Nanoelectronics, Circuits and Communication Systems

Nowadays, medical applications need a lot of storage for storing and providing access to the medical information seekers. Moreover in medical applications, information grows tremendously and hence they must be stored using a suitable storage structure so that it is possible to retrieve them faster from the text corpus in which the medical information is stored. The existing methods for storage and retrieval do not focus on classified organization. However, classified data storage will facilitate fast retrieval. Therefore, a new Latent Dirichlet Allocation (LDA) based topic modeling approach is proposed in this paper which uses temporal rules for effective manipulation of stored data. Therefore, a temporal rule based classification algorithm is proposed in this work by combining Naïve Bayes Classifier with LDA and temporal rules to store the data more efficiently and it helps to retrieve the documents faster. From the experiments conducted in this work by storing and retrieving medical data in a corpus, it is proved that the proposed model is more efficient with respect to classification accuracy leading to organized storage and fast retrieval.

Cloud computing will offer the adaptability of adaptably outsourcing programming framework for giving chain joint effort and foundation wants in an exceedingly higher way. The cloud computing is reshaping the information and data and correspondence innovation (ICT) store system and making a more interesting ICT condition and related advances like virtualization, Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) have been touted as the “accompanying tremendous thing” and “unmistakable favorable position” for endeavors to improve their best line and essential concern. Proficient supply chains are an imperative need for some organizations. Inventory network administration follows up on operational procedures, different and merged data streams and cooperation forms with an assortment of business accomplices. This computing is transforming into an adoptable advancement in most of the relationship with its dynamic flexibility and utilization of virtualized resources as an organization through cloud. It will probably significantly affect the numerous ventures in future days to come. It is an amazing option for the businesses which are having lack of assets so as to work their data frameworks successfully without spending any more capital for the PCs and system gadgets. Endeavors of late years are typically confronting this decent variety by making and arranging focal data framework arrangements.

This paper comes up with “Hybridized Delta Spectral Cepstral Coefficients” or HDSCC features. The HDSCC feature is obtained by processing concatenated Mel Filter Cepstral Coefficients and Delta Spectral Cepstral Coefficients (DSCC) with Quantile based Dynamic Cepstral Normalization technique (QCN). The strength of proposed HDSCC feature set has been evaluated for Hindi Vowel classification in noisy environments. The results have been compared with that obtained with MFCC and QCN-MFCC features. HDSCC feature set outweighs classification accuracy compared to that obtained with MFCC features and QCN-MFCC features in various operating conditions. Quantitatively for clean database results revealed an improvement of 20.33 and 15.93% as compared to that obtained with MFCC and QCN-MFCC features respectively for context independent (CI) cases. For context dependent (CD) cases an improvement of 18.59 and 11.69% has been detected for MFCC features and QCN-MFCC features respectively. For noisy database also HDSCC features demonstrate better efficiency than baseline features. The experiments for the classification of Hindi Vowels, revealed as high as 97.87 and 94.85% average % Vowel Classification Accuracy (% VCA) in noisy environments for CD cases and CI cases respectively. The maximum comparative improvement in % VCA for CI cases is noted down as 29.45 and 25.88% over MFCC and QCN-MFCC features respectively while the same for CD cases has been detected as 27.09 and 24.12% over MFCC and QCN-MFCC features respectively. All results are obtained on Matlab R2009b with HMM classifier.

The marketing trend in the service sector is changing at a rapid pace due to fierce competition and ever-growing innovation in the field of information technology. The marketing approach has transformed from product-centric concept to customer-centric concept. Adopting customer-centric strategy has become the essential component of business philosophy especially in the service sector, where customers are treated like guests. The whole concept of business performance is based on the philosophy of hospitality. Efforts are on satisfying individual needs of the customer and thus creating personal relationship by offering customised service. Customer relationship management (CRM) has replaced the traditional concept of marketing. Successful implementation of CRM program requires effective database management system to facilitate acquiring and maintaining customer information and thus applying this information to extend customised service based on individual needs thereby, supporting customer satisfaction and retention. Hence, it is evident that technology-based CRM has become essential for the survival and growth of business organisation mainly, the hotel sector and maintaining effective CRM strategy has its origin in knowledge management and data mining technique.

Dementia is a broad category of brain-related diseases that continues for a long term and severely affects thinking and daily functioning of a human being. Among different types of dementia the fatal type of brain problems are Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). More than 70% of cases are reported as dementia is in the Alzheimer’s category. In AD, the patient’s brain gets severely damaged, especially the outer part of the brain like cerebral cortex, hippocampus, ventricles, etc. The AD patients have enlarged ventricles, shrinkage in hippocampus and cortex. PD is also a common dementia after AD. In PD, the patient’s mid-brain gets damaged, i.e., substantia nigra. The proposed work presents an efficient automation for the detection of the AD and PD with Machine Learning Techniques (MLT). To detect the presence of PD and AD, two different types of brain image databases have to be selected: Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) database images, both of them contain data for AD and PD patients in comparison with the healthy brain images. From the input image, different features have to be extracted like statistical moments, geometrical moments, texture features, etc. Then Region of Interest (ROI) has to be selected to differentiate disease-affected areas. The results have to be generated automatically by comparing input image with the trained samples in the database. The proposed system concentrates on applying the MLT for segregating the outer part of brain with central part of brain for diagnosing the AD and PD in comparison with the healthy brain data.

The interest has grown in hybrid electric vehicles owing to the necessities of decreasing the environmental pollution and fuel saving. Hybrid electric vehicle (HEV) consists of electric motor, power electronic converters, batteries and microcontrollers and internal combustion engine (ICE). A Voltage Source Inverter (VSI) has been designed in the power electronics laboratory dedicated to HEV. The experimentation was done using three phase balanced star connected load and a three-phase induction motor drive. The various waveforms were obtained and comparison was made between two different loads. Total Harmonic Distortion (THD) has been calculated and found to be in the range of 9.44%.

