Advances in Electrical and Computer Technologies

Our paper provides an extensive study of detecting fraudulent claims in healthcare insurance by leveraging machine learning algorithms. By using the publicly available medicare dataset, we are able to classify as fraud and non-fraud providers. Moreover, synthetically minority oversampling technique is used to avoid the class imbalance problem. Furthermore, a hybrid approach is used which is based on clustering and classification. Additionally, we have used other machine learning algorithms to check the efficiency of the best-suited algorithm.

In graphs, data is organized in such a way so that the information becomes clearer to draw conclusions which help to make decisions. Graphical visualization of the data gives better insights for decision making, forecasting, and prediction. Graphs clustering tools helps and assist in completing the task in a faster way. It also helps in graphical visualization of data based on features. Identifying the best tool for visualization is a time-consuming task. In this study, graph clustering tools are reviewed. Experiments are performed on few graph clustering tools for further comparative study. Graph clustering tools mainly Gephi and Tableau are considered for experiment purpose. The comparison is based on visualization and clustering effect in both tools.

The popularity of mobile devices and the availability of the Internet increase the use of various online platforms for chatting and communicating with others. Due to the use of such platforms, the use of local languages is also increasing because everyone feels comfortable with his/her mother tongue. In this research work, clustering of Assamese words is done by using N-gram models. This clustering is context-based and preserves the contextual information of the words. This Assamese word clustering will help in the development of the Assamese language in Parts of Speech tagging, spell checker, word prediction, etc. In this research work, word clusters are designed using bigram, trigram, and quadrigram models. Words that appear in a particular context are stored in the same list of clusters. A corpus of size almost 600K words is used to design the Assamese word clusters. A similarity score is calculated between two words to keep them in the same cluster. The accuracy of the clustering system is around 60%, and 733 different clusters are extracted with a maximum of 15 words in a cluster.

The usage of anonymous proxies and virtual private network has increased due to the privacy and Internet censorship issues. The traffic passing through proxies (Middle boxes) can be easily intercepted and modified by the controller to perform man-in-the-middle attacks like data injection, data tampering, and data deletion. A stealthy attack called cryptojacking started infecting the popular Web sites to mine cryptocurrency without the Web site visitor’s consent. This paper proposes an effective and stealthy approach to perform cryptojacking attack by injecting cryptomining script on anonymous proxy’s Web site traffic. To increase the efficiency of the attack on larger scale, a testbed environment for private The onion router (Tor) network is deployed to implement the same attack on tor exit node. Our study shows that covertness of the attack can be improvised by varying the central processing unit usage of the victim during mining to avoid detection. The existing defensive mechanism to prevent this attack is also reviewed.

More than sixty years have passed since it has disclosed that protein can be developed from amino acid sequence. Structures of protein have proven to be a key piece of information for designing a drug, synthetic biology, and diagnosis of diseases. By knowing the protein structure, one can predict its function, find out how molecules or drugs skillfully bind with proteins and structural behavior of protein can help us to diagnose diseases and to invent new drugs. Due to the fast growth of protein sequence from the amino acid, it finds to be much more difficult with experimental techniques to obtain three-dimensional protein structures. Hence protein structure prediction (PSP) is still seen to be more important. Here in this paper, we are presenting a review about the formation of protein, different protein structure, factors affecting the tertiary structure, the importance of protein structure prediction, structural database, experimental and recent computational approaches for PSP, and similarity measures.

Studies show that in past few years out of four deaths, one is due to cancer. The area of molecular oncology witnessed the involvement of small non-coding RNAs known as MiRNAs in these complex disorders. Using MiRNA expression profiling, the stages of complex diseases can be analyzed, and suitable treatment strategy can be devised from the very beginning. The current study focuses on the importance of performing feature selection before classification. Genetic Algorithm (GA) is used for feature selection to select a set of MiRNAs from a large dataset that can classify early and advanced stages of breast cancer. Two classifiers, namely Support Vector Machines (SVMs) and random forest, were analyzed here. The result signifies that random forest classifier performs much better than SVM, and feature selection improves the prediction accuracy. SVM performance was studied by using different kernel functions and concluded that polynomial kernel gives a better result.

In Data mining, Fuzzy or soft clustering is one of the popular approaches proposed to solve several real-world problems. The Fuzzy C-Means (FCM) algorithm is the famous algorithm in fuzzy clustering because of its straightforwardness and short computational effort. But it has the problem of local optima. To overcome this local optima problem, many optimization algorithms have been developed and try to attain a global optimum solution. In this research work, two kinds of enhancement are proposed to solve clustering problem and overcome the above-mentioned shortcomings. First, Bacterial Colony Optimization (BCO) algorithm is integrated with fuzzy theory called Fuzzy BCO (FBCO). Second, Hybridization of FCM with FBCO is developed to obtaining good optimal clusters are called as Hybridization of Fuzzy Clustering Algorithms (HFCA). The experimental results of proposed algorithms are demonstrated using six machine learning datasets and the results produced by proposed FBCO and HFCA generates higher performance while match up with FCM, FPSO (Fuzzy Particle Swarm Optimization) and FBFO (Fuzzy Bacterial Foraging Optimization) algorithms.

The purpose of organizational e-governance is to structure the working set up of the organization appropriate to the business process to derive optimum efficacious output resources to the prioritized sink actors. The level of optimality of the business process determines its rank in the specific business process cluster. In the extant procedure, the ranking systems determine the ranks of organizations by gauging solely from the organizational different criterion values attainable to the criterion values of fixed benchmarks of the business process. This is proved to be futile, if the criterion values remain unutilized or damply utilized. The attainability per se determines merely its potentiality and not the actual efficacy or rank. The efficacy should be based on the utility of potentiality in providing resource to the sink actors from these potential criterion values. In this paper, attempt has been made to develop the ranking methodology in the form of automated algorithm, by blending this potentiality with resource flows from source actors to the sink actors via appropriate transformation within the organizational business process. Further, the synergistic efficacy is derived pedestalled on prioritized sink actors’ resource satiation. Further, the algorithm reduced the running time complexity from O(n3) to O( $$\sqrt{n}$$ n ).

Development of breast cancer detection and its usage by different health care industries in their diagnostic center is a very much serious task for classifying cancer cells based on its specific characteristics. As a consequence, the classification process of the cancer becomes incredibly complicated for the potential users because they have a large set of attributes and parameters of the cancer cells which are available at their disposal in laboratory for diagnosis. Moreover, the proposed work gives the efficient decision for the classification of the cancer cells to diagnose the patients at there earlier stage of breast cancer. Design Methodology/approach: In this chapter, it has been proposed a layered neural network model which uses this back propagation algorithm along with scaled conjugate gradient for optimized way of classification of cancer cells by considering the appropriate parameters. Findings: The classification of cancer cells is evaluated using the proposed algorithm by designing a layered neural network model. For training the model, 70% of instances are used, for verification, 15% instances and for testing, 15% instances are used of 699 samples. After successful training of the model, the model classifies the cancers as benign (2) or malignant (4). Originality/value: The proposed methodology is an original scientific work and the algorithm used is an efficient algorithm for the classification of cancer cells. In this work, eleven data attributes are used for the classification from cancer data set.

Rapid advances in digital image technology contributes huge amount of images in the many fields like digital libraries, medical imaging, Web image searching, etc. This chapter attempts to present novel approach in CBIR, which is based on the combined texture features. This approach mainly aims at designing the effectiveness of the image retrieval system. Thus, in this chapter, multiple features method has been proposed in order to create a novel hybrid features by 4Dir-GLCMEDGE method. This method majorly has three process flows, (a) a process to find the four diagonal texture features (b) a process to find the local binary pattern (LBP) features (c) It is a fusion of diagonal texture features and edge features. This fusion aims at combining and reducing multiple image features into single image feature. Selecting a query image as input, more similar images can be retrieved with the help of the proposed hybrid features. Using the hybrid features, based on two distance metrics such as euclidean and manhattan metics, results in more similar imges. This proposed method has been tested with Corel and Wang datasets. The test results, compared to the previous methods, are encouraging and found to be better than existing methods. The image retrieval result improvements in the new approach method over existing methods have been proved by in terms of precision and recall.

This chapter proposes an approach for product ranking with SEO (PR-SEO). PR-SEO is such a way with which all the product in an e-commerce site can get a rank. Search engine optimization (SEO) is now become one of the most useable technique because of the e-commerce business. With the development of technology, nowadays online shopping has become more attractive to all the users. To represent the product in front of all the user, search engine optimization (SEO) helps a lot by ranking the product. In recent few days, the search engine optimization (SEO) becomes more popular because of only the e-commerce sites. Now in this era of digital marketing, all the e-commerce sites are in a competition because of their profit for the product. For generating a new lead of organic traffic, all the e-commerce sites need to be available in the top search when a customer is willingly searching for it without this new lead of organic traffic will not be generated. This proposed product ranking with SEO (PR-SEO)mainly focuses on the SEO tactics for digital marketing as well as e-commerce sites.

Computer graphics is extensively used in the applications such as video gaming, cinemas, advertisements, training, 3D modeling and many more. To classify images used in these fields, pre-trained convolutional neural network models such as VGG16 and VGG19 are adopted through transfer learning and also performance of these models are evaluated using stochastic gradient descent (SGD) with momentum and adaptive moment (AdaM) optimizers. Experimental results show that VGG19 outperforms VGG16 by obtaining an average classification accuracy of 97%.

The development of the World Wide Web has evolved in an enormous volume of data; consequently, drawing out of useful knowledge is a demanding research issue. Use of data mining methods to the Web mentioned as Web mining. It goals at finding and extracting hidden knowledge from Web pages, and services bring out useful information from Web resources and discover common patterns on the Web for examining user activities in network-based systems. The key aim of this paper is to find interested users for a particular website. Hence, the requirement is to build a classification model that can categorize users into interested and non-interested visitors for a particular website based on their access pattern using Web server logs by making use of decision tree classification technique. It will be useful for website owners, to get potential users who are more interested in their website, so that the administration can provide higher privileges for them. Users will be benefited by the special service, which is not available for other users.

