Miracle-dz/UniFAR-FTUs huggingface dataset:
‘My watch kept on alarming all night about my heart rate’: diagnosis of asymptomatic atrial fibrillation with fast ventricular response in a patient with a recent TIA as the result of a smartwatch alarm

Abstract Atrial fibrillation is a leading cause of stroke and early detection and treatment of the condition are critical. Paroxysmal atrial fibrillation is often asymptomatic and may go undetected and untreated in the routine management of patients with ischaemic strokes or transient ischaemic attacks. Prolonged monitoring does increase the diagnosis rate of atrial fibrillation after an ischaemic cerebrovascular event. Biometric and ECG sensors have been integrated with smartphones, apps and wearable devices which may increase rates of diagnosis of arrhythmias. This case study describes an asymptomatic patient who two months after her initial transient ischaemic attack was alerted by her smartwatch about her nocturnal tachycardia and was subsequently diagnosed with atrial fibrillation ensuring appropriate secondary prophylaxis.
Container number localization based on SSD algorithm

After R-CNN and other classical frameworks were proposed, deep learning based target positioning framework has gradually become the mainstream. The SSD (Single Shot MultiBox Detector) framework model was used to locate the container number. By comparing the locating effects of different labeling methods for training samples, the basic unit that label as Single row or Single column is much better. With the actual size and proportion of the container number as reference, that modifying the feature layers which are used in SSD frame to generate the default box and adjusting the size of the feature map could adapt to the localization better. The result show that in the complex container number scenario, the accuracy based on SSD network to detect container number reaches 78%.
Optimization of Cooperative Spectrum Sensing for OFDM in Multiuser Scenarios

Efficient and accurate spectrum sensing is a necessary part in cognitive radio networks. In order to solve the problem of OFDM spectrum sensing in multiuser scenarios, a cooperative spectrum sensing technology based on OFDM is proposed according to the difference reliability of the users. Firstly, by analyzing the structural characteristics of the OFDM signal, the detection feature of the real correlation function is established. Then combined with the traditional collaborative spectrum sensing method, the system weight vector is optimized to ensure the detection probability maximization in the case of a given false alarm probability. Finally, the weight vector and the decision threshold which is used to complete cooperative spectrum sensing OFDM is derived. The simulation results show that the proposed algorithm is superior to the classical cooperative spectrum sensing algorithm based on energy features.
Analytical Switching Model of a 1200V SiC MOSFET in a High-Frequency Series Resonant Pulsed Power Converter for Plasma Generation

The circuit parasitic components have a significant effect on switching performance in the high-voltage and high-frequency pulsed power converter. The SiC MOSFET has better electrical characteristics than Si MOSFET and it is preferable in this application. In this paper, the characteristics of comparative study between Si and SiC MOSFET is provided first. Then, a converter-level analytical switching model for a 1200 V SiC MOSFET is proposed in a high-frequency series resonant pulsed power converter for the application of plasma generation. To increase the accuracy of predicting the behavior of the SiC MOSFET, the proposed analytical model involves all parasitic components in the converter, including the nonlinearity of the junction capacitances and transconductance, stray inductances from the package, printed circuit board (PCB) and transformer, parasitic capacitances from the transformer, and the capacitive load of plasma. The turn-on and turn-off transitions are analyzed in detail and the modeling mathematical equations are resolved by the MATLAB ode-45 command. The accuracy of this model is validated by comparing the analytical, LTspice simulated and experimental waveforms of a 400 VDC input, −8.2kV output prototype. The proposed model enables us to evaluate and optimize the switching solution of a SiC MOSFET for the pulsed power converter.
A Novel Fault Tolerant Machine With Integral Slot Non-Overlapping Concentrated Winding

A novel fault tolerant machine with integral slot non-overlapping concentrated winding (ISNCW) is proposed in this paper. With the distribution of phase windings in axial, the proposed machine realized excellent isolation capability between different phases. The performance comparisons between the proposed machine and the traditional integer slot overlapping winding (ISOW) machine are made by the analytical method and finite element analysis (FEA). Compared to the ISOW machine, the proposed machine has better performance in low speed high torque applications, and much better fault tolerant capability and flux weakening capability. Especially, the proposed machine appears excellent performance in limiting the short circuit current and preventing the irreversible demagnetization of permanent magnet under short-circuit fault. The phase independent structure makes the maintenance and replacement of the fault module convenient and saves the maintenance cost further.
Systematic Design of Bandgap Voltage Reference Using Precomputed Lookup Tables

