Miracle-dz/UniFAR-FTUs huggingface dataset:
Incident Detection over Unified Threat Management Platform on a Cloud Network

Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves security mechanisms like antivirus, firewalls, intrusion detection system (IDS) and cryptography by using machine learning methods and data analysis techniques. As the modern AI techniques help improving security systems, criminal activities are also becoming updated simultaneously. Machine learning methods along with data analysis tools have become popular to prevent security systems from threats and hacking activities. This work contributes to secure cloud networks and help them prevent malicious attacks. In this paper, Bidirectional long short-term memory (BLSTM) is used to detect incidents over unified threat management (UTM) platform operated on cloud network. Results are compared with K-nearest neighbor which is a baseline technique. Time series input samples recorded over UTM platform are used for training and testing purposes. We obtain accuracy score of 98.47% with 0.0186 mean squared error (MSE) using KNN while BLSTM provides 98.6% accuracy score with 0.002 loss, which is better than the KNN.
Semi-physical Simulation Operation Monitoring System of Synchronous Generator Based on DSP and LabVIEW

In this paper, in order to verify the performance of the excitation system, a semi-physical simulation operation monitoring system of synchronous generator is developed based on DSP and LabVIEW. With the increase of capacity of synchronous generator, it is difficult to verify the performance of the excitation system with an actual synchronous generator set. The semi-physical simulation system is introduced to achieve the demands. Design human-machine interface based on LabVIEW to control the operation of simulation model in DSP. The industrial Ethernet is used to connect the DSP with the human-machine interface to exchange information. By observing the transient phenomena of synchronous generator under simulated faults, verify the regulation performance of excitation system.
User Association and Radio Resource Allocation Combined with Multiple Frequency Bands and ICIC in Small Cell Deployments

To support the continuing growth in the amount of mobile cellular traffic, small cell deployments represent a promising option. In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. Furthermore, higher frequency bands are also expected to be one of the promising solutions. This paper investigates user association and radio resource allocation combined with ICIC in small cell deployments using multiple frequency bands. The optimum user association and resource allocation for the multiple frequency bands are derived by solving a convex optimization problem based on proportional fair criteria. The performance in various small cell deployment scenarios with multiple frequency bands is investigated, and in all scenarios, the algorithm outputs the optimum user association and resource allocation.
Face Recognition based on Illumination Invariant Techniques Model

In modern years, face recognition is becoming popular in many applications in different areas being videoconferencing, security, banking, law requirement, and human-computer interaction. The performance of the face verification system depends on many challenges. The Most challenge in face recognition is illumination variations. In this approach, Median Filter, Gabor Filter, and Histogram Equalization are used to reduce the illumination effect of the face images as the preprocessing stage. The extraction of the Eigen faces features use Principal Component Analysis (PCA). After that, recognize face by using multiclass Support Vector Machines. The standard databases of ORL and Yale are used in the experiment. The results show that the system efficiently increased the accuracy of face recognition rate of the system, especially in various lighting situations.
A New Multi-Port Power Electronic Transformer for Distribution Gri

A new multi-port power electronic transformer (PET) for distribution grid is presented in this paper. The proposed PET includes high voltage stage, isolation stage and low voltage stage, which can provide high voltage DC port, high voltage AC port, low voltage DC port and low voltage AC port. The high voltage stage adopts three-phase four arms Modular Multilevel Converter (MMC) structure, which can reduce the number of sub-modules and associated elements compared with three-phase six arms MMC. The third phase of three-phase four arms MMC consists of batteries in order to achieve energy storage capability.
Memory-Centric Neuromorphic Computing With Nanodevices

When performing artificial intelligence, CPUs and GPUs consume considerably more energy for moving data between logic and memory units than for doing arithmetic. Brains, by contrast, achieve superior energy efficiency by fusing logic and memory entirely. Currently, emerging memory nanodevices give us an opportunity to reproduce this concept. In this overview paper, we look at neuroscience inspiration to extract lessons on the design of memory-centric neuromorphic systems. We study the reliance of brains on approximate memory strategies, which can be translated to AI. We give the example of a hardware binarized neural network with resistive memory. Based on measurements on a hybrid CMOS/resistive memory chip, we see that such systems can exploit the properties of emerging memories without error correction, and achieve extremely high energy efficiency. Second, we see that brains use the physics of their memory devices in a way much richer than only storage. This can inspire radical electronic designs, where memory devices become a core part of computing. We have for example fabricated neural networks where magnetic memories are used as nonlinear oscillators to implement neurons, and their electrical couplings implement synapses. Such designs can harness the rich physics of nanodevices, without suffering from their drawbacks.
Compressed Cache Layout Aware Prefetching

