Miracle-dz/UniFAR-FTUs huggingface dataset:
Control and Protection of a Synergetically Controlled Two-Stage Boost-Buck PFC Rectifier System Under Irregular Grid Conditions

There is an increasing demand for three-phase mains interfaced high power EV chargers that provide a wide output voltage range while featuring low losses and a high power density. In this context, a synergetic control structure for a two-stage three-phase boost PFC rectifier with subsequent DC/DC buck stage was proposed, which allows to only switch one out of the three rectifier bridge legs, leading to switching loss savings of over 66 %. This is achieved by varying the voltage of the intermediate DC-link, which features a small capacitance, with six times mains frequency, always following the maximum three-phase mains line-to-line voltage. Although this concept has been proven to work correctly for ideal three-phase mains conditions, it is unclear whether it can safely operate under irregular three-phase grid conditions such as heavy harmonic distortions, unbalances, phase failures, and grid overvoltages caused by, e.g., lightning. Hence, after a short review of the employed synergetic control structure, this paper focuses on the operation of the proposed boost-buck converter for an application subject to a wide variety of three-phase grid disturbances. Comprehensive analysis with respect to irregular grid conditions is performed, resulting in a slight modification of the converter’s front-end and its control structure in order to be able to handle faulty mains conditions. The proposed hardware and control modifications enabling the proper converter functionality under regular and irregular grid conditions are verified by circuit simulations. Finally, design guidelines are given for the hardware implementation of a resilient three-phase grid interfaced EV charger that can fulfill all required grid standards.
Strokes Classification in Cricket Batting Videos

In this paper, we present a novel approach to classify strokes in cricket batting. To classify front foot and back foot strokes, we created a feature space consisting of spatiotemporal time series obtained from generated stick Figure video overlays. Classification was performed using Long Short Term memory (LSTM) and Bidirectional LSTM networks. Both LSTM models accurately classified (100%) of all the videos from the testing split for a dataset created using publicly available videos (63 strokes). The presented approach has the potential to contribute to sport analytics and advance augment coaching systems and cricket viewing experience (including automated body segments annotations). Future work will include application in other sport disciplines and advancing prototype implementations on various platforms.
A Distributed Resource Allocation Scheme for Self-Backhauled Full-Duplex Small Cell Networks

Full-duplex (FD) radio with wireless backhauling, is a key technology for dense small cell deployments in fifth generation (5G) cellular networks. In this paper, the problem of joint user scheduling, operation mode selection and power allocation in a two-tier cellular network with FD–capable small basestations is considered. The objective is to maximize the network sum rate under quality-of-service requirements and backhaul capacity constraints. In order to solve the problem, a distributed algorithm is developed which divides the original problem into two subproblems, viz., (i) user scheduling and mode selection; and (ii) power allocation. For the first subproblem, a low-complexity search method is proposed for opportunistically finding the best user scheduling and operation modes. In the second subproblem, an iterative successive convex approximation method is proposed to transform the nonconvex power allocation problem into a sequence of convex subproblems. The proposed solution provides a hybrid strategy which opportunistically selects FD or half-duplex (HD) operation modes for access and backhaul links. It is observed that the proposed algorithm converges in few iterations and mitigates the interferences by properly adjusting the transmission powers. Numerical results demonstrate the superiority of the proposed scheme over other possible scenarios. In particular, the proposed algorithm can achieve up to 44% gain in overall sum rate compared to a network that solely uses HD.
Rapid Sorting and Regrouping of Retired Lithium-Ion Battery Modules for Echelon Utilization Based on Partial Charging Curves

The lithium-ion battery of an electric vehicle continues to have available capacity even after it is retired, thus representing good echelon utilization value. The ideal regrouping form for echelon utilization is conducted at the module level. However, existing sorting methods are generally only suitable at the cell level. To address this issue, a fast sorting and regrouping method is proposed at the module level based on a machine learning algorithm. First, the correlation between the charging curve and the remaining useful capacity of the battery is investigated. The charging curves of cells in a module are translated and supplemented to extract the capacity characteristics without disassembling the modules. Next, a rapid sorting model based on the support vector machine is proposed to estimate the capacity. Then, a regrouping method based on an improved K-means algorithm that considers different echelon utilization scenarios at the module level is proposed. Finally, simulations and experiments are conducted to verify the effectiveness of the proposed method. The results show that the capacity prediction accuracy is within 3%, and the consistency of the echelon utilization battery system obtained by the proposed regrouping method is higher than that obtained by the conventional method.
Drosophila image Segmentation using Marker Controlled Watershed

