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Current non-intrusive load monitoring (NILM) algorithms are reasonably accurate when the sampling rate is high. However, in practical scenarios, when the sampling frequency is low, the performance of most algorithms deteriorates. This work proposes a solution for low-frequency NILM. We propose to modify the smart-meter such that it can transmit at low frequency using principles of compressed sensing (CS). From such CS samples, we propose to detect the state of the appliance by using a multi-label consistent version of deep blind compressed sensing. Comparison with existing techniques shows that our proposed approach yields considerably better results.

As there is a quick development in human populace and clinical use and furthermore the medical services have gotten quite possibly the most critical issue for the two people just as the public authority. Then, as per a report from World Health Association (WHO), the issue of an exceptionally expanding populace is getting more genuine. The strength of matured individuals should be checked all the more often, which represents a more noteworthy challenge to the existing clinical framework. Henceforth, to distinguish human illnesses as expected and precise way with minimal effort has been given an expanding consideration. Because of the strength in the conclusion of heart-related infections, electrocardiogram (ECG) observing has been broadly utilized in the two emergency clinics and clinical examination. An electrocardiogram gadget screens the cardiovascular status of a patient by recording the heart’s electrical potential versus time. Such gadgets assume a vital part to save the existence of patients who endure a respiratory failure or experience the ill effects of these patients. Early identification of conditions that lead to the beginning of heart failure permits specialists to give legitimate treatment on time and keeps passing or incapacity from heart failure. Most non-industrial nations have very helpless data about these medical services issues. Here we proposed an approach that is able to check the ECG level with low cost and power consumption.

International audience; This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inherently time varying. However prior multi-label classification techniques could not model this dynamical behaviour. They used off-the-shelf algorithms for classifying static signals on NILM problems. This is the first work that shows how to account for the temporal variability of input signals in a multi-label classification framework. Results on benchmark datasets like REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques.

This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approach (developed for single label classification) to solve multi-label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state-of-the-art techniques with small volume of training data.

Agent-based Modelling is a simulation technique that is used to solve critical problems in several fields. Separate agents are used as the input, and then the simulation starts. In this paper, a literature review of 10 years papers is done to analyze the basic features, benefits, applications and challenges of using Agent-based modelling in different sectors. A detailed description is given about the characteristics of Agent-based Modelling in the operational sector.

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

In this paper, Detailed study is done for power quality and role of Dynamic Voltage Restorer (DVR) in solutions of power quality problems those occur into the power supply at the time of power distribution and improper loading such as mitigation of voltage dips for aim of protection of power quality sensitive devices. Further we will be comparing the compensation results DVR with compensation results of FACTS devices.

SIGCSE is packed with teaching insights and inspiration. However, we get these insights and inspiration from hearing our colleagues talk about their teaching. Why not just watch them teach? This session does exactly that. Six exceptional educators will present their favorite piece of innovative lecture content just as they would to their students. The moderator, Colleen Lewis, will describe the central pedagogical move within the innovation and how this connects to education research. The goal of the session is to inspire SIGCSE attendees by highlighting innovative instruction by exceptional educators. The specific content of the innovative instruction may be applicable for some attendees, and the discussion of the underlying pedagogical move within each innovation can be applied across the attendees' teaching.

Jerk chaotic systems have many applications in science and engineering. In the past few decades, significant attention has been paid on the modeling of new jerk differential equations arising in engineering. An important model of a plasma torch jerk differential equation exhibiting chaos was reported by Ghorui et al. (2000) . The Ghorui jerk system is a recent example of a jerk chaotic system consisting of a just cubic nonlinearity and linear terms in its dynamics. This work announces a new plasma torch jerk chaotic system by adding a quadratic nonlinearity to the Ghorui jerk chaotic system. We show that the new plasma torch jerk system has a self-excited chaotic attractor with three unstable saddle-foci rest points. We give a detailed bifurcation analysis of the new plasma torch chaotic jerk system. We also report that the new plasma torch chaotic jerk system has multistability and coexisting chaotic attractors. As control applications, we use active backstepping control laws for the global chaos stabilization and global chaos synchronization for the new plasma torch chaotic jerk system. Finally, we design an electronic circuit of the new plasma torch chaotic jerk system using Multisim for practical implementation in engineering.