The speed gap between CPU and memory is impairing performance. Cache compression and hardware prefetching are two techniques that could confront this bottleneck by decreasing last level cache misses. However, compression and prefetching have positive interactions, as prefetching benefits from higher cache capacity and compression increases the effective cache size. This paper proposes Compressed cache Layout Aware Prefetching (CLAP) to leverage the recently proposed sector-based compressed cache layouts such as SCC or YACC to create a synergy between compressed cache and prefetching. The idea of this approach is to prefetch contiguous blocks that can be compressed and co-allocated together with the requested block on a miss access. Prefetched blocks that share storage with existing blocks do not need to evict a valid existing entry; therefore, CLAP avoids cache pollution. In order to decide the co-allocatable blocks to prefetch, we propose a compression predictor. Based on our experimental evaluations, CLAP reduces the number of cache misses by 12% and improves performance by 4% on average, comparing to a compressed cache.
An Edge AI System-on-Chip Design with Customized Convolutional-Neural-Network Architecture for Real-time EEG-Based Affective Computing System

In this work, we proposed an edge AI CNN chip design for EEG-based affective Computing system by using TSMC 28nm technology. To improve the performance, Artifact Subspace Reconstruction (ASR) and Short-Time Fourier Transform (STFT) were used for our signal pre-processing and features extraction. The time-frequency EEG feature map was obtained with a multi-channel Differential Asymmetry (DASM) method on 6 EEG channels: FP1, FP2, F3, F4, T7, and T8 according to 10–20 system. The total power consumption of the proposed CNN chip was 71.6mW in training mode and 29.5mW in testing mode. We used 32 subjects data from the DEAP database to validate the proposed design, achieving mean accuracies of 83.7%, 84.5%, and 70.51% for Valence-Arousal binary classification and quaternary classification respectively, showing significant performance improvement over the current related works.
Performance Analysis and Optimization of Automotive GPUs

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.
Suppression of Resonant Frequency Drifting of Transmitter for One-to-Multiple Wireless Power Transfer System by Utilizing Hybrid Compensation Topologies in Receivers

This paper aims to suppress the resonant frequency drifting of the transmitter of the one-to-multiple wireless power transfer (OWPT) system, which is caused by the reflected impedances of the unselected receivers. To overcome the frequency drifting of the transmitter and realize the full resonant of the whole circuit, it is necessary to dynamically change the compensation capacitor with tuning the circuit of the transmitter. However, with more receivers, the range of capacitance of the compensation capacitor increases. So, it leads to the increasing of the difficulty in making the capacitor array. In this paper, to cancel the unexpected frequency drifting and narrow the range of the compensation capacitor, as well as to decrease the size of the capacitor array of the transmitter, the using of hybrid compensation topology in receivers is proposed. First, based on the existing OWPT system, the hybrid compensation topology is proposed and analyzed in details. Then, the guidelines for parameters design of the transmitter is presented and analyzed in details. Finally, the simulation results are given to validate the proposed method.
Optimum Leakage Inductance Determination for a Q2L-Operating MMC-DAB with Different Transformer Winding Configurations

This paper discusses the procedure for the determination of the optimum leakage inductance of a medium frequency transformer (MFT) for a quasi-two-level (Q2L) operating three-phase modular multilevel converter dual-active bridge (MMC-DAB) considering the effects of the MFT winding configuration. Three different winding configurations-namely, Y-Y, D-D, and Y-D-are considered for the connection of the MFT windings. The optimum leakage inductance requirement of the MFT, the currents total harmonic distortion (THD), and the transformer utilization factor (TUF) are compared for the three winding configurations. It is found that the Y-Y and the D-D configurations have similar optimum leakage inductance patterns, which are different from the Y-D configuration. Furthermore, it is established that an optimized leakage inductance value for a conventional DAB, can be utilized for the Q2L-operating MMC-DAB for a wide range of transition time with the Y-Y and the D-D winding configurations if less than a 5% error could be tolerated in its value. A comparison with respect to the TUF and the currents THD revealed that the Y-Y and the D-D configurations result in a higher TUF compared to the Y-D configuration; However, the Y-D configuration has approximately two times lower currents THD compared to the other two configurations.
Parameters Optimization of the Permanent Magnet Linear Synchronous Machine Using Kriging-based Genetic Algorithm

