Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Enhancement of Real Time System using Dynamic Scheduling Algorithms

This paper presents the Enhanced Dynamic Scheduling Algorithm with Combine Scheme of Earliest Deadline First (EDF) and Modified Least Laxity First (MLLF). This proposed work is the further improvement in previously developed algorithm [3] that is combined the advantages of both EDF and LST scheduling algorithm for task scheduling to enhance the performance of the real-time system. This paperwork will focus on to meet deadlines which is the major criteria for scheduling task in the real-time system to achieve feasibility. The proposed algorithm enhances the performance of real-time task scheduling to improve the previous algorithm proposed by Dr. Radha Krishna et. al [3]. The proposed dynamic scheduling algorithm minimizes the system overhead, average waiting time and number of context switching.
A Sensorized Hybrid Gripper to Evaluate a Grasping Quality Based on a Largest Minimum Wrench

Soft pneumatic grippers, which are based on soft pneumatic actuators have been widely studied owing to their simple morphological structure, inherent compliance, and pliable grasp. Additionally, the integration of the soft gripper with various sensors to improve its functionality has also been extensively studied. Although the soft gripper is known to exhibit a robust grasping performance without accurate control, the grasping quality of the soft gripper has rarely been studied due to the lack of adequate embedded sensors and quality metrics of the soft gripper. Therefore, a hybrid gripper, which is a soft gripper with rigid components, was sensorized by embedding a soft force sensor and a bending sensor to evaluate the grasping quality. Furthermore, a new grasping quality metric for a soft gripper was proposed, which calculates the largest minimum wrench of a convex hull in the wrench space. The proposed grasping quality metric was experimentally verified, and a real-time program was developed to evaluate the grasping quality.
Fieldbus Physical Layer Simulation Using HSPICE

This paper presents a novel electrical modeling of a fieldbus physical layer. The main objective is to model a Fieldbus Physical Layer to allow a practical and flexible way to collect and analyze the different kinds of physical layer structure signaling and data. The fieldbus physical layer is a bidirectional low frequency and power transmission line with several nodes which allows the fieldbus signal to travel in a network between nodes and circulates the data stream. The proposed model is created based on transmission line modeling concept and has been simulated using HSpice. The performance of the proposed scheme is evaluated in comparison with real environment signal and compliance with the physical layer standard. The results verify the simulation performance as well as the model competency.
Variable Selection for a Nonparametric Nonlinear System by Directional Regression

The importance of discovering significant variables from a large candidate pool is now widely recognized in many fields. There exist a number of algorithms for variable selections in the literature. Some are computationally efficient but only provide a necessary condition, not a sufficient and necessary condition, for testing if a variable contributes or not. The others are computationally expense. The goal of the paper is to develop a directional variable selection algorithm that performs similar to or better than the leading algorithms for variable selections, but under weaker technical assumptions and with a much reduced computational complexity.
Variational Autoencoder With Optimizing Gaussian Mixture Model Priors

The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem. This paper proposes a variational autoencoder with optimizing Gaussian mixture model priors. This method utilizes a Gaussian mixture model to construct prior distribution, and utilizes the Kullback-Leibler (KL) distance between posterior and prior distribution to implement an iterative optimization of the prior distribution based on the data. The greedy algorithm is used to solve the KL distance for defining the approximate variational lower bound solution of the loss function, and for realizing the VAE with optimizing Gaussian mixture model priors. Compared with the standard VAE method, the proposed method obtains state-of-the-art results on MNIST, Omniglot, and Frey Face datasets, which shows that the VAE with optimizing Gaussian mixture model priors can learn a better model.
A Review on RFID based Image recognition for Driver Eligibility and Car Security

In the current world, the usage of car transportation has been increasing rapidly. Adults without proper identity are involved in road transportation, which leads to accidents and becomes a significant concern in our country. World Health Organization surveyed traffic collisions, India has the highest fatality rate of 16.6 per 100,000 people of which Tamil Nadu has more number of road collisions for a decade, and Chennai has more crashes in India than any other city. Based on the scenario of accidents and car thefts, various technologies have been designed for reducing the accident rate and also for identifying the car theft on average. Many researchers take several safety measures with recent technologies such as the clonal selection algorithm, artificial fish swarm algorithm, and intelligent car security system. Recognition of numbers in the license plate plays a significant role in identifying the car theft. This paper describes a detailed review on various technologies through which burglary of the car can be detected, and the rate of accidents can also be controlled.
BER Performance of Spatial Modulation Systems Under a Non-Stationary Massive MIMO Channel Model

