Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nonlinear Controller Based on the Convolutional Neural Networks

This paper focuses on developing a nonlinear controller based on the convolutional neural networks to control different plants. It is assumed that the prior knowledge about the plants is very limited and there are only sensory input-output data history of them. The neural network is trained in supervised learning method without having a target controller. As manipulating data are not picture frames, they are preprocessed and concatenated to form adequate frames required by the convolutional neural networks. A convolutional neural network with a simple structure is proposed for the problem. The trained controller is applied to six different linear and nonlinear plants, one of which is inherently unstable and different from the plants utilized in the training process. Furthermore, an important parameter of this unstable plant is considerably changed and the controller performance is analyzed. The simulation results show that the proposed controller can properly control all plants.
Communication, Computation, and Caching Resource Sharing for the Internet of Things

The Internet of Things connects a large number of smart mobile devices with the Internet, where these devices are embedded with often limited communication, computation, and caching resources. To address the heterogeneity of these devices and achieve efficient overall system resource utilization, researchers have proposed various device-to-device resource sharing models, enabling mobile devices to form device-todevice connections and to share their resources for cooperative task execution. Most of these existing works, however, considered scenarios where mobile devices can share one or two types of resources, and hence inadequately explore the potential of resource sharing among mobile devices. In this article, we introduce a general framework where mobile devices can share any combination of the three types of resources, and it can generalize many existing deviceto- device resource sharing models. In addition, it can achieve more efficient resource allocation by offering mobile devices more flexibility in terms of resource sharing. Based on the proposed framework, we focus on discussing two issues: the optimization issue, regarding how to schedule resources among devices; and the economic issue, regarding how to motivate the device owners to share their resources. We introduce the challenges and potential solutions to these two issues. We further outline several open issues and future directions for the proposed general resource sharing framework.
Evaluation of the performance of supervised and unsupervised Machine learning techniques for intrusion detection

machine learning techniques are widely used in the research for intelligent solutions anomalies detection on different computers and communications systems, which have allowed to modernize the intrusion detection systems, to ensure data privacy. For that, this paper evaluates the performance of some supervised (i.e., KNN and SVM) and unsupervised (i.e., Isolation Forest and K-Means) algorithms, for intrusion detection, using data set UNSW-NB12. The results show that the supervised algorithm SVM gaussiana fine, obtained 92% in accuracy, indicating the ability to correctly classify normal and abnormal data. With regard to the unsupervised algorithms, the K-Means algorithm groups the data together correctly and allows the appropriate number of groups to be clearly defined; however, this data set is highly agglomerated. For Isolation Forest, despite being a robust algorithm for the separation of atypical values, it presented difficulty for it. Finally, it should be made clear that not all methods of detecting anomalies by distance work properly for all data sets.
Arithmetic Coding for Length-constrained Joint Source Compression and Error Detection

Arithmetic coding (AC) is an entropy achieving lossless compression algorithm, which efficiency motivated its integration in emerging image and video compression standards like JPEG2000 and HEVC. However, this compression efficiency comes at a cost of a very sensitive compressed bitstream, not convenient when transmission over lossy channels is considered. This motivated researchers to propose the use of error detection and correction schemes combined with joint source channel coding for AC. In this paper, we change the paradigm and propose to use the arithmetic codes for error detection too based on length constraints. In fact, the starting observation is that the arithmetic encoded stream is very sensitive to errors, and tends to have a random behavior when errors are introduced. This random behavior induces, in general, a length change after arithmetic decoding which can be simply used to detect errors. The idea is very simple and can improve the decoding system quality when combined with retransmission strategies or appropriate error correction mechanisms, without adding any extra redundancy. In fact, the compression efficiency of the AC remains untouchable. This paper investigates the length-constrained error detection performance of AC with different source and errors configurations. The error detection efficiency is demonstrated and, unlike the commonly used Cyclic Redundancy Check (CRC) codes, the proposed method is more efficient when the number of errors is high. Finally, we present a review of the Chase-like arithmetic decoder that exploits the length-based error detection capacity of AC to make source error correction.
Designing a Fractional Order Back-Stepping Controller Based on GPI Observer for a 3D Biped Robot