This paper presents an efficient optimization design method for a permanent magnet linear synchronous motor (PMLSM) to suppress thrust ripple and maintain high average thrust. The effects of twelve design parameters on the performance of PMLSM are studied, and five key design parameters are selected through Taguchi method. To obtain the optimal configuration of five design parameters, an improved Latin hypercube sampling is adopted to generate samples for constructing the Kriging model, and the Kriging-based genetic algorithm is applied to increase the optimization efficiency. The mean square error function as a criterion of response accuracy is to make the optimization result more robust. A two-dimensional finite element analysis is used to evaluate performances of the original and optimized PMLSM. The results show that the optimized PMLSM has a 68% reduction in thrust ripple and a slight increase in average thrust compared with the original PMLSM. Furthermore, the optimized PMLSM with skewing pole will achieve better thrust characteristics.
Resource Efficient Authentication and Session Key Establishment Procedure for Low-Resource IoT Devices

The Internet of Things (IoT) can includes many resource-constrained devices, with most usually needing to securely communicate with their network managers, which are more resource-rich devices in the IoT network. We propose a resource-efficient security scheme that includes authentication of devices with their network managers, authentication between devices on different networks, and an attack-resilient key establishment procedure. Using automated validation with internet security protocols and applications tool-set, we analyse several attack scenarios to determine the security soundness of the proposed solution, and then we evaluate its performance analytically and experimentally. The performance analysis shows that the proposed solution occupies little memory and consumes low energy during the authentication and key generation processes respectively. Moreover, it protects the network from well-known attacks (man-in-the-middle attacks, replay attacks, impersonation attacks, key compromission attacks and denial of service attacks).
A Broadband High Gain SIW Dielectric Rod Antenna

A new broadband high gain planar dielectric rod SIW antenna is proposed in this paper. SIW transmission line structure is applied to minimize transmission losses and Vivaldi structure is used to achieve a wide bandwidth. The proposed antenna has the high gain property (9–13 dBi) within the wide bandwidth of 73% from 24 GHz to 50 GHz.
Fast extraction of predictive models for integrated circuits using n-performance Pareto fronts

Predictive models based on Pareto fronts are key tools to understand and leverage tradeoffs in electronic circuit and system design. However, their generation conventionally requires the extensive use of numerical simulation and multi-objective optimization methods, resulting in significant computational cost. This cost increases exponentially with the number of parameters, and visualization also becomes an issue as the number of performance metrics increases. In this paper, we present a method to extract predictive models efficiently for electronic subsystems based on Pareto fronts. We use a very fast design and migration software called ID-Xplore™ to generate the performance space of sub-blocks in order to generate Pareto fronts for any block, thereby circumventing the traditional use of numerical optimization and thus accelerating the generation of Pareto-fronts for n-performance. We also combine the Pareto fronts in order to obtain one single Pareto front that represents all specifications, and use the Hyper-Space Diagonal Counting (HSDC) methodology to visualize n-performance Pareto fronts and combine the overall approach to help the designer in the final choice of optimal design points in the design of a state of the art OTA.
Reducing Ambulances Arrival Time to Patients

Minimizing access time for patients in the case of accidents and/or cure needs is a very important issue related to saving lives. It is very important for ambulances to reach places of accidents and/or medical requests in a minimized time. Several solutions were proposed to handle this issue. With the prevalence of GPS system, it is possible to develop mobile applications to direct ambulance drivers to reach patients' places in a minimized period of time. However, it is possible to minimize this time more and more when people with emergency cases are able to drive to a point where they can reach ambulances moving ahead towards them. In this work, we developed a mobile-based application that enables patients to request ambulances after searching for the nearest ones depending on the patients GPS locations. Upon a request, the application, and depending on the GPS location of the patient and the nearest ambulance, draws the shortest route for both (on their handheld devices) after the ambulance crew receives the request. The application keeps updating the route while both are moving with their vehicles until they both reach a contact point. The application enables contacting involved hospitals and/or health care centers for curing purposes. A website is also developed for managerial purposes by all application stakeholders. Experimental tests were conducted and promising results were achieved in terms of minimizing patients' access times.
Finger Movement Regression with Myoelectric Signal and Deep Neural Network