In this paper, the bit error rate (BER) performance of spatial modulation (SM) systems is investigated both theoretically and by simulation in a non-stationary Kronecker-based massive multiple-input-multiple-output (MIMO) channel model in multi-user (MU) scenarios. Massive MIMO SM systems are considered in this paper using both a time-division multiple access (TDMA) scheme and a block diagonalization (BD) based precoding scheme, for different system settings. Their performance is compared with a vertical Bell labs layered space-time (V-BLAST) architecture based system and a conventional channel inversion system. It is observed that a higher cluster evolution factor can result in better BER performance of SM systems due to the low correlation among sub-channels. Compared with the BD-SM system, the SM system using the TDMA scheme obtains a better BER performance but with a much lower total system data rate. The BD-MU-SM system achieves the best trade-off between the data rate and the BER performance among all of the systems considered. When compared with the V-BLAST system and the channel inversion system, SM approaches offer advantages in performance for MU massive MIMO systems.
Characterization of the HCA2 Receptor in a Murine and a Feline Model of Human Obesity

ii Acknowledgments . vi List of Tables . ix List of Figures . x List of Abbreviations . xii Chapter 1: Review of the Literature . 1 The HCA2 Receptor . 2 The HCA2 Receptor in Various Tissues and Disease States . 5 Obesity in Companion and Food Animal Species. 24 HCA2 Receptor in Companion and Food Animal Species . 36 Conclusions and Objectives . 39 Chapter 2: High-Fat Diet-Induced Adipose Tissue Inflammation is Attenuated by Niacin in Both an Adiponectin-Dependent and Independent Manner . 42 Chapter 3: Characterization of the HCA2 Receptor in Cats. 64 Chapter 4: Characterization of Immunologic Response to the Development of Obesity in Cats . 83 Chapter 5: Conclusions and Future Objectives . 119 References . 123
A Survey of Network Attacks on Cyber-Physical Systems

A cyber-physical system (CPS) typically consists of the plant, sensors, actuators, the controller and a communication network. The communication network connects the individual components to achieve the computing and communication in the CPS. It also makes the CPS vulnerable to network attacks. How to deal with the network attacks in CPSs has become a research hotspot. This paper surveys the types of network attacks in CPSs, the intrusion detection methods and the attack defense strategies. The future research directions of CPSs network security are also presented.
On the Phase Noise Enhancement of a Continuous Wave in Saturated SOA Used for RIN Reduction

We report on the spectral characteristics of the phase noise enhancement of continuous-wave (CW) optical signals passing through a semiconductor optical amplifier (SOA) operating in the saturation regime and used for relative intensity noise (RIN) reduction. We show that the known effect of phase noise enhancement, attributed to the linewidth enhancement factor of the device, happens only at a limited band of the phase noise spectrum, and the actual measurable linewidth of the output CW signal may not be affected. While this phase noise enhancement is not shown as an increase of spectral linewidth, it can still affect system performance when coherent detection is used, especially in applications with relatively low symbol rates. Numerical simulations and experimental results are used to support the observation. A single spectral line from a quantum-dot mode-locked laser is used as the light source, which is known to have relatively high RIN (> −120 dB/Hz in the low frequency region). Experimental transmission of 16-QAM modulation with coherent detection has been performed at 5 GBd to assess the implication on system performance.
Portal: A user-friendly environment for BCI models training

This paper describes an application dedicated to noninvasive electroencephalography (EEG) signal processing and recognition. The goal of the designed software is to provide endto-end support for brain-computer interface (BCI) routines including data acquisition, signal filtering and preprocessing, feature extraction, classification and subject feedback. The software is supplied with a large number of data processing tools including ready-to-use machine learning and deep learning techniques. The real-time processing mode is also implemented.
Dynamic Model of Cyber Defense Diagnostics of Information Systems With The Use of Fuzzy Technologies

When building the architecture of cyber defense systems, one of the important tasks is to create a methodology for current diagnostics of cybersecurity status of information systems and objects of information activity. The complexity of this procedure is that having a strong security level of the object at the software level does not mean that such power is available at the hardware level or at the cryptographic level. There are always weaknesses in all levels of information security that criminals are constantly looking for. Therefore, the task of promptly calculating the likelihood of possible negative consequences from the successful implementation of cyberattacks is an urgent task today. This paper proposes an approach of obtaining an instantaneous calculation of the probabilities of negative consequences from the successful implementation of cyberattacks on objects of information activity on the basis of delayed differential equation theory and the mechanism of constructing a logical Fuzzy function. This makes it possible to diagnose the security status of the information system.
Deep Ensemble Network for Handling Class-Imbalance Problem in Land-Cover Classification