A Wireless Sensor Network (WSN) is a group of sensors which communicate with each other and perform some specific task. Clustering is used to conserve energy in a WSN. In this work, the aim is to minimize the energy consumption and maximize the network lifetime of a homogeneous WSN using PSO (Particle Swarm Optimization) based Clustering algorithm in conjunction with quantum computing. In quantum computing, a bit is known as a qubit and it can exist in the following states: a ‘0’, a ‘1’ or a superposition of ‘0’ and ‘1’. In this chapter, the Quantum Computing based PSO clustering algorithm for Optimizing Energy consumption and Network lifetime (QCPOEN) algorithm for homogeneous wireless sensor networks is proposed. The proposed algorithm is compared with the PSO-ECHS algorithm and the LEACH algorithm. The superiority of the algorithm can be verified from the results.

This chapter proposes a new and innovative method of smart optical character recognition (OCR) for translation (SOT) from an English image to Bangla and Hindi language. For translation, this OCR system can take any picture from any documents which contain printed English sentences. SOT can translate the image into text within two languages. The translated text from SOT can also be copied and kept for further use. SOT can convert the full image in a text format and then can translate some of the portions of an image by cropping. Google translator has been used here for the OCR. This API can translate the image into a printed text or a machine-encoded text. But the translated text cannot be kept for further use which is an immense disparity between SOT and Google translator. Moreover, many APIs also deploy, which can translate the image into a text format but cannot ferry the limit of copying text which is collected after translating text from the image. SOT can be beneficiary among all the OCR as it struck the limit of copying text.

Fuzzy clustering is most interested research problem in numerous real-world applications. The fuzzy c-means (FCM) is a most interested and widely used fuzzy clustering algorithm. However, it is easily stuck in the problem of local optima. Hence, the fuzzy social spider optimization (FSSO) technique has applied to solve fuzzy clustering problem in order to solve the shortcomings of FCM algorithm. Both FCM and FSSO have own advantages and disadvantages. In this presented research paper, the FCM clustering algorithm is an integrated with FSSO as a proposed method (FCM-FSSO) for solving fuzzy clustering problem in order to enhance clustering efficiency. The experimental outcome confirmed that the proposed hybrid FCM-FSSO clustering algorithms reveal improved performance compared with FCM and FSSO.

The emergence of Internet of Medical Things (IoMT) has opened up enormous opportunities in the path of advanced healthcare. The advancement in wearable sensors and integration of IoT technologies into medical applications is transforming the way we deliver healthcare. Also, the advent of Social Internet of Things (SIoT) paradigm has introduced a scalable approach to handle interactions among IoT devices. Recognizing the merits of a SIoT network over traditional IoT network, this paper envisages the incorporation of socially enabled IoT into smart healthcare termed as Social Internet of Medical Things (SIoMT) by proposing a cloud-based framework for SIoMT systems. The proposed framework is modeled semantically and instantiated with the help of semantic web languages. In addition, a relationship management approach has been defined to handle relationships among SIoMT devices.

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]).

This paper proposes a combined approach involving image processing and deep learning algorithms to detect the cracks present in building structures such as bridges and other massive structures. The deep learning model used in the proposed work is the Mask R-CNN model. The model is trained on a total of 40,000 images with the help of a Supervisely software to perform crack detection. To distinguish the results, the images are also segmented using active contour model and Chan-Vese segmentation algorithm. The segmented images obtained are trained and validated using a fully convolutional network model. The results obtained on using the pre-trained model and FCNN algorithms are detailed and the study yields effective research. The study shows that deep learning scheme results in an alternative to the current visual examination.

Advanced agriculture system includes a method of allowing water to irrigate the roots of plant according to the soil moisture status and it takes care of the plant health by checking the leaf for disease detection. Agriculture is considered as a main source of income in our country because a majority of people are dependent on farming. From last few years, old style farming is getting merged with smart farming. Many methods has been used for smart farming, IOT (Internet of things) is one of them. Using IOT, we get benefits of cost reduction, improve efficiency, less time consumption in the farming. This project aims at designing a advanced agriculture system to irrigate the plants smartly and remotely. It also has a feature of checking the plants disease by seeing the leaf image of plant with image processing technique. Camera is used there with the system for seeing the plant leaf and soil moisture sensor is attached with raspberry pi for checking the soil status. The system is connected with server and we achieve all the facilities by accessing the web browser. This paper presents all the details about the experiments details done for the project.

Glaucoma, a grievous eye disease, is caused due to high pressure in the eye. It should be diagnosed at an early stage. It may lead to loss of vision when it is not diagnosed early. Glaucoma can be easily detected by the advancements in recent technologies. Deep learning is a latest successful research field in medical image processing. The convolutional neural network, a prominent deep learning model, is used for image classification. A large number of datasets are required for training, and it takes longer time for training the model in order to get more accuracy. To overcome this problem, transfer learning has been used to diagnose glaucoma. A pre-trained model, Inception V3, is used for transfer learning. It can be applied for improving the accuracy of glaucoma diagnosis. ORIGA dataset is used in this work. The performance of the automatic glaucoma diagnostic system is analyzed by sensitivity, specificity and accuracy metrics. Test accuracy of 91.36% has been achieved with minimum 20 numbers of epochs. This automated glaucoma detection system can be used as an analysis tool that helps to detect glaucoma.

In underwater wireless sensor networks ( $${\text{UWSNs}}$$ UWSNs ), sensor allocation is a vital issue for target tracking applications. Sensor nodes are used to track the particular target in the predefined region. But, some targets are not covered because of the fewer sensor nodes, limited energy and random deployment. In the existing literature, most of the researchers have used static nodes which diminished the energy consumption of the network. However, due to harsh water and underwater habitat, mobile nodes are also displaced from their actual position and get stuck. Thus, the mobile node is unable to track the particular target which disrupts the network stability. So, we have to choose an optimal set of mobile nodes to strengthen energy efficiency and network lifetime. Hence, we have proposed the target tracking scheme through multi-objective differential evolution ( $${\text{MODE}}$$ MODE ) in $${\text{UWSNs}}$$ UWSNs . This scheme incorporates the crossover operator with the non-dominated sorting approach. It is applied to balance the diversity in the population set with a high convergence rate. Fitness function is also described with packet delivery probability ( $$PDP$$ PDP ) and residual energy over distance to determine the best mobile node in the direction of the target. The proposed scheme dominates the existing one in terms of the percentage of the allocated nodes, consumed energy and network lifetime.

Triple-band antenna suitable for WLAN applications is designed using ANSYS electronics software. The dimension of the antenna is 50 × 50 × 1.6 mm3 designed on a FR4 epoxy substrate with dielectric constant 4.4. The antenna comprises of a coplanar waveguide (CPW) fed triangular patch with cross-shaped slot. The antenna exhibits resonance around 2.5 and 5.2 GHz (WLAN). The antenna’s performance is also compared using polyimide substrate in view of wearable applications. The resonances were inferred around 2.5, 5.2, and 7.3 GHz (S and C-band). The radiation is realized to be nearly omnidirectional in all the operating frequencies which are in adherence to our application.

A sentence can be interpreted in more than one way depending on the how it is conveyed to a person, and this in turn determines how a person responds. There are several factors that determine interpretation of a spoken sentence, with emotion being one of the major factors. Emotions not only play an important role in human communication but also in human–machine interaction. Proper identification of emotion in a context can be used to increase the effectiveness and responsiveness of various applications like digital voice assistants and automated customer support and in other smart devices. Speech emotion recognition (SER) is a technique by which recognition of emotion is made possible. This thesis focuses on understanding the scope and significance of SER and use of deep learning architectures to achieve better classification rate. The dataset and the features used in the training of the convolutional neural network are also explained.

This research explores recent studies on the implementation of deep learning methodologies such as CNN, RNN and LSTM for news sentiment analysis and examines the methods that provide qualitative results. The various literature in this research gives its own architecture in order to address specific issues that are generally encountered while analyzing news sentiments. It asserts various methods for feature representation like simple encoding of words, word2vec-based encoding, etc. and various other word embedding techniques like word2vec, FastText and GloVe, whereas word2vec is the popular and frequently used technique. The other types of embedding methods like sentence embedding and event embedding are considered to be productive than word embedding; however, event embedding is more potent among all. This research focuses on the combination of deep learning methodologies manipulated for sentiment analysis.

Advancement of Information and Communication Technology (ICT) has facilitated delivery of timely and cost-effective services to the doorstep of populace. This approach becomes popular in developing countries, which are striving hard to maintain good infrastructure and manpower to provide services to its Citizen. In case of conventional service delivery system, Citizen has to attend SERVICE PROVIDER physically to avail the desired services, which may be missed out by Citizen due to lack of proper time management. Technology-based message communication may provide sufficient flexibility to Citizen to avail those electronic services at their suitable time, thereby giving them liberty from obligation of physical presence for the same. Even Government can also monitor these electronic message communications using advanced technologies to prevent fraudulent activities. In this paper, authors have proposed an Integrated Cloud Education System (ICES) through single window interface, namely multipurpose electronic card (MEC), which will help Citizen (i.e., student) to avail online education as per their convenient time schedule. To ensure security of message communication, application of blockchains will explore future scope of research for our proposed system Integrated Cloud Education System (ICES).

The OpenID Connect is an open standard authentication protocol used to authenticate users across multiple domains using a single identity. The Identity Provider(IdP) provides a unique account to each user, which helps them access multiple domains called Relying Parties(RP).Since many web services and applications rely on this protocol for user authentication, it is important to verify the security properties of this protocol. The protocol was modeled and the properties of interest were verified using Tamarin Prover, a tool used for symbolic modeling and formal verification of cryptographic protocols. The results of the protocol verification demonstrate the presence of the Identity Provider (IdP) Mix-up attack and HTTP 307 Redirect attack in the protocol.

