Miracle-dz/UniFAR-FTUs huggingface dataset:
Helmet Detection Using ML & IoT

This paper is about detecting two-wheeler riders without helmet with the help of machine learning and provide them with a user interface to pay challans. The proposed approach first captures the real time image of road traffic and then differentiates the two wheelers from other vehicles in the road. It then processes to check whether the rider and pillion rider are wearing helmet or not using OpenCV. If any one of the riders and pillion rider found not wearing the helmet, their vehicle number plate is processed using optical character recognition (OCR). After extracting the vehicle registration number, a challan will be generated against respective vehicle and all the details of the challan will be sent via E-mail and SMS to the concerned person. An user interface (an app and a website) will also be provided to pay their challans.
Good data? The EEG Quality Index for Automated Assessment of Signal Quality

Translating EEG technology into real world applications is limited by a variety of challenges, one of which is signal noise from portable, low-density EEG devices operating in uncontrolled environments. We have developed an automated general-purpose framework, the EEG Quality Index, for the objective assessment of EEG signal quality. This framework currently uses 6 metrics that are shown to be highly sensitive to various typical artifacts. Normalized scores for each metric can be used in a framework to provide indications of the overall quality of the recorded EEG signal. Using simulated clean and artifact-contaminated EEG in a Support Vector Machine classifier, the EEG Quality Index metrics can identify artifact-contaminated EEG with 90.75% accuracy, 97.7% sensitivity, and 83.8% specificity. This is the first demonstration of a general-purpose framework for the automated and objective assessment of the quality of EEG data. With portable, low-density EEG systems being increasingly adopted in real-world clinical applications, this type of quality assessment is important.
Toward Requirements Specification for Machine-Learned Components

In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead, they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.
Profit Maximization for SaaS Provider using Improved Strategy for Resource Allocation in Cloud Computing Environment

In the present era of cut-throat competition, IT tools and services provided by cloud computing environment plays an important role in the success of any business organization. These services are offered by the SaaS providers, who act as a bridge between organizations and the available resources. These providers focus on the optimum utilization of available resources without violating the quality constraints and maximization of profit for gaining competitive advantage. The present research is an effort to maximize the profit of SaaS providers by devising an algorithm to overcome the limitation of present system. Renting of resources in heterogeneous environment has been suggested as a way to improve earnings. Also, dynamic scheduling and re-scheduling of available resources has been used to improve utilization of available resources. Aim is to control cost and gain customer loyalty. Thus, the objectives of the research are: 1) to design a knowledge-based algorithm for optimal allocation of resources; 2) to design an algorithm for scheduling of resources to maximize profit and control cost and 3) to propose a system model to earn maximum customer satisfaction.
Analysis of low power novel SRAM design with self-biasing approach for ultra-low power applications

Leakage power dissipation has been an overriding constraint for the digital device, as portable devices contain static random access memory (SRAM) units. The memory units have longer standby mode than the active mode, which further increases the leakage current. In this paper, the literature based on the classification of leakage reduction techniques for low power SRAM has been explored. In order to achieve low leakage power in scaled SRAM cell, we have been proposed a novel SRAM cell with the self-biasing (SB) approach. Four topologies are introduced based on PMOS self-biasing and NMOS self-biasing approach. The proposed design was analyzed and Simulated with cadence virtuoso simulator using gpdk 45 nm technology. The proposed design 1 and design 2 significantly minimizes the leakage power by 78% and 51% respectively as compared to the basic design. However, the proposed designs marginally reduce the signal to noise margin.
Designed Model of Supporting System for Healthcare Facilities: Home-based Supportive Service

As the demand of patients for long term care significantly increased, caregivers also needed the intensive home care and accessibility of healthcare facilities more. The mobile health or mobile applications became a part of technologies which adapted into the healthcare to improve services of healthcare facilities and increase outcome of caring because it was accessible through internet connected and smartphone was highest used in technology term. With advanced information technology in health care and mobile technology, electronic health record (EHR) and other health data were provided to many wireless telemedicine systems including the home health care system. This study aimed to provide the new designed model of health care system for home-based service to support long term care as mobile health.
Suppression of Antenna Backscatter on a Nanosat Using a Resistively Loaded FSS Absorber