Education loan is a significant factor contributing to one’s decision to pursue studies. Students now do not hesitate about taking the risk of being in debt of thousands of dollars. They believe in getting the highest quality of academic qualification first without realizing how risky it can get to be in such an enormous amount of debt. Banks nowadays are also wary of approving loans because they face difficulty in analyzing how credible the borrower is. It makes the whole process very tedious and time taking and often proves to be inefficacious. Prediction of education loan repayment rate can make the job easier for both banks and the applicants. This research paper aims at analyzing the education loan repayment rate by the use of machine learning algorithms. Machine learning is extensively used now and finds application in almost every domain. Predictions and analysis carried out using ML helps in making an informed decision and gives an idea of how future trends predict to look. In this research, various features are analyzed and researched thoroughly by the use of Python language. Its extensive set of libraries enables easy manipulation and visualization of the data. The paper contains a description of the analysis and rich visuals to produce a clearer image of the dataset. Various models are implemented, and their accuracy is measured using the R2 score.

This chapter presents the multi-switching synchronization of nonlinear hyperchaotic systems via active backstepping control. The multi-switching complete synchronization scheme has been examined such that the state variables of slave system synchronize with different state variables of master system. The controllers are computed via active backstepping control design and Lyapunov stability criteria is used to ensure the stability of the dynamical system. To perform this methodology a hyperchaotic system is considered and it is applied on three switches. Numerical simulation results are performed to demonstrate the validity and feasibility of the applied method.

This paper summarizes Data science which is an upcoming and advanced field in technology. We know that in today’s world storing large amounts of data is not easy and hence we use data science for it. Data science is a multi- disciplinary field that makes use of algorithms, processes to extract information from structured and unstructured data. Breast cancer is a form of cancer that develops in the breast cells. Breast cancer is the second most common cancer diagnosed in women all over the world, after skin cancer. Breast cancer can strike both men and women, but it affects women much more frequently. Major support for breast cancer awareness and education has assisted in the advancement of breast cancer diagnosis and care. This paper focuses on the use of data science to predict Breast Cancer. Generally, we use two languages for coding in Data Science – Python and R. Authors in this paper have used Python for implementation of the work. The main focus of this paper will revolve around Data Science, Python and Breast Cancer Prediction. Data science has importance in various fields and has shown itself as well be it medical or any other it has been on the list. The main focus of the paper is to lay emphasis on breast cancer prediction and use of python and data science in it.

Motion planning problems of a wheeled robot remain challenging for the researchers and are being solved by applying various nature-inspired algorithms. Therefore, this article implements the sunflower optimization algorithm and tries to solve the motion control and planning of a wheeled robot. A sunflower optimization algorithm is a newly invented algorithm, and it is inspired by the motion of sunflowers to capture solar radiation. In this study, the differential driven two-wheeled Pioneer P3-DX (P3D) robot has been chosen for experiments. The real-time ultrasonic sensors data (obstacles distance) information have been taken as inputs of the algorithm, and the steering control angle of the P3D robot is an output of the algorithm. The objective function of the algorithm has been made by taking these inputs and output data. Further, the implemented algorithm has been tested in the Virtual Robot Experimentation Platform (V-REP) software scenarios on differential driven two-wheeled P3D robot. Next, this implemented algorithm has been compared with previously developed particle swarm optimization algorithm to verify the effectiveness and authenticity of the implemented algorithm.

Path planning for robots is one of the primary parameters to justify the implementation of robots in any particular application. Path planning hugely depends on the computation time required to derive the optimal path from the start point to the goal point. Most of the approach follows a linear approach to derive an optimal path for robots while traversing from source to goal. This chapter proposes a novel method of using a non-linear mechanism within the cuckoo search algorithm to approach the goal. Using the core concept of the Cuckoo Search algorithm for finding path, this chapter proposes an approach of implementing an alternative mechanism of selecting the next best solution in the given workspace. After getting stuck with the obstacles, the proposed approach opts an alternate way to get out of that obstacle, i.e. one-time considering the minimum distance and another time maximum distance from the obstacle. The proposed non-linear approach converges rapidly towards goal and similarly comes out of the obstacle zone. The proposed approach is implemented and compared with the original CS algorithm which shows a noticeable improvement in deriving path from source to goal and a considerable reduction in computation time required to derive the optimal path for robots traversal in the workspace (Sharma and Doriya, 2019 4th international conference on computing, communications and security (ICCCS), pp 1–4. IEEE (2019) [1]).

