Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Techniques to Detect Maliciousness of Portable Executable Files

In the past few years, malware has become one of the most significant threats to computer security. Malware or malicious is software that attackers use or program to interrupt the operations of a computer, to collect secret or private information, or to access computer systems without being authorized to do. In this paper, we presented a machine learning based approach to classifying a portable executable (PE) file as benign or malware with high accuracy. The proposed approach used the static analysis technique to extract the integrated feature set, which was created by combining a few raw features selected from the three main headers of PE files and a set of derived features. Seven supervised learning algorithms are used in the classification of malware. We compared the performance of each classifier in terms of accuracy, precision, and F -measure. The experimental results indicate that the integrated feature set performs better than the raw feature set on all metrics. Integrated dataset accuracy values are between 91% and 99%, against the raw dataset values which are between 71% and 97% using (70/30) split method. Random Forest has outperformed all classifiers on both datasets (with accuracy of 99.23%).
Low-Complexity Beamspace Massive Access for B5G Cellular Internet of Things

In this paper, we investigate the issue of massive access in beyond fifth-generation (B5G) cellular internet of thing (IoT) network. To reduce the overhead of channel state information (CSI) acquisition and the complexity of beam design in the scenario of massive IoT devices, we propose a framework of massive access in the beamspace. Then, we analyze the performance of beamspace massive access, and reveal the impacts of beamspace parameters on the sum rate. Furthermore, in order to improve the overall performance, we present a low-complexity beamspace massive access algorithm by jointly optimizing transmit and receive beamforming. Finally, simulation results validate the effectiveness of the proposed beamspace massive access algorithm.
Analytical modeling method of thermal spreading resistance in BAW filters

As the power requirements in BAW filters steadily increase, thermal behaviour emerges as a key asset to ensure device performance and reliability. Under these conditions, self-heating mechanisms drive to possible situations where the filters no longer meet the electrical specifications. In addition to that, the evaluation of the heat spreading might help to define the resonators and pillars distribution along with the overall performance of current BAW filters. In the past, numerical methods based on FEM have been used to study the temperature distribution under high-power (HP) signals, lacking efficiency in terms of computational time. The motivation of this work is to describe an analytical method that is able to simulate very fast, without the need of costly FEM simulation, the heat spreading in a filter according to the spatial distribution of the resonators.
Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC

We present Newtonian Monte Carlo (NMC), a method to improve Markov Chain Monte Carlo (MCMC) convergence by analyzing the first and second order gradients of the target density to determine a suitable proposal density at each point. Existing first order gradientbased methods suffer from the problem of determining an appropriate step size. Too small a step size and it will take a large number of steps to converge, while a very large step size will cause it to overshoot the high density region. NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension. However, our objective is not to find a maxima but instead to find a parameterized density that can best match the local curvature of the target density. This parameterized density is then used as a single-site Metropolis-Hastings proposal. As a further improvement on first order methods, we show that random variables with constrained supports don’t need to be transformed before taking a gradient step. NMC directly matches constrained random variables to a proposal density with the same support thus keeping the curvature of the target density intact. We demonstrate the efficiency of NMC on a number of different domains. For statistical models where the prior is conjugate to the likelihood, our method recovers the posterior quite trivially in one step. However, we also show results on fairly large non-conjugate models, where NMC performs better than adaptive first order methods such as NUTS or other inexact scalable inference methods such as Stochastic Variational Inference or bootstrapping.
Sinusoidal PWM for Flying Capacitor Voltage Balancing of a Six-Level Inverter

This paper presents a control technique developed based on Sinusoidal Pulse Width Modulation (SPWM) to control the flying capacitors voltages' of a recently published six-level topology inverter. The presented inverter has a fewer number of components compared to the existing six-level topologies, which makes it a cost-efficient solution for medium-voltage applications. To produce a six-level voltage at the output of the inverter, the flying capacitors voltages should be controlled at their nominal values. The six-level inverter is controlled based on Space Vector Modulation scheme (SVM), which is complex and not convenient to implement. Therefore, a control method based on Sinusoidal Pulse Width Modulation (SPWM) technique is developed to control the capacitors voltages at different operating conditions. The simulation results in MATLAB/Simulink environment has been obtained to show the performance of the developed control method. A prototype of the proposed inverter is implemented, and the experimental results are given to show the feasibility of the proposed inverter with the developed control technique.
Adaptive Stiffness and Damping Impedance Control for Environmental Interactive Systems With Unknown Uncertainty and Disturbance

