Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of a Hand Gesture Based Digital Lock Demonstrator

The security is a vital apprehension. An omnipresent part of all security systems is a key protected admittance control. It allows only the authorized people to enter into a restricted zone. This paper focuses on designing and implementing hand gesture recognition based digital lock system. The objective is to design a functional lock system using Human-Computer Interaction (HCI) with display for theeducation purpose. Keeping in mind the demonstration purpose, the system modules are kept easily accessible. The solution is based on a web camera, which is used as the main component to captures a live-stream video with the help of a specifically designed algorithm in the MATLAB. The system is able to identify the hand-gesture. This algorithm is designed to enhance a specific frame from the video to get better results. Later, this frame is converted into a binary image so it can be easily subtracted from a background image that has been provided in the database. A system prototype is implemented and its functionality is tested. The results are presented.
Improving QoE Fairness Using the IEEE 802.11e Priority Control Mechanism

QoE (Quality of Experience) represents the overall acceptability of communication services perceived by end-users. In this paper, we study QoE in a wireless LAN, and propose to use the IEEE 802.11e MAC (Medium Access Control) in order to improve the QoE fairness among users sharing the wireless channel. We assume that users can be classified into several classes according to context parameters affecting QoE, and assign different access categories to different classes. We evaluate our approach with simulation experiments and show that it can improve the QoE fairness.
Tuning Cascade PID Controller for Valves Using optimization Methods

Precise control of proportional valves is an important subject for industry. In this work, we implemented and evaluated three different population-based heuristic optimization methods from literature and propose two different fitness functions to tune the closed-loop gains of a cascade PID flow and current control system for a directional proportional valve for operating hydraulic axes. Lastly we reached under 0.1% total steady state error at minimum.
Low Power Class-AB Line Driver with Adaptive Digital Impedance Control for Fast Ethernet

This work presents a low power analog class-AB line driver with adaptive output impedance for Fast Ethernet. In order to provide a good matching to the line, a digital impedance control is implemented to adapt the output impedance of the line driver to the load connected to it. The advantage of this architecture is a low supply voltage requirement compared to voltage mode line drivers, while still maintaining a very low power consumption. A prototype of the line driver is fabricated in an 180nm technology. Measurements demonstrate the ability to adapt to an output impedance in the range of $80 \Omega$ to $130 \Omega$, while the 3dB bandwidth is 200MHz. The power consumption is as low as 24.3mW for the Fast Ethernet mode operating from 2.5V. If the supply voltage is increased to 3.3V the line driver is compatible to the older 10Mb/s Ethernet standard requiring larger output voltages up to $5\mathrm{V}_{pp}$. To achieve the low power consumption, an aggressive duty cycling approach is used. The impedance control is only activated a fraction of the time, to effectively lower the mean power consumption.
A Survey on Cloud Security Issues and Solution

Cloud computing is modem system provides software tools and resources by internet as user demand. in the traditional way the user must download files to use it but now in cloud computing system the user can uses the shared software without need to download it in the device. Cloud computing based on distributed resources and services related to various organizations, so providing the security is more important process in cloud computing to allow user of protecting their resources from any attack. Cloud computing is one of interested system which used to solve of complex tasks and function using Artificial intelligence. It used in a large area of application filed which provides share files and devices based on cloud system. It is cover huge numbers of users. The main challenges in the cloud computing is related to provides security of the system and protect the system area which faced a number of attracters who aims to stole data or damage system. The study review the attaint of using block chain system to solve security issues in cloud storage.
Experiments with Declarative Modeling of Maximum Clique Problem Using Solvers Supported by MiniZinc

In this paper, we present the results of solving the Maximum Clique Problem using declarative modeling approaches. Our goal is to create a single reusable model that can be utilized to compare several solvers supported by MiniZinc. For the comparison, we have used data sets from the DIMACS benchmarks. We present the performance results of the solvers used in experiments using single as well as 16-threaded configurations on data set instances of 125, 250, 500, 1000, and 2000 nodes. Our initial results revealed that the Gurobi solver handles the small data sets better than the Gecode or COIN-OR, but the Gecode solver is better over the larger data sets. We found that COIN-OR was not able to find a solution for data sets of more than 500 nodes, on the single thread configuration, within our chosen time limit of 120s.
Fingerprint Spoofing Detection Using Machine Learning

