Miracle-dz/UniFAR-FTUs huggingface dataset:
TSSBV: A Conflict-Free Flow Rule Management Algorithm in SDN Switches

Software-Defined Network (SDN) radically changes the network architecture in a mobile broadband network by decoupling the network logic from the underlying forwarding devices. The application in 5G systems will face the challenge of detecting conflict policy provided by the SDN controller. Multiple active network functions in 5G systems with the same priority will potentially trigger conflicts among policies with overlapped flow space, causing the flow table explosion. Focusing on the problem that state-of-art flow rule management methods may cause high consumption of ternary content addressable memory (TCAM) during the process of conflict detection and resolution, we propose TSSBV. Our TSSBV algorithm groups the flow entries with the same prefix length together and decreases the redundant bit in vectors. Also, we present techniques for global prioritization of flow rules in SDN switches and provide dynamic priorities for unassisted resolution of these conflicts. We demonstrate the effectiveness and feasibility of TSSBV in a simulated SDN networks through generating synthetic flows that reflect the characteristics of the flows used in real OpenFlow scenarios. Both the emulations and results confirm that TSSBV is more efficient than state-of-art methods in terms of less initialization time and lower conflict search time.
EEG Signal Epilepsy Detection System Based on Convolutional Neural Network and Memristor Array

Epilepsy is one of the most harmful diseases to human society. EEG(Electroencephalogram, EEG) can be used to detect the onset of epilepsy in a timely and effective manner. In this work, a detecting system based on convolutional neural network is designed. The system consists signal acquisition module, pre-processing module and neuromophic calculation module, and is capable of detecting epilepsy activity from EEG signal, the neuromophic module is designed based on memristor array. The system is simulated and tested on a 480-subject database corresponding to the 8 most significant channels from each patient for a total of 64 EEG signal channels, and have obtained the detecting accuracy of 98.46%, which proved its excellent performance on epilepsy detecting.
A State-space Approach for Efficient Channel Tracking in Hybrid Analog/Digital MIMO Architectures

In this paper we consider a multipath channel subject to variations in time due to the presence of a Doppler drift which imposes a phase rotation on the original channel impulse response. Accordingly, a decision-directed state-space approach based on the extended Kalman filter (EKF) is proposed to track the channel variation. This approach is particularly efficient since we are able to reduce the number of training symbols used for channel tracking to be as few as two times the number of transmitters. These training symbols are required to initialize the EKF. Furthermore, using a decision-directed approach we are able to provide the EKF with state-observations without additional training symbols. Our approach is specially designed to work with hybrid analog/digital multiple-input multiple-output (MIMO) architectures.
Comparison of the Power Cycling Performance of Silicon and Silicon Carbide Power Devices in a Baseplate Less Module Package at Different Temperature Swings

In this work, the power cycling performance of silicon carbide MOSFETs in comparison to silicon IGBTs was investigated. For this purpose, several power cycling tests with multiple temperature swings on silicon IGBTs and silicon carbide MOSFETs in identical packages and with the same package technology (die attach, number and diameter of bond wires) were performed, to allow a direct comparison between both device types. Subsequently, the applicability of the CIPS08 lifetime model on the obtained lifetime data is investigated. The test results show, that the data for the silicon IGBTs are in very good agreement with the CIPS08 model. In contrast, the data for the SiC MOSFETs deviate significantly from the modeled curves and neither the CIP08 model with its original parameter nor a parameter fit on the model yield satisfactory results.
A Target Location Method Based on Swarm Probability Fusion

The existing target location methods either depend on the sensors’ high performance or the communication bandwidth, which are not suitable to directly apply to the effective fusion and target recognition of observation information of the large-scale heterogeneous swarm. In this paper, a target location method based on swarm probability fusion is proposed, which can represent the sensor's data in the heterogeneous swarms as the probability distribution map, and then carry out image recognition processing after normalized distributed fusion to obtain target information. The effectiveness of the method is verified by the effectiveness tests, the singlenode’s direction finding accuracy tests, and the swarm nodes’ number tests. Preliminary analysis shows that single-node’s direction finding accuracy has a significant effect on the swarm positioning accuracy and the convergence speed, while the number of nodes has a small effect on positioning accuracy and mainly affects the convergence speed.
Cellular Network Planning via Local Regularity Optimization

