameters and structures are essentially consistent, with a difference in current of no more than 1 mA, thereby verifying the effectiveness of the nonlinear excitation analysis method.

A Nine-Level Inverter with Adjustable Turn-Off Time for Helicopter Transient Electromagnetic Detection

The current inverter is the core component of the helicopter transient electromagnetic (HTEM) detection system. It should meet the concerns of low loss, high power, and fast turn-OFF time. This article proposes a new circuit topology based on nine-level inverter technology to overcome the drawbacks of typical PWM (pulse width modulation) inverters, such as switching losses and harmonics. This circuit topology overcomes the shortcomings of the traditional single constant voltage clamp circuit in which the turn-OFF time is not adjustable. Using an inverter with the proposed topology is able to avoid the complex PWM control method and switching loss. In this way, the current rising edge and falling edge of this inverter are also improved effectively. The proposed inverter has adjustable turn-ON-time and turn-OFF time, which is significantly different from the conventional single-clamp inverter. Through subsequent experiments, the inverter proved to have the capability of generating trapezoidal current waveforms. Moreover, by modifying the FPGA (Field Programmable Gate Array) control program, three different turn-OFF times are achieved. The nine-level inverter has a peak current of 1.5 A with an adjustable turn-OFF time from 129 μs to 162 μs. Moreover, the switching frequency of the inverter is reduced from 10 kHz to below 100 Hz. The experimental results further demonstrate that it achieves lower switching losses and more flexible transmission. Our work in this article provides an efficient way to improve the performance of HTEM detection systems.

A survey on computer vision based human analysis in the COVID-19 era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of ( i ) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and ( ii ) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.

A Novel Ultrasound Robot with Force/torque Measurement and Control for Safe and Efficient Scanning

Medical ultrasound is of increasing importance in medical diagnosis and intraoperative assistance and possesses great potential advantages when integrated with robotics. However, some concerns, including the operation efficiency, operation safety, image quality, and comfort of patients, remain after introducing robotics into medical ultrasound. In this paper, an ultrasound robot integrating a force control mechanism, force/torque measurement mechanism, and online adjustment method, is proposed to overcome the current limitations. The ultrasound robot can measure operating forces and torques, provide adjustable constant operating forces, eliminate great operating forces introduced by accidental operations, and achieve various scanning depths based on clinical requirements. The proposed ultrasound robot would potentially facilitate sonographers to find the targets quickly, improve operation safety and efficiency, and decrease patients' discomfort. Simulations and experiments were carried out to evaluate the performance of the ultrasound robot. Experimental results show that the proposed ultrasound robot is able to detect operating force in the z-direction and torques around the x- and y- directions with errors of 3.53% F.S., 6.68% F.S., and 6.11% F.S., respectively, maintain the constant operating force with errors of less than 0.57N, and achieve various scanning depths for target searching and imaging. This proposed ultrasound robot has good performance and would potentially be used in medical ultrasound.

FDD: a deep learning-based steel defect detectors

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.

A miniaturized transit-time ultrasonic flowmeter based on ScAlN piezoelectric micromachined ultrasonic transducers for small-diameter applications

Transit-time ultrasonic flowmeters (TTUFs) are among the most widely used devices for flow measurements. However, traditional TTUFs are usually based on a bulk piezoelectric transducer, which limits their application in small-diameter channels. In this paper, we developed a miniaturized TTUF based on scandium-doped aluminum nitride (ScAlN) piezoelectric micromachined ultrasonic transducers (PMUTs). The proposed TTUF contains two PMUT-based transceivers and a π-type channel. The PMUTs contain 13 × 13 square cells with dimensions of 2.8 × 2.8 mm². To compensate for the acoustic impedance mismatch with liquid, a layer of polyurethane is added to the surface of the PMUTs as a matching layer. The PMUT-based transceivers show good transmitting sensitivity (with 0.94 MPa/V surface pressure) and receiving sensitivity (1.79 mV/kPa) at a frequency of 1 MHz in water. Moreover, the dimensions of the π-type channel are optimized to achieve a measurement sensitivity of 82 ns/(m/s) and a signal-to-noise ratio (SNR) better than 15 dB. Finally, we integrate the fabricated PMUTs into the TDC-GP30 platform. The experimental results show that the developed TTUF provides a wide range of flow measurements from 2 to 300 L/h in a channel of 4 mm diameter, which is smaller than most reported channels. The accuracy and repeatability of the TTUF are within 0.2% and 1%, respectively. The proposed TTUF shows great application potential in industrial applications such as medical and chemical applications.