In non-intrusive load monitoring (NILM), an increase in sampling frequency translates to capturing unique signal features during transient states, which, in turn, can improve disaggregation accuracy. Smart meters are capable of sampling at a high frequency (typically 20kHz). However, transmitting signals continuously would choke the network bandwidth. Given the deployment of millions of smart meters which communicate over a wireless wide-area network (WAN), utilities can only collect power signals at very low frequencies. We propose a compressive sampling (CS) approach. After measuring the high-frequency power signal from a smart meter will be encoded (by a random matrix) to very few samples making the signal suitable for WAN transmission without choking network bandwidth. CS guarantees the recovery of the high-frequency signal from the few transmitted samples under certain conditions. This work shows how to simultaneously recover the signal and disaggregate it; hence, the name Compressive NILM.

This research is focused on the data analytics for the available data for COVID-19 pandemic disease. In this research work, Python and its libraries are applied for the exploratory data analysis of this secondary dataset. Considering the variation of the scenario with time, it has been observed to analyze the data with the time series analysis in order to forecast the future effect of Coronavirus globally or individually. This analysis has been conducted using seven forecast methods. But only the four methods with the least errors are reported here, viz. NAIVE, Holt's linear trend method, Holt's Winter seasonal method, and ARIMA. It is so much required to forecast the future possibilities in such random and unique occurrence of the pandemic around the world. This analysis helps many researchers and scientists to understand the statistical forecasting which will be a great support for future preparedness. This analysis will be helpful for the organizational and social entities to tackle this pandemic across the country.

An Electroencephalograph (EEG) is widely used to study the working of brain. The main challenge in dealing with such signals is its non-stationary nature and high dimensionality. The propagation of signals from one electrode to other gives rise to presence of artifacts in the data. This can lead to generation of false results while working on the data. Ocular, Muscular, and Cardiac are some of the very common artifacts for a dataset dedicated for motor imagery classification. This paper deals with the techniques and their comparative analysis for removal of ocular artifact from EEG signals. On the basis of dataset, the paper shows a study of how Linear Regression, Filtering, and Independent Component Analysis(ICA) works on the removal of ocular artifact in this data. To verify the effect of artifact removal technique features were extracted from the data, before and after applying different techniques and further fed to the classifier. An improvement in the accuracy of motor imagery classification is observed when we apply various techniques of artifact removal. ICA proves to remove the artifacts most efficiently as compared to the other two techniques on this dataset. Precision, Recall, and F1-Score values are also determined for the comparative analysis.

In the competitive world, many semiconductor organizations are facing difficulties due to the scarcity of resources, and this creates a challenge for organizations to sustain in the market. Cloud technology is boon for these organizations, as cloud technology facilitates the pay per use model. Even after the acquaintance of the benefits of the cloud, the organization faces difficulties while adopting this technology in the semiconductor domain. The objective of our research is to understand the factors which impact the adoption of cloud technology in the semiconductor industry and how these factors are inter-related. We adopted the TOE (technical, organizational, and environmental) framework to determine the success factors. In this research, we used multiple criteria decision-making methods to assess the causal relationship among the dimensions and their corresponding factors, which further helps to calculate the overall priority of factors. We found that factors under technological and organizational dimensions are the causal factors that are affecting the adoption of cloud technology in the semiconductor industry.

