Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive tracking control of the quadrotor with state constraints and bounded disturbances

For quadrotor system with state constraints, unknown mass and bounded disturbances, the problem of tracking control is considered in this paper. To overcome the underactuated, the system is first divided into position loop and attitude loop by introducing internal variables. Barrier Lyapunov Functions are introduced to maintain state constraints. Different from usual controller design, a tuning adaptive state feedback tracking controller in vector from is skillfully designed such that tracking error can be made small enough, while keeping all signals in closed-loop system bounded. The simulation results demonstrate the effectiveness of the control scheme.
Development of Double Closed Loop Control based on Explicit Model Predictive Control for PMSM

This work deals with the development of double closed loop control based on explicit model predictive control (MPC) for permanent magnet synchronous motor (PMSM) system with constraints. The aim of the proposed controller is to solve the constrained optimization problem for fast-sampling time PMSM system and improve the robustness of the system. The recreated mathematical model of PMSM is introduced at first. Then the proposed explicit MPC controller is designed based on the reformed model. The explicit controller is employed to control the mechanical speed and the q-axis current, and the proportional-integral (PI) control is employed to control d-axis current. The simulation results demonstrate that the proposed double closed loop control method is effective when the working condition of PMSM changes and the performance of the speed and current is satisfactory.
GaS-PBFT: A Game-based Node Selection Consensus Mechanism for Internet of Things

Future communication systems are trending to embrace an open and collaborative ecology for a rising number of edge services and applications, enabling the evolutions of multiple wireless ecosystems such as the Internet of things (IoT). The collaborations among multi-party IoT users, devices, and infrastructure require further designs in terms of security, trust, and efficiency. Blockchain is considered a promising solution to facilitate trusted multi-party collaborations, enhance security, and protect privacy in the IoT. However, the research on IoT-friendly blockchains is still facing a number of challenges due to the heterogeneity and limited capabilities of IoT devices. In this paper, we propose a hierarchical blockchain consensus combining practical Byzantine fault tolerance and a game-based node selection mechanism (GaS-PBFT). GaS-PBFT enables a logical two-layer consensus network structure, groups heterogeneous IoT participants into multiple collaborating consensus groups, and imposes an efficient and fair game on each group for selecting block generators of the IoT blockchains. Finally, we conduct comprehensive simulations to show the performance of the proposed GaS-Pbftconsensus in terms of consensus latency, transaction speed, node capacity, and security.
Unleashing Parallelism in Elastic Circuits with Faster Token Delivery

High-level synthesis (HLS) is the process of automatically generating circuits out of high-level language descriptions. Previous research has shown that dynamically scheduled HLS through elastic circuit generation is successful at exploiting parallelism in some important use-cases. Nevertheless, the literal conversion of a standard compiler's control-data flow graph into elastic circuits often produces circuits with notable resource demands and inferior performance. In this work, we present a methodology for generating more area- and timing-efficient elastic circuits. We show that our strategy results in significant area and timing improvements compared to previous circuit generation strategies.
ED.Ai: A Ballistic Simulator for Cardiac and Respiratory Micro-vibration Testing

Ballistocardiography (BCG) is a non-invasive tech-nology used to record ultra-low-frequency vibrations of the heart, allowing for the measurement of cardiac and respiratory cycle events, including timing and amplitudes of contraction. Recent developments in BCG have made this technology simple to use, as well as time and cost-efficient compared with other more complicated and invasive techniques used to evaluate cardiac performance. The BCG -based Dozee device can contactlessly monitor Heart Rate (HR) and Respiratory Rate (RR) using a sensor sheet placed under the mattress. While most sensor based devices in medicine have a simulated test bed for validation of HR and RR, there are no such standard simulators for BCG signals. Hence, we developed a heart and lung simulator and its accuracy was tested with an FDA-approved BCG -based device, the EarlySense 2.0 System. The HR and RR claims for the Dozee VS device were then validated using the simulator, especially for extreme cases. This paper aims to present the validation of a novel micro-vibration simulation setup to validate micro-vibration based HR and RR monitoring systems.
On the Congruences of the Tangents to a Surface

Inthispaper,forthecongruencesgeneratedbythetangentstothelinesof main curvature on a surface, necessary and su⁄cient conditions that the straight lines of these congruences are preserving the lines of curvature on their focal surfaces are derived and explained. Finally, two rectilinear congruences are introduced and investigated.
ReFleX: Enabling Full Duplex Relay Cluster