Heart rate estimation network from facial videos using spatiotemporal feature image

Remote health monitoring has become quite inevitable after SARS-CoV-2 pandemic and continues to be accepted as a measure of healthcare in future too. However, contact-less measurement of vital sign, like Heart Rate(HR) is quite difficult to measure because, the amplitude of physiological signal is very weak and can be easily degraded due to noise. The various sources of noise are head movements, variation in illumination or acquisition devices. In this paper, a video-based noise-less cardiopulmonary measurement is proposed. 3D videos are converted to 2D Spatio-Temporal Images (STI), which suppresses noise while preserving temporal information of Remote Photoplethysmography(rPPG) signal. The proposed model projects a new motion representation to CNN derived using wavelets, which enables estimation of HR under heterogeneous lighting condition and continuous motion. STI is formed by the concatenation of feature vectors obtained after wavelet decomposition of subsequent frames. STI is provided as input to CNN for mapping the corresponding HR values. The proposed approach utilizes the ability of CNN to visualize patterns. Proposed approach yields better results in terms of estimation of HR on four benchmark dataset such as MAHNOB-HCI, MMSE-HR, UBFC-rPPG and VIPL-HR.

Ion-specific nanoscale compaction of cysteine-modified poly(acrylic acid) brushes revealed by 3D scanning force microscopy with frequency modulation detection

Stimuli-responsive polyelectrolyte brushes adapt their physico-chemical properties according to pH and ion concentrations of the solution in contact. We synthesized a poly(acrylic acid) bearing cysteine residues at side chains and a lipid head group at the terminal, and incorporated them into a phospholipid monolayer deposited on a hydrophobic silane monolayer. The ion-specific, nanoscale response of polyelectrolyte brushes was detected by using three-dimensional scanning force microscopy (3D-SFM) combined with frequency modulation detection. The obtained topographic and mechanical landscapes indicated that the brushes were uniformly stretched, undergoing a gradual transition from the brush to the bulk electrolyte in the absence of divalent cations. When 1 mM calcium ions were added, the brushes were uniformly compacted, exhibiting a sharper brush-to-bulk transition. Remarkably, the addition of 1 mM cadmium ions made the brush surface significantly rough and the mechanical landscape highly heterogeneous. Currently, cadmium-specific nanoscale compaction of the brushes is attributed to the coordination of thiol and carboxyl side chains with cadmium ions, as suggested for naturally occurring, heavy metal binding proteins.

Retinal imaging using adaptive optics optical coherence tomography with fast and accurate real-time tracking

One of the main obstacles in high-resolution 3-D retinal imaging is eye motion, which causes blur and distortion artifacts that require extensive post-processing to be corrected. Here, an adaptive optics optical coherence tomography (AOOCT) system with real-time active eye motion correction is presented. Correction of ocular aberrations and of retinal motion is provided by an adaptive optics scanning laser ophthalmoscope (AOSLO) that is optically and electronically combined with the AOOCT system. We describe the system design and quantify its performance. The AOOCT system features an independent focus adjustment that allows focusing on different retinal layers while maintaining the AOSLO focus on the photoreceptor mosaic for high fidelity active motion correction. The use of a high-quality reference frame for eye tracking increases revisitation accuracy between successive imaging sessions, allowing to collect several volumes from the same area. This system enables spatially targeted retinal imaging as well as volume averaging over multiple imaging sessions with minimal correction of motion in post processing.

Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture

In recent years, coronavirus (Covid-19) has evolved into one of the world’s leading life-threatening severe viral illnesses. A self-executing accord system might be a better option to stop Covid-19 from spreading due to its quick diagnostic option. Many researches have already investigated various deep learning techniques, which have a significant impact on the quick and precise early detection of Covid-19. Most of the existing techniques, though, have not been trained and tested using a significant amount of data. In this paper, we purpose a deep learning technique enabled Convolutional Neural Network (CNN) to automatically diagnose Covid-19 from chest x-rays. To train and test our model, 10,293 x-rays, including 2875 x-rays of Covid-19, were collected as a data set. The applied dataset consists of three groups of chest x-rays: Covid-19, pneumonia, and normal patients. The proposed approach achieved 98.5% accuracy, 98.9% specificity, 99.2% sensitivity, 99.2% precision, and 98.3% F1-score. Distinguishing Covid-19 patients from pneumonia patients using chest x-ray, particularly for human eyes is crucial since both diseases have nearly identical characteristics. To address this issue, we have categorized Covid-19 and pneumonia using x-rays, achieving a 99.60% accuracy rate. Our findings show that the proposed model might aid clinicians and researchers in rapidly detecting Covid-19 patients, hence facilitating the treatment of Covid-19 patients.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

A review of generative adversarial network applications in optical coherence tomography image analysis

Optical coherence tomography (OCT) has revolutionized ophthalmic clinical practice and research, as a result of the high-resolution images that the method is able to capture in a fast, non-invasive manner. Although clinicians can interpret OCT images qualitatively, the ability to quantitatively and automatically analyse these images represents a key goal for eye care by providing clinicians with immediate and relevant metrics to inform best clinical practice. The range of applications and methods to analyse OCT images is rich and rapidly expanding. With the advent of deep learning methods, the field has experienced significant progress with state-of-the-art-performance for several OCT image analysis tasks. Generative adversarial networks (GANs) represent a subfield of deep learning that allows for a range of novel applications not possible in most other deep learning methods, with the potential to provide more accurate and robust analyses. In this review, the progress in this field and clinical impact are reviewed and the potential future development of applications of GANs to OCT image processing are discussed.

Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates

The last two years have been marked by the emergence of Coronavirus. The pandemic has spread in most countries, causing substantial changes all over the world. Many studies sought to analyze phenomena related to the pandemic from different perspectives. This study analyzes data from the governorates of the Kingdom of Saudi Arabia (the KSA), proposing a broad analysis that addresses three different research objectives. The first is to identify the main factors affecting the variations between KSA governorates in the cumulative number of COVID-19 infections. The study uses principal component regression. Results highlight the significant positive effects of the number of schools in each governorate, and classroom density within each school on the number of infections in the KSA. The second aim of this study is to use the number of COVID-19 infections, in addition to its significant predictors, to classify KSA governorates using the K-mean cluster method. Findings show that all KSA governorates can be grouped into two clusters. The first cluster includes 31 governorates that can be considered at greater risk of Covid infections as they have higher values in all the significant determinants of Covid infections. The last objective is to compare between traditional statistical methods and artificial intelligence techniques in predicting the future number of COVID-19 infections, with the aim of determining the method that provides the highest accuracy. Results also show that multilayer perceptron neural network outperforms others in forecasting the future number of COVID-19. Finally, the future number of infections for each cluster is predicted using multilayer perceptron neural network method.

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic

Background

At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.

Methods

Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.

Main results

With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 ± 14.56% with LDA classifier, 89.73 ± 5.71% with QDA classifier, and 87.04 ± 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks.