This paper proposes an active disturbance rejection control (ADRC) method based on generalized proportional integral (GPI) observer to control of a biped robot while considering its complicated structure and possible disturbances. These observers are used to estimate time-varying disturbances and possible uncertainties. Generally, implying GPI observer on robotic systems is accompanied by a PD-controller in feedback, which is not efficient enough due to abrupt velocity changes of the biped in impact phase, and consequently, a higher level of input torque is required. Thus, applying the back-stepping technique to the mentioned method is suggested. In this study, a two-nested control loop is designed for a 3D biped robot. The back-stepping method with a GPI observer is used in the inner control loop to improve the custom method. Joint trajectories are designed by an offline method and modified by an online method in the outer loop to maintain the stability of the biped. The efficiency of the proposed control strategy is depicted for the biped in the presence of time-varying disturbances through MATLAB simulation.
A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

Failure mode and effects analysis (FMEA) is a typical risk assessment and prevention technology, in which different experts provide different assessments on the target system to identify the risk grades of its components. Sometimes, the assessments contain conflict information. How to manage and fuse the conflict assessment information is an open issue. We propose a triangular distribution-based basic probability assignment (TDBPA) method to model and fuse the conflict risk level coming from different experts’ assessments in the frame of Dempster-Shafer evidence theory. First, the subjective assessments of risk analysis from domain experts are modeled with belief structure in Dempster-Shafer evidence theory. Then, the assessments are transformed as the TDBPA function. Thirdly, the conflict risk assessments from the FMEA team for failure analysis can be fused with Dempster rule of combination. After that, the modified risk priority number (RPN) model based on fused assessment can be calculated for ranking of failure modes. Finally, recommended actions should be taken for prevention of potential risk items. We verify the rationality and efficiency of the proposed method with a case study in the blades of an aircraft turbine. In short, the presented FMEA methodology procedure in this paper is well organized so that we can apply it in a more simple and understandable way. Utilizing the character of triangular distribution, taking adjacent values into account, TDBPA method can smooth the conflict assessment for information fusion. In addition, the shortcoming of repeating values in classical RPN is eliminated in the proposed method, which improves the ability for risk assessment of FMEA.
Optimal Sensor Configuration for Activity Recognition during Whole-body Exercises

Advances in wearable devices with inertial measurement units (IMUs) for the detection of different motor activities and monitoring training tasks have important applications in tele-rehabilitation. These technologies can play an effective role in improving the quality of life for people with progressive movement disorders such as Parkinson's disease (PD). Considering cost, simplicity, and practicality, a smaller and more efficient number of IMUs that can accurately recognize the type of movement is preferable. The purpose of the current study was to design an affordable and accurate wearable device with IMUs to detect thirty four different motor activities in a customized training program called LSVT-BIG11Lee Silverman Voice Technique-Big https://www.lsvtglobal. com/LSVtbig[1], which is usually used for people with PD. Nine neurologically healthy individuals performed all 34 tasks. The collected data were processed in windows of 2.5 seconds. Eight features in time and frequency domains and discrete wavelet transforms were calculated. Dimension reduction was performed using the PCA22Principal Component Analysis algorithm. NM33Nearest Mean, RBF44Radial Basis Function, SVM55Support Vector Machine, and k-NN66k-Nearest Neighbors classifiers were then trained and used to recognize the activity. A genetic algorithm was utilized to decide which sensors and signals took part in the classification to produce the best accuracy. Our results showed that the four sensors installed on the left shank, right thigh, left forearm, and right arm provided the optimal number and arrangement to achieve a precision of 94.3% and sensitivity of 93.4% using NM classification. Also, an adaptation algorithm was utilized in order to maintain the quality of recognition for new users.
Predictive Speed Estimation for Encoderless Control of an Induction Motor Fed by Delta Sigma Modulated Matrix Converter

Predictive control has emerged as a popular tool in the modulation of power converters and control of sensorless motor drives. However, it is associated with inherent limitations such as increased processor burden and heuristic weight tuning when dealing with multiple objectives. Moreover, in applications such as modulation of matrix converters, predictive control necessitates input filter modeling, besides requiring repeated reruns of load (and input filter) models for securing the most optimum state selection. This paper proposes the restricted use of predictive control as an estimation technique only, for speed sensorless control of an induction motor through deltasigma modulated matrix converter. Delta sigma technique allows conveniently implementable modulation for the power converter, whereas predictive estimation of flux, torque and speed, ensures high precision even at near zero motor speeds. Both predictive control and delta sigma modulation work in tandem, to provide a robust drive solution.
A Comparative Analysis on Coordinate Rotation Digital Computer (CORDIC) Algorithm and Its use on Computer Vision Technology