Research on electromyographic (EMG) signals is intensively carried out to help disabled people to control prosthetic hands. Neural Networks have been widely used in research on the classification of finger movements using EMG. The study of a classification system generally still works on a limited number of movements, even though the human body, especially fingers, has a nearly unlimited combination of movements to help do daily activities. To overcome this, a proportional control system is needed. In its recent development, research on myoelectric control using EMG devices is still in a laboratory environment. Hence, the results obtained in a clinical setting are often different. However, along with technological developments, the emergence of affordable and wearable commercial EMG devices such as Myo Armband, has encouraged this study to develop control systems of prosthetic fingers using regression. One of many options available is neural networks that have been widely used in various fields. By estimating each joint with a different neural network, the result shows the predicted is fitted to the actual angle with R2 as high as 99%.
Research on the Effect of Pulse Width Modulation Frequency on Radial Force and Vibration of Switched Reluctance Motor

The large vibration and noise of the switched reluctance motor (SRM) restrict its wide application in various industries. Compared with the current chopper control, the pulse width modulation (PWM) control will introduce high frequency harmonics with fixed frequency in the excitation force. Thus the high frequency component is introduced into the motor vibration. In this paper, the radial force waveforms of four-phase 8/6 switched reluctance motor at different PWM frequencies are analyzed. The finite element model of SRM vibration is established by using radial force as excitation to study the influence of high frequency harmonic excitation on vibration. The theoretical analysis is verified by experiments. It is concluded that high frequency harmonic excitation has a significant impact on the vibration of SRM. Pulse width modulation technology can be used to make the excitation frequency far away from the natural frequency to reduce the vibration of SRM.
A New Dual-Permanent-Magnet-Excited Motor with Hybrid Stator Configuration for Direct-Drive Applications

This paper proposes a new hybrid-stator dualpermanent-magnet-excited (HS-DPME) motor for direct-drive applications. Compared with the existing straight-tooth-stator dual-permanent-magnet-excited (STS-DPME) motors, its notable structural features are that the stator employs a socalled hybrid stator, by combining a straight-tooth stator and a split-tooth stator; stator permanent magnets (PMs) with unified direction of magnetization are surface-mounted under the straight and split teeth, and each two adjacent PMs are either separated by a straight tooth or by a split tooth. The major benefit of the proposed motor is to reduce the consumption of precious PMs, especially the stator PMs. Nevertheless, simulation results obtained by finite element method (FEM) show that performances of the proposed motor can be comparable to that of the existing STS-DPME motors, including cogging torque, back-EMF, torque capability, torque ripple and speed-extension capability. Moreover, working principle of the proposed motor and influence of slot-pole combination are also discussed in this paper.
Online monitoring of IGBT junction temperature based on Vce measurement

In this paper, an online junction temperature monitoring method based on the on-state voltage under high collector current density measurements for IGBT power modules is proposed. Unlike the conventional junction temperature monitoring method, the presented method can extract the junction temperature during operation without altering the modulation strategy or topology of the converter. The proposed method is verified by simulations and the JEDEC-51 standard recommended approach. To accurately extract the on-state voltage during operation, a measurement circuit that combines the advantages of both the active MOSFET clamp and the diode clamp is designed and tested. It has been shown to have good accuracy and a rapid response time. After the calibration results are obtained, the presented method is applied to a three-phase voltage source converter controlled by a closed-loop SVPWM modulation strategy.
SpotFake: A Multi-modal Framework for Fake News Detection

A rapid growth in the amount of fake news on social media is a very serious concern in our society. It is usually created by manipulating images, text, audio, and videos. This indicates that there is a need of multimodal system for fake news detection. Though, there are multimodal fake news detection systems but they tend to solve the problem of fake news by considering an additional sub-task like event discriminator and finding correlations across the modalities. The results of fake news detection are heavily dependent on the subtask and in absence of subtask training, the performance of fake news detection degrade by 10% on an average. To solve this issue, we introduce SpotFake-a multi-modal framework for fake news detection. Our proposed solution detects fake news without taking into account any other subtasks. It exploits both the textual and visual features of an article. Specifically, we made use of language models (like BERT) to learn text features, and image features are learned from VGG-19 pre-trained on ImageNet dataset. All the experiments are performed on two publicly available datasets, i.e., Twitter and Weibo. The proposed model performs better than the current state-of-the-art on Twitter and Weibo datasets by 3.27% and 6.83%, respectively.