A novel autoencoder approach to feature extraction with linear separability for high-dimensional data

Feature extraction often needs to rely on sufficient information of the input data, however, the distribution of the data upon a high-dimensional space is too sparse to provide sufficient information for feature extraction. Furthermore, high dimensionality of the data also creates trouble for the searching of those features scattered in subspaces. As such, it is a tricky task for feature extraction from the data upon a high-dimensional space. To address this issue, this article proposes a novel autoencoder method using Mahalanobis distance metric of rescaling transformation. The key idea of the method is that by implementing Mahalanobis distance metric of rescaling transformation, the difference between the reconstructed distribution and the original distribution can be reduced, so as to improve the ability of feature extraction to the autoencoder. Results show that the proposed approach wins the state-of-the-art methods in terms of both the accuracy of feature extraction and the linear separabilities of the extracted features. We indicate that distance metric-based methods are more suitable for extracting those features with linear separabilities from high-dimensional data than feature selection-based methods. In a high-dimensional space, evaluating feature similarity is relatively easier than evaluating feature importance, so that distance metric methods by evaluating feature similarity gain advantages over feature selection methods by assessing feature importance for feature extraction, while evaluating feature importance is more computationally efficient than evaluating feature similarity.

Fiber structures and material science in optical fiber magnetic field sensors

Magnetic field sensing plays an important role in many fields of scientific research and engineering applications. Benefiting from the advantages of optical fibers, the optical fiber-based magnetic field sensors demonstrate characteristics of light weight, small size, remote controllability, reliable security, and wide dynamic ranges. This paper provides an overview of the basic principles, development, and applications of optical fiber magnetic field sensors. The sensing mechanisms of fiber grating, interferometric and evanescent field fiber are discussed in detail. Magnetic fluid materials, magneto-strictive materials, and magneto-optical materials used in optical fiber sensing systems are also introduced. The applications of optical fiber magnetic field sensors as current sensors, geomagnetic monitoring, and quasi-distributed magnetic sensors are presented. In addition, challenges and future development directions are analyzed.

Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images

Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.

The Robust Multi-Scale Deep-SVDD Model for Anomaly Online Detection of Rolling Bearings

Aiming at the online detection problem of rolling bearings, the limited amount of target bearing data leads to insufficient model in training and feature representation. It is difficult for the online detection model to construct an accurate decision boundary. To solve the problem, a multi-scale robust anomaly detection method based on data enhancement technology is proposed in this paper. Firstly, the training data are transformed into multiple subspaces through the data enhancement technology. Then, a prototype clustering method is introduced to enhance the robustness of features representation under the framework of the robust deep auto-encoding algorithm. Finally, the robust multi-scale Deep-SVDD hyper sphere model is constructed to achieve online detection of abnormal state data. Experiments are conducted on the IEEE PHM Challenge 2012 bearing data set and XJTU-TU data set. The proposed method shows much greater susceptibility to incipient faults, and it has fewer false alarms. The robust multi-scale Deep-SVDD hyper sphere model significantly improves the performance of incipient fault detection for rolling bearings.

Application of Multislice Spiral CT and Three-Dimensional Image Reconstruction Technology in the Observation of Ankle Sports Injury under the Microscope

In order to solve the problem of multislice spiral CT and three-dimensional image reconstruction technology in the observation of ankle sports injuries under the microscope, to meet the needs of the accuracy of the diagnosis of traumatic fractures, to make up for the shortcomings of the traditional treatment cycle, and to improve the recovery speed of patients. The subjects were inpatients in the Orthopedics and Traumatology Department of a hospital from January 2020 to January 2021.The ankle joint belongs to the flexion joint, which is formed by a dense joint at the lower end of the fibula, tibia, and talus. Osteoarthritis is the most common type of bone fracture, accounting for approximately 3.9% of the total skeletal system, and is most likely to occur in adolescents.

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Purpose

A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity.

Patients and Methods

Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30.

Results

According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool.

Conclusion

Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.