We propose a cell planning method for wireless cellular networks to simultaneously balance the volume shares of the cells and to preserve their regularity. In this framework, we first optimize the base station locations to minimize the coefficient of variation (CoV)-based service demand share metric using the power Voronoi algorithm. Then, we maximize the downlink coverage probability by defining a local regularity optimization problem and solve it through particle swarm, farthest point, average point, and spatial median optimization methods. Finally, we apply the proposed methods on a real network to show how the proposed methods simultaneously minimize both CoV-based service demand share and local regularity metrics.
Index Coded PSK Modulation in Vehicle to Vehicle Communication

Vehicle to vehicle (V2V) communication has gained its importance in recent years. In this work we consider the index coding problem (ICP) over noisy channel in V2V communication phase of message dissemination. The ICP in V2V communication is considered as device to device ICP with transmitting nodes as receiving nodes also. The index code is used in noisy scenario over AWGN channel and the broadcast vectors are mapped to suitable M-PSK signal constellations to save bandwidth, thus increasing the bandwidth efficiency. An algorithm is proposed to derive suitable mapping of the broadcast vectors to M-PSK signal constellation for improving the error performance. Performance improvement in terms of PSK index coding gain is discussed. While one vehicle transmits, the proposed algorithm provides performance improvement for at least one of the remaining vehicles, in most of the scenarios, while not penalizing the other vehicles.
New Four-Channel Resonant Boost DC/DC Converter

Multichannel resonant DC/DC converters have recently been popular in diverse fields, where multiple output voltage levels are required, such as supplying multilevel inverters. This paper is devoted to present the new member of a four-channel resonant DC/DC converter family, having consisted till now of step down as well as step-up/down configurations. The new converter is capable of realizing the power flow amongst the two input and the four output channels in order to maintain the required equal or unequal output voltages. In the meanwhile, it offers the benefits of soft-switching capability, including zero voltage (ZVS) and zero current switching (ZCS), resulting in high efficiency and reduced electromagnetic interference (EMI). The presentation of the new boost converter topology is followed by a detailed steady-state analysis. The focus is on the symmetrical operation of the converter in discontinuous current conduction mode (DCM). Possible sets of control variables are revealed and their effect on the operation, especially on the output voltages and powers is analyzed in detail. A step-by-step calculation procedure is also proposed to determine the circuit parameters. The outcomes of simulations and experiments validate the theoretical results.
Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

These days, cloud computing is one of the most promising technologies to store information and provide services online efficiently. Using this rapidly developing technology to protect computer-based systems from cyber-related attacks can bring many advantages over traditional protection schemes. The protected assets can be any computer-based systems such as cyber-physical systems (CPS), critical systems, desktop and laptop computers, mobile devices, and Internet of Things (IoT). Malicious software (malware) is any software which targets the computer-based system to launch cyber-attacks to threaten the integrity, confidentiality and availability of the data. To detect the massively growing malware attacks surface, we propose an intelligent behavior-based detection system in the cloud environment. The proposed system first creates a malware dataset on different virtual machines which identify distinctive features efficiently. Then, selected features are given to the learning-based and rule-based detection agents to separate malware from benign samples. Totally, 10,000 program samples have been analyzed to evaluate the performance of the proposed system. The proposed system can detect both known and unknown malware efficiently with high detection and accuracy rate. Besides, the proposed method results have outperformed the leading methods’ results in the literature. Our evaluation results show that the proposed algorithms along with machine learning (ML) classifiers achieve 99.8% detection rate, 0.4% false positive rate, and 99.7% accuracy. Our proposed system and algorithms may assist those who would like to develop a novel malware detection system in the cloud environment.
LoRa Signal Demodulation Using Deep Learning, a Time-Domain Approach

The LoRa modulation scheme is becoming one of the most adopted Internet of Things wireless physical layer due to its ability to transmit data over long distances with low power requirements. Typical demodulation techniques for LoRa utilize variants of non-coherent Frequency Shift Keying demodulation. This paper aims to capitalize on the robustness of deep learning techniques, specifically by using convolutional neural networks to demodulate LoRa symbols. We achieve this by building a dataset consisting of emulated time-domain LoRa symbols across a range of channel impairments; namely, we examine additive white Gaussian noise together with carrier frequency offset and time offset. The presented results show an improvement when utilizing deep learning over typical non-coherent detection while performing very close to the optimal matched filter.
Convolutional Neural Networks based Denoising for Indoor Localization

Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.
CSI Quantization for FDD Massive MIMO Communication