Computer Vision is a needful technology of present time, further augmented by the current era of Multi Dimension vision System based on high speed network. It has been an established belief that Social media is most needful andpromising application for all of us in the present scenario. This generates the need for various computer vision algorithms in order to further improve the quality of social media which in turn leads to impact on the consumer. Most of the computer vision algorithms need the computation consisting of two steps-namely arithmetic computation and trigonometric computation. The complexity is mostly by the trigonometric computations, which generates a need for separate algorithms which pefforms the entire trigonometric task. In this work one well known algorithm called CORDIC (Rotation Digital Computer), another name is digit by digit approach and Volder’s calculation has been addressed. In this, a novel processing strategy is utilized which is particularly reasonable for understanding the trigonometric connections associated with plane organize revolution and change from rectangular to polar directions. CORDIC calculation is additionally appropriate for square root, Logarithmic, Exponential capacity and for computer based computations. In this paper, a comparative study amongst the available CORDIC algorithms has been presented with study of theirpefformance on discrete cosine transform (DCT)application as an example case.
Efficient Operation of Power System with FACTS controllers using Evolutionary Techniques

In this paper, Moth flame optimization (MFO) technique is used for the optimal placement of flexible ac transmission system (FACTS) controllers for efficient operation as well as reduction of the transmission losses in the power system under various loading conditions. The aforesaid approach is implemented on standard 14 bus systems and the results obtained are compared with symbiotic organism search (SOS) algorithm and particle swarm optimization (PSO) based approach. The locations for installation of FACTS controllers are chosen by estimating the line power flows. The comparisons between these three techniques are presented in this paper to show the efficacy of the suggested approach.
A Survey on Steiner Tree Construction and Global Routing for VLSI Design

Global Routing (GR) is a crucial and complex stage in the Very Large-Scale Integration (VLSI) design, which minimizes interconnect wirelength and delay to optimize the overall chip performance. Steiner tree construction is one of the basic models of VLSI physical design, which is usually used in the initial topology creation for noncritical nets in physical design. In a GR process, a Steiner Minimum Tree (SMT) algorithm can be invoked millions of times, which means that SMT algorithm has great significance for the final quality of GR. Some of the research works are surveyed in this paper to understand GR and SMT problems and to learn the available solutions. Firstly, we systematically dissect three types of subproblems in Steiner tree construction and three types of GR methods. Then, we investigate the recent progress under two new technology models. Finally, the survey concludes with a summary of possible future research directions.
Designing a self-tuning regulator controller for a non-linear and MIMO Exoskeleton system assist test setup with adaptive decoupling

The assistive exoskeleton robots are designed in order to help immobile patients and augment healthy individual's muscle power. Assistive robots help humans with control algorithms and providing the needed power for users walking. The control algorithm must be in a manner that provides a share of the torque the person requires for walking and consequently, the force the person needs to endure in order to walk will reduce. Considering the integration of the robot to the human, it is essential to assure the safety and practicality of the control algorithm before implementing it on a real user. In this paper, a new test setup is designed and built for simulating human joints and implementing the assistive control algorithm on the robots provided with the series elastic actuator. In this test setup incorporation of a set of motor and gearbox along with a series elastic actuator for simulating motion and torques exerted to human joints is used. A series elastic actuator is considered as an actuating system for the robot and for implementing the assistive control algorithm. This test setup includes a non-linear and multiple inputs-multiple outputs (MIMO) system. Considering the complexities of MIMO non-linear control methods, in this paper after linearization, the test setup is decoupled. Subsequently, an adaptive control method for the trajectories and actuator torques is employed. The proposed control method is immune to systems parameters uncertainty and is able to adapt to the system parameters varying. In the end, the practicality of the proposed controller is shown in comparison with the PD controller.
Improved H-bridge Multilevel Inverter for Grid Integration of Photovoltaic Power Conversion System with Power Quality Enhancement

This paper proposes an improved H-bridge multilevel inverter in the photovoltaic power conversion system (PPCS) system which is connected to the distribution grid. The power quality is improved with the number of levels and balanced active power sharing between the sources has been achieved. The bidirectional power flow is achieved. The two-stage SPVS extracts the maximum power from the solar panels through the incremental conductance (IC) algorithm based control of the DC-DC converter. The system is designed with multi-functional objectives such as reactive power support and load harmonic mitigation in addition with the active power penetration to the grid. The system is modelled and simulated in MATLAB/Simulink platform and the laboratory prototype is developed, and test results are produced for result validation.
Design and Analysis of Twin-Rotor Axial Flux Permanent Magnet Synchronous Motor for Electric Bicycle using 3D Finite Element Analysis