Dielectric characterization and modelling of aqueous solutions involving sodium chloride and sucrose and application to the design of a bi-parameter RF-sensor

This paper reports on dielectric properties of ternary mixtures involving sodium chloride (NaCl) and sucrose (C₁₂H₂₂O₁₁) dissolved into water (H₂O). Broadband electromagnetic characterizations of such mixtures at various concentrations were performed, evidencing a dual behavior made of conductive effects at low frequencies and dipolar relaxation at microwave frequencies. Conductive and dielectric properties resulting from these both effects were integrated into predictive models for variations of Cole-Cole model parameters. Based upon this modelling, an innovative microwave-based sensor able to retrieve concentrations of both sodium chloride and sucrose in ternary aqueous solutions was introduced, designed, realized and assessed. The proposed sensor shows an error lower than 5.5% for concentration ranges of 0 to 154 mmol/L for sodium chloride and 0 to 877 mmol/L for sucrose.

A method of blasted rock image segmentation based on improved watershed algorithm

It is of great theoretical significance and practical value to establish a fast and accurate detection method for particle size of rock fragmentation. This study introduces the Phansalkar binarization method, proposes the watershed seed point marking method based on the solidity of rock block contour, and forms an adaptive watershed segmentation algorithm for blasted rock piles images based on rock block shape, which is to better solve the problem of incorrect segmentation caused by adhesion, stacking and blurred edges in blasted rock images. The algorithm first obtains the binary image after image pre-processing and performs distance transformation; then by selecting the appropriate gray threshold, the adherent part of the distance transformation image, i.e., the adherent rock blocks in the blasted rock image, is segmented and the seed points are marked based on the solidity of the contour calculated by contour detection; finally, the watershed algorithm is used to segment. The area cumulative distribution curve of the segmentation result is highly consistent with the manual segmentation, and the segmentation accuracy was above 95.65% for both limestone and granite for rock blocks with area over 100 cm², indicating that the algorithm can accurately perform seed point marking and watershed segmentation for blasted rock image, and effectively reduce the possibility of incorrect segmentation. The method provides a new idea for particle segmentation in other fields, which has good application and promotion value.

A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples could be obtained. With a small amount of training data, traditional fault diagnosis models that need huge samples under a deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples in model training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline (MAMB). The training data is input to the base classification model for pre-training, then, some data is randomly selected from the training set to form multiple meta-learning tasks that are utilized to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The proposed model was analyzed by the small samples of the bearing data from Case Western Reserve University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines under randomly changing operating conditions. The results verified that the proposed method was superior in one-shot, five-shot, and ten-shot tasks of wind turbines.

Analytical Model of Eccentric Induction Machines Using the Conformal Winding Tensor Approach

Induction machines (IMs) are a critical component of many industrial processes, and their failure can cause large economic losses. Condition-based maintenance systems (CBMs) that are capable of detecting their failures at an incipient stage can reduce these risks by continuously monitoring the IMs’ condition. The development and reliable operations of CBMs systems require rapid modeling of the faulty IM. Due to the fault-induced IM asymmetries, these models are much more complex than those used for a healthy IM. In particular, a mixed eccentricity fault (static and dynamic), which can degenerate into rubbing and destruction of the rotor, produces a non-uniform IM air gap that is different for each rotor position, which makes its very difficult to calculate the IM’s inductance matrix. In this work, a new analytical model of an eccentric IM is presented. It is based on the winding tensor approach, which allows a clear separation between the air gap and winding-related faults. Contrary to previous approaches, where complex expressions have been developed for obtaining mutual inductances between conductors and windings of an eccentric IM, a conformal transformation is proposed in this work, which allows using the simple inductance expressions of a healthy IM. This novel conformal winding tensor approach (CWFA) is theoretically explained and validated with the diagnosis of two commercial IMs with a mixed eccentricity fault.

Detection of Surface and Subsurface Flaws with Miniature GMR-Based Gradiometer

The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples.

