Miracle-dz/UniFAR-FTUs huggingface dataset:
CP-OFDM radar range reconstruction in a chaotic Doppler disturbed scenario

Considering the serious performance degradation of cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) radar range reconstruction caused by chaotic Doppler disturbance, an inter-carrier interference (ICI) cancellation problem is investigated in the scene of multiple unknown moving targets. The ICI cancellation problem is solved by proposing a two-step subcarrier alignment method. Firstly, we construct the signal model and separate the intra-pulse and inter-pulse Doppler modulation term. Then, the two-step orthogonal alignment of the subcarrier method is put forward to against ICI by Fourier analysis on the converted signal model. Later, inter-range-and-carrier (IRCI) free range reconstruction is implemented with the assistance of the CP-OFDM pulse compression method. Finally, numerical results are provided to verify the validity of the proposed algorithm.
Dynamic Heart Disease Prediction using Multi-Machine Learning Techniques

Health Care Field having enormous data, for processing these data we must use any advanced techniques which will be helpful to provide the effective results and making effective decisions on data and getting the appropriate results. Heart disease is the leading problem and one of the biggest causes for no. of deaths happening all over the world. In this paper, an effective Heart Disease Prediction framework is implemented using algorithms in Machine Learning such as Gaussian Naïve Bayes, Random Forest, K-Nearest Neighbour, Support Vector Machine, Xg-Boost. The framework uses 13 features such as age, gender, blood pressure, cholesterol, obesity, cp, etc. It is a user-friendly system where we are having some phases.In the first phase, we upload the dataset file and select the algorithm to perform on the selected dataset. Then the accuracy is predicted for each selected algorithm along with a graph, and the modal is generated for the one having highest frequency by training the dataset to it. In the next phase, input for each parameter of the heart is given and based on that modal generated, the diseased stage of the heart gets predicted. We then take the precautions based on the condition of the patient. Our strategy is effective in foreseeing the heart illness of a victim. The Heart Disease Prediction Framework progressed in this view is a one of a unique methodology that might be utilized inside the class of heart disease.
AI-Powered Honeypots for Enhanced IoT Botnet Detection

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users’ IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.
Realtime Detection Of Network Anomalies Using Neural Network

An Intrusion Detection System is model which monitors the network security from various type of Attacks. Intrusion Detection plays an important role in order to provide Network Security. In this paper we implement an Intrusion Detection System by building a Deep Learning Model using Feed Forward Neural Network (FFNN) and Long Short Term Memory Neural Network(LSTM). The study of model is based on Binary Classification and Multiclass Classification. The Model is implemented on Realtime Datasets or Dynamic Datasets. There is an comparative study between Feed Forward Neural Network and Long Short Term Memory Neural Network. The Intrusion Detection System(IDS) model improves the acccuracy and enlarge the further implementation for an Intrusion Detection Systems.
Analyzing the Evolutionary Characteristics of the Cluster of COVID-19 under Anti-contagion Policies

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.
Two-component optical vortices at zero group velocity dispersion

By means of numerical simulation, we study the formation and propagation of vortex solitons in a quadratic nonlinear medium with competing diffraction, group velocity dispersion close to zero (GVD) and third-order dispersion in the presence of the waveguide structure. We assume that balance between nonlinearity, inhomogenity, diffraction, and third-order dispersion near zero GVD can support vortex soliton.
A Multi-Task Algorithm for Autonomous Underwater Vehicles 3D path planning

When considering multiple Autonomous Underwater Vehicles (AUV) path planning simultaneously, problems can be seen as multi-task optimization (MTO) problems. The Multifactorial evolutionary algorithm (MFEA) has many obvious advantages in handling multi-task optimization, but it is rarely used in three-dimensional (3D) path planning. Therefore, using the individual gradient based on MFEA (MFEA-IG), this paper solves the global path planning problem of AUV in the 3D environment. We construct a 3D space model, effectively reducing the computational complexity from 3D to two-dimensional (2D). Besides, we also consider the impact of ocean currents on path planning. In this paper, both MFEA-IG and MFEA are used for path planning, and time cost is selected as their optimization target. Experimental results show that the two algorithms can be effectively used for AUV 3D path planning, and MFEA-IG has a better result than MFEA.
A Mixed Integer Programming Approach to Air Cargo Load Planning with Multiple Aircraft Configurations and Dangerous Goods

