Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantized Synchronization of Master-Slave Systems under Event-Triggered Control against DoS Attacks

Quantized synchronization of master-slave neural networks with an event-triggered scheme under denial-of-service (DoS) attacks is studied in this paper. A kind of DoS attacks with constraints on attack frequency and duration is considered. Firstly, a quantized output feedback control scheme based on event-triggered strategy is proposed. Then a sufficient condition for secure event-triggered feedback control based on quantized output measurements is derived, from which the attack frequency and duration that the system can render is given. It is shown that the slave system is able to synchronize with the master system meanwhile the Zeno behavior can be excluded. The controller design is also included. Finally, an example is given to verify the theoretical results.
On Path Following Evaluation for a Tethered Climbing Robot

Over the last years, there is a growing need for climbing robots performing autonomous inspection tasks of large-scale infrastructure, to reduce inspection time and the overall operation costs. Thickness measurement, visual inspection, fault detection, etc. are a few examples of inspection and maintenance applications that could be performed autonomously by robotic platforms like climbing robots. One of the main challenges of inspecting large infrastructures, is the problem of path planning, as the path should be optimal to reduce the inspection time, incorporate sensor properties, and account for important robot requirements such as power supply cabling. This article proposes a novel path planner targeting inspection tasks, where the restrictions posed by the cabling on a Vortex Robot (VR), the attached sensor, and the properties of the scanned surfaces are taken into consideration. The presented framework is successfully evaluated in multiple closed-loop experiments, under different surface inclinations and VR orientations to demonstrate the efficacy of the path planning and control scheme.
Control and Stabilization of Centimeter Scale Glow Discharge in Ambient Air Using Pulse-Width Modulation

Re-pulsing glow discharges in atmospheric-pressure air were experimentally investigated using a push–pull generator with pulse-width modulation. Discharges were ignited as a spark-glow discharge sequence and sustained in a glow regime after ignition in a gap of 2 cm and characterized by current and voltage measurements, as well as by optical emission spectroscopy. It was found that at a certain range of parameters, the discharge could be stabilized even in the presence of external airflow. It was demonstrated that the type of discharge and total power dissipated in the plasma volume could be precisely controlled by pulse-width modulation. Additionally, it was confirmed that the rotational temperature varied across a wide range (1640–2440 K) by using pulse-width modulation with gas-flow control, when vibrational temperature was around 4610 ± 770 K. The generation of stable glow discharge in the presence of gas flow with a wide range of parameters that could be precisely controlled by pulse-width modulation looks promising for use in energy-efficient gas conversion.
Swarm Intelligence Aided Parameter Design for the Symmetrical CLLC-Type DAB Converter with Robust Voltage Conversion Gain

The symmetrical CLLC-type DAB converter has become more popular as a DC transformer (DCT) in the DC microgrid for its galvanic isolation, high power efficiency and high power density. As a DCT, the CLLC-type DAB converter is expected to regulate the DC voltages on the bus and keep a robust voltage conversion gain (VCG). However, because of the practical values of the inductors and capacitors may fluctuate with varying temperature and operating power, VCG of the CLLC-type DAB converter may deviate from the designed one, negatively affecting power supplies. Therefore, this paper proposes a swarm intelligence aided parameter design approach for the CLLC-type DAB converter to ensure its robust VCG against fluctuating inductors and capacitors. In this design approach, the particle swarm optimization (PSO) algorithm is adopted to facilitate design process with high accuracy and computational speed. Finally, the effectiveness of the proposed design approach has been verified with experiments.
Sensitivity to Parameter Mismatch in a Bi-Subspace Predictive Current Control Strategy for Six-Phase PMSM Drives

The application of finite control set model predictive control (FCS-MPC) to multiphase electric drives has been widely studied in recent years due to its enhanced transient performance, simple structure, and low tuning requirements. However, the selection of the optimal control actuation by FCS-MPC strategies relies on the accuracy of the system model, which depends on the knowledge of the parameters of the machine. Moreover, depending on the operating conditions of the electric drive, the parameters of the machine also change during operation due to thermal and saturation effects. Hence, this paper studies the sensitivity of parameter mismatch in the bi-subspace predictive current control strategy based on virtual vectors (BSVV-PCC) applied to a six-phase permanent magnet synchronous machine (PMSM) drive. Several simulation results are provided to analyze the impact on the performance of the drive caused by a variation of the machine parameters used in the prediction model of the control strategy.
Short-Term Electric Load Forecasting Based on Data-Driven Deep Learning Techniques