Bandgap voltage references are indispensable in any analog/mixed-signal system. In this paper, we introduce a systematic gm/ID-based procedure to design a CMOS bandgap reference. The proposed iterative methodology relies on one-time-generated precomputed lookup tables (LUTs); thus, it does not require invoking a simulator in the loop. Despite the inherent finite accuracy of the LUT-based design approach, we demonstrate that a precision bandgap circuit can be designed with less than 1-ppm error. We verified the proposed procedure against the Spectre simulations and probed the design space using a symbolic circuit solver. Moreover, we demonstrated how variations and mismatch can be taken into account in the context of the proposed methodology. The results demonstrate that the proposed procedure can provide very accurate results in a short execution time, enabling the designer to explore the design space of key performance metrics, such as power-supply rejection (PSR) and noise.
General Target Detection Method Based on Improved SSD

In order to improve the accuracy of general target detection by the surveillance camera in the monitoring area, an improved SSD model named DC-SSD that integrates the deformable convolution network was proposed. This model is based on the SSD convolutional neural network model, and the idea of deformable convolution is used for reference. The deformable convolution module is added into the three high level convolution modules of SSD to make the sampling point position of the high level convolution kernel adaptively change according to the image content, so as to adapt to the geometric deformation of different objects such as shape and size. In the open source neural network framework Caffe, DC-SSD was tested with PASCAL VOC 2007 (train+val) as the training set and PASCAL VOC 2007 (test) as the test set. Under the same training and test conditions, the mAP of DC-SSD reached 72.0%, 4.0% higher than the original SSD model, and the detection frame rate reached 39fps. Experimental results show that DC-SSD model can effectively improve the accuracy of universal target detection and meet the real-time requirements.
Optimization of Transverse Flux Permanent Magnet Machine with Double Omega-Hoop Stator

Transverse flux permanent magnet machine (TFPMM) has advantages of arbitrarily set poles and adjustable cross-section of armature winding, which is suitable for the traction motor geared to urban rail transit industry. This paper analysis and optimizes the shape of the omega-hoop stator of the proposed TFPMM. The original and optimized models are described by three-dimensional (3-D) finite element analysis (FEA). The electromagnetic characteristics of both structures are evaluated and analyzed. The results indicate that the reduction of stator hoop brings about shorter magnetic circuit to achieve better magnetic field and compact volume performances.
Semantic Role Labeling For Russian Language Based on Ensemble Model

Traditional Russian semantic role labeling (SRL) methods heavily rely on complicated and hand-crafted rules or semantic dictionaries, which increase the difficulty of this task. In this paper, bidirectional Gated Recurrent Unit (Bi-GRU) and attention mechanism are respectively used to extract the potential features of arguments which are prepared to be classified, and then we input above features and the basic features of the argument into a neural network to identify its role. In addition, we use voting ensemble mechanism to determine the final category. Experiments show that our model achieves macro F1=78.8 and micro F1=81.7 on FrameBank dataset, which outperforms the previous state-of-the-art results by Δmicro F1= 1.7, and Δmacro F1= 2.0.
Mobile Mental Health: A Review of Applications for Depression Assistance

Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.
AC Winding Losses in Automotive Traction E-Machines: A New Hybrid Calculation Method

This work deals with the AC winding losses calculation in electrical machines used in modern automotive traction applications; considering both hybrid and fully electric vehicles. The research is carried out estimating the AC winding losses amount for various configurations, through the use of detailed finite element analysis (FEA) and of a new hybrid analytical-FEA calculation method. A comparison of the obtained results by the two methods is carried out, in order to prove the proposed methodology effectiveness and to evaluate the winding losses impact on the overall design performance.
Task Scheduling Algorithm for Heterogeneous Real-time Systems Based on Deadline Constraints

With the wide application of real-time information processing systems, real-time task scheduling in heterogeneous environments has become the most important research area. According to the processing stage and urgency of tasks, a task scheduling algorithm for heterogeneous real-time systems based on deadline constraints(DCSA) is proposed. Firstly, according to the free time of the ready tasks and the estimated execution time of each node in the system, the classified scheduling strategy is adopted to guarantee the priority of the emergency tasks. Then, different task switching strategies are proposed respectively corresponding to the assigned tasks and the relative deadlines of execution tasks in the processing nodes. The analysis shows that the algorithm can avoid the thrashing of scheduling system, and there is no case that other suspended tasks miss deadlines due to long waiting time, which effectively guarantees the successful-scheduled tasks ratio.
Whispering Botnet Command and Control Instructions