Air cargo business is highly uncertain and dynamic. An airline needs to manage its air cargo loading operations with efficient cargo consolidation, accurate load planning, high level of safety precautions, limited cargo planning period and careful yield management in order to stay competitive and, in extreme situations, survive. To achieve these requirements for air freighters, a mathematical model and optimisation tool is developed to assist its air cargo load planning for the airline which handles about 327,500 tons of cargoes each year. A small improvement in its load planning, in terms of the best allocation and position of unit load devices to be stowed in an aircraft, can contribute substantially to its operational and financial performance. This study, applying in an cargo airline, aims to maximise cargo loading profit as well as improve the operation efficiency under the constraints of aircraft configuration, dangerous goods segregation, weight and balance, and flight safety. In future, the model can cater not only the cargo load planning of freighters but also passenger aircraft to improve the operational performance by considering over-sized cargoes.
Bringing Energy Information to the Instruction Set

In the last decades, many advances have been made in clock frequencies and silicon integration. With the increase in energy consumption, energy-aware computing has become a topic of great interest to the community. A wide variety of works focus on software techniques to reduce energy consumption when a device's battery is running out. Even then, by not knowing how much energy is available on a device 1) some routines can start and do not finish due to an energy outage, 2) non-critical routines can be called even when the device's battery is running out, and 3) critical and energy-intensive routines can quickly drain the rest of the device's battery, even though these routines may have an alternative implementation (more simplified or less accurate). The current way of collecting energy information is hardware and Operating System dependant and may take too much time. This work proposes a new branch instruction to the RISC- V instruction set architecture, called Branch if Energy Low (BEL). Using a single instruction, compilers/software developers can evaluate if the available energy is bellow a threshold and branch to routines with less energy consumption. BEL can be used on intermittent devices and approximate computing. We evaluate BEL on approximate computing benchmark applications and showed that it is possible to reaches an average of 52.32 % energy savings with negligible 0.04 % of accuracy degradation.
Run Time Fault Tolerant Mechanism for Transient and Hardware Faults in ALU for Highly Reliable Embedded Processor

Reliability and low power consumption are important design metrics of any critical embedded systems. With the advancements of fabrication technology reaching to the nano levels and complexity of system is increasing, systems are more exposed to manufacturing defects which leads to faults in the system. This paper is presenting a method to design ALU which employs a run time recovery mechanism in order to detect both hardware and transient faults. The proposed method is a recomputing using duplication with comparison (RDWC) based on combination of time and hardware redundancy techniques. Simulation results indicate, RDWC incurs a decrease in LUT overhead of 124%, IO (input output) overhead by 129% and power overhead of 35% compared to the existing TMR technique.
Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this article, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers’ distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.
Development of a smart extruder as a demonstrator for an industrial 4.0 component with EtherCAT and MQTT connection

According to the objectives of industry 4.0 (I4.0) and the associated demands on the communication capability of the participating entities, the smart extruder represents a demonstrator using a two-part communication structure consisting of a real-time interface and an IoT protocol. An application example of the I4.0 context offers the implementation of a force sensor for monitoring the required feed force as a function of the feed speed, which is generated by a stepper motor. The exchange of the sensor data as extended functionality of the smart extruder is done by MQTT, while the control is realized via EtherCAT; the central element is the microcontroller, which realizes both a real-time interface and a non-real-time interface via a Y-structure. Thus the data exchange in the sense of I4.0 can be illustrated.
Social Emotion Cause Extraction from Online Texts