Character recognition is an active research area for recognizing handwritten or optical characters using computers. The number of languages in the world is about 7000, which demands different technologies to deal with the characters. Different tools help to get the scanned documents in the form of images and then technologies are used to recognize the characters. In this paper, a convolution neural network (CNN) model is proposed for recognizing 52 Telugu characters. The performance of the proposed model has been evaluated by optimizers such as Adam, Adagrad, Adadelta, and Stochastic Gradient Descent. The preprocessing step is also supported in improving the accuracy. The CNN model is compared with VGG-16 and the accuracy obtained is less. A highest accuracy of 90.8% is achieved by Adam for the proposed CNN model.

In this paper, an automated system is proposed to crack the WPA2 passphrase much efficiently. Two different schemes are introduced which can improve the dictionary attack and overcome the existing issues. Firstly, a method to validate the captured handshake is introduced. This helps to enhance the dictionary attack to retrieve the passphrase easily. Secondly, a design flaw in the WPS implementation is explained and used to exploit the same to crack the WPS pin. This attack is much efficient compared to existing methods. Moreover, the automated script which probes and intimates whether access point supports WPS. Access point which supports WPS will perform WPS pin attack and which does not support WPS undergoes improved dictionary attack and will obtain the passphrase.

Routing is essential for data collecting in underwater network applications. In the routing, a set of best forwarding nodes are required for transferring the packets toward the sink. In the case of natural disasters, nodes are stuck and unable to communicate with other nodes which create node disjoint path issues. The existing work has not considered this issue during obstacles such as a natural disaster. This strengthens the energy consumption, packet loss, and degrades the network lifetime. Therefore, we have proposed an interval type 2 takagi-sugeno-kang fuzzy interference system ( $${\text{IT}}2 {\text{TSK}} {\text{FIS}}$$ IT 2 TSKFIS )-based energy efficient routing scheme for the selection of the best autonomous underwater vehicle $$\left( {{\text{AUV}}} \right)$$ AUV -based forwarding nodes. In the proposed scheme, three parameters are used such as depth difference, remaining energy, and link quality as the input of the $${\text{IT2 TSK FIS}}$$ IT2 TSK FIS rule base system. The node which has high link quality and less depth difference is elected as the best forwarding node. The simulation of the proposed scheme upgrades the performance in terms of the $${\text{PDR}}$$ PDR , $${\text{EED}}$$ EED and total energy consumption as compared with the existing scheme.

In this paper, we make a comparison of the two schools of mathematics that flourished in two distant parts of the world. One is the Kerala School of Mathematics and the other is the European School of Mathematics. Kerala School of Mathematics contributed extensively to early developments in the field of mathematics. Their contributions in the field of astronomy and calculus have increased the pace of knowledge explosion in classical mathematics that took place in Europe. Later, we witnessed the continuous expansion of European School while Kerala School diminished. On the basis of network analysis, we try to explain the reasons for these developments.

Cervical cancer is a major cause of death in women, affecting over five hundred thousand women worldwide each year. It is one of the leading causes of death in women. Technology plays an indispensable role in medicine and health to assess cancer research studies. In this study, we integrate diverse machine learning models in order to predict the presence of the cancer. The data set is characterized by 32 features and 4 labels—Biopsy, Hinselmann, Cytology and Schiller. Neural Network and Ensemble methods are applied to predict the four target variables independently. Naïve Bayes, K-Nearest Neighbour, Logistic Regression, Support Vector Machine and Decision Tree are used for Multi-label classification. Stacking classifier, which is an ensemble based machine learning technique, provided the best accuracy for classifying each target variable, with the best one being 98.02%. In Multi-label classification the best mean accuracy is 91.44%, given by decision tree, while the best accuracy given by neural network is 91.44% for Hinselmann.

Teaching Learning Based Optimization (TLBO) algorithm simulate the teaching learning peculiarity of a classroom to solve multidimensional, linear and nonlinear problems with appreciable efficiency. In order to accelerate the execution time of software implementation, the TLBO algorithm is implemented on hardware. Then the TLBO hardware is developed as TLBO Intellectual Property and it is interfaced as a peripheral to the System on Chip platform. We compared the performance of the floating point TLBO IP is realized by solving the benchmark functions, results 183–224X times faster than the software implementation of the same algorithm. As a case study, the same TLBO IP is used to solve the spectrum allocation problem by optimizing Max-Sum-Reward (MSR) function and it results 69–78X times faster than the software implementation of the same algorithm.

In wireless communications, Bluetooth technology is a key component. Bluetooth, more specifically Bluetooth Low Energy (BLE), provides a short-distance wireless communication between devices and other networks with low cost and low power. The security issues in the Bluetooth networks give the advantages to the attacker for unauthorized access to the information perform internal attacks and do vulnerable attacks that can corrupt data on the wireless devices. As technology is growing, the attackers are finding new ways to exploit. This paper describes the Bluetooth technology, its security, vulnerabilities, threats, and risk mitigations, as well as real-life examples of exploits and its results.

There had been numerous explores on scheduling problems till date. Techniques to solve scheduling problems are frequently unfeasible, as problems are strongly NP hard. Meta-heuristic techniques are more generic and applicable to solve wider range problems. we choose master–slave, adaptiveness approaches in genetic algorithm because they are known for parallelisation, use optimistic selection techniques and use dynamic features to yield better result. In this paper, we propose and implement workforce planning problem using serial genetic algorithm and parallel genetic algorithm with both dynamic and adaptive behaviour which we believe is the first of its kind and provides possible optimal solutions. This approach fastens the algorithm performance. Computational results for our proposed model thrive towards optimality, in scheduling.

Industry is growing rapidly these days; the greatest problem for any nation would be the environmental protection. Industries and vehicles which are producing poisonous gases are creating challenges. Predicting the presence and density of harmful gases, finding the right predictive value, and raising notifications in real-time are the biggest challenges. Smart cities use a lot of real-time systems; the data generated from these systems can be better utilized if it is exchanged with other organizations. Sharing the real value data of air quality with the surveillance systems in several smart cities will help in developing a solution for air pollution. Many sensors are available for sensing the poisonous gases in air. Implementing machine learning algorithm along with the collected sensor data could help in predicting the air quality with high accuracy. One-third of the total pollution comes from traffic due to the congestion of vehicles there. Hence, a smart traffic management which operates on the expected value of the air quality is necessary to control air pollution. MQ series sensors have been used for collecting the poisonous gas data present in the air. A wireless network model (WSN) is used for communication with the gateway. WSN will also provide a service protocol to send the data from one place to another (José Luis Herrero Agustın, Wireless sensor network for air quality monitoring and control). Implementing communication protocol LORA (short for long range) will provide more advantages compared to other protocols in long-range communication. Collected data from sensors can be easily sent on a private cloud and the machine learning algorithm can also use the value for further prediction. The user can access any information from the cloud through an open API and library. With such a model, the society can develop an understanding of air quality in real time. Smart traffic management with the support of the recurrent neural network will help in protecting the future generation and will provide a solution for the challenges.

Digital signal processing (DSP) architectures primarily involve mathematical operations such as multiplication and addition. Hence, optimizing the multiplier circuit architecture essentially improves the efficiency of DSP systems. In this paper, two Vedic sutras, namely Yavadunam and Urdhva-Tiryagbhyam, have been identified and integrated for computing the cube of a 4-bit binary number. The Vedic multiplier architecture has been designed using different adders, namely CLA, carry-select adder (CSLA) and carry-skip adder (CSA), and used in cubic computation. The cubic computation performed using Vedic multiplier designed using CLA is 11 and 17% more power efficient when compared with that of the Vedic multiplier employing CSA and CSLA. There also found a subsequent reduction in area and cell count while using CLA in the multiplier design. The cubic architecture has been designed using Altera Quartus-II tools and the simulations have been carried out using with ModelSim-Altera 10.0c. The performance parameters such as area and power have been calculated with the help of Cadence Genus EDA tool employing 180 nm technology library files.

Multiplier is one among the essential hardware blocks in present-day communication and digital signal processing (DSP) systems. The primary advantage of Vedic multiplier is that delay increases slowly with the increase of input bits. Vedic multiplier has supreme advantage when compared with other multipliers over regularity of structures and gate delays. In this paper, the different designs of 8 * 8 bit Vedic multiplier are implemented with different adders, and these multipliers and adders are structured using suitable reversible logic gates, that is, Feynman gate, Peres gate, Toffoli gate, DPG gate and modified Fredkin gate. The different designs of Vedic multipliers designed with the help of different adders, that is, ripple carry adder, carry skip adder and carry select adder, and hence compared their implementation time as well as area required for the circuitry. This comparison will help designers to choose the preferable multiplier according to the application specific demands.

Nowadays, the wireless communication systems have experienced a great advance in efficiency, infrastructure and coverage, which has allowed the appearance of new standards that help in the digital interconnection of different devices in a local network. Today, the Internet of things (IoT) is considered the next great opportunity and challenge for the Internet engineering community, wireless technology users, companies and society in general. There are several wireless transmission standards such as Wi-Fi, Bluetooth, ZigBee and all of them are designed for low power operations; they can be left unused for a long period of time without the need to recharge the battery of the device, which avoids the need to recharge the battery frequently. This paper aims to analyze the implementation of a gateway for the IEEE 802.15.4 standard with open source tools and the Raspberry Pi 3 development board, using the agile development methodology.

This paper deals with the scheduling of parallel works in a two-stage hierarchical grid. In this configuration, one of the great challenges is to assign the tasks in order to allow an efficient use of resources, while satisfying other criteria. In general, the optimization criteria are often in conflict. For solving this problem, a bi-objective genetic algorithm is proposed presenting an experimental study of six cross operators, and three mutation operators. The most influential parameters are determined through a statistical analysis of multifactorial variance which compares the proposal with five allocation strategies found in the literature.

