Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy-Complexity Tradeoff Analysis and Complexity Reduction Methods for Non-Stationary IMT-A MIMO Channel Models

High-mobility wireless communication systems have attracted growing interests in recent years. For the deployment of these systems, one fundamental work is to build accurate and efficient channel models. In high-mobility scenarios, it has been shown that the standardized channel models, e.g., IMT-Advanced (IMT-A) multiple-input multiple-output (MIMO) channel model, provide noticeable longer stationary intervals than measured results and the wide-sense stationary (WSS) assumption may be violated. Thus, the non-stationarity should be introduced to the IMT-A MIMO channel model to mimic the channel characteristics more accurately without losing too much efficiency. In this paper, we analyze and compare the computational complexity of the original WSS and non-stationary IMT-A MIMO channel models. Both the number of real operations and simulation time are used as complexity metrics. Since introducing the non-stationarity to the IMT-A MIMO channel model causes extra computational complexity, some computation reduction methods are proposed to simplify the non-stationary IMT-A MIMO channel model while retaining an acceptable accuracy. Statistical properties including the temporal autocorrelation function, spatial cross-correlation function, and stationary interval are chosen as the accuracy metrics for verifications. It is shown that the tradeoff between the computational complexity and modeling accuracy can be achieved by using these proposed complexity reduction methods.
Comparative Analysis of Abstraction Layer Performance of IoT Devices Management Platforms

The Internet of Things (IoT) contributes to the advancement and optimization of systems in different contexts by reducing human effort, cost, and time. It is estimated that by 2020 there will be more than 20 billion connected IoT devices. Though justified by emerging IoT applications, such growth requires the implementation of more robust, and consequently, more complex IoT systems. Hence, the optimization of IoT hardware and software design is fundamental towards achieving scalability. It allows for the control and capture of increased data from more connected devices. For this purpose, this work analyzes and compares the scalability of three architectures for IoT devices abstraction of IoT management platforms, and for this, two experiments were carried out. The results show that the Architecture NxN has made better use of computing resources, obtained the average latency value close to zero and connected 100,000 physical emulated devices. Also, being the most optimized architecture for IoT device abstraction of IoT management platforms, in the context of big data, it works in real time.
Performance Evaluation of Active Queue Management in Fat Tree Architecture on Data Center Network

In the age of the rapid growth of Internet technology, people are connecting to the0020network continuously. Request is made in seconds, resulting in congestion in the data center network. This makes the researcher tried to make a breakthrough in order to reduce the network latency. One of many proposals is the use of the active queue management (AQM). Active queue management focuses on dropping the network packets inside a buffer to reduce network congestion. This method is capable to dropping the packets according to probabilistic manner. In this paper, we evaluate different AQM algorithms to the Data Center Network (DCN) using fat tree architecture. We conducted some experiments using NS-3 to evaluate the network performance of different AQM algorithms. The simulation is done using a research based tool for computer network design, NS- software on Ubuntu Operating Systems. NetAnim, the visualization and monitoring tool for network is used for the evaluation. Throughput, packet drop, and delay are measured and compared. Best throughput is gained when ARED AQM is used which is 498.5 kpbs. It is 37 kbps higher than NLRED and 59 kbps higher than those of RED. In terms of packet drop, the ARED shows the lowest packet drop of 2230 packet drop. ARED also has the lowest delay, 0.132 second, which is slightly different from NLRED with 0.133 second delay, whereas RED’s delay is 0.177 second.
A Wide Stopband Wideband HTS Filter Using Stepped-Impedance Resonators With an Interdigital Capacitor Structure