Social emotion refers to the emotion evoked to the reader by a textual document. Compared to classical sentiment analysis conducted from the author’s perspective, emotion analysis from the reader’s perspective (i.e. social emotion mining) is an important task in web-based social media analytics, and particularly meaningful for security related applications. Mining the causes of social emotions, i.e. Social Emotion Cause Extraction (SECE), is a new challenging task in social emotion mining, which can help better explain the elicited social emotions in text. In this paper, we propose the SECE task for the first time, and construct a new SECE dataset to support our study. We develop the first computational method for this new task, and conduct experimental studies to evaluate the effectiveness of our proposed method based on the dataset we construct.
Optimal Capacity Planning of Isolated Multi-energy Microgrid Considering Multi-dimensional Uncertainties

In recent years, microgrid technology has gradually been proved to be an effective method to solve the power supply problem in remote areas. The optimization goal of the planning model is to minimize the investment, operation and maintenance costs of the microgrid system, and the constraints include system power balance constraint, ‘abandon wind and light’ constraint, energy supply reliability constraint, etc. Then a mixed-integer nonlinear programming model is established, and the particle swarm optimization algorithm is used to find the optimal solution of this problem, after calculation, the optimal capacities of the WT, PV and battery in the microgrid are obtained. Simulation results indicate that the optimal configuration capacity obtained can minimize the total cost of the system while ensuring energy supply reliability and reducing wind and light abandonment as much as possible.
Towards an Objective and Precise Moisture Content Measurement of Textiles Using a Chipless RFID Tag-Sensor

One of the challenges in textile engineering is evaluating the moisture retention, or moisture releasing of fabrics, that is a key factor determining wear comfort. Conventionally, this process involves a human decision based on the sensation felt by the assessor, or methods that involve weight uptake or travel distance of water. In this paper, moving toward an automated approach, a chipless RFID tag-sensor is proposed to evaluate the wetness level of cotton fabric. The principle of this sensing system is based on measuring and processing of the reflected radio frequency wave from the wet fabric sample. The preliminary experimental results of the investigation are presented which show that using chipless RFID approach has the potential for a precise automated wetness level measurement.
ControlDroid: A m-learning platform to learn and teach control systems in technology and engineering

In this paper, we describe the m-learning platform ControlDroid, which allows the students to design and implement SISO control systems with their smartphones or tablets. The platform is composed by an application in Android and a development board to connect sensors and actuators for the control system. Regarding the application, the digital classic controllers (P, PI, PID) are built using the Google visual Tool Btockly that simplifies their implementation, focusing the attention of the students in the aspects of plant identification, simulation and design, instead of the algorithms needed in the implementation of a controller. To test the platform, we perform an experiment to control the velocity ω of the DC motor (EMG30) and we expose the respective results in the manuscript. The concept of the platform represents a curricular transformation in the area of control systems, taking the advantage of the mobile devices and offering the possibility to the students learn control systems in their homes without an expensive infrastructure, aspect that takes more relevance given the current challenges of the education in the COVID-19 pandemic.
Cognitive Wideband Beamforming for Sparse Array

This paper deals with the constrained design of the weighting coefficients to achieve a desired receiving beampattern for wideband sparse phased array. A novel optimization model that minimizes beampattern peak sidelobe level in a space-frequency region of interest (SFRI) along with mainlobe level and weight coefficients quantitative restrictions is proposed. An Alternating Direction Penalty Method (ADPM) formulation to tackle the resultant nonconvex problem is introduced. In each iteration, the original problem is executed via solving multiple tractable subproblems. The simulation results highlight that in comparison with the available approach, the proposed method realizes a deeper nulling in the given space-frequency region capable of rejecting the strong interference.
Various Reliability Investigations of Low Temperature Polycrystalline Silicon Tunnel Field-Effect Thin-Film Transistor

