Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing the Effectiveness of EMG and Electrical Impedance myography Measurements for Controlling Prosthetics

In recent years, the field of prosthetics has made significant progress towards creating prosthetic devices that are more functional, comfortable, and user-friendly. However, achieving intuitive control over prosthetic hand movements remains a significant challenge, especially for individuals with limb loss who rely on prosthetics for independent daily activities. To address this challenge, researchers have explored the potential of non-invasive techniques as electromyography (EMG) for prosthetic control. This paper aims to investigate the potential of using EMG and the electrical impedance myography (EIMG) techniques jointly for the measurement of hand movements. The study involved recording and comparing EMG and EIMG signals from a cohort of healthy individuals. These signals were captured during four distinct hand gestures: opening and closing the hand, as well as extending and flexing it, under varying time conditions, allowing for categorization into low and high-intensity movements. Data collection employed the Open BCI and ZRPI devices. The analysis of these signal waveforms revealed compelling results. Brachioradialis activity in EMG 2 exhibited an increase during open hand (0.015mV) and extension hand (0.009mV in low and 0.013mV in high intensity) gestures, accompanied by increased EIMG activity (56mV and 52mV respectively). Additionally, close hand (0.0018mV in low and 0.05mV in high intensity) and flexion hand (0.0075 in low intensity and 0.002 in high intensity) gestures exhibited heightened flexor carpi ulnaris activity with raised EIMG activity (57mV and 45mV respectively). These results proved to be consistent, acceptable, and aligned with existing literature. The findings of this paper indicate that both EMG and EIMG techniques could be used together to control custom-made hand prosthetics, demonstrating a significant development that could lead to more intuitive and easier-to-control prosthetics. Also, the results obtained could be valuable to researchers and engineers working in the prosthetics field, as it provides insights into the potential of non-invasive techniques for prosthetic control.
A Deep Learning-based Air Quality Index Prediction Model Using LSTM and Reference Stations: A Real Application in Taiwan

Air pollution has attracted significant attention as a public concern, prompting government efforts towards air quality monitoring. With the accumulation of data, the feasibility of utilizing artificial intelligence (AI) to aid in air quality prediction has grown. Air quality prediction plays a pivotal role in policy formulation and environmental management. We propose a neural network model framework constructed using LSTM (Long Short-Term Memory) model and fully connected model. It employs pollutant and meteorological data from target stations, neighboring stations, and outpost stations as feature factors. By utilizing the previous 24 hours monitoring data, it predicts the hourly air quality index (AQI) for the next 12 hours. Experimental results demonstrated the effectiveness of this approach in enhancing prediction accuracy and providing early forecasts of long-range transport of air pollutants. This prediction model is applied within the Taiwan Air Quality Monitoring Network [1] and the Environment Info Push app [2] both maintained by the Ministry of Environment of Taiwan (MOENV), allowing citizens to access information of environment at any time and receive activity recommendations based on real-time monitoring values.
Optimized Finite Control Set Model Predictive Control for a Three-Phase Five-Level Cascaded H-Bridge Multilevel Inverter fed Interior Permanent Magnet Synchronous Machine With On-Line Candidate Switching State Selection

Model predictive control is a novel control strategy that is attracting the scientific community due to the several advantages it offers, such as the ability to consider system nonlinearities, the possibility to synthesize a control for a MIMO system instead of multiple SISO, and so on. Control feasibility, due to the very high computational cost required to solve the optimal control problem, is a challenge. By considering electric drives fed by multilevel inverters, the control design is more challenging due to the increased number of available output voltage vectors. In this work, a simple algorithm for the voltage candidate reduction is presented: it allows for reducing the control computational cost, minimizing the switching losses, and minimizing dv/dt on phase voltage waveforms.
Weakly Supervised Semantic Segmentation for Point Clouds with Self-Purification on Pseudo Labels

