Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Neural Control with Prescribed Performance for Strict-Feedback Systems with Input Saturation*

This paper presents a novel adaptive control scheme that is able to achieve given tracking performance for a class of uncertain nonlinear systems in strict-feedback form with input saturation. The neural networks (NNs) are utilized to estimate the unknown nonlinearities, and an auxiliary system is designed to compensate the effect of input saturation. Different from the existing results, a novel barrier Lyapunov function is firstly introduced into the backstepping design step to deal with the tracking error performance. Therefore, it is a unified design approach for systems with or without constraint requirements. Finally, by utilizing the Lyapunov method, the boundedness of the closed-loop signals is guaranteed, and the tracking error is constrained within prescribed performance bound. The simulation results illustrate the effectiveness of the proposed control approach.
Algebraic Observer for the Three Cell Multicellular Converter

this paper is devoted to design an algebraic state observer of three-cell multicellular converter for a predetermined switching sequence. The aim of this observer is to ensure a finite time convergence of the capacitor voltages in presence of measurement noises. For the converter, one differentiates robustly the load current to get the observable part. Then, one gathers the information from each mode and finally gets the capacitor voltages. The simulation results seemed to be satisfactory especially in terms of the efficiency and this confirms the theoretical synthesis.
Analytical Formulas for Frequency-unbalanced Periodic Leaky-wave Antennas

Periodic leaky-wave antennas (P-LWAs) are usually optimized in terms of frequency balancing (w-balancing) and $Q$ balancing, where the resonance frequencies of the series and shunt modes (ωse, ωsh) and their quality factors (Qse, Qsh) that contribute to radiation are equalized. This paper performs a general study of the four radiation regimes in P-LWAs (ω-balancing/ Q-unbalancing, ω-balancing/ Q-unbalancing, ω-unbalancing/ Q-balancing and ω-balancing/Q-balancing) and provide analytical formulas of 1D P-LWA based on a network model. Finally, analytical results of important P-LWA parameters, such as propagation constant, Bloch impedance and radiation efficiency, are provided and discussed for the four regimes.
A Deep Learning Based Framework for Textual Requirement Analysis and Model Generation

Requirement analysis is a key part of systems engineering process. Analyzing requirements correctly and creating design model sequentially could be critical to the whole process of a product development. Nevertheless, requirement text handling and model transferring could be really time-consuming and error-prone. Thus, we proposed an artificial intelligence based framework to deal with textual requirement handling and model creation. With deep learning and natural language process skills, our approach could be able to analyze textual requirements automatically, and then create the related models. This would indeed alleviate the work of engineers and promote the efficiency and quality of product development process. With our limited knowledge, our paper is the first one to propose the deep learning and NLP based framework to automatically create requirement models.
A Binary-switch Controlled Periodic Half-width Leaky-wave Antenna for Fixed Frequency Beam Steering near the Endfire Region

A leaky-wave antenna (LWA) with fixed-frequency beam scanning capabilities is presented in this paper. The main structure is a half-width microstrip LWA (HW-MLWA), and the direction of the main beam at a fixed operating frequency is controlled by using a group of gap capacitors and binary switches. Different binary switching patterns are applied to change the reactance profile of the radiating structure and hence beam scanning at a fixed frequency is achieved in discrete steps. Results from the full-wave simulation demonstrate that the radiating antenna beam can be steered from 38° to 68° in a discrete step at 7 GHz.
Fourier Analysis for the Reconstruction of Radiating Sources on Conic Curves

In this paper, we present useful upper-bounds to the Number of Degrees of Freedom (NDF) for some 2D source conic geometries and for two different representation variables choices. These results come out from asymptotic considerations on the radiation integrals and are shown for an elliptic and a parabolic arc source observed in far zone Since an estimation of the NDF is available for an elliptical source, the obtained results are compared with those ones available for the smallest elliptical source incorporating the considered arcs. Then, the role of the source length in the NDF is pointed out thanks to numerical examples and it is shown that a more accurate estimation for the NDF can be obtained.
A Machine Learning based Tool for AGC Performance Evaluation

The Automatic Generation Control (AGC) system provides secondary frequency regulation by commanding online generating units in order to compensate the active power imbalances in power systems. System operators are responsible for monitoring and evaluating the performance of this service that guarantee the secure operation. However, due to the complexity of the AGC system and the amount of available data, the performance evaluation could be challenging. As an alternative, a data-driven approach was used to develop a tool that support the operator performing this task, providing an insight in a few minutes. The proposed machine learning-based tool for AGC performance monitoring and evaluation was tested on the Colombian system operator.
Polarized Bistatic Scattering from a Spatially Anisotropic Rough Surface with Inhomogeneous Dielectric Profile

