Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization and Model Parameters of Large Commercial Supercapacitor Cells

The paper reports the results of the test and modelling activities carried out on large commercial supercapacitor cells at high current. Four commercials cells, with rated capacitance of 3000 F and rated voltage of 2.7 V, are considered. All cells are submitted to consecutive charge/discharge cycles at constant current. A test current in the range 80–130 A is used, which is comparable with the maximum operating current (up to 150 A) that the cells can reach in practical operations. A circuit model of the cells, able to reproduce the most relevant dynamic behavior, with a good compromise between accuracy, simplicity and robustness of the model’s parameters, is also developed and validated against the experimental data. It is shown that all the investigated commercial cells are characterized by similar phenomena and comply with the same circuit model. Moreover, it is shown that the circuit parameters of the cells are in the same range and are weakly dependent on the test current. The obtained circuit parameters are accurately reported for all the cells and made fully accessible to users.
Source Number Estimation Method of Multi-Antenna system for Multi-Satellite TT& C

Using a multi-antenna system to realize multi-satellite simultaneous TT& C (telemetry, tracking, and command) is the main method to solve the contradiction between the increasing number of spacecrafts in LEO and the limited TT& C resources. The key to the normal operation of the multi-antenna system is multi-antenna signal synthesis. As a generalized array antenna, the DOA (Direct of Arrival) estimation method in array signal processing can be used to align the delay difference for multi-antenna in multi-satellite TT& C scene, to make signal synthesis possible. Multiple signal classification is a widely concerned DOA estimation method, which is based on accurate source number estimation. In this paper, based on the classical Gerschgorin’s Disk Estimation (GDE), an array manifold Exchanged GDE (EGDE) method for nonuniform array source number estimation is proposed. In addition, a Modified Gerschgorin’s Disk Estimation method is improved to enhance the estimation accuracy under low snapshot number. Simulation results verify the correctness and effectiveness of the method.
An Isolated Bridgeless Cuk-SEPIC Converter Fed Electric Vehicle Charger

A single stage electric vehicle battery charger with an isolated bridgeless (BL) Cuk-SEPIC converter is developed in this work. The design of this converter is ensured in discontinuous conduction mode with intrinsic power factor correction (PFC) at AC mains. This topology offers the advantage of reduced number of components with improved efficiency due to integration of Cuk and SEPIC converters during the individual half cycle. The presence of output inductor at Cuk converter side also smoothens the output current, as compared to SEPIC converter used for both cycles. A prototype is designed and developed for a battery load of 48V/100Ah to demonstrate the unity PF based charging. The efficacy of charging is tested in constant current (CC) and constant voltage (CV) modes to validate the performance at rated and different supply voltages. Different power quality parameters are observed to be within the IEC 61000-3-2 standard.
A High-Throughput FPGA Accelerator for Short-Read Mapping of the Whole Human Genome

The mapping of DNA subsequences to a known reference genome, referred to as “short-read mapping”, is essential for next-generation sequencing. Hundreds of millions of short reads need to be aligned to a tremendously long reference sequence, making short-read mapping very time consuming. In this article, a high-throughput hardware accelerator is proposed so as to accelerate this task. A Bloom filter-based candidate mapping location (CML) generator and a folded processing element (PE) array are proposed to address CML selection and the Smith-Waterman (SW) alignment algorithm, respectively. It is shown that the proposed CML generator reduces the required memory access by 40 percent by employing a down-sampling scheme when compared to the Ferragina-Manzini index (FM-index) solution. The proposed hierarchical Bloom filter (HBF) that includes optimized parameters achieves a 1.5×104 times acceleration over the conventional Bloom filter. The proposed memory re-allocation scheme further reduces the memory access time for the HBF by a factor of 256. The proposed folded PE array delivers a 1.2-to-3.2 times higher giga cell updates per second (GCUPS). The processing time can be further reduced by 53-to-72 percent by employing a fully pipelined PE array that allows for a tailored shift amount for seeding. The accelerator is realized on a Stratix V GX FPGA with 16GB external SDRAM. Operated at 200MHz, the proposed FPGA accelerator delivers a 2.1-to-11 times higher throughput with the highest 99 percent accuracy and 98 percent sensitivity compared to the state-of-the-art FPGA-based solutions.
An equivalent circuit diagram for a hexagonal ring frequency selective surface

An equivalent circuit diagram for a frequency selective surface (FSS) consisting of hexagon ring elements is presented. It provides the designer with a profound physical understanding of the high frequency behaviour of the FSS. In addition to a semi-analytical approach with element value extraction from full wave simulations, an approximation formula is presented for a quick estimation of the element values. This makes it easier to apply filter theory and thus the optimization process can be accelerated.
Link Congestion Aware Proactive Routing for Dynamic Traffic in Elastic Optical Networks