Given an input stream of size N, a φ-heavy hitter is an item that occurs at least φ N times in S. The problem of finding heavy-hitters is extensively studied in the database literature. We study a real-time heavy-hitters variant in which an element must be reported shortly after we see its T = φ N-th occurrence (and hence becomes a heavy hitter). We call this the Timely Event Detection (TED) Problem. The TED problem models the needs of many real-world monitoring systems, which demand accurate (i.e., no false negatives) and timely reporting of all events from large, high-speed streams, and with a low reporting threshold (high sensitivity). Like the classic heavy-hitters problem, solving the TED problem without false-positives requires large space (Ω(N) words). Thus in-RAM heavy-hitters algorithms typically sacrifice accuracy (i.e., allow false positives), sensitivity, or timeliness (i.e., use multiple passes). We show how to adapt heavy-hitters algorithms to external memory to solve the TED problem on large high-speed streams while guaranteeing accuracy, sensitivity, and timeliness. Our data structures are limited only by I/O-bandwidth (not latency) and support a tunable trade-off between reporting delay and I/O overhead. With a small bounded reporting delay, our algorithms incur only a logarithmic I/O overhead. We implement and validate our data structures empirically using the Firehose streaming benchmark. Multi-threaded versions of our structures can scale to process 11M observations per second before becoming CPU bound. In comparison, a naive adaptation of the standard heavy-hitters algorithm to external memory would be limited by the storage device's random I/O throughput, i.e., ~100K observations per second.

Convolutional transform learning is an unsupervised framework we introduced recently, for feature generation based on learnt convolutions. In this work, we propose a supervised formulation for convolutional transform so as to address the multi-label classification problem. Unlike the simple multiclass classification, in multi-label problems, each sample can correspond to multiple classes simultaneously, making the problem quite challenging. We propose to make use of a label consistency penalty and develop a suitable minimization algorithm for the training step. We illustrate the performance of the developed formulation on the practical problem of nonintrusive load monitoring. Comparisons with popular techniques show that our proposed approach yields better results on benchmark datasets.

In this paper, we study the problem of incremental maintenance of Datalog programs and model it as a scheduling problem on DAGs. We design provably good time- and memory-efficient scheduling algorithms for (re)executing a Datalog program where some (but not necessarily all) of the inputs have changed. We prove that our schedulers, called LevelBased and LevelBased with lookahead, have asymptotically improved running time and space efficiency when compared with benchmark algorithms used in production at LogicBlox.The main result of the paper is a hybrid scheduler, which combines LevelBased with the production LogicBlox scheduler (or any other heuristic scheduler). The hybrid scheduler achieves strong worst-case guarantees and robustness without losing out on the best-case behavior of the production LogicBlox scheduler. Our experiments show that the hybrid scheduler results in similar or improved total execution times compared to LogicBlox scheduler, while consistently reducing the scheduling overhead—by as much as 50% on some datasets. This hybrid scheme requires little to no overhead but provides predictability and reliability, which are crucial in a commercial application such as LogicBlox.

Purpose The purpose of this paper is to study the barriers to institutionalize the product lifecycle management (PLM) in large manufacturing organizations. The paper explores the hurdles and identifies the causal barriers to support the organizations’ transformation into digitized firms. Design/methodology/approach The paper utilized the multi-criteria decision making technique, i.e., DEMATEL (DEcision MAking Trial and Evaluation Laboratory) method to find the causal barriers, and adopted maximum mean de-entropy (MMDE) algorithm to determine the threshold value based on the information entropy of the relations among the barriers to PLM institutionalization. Findings This study explored nine barriers to PLM institutionalization and empirically identified the four critical barriers among the nine. Research limitations/implications The present work is exploratory case-based research which is limited to a case of an Indian aircraft manufacturing firm with a limited number of respondents. More sophisticated statistical tools can be utilized to consider the subjectivity of the respondents. However, this research explores the various hurdles to PLM success and serves as a relevant outcome to identify the critical barriers to institutionalize the PLM concept. Practical implications The findings of the paper provide guidelines to the case company and similar firms for obtaining maximum benefits of PLM. The methodology shown in this paper will be useful to various large scale industries in identifying the critical barriers to PLM institutionalization among all existing barriers so that they can take appropriate measures before they proceed to adopt PLM. Originality/value The present work discusses the different reasons for which the companies are not able to derive the maximum benefits of PLM even after the implementation of PLM systems. This work uniquely applied the DEMATEL and MMDE methods to investigate the critical barriers to PLM institutionalization in an aircraft manufacturing firm.