This paper presents full polarization bistatic scattering from a spatially anisotropic rough surface with inhomogeneous dielectric profile modeled by a transitional layer as a function of depth. The spatial anisotropy is geometrically described by directional correlation function. Both linearly and circularly polarized scattering are investigated in light of azimuthal dependence, degree of anisotropy, and inhomogeneity. Numerical results show that the bistatic scattering in the incidence plane reveals strong dependence on degree of anisotropy near the specular direction. By comparison, for co-polarization, the linearly polarized scattering reveals stronger directional dependence, but the circularly polarized scattering tends to be more omnidirectionaland the scattering pattern seems to be “concentric circles” with the strong scattering locating at specular direction. For linear polarization, in virtue of dielectric inhomogeneity, the scattering pattern reveals several distinct features including: HH polarization increases significantly in the backward region but decreases slightly in the forward region; the forward and backward scattering of VV polarization are enhanced; HV polarization can be greater than VH polarization. For circular polarization, both LR and RR polarizations on the whole scattering plane are enhanced due to the inhomogeneity. As the transition rate increases from 6 to 12, for linear polarization, the HH polarized scattering coefficient increases 5 ~ 10 dB in backward region, and VV polarization increases 8 ~ 15 dB both in backward and forward region, that is, VV polarization is more sensitive to the transition rate; secondly, the HV polarization increases faster than VH polarization does; for circular polarization, both LR and RR polarizations are simultaneously enhanced but the difference between them shrinks. As the background dielectric constant increases, for linear polarization, the strong scattering of HH polarization tends to move to the forward region, while the VV polarized scattering coefficient reduces about 3 ~ 6 dB in forward region and is enhanced 5 ~ 10 dB in backward region; for circular polarization, both LR and RR polarizations are enhanced in backward region, but in forward region, LR polarization decreases and RR polarization increases.
Implementation of TBSC Compensator for Reactive Power Compensation together with Transient Free Switching of Capacitor Bank

This paper presents a binary control scheme for reactive power compensation together with transient free switching of capacitor banks. The work approaches with performance estimation through analysis, design and practical realization of actual system. The strategy composed of Thyristor Binary Switched Capacitor (TBSC) banks. TBSC is in accordance with a set of Thyristor Switched Capacitor (TSC) banks organized in binary consecutive way. Numerous switching of capacitor affects on life of switched capacitor banks. Thus a managing circuitry has been established in such a manner that transient free switching of TBSC bank will occur. The proposed strategy can realized into the reactive power compensation on sequential way and the harmonic content of the source are maintain at the negligible levels owing to the filtering action of TBSC and also the switching of capacitor banks is transient free.
A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems

The cybersecurity of industrial control systems (ICSs) is becoming increasingly critical under the current advancement in the cyber activity and the Internet of Things (IoT) technologies, and their direct impact on several life aspects such as safety, economy, and security. This paper presents a novel semi-supervised dual isolation forests-based (DIF) attack detection system that has been developed using the normal process operation data only and is demonstrated on a scale-down ICS known as the Secure Water Treatment (SWaT) testbed and the Water Distribution (WADI) testbed. The proposed cyber-attack detection framework is composed of two isolation forest models that are trained independently using the normalized raw data and a pre-processed version of the data using Principal Component Analysis (PCA), respectively, to detect attacks by separating-away anomalies. The performance of the proposed method is compared with the previous works, and it demonstrates improvements in terms of the attack detection capability, computational requirements, and applicability to high dimensional systems.
Information Abstraction from IoT Streaming Greenhouse Data

Internet of Things (IoT) is a platform which gives the billions of computing devices, sensory devices that constantly produce and exchange the huge amounts of data over the network. Several interesting applications, like Greenhouse, involving extracting the higher level information from the raw data and representing it in human readable format, exists. An effective mechanism is required to process streaming data and inferring it, to get insight about the data and get some actionable information from processing and measurements. The aim is to represent the data from device point of view to user-centric point of view using Data analytics and Machine learning mechanism. Raw sensor data is created, based on the sensor threshold values. Parameters considered are temperature, humidity, co2 concentration, luminosity, radiation, soil moisture and soil temperature. Numerical values are converted to the strings to create higher level abstraction by applying set of rules. Abstracted data is cleaned by some cleaning processes. Then Latent Dirichlet Allocation (LDA),a topic extraction model, is applied to extract the hidden correlation among the data
Conical error compensation algorithm based on particle swarm optimization algorithm

