Showing papers by "Narula Institute of Technology published in 2022"

Brain Computer Interface: A Review

Abstract: Brain-computer interface (BCI) enables their users to use brain signals instead of the brain’s normal peripheral nerve and muscle output paths to communicate or control external devices. Several methods can be used to obtain data from the brain sensors that basically monitor physical processes Brain computer interface technology is an emerging area of research with several applications in medical fields. In this review, we discuss the current status and future prospects of BCI technology and its applications in several fields. We will define BCI, examine BCI-related signals from the human brain, and describe the functional components of BCI. We will also review the different applications of BCI technologies in the field of medicine, in entertainment and games, safety and security and in biomedical. Finally, we will discuss the current restrictions of BCI technology, obstacles to its widespread clinical application, and expectations for the future.

Quantum Random Number Generators for Cryptography: Design and Evaluation

Abstract: In this article, quantum circuit-based secure communication architecture has been envisioned. Herein, all the proposed circuits and architecture are verified by IBM Qiskit and established on the quantum nanostructure. The salient goal of this hardware-based cryptographic structure in the quantum domain is to attain a diversified invulnerable quantum communication arrangement through a commendable post-CMOS technology. This architecture consists of a novel quantum random number generator (QRNG) and swap gate-based quantum shuffler. In our intended framework for cryptographically secured communication model as well as its implementation through a novel quantum encryption–decryption prototype by the random bits extracted from quantum Hadamard gates, rotation gate is (Rz) postulated on QRNG.

Implementing Data Security in Delay Tolerant Network in Post-disaster Management

Abstract: Disaster causes severe destruction to physical infrastructures. As a result, communication infrastructure has been getting disrupted for weeks. Wireless ad-hoc networks use mobile devices to deliver services. In any critical situation, ad-hoc network acts as delay-tolerant network (DTN). DTN is resource-constrained network, where nodes are required to cooperate with each other to relay messages in store-carry-forward feature. These messages are re-addressed to other nodes based on prearranged criteria and finally are conveyed to a destination node via multiple hops. Meanwhile, during the transmission of message from sender node to receiver node, the privileged message may be disclosed to the other node except sender node and receiver node. So, message should be encrypted by the sender and decrypted by the receiver to maintain proper data security in a DTN network; there may be periodic disruptions or long delays in the connection between the network devices. Opportunistic network environment (ONE) simulator is used for performance evaluation and comparison with other state-of-the-art schemes.

Diagnoses of Melanoma Lesion Using YOLOv3

Abstract: The most modifiable risk factor for skin cancer is ultraviolet radiation (UVR) exposure. Melanoma or malignant melanoma is the rarest but at the same time deadliest form of skin cancer. While prevention of melanoma is possible to some extent by educating masses to involve in safe sun practices as avoiding sun exposure during peak radiation hours, using protective clothing, applying sunscreen and distancing oneself from artificial sources of UV light, early detection and accurate treatment of the disease may curtail the fatality of the deadly disease. If statistics are to be believed, the lifetime risk of developing melanoma in the year 1935 was 1 in 1500 as compared to 1 in 50 in 2010, indicating its dramatic increase in the last century. While effective and timely treatment of melanoma has been a subject of prime importance for researchers and the medical fraternity alike, several invasive and non-invasive techniques have come to the fore from time to time for diagnosis of melanoma. Analysis of the several methods developed during the years suggests that easier access to skin examinations increase the chances of accurate and well-timed detection of melanoma and computer-aided diagnosis (CAD) has played a major role in fulfilling the same. This work proposes a novel CAD approach which includes preprocessing of the dermoscopic images by Dull Razor algorithm followed by classification by deep learning-based algorithm ‘You Only Look Once’ (YOLO) and finally segmentation of the identified image by a self-designed algorithm. The experiments have been conducted on three publicly available datasets—PH2, ISBI 2017 and ISIC 2016. The combination of the total methodology offers a Jac score of 86.12% and Dic of 92.55% which is way superior to results of contemporary works in the area.

