Miracle-dz/UniFAR-FTUs huggingface dataset:
A Teleoperated Manipulator Control System Based on Surface Electromyography Using Deep Learning

Gesture recognition based on surface electromyography (sEMG) is a popular topic in teleoperation field, which has the characteristics of convenience, maneuverability, flexibility and adaptability. With the continuous development of deep learning technology, the variety and accuracy of gesture recognition are constantly improving. In our previous research, a new neural network based on graph convolutional network and temporal convolutional network (STCN-GR) was proposed and achieved good results in gesture recognition task. In this paper, we develop a teleoperated manipulator control system based on STCN-GR combined with majority voting. The experimental results show that the performance of proposed system can achieve relatively excellent performance in recognition accuracy and computation speed. In this paper, we choose a mechanism hand as a peripheral to test the performance of the STCN-GR based control system, and the results show that this system can be applied in teleoperation in feature work to provide convenience for human life.
A Novel Modified-TOGI based PLL for the Three-Phase Unbalanced and Distorted Grid Conditions

This paper proposes a novel modified third-order generalized integrator phase-locked loop (TOGI-PLL) for balanced, unbalanced, and distorted grid conditions. This technique also eliminates the error factors that cause in experimental such as DC-offset and removing the second-order oscillation noise by adding the low-pass filter when generating the estimated filtered frequency. A step-by-step controller design procedure, system stability analysis, and comparison with the traditional second-order generalized inte-grator (SOGI), dual second-order generalized integrator (DSOGI), and decoupled double synchronous reference frame PLL (DDSRF-PLL) are carried out. The proposed PLL approach is simulated and ex-perimentally implemented on a three-phase 7.5kW grid-connected inverter system.
Few-shot Partial Discharge Pattern Recognition for Power Cables

The data set of phase-resolved partial discharge (PRPD) grayscale image of power cables is usually small, and it is difficult to train complex deep residual networks for high-efficiency defect recognition. In this paper, a novel method combining data augmentation with network optimization is proposed to tackle this problem. Deep convolutional generative adversarial network (DCGAN) is employed firstly to augment training data set, in particular, an evaluation indicator based on the box dimension is proposed to verify the effectiveness of the generated samples. In addition, a deep residual network is optimized from the perspectives of network depth, convolution kernels and shortcut type to adapt to the small-scale PRPD grayscale image data set. Compared with the original deep residual network trained with the small sample, the proposed method achieves faster training speed and higher recognition accuracy. The recognition accuracy could reach 98.5% in the experiment, which effectively improves the effect of partial discharge pattern recognition for power cables under small sample condition.
Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users

In this paper, we study the localization of ground users by utilizing unmanned aerial vehicles (UAVs) as aerial anchors. Specifically, we introduce a novel localization framework based on Federated Learning (FL) and Reinforcement Learning (RL). In contrast to the existing literature, our scenario includes multiple UAVs learning the trajectory in different environment settings which results in faster convergence of RL model for minimum localization error. Furthermore, to evaluate the learned trajectory from the aggregated model, we test the trained RL agent in an alternative environment which shows the improvement over the localization error and convergence speed. Simulation results show that our proposed framework outperforms a model trained with transfer learning by %30.
A V2G-enabled Seven-level Buck PFC Rectifier for EV Charging Application

This article presents a novel bidirectional multilevel buck rectifier with power factor correction for the charging systems of currently available commercialized electric vehicles. As it synthesizes seven voltage levels, the proposed rectifier entails low harmonics. This rectifier enables grid-to-vehicle (G2V) operation in buck mode and vehicle-to-grid (V2G) operation in boost mode. The proposed topology utilizes the switched capacitors principle to achieve a self-voltage balancing of the capacitors. Experimental results are presented to validate the proposed rectifier.
Integrating Software-Defined Metasurfaces into Wireless Communication Systems: Design and Prototype Evaluation

Software-Defined Metasurfaces (SDMs) have ad-vanced from basic theoretical ideas to a key enabler for next generation wireless systems. When massively deployed in a wireless environment, they can realize precise, software-defined propagation of signals and wireless channel customizations. A plethora of studies have thoroughly investigated the physical design of the SDMs and their wireless channel engineering capa-bilities. This work introduces a novel architecture and associated processes for integrating SDMs into existing networked wireless systems. First, the integration architecture and the according complex underlying physics of the SDMs are presented. Then the software abstractions are proposed that enable the interaction of the surfaces in a physics-agnostic manner. These software abstractions require an SDM profile to operate in real-time, i.e., a database that accurately describes the capabilities of a given metasurface. Thus, the manufacturing time workflows are suggested to produce such a profile. The proposed solutions are evaluated in an actual physical setup/testbed.
FSS based CPW Fed Hexa Loaded Folded Dipole Shape Antenna for mm-Wave Applications