Fixed alternate routing is a potential routing scheme for routing and spectrum allocation (RSA) in elastic optical networks (EON) which has less complexity and time consumption compared to adaptive routing scheme. However, adaptive routing scheme efficiently reduces the amount of network bandwidth blocking probabilities (BBPs) which uses traffic engineered paths and tunes according to the current network status. In this paper, an algorithm for routing is proposed for dynamic traffic in EON which works iteratively to arrange the pre-computed fixed alternate routes offline to incorporate link loading. During the offline process, the pre-computed routes are arranged with an objective to reduce link congestion and diverts lightpaths to the under-utilized links. The proposed scheme merges the properties of fixed alternate routing and adaptive routing and is utilized for dynamic traffic in EON. It has been shown through simulation results that the proposed scheme efficiently improves the performance of RSA in EON and reduces the amount of BBPs compared to the fixed alternate routing and an existing constrained-lower-indexed-block (CLIB) based adaptive routing algorithm. The proposed LCA greatly reduces congestion over all links during dynamic network operation and lowers congestion spikes over some links which occues in the existing alternate routing scheme in different network scenarios.
Research on water absorption characteristic of cycloaliphatic epoxy resin insulator

The water absorption characteristic of the base resin of cycloaliphatic epoxy resin insulators were studied. Hydrogenated bisphenol A glycidyl ether resin (EP-A) and diglycidyl 1,2-cyclohexanedicarboxylate resin (EP-B) were immersed in deionized water at 80°C. It was found that the water uptake of the both of cycloaliphatic epoxy resins increased with the increase of soaking time. The water absorption of EP-A is mainly caused by Fick diffusion and internal molecular relaxation. The water absorption of EP-B is mainly attributed to Fick diffusion and surface micro cracks and voids formed by aging. The water absorption of the two kinds of resins can be described by Diffusion-Relaxation model. The moisture diffusion coefficient D of EP-B is less than that of EP-A at the initial stage of water absorption, which indicates the better property to inhibit the water penetration into the interface. Compared with EP-B, EP-A has better hydrolysis resistance.
A High Efficiency MMIC X-band GaN Power Amplifier

This paper describes the design methodology and the On-Wafer (OW) measurement results of two broadband two stages MMIC High Power Amplifiers (HPAs) operating in the 8-12 GHz bandwidth. A Balanced HPA (BA-HPA) and a Single-Ended HPA (SE-HPA) are considered, they are fabricated on 150 nm HEMT Gallium Nitride on Silicon Carbide technology (AlGaN/GaN on SiC) called GH15 from UMS. The main aim of this realization was the achievement of a high Output Power associated with a very high Power Added Efficiency. To obtain a good compromise between output power and power added efficiency, a multi harmonic tuning approach of load and source impedances has been applied. In addition, particular attention was given to the way of drawing the matching network layouts to minimize the coupling effect between different elements.The On-Wafer measured power results under pulsed wave signal of the BA-HPA demonstrate a maximum output power (POUT) higher than 15 W (42 dBm) with 52% Power Added Efficiency (PAE) and 22 dB of insertion gain (GI) in the 8-12 GHz bandwidth. Moreover, in the same previous bandwidth, the SE-HPA reaches a maximum POUT equal to 43 dBm associated with54% PAE and 23 dB GI.To our knowledge, these results are one of the best in terms of PAE performances and frequency bandwidth trade-offs.
Continuous Estimation of Human Upper Limb Joint Angles by Using PSO-LSTM Model

Aiming at the continuous motion control problem of the upper limb auxiliary exoskeleton. In this paper, we use particle swarm optimization (PSO) to optimize the long short-term memory network (LSTM), and use the optimized network to establish a map between surface electromyography (sEMG) signals and joint angles. The inputs of the model are sEMG feature time series, which have been dimension reduction processed, and the outputs of the model are the joint angles of the elbow and wrist. To validate the effectiveness of the PSO-LSTM model, eight healthy subjects participated in the experiment. Eight channels of sEMG from eight human upper limb muscles were recorded and two joint angles including the elbow joint and wrist joint were acquired. The proposed PSO-LSTM model and back propagation neural network (BP) model were trained and tested, by using the sEMG feature time series. These time series have been processed for continuous estimation of human upper limb movements. Our experimental results showed that the proposed PSO-LSTM model could achieve a significantly lower estimation root mean square error (RMSE) than the BP model. The RMSE of PSO-LSTM is 0.0599 and the RMSE of BP is 0.1986 in the experiment of continuous elbow movement. The RMSE of PSO-LSTM is 0.1025 and the RMSE of BP is 0.2348 in the experiment of continuous wrist movement. These results suggest that the proposed PSO-LSTM model has a good performance on joint angles estimation by using sEMG feature time series that have been processed for subjects. This model would be used on rehabilitation robots for active rehabilitation of spinal cord injury (SCI) patients or stroke patients.
RMPD: Method for Enhancing the Robustness of Recommendations With Attack Environments