Face Detection and Extraction Using Viola–Jones Algorithm

Abstract: In the current times, face detection by computer system has become a major field of interest. Face detection technology can be applied to various fields-including security, biometrics, law enforcement, entertainment, and personal safety—to provide surveillance and tracking of people in real time. Face detection applications use algorithms to find only the human faces within larger images. Face detection algorithms typically start by searching for human eyes, one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, mouth, nose, nostrils and the iris. Once the algorithm concludes that it has found a facial region, it applies additional tests to confirm that it has, in fact, detected a face. In this report, we propose a human face detection method for colored as well as gray images. Also, we cropped the particular detected facial image and extracting and showing the individual cropped image if the input image contains many faces.

A QCA-Based Improvised TRNG Design for the Implementation of Secured Nano Communication Protocol in ATM Services

Abstract: In this paper, an attempt has been made for quantum-dot cellular automata (QCA) based design of TRNG (True Random Number Generator) to support the implementation of developed nano communication protocol targeting more secured operation over automated teller machine (ATM). TRNG is an ingenious design can generate non-deterministic and distinctive stream digital bit, and a major aspirant for any secured cryptography process. Here, the mode of design is using QCA technology due to its advantageous aspects of consuming low-design area and ultra-low power during high-frequency operations. Overall the functional verification of our proposed setup is carried out using QCA Designer 2.0.3 where from its efficacy is rightly depicted.

Use of Convolutional Neural Network (CNN) to Detect Plant Disease

Abstract: The automatic and accurate detection of diseased leaves is a challenging job for researchers. It offers a promising step towards food security and agricultural growth. On contrary, the conventional manual interpretation is time-consuming and expensive. In this paper, it proposes a new approach to detect plant diseases using the deep learning Convolutional Neural Network. We have used 1900 images, taken from a public dataset to train our model. This deep learning model is designed to consist of 25-layer for plant disease classification. The trained model achieved 96.64% accuracy to detect the plant disease. The proposed deep learning convolution neural network model may have great potential in disease detection for current cultivation on large scale.

Influences of Solar Activity on Food Grains Yield

Abstract: The increased imbalance between demand and supply of food grains in India is due to the population burst and limited land of cultivation. Every year Government used to fix a target of food grains production which is rarely achieved due to various factors. Climatic conditions are one of the factors. India has the second largest cultivable land in the world and approximately 65% of the crops cultivated are food grains in spite of that food grains per person per month are decreasing continuously. New technologies are continuously adopted to increase the food grains yield but the situation is still not good. Along with the other factors the crop yield may depend on solar activity as earth is a solar planet from whom cosmic rays are continuously coming to the earth. So, in this paper, we studied the influences of the Sun’s activity on the food grains yield. For this purpose, all India food grains yield, rice yield, and yearly average number of sunspots data are used to analyze the characteristic variations and to find any possible correlation between them. A strong linkage between solar activity and all Indian food grains yield and rice yield is obtained from the analysis.

Evaluation of Azimuth Angle Profile for Solar Photovoltaic System in Humid Subtropical Climate of Varanasi City

Abstract: This research work focuses on the prognosis of energy exploration opportunity due to geographical coordinates and celestial positioning of Sun at Varanasi city. As per the Koppen classification, the city has its humid subtropical climate with pretty higher temperature and scattered precipitation all over the year. The prime objective of this research work is to predict the solar energy security of a future smart city like Varanasi with its geographical circumstances. Computer program with MATLAB coding is used for mathematical computation of solar azimuth angle profile assessment of Varanasi city. The mathematical computation of azimuth angle profile relates to the mathematical expression of altitude angle, longitude angle and angular measurement of Sun position. The summary of experimental results shows significant variation of azimuth angle profile corresponding to different seasons of the year of the city.