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. The traditional way to address this is via stochastic finite state machines (e.g., factorial hidden Markov model). In recent times, dictionary learning-based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learned dictionaries as basis for blind source separation during disaggregation. Prior studies in this area are shallow learning techniques, i.e., they learn a single layer of dictionary for every device. In this paper, we propose a deep learning approach—instead of learning one level of dictionary, we learn multiple layers of dictionaries for each device. These multi-level dictionaries are used as a basis for source separation during disaggregation. Results on two benchmark datasets and one actual implementation show that our method outperforms state-of-the-art techniques.

Power Supplies (PSs) employed in personal computers pollute the single phase ac mains by drawing distorted current at a substandard Power Factor (PF). The harmonic distortion of the supply current in these personal computers are observed 75% to 90% with the Crest Factor (CF) being very high which escalates losses in the distribution system. To find a tangible solution to these issues, a non-isolated PFC converter is employed at the input of isolated converter that is capable of improving the input power quality apart from regulating the dc voltage at its output. This is given to the isolated stage that yields completely isolated and stiffly regulated multiple output voltages which is the prime requirement of computer PS. The operation of the proposed PS is evaluated under various operating conditions and the results show improved performance depicting nearly unity PF and low input current harmonics. The prototype of this PS is developed in laboratory environment and test results are recorded which corroborate the power quality improvement observed in simulation results under various operating conditions.

The concept of Multi-Scale Transform (MST) based image de-noising methods is incorporated in this paper. The shortcomings of Fourier transform based methods have been improved using multi-scale transform, which help in providing the local information of non-stationary image at different scales which is indispensable for de-noising. Multi-scale transform based image de-noising methods comprises of Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). Both DWT and SWT techniques are incorporated for the de-noising of standard images. Further, the performance comparison has been noted by using well defined metrics, such as, Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT). The result shows that SWT technique gives better performance as compared to DWT based de-noising technique in terms of both analytical and visual evaluation.

This paper proposes a framework for Rule Based inhibition on e-commerce website for prevention of double payment and computing time invariant for concurrent event handling. Authors have analyzed computational models in terms of Customer segmentation replicating their buying characteristics and Dialogue level constraint establishment through OCL. The tool used are MDT-OCL and matching logic for logic level interpretation. The MDT tool generates Context Syntax Tree. We have used LPG grammar to be applied WBS codification by differentiating Descriptive Noun containing description about the products and associated verbs about the product. Authors have used Eclipse plug-ins to embed it logic constraint mapping to check for any ontological errors and double selection and payment errors.

Sleep spindles are one of the unique rhythmic activities observed in sleep electroencephalogram (EEG). Detecting sleep spindles visually by sleep spindles is a difficult task as high skills and efforts are required. In this study, a methodology for detecting sleep spindles automatically has been proposed using band-pass filtering. Time domain features (energy and entropy) are used for classification. The extracted features have been used as inputs to Linear, Quadratic, and Mahalanobis classifier for spindle detection. Results show that the proposed method yields best results when using a Mahalanobis Classifier. The accuracy, sensitivity, and specificity recorded are 91.11%, 84.86%, and 89.73%, respectively. The sensitivity obtained in this study is more than most of the work done in sleep spindles detection using the same dataset.

E-commerce is the most efficient and time-effective method to promote a product to worldwide customers. Its transaction may require submitting customer’s personal information such as username, password, credit card numbers, and financial data. Unified Modeling Language (UML) or Object Constraint Language (OCL) was one of the standardized languages for software developers with visualizing the software system in e-commerce. In e-commerce, purpose security is the primary issue meant for transferring personal data, online payment, and user details. Due to the lack of such security, a significant portion of the customers is feeling uncomfortable to send their respective information over the Internet. This study investigated completely 40 different kinds of literature which are collected from standard publications. Each research paper analyzes different ideas to secure online shopping based on the UML/OCL. In the existing papers, some of the security drawbacks are pointed out and they are overcome by various languages with different software effectively is reviewed by using new research papers.