Many studies had used different techniques to produce liveness fingerprint detection schemes. Our paper aims to review the different studies proposed in liveness fingerprint detection systems that able to classify real fingerprint images and fake ones using machine learning techniques, and to analyze different schemes. A comparison between datasets used in the literature was made based on specific metrics. The results indicate that the most extracted features are BSIF, LPQ. Support vector machine algorithm SVM were widely used as classifier.
A Many-Core Accelerator Design for On-Chip Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is substantially resource-consuming, and it requires large-scale distributed computing-nodes to learn complicated tasks, like video-game and Go play. This work attempts to down-scale a distributed DRL system into a specialized many-core chip and achieve energy-efficient on-chip DRL. With the customized Network-on-Chip that handles the communication of on-chip data and control-signals, we proposed a Synchronous Asynchronous RL Architecture (SARLA) and the according many-core chip that completely avoids the unnecessary data duplication and synchronization activities in multi-node RL systems. In evaluation, the SARLA system achieves considerable energy-efficiency boost over the GPU-based implementations for typical DRL workloads built with OpenAI-gym.
Emotion Classification with DEAP Dataset:Survey

Emotions affect our decision making as well as human interactions in our daily lives. Therefore, contributing to commercial activities such as the classification of emotions, neuromarketing and game industry; to be able to communicate with people who cannot express their feelings due to their disability; It has been a popular research topic for many purposes such as being able to determine the accuracy of expressions during the query and creating emotions in humanoid robots. In the researches, it is aimed to increase the classification success by using various methods. Since it is difficult for people to interfere with EEG signals in emotion classification, more realistic determinations can be made. Therefore, the examination of EEG data for emotion classification is an area that needs to be updated and developed. The most popular open source dataset used in emotion classification studies with EEG is the DEAP dataset. In this article, it is aimed to shed light on future studies by examining the classification studies made with DEAP data set.
CEPA: CNN-based Early Performance Assertion Scheme for Analog and Mixed-Signal Circuit Simulation

The design and verification of analog and mixed-signal (AMS) circuits typically involve many time-consuming simulations to qualify target specifications or optimize the design parameters for better performance. The long simulation time significantly slows down the speed of optimization iterations for both human designers and automatic AMS optimizers, inevitably resulting in high design costs and less-optimized designs. In this work, we propose a convolutional neural network (CNN)-based early performance assertion (CEPA) scheme to identify designs with unsatisfactory performance quickly and accurately. Thanks to the feature extraction capability of CNN, CEPA only requires a short duration of transient waveform to predict the satisfaction of the target specifications for a certain design that is otherwise obtained by a long transient simulation. In addition, applying the fine-tuning technique to the proposed CEPA scheme can further extend the inference from schematic-level simulation to post-layout simulation with only a few training samples (i.e., enhancing CEPA's usage with a low training cost). A sample-and-hold circuit and a delta-sigma digital-to-analog converter are presented to prove the effectiveness of the proposed CEPA scheme. With its maximum assertion accuracy of 99%, CEPA reduces the simulation time for assertion by orders of magnitude.
Performance Analysis of Hybrid Symmetrical Voltage Multiplier Using Low Pass Filter

The voltage multiplier comprises both redesigned Cockcroft-Walton circuit, namely hybrid symmetrical voltage multiplier (HSVM) and low pass filter (LPF). An HSVM converts the alternating current to high voltage direct current (HVDC) with a low current according to the used number of stages. The LPF generates an output signal with reduced ripple voltage since the output voltage of HSVM has a high ripple. The work within this study develops a five stages HSVM with an LPF circuit to analyze output voltage with three different input waveforms, as well as varying frequencies. Furthermore, theoretical calculation and simulated examination are employed to compare the output voltage before and after LPF. Besides, the relative error verifies the feasibility of this restructured HSVM for wide applications. The results indicate that the developed HSVM circuit is capable to produce step-up DC voltage respected to each input waveform.
Contention-based nonorthogonal massive access with massive MIMO

The sporadic communication character of massive machine-type communication systems provides natural advantages to utilize the principle of compressive sensing (CS). However, due to the high computational complexity of CS algorithms, CS-based contention-free access schemes have limited scalability and high computational complexity for massive access with user-specific pilots. To address these problems, in this paper, we propose a new contention-based scheme for CS-based massive access, which can support the sporadic access of massive devices (more than one million devices) with limited resources. Furthermore, an advanced receiver algorithm is designed to solve the optimal solutions for the proposed scheme, which utilizes various prior information to enhance the performance. In specific, the joint sparsity between the channel and data is used to improve the accuracy of pilot detection, and the information of modulation and cyclic redundancy check is exploited for channel correction to improve the performance of data recovery. The simulation results show that the proposed scheme can achieve improved active user detection performance and data recovery accuracy than existing methods.
Response to vitamin D replacement in overweight and normal weight children with vitamin D deficiency

