Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Performance Enhancement Techniques of Antipodal Vivaldi Antenna

The increasing proliferation of advanced devices for UWB, 5G communication, micrometer-wave, and millimeter-wave communication demands an antenna which can handle huge data rates, provides high gain and stable radiation pattern as a panacea of most of the current wireless communication problems. Many different antenna designs have been proposed by the researchers but, Antipodal Vivaldi Antenna (AVA) has drawn the attention of most of the researchers because of its high gain, wide bandwidth, less radiation loss, and stable radiation pattern. Different methods are presented to make AVA more compact while maintaining the performance of an antenna to an acceptable level. These different methods are substrate choice, flare shape, slots, and feeding connectors. Also, AVA performance can be enhanced by incorporating corrugation, dielectric lens, patch in between two flares of AVA, balanced AVA (BAVA), metamaterial, computational intelligence (CI), and AVA array. The AVA performance enhancement techniques modify the electrical and physical properties of an antenna which in turn improves its performance. A large number of performance enhancement methods of AVA design have been proposed, however, no comprehensive study exists to categorize these performance enhancement techniques and outline their concepts, advantages, disadvantages, and applications. So, in this paper, we have attempted to outline all methods available for enhancing and optimizing the parameters of AVA. Additionally, to validate some of the important performance enhancement methods, they are incorporated in the basic conventional AVA design and further simulation results are obtained for the same which are in line with the surveyed literature. Each method is explained in detail by incorporating its key points, merits, and demerits. Moreover, illustrations from the literature are given to demonstrate improvement in the parameters as a result of applying a particular performance enhancement technique.
Mitigation Mechanisms Against the DAO Attack on the Routing Protocol for Low Power and Lossy Networks (RPL)

Destination Advertisement Objects (DAOs) are sent upward by RPL nodes toward the DODAG root, to build the downward routing paths carrying traffic from the root to its associated nodes. This routing mechanism can be exploited by a malicious node periodically transmitting a large volume of DAO messages towards its parent, which in turn will forward such messages to its own parent and so on, until they arrive at the Direction-Oriented Directed Acyclic Graph (DODAG) root. This ultimately results in a negative effect on network performance in terms of energy consumption, latency and reliability. The first objective of this paper is to evaluate the effect of such a DAO attack in the context of an RPL IoT network. In particular, identifying the particular performance metrics and network resources affected most greatly. The second objective is the proposal of mitigating security mechanisms in relation to DAO attacks and to evaluate their effectiveness. The simulation results have shown how the attack can damage the network performance by significantly increasing the DAO overhead and power consumption. It also demonstrated that the DAO attack affect the reliability of the downward traffic under specific conditions. The proposed mechanisms showed a good capacity in restoring the optimal performance of the network by up to 205%, 181%, 87% and 6%, in terms of overhead, latency, power consumption and packet delivery ratio respectively.
Precision Robotic Leaping and Landing Using Stance-Phase Balance

Prior work has addressed control of continuous jumping using touchdown angle from flight, but greater precision can be obtained by directing individual leaps using liftoff angle from stance. We demonstrate targeted leaping as well as balanced landing on a narrow foot with a small, single leg hopping robot, Salto-1P. Accurate and reliable leaping and landing are achieved by the combination of stance-phase balance control based on angular momentum, a launch trajectory that stabilizes the robot at a desired launch angle, and an approximate expression for selecting touchdown angle before landing. Dynamic transitions between standing, hopping, and standing again are now possible in a robot with a narrow foot. We also present approximate bounds on acceptable velocity estimate and angle errors beyond which balanced landing is infeasible. Compared to a prior Spring Loaded Inverted Pendulum (SLIP)-like gait, the jump distance standard deviation is reduced from 9.2 cm to 1.6 cm for particular jumps, now enabling precise jumps to narrow targets.
Robust stabilization of discrete-time switched systems with state delay and parametric uncertainty via model predictive control approach

This paper is devoted to investigating the robust stabilization of a class of nonlinear switched systems with state delay and uncertainties under arbitrary switching signals. For this purpose, a robust model predictive control (RMPC) method is developed for online optimization of an infinite horizon cost function. Sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to guarantee the robust stability of the closed-loop system. To reduce the computational load and the conservatism of the stability analysis, parametric uncertainties are considered. Numerical and practical examples are presented to demonstrate the effectiveness of the proposed method.
Using Computational Methods to Analyze Educational Data

This paper proposes a special session on the use of computational methods for analyzing educational data. Computation has permeated all disciplines because it provides unique opportunities to represent knowledge and understand complex phenomena. In education, disciplines such as learning analytics and educational data mining have emerged to better understand educational phenomena. This special session will discuss three different approaches to use computational methods to analyze qualitative educational data. After the discussion, the participants will be able to implement these methods using R programming, while reflecting on how they can use these methods in their own context.
Hitless Wavelength-Selective Switch Using a Single Microring Resonator Assisted With a Symmetric MZI