Accurate Short-Term Load Forecasting (STLF) has been considered a topic of extreme importance for efficient energy management, reliable energy transactions, and economic operation dispatch in smart grids. However, the continuous instability of the load demand essentially due to the high volatility of weather conditions and customers’ demand behavior dramatically affects the STLF accuracy. In order to overcome this problem, five effective Deep Learning (DL) techniques are proposed for multivariate time series STLF based on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and stacked Auto-Encoder (AE). These DL based techniques are consolidated to build stacked Bidirectional GRU (BiGRU), Convolutional LSTM (ConvLSTM), stacked Bidirectional LSTM-AE (BiLSTM-AE), hybrid CNN-LSTM-AE (CNN-LSTM), and LSTM-AE (LSTM-AE) techniques. Simulation studies are conducted to demonstrate the performance superiority of BiLSTM-AE compared to the other DL models. The main contributions of this paper include 1) integrating a variety of deep neural networks for STLF; 2) employing time series as a benchmark to compare between heterogeneous DL architectures; 3) conducting the analyses on real data set.
Smart Prison - Video Analysis for Human Action Detection

Prisons or correctional facilities require a high amount of manpower for maintaining safety whilst ensuring service quality. However, high turn-over rate (5% per year) commonly seen in these institutions often brings operational challenges, which may entail risk to safety. Prisoner’s abnormal behaviors, such as self-harming and fighting, are the major concerns posing the highest safety hazards to prisoners and front-end staff. Traditional human-dependent method used for monitoring prisoners’ abnormal behaviors is labor-intensive, and may give rise to problem such as misdetection. A Smart Prison system has been developed to assist front-end staff in detecting prisoners’ abnormal behaviors. Results shows that over 95% of the abnormal behaviors can be detected by the system. This supporting system can lower the operational pressure resulted from shortage of manpower, and reduce the rate of abnormal behavior misdetection. This will improve the safety of both front-end staff and prisoners.
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images

Neural saturation in Deep Neural Networks (DNNs) has been studied extensively, but remains relatively unexplored in Convolutional Neural Networks (CNNs). Understanding and alleviating the effects of convolutional kernel saturation is critical for enhancing CNN models classification accuracies. In this paper, we analyze the effect of convolutional kernel saturation in CNNs and propose a simple data augmentation technique to mitigate saturation and increase classification accuracy, by supplementing negative images to the training dataset.We hypothesize that greater semantic feature information can be extracted using negative images since they have the same structural information as standard images but differ in their data representations. Varied data representations decrease the probability of kernel saturation and thus increase the effectiveness of kernel weight updates. The two datasets selected to evaluate our hypothesis were CIFAR-10 and STL-10 as they have similar image classes but differ in image resolutions thus making for a better understanding of the saturation phenomenon. MNIST dataset was used to highlight the ineffectiveness of the technique for linearly separable data. The ResNet CNN architecture was chosen since the skip connections in the network ensure the most important features contributing the most to classification accuracy are retained. Our results show that CNNs are indeed susceptible to convolutional kernel saturation and that supplementing negative images to the training dataset can offer a statistically significant increase in classification accuracies when compared against models trained on the original datasets. Our results present accuracy increases of 6.98% and 3.16% on the STL-10 and CIFAR-10 datasets respectively.
Class E Active Rectifier with Controlled Output Voltage for MHz Wireless Power Transfer

Wireless power transfer (WPT) operating at megahertz (MHz) frequency, i.e., MHz WPT, is favorable for its compact and light circuit component and higher special freedom (longer transfer distance). This paper proposes a Class E active rectifier for MHz WPT applications. The active rectifier is consist of a ac inductor (the receiving coil), a resonant capacitor, an active switch (MOSFET) and a filter capacitor. The duty cycle of the switch can be controlled to achieve output voltage regulation. Voltage and current waveforms of the active rectifier as well as the relationship between output voltage and switch duty cycle are derived. Proposed derivation and analysis are well verified in the simulation platform and real experiments. The proposed active rectifier is favorable for its concise circuit topology and can be widely used in the consumer electronic devices applications.
A V2G Integrated Battery Charger Based on an Open End Winding Multilevel Configuration

A new approach to obtain an integrated battery charger is described in this paper, based on the Asymmetrical Hybrid Multilevel Converter topology. Such a particular open-end winding motor configuration, which has proved to be more efficient than conventional inverter topologies in EV motor drive applications, can be turned in an on-board battery charger only by acting on the control system. Thus, no circuit reconfiguration through electro-mechanical switches is required. Moreover, by introducing a single extra power switch, a bilateral power flow can be managed enabling vehicle to grid operations. The obtained integrated battery charger can be supplied either by a standard ac single-phase grid, either by a dc power source for direct connection to domestic energy resources. The proposed approach enables a new remarkable function to the asymmetrical hybrid multilevel converter at a marginal extra cost, thus mitigating the larger complexity and cost of such an inverter if compared with conventional topologies.
Model predictive control with PWA models