This paper presents an impedance controller with adaptive stiffness and damping terms for environmental interactive system with unknown modeling error and external disturbance. The objective is to compensate the uncertainty and disturbance and form a desired close-loop impedance architecture. A disturbance observer for nonlinear system is employed to estimate the lumped uncertainty and disturbance. The stiffness and damping are adjusted by the state-variable-related negative power type parameters. Consequently, the control input is designed with the consideration of the estimation of lumped uncertainty and disturbance as well as the regulated stiffness and damping. The stability is proved and the criterion of parameter selection is pointed out. Although the state-variable-related stiffness and damping parameters will intuitively be singular, the system works without any singularity due to the independence of these parameters in stability analysis. To verify the proposed approach, a one degree of freedom mass system is adopted and numerical simulation is performed accordingly. The simulation results demonstrate the effectiveness and the superiority of the proposed control scheme for environmental interactive system with unknown modeling error and external disturbance.
A Time-Frequency Independent Component Analysis Method for Group Velocity Extraction of Ultrasonic Guided Waves

Ultrasonic guided waves play an important role in non-destructive evaluation (NDE). Nevertheless, the multi-mode dispersion still brings challenges for mode identification and the waveguide evaluation. In this study, we propose a multimodal separation method combining short-time Fourier transform (STFT) with independent component analysis (ICA). The simulation result shows that this method can realize an automatic mode identification and group velocity extraction, which can be helpful in the waveguide assessment.
Design of Pulse Width Modulator based Sliding-Mode Control (SMC-PWM) for Sensor-less Single-Phase Packed U-Cell Inverter

In this paper a fixed-switching frequency Pulse width modulator (PWM) based-Sliding Mode Control (SMC) is proposed and applied to a single phase/Five-Level/Sensor-less Packed U-Cell (PUC5) interfacing the utility. The new design takes the advantage of chattering compensation when Gao's reaching law is adopted. SMC-based controller takes in action of maintaining the utility current at a desired reference value with unity power factor and fixed-switching frequency operation. Due to self-voltage balancing feature of PUC5 inverter, auxiliary DC capacitor voltage is regulated through redundant switching states while no sensor, neither regulator are needed. The overall SMC-PWM proposed controller is less complex, more robust towards external disturbances making it suitable for single-phase renewable energy systems interfacing the utility. Simulation results on MATLAB/Simulink are provided to confirm the effectiveness and robustness of the proposed controller
Mobile Traffic Prediction Based on Densely Connected CNN for Cellular Networks in Highway Scenarios

With the explosive growth of communication traffic and the arrival of 5G technologies, wireless big data has become an enabler for operators to manage and improve their wireless communication systems. Although many mobile traffic prediction methods have been proposed in the past few years, few prediction methods combine with the distribution features of base stations to predict the mobile traffic of cellular networks. In this paper, by leveraging on the 4G mobile data collected from one typical city in southeastern China, we propose a mobile traffic prediction approach based on one-dimensional densely connected convolutional neural networks (CNN) to predict the mobile traffic of base stations in highway scenarios. After data acquisition, data analysis and modeling, comparisons are made between the proposed mobile traffic prediction approach and the widely used prediction approaches based on machine learning models like LSTM and SVR, and numerical results show that the proposed mobile traffic prediction approach has outstanding performances.
Concatenated Reed-Solomon/Spatially Coupled LDPC Codes

Spatially coupled (SC) low-density parity-check (LDPC) codes can achieve capacity approaching belief propagation (BP) decoding performance with low message recovery latency when using the sliding window decoding (SWD). The SWD is suitable for data stream transmission but cannot always correct all errors in the targeted symbol set. A further decoding stage would be desirable to eliminate the remaining errors from SWD. This paper proposes a novel concatenated coding scheme where the Reed-Solomon (RS) codes and the SC-LDPC codes are chosen as the outer code and the inner code, respectively, namely the RS-SC-LDPC codes. Consequently, the remaining errors from SWD can be corrected by the outer RS codes. In the concatenated coding scheme, the inner SC-LDPC code is designed such that it has a large girth and locally systematic encoding property. Our simulation results show the RS-SC-LDPC codes can achieve a high decoding performance, while the error floor of the inner code can be removed.
A Graphical Tool for Parsing and Inspecting Surgical Robotic Datasets

Skill and practice of surgeons greatly affect the outcome of surgical procedures, thus surgical skill assessment is exceptionally important. In the clinical practice, even today, the standard is peer assessment of the capabilities. In the case of robotic surgery, the observable motion of the laparoscopic tools can hide the expertise level of the operator. JIGSAWS is a database containing kinematic and video data of surgeons training with the da Vinci Surgical System. The JIGSAWS can be an extremely powerful tool in surgical skill assessment research, but due to its data-storage it is not user-friendly. In this paper, we propose a graphical tool for the JIGSAWS, which can ease the usage of this annotated surgical dataset.
Adaptive Neural Fuzzy Inference System Controller for Seven-Level Packed U-Cell Inverter