Automatic segmentation of biological image is an important step to understand a biological process. Moreover, the task is difficult due to ambiguous boundaries and intensity inhomogeneity. The Marker Controlled Watershed (MCW), was introduced for biological image segmentation as an alternative to the Watershed algorithm which suffers from oversegmentation. However, in the basic version of the MCW, the chosen markers directly impacts the segmentation performance. In this paper a new algorithm is proposed and applied for Drosophila image segmentation. The idea is to consider the characteristic of Drosophila image and use kernels of cells as objects markers. The Kernels are constructed using the Fiji software. The performance of the proposed algorithm is attested and compared to the basic version of the MCW. Results show the efficiency of the proposed algorithm for Drosophila image segmentation in term of the Dice coefficient and F1 measure.
Crucial or Unnecessary? Analysis on Phase Differential in Holographic SAR RFID Localization

The development of synthetic aperture radar (SAR) radio frequency identification (RFID) localization technology has taken the RFID based indoor localization research to a new level, among which the holographic localization is the most typical approach. Holographic SAR RFID usually performs phase differential to deal with the problem of unknown initial phase. However, problems with regard to the phase differential have emerged in prior literature, some of which make the localization system perform extra but useless efforts, some affect the comprehensibility of the algorithm, and some even reduce the reliability of localization results. Therefore, the analysis of phase differential is very helpful for researchers and practitioners to complete and improve their work. In this paper, several conclusions concerning the phase differential are drawn and analyzed, which are verified by real experiments.
Using Machine Learning to Forecast Time Series in Spacecrafts

Spacecrafts are intelligent and highly sensitive systems that are constantly exposed to environmental impact. Complex systems like those found in spacecrafts are designed to reduce faults and fatalities utilizing machine learning - more specifically known as Telemetry Mining. This analysis predicts satellite behavior which allows spacecrafts to correct their course or take other measures to mitigate potential negative impact. This paper aims to implement the statistical Autoregressive Integrated Moving Average (ARIMA) algorithm, used to forecast time series to predict spacecraft failure with the aim of saving investment and lives. To identify potential failure, results from predictions are evaluated through mean, standard deviation, covariance and Pearson's correlation square using the ARIMA algorithm. The data received from the Egyptsat-1 satellite's battery temperature is used as the parameter for input into Matlab This paper summarizes the performance and health of the spacecraft with the result of the implementation of the ARIMA algorithm on the basis of input parameters. Finally, the output from this algorithm is compared. This paper proposes a new framework for Machine Learning to Forecast Time Series in Spacecrafts. Prediction accuracy helps to decrease the failure rate of and improves the performance of the spacecraft.
Industrial Fault Diagnosis using Hilbert Transform and Texture Features

An automated fault detection is a vital issue in smart industries of Industry 4.0. This paper presents a model of industrial fault diagnosis using deep learning algorithms. In the proposed model, a standard induction motor dataset that consists of six different types of fault is used as an input. Then as a preprocessing method we utilized Hilbert transform to extract the pre-processed signals with absolute values. After that, texture images are generated from the pre-processed signals. The texture pattern of the images is used for training and testing the deep convolutional neural networks. For analyzing the performance of the proposed system, we used the F1-score which is derived from precision and recall. Experimental results demonstrate that the proposed model exhibited average 98.48% F1 score for the dataset, where HC (98.33%), IRF (98.57%), BF (98.41%), and ORF (98.56%), respectively. In addition, the proposed model shows comparatively higher classification accuracy compared to the four state-of-art models by showing the higher F1 score.
A Novel Dual-Polarized Wideband and Miniaturized Low Profile Magneto-Electric Dipole Antenna Array for mmWave 5G Applications

A Ka-band dual-polarized low profile magneto-electric (ME) dipole antenna with wide operational bandwidth is proposed in this paper. A thin substrate with two-stage meandered vias is utilized for realization of the proposed antenna. The meandered vias aid in compensation of phase difference caused due to utilization of thin substrate. The thickness of proposed antenna is reduced from <inline-formula> <tex-math notation="LaTeX">$0.25~\lambda _{g}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$0.11~\lambda _{g}$ </tex-math></inline-formula> by using these two-stage meandered vias. The low profile antenna topology results in bandwidth reduction which is neutralized by introducing asymmetric slots in the ground plane. Shorting pins are inserted for elimination of higher order TM<sub>21</sub> mode of Ka-band that results in a uniform broadside radiation pattern due to excitation of fundamental TM<sub>10</sub> mode. The proposed antenna covers whole Ka-band with operating frequencies from 26 – 42 GHz. A <inline-formula> <tex-math notation="LaTeX">$1\times4$ </tex-math></inline-formula> antenna array is also designed and fabricated with a reasonable gain of 12.06 dBi and stable radiation patterns. Simulated and measurement results are presented with detailed justification.
Comprehensive Control of Voltage Quality in Distribution Network based on Reactive Power Optimization