The Stressful Experience of Goal Orientations Under Frustration: Evidence Using Physiological Means

The purpose of the present study was to test the hypothesis that goal orientation is associated with divergent forms of emotional reactivity under frustration. Goal orientations were assessed using bifurcations of performance goals described earlier. Physiological stress levels were measured via a blood volume pulse analysis after individuals were subjected to a computerized Stroop task using a malfunctioning mouse to induce enhanced frustration. The results indicated that performance-avoidance goals were associated with the highest levels of emotional reactivity, with normative outcome goals being significantly more detrimental than ability goals. We concluded that the motivation to avoid failure or to outperform others is the most detrimental determinant of stress and needs to be avoided by all means. Instead, it is suggested that educators emphasize performance using personal best outcomes or by valuing engagement, deep processing and task completion.

Research on Several Key Problems of Medical Image Segmentation and Virtual Surgery

Medical images play an important role in modern medical diagnosis. Many clinicians make correct and appropriate diagnosis and treatment plans by means of medical images. With the development of science and technology, the application of medical image needs not only to simply read the image, but also to fuse advanced technology to analyze and process the image from a deeper level, such as the proposal of virtual surgery. Therefore, this article focuses on several key issues of medical image segmentation and virtual surgery. First, medical images are preprocessed by gray level transformation, interpolation, and noise elimination techniques. Second, level set model-based segmentation algorithm is adopted and improved. Finally, a constrained Delaunay tetrahedron method based on a point-by-point insertion method is proposed to reconstruct the tetrahedron mesh model. In order to eliminate the thin element, the tetrahedron mesh model is optimized. The simulation results show that this article improves the segmentation algorithm based on the level set model, which effectively improves the contradiction between the convergence accuracy and the convergence speed of the algorithm. The proposed tetrahedral mesh reconstruction algorithm realizes the generation of tetrahedral finite element meshes with complex boundaries and improves the quality of the volume model by optimizing the model.

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

A low-cost, portable, two-dimensional bioimpedance distribution estimation system based on the AD5933 impedance converter

This study proposes a low-cost, portable, eight-channel electrical impedance tomograph based on the AD5933 impedance converter. The patterns for current injection and voltage measurement are managed by an Arduino Mega 2560 board and four 74HC4067 Texas Instruments multiplexers. Regarding the experimental results, the errors in the impedance estimates of an electrical circuit that represents a Cole model were less than 1.14% for the magnitude and 4.15% for the phase. Furthermore, the signal-to-noise ratio measured in a resistive phantom was 55.23 dB. Additional experiments consisted of placing five spheres of different size and conductivity in a saline tank, measuring their impedance through eight electrodes, and then generating impedance maps using the Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS). These maps were different for each sphere, suggesting the proposed prototype as a promising alternative for medical applications.

Evaluating the robustness of a contact-less mHealth solution for personal and remote monitoring of blood oxygen saturation

MHealth technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow people to self-monitor their health status (e.g. vital parameters) at any time and place, without necessarily having to physically go to a medical clinic. Among vital parameters, special care should be given to monitor blood oxygen saturation (SpO2), whose abnormal values are a warning sign for potential COVID-19 infection. SpO2 is commonly measured through the pulse oximeter that requires skin contact and hence could be a potential way of spreading contagious infections. To overcome this problem, we have recently developed a contact-less mHealth solution that can measure blood oxygen saturation without any contact device but simply processing short facial videos acquired by any common mobile device equipped with a camera. Facial video frames are processed in real-time to extract the remote photoplethysmographic signal useful to estimate the SpO2 value. Such a solution promises to be an easy-to-use tool for both personal and remote monitoring of SpO2. However, the use of mobile devices in daily situations holds some challenges in comparison to the controlled laboratory scenarios. One main issue is the frequent change of perspective viewpoint due to head movements, which makes it more difficult to identify the face and measure SpO2. The focus of this work is to assess the robustness of our mHealth solution to head movements. To this aim, we carry out a pilot study on the benchmark PURE dataset that takes into account different head movements during the measurement. Experimental results show that the SpO2 values obtained by our solution are not only reliable, since they are comparable with those obtained with a pulse oximeter, but are also insensitive to head motion, thus allowing a natural interaction with the mobile acquisition device.