This paper presents an innovative coplanar waveguide fed wideband hexa loaded folded dipole shape antenna (HLFDA) with a frequency selective surface (FSS) for mm-wave application in the 5G spectrum. In this design, the hexa structure had inserted inside folded dipole in the shape of a square to enhance behavior. The overall size of the antenna is 0. 184λ0mm x 0. 119λ0mm x 0. 006λ0mm and FSS unit cell is 0. 06λ0mm x 0. 06λ0mm. The FSS structure loaded antenna gain and bandwidth are simultaneously improved to 6.4dB and 8500MHz respectively. A dielectric substrate of epsilon value 4.3 and dissipation factor (tanδ) of 0.02 is used for the fabrication of HLFDA. HLFDA is an auspicious performance prototype and well suitable for the mm range 5G applications.
Characterization of Si-IGBT Crosstalk with a Concentration on Power Circuit Parasitic Elements and the Device Operation Point

Crosstalk is a serious issue in power electronics converters having a phase-leg in their structure. The crosstalk destructive effect can lead to failure of the device. Hence, several models are presented in the literature to analyze the crosstalk and prevent the undesired failures. However, these models need to be enhanced so as to be characterized in different aspects. In this paper, a new comprehensive model based on the last models in state-of-art has been presented. This model includes the parasitic elements of the power circuit and parasitic capacitances of Si-IGBT. Thus, the effect of variable elements can be investigated. In this paper, the effect of the high-side switch specifications such as off-sate voltage, conducting current, and turning-on time on the crosstalk has been figured out. It is found that the most critical condition in terms of the crosstalk is when the converter operates on the high voltage levels of DC-bus and light-load conditions. Moreover, the different values of parasitic inductance are considered in the model, and their effect on the crosstalk is evaluated. Furthermore, the experimental setup has been introduced in order to check the model accuracy. In order to study the effect of Si-IGBT parasitic capacitances, the ratio of $C_{\text{GC}}/C_{\text{GE}}$ has been changed, and results have been presented. The case study device for the investigation of the crosstalk and experimental tests is IXGH60N60.
Decontamination of galaxy spectra using four dispersion directions

This paper addresses the problem of spectra decontamination in slitless spectroscopy in the context of the Euclid space mission. This problem can be treated as a source separation problem which consists of estimating a set of unknown sources from a set of their mixtures. We first present a mixing model linking observed data to source spectra that simultaneously takes into account four light dispersion directions of the grism, then we propose two methods to decontaminate the spectra from the mixed data. Exploitation of all these dispersion directions improves the estimation of the spectrum of an object of interest. Preliminary results obtained using realistic noisy data show the effectiveness of the proposed methods.
Designing Data-Aware Network-on-Chip for Performance

Network-on-Chip (NoC) is a widely adopted communication infrastructure in Tiled Chip Multi-Processors (TCMPs) due to its high transfer bandwidth, scalability and reliability. As the number of cores continues to increase in modern TCMPs, NoC plays a pivotal role in determining the performance. In fact, it is reported that NoC is responsible for 60 % to 75% of the miss latency experienced by the applications run on TCMPs. Nevertheless, most of the existing memory access techniques and optimisations are NoC oblivious, which limits their performance. This work advocates for considering NoC and memory hierarchy together when designing techniques and proposing optimisations for TCMPs. It shows that designing data-aware NoC with the help of memory hierarchy improves resource utilisation, reduces memory access latency and improves system performance. Specifically, the architectures proposed in this work can improve overall system performance by up to 19 % with negligible storage, area and power overhead. While TCMPs continue to scale with the help of electrical, wireless and photonic NoCs, the proposed work can influence future design decisions.
A Privacy-preserving Wind Speed Prediction Method Based on Federated Deep Learning

Renewable energy, such as wind power, has witnessed rapid development recently on account of increasing public awareness of environmental protection along with sustainable power supply. In this background, wind speed prediction plays an important role in helping ensure stable and economic operation of modern power systems with a high renewable penetration. However, the stricter data regulation rules and competitive demands in power markets set a limit to employing datasets from different wind farms to train a more accurate prediction model via machine learning techniques. Therefore, a privacy-preserving wind speed prediction framework based on federated deep learning is proposed to tackle the challenge, which utilizes multi-input inner-product functional encryption to offer extra data protection. A case study on the Wind Integration National Dataset proves that our method excels isolated training scenes and approaches the ideal centralized one in prediction performance without leakage of sensitive data.
A Permutation Challenge Input Interface for Arbiter PUF Variants Against Machine Learning Attacks