A stepped-impedance resonator with an interdigital capacitor (IDC) structure has been proposed to design a wideband filter with a wide and deep stopband. With the increase of the IDC part, there is always a maximum ratio of a spurious resonance frequency to a fundamental resonance frequency whether we change the length or number of interdigital fingers. We increase the width of a microstrip line loaded with the IDC structure to reduce the fundamental resonance frequency and increase the spurious resonance frequency. The harmonic ratio of the proposed resonator is raised to 5.6. Moreover, we bend the narrow microstrip line into a meander line to realize the strong coupling for a wideband filter. A ten-pole high-temperature superconducting filter centered at 2.128 GHz with a 22.7% bandwidth is fabricated for demonstration. The filter is fabricated on a 0.5-mm-thick MgO substrate with a circuit size of 26 × 10 mm. The measured maximum insertion loss in passband is 0.11 dB and the return loss is better than 17.3 dB. The measured out-of-band rejection is better than 80 dB up to 8.728 GHz, which is about 4.1 times of the center frequency.
Cross-Lingual Transfer Learning of Non-Native Acoustic Modeling for Pronunciation Error Detection and Diagnosis

In computer-assisted pronunciation training (CAPT), the scarcity of large-scale non-native corpora and human expert annotations are two fundamental challenges to non-native acoustic modeling. Most existing approaches of acoustic modeling in CAPT are based on non-native corpora while there are so many living languages in the world. It is impractical to collect and annotate every non-native speech corpus considering different language pairs. In this work, we address non-native acoustic modeling (both on phonetic and articulatory level) based on transfer learning. In order to effectively train acoustic models of non-native speech without using such data, we propose to exploit two large native speech corpora of learner's native language (L1) and target language (L2) to model cross-lingual phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Experimental evaluations are carried out for Japanese speakers learning English. We first demonstrate the proposed acoustic-phone model achieves a lower word error rate in non-native speech recognition. It also improves the pronunciation error detection based on goodness of pronunciation (GOP) score. For diagnosis of pronunciation errors, the proposed acoustic-articulatory modeling method is effective for providing detailed feedback at the articulation level.
Neural Network-Based Adaptive Learning Control for Robot Manipulators With Arbitrary Initial Errors

In this paper, a neural network-based adaptive iterative learning control scheme is developed to solve the trajectory tracking problem for rigid robot manipulators with arbitrary initial errors. Time-varying boundary layers are used to relax the zero initial error condition which must be observed in traditional iterative learning control design, and adaptive learning neural networks are constructed to approximate uncertainties in robotic systems, whose optimal weights are estimated by using partial saturation difference learning method. For arbitrary bounded initial state errors, the tracking error of robot manipulators will asymptotically converge to a tunable residual set as the iteration number increases. An illustrative example and the comparisons are provided to demonstrate the effectiveness of the proposed neural network-based adaptive iterative learning control scheme.
Gait-based Gender Classification Using Smartphone Accelerometer Sensor

People gender and activities recognition are becoming a hot topic in our daily applications through gait information. The very well-known applications are safety-health, security, entertainment and billing. Numerous data mining and machine learning algorithms have been proposed for such issue. Equally, many technologies could be used to observe the people activities to identify their gender and activities. However, the existing solutions and applications suffer from privacy and cost to deploy the solution and their obtained accuracy. For example, when the CCTV camera or Kinect sensors technology are used to identify people, such technologies will violate the privacy since most of the people do not want to take their pictures or videos during their daily activities. Therefore, to tackle such issue, this paper presents a new scheme to identify the gender of the people via onboard Smartphone sensors including accelerometer sensor. Such a scheme requires little interaction with the people; individuals would simply have to hold his/her smartphone and walk normally. Four different data mining techniques and machine learning algorithms are used to identify people gender including: Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Deep learning algorithm (recurrent-neural-network long-short-term-memory ‘RNN-LSTM’). Further, a set of experiments are conducted via Android-based smartphones (to read smartphone accelerometer sensor) and MATALB-2018a packages used to perform the validity of the scheme. The obtained results from the experiments show that the accuracy of the gender identification is about 94.11% via deep learning algorithm (RNN-LSTM) and is around 83.75% by using DT algorithm.
Accelerator Implementation of Lenet-5 Convolution Neural Network Based on FPGA with HLS