The authentication process is a key component to increase security in a vehicle network. Most of the authentication protocols proposed in the literature are based on asymmetric cryptography, and specifically on the use of the RSA algorithm. In addition, the use of digital certificates and a public key infrastructure is considered. Therefore, the authentication process is often complex. In order to propose a secure solution, without the use of digital certificates and the RSA algorithm, a mutual authentication protocol based on the Diffie-Hellman algorithm is presented to establish a session key between vehicle (OBU) and road unit (RSU). From the session key, a secure communication channel can be established to transmit the identifier of each participant and the respective security parameters. To perform the authentication process, the entities perform low-cost computational operations such as hash and XOR functions. Once the mutual authentication protocol is completed, the vehicle and the road unit can exchange messages securely.

Metamaterials are formed by inclusions in material components to achieve qualitatively new physical realizable properties and responses that do not be readily available in nature. This field has gathered a wide research interest in recent years. This paper highlights the use of metamaterial concept in the size reduction and increasing the performance of an antenna. Here, a planar microstrip s-patch antenna is proposed for the LTE band 2.6 GHz. The achievement of compact size in the antenna has been a greater advantage, which helps in making the designed antenna for the LTE-WLAN application with major concentration on Fresnel region.

Primary focus of the present project is to design a coplanar waveguide for UWB antenna used in wireless applications capable of operating at multiple resonating frequencies lying in S-band, C-band and X-band of frequency 2–4 GHz, 4–8 GHz, and 8–12 GHz, respectively. The antenna comprises an arc-loaded-slotted rectangular patch and two parasitic square patches adjacent to the coplanar ground. The CPW antenna firmly fixed on FR—4 substrate fed by 50 Ω strip line. The parametric studies are carried out to analyze the resonant behavior of the expected antenna. The antenna achieves a impedance bandwidth of about 12.4 GHz (1.9–14.3 GHz) and has higher gain. The measured results show that ultra wideband, operating band of 3.1 GHz to 10.6 GHz, has been achieved with multi–directional radiation pattern with efficient peak antenna gain.

An inverted L-shaped monopole antenna loaded with split rings with partial ground plane is proposed in this paper for WLAN, C and X band applications. The antenna is highly compact with an overall size of 25 × 25 × 1.6 mm3. The L-shaped monopole with a partial ground plane is designed to yield resonance at 2.5 GHz. Subsequently, square split rings are loaded onto the monopole to create multiple higher order resonances which then merge together to yield a wide band from 5 to 14 GHz. The measured radiation pattern exhibits an omnidirectional pattern in one plane and nearly bidirectional pattern in the other plane over all the operating regions.

A microstrip two-port multiple-input multiple-output (MIMO) antenna has been designed for S-, C-, X-, Ku-, K- and Ka—band applications covering super wideband range. In this work, partial ground has been used to enhance the gain and bandwidth values. These antennas are with a size of 35 mm × 60 mm and 35 mm × 65 mm. The bandwidth of proposed MIMO antennas is 35.55 GHz for linear, 34.82 GHz for orthogonal and 34.82 GHz for out of phase arrangements. For all these models, the error correlation coefficient (ECC) is less than 0.02 and corresponding diversity gain (DG) values are approximately equals to 10 dBi. The channel capacity loss (CCL) for MIMO satisfies less than 0.4 bits/S/Hz. The proposed structure has been simulated using HFSS software. The parameters of proposed antenna like return loss, radiation pattern; ECC, DG, TARC and CCL are optimized within the bandwidth.

Humans have the capability of recognizing objects at a glance and tell its category or the name despite of the variation in illumination pose, appearance, deformation and texture. But developing algorithms to make a computer to understand an image is really a challenging task. In this work a modified pulse coupled neural network (MPCNN) is implemented for extracting the features of object. The proposed MPCNN was analyzed with various parameter settings and suitable parameters for identifying the objects in COIL dataset were identified experimentally. The performance of this object recognition system is evaluated using neural network classifier. The object recognition was invariant to transformations and view point changes, and robust to noise and occlusion.

The concept of non-orthogonal multiple access(NOMA) is to provide the far users with high power modulated signal in order to increase the transmission efficiency. Power allocation method is used to design non-orthogonal multiple access algorithm, where the signals are modulated with respect to power and time. This type of design improves the energy efficiency and even the far users will be receiving the data bits successfully. The major threat is the fading noise that are added onto the channels. Gaussian distribution and spectral density cause AWGN fading, whereas the presence of scatters between the receiver and transmitter causes Rayleigh fading. These fading channels are computed and the simulation results inferred illustrates that AWGN fading channel performance is better when compared with Rayleigh fading channel. Estimation of channel helps to attain spectral efficient wireless communications. Error correcting block (ECB) based on Hamming code is been implemented. Whenever bit error is generated may be due to hardware faults or software faults it will be detected and corrected by ECB and this improves the data transfer.

Home automation system is one of the developing systems nowadays. This project is concentrating on automatically controlling the devices or appliances working at home based on the comfort of the person living there. Arduino technology is used as the control system. This system designed to enhances the comfort and safety of the user and to the effective usage of the power. Bluetooth peering between client and server act as a wireless communication system. In this work, mobile station is the client and HC6 Bluetooth module is the server that is connected directly with Arduino Uno. It is an automatic system that can work sensing the movement of the human in the confined space and Bluetooth is attached to observe the working devices.

One of the developmental disorder found in early childhood is Autism Spectrum Disorder (ASD). Kids suffering from ASD are affected by the way they act in society and interact with others. Usually, ASD kids are associated with excess or poor emotional facial-expressions. The primary focus of this paper is to use advanced deep learning techniques to classify ASD kids into two classes namely Low ASD kids and High ASD kids. Low and High here mentions the intensity of the ASD disorder in the kids. The proposed work aims at achieving this classification with the computer vision techniques and by learning on their facial expressions. Several videos of Low ASD kids and High ASD kids were collected. Each frame of these videos was then parsed into images to train and test an Attention based Residual Neural Network. This proposed model brings a novel method of embedding Attention mechanism on Residual Convolution Neural Networks, which results in carrying the most significant features from the initial and primary layers to the very end with very little distortion. This is done by the attention block, which weighs every parameter according to its significance. This way, the features with higher weighs are passed till the deep layers of the network without any loss of information. Thus, the proposed work is able to classify effectively Low and High ASD kids based on the videos collected successfully yielding results with a state-of-the-art accuracy of around 94%.

In Ultra Deep Sub Micron (UDSM) Technology, accomplishment of error free data transmission through Network on Chip (NoC) interconnect becomes impractical. This difficult state is widely managed by the use of Error Correction Codes (ECC) in NoC. A novel Hamming based Multiple Transient Error Correction (HMTEC) code is proposed to enhance the NoC interconnect reliability. Implementation of this code corrects all single bit errors and most of the combinations of errors upto 8 bits for a flit size of 32. Furthermore, this code allows error correction in both data and redundant bits. This coding method is performed at lower power consumption, area occupancy and delay when measured against similar codes proposed in recent years.

Battlefield surveillance is one of the most important military applications of wireless sensor networks (WSN). Sustainability of the network is still a major challenge due to the energy constraint of the node, and in most of the cases, battery replacement is not feasible due to the hostile and hazardous environments in which these networks are deployed. In this paper, we evaluate the solar energy harvested by small footprint devices that are suitable for wireless sensor nodes. Based on the energy requirements of the commercially available devices, we infer that, in spite of the unpredictable nature, the solar energy harvested can be effectively utilised to keep the nodes operational for long periods of time. We propose a novel scheme based on Markov decision process that enhances the performance of a wireless node with energy harvesting capability. We apply our algorithm to IEEE 802.15.4 which is a widely implemented standard in WSNs and show that significant enhancement in throughput can be achieved while sustaining the life of the node.

Leakage power minimization is a very important issue in today’s VLSI design, as the technology scales down the supply voltage as well the threshold voltage of the transistor should be reduced. In submicron technology sub-threshold leakage current.Is a source of leakage power. One more issue is the draining of battery when it is in standby mode. Many researchers were proposed different leakage current minimization techniques. In below paper, we initially review the previous research works with their advantages and drawbacks. Based on this literature survey the new leakage power minimization techniques are proposed and it is worked on simple digital circuit and a combinational circuit it resolves the above-discussed issues and gives the comparatively good power reduction.

This paper presents design of 3-bit power-efficient Flash Analog to Digital Converter for high-speed applications with single inverter comparator and Ex-OR based ROM encoder circuits. The Single Inverter Comparator circuit compares applied input voltage with the reference voltage and results in ‘0′ or ‘1′ output depending upon their magnitudes. The designed single inverter comparator consumes just 2.94pW amount of power. Outputs of these comparators form thermometer code which has to be applied to an encoder to obtain the digital code. A high speed Ex-OR based ROM encoder is designed to convert comparator outputs to the corresponding digital equivalents. A 3-bit Flash ADC is designed and simulated using Cadence analog design tools with 180 nm process technological library. The simulated results of the design show an average power consumption of 8.157uW and delay of 2.832 ns at 10 MHz. The area occupied by the design is 205.536um2.

Automatic speech recognition (ASR) technologies and systems have made remarkable progress in the last decade. Now-a-days ASR based systems have been successfully integrated in many commercial applications and they are giving highly satisfactory results. However, speech recognition technologies as well as the systems are still highly dependent on the language family for which it is developed and optimized. The language dependency is a major hurdle in the development of universal speech recognition system that can operate at any language conditions. The language dependencies basically come from the parameterization of the speech signal itself. Tonal languages are different category of language where the pitch information distinguishes one morpheme from the others. However, most of the feature extraction techniques for ASR are optimized for English language where tone related information is completely suppressed. In this paper we have investigated short-time phase-based Modified Group Delay (MGD) features for parameterization of the speech signal for recognition of the tonal vowels. The tonal vowels comprises of two categories of vowels—vowels without any lexical tone and vowels with lexical tone. Therefore, a feature vector which can recognize the tonal vowels can be considered as a speech parameterization technique for both tonal as well as non-tonal language recognizer.