Internet of Things (IoT) have broad and deep penetration into our society, and many of them are resource-constrained, calling for lightweight security protocols. Physical unclonable functions (PUFs) leverage physical variations of circuits to produce responses unique for individual devices, and hence are not reproducible even by their manufacturers. Implementable with simplistic circuits and operable with low energy, PUFs are promising candidates as security primitives for resource-constrained IoT devices. Arbiter PUF (APUF) and its variants are lightweight in resource requirements but suffer from vulnerability to machine learning attacks. To defend APUF variants against machine learning attacks, in this paper we investigate a challenge input interface, which incurs low overhead. Analytical and experimental studies were carried out, showing substantial improvement of resistance against machine learning attacks when a PUF is equipped with the interface, rendering interfaced APUF variants promising candidates for security critical applications.
Trespassing Detection using CCTV and Video Analytics for Safety and Security in Railway Stations

Smart station has its concern to passengers safety and security as it is the fundamental needs of the passengers satisfaction. CCTV (circuit closed television) has been commonly used as surveillance system in public places including railway stations. CCTVs could be optimized using video analytics to automized surveillance using machine learning. Trespassing is one of threatening activities that is happening in railway stations. The objective of this paper is to add trespassing recognition feature to VIANA existing platform using YOLOv5 as the detection model and DeepSort as the tracking model.
Multiclass Skin Lesion Classification Using Densenet

Skin cancer is the most frequent type of cancer. It must be detected and treated as soon as possible because it can be fatal. The distinction between cancerous and benign skin lesions is difficult to discern with the naked eye, making cancer detection difficult. Because several lesions may have similar appearances, precise identification of skin cancer is challenging. With the growth of technology and computer vision, several machine learning and deep learning techniques are getting popular in skin lesion categorization tasks. In this paper we proposed a deep learning model using a pretrained DenseNet architecture. For our work, we used the HAM10000 dataset, which contains 10015 dermoscopic images. To demonstrate the significance of using balanced dataset in classification tasks, we conducted two experiments. The imbalanced dataset was employed in the first experiment, while a resampled dataset with balanced classes was used in the second task. In the classification task, employing balanced classes resulted in better performance having accuracy 82.1%.
Bidding Strategy of Electric Vehicle Aggregators for the Spot Market Based on Bayesian Updating

The rapid development of electric vehicles (EVs) has created more possibilities for their flexible participation in electric power dispatching. Considering the clustering and fast mobility of EVs coinciding with real-time market requirements for responsive demands, a bidding strategy is proposed to assist EV aggregators with submitting reasonable quotations for the real-time market. Based on Bayesian updating, the strategy in this paper considers relevant factors such as aggregator cost and expected return. First, a quotation strategy is designed and its application under different scenarios is discussed. Then, the objective function of learning is built with a specific process of Bayesian updating demonstrated. Finally, a case study of the aggregator quotation strategy based on Bayesian updating is simulated to verify the effectiveness and feasibility of the proposed strategy.
Design of Wide Load Class-E Inverters for Radio Frequency Plasma Generation

This paper presents a design method for wide load operation of high frequency Class-E inverters based on GaN power devices in radio frequency (RF) plasma generation applications. The mathematical model considering the reverse conduction of the Class-E inverter feeding any loads on the complex plane is introduced and the total power loss of the power switch could be calculated then. The safe operating area (SOA) of the Class-E inverter could be established based on the thermal limits of the power switch. With an impedance matching network, the varying plasma load could be transformed into the SOA of the Class-E inverter. Both simulation and experimental results verify the effectiveness of this design methodology.
A CL-MDT Method of Multi-energy Load Forecasting in Integrated Energy System

Source-load scheduling is based on multi-energy-load forecasting. The integrated energy system mainly includes three types of energy: electricity, cooling and heating. Studying the correlation among electricity, cooling and heating may improve the accuracy of multi-energy-load forecasting. This paper considers the correlation of three energy sources, fully analyzes the correlation, and applies the correlation of the three in the forecasting model. This paper proposes a CL-MDT (CNN-LSTM-Multi-Decoder-Transformer) model for multi-energy-load forecasting. The model is based on the Transformer, and the Multi-Head Attention part in the Encoder is replaced by a 2dimensional 3*3 CNN (Convolutional Neural Network) feature extraction module for feature extraction of data. And a 1dimensional CNN feature extraction module and LSTM structure are added to the Decoder. The structure of single Encoder and multiple Decoders is used in this paper to realize the application of the correlation of the three in the forecasting model. Finally, the model is tested on public datasets and the forecasting results of CL-MDT are compared with that of LSTM model for multi-energy-load joint forecasting. The results show that the CL-MDT model proposed in this paper has better forecasting accuracy.
Teaching & Learning Analytics for Data-Based Optimization of Teaching and Learning Processes in Courses with Blended Learning