We demonstrate an optical hitless wavelength-selective switch using a single microring resonator assisted with a symmetric Mach-Zehnder interferometer (MZI). A part of the ring waveguide and its symmetrical waveguide are designed to be an equally armed MZI as an on-off switch for the resonant state of the microring. The symmetric MZI is insensitive to the spectrum and it avoids free spectral range (FSR) matching or alignment in use. The FSR of the structure is 2.18 nm and the insertion loss (IL) at the drop-port is 1.8 dB. Our experimental results reveal that the fabricated device permits on-chip hitless rerouting of multi-wavelength signals, which means the intermediate wavelength remains unaffected during wavelength switching.
Intelligent Active and Passive Learning for Integrated Semantic Computing for Vision Data Annotation

This paper proposes an intelligent control, which generates semantic meanings in natural language descriptions from media data, and discusses its application for semantic image retrieval. The new intelligent control for active and passive learning in the integrated semantic space works in a semantic space that is dynamically formed by the combinations of meaning space attained from the semantic associative calculation and the learned visual feature space derived by deep learning over the media data. Our model is inspired by the human ability to understand the semantic meaning of information by using active and passive learning. To get the query image results with similar semantic meaning, we define two types of knowledge representations: known and unknown semantic. Known semantic, is a set of the comprehensive concepts of meaning that is embedded in the system and used for the semantic associative calculation to construct the meaning space. We associate it with the passive learning. On the other hand, the unknown semantic knowledge is a set of comprehensive concepts of meaning that is not discovered in the system and has to be sought from the pattern of media through statistic and/or learned patterns. This deep active learning can be associated with the active human learning. We describe how the system chooses the subspace from the semantic space through the intelligent control for active and passive learning in the integrated semantic space by slicing, dicing and pivoting dimensions to identify and retrieve the meaning of images. The proposed system is an experimental modular model for database developed to answer the given semantic query in which the axis is represented in the columnar model database. We performed an experimental study on a large image dataset for deep learning. Our model outperforms the state-of-art in annotation. In segmentation, it compares favorably with other methods that use significantly more labeled training data
Support Vector Machine: A Machine Learning Approach for Power Quality Application

The support vector machine (SVM) is an innovative tool for scientific classification of data and delivers solutions to the problems associated with several industries and other sectors. This paper relates a recently presented learning-based linear and nonlinear classifier, the SVM, displaying its appropriateness to predict most severe power quality problems such as sag, swell and interruption. The performance of the proposed SVM classifier is investigated with the voltage samples, i.e. expected and unexpected voltages obtained from Maharashtra State Electricity Distribution Company Ltd. The learner classifier is trained for predicting/classifying voltage events using linear and nonlinear classification based decision rule. The linear, polynomial, and radial basis function (RBF) kernels are taken into consideration to fit the training data correctly. The demonstrated technique is found suitable for detecting and predicting power quality events in addition to the better accuracy, improved flexibility and short computational response time.
Tunable Adaptive Target Detection With Kernels in Colocated MIMO Radar

We introduce a framework for exploring adaptive target detection in colocated MIMO radar in non-linear feature space by exploiting the theory of kernel. The kernel theory inspires us to replace the inner products of test statistics with that of nonlinear mapped data in the feature space or that of their kernel tricks to achieve better detection performance. We apply this framework to the problem of tunable adaptive target detection in colocated MIMO radar, according to the principle of the generalized likelihood ratio (GLR), Rao and Wald (RaW) tests, and propose several new detectors under two new tunable detector forms, namely kernel tunable GLR-based and kernel tunable Raw-based detectors. The proposed tunable detectors include most of the prior detectors in colocated MIMO radar as special cases. One capability of the proposed detectors is that their robustness or selectivity (RoS) to steering vector mismatch (SVM) can be tuned flexibility through an RoS tuning parameter. In addition, we are able to incorporate the prior distribution (PD) of the disturbance covariance matrix, if available, through a PD tuning parameter. Therefore, the proposed detectors can be tuned based on the tuning RoS parameter to achieve robust or selective performance in the presence of SVM as well as to switch between the Bayesian or non-Bayesian based detectors through the PD parameter. For practical situations, we show that the proposed detectors possess CFAR property against disturbance covariance matrix by resorting to the invariance principle. Extensive Monte Carlo simulation results are provided to indicate that the proposed detectors have better detection performance than their counterparts in both single-target and multi-target scenarios.
Sensorless Low/Zero Speed Estimation for Permanent Magnet Synchronous Machine Using a Search-Based Real-Time Commissioning Method