According to a survey of the year 2011, in India there are 600 coal mines are running and over one million peoples are working in those mines. In underground mines, there are various cases where people die due to the leakage of toxic gasses. Nowadays, the accident rate in underground coal mines will decrease with the help of sensor technology and quick gas testing results. There are various traditional methods for gas detection and testing such as methane lamp, carrying live canaries etc. The most common sensors for gas detection are electrochemical gas sensors, semiconductor sensors etc. But the reliability of this method is not accurate and impressive. In this survey paper, our prime focus on the safety of personnel by using IoT based on ZigBee techniques for monitoring the peoples in the underground mines and provide the immediate assistance and repositioning of personnel in an emergency situation.

This paper is going to deal with the implementation of two-layer encryption; RSA along with AES Cryptography Algorithm. It is a cryptography application coupled with a secret user directory where certain files are encrypted for better security. The user has access to his particular folder and can store any files inside it and also has an option of encrypting certain files. This paper implements trapdoor functionality to encrypt and decrypt files through a terminal using Cryptography.

Software-defined radio (SDR) and cognitive radio (CR) system technologies provide added flexibility and offer improved efficiency to complete spectrum use. CR is obstreperous radio communication and networking technology. It is presently undergoing drastic development due to its potential to resolve many of the problems affecting existing systems. Sensing a spectrum is the crucial part of CR to sense the existence of primary users (PUs) or licensed user signals and exploit the spectrum holes. Hence, in this work, the testing of spectrum sensing technique for 16QAM and 64QAM transceivers on SDR testbed is carried out. Furthermore, this paper also addresses the practical signal detection and the impact of various filtering windowing methods on detected signals. Experimental tests are performed on SDR testbed, which employs universal software radio peripheral (USRP) N210 and GNU Radio as hardware and software platforms, respectively.

The rapid depletion of natural resources has increased the need for energy conservation. This project has been developed with the intention to enhance the power saving in public buildings such as educational institutes and hospitals. The basic principle of this project is to switch the power based on some conditions. It consists of two smart switches. The first detects the presence of an animate object and allows power supply as long as the animate object stays in the monitored area. The second switch takes a time input and provides supply for the requested period. A case study was performed in Muffakham Jah College of Engineering and Technology and it was observed that a total power of 65,780 KW per year could be saved.

In recent years, vehicular communication industries focus on the live information transfer with high-speed mobility of vehicles. Though vehicles are normally equipped with a single communication system, they may find different networks for communication during travel. In order to access, seamless connectivity vehicles needed to be connected in different networks and vertical handover comes into the picture. Even future generation wireless communication systems require support for multimode communication of Heterogeneous Networks (HetNets). This paper introduces a novel Road Side Unit (RSU) selection scheme for vehicular communication. The objective is to provide travelers with seamless connectivity and increase the network utilization. This scheme sense of balances between the interests of RSUs and On-Board Unit (OBU) via a suitably Software-Defined Network (SDN) control. The SDN assesses the HetNet performance based on user preferences and finds the proper RSU for the OBUs. This scheme maximizes the HetNet utilization and minimizes handover by properly balancing the load among the RSUs in the HetNet. The scheme enhances the performance by rejecting unnecessary requests. The simulation model claims that the proposed RSU selection scheme improves the HetNet functionality for vehicular communication in terms of Handover Served Ratio (HSR), OBU Served Ratio (OSR), and total throughput.

In the recent past, solar panel (Konar and Mandal in IEE Proc A Sci Meas Technol 138:237–241, 1991, [1]) has been under use to convert solar energy into electrical energy. The solar systems can be connected to electrical grids and also be used as a stationary system. The maximum solar power received by the earth is around 84 TW, of this, 12 TW of power is consumed every day by the world. A concept has been proposed (Gruber in Proceedings of IEEE power electronics specialists conference, pp 128–138, 1970, [2], Snyman and Enslin in Proceedings of 20th annual IEEE power electronics specialists conference, vol 1, pp 67–74, 1989, [3], Yang et al. in IEEE Trans Circuits Syst I Regul Pap 59:1546–1556, 2012, [4]) that by using Arduino extract maximum energy from the solar panel (Ram and Rajasekar in IEEE Trans Power Electron 32:8486–8499, 2017, [5]). If the solar panels are placed perpendicular to the Sun maximum amount of solar energy can be extracted (Kim et al. in Proceedings of the 31st intersociety energy conversion engineering conference, vol 3, pp 1673–1678, 1996, [6], Luque-Heredia et al. in Proceedings of 3rd world conference on photovoltaic energy conversion, vol 1, pp 857–860, 2003, [7]). So, it is important to track the Sun’s location and position of the solar panel. In the case of fixed solar panels, energy conversion at morning and evening is less, but it is more at noon (Liu et al. in Proceedings of IEEE 3rd international future energy electronics conference and ECCE Asia (IFEEC 2017—ECCE Asia), pp 932–935, 2017, [8]). As at noon, the solar panel is placed perpendicular to the sun. The proposed work is to design a system to track the sun’s position (Kobayashi et al. in IEEE Trans Industr Electron 53:495–499, 2006, [9]). Photoresistors will be used as sensors in this system. The system consists of light-dependent resistors, Arduino Uno, RC servomotors, and a solar panel. The proposed method will give an output up to 20% more energy than solar panels without entering tracking systems.