Purpose It is well known that obesity is related to vitamin D deficiency (VDD). We investigated the response to vitamin D replacement in normal-weight and overweight children. Methods This was a prospective study including 62 Korean children with VDD. VDD was defined as a serum 25-hydroxycholecalciferol (25(OH)D) concentration <20 ng/mL. Overweight was defined as a body mass index (BMI)≥the 85th percentile (n=21), and normal weight as a BMI between the 5th and 84th percentiles (n=41). All participants received vitamin D3 supplementation (2,000 IU/day) for 8 weeks. The serum levels of 25(OH)D, PTH and biochemical parameters were measured before and after treatment. Results The mean age was 10.0±1.4 years in normal-weight children and 10.0±2.1 years in overweight children (P=0.93). After 8 weeks of treatment, 61.9% of normal-weight children and 47.6% of overweight children achieved vitamin D sufficiency (P =0.30). The mean serum 25(OH)D levels after vitamin D replacement were 33.8±7.6 ng/mL and 30.3±6.6 ng/mL in normal-weight and overweight children, respectively (P =0.10). The mean calcium/creatinine ratios after treatment were 0.09±0.07 and 0.08±0.06 in the normal-weight and overweight groups, respectively, and no hypercalciuria was found. In multiple regression analysis, the response to vitamin D replacement was influenced by the BMI (β=-1.0, P=0.03) and sex (β=-4.0, P=0.04). Conclusions Eight weeks of vitamin D replacement (2,000 IU/day) is sufficient to overcome vitamin D deficiency in normal-weight and overweight children without any complications.
Differential Evolution-based MPPT with Dual Mutation for PV Array under Partial Shading Condition

The performance of the photovoltaic system heavily depends on solar irradiance and temperature conditions. A condition where photovoltaic received non-uniform solar irradiance is called partial shading condition. Partial shading can affect the power-voltage characteristics of the photovoltaic system resulting in more than one maximum power points. This reason can be one of the leading causes of some energy losses for many photovoltaic systems. In order to overcome the mentioned problem, this paper proposes a differential evolution (DE) based MPPT algorithm with dual mutation factors to track the global maximum power point of photovoltaic systems under partial shading. The mutation step is modified by using two mutation factors to enlarge the search space and optimize the convergence speed in the tracking process. The photovoltaic system in this paper uses two 100 Wp photovoltaic modules configured in series and a DC-DC boost converter. The performance of the proposed algorithm is validated in PSIM simulation and compared to the PSO algorithm. The proposed MPPT is tested with a rapid change of solar irradiation on both two modules. The result shows that the MPPT is able to track the GMPP under fluctuating partial shading conditions fast, with no oscillation and accurate. The average tracking speed of the proposed algorithm is 0.309 s, and the MPPT accuracy is 99.9%.
An Improved Double-Layer Kalman Filter Attitude Algorithm For Motion Capture System

The improvement of measurement accuracy for motion capture systems has been drawing the great concern of researchers. And attitude angle plays as a key factor in measurement accuracy, which is obtained from the multi-sensor fusion. This paper proposes an improved attitude algorithm, that is, a combination of the complementary filter (CF) and the double-layer Kalman filter (DLKF) algorithm. We get the attitude angle after a complementary filter and take it as the observation variable, and then uses a double-layer Kalman filter for data fusion. Meanwhile, to avoid magnetic interference, the least square method(LSM) is taking to fit the magnetometer data, as well as avoid magnetic interference. To verify the feasibility and effectiveness of the algorithm, a self-designed MPU9250 sensor-based inertial measurement unit (IMU) was used for testing. The results show that the algorithm can realize accurate attitude angle calculation with better suppression of noise and drift effects, and have better accuracy in calculating the yaw than the Mahony algorithm.
Design and Implementation of Object Tracking System Based Mean-Shift with Locust Search Optimization on Raspberry Pi

Having an optimal object tracking system is an advantage especially in everyday use. One of the example is use in victim localization. The optimal object tracking system as mentioned earlier means a system that capable of reaching the convergence point of region of interest (ROI) with small number of iterations. The smaller the number of iterations needed for reaching the convergence point, the faster the system follows the object’s movement. A system that is able to follow the movement of objects quickly, especially in accordance with case studies of victim localization, is certainly needed. In this paper, the object tracking system is built on Mean-Shift algorithm. Mean-Shift has a convergent search technique that requires a large number of iteration processes. In order to achieve an optimal object tracking system as the purpose of this paper, an optimized algorithm is carried out. Instead of shift the ROI point sequentially until reach the optimum point as done in Mean-Shift, the optimization algorithm will search the ROI optimum point randomly with a movement that pays attention to the previous optimum point. Optimization algorithm used in this paper is Locust Search algorithm. Object interest being tracked in this paper is human face. Object tracking system will be deployed in Raspberry Pi 3 Model B+ because of its characteristics that are suitable to be implemented in this paper’s case, in form of prototype.
Investigation Into Self Actuation Limitation and Current Carrying Capacity of Chalcogenide Phase Change GeTe-Based RF Switches