A novel architecture of power-efficient 4:16 active low decoder is proposed and compared with the existing 4:16 decoder. The proposed 4:16 decoder using a variable bias gate diffusion input (GDI) NAND and NOR technique. It is examined from the results that the proposed 4:16 decoder using a variable bias GDI technique operates with 47.46% less energy consumption than the recently reported decoders. The proposed simulation results are carried out using spice software.

The innovative fractal-based electromagnetic bandgap (FEBG) organization has affected due to this work. The recommend designed antenna can utilize 2.4 GHz for an application of wireless long-term evolution (LTE) with mini designed dimensions. The simulation is performed with an IE3D simulator version 15.0. The secondary intrusive order bandgap distinctive of FEBG is established by operating more problematically well-organized examination. An inspection on coupling depletion shows above 27 dB and 40 dB in azimuth as well as elevation. The recommend antennas without and with secondary intrusive order FEBG is manufacture as well as steady. The steady consequence is in good acceptance with the simulated consequences. The recommend structure gives an outstanding multiple representations along with acceptable use in low-frequency narrow-band MIMO administration.

Parkinson’s disease is a movement hampering, neurodegenerative and intensifying disorder. The symptoms of this disease are basically divided into non-motor and motor symptoms. The non-motor symptoms include REM sleep disorder, depression, difficulties in speech and swallowing, change in the body odor, while the motor symptoms include tremor, bradykinesia and changes in the body posture. These symptoms gradually intensify and worsens. Out of these two, non-motor symptoms can be recognized at an early stage. Due to this reason, we have focused on detection of Parkinson’s disease using speech and smell signatures which is a part of non-motor symptoms in this paper. The speech and the smell signature of a patient affected with Parkinson’s disease as well as healthy individuals are recorded using a MATLAB toolbox and VOC sensor, respectively. It is further programmed, compared and with the help of both the values, decision-making is carried out. It is a non-expensive and a non-interference technique. The proposed work can be used in clinics and can be introduced in their check-up procedures which can improve a person’s well-being.

In the modern age, where the digital communication systems play a vital part in our day-to-day activities the amount of digital data generated has increased exponentially. Limited security and bandwidth are the major constraints that have to be faced. One of the most excessively used multimedia data are images. A lot of studies indicate that the confidentiality and authenticity of the data are vulnerable from attacks no matter the level or method proposed to secure the data. In this paper, an algorithm is proposed to encrypt color image where Lorenz attractor is used to generate keys that are used in the process of image encryption. The proposed solution is focused of reducing the computational complexity by reducing the encryption process to most significant nibble plane of the image.

The formal definition of urban population flow forecasting problem is the basis for solving this problem. This paper firstly models the regional population flow representation, and based on this, formalizes the urban regional population flow forecasting problem. Based on the problem definition, the network structure of the STRCNs model and how the model solves the space-time dependence in the urban population forecasting problem will be described in detail.

In this paper, a double-band MIMO antenna for wireless applications is proposed. A typical FR4 substrate is used with a thickness is 1.6 mm and size of 80 × 40 mm2, and having two operating bands, one is from 4.7 to 5.4 GHz with the center frequency at 5 GHz and the other is from 5.6 to 6.4 GHz with the center frequency at 6 GHz. The defected ground structure (DGS) is utilized to improve the isolation of the double-band MIMO antenna system. The maximum isolation achieved at both the 5 GHz and 6 GHz center frequencies is 48 dB and 22 dB, respectively. The proposed MIMO antenna is useful for the Wi-Fi application; these bands come under the IEEE standard 802.11 ac with 700 MHz bandwidth. The various diversity performance parameters are also observed. The outcomes of these parameters show that the diversity characteristic performance of this dual-band MIMO antenna design is more effective for the Wi-Fi application.

Early diagnosis of leaf ailments is the most necessary and prominent way to increase agriculture production. This paper introduces a computer-aided approach for detecting, localizing and classifying the ailments in plant leaves. The proposed method consists of two phases: In the fundamental phase, feed-forward back propagation neural network (BPNN) is used to classify leaf disorder by extracting features such as gray-level co-occurrence matrix (GLCM) and statistical features from the leaf. A novel training function is proposed for enhancing the classification accuracy of BPNN. The proposed training algorithm gives maximum classification accuracy of 95.64%, the sensitivity of 96.69%, selectivity of 96.54% and mean square error of 0.02 as compared to other existing training functions that produce a maximum accuracy of 93.4%. In the second phase, the diseased region is localized using radon transform (RT) by applying suitable morphological operations.

Analysis of ionospheric variability is imperative for developing the day-to-day ionospheric modeling and prediction services of the global navigation satellite system (GNSS) applications. The highest-quality GNSS information is provided by the International GNSS Service (IGS) on open access to users. The GNSS information that accompanies them also covers services in support of positioning, navigation and timing information, which benefits science and society. In this paper, retrieve, processing and analysis of Global Positioning System (GPS) derived ionospheric total electron content (TEC) using the IGS Hyderabad GNSS station (17.41° N, 78.55° E; geographical). The presented work would be useful for download, process and analysis of the IGS GNSS data.

For orthogonal frequency division multiplexing (OFDM) technology, if the number of antenna or modulation increases, the most accurate maximum likelihood (ML) detection will become extremely complex. A new suggestion for an algorithm is introduced in this paper with a significantly reduced calculation complexity to perform an optimal ML detection. With the use of minimum mean square error (MMSE) criterion, the suggested method filtered and avoids undependable candidate signals from data streams. While using the metric probability to analysis the dependability of each and every symbol candidate with normalized likelihood functions, the most accurate ML detection is achieved. The authenticity of suggested method is strengthen with the performance examine. To offset a balance between system performance and calculation complexity, a threshold parameter is introduced. The suggested method achieved nearly comparable results as in the ML detection at a bit error rate (BER) of 10−4 with 29 and 16% of true calculations with the conventional QR methods.

With the fast-growing wireless communication and limited spectrum availability, cognitive radio is emerging as promising solution to support ever increasing demand for applications and services. In weak signal conditions, spectrum sensing by unlicensed users gets severely affected by shadowing and fading conditions. In such conditions, channel state estimation of the primary channel would be useful. However, this would lead to significant transmission overheads. In this paper, an innovative fuzzy-based spectrum sensing technique to overcome the drawbacks in conventional cooperative spectrum sensing is proposed. The fuzzy-based cooperative spectrum sensing technique presented in this paper considers sensing results of three secondary nodes; i.e., the node under consideration along with two neighboring nodes, to decide upon the sensing result from any node, which makes this technique very robust and efficient in noisy conditions. Diversity combining techniques are implemented to calculate the signal energy values for consideration at the fuzzy center. These energy values are considered, and final decision on the sensing energy is evaluated by a novel rule-based algorithm, which resulted in improved sensing accuracy for finding the presence of primary user. Further, the proposed algorithm uses multiple threshold values to determine the primary user presence to tackle low signal-to-noise ratio conditions. Results obtained prove that the proposed method in this work outperforms the existing schemes and is able to achieve accurate sensing results and gives better results than conventional schemes in noisy conditions.

One of the most important hardware blocks in processors is the multiplier circuit module. Area, power, and delay rule as primary factors deciding VLSI design methodologies. The work presented in this paper focuses on these three factors using Vedic multiplication approach and gate-diffusion input (GDI) logic. The ancient system of Indian mathematics being the Vedic mathematics, rediscovered from the Vedas, is more simplified, faster and accurate as compared to normal multiplication methods. The primary advantage of gate-diffusion input (GDI) logic is helping in reduced transistor count. Hence, the combination of these approaches of Vedic multiplication implemented using GDI logic results in reduced propagation delay time, lower power consumption, and less silicon area. In this paper, the proposed 2-bit Urdhva cell used is implemented using AND gate, XOR gate, and an inverter unlike the existing literature in which it is implemented using two half adders. The results are validated for the comparative advantages of our approach. The circuit simulations are carried out using UMC 90 nm technology nodes in Cadence Virtuoso.

Virus like any other object has its own unique frequency signature that makes the virus vibrate at a certain frequency. Due to this natural frequency of vibration, the virus is susceptible to resonate at a frequency that is tuned to its own fundamental frequency. In the research proposal, we have established a theoretical model for COVID-19 virus inactivation. The disclosed research deals with the transfer of energy wave to the structure-resonant virus, wherein the virus is inactivated by physically rupturing the virus structure. The proposal provides a novel method and apparatus for developing a resonant-energy induced virus inactivation strategy as a solution for COVID-19.

This paper presents a low power architecture for linear periodically time varying (LPTV) filter by decomposing into finite computational threads. An N-tap, M period LPTV filter architecture is minimized into a single LTI filter by enabling the thread decomposition (TD) of the LPTV filtering operation. The proposed architecture is a generalization to the transposed form structure. A new insight, derived from TD enabled this generalization, which is otherwise not possible. Implementing the LPTV filter with multiplier less functional blocks based on binary common sub-expression elimination (BCSE) algorithm reduced the critical path delay. Experimental results show that the proposed design offers 48.9% reduction in area delay product (ADP) and 14.2% reduction in power delay product (PDP). The LPTV filtering operations in various applications can be realized with the proposed architecture. This work is the first attempt to the ASIC implementation of an efficient architecture for LPTV filter.