This article presents the sensorless low/zero speed estimation and control for permanent magnet synchronous machines (PMSMs) using the innovative search-based real-time commissioning method. An overview of continuous pulsating high frequency injection used in this article is given for different injection angles in the stationary frame of reference. The variations of the saliency at different injection angles and loading operating points of the machine are presented and analyzed. The commissioning, search, and offset of the proposed observer are also discussed. Experimental results showing the performance of the sensorless method are presented in both open-loop and sensorless closed-loop current control. In sensorless mode the PMSM drive is controlled through a rotor flux oriented algorithm which uses the estimated electrical rotor position instead of the encoder measurement. Results for both the estimated mechanical and electrical rotor positions are presented under varying load conditions and speed reversal through the zero-speed region.
Prediction of Hepatitis Disease Using Machine Learning Technique

The objective of this work is to choose the best tool for diagnosis and detection of Hepatitis as well as for the prediction of life expectancy of Hepatitis patients. In this work, a comparative study between various machine learning tools and neural networks were carried out. The performance metric is based on the accuracy rate and the mean square error. The Machine Learning (ML) algorithms such as Support Vector Machines (SVM), K Nearest Neighbor (KNN) and Artificial Neural Network (ANN) were considered as the classification and prediction tools for diagnosing Hepatitis disease. A brief study on the above algorithms were performed based on the prediction accuracy of disease diagnosis. All the ML algorithms were implemented and validated using MATLAB software.
Real-Time Data Acquisition, Measurement and Monitoring Module for Electric Power Network

This paper provides a step by step design and implementation details to acquire, measure, and monitor different parameters (voltage, voltage phasor, frequency, rate of change of frequency (ROCOF)) of an electric power network. A prototype setup has been developed, and it has been tested in real-time, both in steady-state as well as the transient state. The voltage phasors are calculated using a 64-point discrete Fourier transform (DFT), and the mean square error (MSE) of the voltage phasors are low. Low-cost microcontroller and open-source software platforms have been used to develop this prototype.
An Adaptive Fuzzy Cerebellar Model Articulation Controller for Synchronization of Chaotic Systems

This paper aims to propose an adaptive fuzzy cerebellar model articulation controller (FCMAC) for synchronization of two chaotic systems in the presence of uncertainties and external disturbance. The proposed controller is comprised of parametric fuzzy rules. The antecedent part of every rule remain steady, while the subsequent part is renewed with every time step by establishing adaptation laws. These adaptation laws are derived by the Lyapunov synthesis approach and guarantee the closed-loop system’s stability. Simplicity, low memory demand and rapid control satisfy the practical usage of the adaptive FCMAC. Furthermore, adaptation laws can overcome lack of human experts knowledge about the chaotic plant. Eventually, simulations for synchronization of two Duffing-Holmes chaotic systems reveal that a satisfactory tracking performance can be achieved by using the proposed control system.
Classification of Faults in a Hybrid Power System using Artificial Neural Network

Electrical power transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing an artificial neural network to classify the faults in hybrid power system. This work employs a feed-forward artificial neural network with back propagation algorithm for fault classification. The instantaneous voltages and currents values are extracted and used to train the neural network. Simulation results have been provided to demonstrate the efficiency of the developed intelligent system for fault classification in grid-connected hybrid power system. The performance of the classifier is evaluated using the Mean Square Error (MSE).
Recent Advances in 100+nm Ultra-Wideband Fiber-Optic Transmission Systems Using Semiconductor Optical Amplifiers

We report on the use of semiconductor optical amplifiers (SOAs) to extend the optical bandwidth of next generation optical systems to 100 nm and beyond. After discussing the technological progress and the motivation for rekindling the interest in SOAs for line amplification, we describe the innovative approach developed for the realization of ultra-wideband (UWB) SOAs. Leveraging custom design of singly polarized SOAs to provide gain over 100+ nm bandwidth, we developed a polarization diversity architecture to realize UWB-SOA modules. Embedded in a compact package, the UWB amplifier modules have been successfully used to demonstrate 100+ Tb/s transmissions. We subsequently review recent experimental transmission results based on such novel 100+ nm wide semiconductor optical amplifiers, including our first demonstration of 100+Tb/s transmission over 100 km distance, our field trial using real-time traffic, and finally the transmission of 107 Tb/s throughput over three spans of standard single mode fiber (SSMF) using hybrid UWB Raman/SOA amplification technique.
Robust Nonlinear Consensus Seeking