Various reliability issues of low temperature polycrystalline silicon (LTPS) tunnel field-effect transistor (TFET) are comprehensively studied for the first time and compared with conventional LTPS thin-film transistor (TFT). For the positive and negative gate bias stress (P/NGBS) instability, the NGBS causes more serious electrical degradation for both LTPS-TFETs and TFTs because of the higher trap state generation after the NGBS. However, the local P/NGBS near the source-side and drain-side regions show completely different impacts on the LTPS-TFETs and TFTs. The results reveal that the source-side local P/NGBS dominates the entire P/NGBS effects of LTPS-TFETs, and the drain-side local P/NGBS effects have less impacts on the electrical behavior of LTPS-TFETs compared with the entire P/NGBS effects. For the LTPS-TFTs, the local P/NGBS effects show much less electrical degradation than the entire P/NGBS. The difference of local P/NGBS effects between LTPS-TFETs and TFTs is attributed to the different carrier transport mechanisms. In addition to the P/NGBS effects, the hot carrier stress (HCS) with different drain stress voltages is also studied in this work. The drain stress voltage would introduce the horizontal electric field near the junction and reduce the impact of the vertical electric field in the channel region. For the HCS of LTPS-TFETs, the increase of drain stress voltage only leads to the reduction of vertical electric field stress effect but no horizontal electric field stress effect, which is different from the results of LTPS-TFT.
Potential Benefits and Challenges Associated with the Adoption of Mobile health (m-health) in Kingdom of Saudi Arabia

The healthcare system in the Kingdom of Saudi Arabia (KSA) is going through transformative changes as part of the Kingdom's 2030 vision. One of the main pillars of this vision is to improve the healthcare and wellbeing of Saudi citizen. The area of mobile health (m-health) focuses on the use of mobile and Internet technologies for improving healthcare delivery. From the healthcare perspective, the introduction of new and innovative approaches of mobile and digital health solutions in the KSA are timely to enable efficient and effective healthcare delivery services that are vital for the realization of the country's 2030 vision in the healthcare and wellbeing areas. The KSA has made noteworthy strides in adopting Health IT and e-health technologies into their healthcare system, and recently a national e-health strategy was drafted and adopted by the ministry of health. However, from the m-health and digital health perspectives there are no national strategy in the KSA in these important areas. The increasing patient population in chronic diseases, combined with spiraling healthcare costs and pressure on healthcare services makes it timely and important to introduce these concepts into the wider healthcare system. These are considered pivotal drivers in the country's healthcare and patient care transformation process. Furthermore, the unprecedented penetration and usage of smart phones and internet access by the KSA population can drive this change towards accelerated and wider adoption of m-health and digital health services.
Air-coupled ultrasonic inspection of anisotropic composite plates

The present research is a continuation of our work [1], in which a set of analytically-based computer models for the coupled time-harmonic oscillation generated by a given source in acoustic fluid with an immersed anisotropic plate has been developed. This study is focused on the influence of anisotropy of the immersed composite plate on the dispersion characteristics of guided waves (GWs) excited by the incident acoustic beam.
A Dynamic Bidding Strategy Based on Model-Free Reinforcement Learning in Display Advertising

Real-time bidding (RTB) is one of the most striking advances in online advertising, where the websites can sell each ad impression through a public auction, and the advertisers can participate in bidding the impression based on its estimated value. In RTB, the bidding strategy is an essential component for advertisers to maximize their revenues (e.g., clicks and conversions). However, most existing bidding strategies may not work well when the RTB environment changes dramatically between the historical and the new ad delivery periods since they regard the bidding decision as $\boldsymbol {a}$ static optimization problem and derive the bidding function only based on historical data. Thus, the latest research suggests using the reinforcement learning (RL) framework to learn the optimal bidding strategy suitable for the highly dynamic RTB environment. In this paper, we focus on using model-free reinforcement learning to optimize the bidding strategy. Specifically, we divide an ad delivery period into several time slots. The bidding agent decides each impression’s bidding price depending on its estimated value and the bidding factor of its arriving time slot. Therefore, the bidding strategy is simplified to solve each time slot’s optimal bidding factor, which can adapt dynamically to the RTB environment. We exploit the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to learn each time slot’s optimal bidding factor. Finally, the empirical study on a public dataset demonstrates the superior performance and high efficiency of the proposed bidding strategy compared with other state-of-the-art baselines.