Currently, research in the field about point cloud semantic segmentation primarily focuses on fully supervised learning, which requires expensive manual point-level annotations. Weakly supervised learning is an approach to overcome the time and effort required for such annotations. However, for large-scale point clouds with limited labeled points, it is challenging for networks to learn the differences between point features. Some methods address this issue by pretraining the network with generated pseudo-labels for the point cloud, allowing the network to learn more information. However, pseudo-labels are prone to a certain proportion of mislabeling, and directly using them can lead to erroneous classification results. To address this problem, we propose a pseudo-label self-purification framework. Specifically, we design confidence scores to dynamically select reliable labeled points and employ clustering techniques to partially update the pseudo-labels, thereby providing correct supervision for network learning. Additionally, inspired by unsupervised learning, we introduce a perturbation branch to enhance the consistency of point cloud predictions. The experimental results, obtained from the evaluation of two extensive datasets, demonstrate a gain of 2.0% on average compared to recent weakly supervised method. Furthermore, our approach achieves results that are comparable to certain fully supervised methods.
Assessment of the Effects of Temperature and Precipitation Variations on the Trend of River Flows in Urmia Lake Watershed

analysis is one of the appropriate methods to assess the hydro-climatic condition of watersheds, which is commonly used for analysis of change pattern in a single variable over time. However, in real cases, many hydrological variables such as river flow are directly affected by climate and environmental factors, which usually go unnoticed in routine analyzes. The aim of the present research is to investigate the trend of river discharge in 25 hydrometric stations in Lake Urmia river basin with and without consideration of temperature and rainfall variability. Briefly, the results showed that there is a decreasing trend in all stations, which is significant in 9 cases. Also, it has been shown that regarding to trends in precipitation and temperature, the number of stations with significant decreasing trend will reduce to 7, which shows low impact of climate factors on the reduction rate of discharge in these stations. Based on the results, it can be concluded that climate variations have direct effect in inferring significant trends in river flow, so that considering these variables in studying of river discharge can lead to different results in the detection of significant trends.
A 325 FPS Corner-Detection Accelerator with Hardware-Oriented Optimization

Simultaneous Localization and Mapping(SLAM) is an important technology in robot positioning and navigation. In visual odometry, the extraction accuracy and efficiency of corner detection directly affects the performance of the system. However, due to the complexity of corner detection algorithms, it is difficult to meet the requirements of real-time, efficient, and low-cost simultaneously, especially on robots. GFTT corner detection based on Harris has good performance in accuracy. However, hardware implementation increases computational complexity and resource consumption. In this work, an optimized corner detection accelerator is proposed to replace GFTT, which is hardware-friendly to be implemented on edge embedded devices. Without significant loss of accuracy, its computational complexity and hardware resource consumption are greatly reduced, and it can be good candidate for FPGA and ASIC designs. The detection image frame rate can reach 325 fps that outperforms the previous works.
Median Filters for Switching Noise Mitigation in Oversampled Power Electronics Control Systems

This article addresses switching noise propagation and its suppression in multi-rate control systems, where the inductor current is oversampled, digitally filtered and the control execution is decimated to single(double)-sampled pulse-width modulation (PWM). The operating points where the sampling instants occur near the switching ones are critical for switching noise sensitivity. It is shown that, when the number of the corrupted samples is high with respect to the number of samples used for filtering, typically considered moving average filters (MAFs) bring a detrimental impact. As an alternative, median filter (MED) is proposed. To decouple the ranking within MED from the switching ripple, repetitive ripple removal is added beforehand. Multi-rate PWM control with the proposed feedback filtering outperforms state-of-the-art double-sampled PWM by eliminating the well-known switching noise sensitivity for small and large duty cycles. In addition, noise-sensitive operating point regions that appear with MAF are successfully suppressed.
Nonsingular Terminal Sliding Mode and NN Based Control of Underactuated AUV