A bridgeless power factor-corrected (PFC) converter-fed BLDC motor is modeled and designed for an air conditioning system (ACS). Bridgeless configuration eliminates the requirement of diode bridge at the input side. By this arrangement, overall losses and the total number of conducting devices used in one switching cycle are reduced. The performance of air conditioning system is validated through analysis, design and simulation.

This paper describes multi-switching synchronization schemes between three similar chaotic fractional-order systems. The Rossler fractional system is considered to apply the technique. With multi-switch combination synchronization, two drive system state variables are synchronized with different response system state variables at the same time. The multi-switching combination synchronization of three fractional-order equivalent systems was investigated based on the stability of fractional-order chaotic systems. Appropriate controllers have been configured to synchronize three similar fractional-order chaotic systems. Numerical calculations were carried out in Matlab to support the theoretical argument.

Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. In recent times dictionary learning based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learnt dictionaries as basis for blind source separation during disaggregation. Dictionary learning is a synthesis formulation; in this work, we propose an analysis approach. The advantage of our proposed approach is that, the requirement of training volume drastically reduces compared to state-of-the-art techniques. This means that, we require fewer instrumented homes, or fewer days of instrumentation per home; in either case this drastically reduces the sensing cost. Results on two benchmark datasets show that our method produces the same level of disaggregation accuracy as state-of-the-art methods but with only a fraction of the training data., Final version published in IEEE Transactions on Smartgrids

A performance comparison between without bridge single ended primary inductance converter (SEPIC) and without bridge Zeta converter based air conditioning system (ACS) is presented in this paper. In the presented models, dc motor at the output is controlled by an electronic commutation switching. The power factor at the utility side is improved using these without bridge converter configurations which are operating in discontinuous mode of inductor current. The performance of both the ACSs are analyzed and further compared to select the optimal configuration for a specific case, with power quality improvement that adheres the guidelines of international standard.

The technology of Internet of things, also curtailed as IoT, is an emerging technology these days and is often used to connect various appliances or the gadgets that are generally used by us in our daily life with the facility of internet so as to make us capable of sending commands to these connected devices in order to perform a piece of work or an action in a desired manner without the involvement of any human labor. It is introduced to reduce the mankind labor that is generally involved in performing certain activities and to spawn everything in an automatic environment that works on the basis of commands. IoT technology has its major emphasis on the connection between the “things” and the Internet. This “thing” mentioned here can be a living thing, which may include a man or an animal, so as to sense the occurrence of various activities that take place in them. It can also be a non-living thing including the gadgets or any other electronic appliances. IoT incorporates various devices that are enabled by an active connection to the Internet and are able to transfer information collected by them. These devices then act upon the records collected from their surroundings through sensors that are rooted in them. IoT technology comes up with a large range of applications these days. Some of their uses are in smart watches, smart home security and household appliances, embedded cars, and many more. In the past few years, IoT has emerged out as an innovatory technology by converting the normal conventional industries into smart industries by its numerous applications in the field of Industrial IoT and in our ordinary homes to smart homes by its applications in Home Automation System. This review paper has its emphasis on what is IoT, its working, its various applications in industrial purposes, its beneficial applications to humans and its affect in our daily lives. It also consists of a few setbacks IoT technology and various methods of removing those drawbacks so as to make it more beneficial to mankind.

Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliance-level feedback. This observation has increased the relevance of load monitoring in today's tech-savvy world. Most of the previously proposed solutions claim to perform load monitoring without intrusion, but they are not completely non-intrusive. These methods require historical appliance-level data for training the model for each of the devices. This data is gathered by putting a sensor on each of the appliances present in the home which causes intrusion in the building. Some recent studies have proposed that if we frame Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem, the need for appliance-level data can be avoided. In this paper, we propose Multi-label Restricted Boltzmann Machine(ML-RBM) for NILM and report an experimental evaluation of proposed and state-of-the-art techniques., Comment: 5 pages, ICASSP2019

This manuscript investigates the synchronization between a novel integer order hyperchaotic system and a fractional order hyperchaotic system. The controllers are constructed using the technique of tracking controller and the stability theory of the linear fractional order system. Chaotic analysis of the introduced novel integer order hyperchaotic system is also investigated. The Lyapunov exponent, bifurcation diagram, Poincare section, Kaplan-Yorke dimension, equilibria and phase portraits are given to justify the chaotic nature of the system. Theoretical results are supported with the numerical simulations.