Botnets are responsible for many large scale attacks happening on the Internet. Their weak point, which is usually targeted to take down a botnet, is the command and control infrastructure: the foundation for the diffusion of the botmaster's instructions. Hence, botmasters employ stealthy communication methods to remain hidden and retain control of the botnet. Recent research has shown that blockchains can be leveraged for under the radar communication with bots, however these methods incur fees for transaction broadcasting. This paper discusses the use of a novel technology, Whisper, for command and control instruction dissemination. Whisper allows a botmaster to control bots at virtually zero cost, while providing a peer-to-peer communication infrastructure, as well as privacy and encryption as part of its dark communication strategy. It is therefore well suited for bidirectional botnet command and control operations, and creating a botnet that is very difficult to take down.
Optimal Reactive Power Planning in Active Distribution Systems for Steady-state and Transient Characteristics Improvement

Numerous operational issues including voltage deviation and instability arise in distribution networks with insufficient reactive support. Static issues have been widely studied for years, and some dynamic ones are recently gaining attention due to the proliferation of distributed generation (DG). In this paper, a two-step reactive compensation planning procedure is proposed to address these challenges. In the first step, passive compensators are optimally sited and sized using a metaheuristic algorithm in order to improve steady-state indices including power efficiency, voltage profile, and static voltage stability. In the second step, a static synchronous compensator (Statcom) is located in the network to obtain the fastest voltage recovery at the terminals of DG units. The proposed methodology is applied to a 69-bus distribution system and the operational indices before and after the optimization are compared.
A New Very High-speed True 7-3 Compressor

In this paper, a new high-speed 7-3 compressor for high-speed arithmetic operations is presented. This compressor takes advantage of the current-mode techniques, also inverter gate’s property, especially back to back inverters to achieve the minimum delay. The proposed architecture is an altered size of the input number compressor. This feature will be important when 4, 5 or 6 partial product bits exist to compress in Multiplier, which can reduce the extra area and power. All the proposed designs are evaluated by exhaustive HSPICE simulations in 0.18um standard CMOS technology with 0.18V power supply voltage and supplementary simulation have been done to examine the effect of terms. The results show performance improvements with more than 3 times higher speed and 18 percent lower PDP compared to the purely digital design.
Sequential Behavior Modeling for Next Micro-Video Recommendation with Collaborative Transformer

Micro-video recommendation is important for micro-video social platform, which provides its users with micro-videos they may interested in. In this paper, we propose a new variant of Transformer, alleviating the drawbacks of Recurrent Neural Networks (RNN) which tend to compress all history records in a fixed hidden representation, to model users’ sequential behavior for next micro-video recommendation. Firstly, we employ self-attention to capture micro-video’s multi-modal features of different importance. Secondly, we make use of multi-head attention to learn users’ preference from historical records. We show how the Transformer combined with Collaborative Filtering (CF) and user-video sequential interaction can be used to perform next micro-video recommendation. Extensive experiments are conducted on two collected micro-video datasets, i.e., Toffee and TikTok. The experimental results demonstrate the proposed method is more effective compared with several state-of-the-art sequential recommendation methods.
Effect of interphase on dielectric relaxation mechanisms in

The interphase formed between polymer and a nanoparticle plays a critical role in the dielectric properties of the nanocomposite material. The effect of interphase on dielectric relaxation mechanisms is analyzed in this work. Unfilled epoxy and epoxyalumina nanocomposite samples with 0.l, 1 and 5 wt% filler loadings are prepared. SEM analysis is conducted to ensure dispersion of nanoparticles. Impedance and permittivity measurements are taken over the frequency range of 10 mHz-10 MHz. Equivalent circuit models are derived from impedance spectroscopy measurements for unfilled and nanofilled samples. A method based on genetic algorithm is used to optimize the equivalent circuit parameters. Initialization of parameters for the algorithm is selected based on the shape of Cole-Cole plot. The proposed equivalent circuit models based on relaxation time constants indicate the presence of polarization at the interphase region in nanocomposite. Variations in configuration and parameters of equivalent circuit for unfilled and filled epoxy are used to analyze the effect of interfacial polarization. Polarization at the interphase is differentiated from other relaxation mechanisms at 0.1 and 1 wt% based on equivalent circuit model. At 5 wt%, the optimized equivalent circuit configuration and corresponding relaxation time constants different from circuit models for other filler loadings is attributed to the presence of overlapping of interphases.
The breakdown characteristics of thermostable insulation materials under high-frequency square waveform