Empowered by recent advances in in-band full duplex technique, it has been shown that full duplex capable relays are possible. Full duplex relays have been employed in many recent works to enlarge the communication range of WiFi system. However, most of these designs can only utilize one relay due to the potential inter-relay interference when multiple relays coexist. While there are recently proposed systems that can cope with the interference, these designs can hardly work with existing systems due to the large extra delay spread introduced. In this paper, we present ReFleX, a system that can employ multiple full duplex relays at the same time, while introducing only the same amount of extra delay as a one-relay system. To design such a system, we propose a novel inter-relay interference cancellation strategy, a practical channel estimation procedure, and an effective power amplification setting. To evaluate the real-world performance of ReFleX, we build a prototype and conduct experiments using software defined radios. We show that ReFleX can enlarge wireless coverage, provide link diversity, and achieve efficient multicasting compared with half duplex relays or single full duplex relay systems.
Controlled Consensus of Networked Multi-Agent Systems with Different Agent Dynamics

This paper focuses on the controlled consensus problem of Networked Multi-Agent Systems (NMASs) with heterogeneous agent dynamics. The bounded and exact consensus criteria is obtained based on the method of the linear and nonlinear negative feedback respectively. The proposed linear and nonlinear negative controllers guarantee the NMASs achieve exact consensus instead of in terms of boundedness. The effectiveness of the main results are verified by means of the Lyapunov stability method and numerical simulation. The paper generalizes the related results for the cases of identical agent dynamics to the non-identical ones, and many existing results can be viewed as the special cases.
Comparative Study of CNN Models on the Classification of Dyslexic Handwriting

Developmental Dyslexia, one of the learning disabilities is a topic of scientific interest in a variety of disciplines such as psychology, speech and language therapy, data science, etc. While the reason for Dyslexia and its symptoms are still being researched by psychologists, data science is providing ways to intervene and detect them with the aid of technological advancements. Dyslexia is a neurological condition that impairs reading comprehension and has long-lasting impacts. But timely detection and intervention programs can alleviate its effects to a certain extent. This study aims to classify images of handwritten English characters into three classes namely: normal, corrected, and reversed, where normal class refers to normal handwriting, and corrected or reversed constitutes handwriting of children with Dyslexia. The dataset used for the study is available publicly on Kaggle. The building of an efficient CNN (Convolutional Neural Network) model for classifying dyslexic handwriting is the major emphasis of this work. This is accomplished by comparing several CNN models and evaluating how well they detect Dyslexia on the same dataset. The proposed CNN approach has demonstrated a sizable improvement in reliably classifying dyslexic handwritten images.
Decision Support System Terms of SME’s Credit Lending Based on Machine Learning Approach

Small and medium-sized (SMEs) companies have proven economic competencies and great potential in light of the recent global economic transformations in developed and developing countries, which qualified them to play a major role in development and reform policy, by providing job opportunities, intensifying the industrial fabric, and supporting large industries. It should be noted that these institutions need greater efforts and important components that respond to their aspirations and idiosyncrasies, to be able to survive and compete in the markets, as we have witnessed the bankruptcy and collapse of many companies due to financial conditions and obstacles. In this paper, we proposed building a lending decision support system (DSS) for SMEs and researching the reasons that prevent them from lending, and what are the pillars on which the credit decision in banks depends, and how difficult it is to analyze the variables and their information, the data set represents SMEs lending requests from Jordan Ahli Bank. Four machine learning algorithms were used to build an automated system to support decision-making in granting credit. The classification accuracy of the models was evaluated where the accuracy exceeded 90%, and SVM outperformed other rating models. Our findings show that the challenges that SMEs face to obtain credit are summarized by the lack of the company's capital, the recent date of incorporation, and the lack of guarantees.
U-Net Based Covid-19 Infected Lesion Detection and Deep Learning Based Classification on CXR

The Covid-19 disease, which emerged in China in December 2019 and caused by the coronavirus virus, soon became a pandemic all over the world. The fact that the Transcription Polymerase Chain Reaction (RT-PCR) test produces false negatives in some studies and the diagnosis time is long, has led to the search for new alternatives for the diagnosis of this virus, which can result in death, especially with the damage it causes to the lungs. Therefore, chest images have become suitable tools for diagnosis from chest images with data obtained from Computed Tomography or CXR imaging techniques. Deep learning studies have been proposed to provide diagnosis with these tools and to determine the infected region of Covid-19 and Pneumonia disease. In this paper, a two-stage system is proposed as segmentation and classification. In the segmentation process, infected regions segmented from the labeled data were determined. In the classifier stage, Covid- 19/Pneumonia/Normal classification was performed using three different deep learning models named VGG16, ResNet50 and InceptionV3. To the best of our knowledge, this is the first attempt to sequentially design classification and segmentation systems into a more precise diagnosis. As a result of the study, 95% segmentation accuracy was obtained. Classifier models achieved 99%, 90% and 98% accuracy, respectively.
Machine Learning Based Traffic Prediction System in Green Cellular Networks