In this manuscript, we investigate the methodology of combination-combination anti-synchronization of four identical fractional order hyperchaotic system. The methodology is implemented by considering a 4D fractional order hyperchaotic system. The controllers are constructed using adaptive control technique to ensure the combination-combination anti - synchronization. The synchronization schemes such as chaos control problem, projective anti-synchronization, combination anti-synchronization becomes the special cases of combination-combination anti-synchronization. The combination - combination scheme can additionally enhances the security of transmission of message signals. The theoretical results and numerical simulations are given to justify the validity and feasibility of the proposed control technique.

the last couple of years Radio Frequency Identification or as often called RFIDs are used to identify unique items using radio waves. RFID is like barcode reader but the reading is done remotely in case of RFID . RFID does not require line of sight for identification. Now a days RFID has a wide application in different fields such as Internet of things(IOT), real-time systems, medical monitoring, Animal tracking, inventory management, etc. In several cases their is a requirement of the localization of the objects, and for this purpose some good localization techniques are required. There are several algorithms which have been proposed for the RFID localization; as distance estimation, scene analysis and proximity. The first technique i.e., distance estimation includes SpotON, SAW ID-tags, Location Position Measurement (LPM), RSP. The second technique i.e., scene analysis includes Landmarc, VIRE, Simplex, Kalman filtering and Scout. This paper summaries and compare the above said techniques and also give a approach that will be fruitful in enhanced efficiency and increase in network system for internet of things.

Risk is a threat to every activity or system due to uncertainty with each component. Uncertainty of a system rises as the number of components increases. While uncertain risk elements cannot be assessed deterministically, a probability measurement helps an organization to combat those threats. Introduction of the Product Lifecycle Management (PLM) concept in an organization has the potential to offer substantial benefits. However, there are multiple risks in implementing PLM systems, and adequate care must be taken during PLM initiatives to get the desired benefits. If there are lacunae in the management of a PLM initiative, the whole purpose of adopting PLM may be lost partially or totally. The risk assessment of PLM systems’ implementation is performed by employing integrated grey DANP (DEMATEL-based ANP) and grey TOPSIS techniques for the first time. By considering the category and attribute of each risk factor, this integrated model reveals the critical risk factors: an inappropriate choice of a PLM system, mismatch in required data formats, and inefficient resource estimation.

Chaos theory deals with the behavior of dynamical systems that are highly sensitive to initial conditions. Chaotic systems are characterized by the property that small changes in the initial conditions result in widely diverging responses. In this paper, new control schemes of synchronization for different arbitrary incommensurate and commensurate fractional order chaotic systems are presented. Synchronization stability, based on stability of linear fractional-order systems and fractional Lyapunov stability, is proved theoretically. Numerical examples are given to show the effectiveness of the proposed method.

This paper deals with an improved power quality multiple output switched mode power supply (SMPS) using two stage DCDC converter. A non-isolated buck-boost DC-DC converter is used as the first stage whereas single ended primary inductance converter (SEPIC) is used in the second stage. The first stage of buck-boost converter is designed in discontinuous conduction mode (DCM) for inherent power factor (PF) correction. Design and simulations of the two stage DC-DC converter with load variations on the SMPS are performed in MATLAB software to demonstrate the effectiveness of the multiple output DC-DC converter to have a stiff voltage regulation and negligible ripple.Keywords: DC-DC converter; Two stage; PFC; THD; Buck-boost converter; SEPIC; SMPS; Power quality

This chapter investigates the multi-switching combination synchronization of three non-identical chaotic systems via active control technique. In recent years, some advances have been made with the idea of multi-switching combination synchronization. The different states of the master systems are synchronized with the desired state of the slave system in multi-switching combination synchronization scheme. The relevance of such kinds of synchronization studies to information security is evident in the wide range of possible synchronization directions that exist due to multi-switching synchronization. Numerical simulations justify the validity of the theoretical results discussed.