For attitude updating algorithm of Strapdown Inertial Navigation System, conical motion is the worst working environment. Due to the use of Taylor’s formula, the traditional algorithm has truncation error. As the frequency of vibration increases, the effect of compensation is getting worse and worse. this paper is based on the traditional subsample algorithm, Using the conical compensation residual error square sum as the fitness function, the particle swarm optimization algorithm is used to optimize the depth learning extremum. Taking the initial weights and initial threshold generated by Monte Carlo as input, the average value of multiple optimization results is used as a coning error compensation optimization coefficient, so that the residual error after compensation is the minimum. The simulation results show that compared with the traditional coning error compensation algorithm, neural network and genetic algorithm, this algorithm improves the accuracy of the attitude calculation of strapdown inertial navigation system, and achieves excellent coning error compensation effect.
Sistema microcontrolado para transmissão de sinais de ECG pela internet

This work presents a system for remote monitoring of patients that comprises a portable device, a microprocessor-based Internet server and software running on a remote computer. The developed system samples and sends the patient's ECG waveforms over the Internet. At the remote computer, the ECG is presented in real time on the screen to be analyzed by a health professional. The portable device connected to the patient samples two ECG leads. The samples are sent to the microprocessor-based Internet server via RF (Bluetooth). Bluetooth was chosen because it operates on ISM band, presenting low power and low cost. The Internet server (based on the eZ80190 Zilog) receives (via Bluetooth), encapsulates and transmits the ECG signals as well as controls the data flow. The ECG samples are packed into RTP (Real Time Protocol). The remote computer receives and unpacks the RTP packets to display the ECG on the screen. The software for Windows OS written in C++ calculates other four ECG leads. To assess the system performance, four different communication topologies were simulated. Parameters related to the quality of the transmission are presented: Jitter and loss rate.
Low-Overhead Compressibility Prediction for High-Performance Lossless Data Compression

As big data has evolved over the past few years, a lack of storage space and I/O bandwidth has become one of the most important challenges to overcome. To mitigate these problems, data compression schemes reduce the amount of data to be stored and transmitted at the cost of additional CPU overhead. Many researchers have attempted to reduce the computational load imposed on the CPU by data compression using specialized hardware. However, space savings through data compression often comes from only a small portion of data. Therefore, compressing all data, regardless of data compressibility, can waste computational resources. Our work aims to decrease the cost of data compression by introducing a selective data compression scheme based on data compressibility prediction. The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression. Additionally, our method reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost. To verify the proposed scheme, we implemented a DEFLATE compression system on a field-programmable gate array platform. Experimental results demonstrate that the proposed scheme improves compression throughput by 34.15% with a negligible decrease in compression ratio.
Power and Delay Optimization of FinFET based Adiabatic Logic SRAM Cell

The present-day scenario, the VLSI field has grown significantly and the restraints on the critical factors such as power, area & speed have tightened to greater extent. The requirement of high speed and low power applications and their efficiency is the need of the hour. The Static Random-Access Memory [SRAM] cell is the most commonly used memory cell for retaining the data until the power supply is active. In this paper we approach to optimize the power and delay constraints of the SRAM cell. The approach uses FinFET technology as well as the Adiabatic Logic approach to meet the required constraints. FinFET based designs proves to be more efficient with faster switching speeds and better power delay product compared to conventional CMOS. The Adiabatic logic further helps in power reduction by using the stored energy. In this paper, the different types of adiabatic logics are applied to SRAM cell designed using 18nm FinFETs to reduce power consumption and delay. It is observed that Efficient Charge Recovery Logic [ECRL] is most optimized in terms of power consumption and Body-Biasing technique is more efficient in terms of delay as it provides faster switching.
Evaluation of Lucas-Kanade based optical flow algorithm

Since the performance of pyramid Lucas-Kanade (LK) based optical flow algorithm would be influenced by different environments, this paper evaluates the simulation accuracy of the pyramid LK algorithm in cities, plains and mountains, respectively. The simulation results show that the LK algorithm can maintain high precision under the large motion, and the flight strategy of UAV under different environment is given according to the algorithm’s accuracy, In order to ensure the high accuracy of the optical flow sensor in different environments. For example, in the urban environment, when the UAV’s velocity is 12m/s-l5m/s, the best altitude is 80m-l50m. Simulation verification experiments are carried out in different environments. The simulation results show that the proposed UAV flight strategy is generally correct.
An Analysis of Health System Log Files using ELK Stack