We consider high-dimensional multiuser MIMO transmissions in Frequency Division Duplexing systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach. We compare the improved quantization scheme to the 5G NR standardized version by simulation in a scenario with a realistic spatial channel model. The improvements lead to a more than 25% improvement in spectral efficiency.
Towards A Microgrid Based Residential Home Energy Management Using Genetic Algorithm

This paper proposes a load scheduling approach for a residential home in an islanded PV microgrid scenario based on a Genetic Algorithm (GA). The primary aim is to inform on how a Demand Side Management (DSM) could reduce the capital cost of the residential home energy use and operational cost of the microgrid by minimizing the use of fossil fuel generator. The research study proposes and describes the design for load allocation to achieve utilization of solar PV resources optimally. The proposed scheme is based on the time-of-use (TOU) and improvement of electricity users’ comfort. The demonstration of the concept is presented and discussed based on a single smart home scenario using a solar PV microgrid and battery in a rural community of Enugu State, Nigeria.
Design and Analysis of Dual Rectangular Slotted Antenna for Space-station Cynosure IOT Security Applications

In this paper, space-station based internet of thing (IoT) security application is presented. The international space-station indicates enormous satellite orbit. This design consists of the inner arrangement of modernized rectangular in shape antenna loaded by dual rectangle-shaped slot (DRSS). The effect novel was study to check performance of the design. Inserting of slot has a very great influence on the behaviour with the specification of the design. The aim is to enhance bandwidth of the novel design, which brought about slot-loaded side-by-side on the rectangular patch with it feeding via the microstrip feeder link. It’s show that loading of the slot in this part of the antenna without degradation in the cross-polarization performance bring good results than other microstrip antenna that are conventional. The proposed design has been simulated using frequency domain-solver of CST Microwave Studio and, validated with time-domain solver. The results show an impressive wide impedance bandwidth more than the initial antenna. Moreover, the input impedance, the S-parameters (S11) parametric study, radiation pattern, Far-field’s and current distribution are investigated, the state of the art comparison was presented. This is suitable for IoT security applications.
Research on Power Distribution Method of OFDM System with Hybrid Energy Supply under Fast Fading Channel

An adaptive power allocation by minimizing grid power consumption for the Orthogonal Frequency Division Multiplexing (OFDM) system with the hybrid energy supply under a fast fading channel is proposed in this paper. To solve the non-convex problem, two adaptation algorithms have been presented. The first is Lagrangian algorithm and the second is a successive convex approximation (SCA) algorithm. The theoretical analysis and simulation results show that the proposed algorithms are effective. Compared with traditional OFDM systems with grid energy supply only, the proposed algorithms can reduce the power consumption of the grid. Also, different proposed algorithms have different performance. Lagrangian algorithms with higher computational complexity have better performance. However, there is a trade-off between computational complexity and performance.
Facilitating a preparatory stage of real-world experiments in a humanoid robot assisted English language teaching using Gazebo simulator

A simulator is a software application that allows imitating an experimental environment and controlling a process or an instrument. Projects in human-robot interaction (HRI) field face different challenges during real world experiments, which include improper robot behavior, mistakes in an algorithm logic, software and hardware failures, and generic lay participants on a user side. Simulators could help to avoid a number of problems that researchers would definitely face within real world experiments at algorithms’ testing stage. This study presents an experimental validation in Gazebo simulator and real-world experiments for English language lessons scenarios with a small size humanoid Robotis DARwin OP2. We investigated advantages of using Gazebo simulator in constructing modular generic HRI blocks and global HRI scenarios in order to optimize a humanoid robot assisted process of studying English. The simulator saved a significant amount of efforts and time at preparatory stages, allowing robot programmers and HRI developers to obtain a quick feedback from users and their requirements for necessary adjustments.
A Hybrid Beamforming-Based Transceiver with Antenna in Package for Millimeter-Wave Small Cell

A millimeter-wave hybrid beamforming-based (HBF) transceiver designed for small cell has been presented in this paper. A large-scale array for massive MIMO is divided into several subarrays. Every subarray has 16 active elements and adopts analog beamforming to save the number of digital processing channels. A dual-polarized antenna in package (AiP) architecture is constructed to implement each subarray, which operates from 23.2 to 28.3 GHz in vertical polarization and from 23.4 to 27.5 GHz in horizontally polarization. By using multiple AiP subarrays, the transceiver has a maximum transmit linear power more than 33 dBm and a receive sensitivity of -76 dBm. The proposed transceiver can meet the need of small cell well and achieve miniaturization, large capacity and high-data rate.
SAPCGAN: Self-Attention based Generative Adversarial Network for Point Clouds