Performing stroke rehabilitation exercises typically requires assistance from a physiotherapist. In the absence of any external help, the patients tend to quickly lose the motivation to carry out the exercises on their own, resulting in poor recovery. To improve the motivation levels of the patient and take his/her mind off the training process, a sensor-driven active game-like feedback-based hand rehabilitation system is proposed, making the rehabilitation exercises more interactive and entertaining to the patient as well as makes the process more productive. Proposed system monitors and analyzes the strength of voluntary grip of the patient. The grip strength is mapped to a game-like interface and each strength test is presented as a game task to the patient. Successfully completing the game task requires the patient to complete the exercise routine. The data gained from monitoring patient’s performance in the exercise is used to track the progress they have made as well as providing more insight on their difficulties.

In this communication, a dual band antenna with defected circular slot in T shape geometry on top and defected ground structure on bottom is presented. The proposed design operates with two frequency bands (3.18–3.87 GHz)/(4.66–10.05 GHz) at resonating frequencies 3.68 and 6.78 GHz which validates the applications of S, C and X-bands.

In this research work, the functional carbon was prepared by direct carbonization method without any physical or chemical activation from the dead plant leaves of Phyllanthus acidus (PAL) and tested for energy storage system of supercapacitors. This technique is very unique and appropriate to various dried biomass. The synthesized functional carbon was characterized by X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FT-IR), Energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy (RAMAN), Field emission scanning microscopy (FESEM), Transmission electron microscope, and Brunauer–Emmett–Teller Analysis. The functional carbon electrode materials were also assessed by Cyclic Voltammetry analysis (CV), Galvanostatic Charge and Discharge analysis (GCD), Electrochemical Impedance analysis (EIS) and Cycles stability studies in aqueous electrolyte (1M H2SO4) by using three-electrode systems. From the results, the electrode material of PAL-1000 indicates high specific capacitance of 347 F/g compared to other samples of PAL-600 and 800. The PAL-1000 exhibits a high energy density 43.38 Wh/kg and more power density of 625 W/kg. The outcomes of the result reflect that the micro porous functional carbon prepared with PAL as raw material provides eco-friendly, non-toxic and suitable as electrode material for supercapacitors.

A cart-pole system is a highly nonlinear as well as an unstable system, which can be utilized as a benchmark system for the testing and designing purposes of different control efforts and it is the widely used application of control system and robotics. For getting the stability of cart-pole system Linear Quadratic Gaussian optimal control problem is formulated which is based on the design of state observer. According to the principal of separation of the problem, at the beginning the control law is generated just after solving ARE using Schur Decomposition to design a controller, which is totally based on principal of state feedback and the point of time comes when all the states of the system can not be measured at the same time there is a presence of process noise as well as measurement noise, optimal state estimator (i.e. Kalman Filter) is made for cart-pole system. Robust $${H}_{\infty }$$ H ∞ controller has been designed using plant augmentation with weighting functions for the system to carry out the frequency domain analysis of the given system. The simulation results reveal that the controllers can stabilize the cart-pole system at the same time it eliminates noise presents in the system and makes the system robust. Numerical experimentation has been carried out to compare the different approaches.

Regulation of second-order plus time-delay systems (SOPDT) plays a vital role because many of the industrial process (such as electrical, mechanical, and electromechanical systems) exhibits delayed response at outputs in response to the inputs. These time-delay systems are responsible for control complexity and degrade the system performance under disturbance conditions. To regulate the performance of these systems, robust controllers are needed. Sliding mode control is a robust control strategy. To regulate these systems and also to improve the performance under disturbance conditions, sliding mode control strategy is adapted. Here, second-order sliding algorithms such as twisting, super-twisting, and adaptive algorithms are incorporated for regulation of these systems. Pade approximations (0/1, 1/1, 1/0) are used for representation of the constant time delay and here (0/1) approximation is adapted. Simulation studies have been performed for these systems in the MATLAB environment.

Sliding mode control (SMC) strategy offers insensitivity to plant parameter variations and also to the external disturbance signals. Second-order SMC approach is helpful to design a robust controller with smooth control action for a dynamic complex uncertain plant operating under disturbance and noise conditions. Here, adaptive second-order SMC (ASOSMC) algorithm is applied for regulation of a simple pendulum. This ASOSMC law not only smoothens the control action but also brings the trajectories to the stable equilibrium point in less time, and it preserves the properties of robustness. The regulation of this system is carried out under various initial conditions along with disturbance and also under parameter variations. The simulation studies are demonstrated in MATLAB environment.

A device or a dynamo which is capable of undergoing electromechanical energy conversion is termed to be an Electrical Machine. It includes both AC machine and DC machine. The main drawback in conventional DC machines are high maintenance cost and commutation risk. Since induction motor has more advantages than other machines, it replaced the conventional DC machines. Most of the machines used in industry nowadays are Asynchronous motors. Though it is used in various applications is has some risk such as poor starting torque, worst lagging power factor which leads to increase in copper losses and poor efficiency of the machine. Special Machine is the one which started emerging as a suitable candidate which could be used to satisfy the demands of various applications. Switched Reluctance Motor (SRM) is the one such special machine that can be used in various application such as electric vehicles and wind energy systems due to its good performance characteristics. SRM is a brushless AC motor. It is simple in construction and rotor does not require permanent magnet. Simple construction, low losses and greater efficiency are the notable merits of Switched Reluctance Motor. By controlling the overlapping phase currents, minimization of torque ripple can be done. Ansys—RMxprt design tool is used to the design a Switched Reluctance Motor (SRM) which has 8 stator poles and 6 rotor poles. Its performance has been analysed through simulation results using the developed machine using design parameters. The SRM hardware development has been done in order to validate the simulation results.

Currently there is large increase in use of prosumer based energy source globally considering the targets to cut down the carbon footprint. This has created a necessity to control all the sources and study their impacts during normal and abnormal conditions. This paper tries to verify and validate the stability performance of proposed hybrid microgrid having controls integrated with each individual distributed generator (DG) sources. The smooth transfer of grid connect mode to island mode is pondered. Effects of large system disturbances while in islanded mode are also simulated. Cases involving different combinations of various types of DGs including renewable energy sources (RES) and non-RES are modelled. The simulation and modelling of test system are carried out on MATLAB platform.

To design robust controllers for dynamic plant operating under uncertain conditions, one of the efficient approaches is sliding mode control. SMC offers low sensitivity to plant parameter variations and has disturbance rejection properties that eliminate necessity for exact modelling. Regulation of speed of a DC motor with prescribed control law of second-order SMC strategy under disturbance, parameter variation, normal operating conditions are presented in this paper. Simulation studies have been performed in the MATLAB environment considering second-order SMC strategy with prescribed law.

Electric vehicles are gaining popularity due to environmental sensitivity, government support and increasing efficiency nowadays. However, a major power quality issue emerges during charging process of the main source of vehicle as a result of the charging procedure the harmonic generation by power electronics converters which is basically the EV chargers. This paper aims at resolving the power quality issue of reduction in THD for off-board DC supercharger of 175 kW and off-board DC fast charger of 60 kW. The proposed system to resolve this issue consists of a three-leg voltage source converter-based D-STATCOM assisted by a DC link capacitor. Synchronous reference frame theory is used to control D-STATCOM. Simulation is performed using MATLAB SIMULINK for various scenarios during EV charging process.

Solar Power is a sustainable renewable energy source which does not produce environmental pollution like conventional sources. The solar PV power can be transferred to grid using single stage Voltage Source Inverter (VSI) topology. Maximum Power Point Tracker (MPPT) will force the PV system to operate at the maximum power condition. Here MPPT will generate a power angle with which the VSC output voltage leads the grid voltage. The nonlinear nature of VSI is removed by an Input-Output Feedbck Linearization Controller (IOFL). The IOFL controller development includes the complete linearization of the VSI and linearized system is controlled with a linear controller. So, in this paper, Power angle control technique is incorporated along with IOFL controller so that active power flow can guided from solar panel to grid. The proposed method is more meaningful when the synchronization of grid voltage with the controller is also a part of it. So a Phase Locked Loop is also discussed for connecting it in the controller side. This paper focused on a simple, convenient method of solar power control to grid with the inclusion of MPPT, IOFL, PLL and power angle control. The simulation results shows the effectiveness of the controller in power transfer capability of grid connected PV system.

In the recent decade, it has become clear that two-wheelers are responsible for about 95% of the road accidents [12]. The apparent reason being due to negligence of wearing helmets, intoxicated driving, and speeding. The lack of immediate emergency services in response to such accidents results in the loss of life. As a result, driving two-wheeler vehicles has become more dangerous and unsafe. In light of this problem, this paper puts forth a solution to overcome a majority of this problem and thus make driving two-wheeler vehicles much safer. A safety device which is attached to the dashboard of the vehicle will scan for the driver wearing the helmet, and upon confirmation only would it start the engine upon ignition. The in-built accelerometer within the safety device will determine if the driver is intoxicated based on the vehicle movement pattern. This behavioral understanding is achieved by training the microprocessor within the device to differentiate normal vehicle movement from intoxicated vehicle movement through machine learning. The accelerometer is also capable of identifying any impact on the two-wheeler and hence, notify the smart system on board the vehicle to contact the nearest emergency centers for prompt action. The novelty of this work is that it is the first among the other works of its kind to ensure that the driver is wearing helmet and is not intoxicated during driving in single device.

In this lavishing and developing technology era, the conventional methods are updated and made them more effective for the usage. Here, the boost converter is the device which increases the voltage from one end to another end. It is the conventional method for everyone to increase the DC-DC voltage. The level of increment will be obtained by varying duty ratio. To make a boost converter more efficient, we can apply cascading property. The application of the cascading property is known to be quadratic boost converter. The quadratic property increases the input voltage to the output voltage ratio by $$\frac{1}{(1-D)^2}$$ 1 ( 1 - D ) 2 . In the same way, the bi-directional converter is more effective than the boost converter because of its charging and discharging properties according to their applications. The components used for the proposed quadratic boost converter [1] are more in number than the bi-directional converter in the quadratic property. So, the proposed quadratic bi-directional converter has both quadratic boost and quadratic buck methodology. The renewable energy is mainly used as a source, showing the closed-loop control using motor drive for bi-directional converter in the form of discharging mode and charging mode with the fuzzy as a switching technique.