This article reports investigation of self-actuation in phase change material (PCM) germanium telluride (GeTe)-based radio frequency (RF) switches as a limitation to RF power handling. PCM switches are fabricated in-house with multiple design dimensions for evaluation and testing under various RF power levels. RF power handling capability and maximum dc current carrying capacity in the ON-state of the switches is studied for four different device layouts. Self-actuation in the OFF-state is reported at varying input RF power and dc voltage levels. A tradeoff between RF power handling capacity and device performance is discussed, establishing a correlation between design parameters and the power handling. The improved PCM switches exhibit a record high continuous wave (CW) RF power handling capability ranging from +35.5 to +39.2 dBm.
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback

We consider reinforcement learning and bandit structured prediction problems with very sparse loss feedback: only at the end of an episode. We introduce a novel algorithm, RESIDUAL LOSS PREDICTION (RESLOPE), that solves such problems by automatically learning an internal representation of a denser reward function. RESLOPE operates as a reduction to contextual bandits, using its learned loss representation to solve the credit assignment problem, and a contextual bandit oracle to trade-off exploration and exploitation. RESLOPE enjoys a no-regret reduction-style theoretical guarantee and outperforms state of the art reinforcement learning algorithms in both MDP environments and bandit structured prediction settings. TL;DR: We present a novel algorithm for solving reinforcement learning and bandit structured prediction problems with very sparse loss feedback.
Software-Defined Radio Assessment for Microwave Imaging Breast Cancer Detection

This paper presents an evaluation of a low-cost apparatus for Stepped-Frequency Continuous Wave (SFCW) radar-based Microwave Imaging (MWI) systems aimed at breast cancer detection. Software Defined Radio (SDR) is a programmable device with the potential to circumvent the Vector Network Analyzer usage to realize an MWI system, especially during the prototyping stage, once the more suitable system is not established yet. BladeRF, an SDR platform, coupled to a power amplifier and a low noise amplifier, and using two Vivaldi antennas, has been assessed in terms of the radio power ability. The measured results have shown that the bladeRF can provide up to +15dBm output power at 1 GHz carrier frequency and close to -10dBm at 6 GHz, and distinguish a received signal as low as -80dBm from the Vivaldi antenna. Moreover, a noise floor larger than -70dB has been seen along with the carrier frequency increase. These results have motivated an MWI system prototype comprised of a bladeRF SDR and Vivaldi antennas.
Remote Control of Haptic-enabled Robotic Manipulator

By experiment, this paper investigates the performance of kinematics stability of a master-slave manipulator system targeting for haptic-enabled remote control. Experiment of master-slave motion reflection was carried out, and the trajectories of both ends had been recorded by motion capture cameras. Experimental results illustrated inverse kinematics algorithm using Pieper's Solution can facilitate motion reflection with adequate stability and accuracy.
Efficient Hybrid Central Processing Unit/ Input–Output Resource Scheduling for Virtual Machines

The use of virtualization technology in industrial control is increasing. However, virtual instances or virtual machines (VMs) are confronted with the unreasonable resource allocation in the industrial control cyber range, thereby highlighting the increasing importance of resource scheduling. In the Xen open source system, the input–output (IO)-intensive task response is extended because the system does not distinguish between CPU- and IO-intensive tasks. Therefore, this article presents an improved task performance through the hybrid scheduling of CPU and network IO resources. This method uses Cap- and Timeslice-scheduling algorithms for CPU resource scheduling. First, the Cap-scheduling algorithm uses historical data to train a recurrent neural network model for classification and then utilizes the heuristic method to set the upper limit of cap value for VMs. Second, the Timeslice-scheduling algorithm adjusts the timeslice based on Q-learning to shorten the execution time of the overall tasks. This article proposes an IOb-scheduling algorithm for network bandwidth scheduling. The part that does not exceed the average bandwidth is eliminated and distributed to other VMs by monitoring the bandwidth usage of each VM, thereby improving the utilization of bandwidth. Experimental results showed that the proposed CPU/IO scheduling algorithms improved the overall benchmark performance substantially.
On Deep Learning Based Feedback and Precoding For Multi-user Millimeter-Wave Enabled VR/AR

Virtual reality (VR)/augmented reality (AR) and its applications have attracted significant and increasing attention recently. However, the stringent quality of service (QoS) requirements and better spectral efficiency have posed the challenges such as higher bandwidth, lower latency and better reliability on the VR/AR communication system. This paper proposes a deep-learning-based (DL-based) precoding and feedback method for mitigating the channel interference of multi-users VR/AR environments. That is, our DL-based method uses the VR/AR channel state information (CSI) to do radio resource allocation for maximizing the millimeter-wave network throughput. Numerical results show that our DL-based design could significantly enhance the throughput.