With the growth of machine data, logging is progressively critical. Logging helps in investigating and diagnosing the issues for the execution of ideal applications. The logs are not only used for discovering issues but also for searching the required data. The ELK stack abbreviated as Elasticsearch, Logstash, and Kibana is mainly centered around the logs. As the majority of logs are centered at one spot so that it can be able to see the procedure stream and query the questions against logs from all kind of applications from one spot. ELK underpins many log the executives and examination use cases that can get experiences from information. This finds what the information is defining all about and what needs to be done for the accomplishment of the business needs. In the current scenario the identification of the defect in the health system and system location is much difficult, So we propose a method to investigation on the log details of the health systems can give the assistance on identifying the defect on the existing safe guards using ELK stack, which in turn gives the assurance to the frameworks, by this it can have a learning from the data that was separated from the information so it can be able to assist us with keeping track of the defects in the system and the health of the system needs to be prioritized. The Log data is filtered based on system priority and country, because to identify the system state and location of the system, and this is visualized on the Kibana dashboard. This helps service engineers to identify the defect and location of system with short period of time.
Representation System of Quality Indicators towards Accurate Evaluation of Medical Services Based on Medical Databases

Quality indicators play an important role in quantitatively measuring the quality of medical services. In this paper, we introduce a representation system that helps define quality indicators and calculate their values in a coherent manner based on the data in medical databases. The representation system primarily consists of three parts. The first one is an ontology to define concepts related to medical services. The second one is a set of graphs that express the targets of quantification. The third one is a set of quantifying concepts that abstract quantities of the target concepts. The proposed representation system adequately divides the work for defining quality indicators and calculating their values from the data in medical databases, and, hence, it assists medical staffs and system engineers who manage medical databases to perform their own work and to collaborate on the evaluation and the comparison of medical services.
Network Routing Optimization Based on Machine Learning Using Graph Networks Robust against Topology Change

There is an increasing demand of real-time routing optimazation using Sotware Defined Networking (SDN) for better Quality of Service. Since the problem of finding the optimum routing for any QoS metric is hard to solve for a medium or larger size network, quasi-optimization using metaheuristics, such as Genetic Algorithm (GA) and Simulated Annealing (SA), have been investigated, but it is still impossible to satisfy the requirement of real-time optimization. There have been some attempts to solve this with machine learning. By learning a model beforehand, it is possible to output a near-optimal solution in a short time during network operation. The open problem with this approach is that machine learning models cannot deal with topology change of the network. In this paper, we create a model which is robust for topology change by using Graph Networks. Applying the proposed model to maximum bandwidth utilization, we have gotten the accuracy of about 61.0% for solution of GA, and the prediction time is 150 times faster than GA.
RCA Prediction using Machine Learning

System developers and administrators are heavily dependent on log files for getting the system information in the event of any failure or disaster. Products that are built on layered architecture with multiple components and sub-components generate numerous logs. In case of hitting any error or failures, analyzing huge sets of log files is a daunting task since it often requires coordinated analysis from multiple product groups which might take several days before it is root caused. Machine learning based techniques can be one of the best possible methodology in classifying logs, which can correlate distributed information points while entropies are spread across several log files. It provides an efficient method to triage complex failure issues in lesser time and also capable to detect the appropriate element of failure at profound granular stage. This paper proposes an ML-based method for root cause analysis that proves to be more efficient with higher accuracy.
Design of Small Sized SFCW Radar for Landmine Detection

Landmines today still represent an active issue in war-field zones where they hurt or kill thousands of people around the globe. Special challenge presenting in soil buried plastic landmines, which are because of the material harder to detect. For this task many promising result have been achieved with use of millimeter wave frequency radars. This paper presents design of a small sized and low cost ground penetrating radar (GPR) with use of standard integrated circuits (IC) components. The used frequency has to be low as possible for the ability of the signal to penetrate through above ground vegetable as also the soil it self. For this purpose a homo dyne structured stepped frequency continuous wave (SFCW) radar has been designed and developed. Used was the frequency from 700 MHz to 2.7 GHz and is limited by the IC components. With this bandwidth a resolution of 15 cm is achieved. For greater penetration depths the transmitted signal is at the end amplified up to 30 dBm. Because of the high bandwidth in this frequency band and desired narrow beam width, a custom TEM Horn antenna had to be developed. With the weight of only few hundred grams and a small size, the system could be also mounted on a drone and scan infected areas. The radar performance has been tested on a indoor polygon with different scenarios of landmines, positioned above and below the ground surface. The main focus has been dedicated to plastic landmines. The signal which is reflected from a plastic landmine is mainly because of the explosive inside, therefore for test purposes material with similar dielectric performances was used. Experimental results have shown that with the designed SFCW radar, landmines above as also below ground surface can be detected.