Convolution neural network is widely used in image recognition because it can imitate the behavioral characteristics of biological visual nerve, and has high recognition accuracy. It is a kind of feed forward neural network which contains convolution computation and has deep structure. Also it is one of the representative algorithms of deep learning. Because the convolution neural network has a special calculation mode, the general processor is not high in the implementation efficiency of the convolution neural network and can not meet the performance requirements. In order to solve this problem, we implement convolution neural network on FPGA and optimize convolution operation to improve computing parallelism, data throughput and energy efficiency of traditional processors. Finally, we implemented the convolution neural network of the Lenet-5 model on the ZYBOZ7 FPGA board and compared it with traditional processor. We realized the fast recognition of a picture at the frequency of 100M Hz with DMA control. The data throughput of FPGA is more than four times higher than that of general processor. , and the power consumption is 1.8W, which is much lower than that of general processor.
Separated Noise Suppression and Speech Restoration: Lstm-Based Speech Enhancement in Two Stages

Regression based on neural networks (NNs) has led to considerable advances in speech enhancement under non-stationary noise conditions. Nonetheless, speech distortions can be introduced when employing NNs trained to provide strong noise suppression. We propose to address this problem by first suppressing noise and subsequently restoring speech with specifically chosen NN topologies for each of these distinct tasks. A mask-estimating long short-term memory (LSTM) network is employed for noise suppression, while the speech restoration is performed by a fully convo-lutional encoder-decoder (CED) network, where we introduce temporal modeling capabilities by using a convolutional LSTM layer in the bottleneck. We show considerable performance gains over reference methods of up to 0.26 MOS points (PESQ) and the ability to significantly improve intelligibility in terms of STOI for low-SNR conditions.
Machine Learning Based Ransomware Detection Using Storage Access Patterns Obtained From Live-forensic Hypervisor

With the rapid increase in the number of Internet of Things (IoT) devices, mobile devices, cloud services, and cyber-physical systems, the large-scale cyber attacks on enterprises and public sectors have increased. In particular, ransomware attacks damaged UK’s National Health Service and many enterprises around the world in 2017. Therefore, researchers have proposed ransomware detection and prevention systems. However, manual inspection in static and dynamic ransomware analysis is time-consuming and it cannot cope with the rapid increase in variants of ransomware family. Recently, machine learning has been used to automate ransomware analysis by creating a behavioral model of same ransomware family. To create effective behavioral models of ransomware, we first obtained storage access patterns of live ransomware samples and of a benign application by using a live-forensic hypervisor called WaybackVisor. To distinguish ransomware from a benign application that has similar behavior to ransomware, we carefully selected five dimensional features that were extracted both from actual ransomware’s Input and Output (I/O) logs and from a benign program’s I/O logs. We created and evaluated machine learning models by using Random Forest, Support Vector Machine, and K-Nearest Neighbors. Our experiments using the proposed five features of storage access patterns achieved F-measure rate of 98%.
Distributed Deduplication System with Improved Reliability and Access Efficiency

Cloud storage is a service model in which data is stored on remote servers, and this data is accessed through the internet. The technique aims to eliminate redundant data segments and keeps a single instance of the data set, even if similar data set is owned by any number of users. Since data blocks are distributed among the multiple individual servers, the user needs to download each block of the file before reconstructing the file which reduces the system efficiency. We propose a server level data recover module in cloud storage system to improve access efficiency. Using the proposed method user can directly fetch the file without downloading each block from the cloud servers. The proposed scheme improves the time efficiency of the system and quick access ability to the stored data. Thus consumes less network bandwidth.
Solar Supplied Two-Output DC-DC Converters in the Application of Low Power