A network may be able to improve its capacity for planning and the quality of service, if it is possible to accurately estimate the volume of mobile phone traffic in advance it provides to customers. Even though there are currently an excessive number of mobile users connected to the network, the major objective is to enhance the network's quality and performance. It takes the idea of improving network performance and minimizes the amount of energy consumed, which can then be utilized to simplify and make consumers' lives easier by serving their needs as accurately as possible. This is done to alleviate the substantial traffic load that its use may cause on a cellular network. The energy consumption of the world's most powerful mobile networks is in the vicinity of gigawatts per year, making it one of the world's highest. In order to improve the service quality of a network, it is necessary to construct accurate and trustworthy time-series models for estimating the volume of cellular mobile traffic. This research project will permit the creation of a model that will aid in the intelligent prediction of cellular network load traffic. To counteract the enormous traffic load that a cellular network is now experiencing, the Quality of service should be simplified and improved. This will facilitate meeting user needs as exactly as possible. It is determined where the users are, what the signal-to-interference-and-noise ratio is, and what the users' quality of service expectations are, and the transmit power is then adjusted to the least amount necessary to meet those needs. There will never be a day when all base stations are completely in switched off state. Adjustments are made to the transmit power to ensure it is set at the lowest possible level, taking into consideration the amount of signal-to-noise ratio and the users' quality of service requirements.
Design-Driven Innovation Role in Customers’ Behavioral Intention Towards Businesses: A Perspective from the Foodservice Industry in Bahrain

The primary purpose of this paper is to explore the relationship between design-driven innovation (DDI) combining interior design (ID), brand design (BD), packaging design (PD), and marketing content design (MCD) on customers’ behavioral intention (CBI) toward restaurants within the food-service industry in the Kingdom of Bahrain. This paper uses a quantitative method using a structured questionnaire targeting restaurant customers in Bahrain. Four hundred respondents participated in the survey. The results revealed a strong positive and significant relationship between DDI and CBI; when design tools drive innovation, the CBI will increase by 101.7%. It is also evident that ID increases CBI by 57.2%, followed by PD with a percentage of 54.2%, BD with a percentage of 52.3%, and MCD by 50%.
Lightweight Neural Networks for Automatic Classification of ECG Signals

Currently, most existing models for the automatic classification of electrocardiogram (ECG) signals demand high computation and memory capacity and thus are not applicable for edge communication scenarios with resources constrained devices. In this paper, we propose a lightweight two-dimensional convolutional neural network architecture (Ghost-VGG-16) for the accurate classification of five typical heart rates. Specifically, the proposed model follows the basic network structure of Visual Geometry Group(VGG)-16 and adopts the Ghost Module instead of the traditional convolution layer to generate more feature maps in terms of inexpensive linear operation, thus substantially reducing the parameters and calculations of the network model. To further simplify the preprocessing procedure, ECG signals are initially segmented into heartbeats and each heartbeat is then converted into a two-dimensional grey-scale image as the input data, which avoids noise filtering and feature extraction procedure. Experimental results show that the proposed method achieves an average accuracy of 99.74% at a lower computation and memory cost, which makes it more suitable for edge computing networks with performance-constrained edge devices.
Distributed Conditional Gradient Algorithm for Two-Network Saddle-point Problem

We consider a two-network saddle-point problem with constraints, whose projections are expensive. We propose a projection-free algorithm, which is referred to as Distributed Frank-Wolfe Saddle-Point algorithm (DFWSP), which combines the gradient tracking technique and Frank-Wolfe technique. We prove that the algorithm achieves O(1/k2) convergence rate for strongly-convex-strongly-concave saddle-point problems. We empirically shows that the proposed algorithm has better numerical performance than the distributed projected saddle-point algorithm.
Synthesis of Nonuniform TL Resonators for Bandpass Filters with Single Passband

This article proposes an approach that makes it possible to provide a single bandwidth in bandpass filter (BPF) based on transmission lines (TL) segments. All another (parasitic) bandwidths except for the main are suppressed. This BPF uses two types of resonators, which are interconnected by TL segments of half-wave length. Proposed resonators are included in the BPF as parallel stubs. Some resonators are nonuniform TL resonators with a shorted end, while others are uniform TL resonators with an open end. The effect of suppressing all parasitic bandwidths is achieved by a special mutual placement of resonant and antiresonant frequencies of resonators of two types. The traditional design, which uses couplings between resonators, is not applicable to the considered filters. Therefore, parameters of a single-band filter, which provide the required bandwidth, are determined numerically. In this way, dependences that allow calculating the filter parameters are constructed.
Guaranteed cost robust weighted measurement fusion Kalman predictors for systems with moving average colored measurement noise, missing measurements and uncertain noise variances