Breakdown tests on four types of thermostable inter-turn insulation material were carried out under bipolar high-frequency square waves from 1~20 kHz. The insulation breakdown voltage under different frequencies were determined from the two-parameter Weibull distribution. The voltage life characteristic was calculated based on the inverse power function model. The relationship between insulation thickness and breakdown voltage at high frequencies was also studied. The results show that, the characteristic breakdown voltage, decreases with increasing frequency, and reaches a maximum at 1~5 kHz. The voltage life characteristic curves shift with increasing frequency and thermal breakdown is the main cause for failure. At high frequencies, the non-uniform electric field and heat causes the breakdown voltage to change nonlinearly, and increased frequency increases this non-linearity.
Passive Dynamic Walking with Neuroevolutionary Controlled Torso

Passive dynamic walking is the simplest and the most efficient form of biped locomotion. Albeit the simplicity, the walking gait of passive dynamic walkers resembles human walking in slops very well. The most important drawbacks of these robots is their sensitivity on initial conditions and the limited range of slopes they can have a stable walking cycle on. The aim of this work is to alleviate these problems by adding a link that resembles a torso and controlling it using a Neuroevolutionary algorithm. To achieve this goal, initially the dynamical equations governing this robot was extracted. Then the control algorithm is applied. The reason to use this control strategy is to mimic how natural evolution could play a role in robust biped walking. It was observed that this strategy improved both shortcomings associated with passive dynamic walkers and interestingly the robot shows torso movements familiar to that of humans when facing steep slopes or zero slope.
Emotion Recognition from Physiological Signals using Multi-Hypergraph Neural Networks

Emotion recognition from physiological signals is an effective way to discern the inner state of users. Existing works are lack in the exploration of latent correlation among multiple physiological signals and relationship among different subjects. To tackle this issue, we propose to recognize emotion from physiological signals using multi-hypergraph neural networks (MHGNN). In this method, the correlation among different subjects is formulated in the multi-hypergraph structure, where each type of physiological signal is used to generate one hypergraph. In each hypergraph, the hyperedges are used to represent the connections among the vertices (subject, stimuli). Thus, the emotion recognition task is modeled as classifying each vertex in the multi-hypergraph. Experimental results and comparisons with the state-of-the-art methods in the DEAP dataset demonstrate the superior performance of our method. The comparative experiments based on available biological knowledge verify that MHGNN can depict the real biological response process in a much more precise way.
Hand Gesture Recognition using sEMG Signals Based on Support Vector Machine

This paper presents a method based on support vector machine (SVM) to recognize hand gestures using surface electromyography (sEMG). In our method, Myo armband (a sEMG device with eight channels) is used to measure subjects’ forearm sEMG signals. The original sEMG signal is preprocessed to reduce noise and detect muscle activity regions. Feature extraction is applied by segmenting a sliding sub-window in the preprocessed signals to get each segment of signals. Connect the signal segment with the results using a bag of functions to generate a feature vector. For classification, we train a SVM classification model which includes 5 sub-models. Each sub-model can recognize a gesture, like fist, wave in, wave out, fingers spread, and double pinch. Finally, we test the proposed model to recognize these gestures, and achieve an accuracy of 89.0%.
Automatic Fuzzy Segmentation of Textural Images Using Adaptive Divergence Affinity Functions

The goal of digital image segmentation is to assign different labels to different objects present in a digital image or volume. A wide variety of sources have been successfully segmented by the traditional fuzzy segmentation algorithm on several different applications. However, the traditional approach of the fuzzy segmentation algorithm usually does not work well when dealing with objects whose materials are characterized by complex textures that cannot be accurately represented by simple fuzzy affinity functions of the traditional fuzzy segmentation algorithm. In this paper, we extend the fuzzy segmentation algorithm to use adaptive textural affinity functions for segmenting these objects in 2D images. These adaptive affinity functions define their optimal appropriate neighborhood size on execution time, by computing texture descriptors surrounding the seed spells, according to the characteristics of the texture being processed. The algorithm then segments the image with the appropriate neighborhood that may be different for each object. We performed experiments on mosaic images that were composed using images from the Brodatz database, and compared our results with the ones produced by a recently published texture segmentation algorithm, showing the applicability of our method.