A fast squaring technique for the operands nearer to a particular radix is reported in this paper. The technique offers significantly less computational complexity which reduces the processing time to a large extent. Considering the applicability of the technique for a particular set of numbers which are in the vicinity of the corresponding radix, it is shown that the technique is equally acceptable for very large radix number. The proposed approach has been verified using Xilinx ISE Vertex-7 FPGA device. Simulated findings showed that with a significant increment in radix value (99.87%), the change of propagation delay is extremely small (2.33%), and corresponding increase in power dissipation is within tolerable range (29.84%). The results show the improvement in speed and computational complexity.

In this paper, various conventional approaches are studied for the task scheduling, precedence–resource constrained, load balancing, and multiprocessor scheduling problems. In parallel machines the sequence of dependent execution setup time for the minimization of makespan in scheduling problems and prepared a concise review. Multiprocessor scheduling is an NP-hard problem, whereas scheduling algorithm schedules the tasks which may or may not be dependent on each other. There are several traditional approaches existing for processor scheduling such as modified critical path (MCP), dominant sequence clustering (DSC), and priority-based multichromosome (PMC). While using these approaches, we achieve partial solutions in less than the minimum computing time. In this paper, an innovative multiprocessor scheduling technique that is inspired by evolutionary techniques has been embodied.

In this paper, a scheme for detection and segmentation of brain tumor from T1-weighted and fluid-attenuated inversion recovery (FLAIR) brain images is presented. To prevent the effect of noise and enhance texture of the brain image, fractional Sobel filter is used. Fractional order (α) of fractional Sobel filter provides additional flexibility in improving the segmentation results. Detection of asymmetry between hemispheres is achieved using Bhattacharya coefficients and mutual information. In order to detect the hemisphere containing tumor, histogram asymmetry method is applied. To segment the tumor region from the tumor hemisphere, the statistical features of a defined window are calculated and classified using support vector machine (SVM). Simulations are performed on the images, taken from the BRATS-2013 dataset, and performance parameters such as accuracy, sensitivity, and specificity for different values of α are computed. The simulation results show that the performance of proposed scheme is comparable to the nearest schemes compared.

Cladding modes of the Photonic crystal fiber (PCF) is investigated under weakly guiding approximation. The propagation constants of the modes are calculated and their respective field patterns are simulated. The modes are nearly confined in the outer silica ring. This analysis also reveals the penetration of evanescent fields in outer medium which would be helpful in the design of the devices for sensing application.

Big data deals with the terrific volume of data. The rapid development of data increases in many fields such as business, biological, stock market, scientific, etc., and our day to day activities. So while handle the growing data can causes many issues in managing, analyzing, storing, capturing and some others. The term microarray data comes under the biological data, consisting of large amount of gene instances. Bioinformatics deals with the interdisciplinary of computer science or information technology with the biological data. Using this the knowledge discovery can be done through analyzing these biological data with the software tools. In this, the knowledge discovery can done through machine learning techniques. For classification using these biological data the feature selection is an essential one. The selecting features work with the classifier model to improve their performance. In this paper a wrapper based sequential forward selection (SFS) with LASSO (least absolution shrinkage and selection operator) hybrid method are used for selecting the variables for cancer classification. It will improve the classifier model for better classification.

With the decrease in tariff rates and growing popularity of telecom options, competition in the field for abstracting customers and expanding market is becoming fiercer. It is evident by research that the cost levied by losing a customer from the telecom affiliation is sixfold more drastic than the profit that of adding a new one. The proposed Indexed Uncorrelated Linear Discriminant Analysis (IULDA) classification model for customer churn prediction effectively handles increased amount and dimensionality of data and has been tested on L-class problems of UC Irvine Machine Learning Repository and real dataset of the train sample—5,200 customers, the calibration sample—3,680, and the test sample—4,500 observations. The objective evaluation of the investigated methods was measured by precision, specificity, sensitivity, and accuracy by implementing the MATLAB tool. The accuracy of the IULDA model was 95% for UCI churn datasets and 72.4% for real customer datasets, respectively.

Problems relating to the achievement of protection for inter-turn insulation in multi-turn coils in PWM-based induction motors have currently aroused the interest of system designers and are being studied in great detail. However, such methods are impracticable to be carried out at every installation. A system designer should have prior knowledge of the transient overvoltages which are likely to occur in a proposed installation. For this, determination of transient overvoltage is essential. Present paper deals with the investigation of transient overvoltages in 5 HP synchronous motor caused by PWM-based inverter as well as the reduction of transient overvoltages at motor terminal by connecting appropriate RC suppressors. The simulation results show the effect of placing RC suppressor in the system to reduce the overvoltages at motor end. For this, a Simulink model is developed and implemented in MATLAB.

Epileptic seizure occurs due to neuronal disorder that results in abnormal pattern of brain signal. Electroencephalogram (EEG) signal represents a modest measure of electric flow in a human brain. An EEG is one of the main diagnostic tests for epilepsy. Due to the presence of seizures, normal pattern of brain waves disappears and different other brain waves can be visualized during the recording of EEG. Approximately, 1% of the total population in the world is affected by this disease. This paper is based on a systematic approach for epilepsy detection of human brain by extraction of features and classification of EEG signal. Feature extraction is completed by discrete wavelet transform (DWT) and multilayer perceptron neural network (MLPNN) with deep learning is used for classification. Experimental study of the proposed work is done through Python platform with an encouraging performance.

In this paper, we have developed a robust classifier by considering the best attributes of biogeography-based optimization (BBO) and artificial neural network (ANN) for classification of fingerprints into five classes. This work involves two phases. In phase one, we extract features by using Gabor filter bank. In the second phase, we optimize the parameters of ANN by using BBO to mitigate the problem of local optima. We have conducted experimental study on a few benchmark functions for verifying the proposed model accuracy. The outcomes of experiment claim that the accuracy of our model is significantly better than other competitive models.