In this paper, a nonsingular terminal sliding mode control (NTSMC) combined with neural network and disturbance observer is proposed for the trajectory tracking of an underactuated AUV. An adaptive neural network (NN) is designed to compensate the complicated uncertain nonlinearity in the dynamics of AUV. Disturbance observer is employed to estimate the external disturbances. Lyapunov method proves the stability of the proposed controller. Simulation results show that the designed controller has good performance.
Results of Dual-Polarimetric Airborne Passive Radar

Polarimetry in remote sensing applications has emerged as a valuable technique for enhancing the information content of Synthetic Aperture Radar (SAR) images. Passive radar exploits existing illuminators of opportunity, e.g. broadcast or communication transmitters, for sensing applications, i.e. target detection or SAR imaging. Terrestrial broadcast transmitters emit the signal typically using only one linear polarization (horizontal or vertical). In this cases, full-polarimetry, as it is known from active remote sensing applications, is not usable for passive radar. However, if the receiver is equipped with dual-polarized antenna elements, dual-polarimetry techniques can be applied. It has the potential to improve the quality and interpretability of passive radar data. In this paper, we present results and analysis of dual-polarimetric passive SAR images. We calculate the coherence between horizontal and vertical polarized SAR images for two different scenes. Additionally, we calculate the Stokes parameter and derive a quantitative measure from the Stokes parameters to improve the interpretability of the SAR images.
A 64-Gb/s 0.33-pJ/bit PAM4 Receiver Analog Front-End with a Single-Stage Triple-Peaking CTLE Achieving 22.5-dB Boost in 40-nm CMOS Process

This paper presents a PAM4 receiver analog-front end (AFE) circuit. Our proposed single-stage triple-peaking continuous-time linear equalizer (CTLE) stage with current reuse technique is adopted to achieve low, middle and high frequency peaking concurrently. Thus, only one-stage CTLE is sufficient to achieve an over-20-dB boost at Nyquist frequency to save power. A variable-gain amplifier (VGA) is used to reduce the DC-gain variation at different CTLE equalization settings. Fabricated in a 40-nm CMOS process, our prototype AFE achieves 64-Gb/s data rate, 22.5-dB maximum peaking boost at 16-GHz, and an energy efficiency of 0.33 pJ/bit at 64-Gb/s data rate. It also features low, middle and high frequency peaking so that it can compensate a 12-dB channel loss at Nyquist frequency of 16-GHz.
Overtemperature Protection of Power Electronic Modules with Reduced Driveline Oscillations in Electric Vehicles

This paper presents the design of a temperature controller to prevent overtemperature failures in three-phase inverters for electric drive applications. Here, a combination of online estimators is utilized to estimate the critical semiconductor losses and junction temperatures. Then, the temperature controller actively manipulates the maximum allowable phase current to ensure safe operation within the power module junction temperature limits. Oscillations in the maximum allowable phase current output of the temperature controller (e.g., due to the dynamics of the device power losses and temperatures at low speed) are mitigated using a phase current limit ripple controller. This ripple controller utilizes a stiffness gain to alter the amount of ripple suppression. Detailed simulation and experimental results for Field-Effect Transistor (FET) and Insulated Gate Bipolar Transistor (IGBT) based inverters show the effectiveness of the proposed controller across various operating conditions.
Smooth Closed-loop Control Strategy Design for Dual-Active Bridge Converters Using Optimized TPS Modulation

In the application of DAB converter in battery energy storage system, it is necessary to provide a closed-loop control strategy of charge and discharge to ensure wide gain and high efficiency. In this article, a kind of modulation with minimum RMS of inductor current as the optimal target is applied, which can satisfy the above requirements. However, there is a dead time effect when it is used for closed-loop control, so that the results are not optimized. In order to mitigate the effect of dead time and ensure the correct implementation of the optimal TPS modulation, this article proposes a strategy of smooth closed-loop control for DAB. Power feedforward is used to adjust the optimization results obtained from the power reference calculation. In addition, the use of feedforward can realize smooth switching of different loop controls and smooth transition between transient soft start process and steady-state optimization. Finally, the proposed strategy is experimentally validated on a 5.4kW DAB converter prototype.
Novel In-Core Cooling of Yokeless and Segmented Armature (YASA) Axial Flux Machines