This paper studies axial flux permanent magnet synchronous motor as an in-wheel electric motor for its application in electric bicycle. Dual rotor axial flux motor is designed and analyzed with 3D finite element analysis using Ansys software. 3D finite element analysis is used instead of analytical approach and 2D finite element analysis to obtain more accurate results even though time consumption for simulation is more. For reducing simulation time, meshing strategy is applied is to refine the mesh in air gap region and relax the mesh in remaining regions for analysis. The characteristics of the motor showing good performance for gearless transmission is inferred from analysis. Ripple in the torque response shows the presence of harmonics in system which is confirmed from Fourier transform analysis of current and back emf.
A Comparative Study on Arrhenius Equations and BP Neural Network Models to Predict Hot Deformation Behaviors of a Hypereutectoid Steel

In order to predict the high-temperature deformation behavior of hypereutectoid steel, the hot compression tests were conducted in the strain rate range of (0.001~1) s−1 and the deformation temperature range of (950~1100)°C. The experimental data were employed to develop the Arrhenius constitutive model and BP neural network model, and their predictability for high temperature flow stress of hypereutectoid steel was further evaluated. Comparatively, a higher correlation coefficient ( $R$ ) can be obtained for the BP neural network model compared with the Arrhenius constitutive equations. And the relative error within ±1% was more than 68.75% for the BP neural network model, while only 18.75% for the constitutive equations. The BP neural network model is considered more efficient and accurate to predict the hot deformation behavior than the Arrhenius constitutive equations. Moreover, the well-trained BP neural network model is employed to predict the flow stress varying with the deformation temperature and strain rate. The flow stress decreases with the increasing deformation temperature and decreasing strain rate, which is in accordance with the experimental evaluation. The results indicate that BP neural network model is an efficient tool for modelling and predicting the flow behavior of hypereutectoid steels in high temperature applications.
Distributed Agent-Based Big Data Network for Secure E-Mobility TCO Analysis: A Use Case for Multi-Agent Systems in Logistics Environment

Media logistics is undergoing radical change and is fundamentally restructuring its processes. Logistics companies and their delivery companies form a widely distributed and branched network. The sharing of data for the optimization and expansion of business segments is fundamentally opposed to the protection of intellectual property (IP). Combining the distributed character of this application domain with the theory of mobile agents leads to a Distributed Agent-Based Big Data Networks (DBDN), in which data stays where they are, and the IP of the company will be preserved. A trade within the network with acting deputies is made possible. The MAS Ellipsis is used.
Virtualizing LoRaWAN Nodes: a CoAP-based Approach

In the near future, Internet of Things (IoT) will play a relevant role in people’s lives and one of the main challenges will be the integration of heterogeneous networks. Among widely adopted network technologies, the development of the so-called Low-Power Wide-Area Networks (LPWANs), in particular Long Range WAN (LoRaWAN) is attracting a significant interest from both academic and industrial worlds. The integration of LoRaWAN with other communication technologies represents a fundamental requirement for a successful rapid and large-scale diffusion of IoT paradigms, such as Smart Farming, Smart Factory, and Smart City. The aim of this paper is two-fold: we propose (i) a mechanism for automatic discovery of the sensors a LoRa device is equipped with; and (ii) a novel networking architecture, based on cloud computing and node virtualization, to enable the interaction of LoRaWAN end-nodes with other IP-based IoT devices. Our solution does not impact LoRaWAN networking and enables a seamless interaction between LoRaWAN end-nodes and other Constrained Application Protocol (CoAP)-based nodes.
Miniaturized Ultrawideband Half-Mode Vivaldi Antenna Based on Mirror Image Theory

A novel miniaturized ultrawideband (UWB) half-mode Vivaldi (HM-Vivaldi) antenna based on mirror image theory is proposed in this letter. The antenna consists of half a modified radiating arm and two rows of metallized vias, which are equivalent as the perfect electric wall. According to the mirror image theory, the induced current on the electric wall can replace the mirror image current of the radiating arm at its symmetrical position, which will result in the HM-Vivaldi structure. The antenna size of 0. 35λ0L × 0. 54λ0L (λ0L is the free-space wavelength at the lowest operating frequency) is reduced by 33% compared to the original antipodal Vivaldi antenna size. In addition, the continuous exponential curved edge on the ground is improved to retain the wideband impedance match characteristic. The operation theory and parametric analysis of this HM-Vivaldi antenna are presented with a prototype being measured. An UWB from 5.3 to 40 GHz with the reflection coefficient lower than –10 dB is experimentally achieved except that |S11| is slightly higher than –10 dB at several frequencies, and the maximum gain reaches 11 dBi.