Element and network management system is used worldwide for remotely managing the network elements. With EMS/NMS, we can provision circuits, reboot network elements, provision protection rings, and provision in-band communication remotely. Due to its wide utility, performance and scalability testing of medium and large optical networks needs to be done extensively. This paper covers how performance and scalability testing of medium and large optical networks is done in both control and managed plane scenarios. Test bench has more than five thousand network elements and more than three lakhs paths. Time to retrieve nodes, optical connections, and alarms will be measured in medium and large network scenarios. Nodes are added into network management system which is mentioned in the above setup.

The increase in energy demand and reduction of greenhouse gas emission is pushing research interest towards distributed generation systems. Distributed generation systems are renewable, non-renewable energy sources and also energy storage devices. The single-stage three-phase boost inverter can provide higher value of sinusoidal AC output voltages from low-voltage DC sources without an intermediate DC–DC boost chopper. This unique property is absent in classical voltage source buck inverter which produces an instantaneous AC output voltage, which is always less than input DC voltage. Also, this topology is a suitable choice in many applications such as AC drives, uninterruptible power supplies (UPS) and distributed power generation scheme (grid integration of renewable sources). Thus, it has an advantage of boosting and inversion process in a single conversion stage, negligible shoot-through problem, reduced components, size and volume, low losses and higher efficiency, in comparison to double-stage conversion, which consists of a DC-to-DC boost converter and a conventional three-phase buck voltage source inverter in between the low-voltage DC sources and AC output. The three-phase DC-to-AC boost inverter can also be applied to storage devices such as batteries, fuel cells and supercapacitors with the grid. Working principle, operations, modelling and simulation of three-phase single-stage DC–AC boost inverter with battery as input are performed under Simulink platform of the MATLAB and different waveforms are plotted. These simulations explain the feasibility of the proposed single-stage three-phase boost inverter.

Vehicle emissions are the great concern of the modern world and hybrid electric vehicles is one of the feasible solutions because it mitigates pollution and increases fuel efficiency. Power electronics plays an imperative role in Hybrid Electric Vehicle because it consists of converters for high-performance electric traction motor drives. It is important that the traction motor must fulfill the demands of the various driving cycles. In this chapter, a detailed performance analysis of controlling the speed of Brushless DC (BLDC) motor with or without disturbances (or load) is addressed. The chapter also involves the design and implementation of a controller which minimizes the initial overshoots. This work was conducted with the purpose of developing a three-phase inverter for BLDC motor drive system. The two different controllers are designed and simulated for closed-loop speed control of BLDC motor at different loading conditions. The simulation work is performed in MATLAB Simulink platform and results match with the theoretical one.

A grid is an infrastructure to meet the ongoing demands of science and engineering (Foster et al. in Int J High Perform Comput Appl 13(3):200–222, 2001) [1]. In the midst of the 1980s and the 1990s, researchers observed that parallel computing and distributed computing was not only sufficient for solving the biggest challenges of engineering problems. They needed some mechanism which could utilize the power of distributed as well as parallel computing. Grid computing (Foster and Kesselmen in The grid: blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, pp 1–593, 1999; Jacob et al. in Introduction to grid computing, 1st edn., 2005) [2, 3] was the solution to their problem. But working in a grid environment is not really easy since the grid users are increasing and services are becoming commercial, so it is desirable to free the users from the load of job handling. The grid scheduler or resource broker performs the task of job handling such as resource management and fulfilling user requirements. In this chapter, an anatomy of grid schedulers has been discussed which discusses intrinsic properties of different grid schedulers.

The present chapter describes the encryption algorithm of a colour image using microcontroller ATMEGA 32. Two microcontrollers are used as driver and driven systems in transmitter and receiver sides. One-dimensional logistic map is used in both the cases for generating the chaotic sequence and synchronization between them through open plus closed loop (OPCL) coupling. Under the synchronized condition, the data file of the input image is encrypted and transmitted to the receiver side. The receiver microcontroller received this encrypted data and using the reverse technique it decrypted the data and send to LCD display to reconstruct the input image. The proposed technique is verified through different images and the results confirm the validity of the algorithm.

This chapter reflects the enhanced Geographical energy-aware routing in wireless sensor network, where the separation of nodes is totally done on the basis of their position which is further obtained by Global Positioning System. Sink node or Base Station is fixed far away from the sensed area and entry node is fixed at the central point of the sensed area. A threshold value of distance has been defined. If the obtained distance of nodes from gateway node and sink node is relatively smaller than the threshold value of distance then in that case transmission of data takes place directly. If the calculated distance is more than that of threshold value then isolate the nodes into the identical areas. Now for each area, a cluster head is nominated for data transmission. Nomination of cluster head is done on the basis of probability function and residual energy of nodes. Finally, the proposed routing protocol displays better results in terms of network lifetime, network energy consumption and packet transmission to the base station compared to the existing routing protocol.

This chapter presents a novel equivalent circuit modeling approach for the modal analysis of a MEMS cantilever beam. The modal solution is implemented in the circuit model, which provides the spatial displacement in steady-state and dynamic conditions. The sinusoidal excitation is employed to study the transient solution of mode shapes. As a test case, the steady-state displacement along the length of 40 µm cantilever is determined under different modes and compared with the results obtained by MATLAB simulation, which indicates a good agreement.