A very thin microwave absorber that can be integrated into the outermost layer of thermal blankets is proposed as a means to electromagnetically decouple payload antennas from spacecraft platforms. The effectiveness of this concept is evaluated for a critical test case where a linearly polarized dipole antenna working at 10 GHz is placed at three different distances above the surface of a $\text{10 cm}$ Nanosat mock-up. This scenario is chosen as the dipole will radiate into both half spaces and any reflections from the vehicle onto which it is mounted will significantly affect its behavior. A resistively loaded frequency selective surface (FSS) absorber is employed to supress the energy contained in the antenna backlobes, and it is shown that the installed performance for all three geometries closely resembles the beam shape of the antenna in isolation. Experimental results have been obtained for one arrangement to confirm that the directive gain is increased by 18 dB in the boresight direction. The FSS absorber is composed of an array of resistively loaded hexagonal patches that are patterned on a 1.12 mm ($\lambda /25$) thick foil-backed polyethylene terephthalate sheet to replicate the physical construction of commercially available space blankets.
Smartphone-Based Indoor Fingerprinting Localization Using Channel State Information

Indoor localization technology plays an important role in many indoor application scenarios. Existing WiFi-based indoor localization methods mainly obtain channel state information (CSI) through the personal computer, or obtain coarse-grained received signal strength (RSS) through the smartphone to finish the localization. Little work has been done on using smartphones to obtain fine-grained channel state information for localization. In this paper, we use the smartphone to collect fine-grained CSI that is more convenient and applicable, and propose a indoor fingerprinting localization. Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly. Hence, we corrects the CSI data through the signal processing technique and selects optimal subcarriers to obtain more stable and effective signals. In order to cope with the noisy WiFi environment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to remove abnormal sample points to reduce environmental interference. Moreover, the support vector machine multi-classification method is used for training and classification to achieve localization. Finally, we use the Google Nexus 5 smartphone to conduct experiments in two typical indoor environments. The localization accuracy is 91% and 86%, respectively, and both average localization errors are less than $0.5m$ . Experimental results show that the proposed algorithm has higher localization accuracy compared with the typical algorithms.
Mechanism of Improved Luminescence Intensity of Ultraviolet Light Emitting Diodes (UV-LEDs) Under Thermal and Chemical Treatments

In this work, the influences of thermal annealing and chemical passivation on the optical and electrical properties of ultraviolet light-emitting-diode (UV-LED) were investigated. The electroluminescence (EL) intensities of the LEDs under KOH treatment and thermal annealing increased by 48% and 81%, respectively compared to as-fabricated LED under current level of 10 mA. Cathodoluminescence (CL) mapping of UV-LEDs confirmed no variation of the density of the non-radiative recombination centers after surface treatments, and no obvious change in surface morphology was identified due to lacking of energy for surface atom migration. However, Raman spectroscopy indicates a relaxation of compressive strains inside the thin film after both thermal and chemical treatments, and conductive atomic force microscopy (c-AFM) also illustrated reduced leakage current after KOH passivation, which are responsible for the improved luminescence properties of UV-LEDs.
Modeling and Design of a Fuzzy Logic Based PID Controller for DC Motor Speed Control in Different Loading Condition for Enhanced Performance

This paper proposes the design and simulation of a Fuzzy Logic based PID controller for DC motor speed control. The speed of a DC motor depends on armature voltage, field current and torque. In this paper, the armature voltage is changed in order to have the required speed output. The armature voltage is firstly controlled by a simple proportional controller and then controlled by PID constants and tuned by a Fuzzy Logic Controller (FLC). The FLC tunes the values of PID parameters based on the fuzzy rules fed into it and gives a satisfactory output wave shape for wide range of motor speed variation. Constant output voltage is maintained in the proposed system though changes are done in input voltage. Hence, significant amount of reduction in overshoot is observed which improves the efficiency of the motor. MATLAB-Simulink environment is used to achieve the results of the simulation and carry out the analysis.
HMM Optimized Modeling of SSD Storage for I/O MapReduce Workloads

Flash-based SSD draws a considerable interest in big data platforms due to its performance and reliability. However, it still has limited usage as a result of its high cost and limited capacity.Control SSD provisioning on big data platforms reduce storage cost and guarantees performance. The workload is an essential SSD provisioning sources, thus analyzing the characteristics of the workloads would help optimize SSD management design. There is a significant correlation between the workload’s IO patterns and the SSD cost and performance.Big data platforms with multi-stage architecture bring challenges into modeling IO patterns where each stage has it is unique IO patterns. Also, big data platforms run on a distributed environment where the workloads are interacting with local and remote storage during the execution. The designed HMM-based IO patterns model considers IO patterns for MapReduce workloads at different stages and different SSD locations.In this paper, we proposed a platform-level SSD, cost-efficiency controller. The controller is responsible for maximizing the SSD lifespan on the Hadoop platform through two phases. First, modeling MapReduce workload’s IO patterns by employing the Hidden Markov Model (HMM). Then, defining platform-level SSD allocation policies. The designed allocation policies reduce SSD utilization and improve SSD lifespan on Hadoop by up to %40 compared to static allocation policies.
Feature Reduction and Selection Based Optimization for Hybrid Intrusion Detection System Using PGO followed by SVM