Personalized item recommendation has become a hot topic research among academic and industry community. But lots of purposeful fraudsters maybe perform different attacks on the recommender system to insert fake ratings, which could reduce the authenticity and reliability of recommendations. For a recommender system with fraudsters, it is crucial to detect malicious ratings and reduce the proportion of fraudster’s ratings. This paper presents a method Prediction and Detection of Rating Matrix(RMPD) combining rating prediction and attack detection. The detection results of the attackers are applied to the rating prediction, thereby controlling the contribution and proportion of attackers to the rating prediction component both in training and learning, and then implementing more accurate item rating projections. The method will also solve the problem of data sparsity in the recommender system to some extent. The superiority of the proposed method in predicting recommendation performance compared with other baseline methods is demonstrated on real-world datasets. The ablation experiment proves the necessity of the components.
Application of Machine Learning Techniques In Forecasting Groundwater Levels in the Grootfontein Aquifer

In this paper, we attempt to provide a data driven solution to model groundwater levels in the Grootfontein Aquifer in the North West Province of South Africa by testing several predictive models. Groundwater plays a crucial role in supplying water to a significant part of the population for agricultural, industrial, environmental and/or domestic use. Recent advancements in data analytics, and the analysis of large data sets has allowed the production of powerful predictive models. Five different data driven techniques namely, support vector regression, gradient boosting trees, decision trees, random forest regression and multilayer feed-forward neural network techniques were applied to predict groundwater levels. Modelling was carried out for four boreholes located in the Grootfontein dolomite aquifer considering discharge, rainfall and temperature as model inputs. Five site specific models were developed for each borehole. Model performance was evaluated using coefficient of determination and root mean squared error. Comparison of goodness of fit revealed that data driven methods can indeed capture the trend of water level fluctuations in the aquifer sufficiently with the GB algorithm performing better than other algorithms in both the training and verification stages. Whilst the models performed adequately when predicting groundwater level on a monthly basis for 36 months, further investigation is needed towards determining their efficacy in longer term projections to assist in the decision making process of sustainable groundwater use. This paper provides the following contributions: (a) a ranking of the attributes according to their mutual information (MI); (b) a reference for model selection; and (c) a predictive model to forecast groundwater levels in the Grootfontein aquifer.
Deep reinforcement learning based socially aware mobile robot navigation framework

In this study, we propose a socially aware navigation framework, which enables a mobile robot to avoid humans and social interactions in dynamic social environments, using deep reinforcement learning algorithm. The proposed framework is composed of two main stages. In the first stage, the socio-spatio-temporal characteristics of the humans including human states and social interactions are extracted and projected onto the 2D laser plane. In the second stage, these social dynamic features are then feed into a deep neural network, which is trained using the asynchronous advantage actor-critic (A3C) technique, safety rules and social constraints. The trained deep neural network is then used to generate the motion control command for the robot. To evaluate the proposed framework, we integrate it into a conventional robot navigation system, and verify it in a simulation environment. The simulation results illustrate that, the proposed socially aware navigation framework is able to drive the mobile robot to avoid humans and social interactions, and to generate socially acceptable behavior for the robot.
Via-Less Waveguide-to-Stripline Transition Using 2D Electromagnetic Bandgap Structure

A novel waveguide-to-stripline transition utilizing an electromagnetic bandgap (EBG) structure is presented. The proposed transition, which does not require vias or waveguide shorts, delivers a simpler implementation option and allows the stripline to be mounted directly to the flange of a waveguide. The EBG structure is added to suppress parallel plate mode propagation and to ensure the transition can maintain the desired performance when used with arbitrary board dimensions. A 1.0dB insertion loss from 10.6GHz to 11.5GHz and a 10dB return loss bandwidth of 8.1% are achieved and verified by measurement.
ProtoViewer: Visual Interpretation and Diagnostics of Deep Neural Networks with Factorized Prototypes

In recent years deep neural networks (DNNs) are increasingly used in a variety of application domains for their state-of-the-art performance in many challenging machine learning tasks. However their lack of interpretability could cause trustability and fairness issues and also makes model diagnostics a difficult task. In this paper we present a novel visual analytics framework to interpret and diagnose DNNs. Our approach utilizes ProtoFac to factorize the latent representations in DNNs into weighted combinations of prototypes, which are exemplar cases (e.g., representative image patches) from the original data. The visual interface uses the factorized prototypes to summarize and explain the model behaviour as well as support comparisons across subsets of data such that the users can form a hypothesis about the model’s failure on certain subsets. The method is model-agnostic and provides global explanation of the model behaviour. Furthermore, the system selects prototypes and weights that faithfully represents the model under analysis by mimicking its latent representation and predictions. Example usage scenarios on two DNN architectures and two datasets illustrates the effectiveness and general applicability of the proposed approach.
Design and Implementation of A Human Prosthetic Hand