In this manuscript, a bagging based random undersampling technique using the ensemble of deep convolutional neural networks (DCNNs) has been investigated to deal with the issue of the class-imbalance problem in land-cover classification (LCC). The proposed method is intended to address a scenario in object-level LCC where some land-cover classes outnumber other classes in a specific region, and as a result, the training set contains fewer samples from some (minority) classes compared to other (majority) classes. Here, initially, some bags consisting of balanced classes from the dataset is acquired using random undersampling technique. After that, samples from each bag are used to independently fine-tune a pre-trained convolutional neural network (CNN). Finally, the decisions of all trained finetuned CNN models are combined to assign the class-label for each unlabeled test sample. In order to validate the efficiency of the proposed scheme, experiments have been conducted on the University of California, Aerial Image Dataset, and a new remotely sensed aerial image dataset obtained across the Asian subcontinent region (ASCD). The results obtained over these three different aerial image datasets provide superior performance for the proposed scheme as compared with the other state-of-the-art techniques.
A Study on the Effect of Ultra-Wide Band WDM on Optical Transmission Systems

In the optical communication, ultra-wide band (UWB) wavelength division multiplexing (WDM) transmission systems have been gathering considerable attention because it is becoming rapidly difficult to increase capacity per optical fiber within conventional limited bandwidths. Our aim is to extend the wavelength for UWB WDM systems from the widely used C or L bands to additional bands such as the S, E, and O bands. For these wavelengths to be used in a practical manner, it is necessary to study how repeater configurations such as multiplexer and optical amplifiers and inter-channel interference caused by stimulated Raman scattering (SRS) will impact optical transmission systems. We study these problems through two UWB WDM experiments. In the first, we demonstrate the first ever 5-band WDM transmission and devise a format for allocating wavelength adaptive modulation to exhaustively use wavelength dependent optical signal-to-noise ratio (OSNR). The experiment setup includes 5 channel signals each allocated in each band and 5-band WDM repeater constructed of WDM coupler, 3-dB couplers, and four type of optical amplifiers. In the second, we investigate the inter-channel interference effect from SRS between 35-channel S- and 40-channel L-band 16QAM signal transmission over 210 km. The experimental results show that there is only optical power transition between S and L bands and that no nonlinear crosstalk interference is observed.
Hybrid Robust Optimal Resource Allocation with Momentum

We study the problem of robust resource allocation with momentum following a dynamical systems point of view. Motivated by a class of existing optimization dynamics with no momentum defined on the general m-simplex, we propose a class of time-varying differential equations with momentum that achieve acceleration and preserve most of the asymptotic properties of its time-invariant counterpart. Since time-varying dynamics with momentum in continuous-time usually lack of structural robustness properties, we present a hybrid regularization that induces the property of uniform asymptotic stability in the system. We show this by using the invariance principle for well-posed hybrid dynamical systems, and we establish the existence of strictly positive margins of robustness with respect to arbitrarily small disturbances. We illustrate our results via numerical simulations.
Arrhythmia Detection - A Machine Learning based Comparative Analysis with MIT-BIH ECG Data

Cardiovascular arrhythmias are most common cardiac problem in the world. This work while focusing on development of automated detection and classification of arrhythmia using MIT-BIH database compared five machine learning algorithms with three different features. Pre-processing followed by beat detection is applied on one channel to get individual beats having PQRST window. Three features (viz. simple amplitude feature of 300 samples, area feature with non-overlapped sliding window, and area feature with overlapped sliding window) were used for five classifiers used in this study. It was observed that Artificial Neural Network with amplitude features gave best result with 99.59% accuracy which is comparable to state-of-the art methods.
Comparison of Ensemble Methods for Real Estate Appraisal

Real estate appraisal is necessary for a range of endeavours, including property insurance, investment analysis, taxation and sales listing. Along with prospective buyers and sellers, estimating the prices of real estates can benefit investors as well. This paper makes a comparative analysis of four ensemble methods, viz. Bootstrap Aggregating, Random Forest, Gradient Boosting and Extreme Gradient Boosting for real estate appraisal. The comparison is based on estimating real estate prices in Mumbai. The data for this study was obtained by web scraping from the real estate website 99acres. The collected data was utilized for training and testing the above-mentioned ensemble models and comparing their performances. Grid search was used for fine-tuning the hyperparameters of the learning models. Also, preprocessing techniques such as dimensionality reduction and one-hot encoding were used for improving the accuracy of the models. Analysis of the results shows that using ensemble methods for estimating real estate prices is a realistic prospect.
Performance Analysis of Enhanced Turbo and Polar Codes with List Decoding for URLLC in 5G Systems