This paper is concerned with the guaranteed cost robust weighted measurement fusion (WMF) prediction problem for systems with moving average colored measurement noise, missing measurements and uncertain noise variances. The under considered system can be converted into that only with uncertain noise variances by the application of rewriting moving average model of state space form, augmenting the original state model, and a fictitious noise technique. Then, two classes of guaranteed cost robust WMF Kalman predictors are presented based on the minimax robust estimation principle and parameterization representation of uncertain noise variances. The proposed guaranteed cost predictors can concurrently give maximal lower bound and minimal upper bound of accuracy deviations. The proof of the guaranteed cost robustness is proved by the Lyapunov equation approach. A simulation example shows the correctness and effectiveness of the proposed results.
Process Mining Analysis and Implementation on Customer Complaints Dataset

The UWV (Employee Insurance Agency) is an autonomous administrative authority (ZBO) and is tasked by the Ministry of Social Affairs and Employment (SZW) to implement employee insurance and provide labor market and data services in the Netherlands. A large amount of data is entered during the daily customer complaint interactions. Therefore, a smart approach is needed in analyzing customer complaints with the aim of improving the quality of insurance services at a lower cost. This study focuses on ProM and highlights two main algorithms, namely alpha miner and inductive miner. the conclusion obtained is that the best accuracy for fitness is generated by the inductive miner algorithm with a score of 0.93 compared to the alpha miner score which produces 0.48. However, in the acquisition of precision and generalization scores, the best value was obtained in the alpha miner algorithm with a precision of 0.79 and a generalization of 0.83 compared to the acquisition of an inductive miner with a precision score of 0.60 and a generalization of 0.52
Analysis and Parameter Design of LCL-Based Parallel T-type Inverters

For medium and high power applications, parallel converters are commonly utilized to boost the power capacity of system. Meanwhile, interleaved operation of pulse width modulation (PWM) is a common and effective method to increase the number of output levels and reduce current ripples. Nevertheless, due to asynchronous switching operation, the problem of circulating current inevitably emerges. To suppress circulating current, this paper aims to analyze the principle of circulating current generation and design the parameters of the inductors for suppression. A 100kW grid-connected system with two T-type inverters parallel connected is built, applying phase opposition disposition (POD) modulation for decreasing circulating current. The LCL filter is designed to achieve low harmonics. Simulation validations are provided to prove the feasibility of the analysis and design.
Open Ring Patch Antenna

An open ring patch antenna and two versions of the antenna arrays based on it are proposed. Experimental samples of antennas are designed for a frequency of 5.8 GHz for use in video signal transmission systems for radio-controlled models. Designs of antennas were investigated using automatic design tools and experimentally. It is shown that the gain factor of a single patch antenna can exceed 10 dBi, and the gain factor of a four-element antenna array is 16 dBi.
Practical Coded Sharding

Blockchain technology is finding applications not only in the cryptocurrency world but also in sectors like healthcare, IoT etc. However, before it can replace centralized counterparts like VISA or PayPal, its transaction throughput has to increase multiple orders of magnitude from its current throughput. Sharding is one solution for increasing the trans-action rate but it weakens the underlying security guarantees. Coded sharding uses information-theoretic approaches to scale blockchains without compromising security. Deploying coded sharding at webscale remains a challenge since it incurs bottlenecks like decoding very large Reed -Solomon codes of length of as much as 218. In this work, we propose a new method that leverages verifiable computing as an alternative to costly decoding strategies. We also simulate a simple balance checking application using our approach to check the feasibility of using verifiable computing with coded sharding. As its overheads are lower, we report transaction rates of 2806 tx/sec on commodity hardware. We also report the throughput of coded sharding based on verifiable computing by varying parameters such as the number of shards, users handled by each shard and complexity of verification used.
A novel method for the short-term power load forecasting

Based on the current mainstream time-series model, this paper proposes a network model that can be used for short-term power load forecasting. For time-series prediction, this paper proposes a hybrid network that integrates a Deep Cross & Temporal convolutional network (DCN) and a Temporal convolutional network (TCN), termed as Deep Cross & Temporal convolutional network (DTCN). This model can automatically extract the effective information contained in the data set, and automatically perform feature extraction and feature fusion. The validity of the model is verified by “Australian Electric Load Data”. The comparison of the results of different tests in multiple periods shows that the model can predict accurate power load forecasting results. It has a clear structure, high generalization, and strong data tolerance.