In this paper, an improved Adaptive Neural Fuzzy Inference System (ANFIS) based controller has been designed to regulate the capacitor voltage and load current of Seven-Level Packed U-Cell (PUC7) inverter. PUC7 is an affordable topology due to the least power switches and DC sources; but, it suffers from unstable adjusting capacitor voltage level. Proportional Integral (PI) as a simple linear control method causes overshoots and undershoots in transient response and conducts the system to unstable mode when some uncertainties are existed in the load. However, the proposed ANFIS control method regulates the load current and balances the capacitor voltage without any overshoot or undershoot and transient response even in presence of nonlinear loads. Simulation results of stand-alone mode of operation of PUC7 obtained by MATLAB software also confirm usability of ANFIS control loop to achieve unity power factor, minimum Total Harmonic Distortion (THD) and minimum capacitor voltage ripple while the PUC7 inverter is connected to an AC dynamic load.
Segmentation of electromyography signals for pattern recognition

The use of gestures as interface between humans and robots to facilitate communication between them is a long-sought goal. Although many gesture solutions have been presented, none of them cope entirely with wrong gesture recognition. This study proposes a novel electromyography (EMG) prototype sensor to capture gestures and also algorithms and procedures to discriminate data containing valid gestures (segmentation). Gestures are recognized using convolutional neural network (CNN) model. The proposed solution presented high recognition accuracy overcoming other similar studies in literature. Test results demonstrated that the proposed solution presents high performance and suggested its use in industrial environment.
Design and Implementation of an Electrical Tamper Detection System

Electrical power theft is one of the main causes of Non-Technical Losses (NTLs) in an electrical power system. NTLs in electrical power grids refers to the transmission and distribution loses originating from electrical theft and various other illegal uses of electricity. According to Eskom, South Africa loses at least R20 billion a year due to electricity theft, three-quarters of which is reported to be losses suffered by municipalities. Thus, to address this issue, this paper presents the design and implementation of a device capable of detecting electricity theft by identifying the small changes between the real-time measurements and that of a trained model. The energy metering system consists of a voltage and current sensing circuits while the theft detection algorithm consists of a Support Vector Machine (SVM) model trained and used to classify users as either a clean or fraudulent user. The data that was used to train the SVM model was obtained from a test bench. The test bench consisted of 100 appliances that were emulated using purely resistive loads. Each appliance has its own probability function which consists of a PRNG that generates pseudorandom numbers according to a chosen distribution type. The probability function also considers the probability of a specific appliance being used for that day and for how long the appliances are active. The detection device was implemented using a Raspberry Pi 3 and several evaluations were carried out. Evaluation result showed that the device can accurately detect electricity theft.
LSTOD: LATENT SPATIAL-TEMPORAL ORIGIN- DESTINATION PREDICTION MODEL AND ITS APPLI-

Origin-Destination (OD) flow data is an important instrument in transportation studies. Precise prediction of customer demands from each original location to a destination given a series of previous snapshots helps ride-sharing platforms to better understand their market mechanism. However, most existing prediction methods ignore the network structure of OD flow data and fail to utilize the topological dependencies among related OD pairs. In this paper, we propose a latent spatial-temporal origin-destination (LSTOD) model, with a novel convolutional neural network (CNN) filter to learn the spatial features of OD pairs from a graph perspective and an attention structure to capture their long-term periodicity. Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrate the advantage of LSTOD in achieving at least 7.94% improvement in prediction accuracy over the second best model.
A Novel Commutation Torque Ripple Suppression Method for Position Sensorless Brushless DC Motor Based on a SEPIC Converter

Commutation torque ripple for a brushless dc motor (BLDC) is a key problem to be researched widely. A novel commutation torque ripple suppression method for the position sensorless BLDC motor based on a SEPIC converter was proposed. In the commutation interval, the dc-link bus voltage is controlled through a new switching circuit based on the SEPIC converter. It gives rise to the equal rates of current decrement and increment between commutated phases, which dramatically reduces the current ripple of the noncommutated phase. Furthermore, a new automatic correction method of phase error with an analog filter was proposed to make the position sensorless BLDC motor commutate precisely and reduce the commutation torque ripple at the same time. The experimental results showed that the new proposed method effectively suppressed the commutation torque ripple for position sensorless BLDC motor.
Crosstalk Study of Large PMUT Array using FEA and Cloud HPC