This paper presents a novel in-core cooling method for yokeless and segmented armature (YASA) axial flux machines to address the challenges posed by high temperatures in these motors, thereby unlocking their full potential. The in-core cooling method involves forming cooling channels within each stator core segment, through which a coolant is circulated to transfer heat from windings and core to the coolant. This solution increases thermal performance and reliability by effectively cooling the stator core and windings. Additively manufactured connectors are utilized to achieve integrity of cooling channels through adjacent segments. As a case study, the 36-kW YASA motor is analyzed using CFD and lumped parameter thermal network models. The results showed that the proposed in-core cooling can boost the current density to 18 Arms/mm2 at the stator windings.
Wide-Stopband EMI Filter Based on Coaxial Metal-Dielectric EBG Structure

A wide-stopband EMI filter based on a coaxial metal-dielectric electromagnetic bandgap (EBG) structure is presented in this paper. The effects of dielectric permittivity, dielectric thickness, inner diameter of the outer conductor, and periods on the stopband characteristics in limited length are investigated. Research has shown that increasing the dielectric constant of a medium or reducing the thickness of a dielectric can effectively broaden the stopband and enhance stopband attenuation in the frequency range of 1–20 GHz. Additionally, increasing the inner diameter of the outer conductor can further improve the filtering performance. Although loading more periods within limited space can achieve stopband attenuation exceeding 150 dB, it results in a higher lower cutoff frequency. For instance, when the number of periods is 20, the lower cutoff frequency is 5 GHz.
An Event-Driven Charge Pump Based OCL-LDO with 410nA IQ and 30ns Recovery Time

To achieve fast transient response with low power consumption for output-capacitorless low-dropout regulator (OCL-LDO), an event-driven charge pump (CP) loop with the dynamic strength control (DSC), is proposed in this paper. A prototype chip was fabricated in 65nm CMOS technology. With 410nA quiescent current (IQ), the proposed OCL-LDO can recover within 30 ns under 200mA/10ns loading change, achieving an excellent FoM of 0.1 fs.
A Polarized Random Fourier Feature Kernel Least-Mean-Square Algorithm

This paper presents a polarized random Fourier feature kernel least-mean-square algorithm that aims to overcome the dimension curve of the random Fourier feature kernel least-mean-square (RFFKLMS) algorithm. RFFKLMS is an effective nonlinear adaptive filtering algorithm based on the kernel approximation technique. However, random samples drawn from the distribution need more dimensions to achieve better-generalized performance because they are independent of the training data. To overcome this weakness, a kernel polarization method is adopted to optimize the random samples. Polarized random Fourier features demonstrate a clear advantage over a method without using the polarization method. The experimental results in the context of Lorenz time series prediction and channel equalization verify the effectiveness of the proposed method.
Investigation of a Multi-Modular IPT System on the Efficiency and Current Balancing due to Inter-/Cross Coupling

Inductive power transfer systems can process higher power using multiple charging pad modules connected in parallel. However, the effects on the system operation of the inter-/cross coupling among the pads have to be studied. This paper analyses the power transfer efficiency and current distribution of a quadruple modular IPT system. Additionally, a sensitivity analysis of the power transfer efficiency is provided based on the tolerances of the secondary resonant capacitors. The analysis shows that the efficiency of a multi-modular IPT system will increase compared to a single module and that there would not be a negligible imbalance in the current due to the inter-/cross coupling. In the experiments, the results of the quadruple modular IPT system’s estimated AC efficiency are lower than a single module. It also shows that the could be bifurcation when multiple modules are deployed. Both can be attributed to the mismatch in resonant frequency, self-inductance and main mutual inductance between the modules. Future work will focus on mitigating the circulating currents caused by the mismatch between modules.
Deep Learning-Based Plant Disease Image Recognition for Cyber-Physical Systems