The requisition or insistence of internet (web) connectivity i.e. wireless network like WSN, MANET, cellular network, broadband increases day by day. So it is obvious that increase demand of connectivity increase the problem also i.e. safety and security. In this paper discusses the security issue or problem on connectivity network generally define as the network intrusion (malicious activity) finding system. This system has to be used for secure or protect the information data from any unwanted activities. In this paper presents the feature reduction and selection based on an optimization mechanism which followed by supervised learning classifier. This paper introduce the hybrid intrusion detection system using supervised classifier i.e. SVM followed by the optimization mechanism i.e. PGO. Every IDS system needs reduce feature data set attributes to perform efficiently and smoothly that has to be major issue for any NIDS. The hybrid optimization mechanism provide the optimal solution, plant growth optimization mechanism inspired the natural tree growth process, here make this an artificial plant growth process and apply for data set attributes and set similar condition. That optimization method provide the best fitness value for branches and leaf for an artificial plant, these branches or leaf fit for artificial plant growth or not. According to these fitness values data set attributes further classified into intruder class. In this paper present mechanism or system use NSL-KDD data set (i.e. basically intruder class attribute data sets contain DOS, PROBE, R2L and U2R intruder class) for evaluation and comparing the mechanism performance in term of accuracy and Kappa. This hybrid mechanism based on optimization decreased the false alarm rate of the system and enhance the performance.
Extended Path-finding RWA Algorithm Based on ACO in Optical Satellite Network

An extended pathfinding routing and wavelength assignment (RWA) algorithm based on ant colony optimization (ACO-EP) is proposed in order to enhance the global exploratory capability. The algorithm introduces heuristic function which is based on wavelength availability to solve the problem of unbalanced wavelength usage and introduces volatilization coefficient and random interference mechanism based on traffic intensity to improve the shortcoming of easily falling into local optimum. The communication success rate and transmission delay performance are simulated in the scene of Iridium satellite constellation. The results show that when traffic intensity is 138.6Erl, compared with the original ant colony algorithm, ACO-EP algorithm can improve the communication success rate by 23.56% and reduce the communication delay by 13.89%.
Intelligent Management of Microgrid for Optimal Participation in Electricity Market Using Game Theory

In this model, the electricity market is highly penetrated with renewable resources. As renewable is the backbone of the system, the system is extremely dynamic in nature. So, in this paper, we have studied the reaction and behavior of both market participants i.e., the supplier and the consumer to the dynamic system. By the means of noncooperative game theory, demand side management or load management or optimized load is generated according to the suitability of the price. By the means of cooperative game theory, each supplier's contribution of power to accomplish the optimized demand is decided. Besides solar and wind, battery and diesel generators (DGs) are also provided as back up supply. Grid is also presented here as an independent system operator (ISO) to feed power during power deficit condition otherwise the renewable suppliers can sell their surplus power to the grid. By keeping in mind both participants profit, an optimum price has generated to maintain equilibrium in the electricity market. The entire study takes challenges for the interaction of the conceivable upcoming technologies, namely smart grids with renewable integration and the use of game theory for the economic development.
An Overview of the Photovoltaic Industry Status and Perspective in China

Photovoltaic (PV) is developing rapidly in China, and the installed capacity and PV module shipping capacity are the first in the world. However, with the changes in the global economic environment and the uncertainty of China’s PV policy, especially after the 531 new policy, China PV has started a new cycle. To understand the laws of the development of photovoltaics in China better, the article first introduces the distribution of China’s solar resources, sorts out the development process of China’s PV, focuses on the development of China’s PV Top-runner project, and emphasizes the role of advanced technology in the application of the Top-runner project for grid parity and industrial development. Then it expounds the evolution of PV module technology, inverter technology and System design technology, and analyzes the development status of photovoltaic industry chain and production of Chinese PV enterprises. Finally, it summarizes and predicts the development trend of China’s PV industry and gives recommendations for China’s PV development.
HfO2 based MIS type RRAM as an Electronic Synapse