This article presents on controlling a fishmeal dryer. A model-based predictive controller was designed to control the amount of moisture present in fishmeal. The Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC) approach has been used. This approach uses a non-linear model in its implementation. In this work we study the use of piecewise affine (PWA) models to approximate the non-linear model. The use of PWA models reduces calculation time but worsens the performance of the controller.
An Early Termination Technique of Polar Codes for IR-HARQ Scheme

In this work, we focus on polar codes in mobile communication systems where a retransmission strategy, namely, the incremental redundancy hybrid automatic repeat request (IR-HARQ) scheme, is performed. We propose an early termination (ET) of polar codes using the interleaved cyclic redundancy check (CRC) codes and the parity check (PC) codes. The parity-check equations of CRC and PC codes are used to detect the incorrectly decoded bits during the polar decoding. The simulation results verify that the proposed ET techniques can detect the erroneous bits in initial transmission and retransmission. The proposed technique provides the ET rate about 4-90%, and the block error rate (BLER) performance degradation is less than 2 dB.
Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model

In the financial world, the forecasting of stock price gains significant attraction. For the growth of shareholders in a company’s stock, stock price prediction has a great consideration to increase the interest of speculators for investing money to the company. The successful prediction of a stock’s future cost could return noteworthy benefit. Different types of approaches are taken in forecasting stock trend in the previous years. In this research, a new stock price prediction framework is proposed utilizing two popular models; Recurrent Neural Network (RNN) model i.e. Long Short Term Memory (LSTM) model, and Bi-Directional Long Short Term Memory (BI-LSTM) model. From the simulation results, it can be noted that using these RNN models i.e. LSTM, and BI-LSTM with proper hyper-parameter tuning, our proposed scheme can forecast future stock trend with high accuracy. The RMSE for both LSTM and BI-LSTM model was measured by varying the number of epochs, hidden layers, dense layers, and different units used in hidden layers to find a better model that can be used to forecast future stock prices precisely. The assessments are conducted by utilizing a freely accessible dataset for stock markets having open, high, low, and closing prices.
qULM-DL: Quantitative Ultrasound Localization Microscopy via Deep Learning

Ultrasound localization microscopy (ULM) has been developed in recent years to significantly improve the spatial resolution of ultrasound imaging by localizing the microbubbles (MBs) flowing in microvasculature. Nevertheless, challenges remain in ULM. In our previous work (IEEE Trans. Med. Imaging, 10.1109/TMI.2020.2986781), by using a modified sub-pixel convolutional neural network (CNN), we have implemented ULM with fast data-processing speed, high imaging accuracy, short data-acquisition time, and high flexibility characteristics. However, the signal intensities of MBs are not quantitatively recovered, as similar to many other ULM techniques. To overcome this limitation, in this work, we propose a new deep-learning (DL) method, termed as qULM-DL, to recover the true distributions (positions and intensities) of the MBs in ULM. A series of numerical simulations and in vivo experiment are performed to evaluate the performance of qULM-DL. The results show that qULM-DL can not only super-resolve the complex structures but also quantitatively recover MBs intensities in these structures.
A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

The rapid growth of Internet users led to unwanted cyber issues, including cyberbullying, hate speech, and many more. This article deals with the problems of hate speech on Twitter. Hate speech appears to be an inflammatory kind of interaction process that uses misconceptions to express a hate ideology. The hate speech focuses on various protected aspects, including gender, religion, race, and disability. Owing to hate speech, sometimes unwanted crimes are going to happen as someone or a group of people get disheartened. Hence, it is essential to monitor user’s posts and filter the hate speech related post before it is spread. However, Twitter receives more than six hundred tweets per second and about 500 million tweets per day. Manually filtering any information from such a huge incoming traffic is almost impossible. Concerning to this aspect, an automated system is developed using the Deep Convolutional Neural Network (DCNN). The proposed DCNN model utilises the tweet text with GloVe embedding vector to capture the tweets’ semantics with the help of convolution operation and achieved the precision, recall and F1-score value as 0.97, 0.88, 0.92 respectively for the best case and outperformed the existing models.
A Survey on Multi-Objective Harmony Search-Based Clustering and Characteristics in WSN

Advancements in Wireless Sensor Network (WSN) technology has allowed lightweight, inexpensive sensor nodes to track diverse physical and environmental environments, data, and so forth. Wireless correspondence, and delivery. The clustering of WSNs has proved to be an efficient method for enhancing data processing and scalability for broad WSNs. Clustering often retains the sensors’ minimal energy supplies. The aim of the paper to present the concept of WSN and their Characteristics and discuss the concept of the cluster in a wireless sensor network.
Modeling the Timing and Duration of Grip Behavior to Express Emotions for a Social Robot