In recent years, innovations have led to the usage of almost all forms of energy available to produce electricity that can be used to light up the World. One such technological novelty that converts mechanical energy wasted into useable electrical energy is piezoelectric energy harvesting technique. This project focuses on conversion of kinetic energy produced during the taping of feet, which is captured using piezoelectric material installed in the dance floors to generate electrical energy. The circuit comprises stacks of piezoelectric transducers in series and parallel arrangement that is designed to be installed in the floor to produce more amount of energy; Synchronized Switch Harvesting on Inductor as the power electronic interface circuit, so as to reduce the losses during conversion and hence enhance the efficiency and a super capacitor connected to a buck converter at the load side to store the energy developed. Using MATLAB/SIMULINK, waveforms of different flipper circuits with the PEH model are studied and the best one producing optimized output is chosen for the further circuit design. This prototype can also be installed in various other platforms where the energy conversions can take place.

Topological index is the molecular-graph-based structure descriptors. Computational chemistry is a discipline in which we use mathematical approaches for the computation and simulation of molecular behaviour or properties. Detour index is one of the topological index in the collected works of computational chemistry. In this article the authors have computed detour index of join of certain graphs.

In computational chemistry, numbers programming certain structural skin appearance of normal molecules with derivative as of the parallel molecular diagram are called the graph invariants otherwise topological indices. In QSAR and QSPR learn, topological indices be utilized to estimate the bioactivity of substance compound. The Sanskruti index is one among them. This index has a very excellent connection with entropy of octane isomers. In this present study we find the Sanskruti index of certain silicate structures.

The automatic classification of glioma on MR images has been widely used in the diagnosis and treatments of cancer. However, due to the shape of prostate varies significantly and low contrast with adjacent structures, the classification of MR images faces great challenges. This method is done by digital image processing and it gives an exact result in the detection of the tumor. Additionally, it proposes an intellectual segmentation technique to distinguish ordinary and irregular slices of brain MRI data. It comprises five stages which include preprocessing, segmentation utilizing simple linear iterative clustering (SLIC) super-pixel segmentation algorithm, feature extraction, selection, and finally, classification is performed using the hybrid support vector machine (SVM). Experimental results show the effectiveness of adversarial learning and SVNN on the classification of prostate MR images. At the same time, the proposed method achieves advanced results, and outperforms most of the other existing methods on various evaluation metrics.

Forest fires are generally Class A fires that are very difficult to extinguish due to the inaccessible terrain and uncontrollable behavior of fire. This project aims at proposing an idea of utilizing unmanned aerial vehicles such as a quadcopter to extinguish a forest fire. Designing a UAV that can handle and operate functionally at these elevated temperatures atmospheric conditions are necessary. These unmanned aerial vehicles, using a payload drop mechanism, can carry and drop fire extinguishing balls above the fire to extinguish it. So the proposed idea is to design a fighting drone with suitable material instead of plastic or fiber and required material properties such that it doesn’t fail at elevated temperatures. Structural analysis is done to verify the same. Thermal insulation is given for sufficient thermal protection for electronics and circuits in the UAV.

Today’s current environment is full of toxic pollutions smoke materials, which causes an unhealthy environment for the human survival. Major shares in pollution are due to the vehicle which includes Internal Combustion Engine (ICE). Electrics drives such as induction motor, brushless DC motor, stepper motor, synchronous motor posses an alternative to ICE’s. Induction motor posses many advantages such as ruggedness, less maintenance, and matured technology in terms of speed control. Five-phase IM posses many advantages when compared with the conventional three-phase IM such as less torque ripple percentage, less per phase current, higher torque density. In this paper, open loop V/F control of five phase induction motor drive is carried out. The performance is compared with three-phase induction motor drive in term of torque ripple. The simulation study is carried out in MATLAB/SIMULINK, and results obtained are presented.

Energy Conservation in industries has always been a concern due to high amount of conventional electrical energy consumption. Energy conservation leads to optimum use of energy to meet the consumer demand without affecting the production. Most of the older industries contribute to a large amount of energy bills every month due to unscheduled way of tea production. The present article emphasizes on energy audit for a medium scale tea manufacturing industry whose garden is located at Biswanath District area of Assam, which is the central part of Assam, India. By executing the audit, amount of energy usage by the machineries and production processes are determined and analyzed. An effective lighting audit is carried out by calculating fixtures according to lux levels of each section of factory. Results infer the optimum use of luminaries which will further reflect in the electricity bill of lighting loads of factory.

Relieving transmission congestion is a prime technical issue in deregulated power systems. The different schemes are available to manage congestion, which already presented in the different literatures. In this work, coordinating flexible AC transmission system device and microgrid-based congestion method is proposed. The objective of this proposed method is to relieve the transmission congestion with more benefits in terms of reducing the operating cost and maintaining the system in a secure state. The first step is to create the congestion problems in multiple transmission lines by increasing load (up to 20%) and line outages (more than two lines). Second step is to relieve multiple lines congestion by coordinating FACTS device and microgrids, where one of the FACTS device TCSC and one of the microgrid-based DG units are used. Combination of these device is placed simultaneously (one by one) at their optimal locations till relieving the congestion in the system. The proposed approach is validated on IEEE 118-bus test systems. The optimal power flow solutions (OPFS) of the test system are obtained by both primal linear programming (PLP) and genetic algorithm (GA) methods. All the results have been obtained from power world simulator and MATLAB.

Distributed series FACTS controllers provides active power flow control through transmission line in cost effective and reliable manner. Distributed Static Series Compensator (DSSC) is one of the distributed series FACTS controller. It is a low power device which can be distributed over a transmission line at regular intervals emulating a small reactance in the line to control active power flow. Like SSSC, it injects a small voltage in series with the line in quadrature with line current. To achieve noticeable change in power flow, multiple number of DSSC devices need to be connected in the line. This paper presents DSSC to enhance system loadability index in the line. Optimization technique is applied to find optimum emulated reactance by DSSC such that all the lines carrying power flow in their thermal limits. Location of DSSC placement is also investigated to maximize system loadability index. MATLAB simulation results are discussed in IEEE-14 bus DSSC connected system.

This article analyses the operation of the decentralized fuel cell (FC) generation system. FC is integrated into the inverter distribution system, and it is suggested that the inverter controller would provide for the actual power in the distribution system. This paper highlights the benefits of using tuned PI controller. Here, the PI controller reduces the drop-in voltage and ensures the proper flow of power and its control in the system. In this article, a hybrid controller for the inverter is proposed so that this controller reduces sag, compensates for reactive power and improves stability in power. Therefore, under the dynamic loading conditions, power quality of the system is maintained. Based on the substantial simulated results with MATLAB/Simulink as platform, it is observed that the controller’s performance enhances in the transient as well as in the steady state.

This paper presents an analysis on the effect of congestion in determining the profit for generating companies. The locational market price (LMP) determined at each node/bus determines the profit for generating companies and benefit for the consumers. The transmission line loading capabilities will have effect on scheduling the generators. The congestion of transmission line influences the LMP at each node where the transmission line is connected. The difference in marginal cost of generator and LMP at the node connected will determine loss or profit to the generating company. In this paper, a three-bus, seven-bus, and nine-bus systems are simulated for different congestions, and effect on profit is studied. The congestion effect on price of generating companies is studied in this paper. A method to relieve congestion by some percentage on transmission line to benefit generating companies is the key study in this paper, and results are presented in detail.

The horizontally integrated power system includes separate entities, namely generation, distribution and transmission companies, and independent system operator. The new framework introduces market to trade the contracted power among the market players. The market forms single buyer, poolco, bilateral and hybrid model. This paper focuses on power transaction in the hybrid market. Under hybrid model, the market players can participate in both auction and bilateral contract. Each generating unit meets a portion of the spot market and bilateral contract power in an area. This participation is represented in terms of hybrid participation factor. The paper presents the determination of hybrid participation factor based on the total contracted power of a generating unit. The system stability and reliability are ensured with the help of ancillary services, one of which is load follower. This service maintains system frequency and power through transmission line steady by adjusting the generation with respect to the contract breach. The determined participation factors are included in the schematic of two-area load follower system to analyze the power of generating unit and transmission line. The obtained results are compared with the tabulated powers to prove for the effective operation of load follower incorporating the participation factors.

Lightning discharge generates high intensity electric and magnetic fields in the vicinity of the discharge point and all structures in the locality are exposed to this strong electromagnetic (EM) environment. Any indoor equipment needs to be designed compatible to the lightning EM field. Distribution of EM field inside the structure where equipment needs to be placed is greatly influenced by the structure geometry. This paper discusses the enhancement in electric field generated by the lightning discharge inside building due to the presence of slots. Lightning channel is modeled as thin wire radiating element and field is computed using method of moments (MoM). Structures constructed in computer-aided design (CAD) platform is exposed to lightning field in EM simulation software and field distribution is studied. Significant enhancement in electric field is observed inside structures due to the presence of slots. Also, the electric field enhancement factor due to slots increases as the overall size of the structure decreases. However, magnetic field distribution remains unaltered by the presence of slots.

This paper focuses on the performance evaluation of a dual stator de-coupled rotor six-phase permanent magnet synchronous generator (DSDRSP-PMSG) for wind power applications. For this application, the generator should have essential advantages like high power density, light in weight, high fault-tolerant capability and high reliability. Due to the dual stator and de-coupled rotor, the power density of proposed generator is high. The two sets of 6-phase windings also ensure their high reliability and high degree of fault tolerance capability of generator. For performance evaluation, the finite element method (FEM) is chosen because of its more accurate method of analysis. For the optimal design, magnetostatic and transient mode of analysis are considered. For the performance evaluation, transient mode of analysis is opted. The electromotive force (EMF), percentage (%) Total Harmonic Distortion (THD) and electromagnetic torque, % ripple in the torque and voltage-current characteristic are investigated for the proposed generator.