Corn, wheat, and rice are vital as staple foods, affecting our economy, culture, health, and environment. Our research focuses on using a convolutional neural network (CNN) to detect food plant diseases. Early and accurate detection is essential to control the spread of infection and maintain the health of the food plant industry, making sustainable food systems a pressing matter. Traditional neural networks can't cope with the weight increase caused by large image sizes and numerous hidden layer neurons. To solve this, we suggest a new method for identifying plant diseases early using CNN. By adjusting the CNN's hyperparameters, we can optimize it for the given dataset. To train the proposed deep CNN model, we use real plant disease datasets such as the PlantVillage dataset [19], Wheat Leaf Dataset [20], Rice Leaf Disease Data Set [21], and Rice Leaf Disease Image Samples [22]. We were able to achieve impressive results for wheat (99.34%), corn (95.15%), and rice (92.47%) plant disease detection, with a promising level of accuracy demonstrated in our experimental findings. The results achieved in this paper exceed the accuracy of other related research works (details in literature review).
Anti-jamming Equilibrium Strategy Learning of Frequency Agile Radar based on Monte Carlo Tree Search

Frequency agility is a promising technique for addressing the threat of mainlobe jamming in modern electronic warfare. This paper investigates the confrontation between a subpulse-level frequency agile radar and a smart mainlobe jammer from a game theory perspective. The interaction process between the two sides is modeled as a two-player zero-sum extensive form game, which can capture the multi-stage and highly competitive nature. To obtain the optimal radar frequency agility strategy in the presence of a smart jammer, we introduce the Simultaneous Move Monte Carlo Tree Search (SM-MCTS) algorithm, which improves upon the classic Monte Carlo Tree Search algorithm in both selection and backpropagation steps. Furthermore, we discuss three selection functions that choose actions for the radar and jammer at each game tree node. Finally, simulation results demonstrate the effectiveness of the SM-MCTS algorithm for approximating the Nash equilibrium.
Generalized High-frequency Small-signal Modeling of Electric Machines using Electromagnetic FEA

With the increased application of variable speed drives for efficient speed and torque control of electric machines, there has been a growing number of practical machine drive-related process failures. Typical variable speed drives have a pulse width modulated inverter switching at tens of kilohertz, with dv/dt of thousands volt per millisecond. The high switching frequency introduces additional electromagnetic losses in electric machine cores, permanent magnets (PMs), and windings. The high dv/dt introduces damaging bearing currents and leakage currents, adversely affects the machine insulation due to terminal overvoltages, and contributes to the electromagnetic interference (EMI). To address these high-frequency issues in a systematic manner, this paper proposes a new framework by including parasitic capacitive effects into the widely-used coupled-circuit model of electric machines. To study the influence of high-frequency voltage harmonics introduced by pulse width modulation (PWM) voltage source inverters, the high-frequency small-signal model is further deduced and its numerical implementation in electromagnetic finite element analysis (FEA) tools is presented. Typical applications of the proposed modeling approach are discussed.
Variable Voltage Phasing Method for DC Bus Power Oscillation Mitigation in Hybrid DC/AC Residential Buildings with Harmonics

This paper proposes a method to mitigate DC bus power oscillations in a hybrid DC-AC microgrid (MG), including double frequency and its respective power harmonics. The considered MG is composed of renewable energy sources, batteries, utility grids, and AC loads. The single-phase inverters connect the AC loads to the DC bus, which increases the oscillations of 2ω0 (and its multiple harmonics) due to the non-linear properties of residential loads. Considering stability and degradation of capacitors and batteries as the main problems generated by low DC bus power quality, this paper proposes a method to reduce the DC bus power harmonic oscillations. The method consists of obtaining the proper voltage reference phase angle for each inverter and implementing adaptive phase-shift control in real-time. The proposed idea does not add any additional passive or active elements to the system. The main building controller receives power values and proceeds to calculate the phase angles. The method achieved a 93% improvement in DC power quality.