The direct extension of 2D image learning to three-dimensional space is 3D point cloud learning. Recently, point cloud learning has shown significant results in 3D shape estimation and semantic segmentation. Despite these advancements, fundamental problems in point cloud learning still pose significant challenges. These problems include representation learning, shape generation, shape segmentation, and shape matching. In this paper, we propose a cognitive self-attention based learning approach to aggregate global representation of 3D shapes from point cloud data. We also integrate 3D point data with a binary tree structure to build a point cloud generator. We further design a novel Generative Adversarial Network (GAN) architecture to generate point clouds resembling the ground truth that could be used for unsupervised learning of 3D shapes. Relying on a self-attention mechanism, our framework, called SAPCGAN, aggregates the global graph features to correct the structural information of 3D shapes in the latent space. Finally, we compare the performance of two gradient penalty methods used in stabilizing the training of our GAN system. We show that our framework has high training efficiency and can provide state-of-the-art results in 3D point cloud generation. The performance of our is demonstrated with both quantitative and qualitative experimental evaluations. Furthermore, the generated 3D point clouds can be segmented into their natural semantic parts, such as, for example the four legs of a chair, the wings of an air plane, or the four wheels of a car.
I/Q Density-based Angle of Arrival Estimation for Bluetooth Indoor Positioning Systems

In recent years, indoor positioning system employing Bluetooth has attracted tremendous attention. However, it is investigated that its received channel information is significantly affected by hardware configuration and wireless environments, especially the received dataset of angle of arrival (AOA), which leads to inaccurate channel estimation and positioning. Further-more, AOA estimation at larger angle direction is severely influenced by the variant wireless environment and signal distortion, which has not been resolve in existing literature. In this paper, we propose an advanced I/Q density-based AOA estimation (IQDAE) to deal with the above-mentioned problem, which consists of two sub-schemes. We firstly employ the designed phase difference (PD) filter to convert I/Q signals to phase information and then select the candidate sets by eliminating outliers. Afterward, we conceive a PD density-based classification algorithm to estimate AOA. The experimental results show that the mean absolute error of proposed IQDAE algorithm is comparably smaller than that from the other schemes, including commercial solutions, especially at larger angles. The results indicate that we can effectively increase the service range for the Bluetooth positioning system by adopting the proposed algorithm.
Active Block Diagonal Subspace Clustering

Subspace clustering aims to find clusters in the low-dimensional subspaces for high-dimensional data. Subspace clustering with Block Diagonal Representation (BDR) maintains the number of connected components of the graph by Laplacian rank constraint, and the learned affinity matrix shows a block diagonal structure, which will achieve a good segmentation for the dataset by spectral clustering. However, the subspaces of real data may overlap and the learned affinity matrix may be imprecise. In this work, we propose an Active learning framework for BDR(ABDR) to acquire and incorporate prior knowledge to improve the subspace clustering performance. An active selection strategy is designed to acquire labels of the informative data points from both the skeleton of clusters and the boundaries of clusters, and then the labeled data are converted into pairwise constraints, which are incorporated into BDR. The optimization of the new objective function is given and the convergence of ABDR is discussed. Experimental results on three images datasets(MNIST, ORL and COIL-20) and one UCI dataset(ISOLET) demonstrate the effectiveness of ABDR on complex clustering tasks and show that ABDR is superior to multiple state-of-the-art active clustering and learning techniques.
Predictive Current Duty Cycle Control for Induction Motor Based on Reference Voltage

Model predictive current control has received a great deal of attention in the field of induction motor drives. However, the most methods select voltage vectors based on enumeration and cost function, so it greatly increases control system complexity. In order to reduce control complexity and further improve steady-state control performance, a predictive current duty cycle control based on the reference voltage (PCDCC-RV) is proposed in this paper. The applied voltage vector is determined based on the location of the synthesized reference voltage, so the complicated enumeration process is cancelled. In addition, the control duty cycle is calculated based on the principle of minimum error between the selected vector and reference voltage. Compared with the traditional method, the proposed method of voltage vector selection and duty cycle calculation are more convenient, and the better control performance is obtained. Finally, simulation results prove its effectiveness.