This research paper proposes a newly discovered current feedback operational amplifier (CFOA) based multifunction inverse filter. This configuration employs 2 CFOAs, 3/4 resistors and 2/3 capacitors to realize inverse low pass filter (ILPF), inverse band pass filter (IBPF) and inverse high pass filter (IHPF). Also, the proposed configuration exploits the advantage of orthogonal tunability between gain, cutoff frequency and bandwidth/quality factor for each type of inverse filter realized. The workability of the proposed inverse filter configuration is verified by PSPICE simulation and hardware experimental results using AD844 type CFOAs. Also, non-ideal and Monte Carlo analysis has been carried out for the circuit which directly indicates the proper functionality of proposed configuration in practical results. Sensitivity analysis is further performed to determine that the proposed configuration also enjoys the advantage of low passive sensitivity.

In recent times, Model-based Finite-Set Predictive Control (M-FSPC) techniques are widely accepted by both academia as well as industrial community. They become popular because of their simple understanding and ability of easy implementation for a wide variety of power electronics and drives applications. The main objective of this chapter is to demonstrate the implementation of these M-FSPC techniques by using the PLEXIM PLECS standalone tool. In this chapter, two M-FSPC techniques are developed in PLECS environment. The first technique is the Predictive Current Control (PCC) of 2-level Voltage Source Inverter (2L-VSI) as an example for converter application and the second technique is Predictive Torque Control (PTC) for induction motor drive.

The technological development, especially in the area of power electronics converters and its control, pulls the machine design engineers to work in the area of the dual-port and multi-phase generating systems for high power density and reliable operation. Due to this, the authors opted for dual stator single rotor 5-phase and dual stator single rotor 6-phase generator for their investigation. This paper is mainly focused on the designing and comparison of the performance of 5 and 6-phase generating system with dual stator single rotor topology for wind power applications. For this purpose, FEM is opted because of its high accuracy. There are two modes of analysis which is needed to be carried out namely, magnetostatic and transient. Using these modes of analysis, the evaluation of electromagnetic performance like generated EMF, %THD and terminal voltage-load current, electromagnetic torque and %ripple in the torque for both the generators have been investigated.

This study aims to recognize the techniques for enhancing the voltage stability using Flexible AC transmission systems (FACTS) devices and investigating their impact on the test system. FACTS devices such as SVC, TCSC and UPFC are optimally sized and placed in the transmission system to enhance voltage stability. The objective function is formulated by combining two conditions such as minimizing voltage stability index (Lj) and minimizing the losses. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and ABC algorithms are applied to solve the problem on IEEE test systems. The outcomes support in drawing conclusions on the effectiveness of each FACTS devices along with its exact site and size.

Restructured electric system introduces a competitive environment among different market players that include power suppliers and buyers. The market players can enter into auction or bilateral contract depending on the model of market that covers single buyer, poolco or bilateral. The power transactions performed among selected players are based on the contract, decided through auction mechanism or bilateral agreement. The generating units of the selected suppliers are scheduled to generate the contracted power. A stable power system operation requires matching between gross generation and gross load. Due to power discrepancy between the gross power generation and demand leads to deviations in frequency and transmission line power. The load frequency controller restores this mismatch. The performance of the controller highly depends on the type of controller and its gain values. The work employs comparison and selection of the suitable among centralized and decentralized controller incorporated in the secondary loop of the load frequency controller. The selection is done based on the performance index and integral square error. The analysis is done employing the analytical model of two-area load frequency control functioning under poolco market model. The gain values of the secondary proportional integral derivative controller are obtained using Ziegler Nichols’ method. The analysis is performed in MATLAB/Simulink.

The proposed work studies the simulation of a standalone Photovoltaic (PV) system. Maximum Power Point Tracking (MPPT) plays an essential role in maximizing the output power from photovoltaic array under varying conditions of irradiation and surface temperatures. This in turn yields maximum efficiency and reduces the overall system cost. For the purpose of tracking maximum power, an intelligent asymmetrical fuzzy logic control MPPT algorithm has been developed for the PV system. The developed algorithm has been simulated using MATLAB Simulink toolbox. In order to show the superiority of the developed algorithm, the results of this technique have been compared with conventional P and O algorithm.

The utilization of electrical appliances all over the world has been increased, which thereby increases the consumption of electricity. For ensuring the continuity in power supply, distributed generators (DG) play a vital role and are placed in parallel with the system. The important factor that influences the DG which is inappropriate placement which may increase the energy losses. The placement of the DG should be done so that the losses are reduced, and the consumer is benefited. With respect to that, an objective function is designed for which the consumer benefits are maximized. For knowing the optimal placement and customer benefit maximization, load flow analysis has been performed with two conventional methods like fast decoupled and newton Raphson load flow analysis. A comparative study has been done to put forth the best load flow method for locating the DG at the optimum position. The factors like the optimum size, optimum placement, voltage deviation index are analyzed from the aforementioned analysis, which thereby reduces the energy losses and maximizes the customer benefit for the placement of the DG. The whole analysis is done for IEEE 14 bus system and is evaluated in MATLAB software.

This paper focuses on the design and analysis of a novel six-phase V-shaped permanent magnet synchronous motor (NSPVS-PMSM) for electric vehicle application. The motor used for electric vehicle application should have essential requirements like low cost, light in weight, high torque density, high efficiency, more reliable and high degree of fault-tolerant capability. The motor has a V-shaped rotor which leads to a high torque density and good dynamic operating performance. For improving the power density, the flux barrier is provided at suitable places inside the rotor so that all the local leakage flux gets diverted towards the direction of main flux. To further improve the torque density, reliability and fault tolerance capability six-phase has been taken in the proposed PMSM. For designing and analysis of the proposed motor, finite element method (FEM) has opted because it more accurate among all methods of analysis. The magnetostatic mode of analysis is taken for flux and flux density distribution in the model. For the performance analysis, transient mode of analysis is chosen. The back electromotive force (EMF), % THD, Torque-time, torque-current torque-speed and power-speed characteristic are investigated for the proposed motor.

The prime focus of this paper is the performance evaluation of a Dual-rotor magnetically decoupled stator five-phase permanent magnet synchronous generator (DRMDCSFP-PMSG) for wind power application. The proposed generator poses essential benefits like high power density, high efficiency, light in weight, high fault tolerant capability, and high reliability. The two sets of five-phase windings of decoupled stators of proposed generator confirm the high reliability and high degree of fault tolerance capability of generator. The finite element method (FEM) is used to evaluate the performance of the generator due to its accuracy. The magnetostatic and transient mode of analysis are considered for the optimal designing parameters of the machine. The following performance parameters are investigated for the DRMDCSFP-PMSG—electromotive force (EMF), percentage (%) total harmonic distortion (THD), generated EMF versus speed, electromagnetic torque, % ripple in the torque, terminal voltage–current characteristic, and % efficiency-current characteristics.

The concepts of power quality depend on full considerations of power electronics based modern technology for the reliable power systems that can be electronically controlled due to high voltage. This paper presents the performance of using static modulation power filter compensator (SMPFC) technique, which is an active device for improving power quality, voltage adjustment, reducing power loss and correction power factor, and smart grid interacting with power distribution systems. This paper (SMPFC) describes a companion on the reliability and exploitation of the intelligent network. The proposition of a flexible AC transmission framework can be extended to renewable energy interfaces and exploitation frameworks, then to adjust voltage fixation to achieve the required power stability. The SMPFC device depends on the dynamic error of the tri-loop that occurs during the coupling contribution to the voltage source controller. The static modulation power filter compensator (SMPFC) method has been approved using MATLAB/SIMULINK conditions.

A thermoelectric generator (TEG) is noiseless, clear renewable power source. The paper represents the modified perturbation and observation (MP&O) maximum power point tracking (MPPT) technique. Comparison to traditional P&O (MPPT) algorithm, the proposed algorithm reduces the oscillations which take place in conventional algorithm, i.e., the steady-state oscillations become very less which results in smooth steady-state MPP response. The simulation and results verify that the proposed algorithm has improved steady-state and dynamic efficiency almost by 2%–2.5%, respectively, as compared to perturbation and observation (P&O) algorithm.

Bearing defects are the most frequent occurring faults in any electrical machine. In this perspective, this paper presents a novel time-domain methods incorporating feature reduction method and back propagation feedforward neural network (BPNN) to identify bearing defects. For this, thirty-six standard vibration datasets related to healthy, inner raceway, and ball defects were derived from the Case Western Reserve University (CWRU) website. Four single point defects levels as 7, 14, 21, and 28 mils of inner raceway and ball defects were investigated for effective diagnosis of bearing defects. Initially, nine time-domain features were extracted from each vibration datasets, and then these features were ranked using Fisher’s ranking method to selected top four most discriminating features for effective classification of bearing conditions using BPNN algorithm. The effectiveness of proposed scheme to diagnose bearing defects was corroborated using performance parameters as accuracy (ACC), sensitivity (SE), and specificity (SP). The proposed algorithm has achieved maximum fault classification ACC as 94.87%.

Servo DC motor has wide variety of application in control and automation industry. Position control plays a major role in areas of robotics. QUANSER QUBE Servo DC motor is a system developed by national instruments for analyzing speed and position control. The system comprises of DC motor, optical encoder, and myRIO with QUANSER Rapid Control Prototype (QRCP) module. LabVIEW with QRCP is required for interfacing system with hardware. The process variable to be controlled in the system is the position of servo DC motor and the manipulated variable for the same is motor input voltage. The work deals with implementation of controllers like PD controller, sliding mode controller (SMC), and linear quadratic regulator (LQR) controller for position control. The results are compared and it is found that SMC had provided better control on the process variable.