This letter addresses the effects of grip timing to express a robot's emotions to people while they are watching a video stimulus together. Past studies on human-robot touch interaction focused on the types of touch behaviors for expressing a robot's emotion, but timing factors have received less attention. In this letter, we conducted data collection to investigate the appropriate grip timing to express heartwarming and horror emotions. Participants identified touch (grip) timing and durations by using a robot while they are watching video stimuli. Typically, they preferred a grip timing before a climax for horror videos but after a climax for heartwarming videos. We modeled the timing and durations by a fitting approach to probabilistic distribution to reproduce human-like touch behaviors, and also we implemented the models to an android robot.
Feasibility Study on the Use of Heart Rate Variability Parameters for Detection of Atrial Fibrillation with Machine Learning Techniques

The paper is devoted to development and studies on atrial fibrillation (AFib) detection in electrocardiogram (ECG) using digital signal processing (DSP) and machine learning (ML). The goal of this pilot study was to find the DSP and ML methods suitable for the AF detection in real-time in short single-lead ECGs containing 32 consecutive cardiac cycles. Three simple Heart Rate Variability (HRV) parameters from the time domain analysis were calculated and used as features for ML algorithms. Binary decision tree and shallow neural network were used for classification, and the impact of metaparameters on the performance of the AFib detection algorithms was investigated to determine the lower limit of their required complexity. In the neural network, different numbers of hidden neurons and different activation functions were examined. In the decision tree, different limits on the maximum number of splits were set. For both AFib detection algorithms, various sets of HRV-based features were tested. With neural network (two features, ten hidden neurons), 98.3% accuracy, 97.1% sensitivity and 99.1% specificity were obtained. With decision tree (two features, seven splits), 96.9% accuracy, 96.3% sensitivity and 97.4% specificity were reached. This study shows the usefulness of neural network and decision tree algorithms for the detection of atrial fibrillation using the simplest HRV parameters. The use of more complex HRV parameters in AFib detection with the proposed ML algorithms requires further investigation.
Lung ultrasound for point-of-care COVID-19 pneumonia stratification: computer-aided diagnostics in a smartphone. First experiences classifying semiology from public datasets

Lung ultrasound (LUS) has demonstrated potential in managing pneumonia patients, and is actively used at the point-of-care in COVID-19 patient stratification. However, image interpretation is presently both time-consuming and operator-dependent. We explore computer-aided diagnostics of pneumonia semiology based on light-weight neural networks (MobileNets). For proof-of-concept, multi-task learning is performed from online available COVID-19 datasets, for which semiology (overall abnormality, B-lines, consolidations and pleural thickening) is annotated by two radiologists. Initial results suggest that individual indications can be classified with good performance in a smartphone. Neural networks may also help to reduce inter-reader variability and objectivize LUS interpretation, especially for early-stage pathological indications.
Multifractal Detrended Fluctuation Analysis of Congestive Heart Failure Disease Based on Constructed Heartbeat Sequence

Heart rate variability (HRV) can be used as a common detection method for congestive heart failure (CHF). Existing researches regarding HRV, including both linear indicators and nonlinear characteristics, are mostly based on the <inline-formula> <tex-math notation="LaTeX">$RR$ </tex-math></inline-formula> intervals of the ECG signal. This article proposed a sequence that can reflect the regulation of sympathetic and parasympathetic nerve on heart rate, and on this basis, conducted multifractal detrended fluctuation analysis (MFDFA). We extracted multifractal features to quantitatively compare the complexity of proposed sequence between the healthy and CHF groups. Results showed that abnormal physiological and pathological conditions due to the weakening of autonomic nerve control can reduce the complexity of the heartbeat signal. Estimate the separation performance of all features, the best discrimination is obtained for the area under the mass index spectrum <inline-formula> <tex-math notation="LaTeX">$S1_\tau $ </tex-math></inline-formula> as providing 100% accuracy in separating the Healthy Young and CHF groups, and 90.93% separation accuracy between the Healthy Elderly and CHF groups. This work provide a good basis for the diagnosis of CHF with a novel perspective.
Energy-Efficient Near-Threshold Standard Cell Library for IoT Applications

In this paper, a low-energy minimum-area CMOS standard cell library suitable for IoT applications is proposed. Energy consumption reduction is achieved by operating the library in Near-Threshold Voltage (NTV) region, and by designing layout of cells at the minimum possible area for the used technology process. Body biasing technique is proposed to boost pMOS performance. Operating voltage and transistor sizing are also selected to achieve the minimum energy consumption while operating at the frequency range of 1MHz to 20MHz which is suitable for IoT applications. The proposed library was designed and characterized in UMC 130 nm CMOS technology process. The library was modeled to be used in synthesis tools. To prove the benefit for IoT applications, the library was benchmarked by implementing 3 cryptographic algorithms: ASCON, AEGIS-128, and AEZ. Synthesis results are showing that the three cores can operate at 18 MHz, 14 MHz, and 16 MHz respectively, while consuming 0.466 pJ, 3.006 pJ, and 5.064 pJ.