Learning Management Systems (LMS), such as Moodle, enable the rapid progress of digitisation in teaching, which is no longer only taking place in the lecture hall, but increasingly “online” and asynchronously. New didactic concepts (blended learning, “flipped classroom”) consist of alternating self-learning and face-to-face phases, with the former taking place in the LMS, i.e. online. However, no analysis has yet been carried out as to how students act with the material in the self-learning phase, or the teachers are not provided with any information about the learning progress of the students during the self-learning phase. In this paper, concepts of learning and teaching analytics are presented to answer these questions and to integrate the measures derived from them into the teaching processes in a sustainable manner.
5G Microstrip Antennas: Design, Analysis and Study of Multi band Antennas with Split Ring Resonators

This paper presents the design and analysis of microstrip patch antenna resonating in the 5G band at 3.45 GHz with split ring resonators in the ground plane. Then split ring resonators are added in the substrate of the antenna, thus changing its permeability. The change in the radiation pattern of the antenna after introducing the metamaterial ring resonator substrate is discussed and compared with previous results. The S parameters, Directivity, gain and size of different designs are presented. Ansys HFSS was used for the study. All the designs are done on FR4 substrate due its low costs and easy availability.
Evaluation of Machine Learning Methods for Predicting Rainfall in Bangladesh

Rainfall is a crucial weather parameter in the context of Bangladesh. Prediction of rainfall can effectively aid the decision making process for agriculture and natural disaster management of the country. However the chaotic nature of rainfall due to climate change has made the task of rainfall prediction challenging through traditional statistical models. In this study, we analyze the performance of six machine learning algorithms: Decision Tree (DT), K-Nearest Neighbours (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB) and Multi-Layered Perceptron (MLP) in predicting daily rainfall as both regression and classification. In addition we try out an approach called Zero Inflated Regression (ZIR) to address the excessive amount of zero rainfall values in the dataset. The models were trained with and without feature selection and/or sampling techniques (for classification). During training 10-fold cross validation and hyperparameter tuning was performed on the train set and afterwards the selected models were applied to the test set for evaluation. For regression LGB with SelectKBest feature selection had the best performance on the test set with R2-score of 0.203, MAE of 6.40 and RMSE of 15.44. Among the classifiers, XGB with no feature selection and no sampling technique resulted with best test accuracy of 0.787 and test macro fl-score of 0.62. The ZIR model consisting of XGB classifier and LGB regressor with no feature selection yielded R2-score of 0.189, MAE of 5.789 and RMSE of 15.575 on the test set. Interestingly the ZIR models produced lower MAE compared to the regression models but the regression models had better R2-score.
A Robust Pipeline Approach for DDoS Classification using Machine Learning

Remote and edge devices have less security features that are easily exploited by hackers. The security of businesses in major domains depends on the security features the infrastructure has to offer. Major breaches have been reported over the past years which have led to compromise of hidden data. DDoS attacks have been a major trend which has brought down many devices using similar techniques. Major vulnerabilities have been found in IoT systems which presents an open door for hackers. To address the upcoming trends in early vulnerabilities detection, a standard predictive model of DDoS attacks needs to be implemented. In this paper we propose a robust pipeline for DDoS classification and the performance of the models are calculated against the metrics such as precision, recall and f1-scores. After evaluating various machine learning models, the XGboost algorithm works well on our data set with an accuracy score of 99% outperforming other models.
PCB Technology Comparison Enabling a 900V SiC MOSFET Half Bridge Design for Automotive Traction Inverters

The design of automotive traction inverters for an $800\ V$ dc-bus typically utilize $1.2 kV$ silicon carbide (SiC) metal oxide semiconductor field effect transistors (MOSFET). The $1.2 kV$ power devices allow for high overshoot voltages during switching transients but experience a high on-state resistance due to the die's thick drift layer region. This paper proposes the usage of $900\ V$ SiC MOSFETs for $800\ V$ automotive traction inverter applications. The proposed half-bridge design combines discrete power semiconductors and the dc-link capacitor on one printed circuit board (PCB). This design approach enables a small current commutation loop and thus a small overshoot voltage. Three different PCB technologies are compared based on their suitability for the traction inverter application. An $800\ V$ half-bridge prototype is designed, simulated, and tested. Measurement results are provided and show an overshoot voltage of $49.6\ V$ at a switching speed of $69 V/ns$ under maximum load conditions.