This paper presents FEA analysis using high-performance computing for crosstalk study in a piezoelectric micromachined ultrasound transducer (PMUT) array. The array comprises 64 1-D elements which each element comprises 80 membranes. The simulation for 5120 membranes (64×80) takes about ~2 hours executed on Amazon Web Services (AWS), and consuming ~700 core hours with 2GB RAM. The crosstalk displacement for several elements has been simulated showing significant impact on adjacent elements that will be reduced when the wave travels farther. The simulation also shows crosstalk frequencies higher than the device center frequency. The effect of pitch size is also studied on crosstalk implying larger pitch mitigates the crosstalk effect.
Comparison of Modulation Methods to Reduce the Circulating Current in Paralleled NPC-Converters with Common DC-link

In medium/high power applications including smart transformers, active power filters and wind turbines, parallel converters are a reliable solution to achieve low harmonic distortion, high efficiency and increased power handling. However, if such topology has a common dc-link source, the circulating current arises between the converters, increasing power losses of the switching components and propagating the stress of the dc-capacitors. This paper provides comparison of four most commonly used pulse-width modulation methods and their influence on the circulating current and the quality of the output current. The efficacy of the investigated method is validated by experiments.
Q-learning based Reinforcement Learning Approach for Lane Keeping

The paper presents the application of a Q-Iearning reinforcement learning method in the area of vehicle control. The purpose of the research presented is to design an end-to-end behavior control of a kinematic vehicle model placed in a simulated race track environment, by using reinforcement learning approach. The varied trajectory of the track to provide different situations for the agent. The environment sensing model is based on high-level sensor information. Track curvature, lateral position, and relative yaw angle can be reached from the current automotive sensors, such as camera or IMU based systems. The objectives were reached through the definition of a rewarding system, with subrewards and penalties enforcing lane keeping. After the description of the theoretical basis the environment model with the reward function is detailed. Finally the experiments with the learning process are presented and the results of the evaluation are given from some aspects of control quality and safety.
Research on Localization Algorithm of Wireless Sensor Networks Based on IoT

Wireless Sensor Networks (WSNs) are multihop self-organizing network systems formed by a large number of sensor nodes deployed in the monitoring area. It is an important technical form of the underlying network of the Internet of Things (IoT). The sensor nodes localization is the premise and hinge to the uses of WSNs. The hop-count of the inter-nodes obtained by the conventional Distance Vector Hop (DV-Hop) localization algorithm is always an integer. Therefore, the connectivity between nodes they described is not sufciently accurate. The compute errors of the separation distance between unknown anchor nodes are signicant. This is the main reason. In the paper, the concept of pseudo hop is raised, and the improved accuracy of hop-relationship between nodes is obtained based on energy. Simulation indicate that the improved algorithm enormously advance the localization accuracy of unknown nodes. Moreover, the algorithm has lower requirements on the quantity of beacon nodes and the communication radius, which primely saves energy consumption and costs.
A Domain Specific Language for Automotive Systems Integration

Developing complex safe and secure Cyber-Physical Systems (CPS) applications for the automotive domain is typically a complex task, due to the criticality inherent to this domain. Considering such known complexity of the development process, we propose a novel solution that aims to provide a quasi-automatic’ integration process between the different components of such CPS systems via the support of a Domain Specific Language (DSL) that provides several views of the system, abstracting away the more technical implementation details, while imposing system properties and restrictions that have the potential to be formally verified (either statically or at run-time) during design, and facilitates the process of customization and quasi-automatic build and deployment processes. In this paper, we briefly analyze the tools that are available and that cover partially the characteristics of our envisioned DSL, describe its building blocks, and show how it can be applied in a small, yet sufficiently complex CPS application whose architecture is very close to what we may expect for the modern and future generation of CPS application in the automotive domain.
Simulation Study of the Effects of Synchronous Generator Parameter Deviations on the Performance of a DFIM-SPS

Targeting at reduce the difficulty of fault protection of a modern more-electric ship (MES), a doubly-fed induction motor (DFIM) based shipboard propulsion system (SPS) is proposed. The on-board electric power supply is provided by a synchronous generators (SG). Therefore, the accurate parameter setting is important for achieving the optimal system performance. In this paper, the effects of parameter deviations (PDs) in SG on the system performance are studied. The detailed models of a salient-pole SG and a type DC1A excitation system (EXS) are presented. Different degrees of PDs (DoPDs) are applied to 8 important parameters of SG to analyze the sensitivity of each parameter. Simulation studies are carried out in Matlab/Simulink to obtain data for investigating the PD effects. Three PD effect indicators (PDEIs) are defined to illustrate the effects on the SG terminal voltage and output active power. Meanwhile, when applying different DoPDs, the increasing rates of PDEIs are different, which are also investigated. Moreover, the two most critical SG parameters are determined, and the overall system performance is analyzed when applying different DoPDs on these two parameters.