Nowadays due to the increase in the cloud computing services, the energy consumption has drawn huge focus in the high-performance computing research community. With increasing utilization of Cloud network infrastructure, the energy consumption has drastically increased to the cloud networks. This has become a critical issue. At the time of data delivery in the cloud, network components consume enormous amounts of energy and connecting to sky-scraping operational expenses and carbon footsteps to the environment. The energy problem in the cloud networks is the key difficulty of relation with network data delivery. Also, it is the main objective to be solved in the cloud computing networks design. The energy-efficient related work in cloud networks combines with characteristics of the network and analyzes the approach energy consumption. The energy-efficient strategies from the aspect of cloud networks node and cloud networks are discussed from the following aspects of data size, data delivery time, bandwidth size and the delivery path between clients to and the server. The aim is to achieve an energy-efficient proportional green cloud network to provide on-demand quality of services (QOS) with energy-efficient data delivery algorithm.

Depression or Major Depressive Disorder (MDD) is a psychiatric disorder. It is the major contributor to overall global burden of disease. Any deterioration in brain functioning is reflected in Electroencephalogram (EEG) signals. EEG signals are highly complex, nonstationary and nonlinear. So, it is very difficult to analyze visually and identify changes in the waveform in order to classify MDD and normal subjects. Thus, computerized quantitative EEG is used for the analysis of signals. Support Vector Machine (SVM) using band power feature reported an accuracy of 98.33% and using Kernel Eigen-Filter-Bank Common Spatial Patterns (KEFB-CSP) gave an accuracy of 91.67% [1, 2]. Logistic Regression (LR) using band power feature reported an accuracy of 98.33%, using 4 nonlinear features combination provided an accuracy of 90%, using wavelet transform provided an accuracy of 87.5% and using only alpha power gave an accuracy of 73.3% [1, 3, 4]. Naïve Bayesian (NB) using band power feature provided an accuracy of 96.8% [1]. Artificial Neural Network using Relative Wavelet Energy (RWE) reported an accuracy of 98.11%, using power spectrum feature gave accuracy of 84% and using Lep–Ziv complexity accuracy of 60–80% was reported [5, 6, 7]. Linear Discriminant Analysis (LDA) reported an accuracy of 91.2% using SASI (Spectral Asymmetry Index) and DFA (Detrended Fluctuation Analysis) [8]. Decision Tree provided an accuracy of 80% using EEG band power as feature [9]. The study reveals that, in general, high classification accuracy is achieved by SVM, LR and ANN and highest classification accuracy of 98.33% is achieved by SVM. Highest accuracy is achieved by SVM because it is more robust and computationally more efficient due to maximal margin gap between separating hyper planes and kernel trick. The study gives some ideas which could be helpful for guiding and improving future researches. Since linear and nonlinear method for feature extraction are both equally efficient. So any of the linear/nonlinear feature can be used. For feature selection and reduction, Genetic Algorithm (GA), Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) can be used. Since SVM, LR and ANN give high classification accuracy and any of them can be used or any hybrid technique like GA-SVM, GA-ANN can be used. This chapter compares various EEG signal analysis techniques, compares their accuracy and methodology used and finally recommends the most suitable technique based on the accuracy for detection of depression. The chapter also summarizes some of the key finding related to EEG features based on present state of art. These results could be helpful for guiding and improving the future research in this area.

An 863 nm, bottom-emitting VCSEL diode is simulated under a wide range of operating temperatures. Based on this VCSEL, a digital optical link comprising of Graded-index Multi-Mode Fiber (MMF) and a Si-based P-type/intrinsic/N-type (PIN) photodetector is numerically evaluated. The working temperature of the laser diode is changed from 20 to 120 °C and its impact on the MMF link is evaluated for 1.25 and 2.5 Gbps. The link performance is evaluated by determining Q factor and BER. For a data rate of 1.25 Gbps, the maximum distance is found to be 0.64 km at 120 °C and 0.3425 km for 2.5 Gbps at 100 °C, for a BER = 10−12, as per IEEE 802.3.z standard.

Sensors in activity based computing enable continuous monitoring of numerous physiological signals when attached to the human body. This finds wide application in areas of activity monitoring, bio-medical rehabilitation, and fitness tracking. Primary challenges in embedded application development for smart wearable include high energy efficiency and user compatibility. Existing algorithms and applications are still unable to fully utilize the true power of the data being collected. They provide lot of descriptive data analytics but lack in predictive analysis. Energy efficiency of computing as predicted by Koomey’s is expected to strike the second law of thermodynamics based on Launder’s Limit within few decades. In this work an energy efficient computing technique for next generation mobile applications is developed. Proposed Artificial Intelligence based energy-efficient embedded algorithm that provide personalized training sequence recommendation in order to achieve desired calorie goals. Suggested training sequence of 6 activities fall under high, medium and low calorie burn with achieved median for 234C:535C:688C respectively. The crux of this implementation is Calorie Matrix Regeneration via state feedback technique using Markov Decision Process (MDP) and Genetic Algorithm (GA). Number of generations required by the GA to reach a suboptimal solution is optimized. While Machine learning algorithms are written in C/C++ for effective embedded implementation, certain computationally expensive modules like MDP and GA are coded in Python with proposed IoT cloud based implementation thereby improving battery efficiency to 12–16 h. This implementation is first of its kind and a step ahead of available state of the art fitness training algorithms/applications.

A human–computer interaction is generally limited to taking input from the user using handheld devices like keyboard, mouse, or scanners. With the advancement in computers, the user interaction approaches have also advanced. Direct use of hands as an input device is an attractive method for providing natural Human–Computer Interaction. It is also helpful for people who use sign language. The chapter aims to study the existing methods for Hand Gesture Recognition and provide a comparative analysis of the same. The entire process of hand gesture recognition is divided into three phases: hand detection, hand tracking, and recognition. The chapter includes a review of the different methods used for the hand gesture recognition. The recognition phase is classified based on the way the input is received as glove based or vision based. For recognition, various methods like Feature extraction, Hidden Markov Model (HMM), Principal Component Analysis (PCA) are compared along with the reported accuracy.