With an aging population and war veterans, the disabilities of a human being are increasing day by day. In particular, prosthesis is finding their way into the lives of many individuals. The aim of the paper is to find cheaper solution for robotic prosthetic hand than the product found in market. Human innovations for any problems or disability should serve both poor and rich person of the world. Lack of money should not restrict the people to use advance technology. In this paper, a detailed design of voice and muscle controlled prosthetic hand is proposed. This paper will mostly on the development and manufacturing of the prototype of the prosthetic hand.
Image Recognition based on Convolution Neural Network

This paper introduces the development of image recognition technology, summarizes several classic traditional algorithms, and then introduces the use of convolution neural network for image recognition, and summarizes the application and development of CNN in various fields in recent years, and proposes to recognize images by deep learning in complex or high-risk environment similar to substation Don’t take the place of manpower to detect the working state of instruments and equipment, which has a development prospect. In the end, it puts forward some suggestions on the direction of image recognition using deep learning. In the future, we need to improve and optimize the model, intelligent algorithm and data set.
BiCMOS Through-Silicon Via (TSV) Signal Transition at 240/300 GHz for MM-Wave & Sub-THz Packaging and Heterogeneous Integration

In this paper, the development of a BiCMOS embedded TSV technology for mm-wave/sub-THz applications is demonstrated. Annular-type TSVs enable low loss TSV transitions up to 300 GHz. Back-to-back TSV transitions are fabricated and characterized from 220 to 325 GHz providing a low insertion loss of 0.83 and 0.94 dB at 240 and 300 GHz. Results show that BiCMOS embedded TSVs are very good candidates to provide RF transitions for mm-wave/sub-THz packaging and 2.5/3D heterogeneous integration solutions.
Resolving Multi-user Conflicts in a Smart Building using RBAC

In the world of cyber-physical systems, the emergence of Smart Living Spaces (SLS) trend gives people the opportunity to benefit from new ways of living. Such technological trend that involves several aspects of daily life, allows the residents of the space to better customize and control their environment. SLS emphasizes, mainly, energy conservation, convenience and comfort as well as health care concerns. They are gradually realized in practical applications e.g. household appliances and medical and care systems. The SLS related applications are intended to improve the quality of life for users by ensuring that their needs are met, thereby producing more sustainable use of energy in a given living space. Moreover, the development of SLS technology makes the devices around us “smart”. This has brought lot of challenges, one of which is the access control management. In a SLS, there has to be a robust access control mechanism in place in order to ensure an authorized access to devices. This paper focuses on the use of Role Based Access Control (RBAC) model as an efficient means to prevent unauthorized users from gaining access to devices in a Smart Building. Then, to guarantee that only the approved users can access the devices, a Smart Building Manager (SBM) is required to ask an evaluation engine to evaluate requests. We separate the evaluation as a third party in the smart building in which we propose RBAC with domains that stands our policy model. We implement the policy model for the aim to ensure a fine grained access control to a smart building application while evolving from a mono-user to a multi-user.
Deep-learning Based Deafness Aware Scheme for mmWave D2D Communications

The 5G core frequency band, mmWave, has the potential to create various services through a wider band than the existing commercial frequency band. However, due to the characteristics of the mmWave frequency, it has various limitations. In particular, since the wavelength is short in the mmWave band, there is a limitation that a directional antenna should be used to compensate for the high path-loss in the mmWave Deviceto-Device (D2D) communications. As a result, directional links are susceptible to adverse effects, such as deafness problem, in which the communication node cannot receive signals from other transmitting nodes. In this paper, we propose a deafness aware scheme based on deep-learning to solve deafness problem. In this paper, each device trains whether the cause of communication failure is deafness or another reason based on its own transmitting parameters. As a result of performance evaluation, it was found that our proposed model distinguishes deafness with a probability of about 96% in fixed scenario model.
Experimental study of Electrohydrodynamic Conduction Pumping Embedded in Micro-Scale Evaporator

Electrohydrodynamic (EHD) conduction pumps are promising for generating fluid flow in thermal control systems for high power electronics, both for terrestrial and aerospace applications. EHD conduction pumps generate a net flow driven by electric body forces acting dissociative molecules in a dielectric fluid. EHD conduction pumps have been previously proven effective for heat transfer enhancement in multiple size scales but were only studied in a flush ring or flush flat electrode configurations at the micro-scale. This study provides the pumping performance for a micro-scale pump with three pairs of perforated electrodes, designed to be manufactured and incorporated into a high-heat-flux evaporator embedded in an electronic cooling system. The pump performance based on generated pressure and flowrate is determined in the fluid loop system with HFE7100 as the working fluid.