The fifth generation (5G) communications has to support multitude of services. The ultra reliable low latency communications (URLLC) is one of the services to be supported by 5G. URLLC requires very low error values with short information block lengths with low latency in encoding and decoding process at the lower rates and very low error floors. Being a part of the physical layer, Channel coding plays key role in deciding latency and reliability. However, the error correction performance degrades with decreased information block lengths. Further, so far designed polar codes are design SNR dependent. Hence, this paper considers the performance analysis of enhanced turbo codes with list decoding and CRC aided polar codes designed based on method independent of design SNR with list decoding for URLLC over additive white Gaussian noise (AWGN) for short information block lengths at low code rates using block error rate (BLER) as the performance measure.
Self-Aligning and Flexible Dielectric Waveguide Plug for MMICs at G-Band

The packaging technology for transitions to dielectric waveguides in the frequency range above 100 GHz is complex and must be highly precise, and the waveguides are usually permanently connected. This letter presents a transition from a monolithic microwave integrated circuit (MMIC) to a flexible dielectric waveguide at <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula>-Band (140–220 GHz), which is self-aligning and, thus, reduces the requirements for packaging accuracy. Furthermore, the transition is mechanically decoupled to avoid mechanical stress to the MMIC and to reconnect it arbitrarily often. A patch radiator on a quartz-glass carrier is excited by a coupler on the MMIC. It feeds the <inline-formula> <tex-math notation="LaTeX">$\mathrm {HE_{11}}$ </tex-math></inline-formula> mode into a rigid, high-permittivity dielectric dome, which increases the coupling efficiency. The flexible dielectric waveguide is placed above the dome and fixed with Rohacell half shells. The minimum insertion loss measured with a back-end-of-line (BEOL) MMIC is 3.0 dB at 168 GHz.
Slip-Aware Motion Estimation for Off-Road Mobile Robots via Multi-Innovation Unscented Kalman Filter

Benefiting from high mobility and robust mechanical structure, ground mobile robots are widely adopted in the outdoor environment. The mobility of skid-steered mobile robots highly depends on the nonlinear and uncertain interaction between the tire and terrain. This paper introduces an approach to estimate the position, orientation, velocity, and wheel slip for the skid-steered mobile robots navigating on off-road terrains. More specifically, a Multi-Innovation Unscented Kalman Filter (MI-UKF) is developed to fusing different sensors’ data. Historical innovations generated along the time sequence are merged into the update process of standard UKF to improve the accuracy of motion estimation. In the proposed estimator, an asymmetric ICR kinematic indicating wheel slip is taken into localization process. A four-wheeled prototype is introduced and three challenging test scenarios are designed. The improvements in orientation and velocity estimation are achieved according to results comparison. In the turning maneuver, the ICRs-based model operates more steady than the traditional wheel slip/skid model.
Node Replacement Method for Disaster Resilient Wireless Sensor Networks

In recent years, wireless sensor networks (WSN) have evolved from single-hop networks to complex multi-hop (mesh) networks. WSN nodes are finite resources that must be replaced periodically, either due to damage or because they have depleted their energy. When multiple nodes fail simultaneously, a replacement priority must be used to replace the most significant nodes first. In this paper, we propose a method to calculate a node replacement priority in multiple situations. Our method is designed to be used in WSN. Unlike traditional networks, WSN values their nodes not only on the amount of data they collect but also the size of the area their sensors can cover. Traditional node replacement approaches rarely consider these factors. Our method uses the sensor's coverage area to assign a priority to each node and determine its relevance in the network. This priority assignation considers not only the sensor coverage area of a single node, but also the aggregated sensors' coverage areas of multiple nodes. The proposed method is particularly useful when a multinode failure occurs (e.g. natural disasters) or when diagnostic and maintenance tasks are necessary.
Automatic Detection of Confidence Level of Examinees in Answering Multiple Choice Questions using P1000 Brain Signal

Although Multiple Choice problem solving tests are quite efficient, they lack the power to detect the condition where a subject correctly responds to an answer by a mere guess. This paper attempts to provide a novel solution to this problem by utilizing Electroencephalography (EEG) technique. The acquired EEG signals of the subjects participating in the experiments undertaken are fed to an eLORETA software to examine the most active brain regions responsible for this cognitive activity. From eLORETA solutions, it is inferred that the frontal and the parietal lobes have enhanced activation during the said task. The signals obtained from the specific brain regions are further analyzed to investigate the presence of one specific (yet novel) Event Related Potential (ERP), called P1000. We found that P1000 is elicited when a subject is fully confident regarding the correctness or incorrectness of the options to a given question. However, when a subject is in ambiguity and attempts to guess a response, no significant ERP component is released. A Convolutional Neural Network (CNN) based classifier is employed to classify the EEG response during selection of the alternatives to a question into 2 classes: confident and ambiguous on the basis of presence or absence of P1000 signal. The proposed classifier yields a classification accuracy of 98.34% during training phase and an accuracy of 94.54% during testing phase. Thus, the proposed method can effectively be utilized to detect subjects free from lack of confidence.