We demonstrate the potential of MIS (metal-insulator-semiconductor) type RRAM as an electronic synapse. The fabricated RRAM stack, $\mathrm{n^{+}-Si/HfO_{2}/Ti/Al}$ shows pertinent switching only when formed in inversion regime. This can be attributed to role played by an oxide interfacial layer (IL) formed at the HfO2/Si interface and oxygen scavenging layer of Ti metal over the HfO2 layer and the variation in density of oxygen vacancies at the respective interfaces, in filament formation and rupture mechanism. The multiple resistive states are attained by the controlled sweeping to emulate the synaptic behavior. Here with the help of physical model we tried to explain switching of a MIS RRAM and demonstrate the learning of a synapse using STDP which shows the change in device conductance as a temporal function of spiking order. We experimentally corroborate that proposed MIS type RRAM manifests bio-synaptic behavior akin to MIM stack and shows feasibility to be deployed as an artificial synapse for neuromorphic applications.
Classification of Delay-based TCP Algorithms From Passive Traffic Measurements

Identifying the underlying TCP variant from passive measurements is important for several reasons, e.g., exploring security ramifications, traffic engineering in the Internet, etc. In this paper, we are interested in investigating the delay characteristics of widely used TCP algorithms that exploit queueing delay as a congestion signal. Hence, we present an effective TCP variant identification methodology from traffic measured passively by analyzing $\beta$, the multiplicative back-off factor to decrease the cwnd on a loss event, and the queueing delay values. We address how $\beta$ varies as a function of queueing delay and how the TCP variants of delay-based congestion control algorithms can be predicted both from passively measured traffic and real measurements over the Internet. We further employ a novel non-stationary time series approach from a stochastic nonparametric perspective using a two-sided Kolmogorov-Smirnov test to classify delay-based TCP algorithms based on the $\alpha$, the rate at which a TCP sender's side cwnd grows per window of acknowledged packets, parameter. Through extensive experiments on emulated and realistic scenarios, we demonstrate that the data-driven classification techniques based on probabilistic models and Bayesian inference for optimal identification of the underlying delay-based TCP congestion algorithms give promising results. We show that our method can also be applied equally well to loss-based TCP variants.
Comparative Study of Multilabel Classifiers on Software Engineering Q&A Community for Tag Recommendation

In the paper, we are analyzing and classifying the tags of the textual content of Software Engineering, Stack Exchange Q&A website by using text pre-processing and classification algorithm. These methods were chosen because the tags provided by the dataset were too general to contextualize the questions. Further the selected classification methods are being compared using accuracy, and used to predict the tags correctly. The accuracy found for LinearSVM is the highest among all the classifiers. The tags predicted by LinearSVM has given the best results of 96% as its ROC score.
Analysis of NCFET with Si Doped HfO2 as Ferro-Electric Material to Achieve Lower SS

By using ferroelectric material as gate insulator in MOSFET (NCFET), Sub-Threshold Swing can be reduced below the fundamental limit of 60mV per decade. Achieving the negative capacitance depends on the ability to drive ferro- electricity away from its local minimum to non-equilibrium state which is very unstable. It can be stabilized by adding a series capacitance. Hence, a ferroelectric capacitor in a series connection with a channel capacitor can be made to model a ferroelectric MOS capacitor to get a steeper Id-Vgs curve than a regular MOSFET.
Optimized Quantum Dot Light-emitting-device with Insulating Layer

We reported the preparation of CdSe/CdS/ZnS based quantum dot light-emitting-devices (QLED) with an ultra-thin PMMA layer. This PMMA layer between the quantum dots layer and the electron-transport layer retards the electron current and results in a more balanced electron and hole injecting into emissive quantum dot layer. The optimized charge balance enhanced the device external quantum efficiency (EQE). The results show that the precise control of the PMMA layer thickness is quite important, otherwise, leading to degraded device efficiency and increased turn on voltage.
Simultaneous Denoising, Dereverberation, and Source Separation Using a Unified Convolutional Beamformer

This article investigates the applicability of a Weighted Power minimization Distortionless response convolutional beamformer (WPD) to simultaneous denoising, dereverberation, and source separation. The WPD is a recently proposed maximum likelihood (ML) convo-lutional beamformer that performs denoising and dereverberation simultaneously by unifying a Weighted Prediction Error dereverberation method (WPE) and a Minimum Power Distortionless Response beamformer (MPDR). In this paper, we extend the application of the WPD not only to simultaneous denoising and dereverberation, but also to source separation. For this purpose, we introduce a source parameter estimation unit that estimates the steering vectors and the time-varying powers of all the sources from noisy reverberant sound mixtures, and integrate the unit with the WPD. We experimentally confirm the effectiveness of the integrated method in terms of objective speech enhancement measures and Automatic Speech Recognition (ASR) performance.