In a developing nation like India, most of the population is dependent on agriculture. 60–70% population [1] depends only on agriculture and related sectors. Despite technological development in the entire field, now full attention is required in agriculture system to enhance the production and quality of crops. The production of crops plays a major role in the enhancement of GDP of the country. Quality of food gives the healthy life to the people. Not only human life depends on the agriculture animals and other creatures too depends on agriculture. There are so many factors that can generate good quality of crops, e.g., good quality seed, fertilizers, soils, and water and weather. As the farmers of our country are not so much literate and aware of the technology, as a result, the crop production is not up to the mark. If the farmers can embrace new technology the condition would be different and the agriculture sector can become the major employment source. Application of IoTs can help the farmer to increase the crop yield and there by minimizing the hard work done by them. In this chapter, the main objective is to let the farmers know about their necessary items such as good quality seeds, fertilizers, soils, water, weather etc. They can also know about the market value and portability of their crop. For a good yield setup, how the farmer will know how much water is required, when it is required and how much fertilizers are required, which type of crops they can plant, etc. These objectives can be achieved with the support of advanced technologies like sensors and GSM. IoTs will help in predicting the crop yield, soil quality, soil temperature, water level, and proper timing of crop to be delivered. Therefore, a system needs to be developed to enhance the farming procedure and reducing human effort and thereby increased farming output.

Trains are a key and cheaper mode of transportation systems in most developed countries around the world. Nowadays, train derailment in India is a major problem, thousands of peoples are dying due to derailing accidents. Other major problems are collision. It is also often occurring and results in severe damage to life and property. This chapter presents a thought of smart train track monitoring system and collision using IoT methods. This chapter presents various innovative ideas to control and detect the cause of derailed systems such as the strength of track, vibration measurement, load measurement, track alignment, conditioning of track. In this proposed smart system, track conditions will be monitored by IoTs systems with the help of different smart sensors pasted in track and information of track will be delivered to nearby substations, drivers, and central railway monitoring office. Before passing of the trains, the track will be scanned by systems and after the green signal of the system, train will be allowed to run on the particular track. The proposed methods can reduce the accidents.

Smart city is a way of developing the new areas in which it aims to integrate either city features or assets like smart homes, smart parking, waste management, traffic control system, and other community services. Each smart city is equipped with various kinds of electronic devices which are either directly or indirectly connected with IoT. This paper provides an inclusive idea of the concept of the smart city with the help of IoT. In the present day scenario, IoT plays an important role and their capabilities are merging into or apply to the different parts of smart cities for better results. The potential application of smart city pops up frequently in the context of urban development with respect to the technology involved in it. Meanwhile, there are some challenges all over the world and its implementation technique are thoroughly expressed in this chapter.

Internet of Things (IoTs) plays a significant role in directly integrating physical world with computer-based systems by sensing and controlling various features, even remotely, which leads to a reduction in human intervention to a larger extent. This results in improved efficiency, better accuracy and easily accessible data. This chapter proposes the study and implementation of remotely accessible health monitoring system to monitor blood pressure level with IoT. It enables the user to improve health-related risks by collecting vital signs, i.e. temperature, blood pressure, sugar level, etc., of the patients and communicating these information to detector in real-time scenario. After proper examination of the current status and full medical information, it also aids in transmitting the accurate decision taken by doctor and precautionary measures required to be taken by the patients. In this manner, patients are constantly under the observation of doctor, even being physically apart with each other. Thus, IoT serves as a catalyst for health care by creating a brilliant and efficient network.

Road transportation in India is assumed to be the second largest network. To manage such a large network, Toll collection method is used. Toll is collected manually, which requires a toll attendant. Manual process is time-consuming, resulting in a huge rush at the toll booth. Toll collection is one of the important problems in India. For making the system smooth IoTs-based systems are required, that maintain the vehicle health as well as toll tax when passing through any toll booth. In one toll, approximately 24 people are working in shift in classical systems. But, in the proposed system no one is required on booth. Everything is monitored remotely. For this setup, main items required are server-based system with a collection of high-speed Internet Aadhar Card, ZigBee and GPS. Then toll charges will display on monitor and message will be forwarded to the owner of vehicles and payment will be deducted from the account of owner. No need to pay in every booth by driver. For this whole system, when vehicles enter in the city, booth officers will collect the relevant information (vehicle papers, owner Aadhar card, etc.) from driver if vehicles are old and they will paste the barcode in that vehicles. In newly purchased vehicles, this system database is created during delivering the vehicles to customers. When compared to the traditional method, this method is very easy and also reduces the user’s wastage of time.

In this paper, a two step gap coupled resonator is modelled for designing a bandpass filter (BPF). The proposed structure is viewed simultaneously as edge coupled section and parallel coupled section. A detailed nodal analysis of the proposed coupled gap sections using different current and voltage equations are described, which is further derived to obtain different admittance parameter equations. Finally comparing those equations with normal two port network, even and odd mode impedance difference (Z0o−Z0e) equations are obtained in terms of parallel coupled admittance inverter (Jg1) and shunt susceptance (Bg1) at the edge coupled section. This equation is further used for designing the resonator with band accepted characteristics. A three pole wideband bandpass filter is demonstrated by series arrangement of four such coupled resonator unit. The proposed bandpass filter exhibits fractional bandwidth (FBW) of 53.5%. All the results are well verified by numerical simulation and validated through experiments.