In this paper we propose a new stochastic consensus algorithm based on the introduction of a nonlinear transformation aimed at robustification with respect to noise influence. The introduced nonlinear transformation is selected according to the methodology of stochastic approximation and robust statistics. The proposed algorithm represents a general nonlinear stochastic consensus seeking scheme, not yet treated in the literature. It is proved, under general conditions, that the algorithm converges almost surely to consensus. The choice of the nonlinearity ensuring better robustness properties compared to the linear algorithm, in the sense of better convergence rate and lower sensitivity of the asymptotic consensus value, is discussed. Illustrative simulation results, demonstrating the obtained advantages, are also provided.
Classification of expressions in Indian Classical Dance using LBP

Face analysis has been a topic of interest since the past few decades and a lot of research has been done in the domains of face detection-recognition and facial expression recognition. Amongst the numerous applications that use face analysis, the application that is not much explored is an analysis of expressions in Indian classical dance. The expressions that are peculiarly used in Indian classical dance strictly follow the ancient literature on drama and dance. Also, the expressions are deliberate as opposed to real-life expressions. This is the motivation behind carrying out a separate analysis for dance-specific expressions. The dataset of facial images with dance-specific expressions is created. The images of trained classical dancers with and without makeup are captured in a controlled environment.The local binary pattern, a widely used texture descriptor, is used for feature extraction. The classification of the expressions into 7 classes viz. happy, sad, angry, fearful, surprised, disgusted, and neutral is carried out using linear SVM with 81.38% recognition rate. The expressions in Indian Classical Dance that are difficult to differentiate from a neutral (due to the nature of expression) need to be identified. To do so, the differentiability of the expression from neutral face is calculated using template matching and SVM. This experiment is carried out on our dataset successfully and more than 90% recognition rate is achieved for most of the expressions. "Anger" is identified as the expression that is difficult to differentiate from neutral in the dance specific dataset.
Millimeter-Wave Imaging Using 1-Bit Programmable Metasurface: Simulation Model, Design, and Experiment

In this paper, a comprehensive study of millimeter-wave (mmW) imaging for far-field and near-field objects using 1-bit programmable metasurface (PMS) is presented. Theoretical PMS imaging simulation models are proposed by using plane-wave angular spectrum approach, in which two simulation models based on the flexible mmW beam-forming of PMS are developed. The first imaging model steers the high-gain beams electronically to scan over the region of interest, which is suitable for far-field object detection and surveillance. In the second model, randomly complex beams are generated that can sample the target objects in time domain, which is suitable for Fresnel region imaging. Physical optics theory is employed in the two imaging models for calculating reflected waves. Then, a 1-bit $20\times20$ PMS imager operating at 35 GHz is designed. The PMS imager is composed of a PIN-diode-loaded metasurface and a control board with 400 output channels. Finally, we measure 64 groups of randomly distributed fields using the planar near-field scanning technique in the anechoic chamber. Through imaging reconstruction, it is verified that the proposed PMS is a promising technique for mmW imaging with excellent performance.
Real Time Speed Trend Analysis and Hours of Service Forecasting Using LSTM Network

Learning to predict waiting time of a fleet truck is a hard problem, specially for long haul journeys. In the logistic industry, unexpected delay is a big concern since it directly correlates to detention cost. Although it is important to drill down to root causes of delay, it is more desired to predict delays, even before they have occurred. In this paper we propose a new field of study called vehicle speed trend analysis and hours of service forecasting using LSTM network. Over the course of journey of a vehicle, location updates are received in real time. The sequence of location updates are then converted to speed. Using historic data for specific route and a sliding window method over sequence of speed, we train layers LSTM network. In real time tracking, speed sequence for the first 20% of the journey is considered as input to the trained LSTM network. We show that the network is able to forecasts speed trend for the rest of the journey. Finally, we also show that for fleet trucks travelling long distance, the network is able to learn trend in hours of service with mean absolute error less than an hour.
Design of Information System in Disaster-robust Zero Energy Vehicle

In Japan, some experts forecast that earthquakes directly under the Tokyo Metropolitan area will happen. Large-scale earthquakes will destroy that city functions and lifelines. It will take much time to start public aid, such as the Self-Defense Forces after a disaster. Therefore, it is essential to provide quick and flexible support to victims until public aid begins. Provisioning information, electric power, and emergency relief are necessary for many victims. Consequently, we are developing a system called D-ZEV and D-ZEV mini to provide information, power supply, and a first-aid space unit. D-ZEV consists of two light commercial vans to start supporting operation in a short time. D-ZEV mini is a bicycle and collects regional damage information. In this paper, we design two systems for D-ZEV and D-ZEV mini: (1) Information provision system for providing information from D-ZEV to victims and (2) Information sharing system for efficient information sharing between D-ZEV minis. Trial manufacture and experiment show the effectiveness of the proposed systems.