This chapter announces a new four-dimensional hyperchaotic system having two positive Lyapunov exponents, a zero Lyapunov exponent, and a negative Lyapunov exponent. Since the sum of the Lyapunov exponents of the new hyperchaotic system is shown to be negative, it is a dissipative system. The phase portraits of the new hyperchaotic system are displayed with both two-dimensional and three-dimensional phase portraits. Next, the qualitative properties of the new hyperchaotic system are dealt with in detail. It is shown that the new hyperchaotic system has three unstable equilibrium points. Explicitly, it is shown that the equilibrium at the origin is a saddle-point, while the other two equilibrium points are saddle-focus equilibrium points. Thus, it is shown that all three equilibrium points of the new hyperchaotic system are unstable. Numerical simulations with MATLAB have been shown to validate and demonstrate all the new results derived in this chapter. Finally, a circuit design of the new hyperchaotic system is implemented in MultiSim to validate the theoretical model.

An approximate membership query data structure (AMQ)—such as a Bloom, quotient, or cuckoo filter—maintains a compact, probabilistic representation of a set S of keys from a universe U. It supports lookups and inserts. Some AMQs also support deletes. A query for x in S returns "present." A query for x not in S returns "present" with a tunable false positive probability epsilon, and otherwise returns "absent." AMQs are widely used to speed up dictionaries that are stored remotely (e.g., on disk or across a network). The AMQ is stored locally (e.g., in memory). The remote dictionary is only accessed when the AMQ returns "present." Thus, the primary performance metric of an AMQ is how often it returns "absent" for negative queries. Existing AMQs offer weak guarantees on the number of false positives in a sequence of queries. The false-positive probability epsilon holds only for a single query. It is easy for an adversary to drive an AMQ's false-positive rate towards 1 by simply repeating false positives. This paper shows what it takes to get strong guarantees on the number of false positives. We say that an AMQ is adaptive if it guarantees a false-positive probability of epsilon for every query, regardless of answers to previous queries. We establish upper and lower bounds for adaptive AMQs. Our lower bound shows that it is impossible to build a small adaptive AMQ, even when the AMQ is immediately told whenever a query is a false positive. On the other hand, we show that it is possible to maintain an AMQ that uses the same amount of local space as a non-adaptive AMQ (up to lower order terms), performs all queries and updates in constant time, and guarantees that each negative query to the dictionary accesses remote storage with probability epsilon, independent of the results of past queries. Thus, we show that adaptivity can be achieved effectively for free.

In this chapter, the multiswitching synchronization scheme has been investigated for a class of nonidentical fractional order hyperchaotic systems. The multiswitching complete synchronization scheme has been examined such that the state variables of the slave system synchronize with different state variables of the master system. For the synchronization of two nonidentical fractional order hyperchaotic systems suitable controllers have been designed using active control technique. The stability of fractional order chaotic systems has been used to stabilize the error dynamical system. Two switches are considered to illustrate the methodology. Numerical simulation results are performed to demonstrate the validity and feasibility of the applied method.

This paper investigates the multi-switching master–slave synchronization of non-identical chaotic systems in which state variables of a master system are synchronized with different state variables of a slave system using the sliding mode control technique. To design the appropriate controllers via sliding mode control for different switches, Lyapunov stability theory is taken into account. Theoretical results are applied by considering two non-identical chaotic systems where one is considered as master system and another is considered as slave system. Numerical simulations are performed to justify the theoretical results discussed.

As modern computing moves towards smaller devices and powerful cloud platforms, more and more computation is being delegated to powerful service providers. Interactive proofs are a widely-used model to design efficient protocols for verifiable computation delegation.

This paper shows the implementation of an artificial intelligent chatterbot with whom human can interact by speaking to it and receive a response by chatterbot using its speech synthesizer. Objective of this paper is to show application of chatterbot that can be used in various fields like education, healthcare, and route assistance. It is statistical model and chatterbot is based on AIML (Artificial Intelligent Markup Language) structure for training the model and uses Microsoft voice synthesizer for providing speech recognition system and natural language processing.