The fusion of EDFA and optical fiber causes an expansion in the transmission capacity over the large distances and thus making optical fiber a prevalent broadband communication technique. The practical execution of EDFA offers long transmission distances with less attenuation. However, in order to get high transmission range with high data rates using existing SMF, then techniques must be there to compensate the dispersion caused by fiber nonlinearity. In optical fiber communication, DCF and FBG are the trending dispersion compensation techniques. The use of DCF and FBG as a method of compensation of dispersion can notably heighten the overall performance of the system. In this paper, DCF and FBG as dispersion compensator are compared in terms of Q-factor and BER at 100 Gbps launched over an SMF of 120 km by using Optisystem 7.0 software. On the basis of results, it is suggested to use DCF as a best chromatic dispersion compensation technique in pre-compensation mode.

Smart grid involves the application of computer, transmission line, and networks into an inactive power distribution system. It is used to improve the maintenance, operations, and technologies of the smart grid, so that the component of the power grid can easily interchange data. However, concerns associated with communication and management must be addressed before full benefits of the smart grid can be accomplished. In this paper, all aspects that are relevant to smart grid communication and information technologies, and also its challenges, have been discussed.

The audio system of the humans is more complicated and not easy to analyze or understand than the visual systems of the humans. Due to this, various effective watermarking algorithms have been implemented and introduced for digital text and digital images. Only fewer algorithms have been introduced or selected for the audio watermarking. This paper proposes a novel robust approach for the audio watermarking using voiceprint of a speech signal with wavelet transform. Haar and different orders of Daubechies wavelets have been used for the abovementioned task. The performance of voiceprint-based audio watermarking using wavelet transform has been compared based on percentage compression, retained signal energy, and signal-to-noise ratio (SNR). Higher order Daubechies wavelets have shown better performance for audio watermarking in terms of various performance parameters as mentioned.

This paper discusses the design and analysis of two-stage CMOS operational amplifier. This design is operated at the supply of 1.5 V in 90 nm CMOS technology. In this design, 75.17 dB open-loop gain is achieved and having 7.73 MHz unity gain bandwidth and 148.8 m degree phase margin. This circuit has 10 pF capacitive load with 0.14 nW average power dissipation and slew rate is 0.25 V/μs. This proposed circuit is designed and simulated in cadence UMC 90 nm technology.

India is a developing country—a nation which always keeps on moving on wheels: stopping is not an option. Where the total number of vehicles (159,490,578 as of 2012) is more than the entire population of US, an efficient traffic system and smart toll booths are necessary for the efficient movement and proper tax collection; for the wheels should never stop.

In this paper, MIMO antenna is designed, which consists of two planar symmetrical monopole antennas and T-slot adjoining to the common ground plane. The return loss is below −15 dB for a wider bandwidth. The overall antenna size is low as 35 mm × 22 mm but the effect of the mutual coupling among the antenna elements is reduced and is below −10 dB in lower range and below −20 dB at higher frequency range within wider frequency range of 4–10.5 GHz. The resonant frequency is taken as 4.4 GHz. The designed MIMO antenna has two ports whose reflection coefficient behaves as shadow of one another which is considered to be better in terms of efficiency. In this process, the maximum gain achieved is approximately 2 dB. The design has been verified and is ready for fabrication so that the MIMO antenna can be practically used. The design has been simulated using Ansoft HFSS software 15.0. The various parameters like S-parameters, VSWR, gain and radiation pattern are shown.

The research article represents the literature review for the designing of the FIR filter. In this research review, various papers of different authors are reviewed. The FIR filter found a number of applications in large applications and designing of DSP and communication systems. As all the work nowadays revolves around the frequencies, so their detection become also an important factor. In the reviewed papers, various techniques, methods, and algorithms are used for the optimal designing of the FIR filter. In the work of the authors, digital filters (FIR) are mainly used as they have an enormous range of handling capability of the frequency and also find a wide range of applications. The design of FIR filter is focused on HDL implementation and FPGA synthesis. They all proposed FIR filter working and improvement in its various parameters to improve the circuit of various designs and reduce the hardware implementation.

In today’s world, it is very much required to a country’s military to be technically advanced. The most famous technology in military field is the use of drones or, in other words, QUADCOPTERS. Therefore, I intend to aid in improving this technology by creating an all-terrain rover quadcopter which can fly and can move also on any type of surfaces (including special case for water), which is required by military forces. It will work on normal principle of quadcopter, and the added feature to it is its wheel and streamlined and aerodynamic central body. It is so designed that in most of the cases, the propellers will not be harmed. Military can use it to pick up any small object from it (say up to dia. of 5 cm) with average payload capacity. It is of grass color so it can move in bushes creating a camouflage effect without being observed.

A new systematic approach towards development of a software fault prediction model for trustworthiness and excellence enhancement of software systems by predicting fault-proneness in different severity level of module after coding has been performed in this paper. The classification ability of data mining technique and important object-oriented metrics of object-oriented software system are used to develop this fault prediction model. This model predict the severity level of object-oriented software module based on the number of faults in nominal, low, mid, high and not fault prone (NFP). A different decision tree like Hoeffding tree (VFDT) is created for existing project data Eclipse 2.0 in order to gain information for the purpose of decision-making whether a particular module is faulty or not, and if faulty then in which category. The rules that are obtained from VFDT decision tree algorithm are transformed into fuzzy rules and join together with the fuzzy inference system (FIS) of soft computing approach to predict fault-proneness of object-oriented module in different categories of fault or also not fault free for target data that is other two version of existing project data Eclipse 2.1 and Eclipse 3.0. The main aim of developing this fault prediction model is that to give a direct help to software manager in identifying fault-prone module by including this model before testing phase and just after coding phase so that testing effort can be reduced. And indirectly, this model helps the common people in getting reliable and quality-based system.