This paper discussed solar fed single-input two-output Converter in the application of low power. The single diode model is used for the implementation of the solar panel. The performance of this model is better in case of varying solar irradiation. It is operated with Maximum Power Point Tracking (MPPT) algorithm to achieve better efficiency. This paper also discusses the comparative study of two important MPPT algorithms such as Advanced Perturb and Observation (APO) and Incremental Conductance (IC) methods. The characteristics curves between power and voltage as well as between current and voltage are obtained from simulation work. and these curves are compared with the actual characteristics curve. The solar-fed Zeta-Buck Boost and SEPIC-Cuk converters are analysed as a two output converter. Moreover, the converter has a DC bipolar output voltage with the ground connected power switch. The ground connected switch reduces the sparks on the switch. The system is modelled and simulated using MATLAB software and its performance is tested using OP-5142 real time system.
All-Digital Timing Recovery for Free Space Optical Communication Signals With a Large Dynamic Range and Low OSNR

The free space optical communication (FSOC) signals often exhibit a much larger dynamic range and much lower OSNR than optical fiber communication signals, due to the large-scale relative motion of the terminals, change of atmospheric channel conditions and large transmission loss. For this reason flexible digital coherent receivers (DCR) capable of adapting to different channel conditions have attracted much attention in the FSOC field. As timing recovery is indispensable for DCRs, the development of a timing recovery algorithm (TRA) capable of processing such FSOC signals is of particular interest in FSOC field. In this paper we evaluate the performance of Gardner's timing error detector (TED) and propose a new blind TED with a lower computation complexity and more stable output characteristics. Based on the proposed TED, we design a feedback parallel TRA suitable for the FSOC signals and implement it in a FPGA to evaluate its real-time performance. Numerical simulations and experiments illustrate the merits of the proposed TRA based on new TED with respect to the TRA based on Gardner's TED.
Three-Dimensional Higher Order PML Based on Alternating Direction Implicit Algorithm

Based on the alternating direction implicit finite-difference time-domain (ADI-FDTD) algorithm, a higher order perfectly matched layer (HO-PML) is proposed to solve the problems in a three-dimensional computational domain. The proposed scheme can not only take advantage of the HO-PML for improving the absorbing performance but also has the advantage of the ADI-FDTD algorithm in terms of the unconditional stability. A dielectric resonator antenna example is carried out to validate its effectiveness. The results show that the Courant–Friedrich–Levy limit can be eliminated and the absorbing performance can be further enhanced.
Image Pattem Classification for Plant Disease Identification using Local Tri-directional Features

Plant diseases are main culprits of reduction in production in agronomy, which inflicts huge economic damages. Efficient detection and classification of plant diseases contributes significantly towards reducing these losses. This paper presents computer vision framework for plant disease identification and classification. The proposed system extracts Local Tri-directional Patterns (LTriDP) from plant leaf images of different classes. LTriDP features efficiently extracts discriminant information and represent each class with reduced dimensions. Classification is performed through multiclass support vector machines (SVM). Experiments are conducted on Tomato leaf dataset comprising five different classes. Experimental results show that the proposed framework out performs other methods based on commonly used feature descriptors and achieved 94% overall accuracy.
Design of cascaded low noise amplifier for see through wall application

This paper explains the design of two stage microwave low noise amplifier (LNA) for see through wall application. The cascade amplifier configuration and stub matching networks are applied in this design which is aimed to obtain high gain with minimum reflected RF power and Noise Figure. First stage and second stage of amplifier uses Low Noise Pseudomorphic HEMT ATF-34143 transistor as an active device and stub matching is deployed to achieve impedance matching. This work uses the Agilent Design System (ADS) software to optimize the performance of LNA circuit design. The designed LNA at frequency 2.4 GHz providing Gain=30.899 dB and Noise Figure (NF)=0.485dB
Holographic Design of Beam-Switchable Leaky-Wave Antenna