Voltage quality impacts the stability, efficiency, and cost effectiveness of the distribution network. With the integration of large-scale wind power and solar photovoltaic power generation, the probability of fluctuations in the feeder network voltage of the distribution network is greatly increased, and the study of voltage stability is more important. This paper proposes a comprehensive scheme to optimize voltage quality problems from the network level instead of the traditional scheme from the point level. The effect of different compensation points on the voltage of distribution network is analyzed by simulation. Then the elite selection strategy of genetic algorithm is used to calculate the optimal location of reactive compensators in distribution network. The analysis of IEEE 18-node distribution system simulation shows that the proposed comprehensive control method can generate low voltage deviation with fewer control points, compared with the full compensation cases.
DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network

Depth Completion can produce a dense depth map from a sparse input and provide a more complete 3D description of the environment. Despite great progress made in depth completion, the sparsity of the input and low density of the ground truth still make this problem challenging. In this work, we propose DenseLiDAR, a novel real-time pseudo-depth guided depth completion neural network. We exploit dense pseudo-depth map obtained from simple morphological operations to guide the network in three aspects: (1) Constructing a residual structure for the output; (2) Rectifying the sparse input data; (3) Providing dense structural loss for training the network. Thanks to these novel designs, higher performance of the output could be achieved. In addition, two new metrics for better evaluating the quality of the predicted depth map are also presented. Extensive experiments on KITTI depth completion benchmark suggest that our model is able to achieve the state-of-the-art performance at the highest frame rate of 50 Hz. The predicted dense depth is further evaluated by several downstream robotic perception or positioning tasks. For the task of 3D object detection, 3~5 percent performance gains on small objects categories are achieved on KITTI 3D object detection dataset. For RGB-D SLAM, higher accuracy on vehicle's trajectory is also obtained in KITTI Odometry dataset. These promising results not only verify the high quality of our depth prediction, but also demonstrate the potential of improving the related downstream tasks by using depth completion results.
Considerations for Classification of Procrastinator Focusing on Periodical Online Exam in Blended Learning Course

Many researchers study to determine the learning behavior regarding the acquisition of knowledge in e-learning from the learning log in LMS. This study focused on procrastination type learners and tried to distinguish between passive and active procrastination types from the learning logs. Characteristic vectors were generated from the learning log and divided into two groups via unsupervised clustering. The group that accessed the online textbook the highest number of times within the time frame of the exam were designated as active procrastination type. Active procrastination type students had higher average exam scores than passive procrastination type students. The results suggest that the number of times that students accessed the online textbook and exam during a period when students were taking the exam were effective in distinguishing between passive and active procrastination types.
Harmonic Analysis in PWM Converters Using Short-Time Fourier Series

Harmonic analysis is fundamental for practical design of power converters or motor drives adopting pulse width modulation (PWM), such as the output filter design of inverters and electromagnetic interference (EMI) filter design of power factor correction (PFC) converters. In PWM inverters, the double Fourier series (DFS) expansion has been widely used to calculate the amplitude of each harmonic. However, when dealing with the conducted EMI, DFS expansion cannot be directly applied, because the intermediate frequency (IF) filter in the EMI receiver has a relatively wide resolution bandwidth (RBW) and may pass several harmonics at the same time. In this paper, short-time Fourier series (STFS) is used to analyze the harmonics in PWM converters, which gives time-variant harmonic amplitude for carrier harmonics. This time-variant harmonic amplitude is proved to be the envelope of the PWM signal through a band pass filter, which helps to predict the electromagnetic interference (EMI) and assists the EMI filter design. In addition, the results from STFS can be converted into corresponding DFS expansion. The validity of STFS for harmonic analysis is verified by simulation and experiment, respectively.
Parallelism in Autonomous Robotic Surgery

Robots can perform multiple tasks in parallel. This work is about leveraging this capability in automating multilateral surgical subtasks. In particular, we explore, in a simulation study, the benefits of considering this parallelism capability in developing execution models for autonomous robotic surgery. We apply our work to two surgical subtask categories: (i) coupled-motion subtasks, where multiple robot arms share the same resources to perform the subtask, and (ii) decoupled-motion subtasks, where each robot arm executes its part of the task independently from the others. We propose and develop parallel execution models for the surgical debridement subtask, a representative of the first category, and the multi-throw suturing subtask, a representative of the second one. Comparing these parallel execution models to the state-of-the-art ones shows significant reductions in the subtasks completion time by at least 40%. In 20 trials, our results show that our proposed model for the surgical debridement subtask, that uses hierarchical concurrent state machines, provides a parallel execution framework that is efficient while greatly reducing collisions between the arms compared to a naive parallel execution model without coordination. We also show how applying parallelism can lead to execution models that go beyond the normal practice of human surgeons. We finally propose the notion of “automation for surgical manual execution” where we argue that autonomous robotic surgery research can be used as a tool for surgeons to discover novel manual execution models that can significantly improve their surgical practice.
Multi-objective Optimization Design of Modular Multilevel Multi-port DC-DC Converter Assessing Efficiency, Volume and Reliability