In this paper, multi-threshold voltage CMOS technique (MTCMOS) is investigated on static random access memory (SRAM) cell. Cell or/and pass transistors with high threshold and various aspect ratios (β) of transistor are presented. This work is focused on stability, power consumption, delay and write-trip point of SRAM cell, which are very important parameters to design any memory circuit. The read, write and hold stability of data is improved by 2.13×, 1.06× and 1.25×, respectively, with new MTCMOS SRAM cells. The read and write power is suppressed by 1.08× and 2.83×, respectively, and the read delay is suppressed by up to 1.96×, and write-trip point is enhanced by 1.03× with proposed MTCMOS SRAM cells compared with conventional 6T SRAM circuit in 70 nm with MTCMOS technique.

A significant share of the cost of owning an aircraft is comprised of the cost incurred on its maintenance. The paper purports to provide an autonomous framework for the maintenance cost reduction. The suggested framework is built upon Robot Operating System (ROS), implementing navigation, semantic mapping and manipulator motion planning to facilitate daily check tasks. In this framework, a robot mounted with a robotic arm having end effector suitable for performing daily check tasks is capable of doing visual inspection of the aircraft, performing maintenance tasks and can change its end effector depending on the tasks needed to be performed. Gazebo was used for simulation purpose.

This paper emphasizes the barcode technology for automatic Electronic Toll Collection (ETC) systems, in order to avoid the ever-increasing stream of traffic and the long queues at the tollbooths of the highways. The proposed techniques make use of digital image processing techniques to scan the barcode and to match it with the existing database, by utilizing the decoded data. The implemented hardware setup has been illustrated and discussed in detail. The proposed technique for barcode detection significantly improves the speed, efficiency and lowers the cost of implementation. The method has been implemented by using Python and OpenCV.

The paper is designed to develop a robot for stocking shelves, using Arduino UNO, Android, and RFID technology. This robot can grab items off a shelf and put them back again using information obtained from RFID cards and RFID card readers. According to the gesture commands received from Android, the movements of the robot are controlled. Gestures are captured through the Android smartphone. The Bluetooth modules are controlled by the microcontroller.

Disaster management is the field dealing with or organizing proper management and use of resources, technology, and information to deal with any disastrous event or emergency. Disaster management is the proper planning to coordinate natural and any man-made disastrous event and is measured how efficiently and effectively is one’s preparedness to respond to calamity and recovery in order to lessen the impact of such events. The purpose of this paper is to design a system to detect an earthquake and give a warning message for home and industry. The design uses Arduino (ATmega328p), accelerometer (ADXL335), LEDs, and buzzer.

It is because of the technological advancements that we can now reach from one place to the other one within few hours. Aviation industry is concerned as one of the busiest industries in the market. Aviation industry not only deals with the design and development but also plays a crucial role in the operations and production of the aircrafts that are to be operational. Aviation deals with the Civil as well as general aviation. However, advanced avionics do not enable either the engineers or the pilot to break the laws of physics. It was designed for keeping utility and safety in mind. Safety field is done by increasing its awareness. In this paper, we discussed the methods adopted by aviation technology and gave a brief description about the principles followed. The various methodologies and technological advancements involved in the aviation system are also discussed which include various improvisation techniques as well. These include the ways to improvise engines for more efficiency, managing fuel system and improving its efficiency. Causes of noise in aircrafts and ways to reduce noise are also discussed. The very important aspect of aviation system is the reduction of noise in its navigation and its engine during flight. Managing air traffic is another major hurdle in aviation system. The methods to control air traffic by improving navigation system are put in a nutshell. There should be a proper use of checklists and systematic training for decreasing the count of casualties during an accident. Aviation is a flourishing industry with many upcoming technologies to enhance the avionics system. Researches have been going on across all countries in improving avionics system. This also helps them to improve the armed forces of their nation. Military aircrafts are imbibed with latest aviation technologies everyday which improve their target sensor and also other functions like multi-targeting, etc. Various researches on how to reduce the weight of the aircrafts by using better heat-resistant alloys and how to make the aircraft robust and resistant to drastic weather conditions and turbulence are going on widely.

Now, the time industry is moving to a smart technique. Industry is facing a new worldwide revolution: integrating Internet-based information and communication technology (ICT) into their complete value chains. This development is often referred to as the Internet of Things (IoT), smart industry. Beyond leading to more efficient and adaptive production and supply chain processes, it also will lead to a complete new generation of always-connected products.

With continuously increasing the use of energy and growth in population, conservation of energy is very much needed. Remote access to electronic appliances can solve the problem. Instructions can be given to these systems through a web or an android application. Some of the technologies which are used can be GSM, ZigBee, Wi-Fi, and Bluetooth, along with different controlling devices. This paper gives a survey of all those systems which have already been used in various different applications.

In this project, we introduce the railway track crack monitoring our system. In this system, sensors such as ultrasonic sensors are used to detect cracks in the railway track and measure distances, while infrared sensors are used to detect obstacles. Whenever a crack is detected in the railway track, a message is sent to the authorized user via GSM module. The GPS module advises the exact position with the help of SMS. This system can be used for daytime and nighttime crack detection process.

The denial of service attack exploits the weakness in the TCP/IP implementation. In a privacy attack, the attacker tries to get access to the resources to which he is not authorized. The DoS attack blocks the authorized users from accessing the resources. The affected systems crash or disconnect from the Internet. In some cases, they are not much harmful. In other cases, they can be harmful, especially when you run a corporate network or ISP. This paper presents the denial of service attacks, and also the difference between DoS and DDoS is mentioned.