A holographic design of beam-switchable leaky-wave antenna (LWA) is presented. The proposed design consists of a holographic impedance modulated surface (HIMS) and control circuit. In this design, the HIMS, which carries impedance characteristics of four beams, is obtained by using a modified modulation method in the same aperture. Thus, the proposed HIMS can generate four switchable beams at a fixed frequency when it is excited by different reference waves. By controlling p-i-n diodes inserted along the feedline, four working states can be obtained. The proposed LWA is fabricated and measured. Four switchable beams with pitch angles of <italic>θ</italic><sub>1</sub> = <italic>θ</italic><sub>2</sub> = −30°, <italic>θ</italic><sub>3</sub> = <italic>θ</italic><sub>4</sub> = 30° and azimuth angles of <italic>ϕ</italic><sub>1</sub> = 0°, <italic>ϕ</italic><sub>2</sub> = 90°, <italic>ϕ</italic><sub>3</sub> = 180°, and <italic>ϕ</italic><sub>4</sub> = 270° are obtained at 18 GHz. The measured peak gain of each beam is better than 17.2 dBi.
Exploiting Mobile Social Networks From Temporal Perspective: A Survey

With the popularity of smart mobile devices, information exchange between users has become more and more frequent, and Mobile Social Networks (MSNs) have attracted significant attention in many research areas. Nowadays, discovering social relationships among people, as well as detecting the evolution of community have become hotly discussed topics in MSNs. One of the major features of MSNs is that the network topology changes over time. Therefore, it is not accurate to depict the social relationships of people based on a static network. In this paper, we present a survey of this emerging field from a temporal perspective. The state-of-the-art research of MSNs is reviewed with focus on four aspects: social property, time-varying graph, temporal social property, and temporal social properties-based applications. Some important open issues with respect to MSNs are discussed.
Impact of SUs under FC in Co-operative Cognitive Radio Network in PUEA Environment

In this paper we determine the performance of a co-operative Cognitive Radio Network (CRN) under Additive White Gaussian Noise (AWGN). The impact of the number of received samples on probability of detection (PD) and false alarm (PFA) are shown graphically to determine the optimum number of samples for perfect detection. Next we show the impact of number of SUs under FC on the performance of cooperative CRN and determine the optimum value of threshold signal to noise ratio (SNR) at FC to combat the effect of PUEA. Analytical results are verified by simulation and found confidence level above 99%.
Machine Learning Approach for Analysing Encrypted Data

Machine Learning has been explored in many fields for solving problems ranging from medicine to finance, including Information Security. At the same time, in the area of cryptography, it is always a challenge to decipher the cryptic text without the knowledge of the encryption key. The challenge becomes even harder in absence of knowledge about the encryption algorithm. In this paper, we propose a methodology to identify the encryption algorithm being used to produce the cryptic text. We apply machine learning approach, to be specific Expectation-Maximization based clustering to identify the encryption algorithm. We conducted several experiments to substantiate our hypothesis and are effectively able to identify the encryption algorithm with over 90 percent accuracy. Our approach finds a useful place in information retrieval system when no meta-knowledge is being extended for crypt-analysis.
Battery State of Charge Estimation Based on Coulomb Counting Combined with Recursive Least Square and PI Controller

Battery is one of the main parts in Electrical vehicles as it serves as energy source. In order to prolong battery lifetime and optimally operate the battery, battery performance needs to be monitored and controlled using Battery Management System (BMS). One among important BMS functions is to determine the amount of remaining energy capacity available in a battery, represented by State of Charge (SOC), through estimation. The SOC estimation accuracy is important since it is used as indicator to prevent battery from failures due to overcharging and over discharging. One of the SOC estimation methods is Coulomb counting (CC) which is easy to implement. However, a false initial value of the SOC in this method will degrade the accuracy of the SOC estimation. In this paper an SOC estimation method is proposed by using CC method combined with recursive least square (RLS) algorithm and Proportional Integral (PI) controller to correct the error of SOC estimation due to false initial value of SOC. The result indicates that the proposed method is able to correct the error of SOC estimation due to false initial value of SOC with average of SOC estimation error equals 0.498%.