In this paper, a modular multilevel multi-port DC-DC converter (M3DC) consist of modular multilevel converter (MMC) and dual active bridges (DAB) is investigated, which is suitable for the DC distribution network integrated energy storage elements (ESE) in the next generation of vessel integrated power system (IPS). M3DC features flexible power control and excellent capability of fault-tolerant operation. To optimize the parameter design of the converter, a multi-objective optimization (MOO) scheme adopting non-dominated sorting genetic algorithm II, assessing efficiency, volume and reliability, is developed. The relationship between the parameters and the indexes is studied, and the constraints are presented. At last, the optimization scheme is implemented on a case study to illustrate the correctness and effectiveness.
Overcoming the Sudden Conversion to Online Education During the COVID-19 Pandemic: A Case Study in Computing Education

This paper presented a case study on an agile shift from brick-and-mortar to online distance learning as a measure to sustain teaching and learning in higher education in response to the interruption and impact brought by the outbreak of Novel Coronavirus Pneumonia (COVID-19) to the society. This case study provided a practical example demonstrating possible practices that can be adopted to deliver different types of modules and carry out assessment under force majeure circumstances. We have applied several methods in module delivery and reflected on their strengths and weaknesses. Our approaches have taken into consideration the different needs of taught and non-taught modules, robustness of online assessment, and quick orientation for students who were unfamiliar with online education. The consequent effectiveness of teaching and learning was evaluated in terms of instructor participation and student participation. The findings indicated a relatively slow start due to the series of necessary adjustments to be made to encounter the shift. However, once the adaptation process was completed, an enhancing trend was observed in students and instructors’ engagement in various online teaching and learning practices. Our study highlighted that pastoral support is essential to maintain the motivation and engagement of students. In addition, adjustments to module delivery and assessment methods are highly recommended.
Blockchains for COVID-19 Contact Tracing and Vaccine Support: A Systematic Review

Several blockchain projects to help against COVID-19 are emerging at a fast pace, showing the potential of this disruptive technology to mitigate the multi-systemic threats the pandemic is posing on all phases of the emergency management and generate value for the economy and society as a whole. This survey investigates how blockchain technology can be useful in the scope of supporting health actions that can reduce the spread of COVID-19 infections and allow a return to normality. Since the prominent use of blockchains to mitigate COVID-19 consequences are in the area of contact tracing and vaccine/immunity passport support, the survey mainly focuses on these two classes of applications. The aim of the survey is to show that only a proper combination of blockchain technology with advanced cryptographic techniques can guarantee a secure and privacy preserving support to fight COVID-19. In particular, this article first presents these techniques, i.e. zero-knowledge, Diffie Hellman, blind signatures, and proxy re-encryption, then describes how they are used in combination with blockchains to define robust and privacy-preserving solutions. Finally, a brief description of blockchain applications beyond contact tracing and vaccine certification is presented.
Configurable Digital Pulse Generator for Neuromorphic Devices

In this paper, we propose a configurable pulse generator for neuromorphic devices to verify the resistance change characteristics of neuromorphic devices. In general, the resistance change characteristics of neuromorphic devices are verified based on both the number of pulses and pulse width. The proposed configurable digital pulse generator consists of two submodules: Pulse Width Controller (PWC) that determines the pulse width and Pulse Count Controller (PCC) that specifies the number of pulses. Due to the configurable property, the proposed pulse generator can be widely applied to the various environment to verify the neuromorphic devices. As an experiment, The digital pulse generator was designed using a CMOS 180nm process, and it was shown that it can be used to verify the resistance change characteristics of neuromorphic devices through operation verification.
Inter-arm Voltage Balance Control of a Modular Multilevel Matrix Converter with Injecting Dual Frequency Circulating Current

This paper presents an inter-arm capacitor voltage balance control strategy for a modular multilevel matrix converter (M3C) with injecting dual frequency circulating currents (DFCC). The proposed method enforces that the injected input-frequency circulating currents and the output-frequency circulating currents only flow within the nine arms of M3C under arbitrary unbalanced power transmission conditions. The strategy aims to balance the energy across the nine arms to provide superior harmonic performance in both input and output grid sides even during transient processes when compared to the existing single frequency injection method. Comparative simulation results obtained using Matlab/Simulink demonstrate the performance and effectiveness of the proposed control strategy.