Spectrum Based Prediction for Seismic Activity

Abstract: Based on the retrospective study of seismic activity we have analyzed prospective seismic activity based on results from Continuous Wavelet Transform (CWT) and from studies on The logged data of Very Low Frequency (VLF) transmitted sub-ionospheric signals at 16.4 kHz from Novik, Norway (Lat: 66.97° S; Long: 13.9° E), 19.8 kHz from North West Cape, Australia (Lat: 21.82° S; Long: 114.16o E) and 25 kHz from Petropavlovsk-Kamchatsky, Russia (Lat: 53.15° N; Long:158.92° E,) at Kolkata (Lat: 22.56° N, Long: 88.5° E) are studied throughout the period of April 3, 2013–April 24, 2013, when there happened 18 large earthquakes with M ≥ 5. Here the introduction of other signals which are generated due to the seismic activity (considered as noise) is captured from the spectrum analysis using the method of CWT. In this method, we can watch a yellow region in the spectrum of blue color. Blue color spectrum is for VLF signals without any noise in fair-weather conditions and in this yellow color indicates the introduced noise which may also start to observe few hours (12 h) prior to the event of the earthquake. The identified event may have been the result of a combination of changes in seismicity patterns and the yellow color gives the forecasting of the main event as a signature of the prediction.

Word Estimation in Continuous Colloquial Bengali Speech

Abstract: Word segmentation is a crucial part in any speech to text conversion. Many works have been done on popular languages, especially on English, but a very few work has been carried out on Bengali language, especially on colloquial speech. In our work, we present a simple pitch profile-based technique to find the words in a Bengali speech. We extract the feature of the existence of words based on the pitch profile of a speech. To find the pitch profile, we have used the state phase technique. A simple deviation of a 20 ms window is studied to find the pitch. In order to reshape the pitch, power profile of the speech is used. Then apply morphology to make the profile more robust. Finally, we cluster the data and use silhouette index to select the number clusters present in the data which in turn estimate the word boundaries. The algorithm is tested over continuous colloquial Bengali speech.

Employment of New Cryptography Algorithm by the Use of Spur Gear Dimensional Formula and NATO Phonetic Alphabet

Abstract: Cryptography is the branch of science. In cryptography, the techniques that are being used to protect data are derived from mathematical theories and a collection of calculations known as algorithms to process messages in many ways to encode it. In this paper, new approach to cryptography algorithm is mentioned in which the equation and numerals are adopted. This new finding is established on the unique amalgamation of mechanical concept and traces of alphabets used in NATO (North Atlantic Treaty Organization) phonetics alphabet.

Development of Interactive Smart Mirror for Implementation in College Environment

Abstract: With the advancement of smart devices in recent years, there is an addition of a smart mirror that provides touch-free interaction to the user. It has gained substantial interest from the researchers and industries to develop smart mirrors for various application areas. An attempt has been given to develop a smart mirror system for the interaction with the user in the college or university premises. As a display, the smart mirror provides information on the time, weather, location, and live news updates. The system also has the capability of voice and image recognition. A machine learning-based voice assistant has been developed a python environment which answers any general questions by searching the Wikipedia to the user and also the questions related to the college like ongoing and upcoming events, the map of the college. The system is unique as it provides valuable information to users at a glance and also acting as a traditional mirror.

Design of Portable Integrated Sensor System for Local Monitoring of Climate Change

Abstract: Integrated sensor has gained popularity because of its compactness, multi-sensing ability, and ease of use in recent days. It is used to measure the multiple physical parameters simultaneously. The paper examines the issues and challenges of implementing an integrated sensor for real-time monitoring of climate change in small locations. The designed experimental system can measure the temperature, humidity, and pressure simultaneously for the local area using an integrated sensor. The developed system is portable and capable of acquiring the data and provides information to various applications remotely. The overall system shows an advantage in cost, power availability, connection range, and inbuilt display of gathered data. The results obtained show the applicability of integrated sensors having very low deviations from standard readings.

Design of FPGA-Based QPP Interleaver for LTE/LTE-Advanced Application

Abstract: Modern wireless communication systems have witnessed increasing use of channel coding techniques to enhance the throughput and to reduce latency. Interleavers are playing an important role to make the communication systems more robust and resilient in such channel coding approaches. The Long-Term Evolution (LTE)/LTE-Advanced of the 3rd Generation Partnership Project (3GPP) uses Quadrature Permutation Polynomial (QPP) interleaver in its Turbo coding scheme. The address generator of the interleaver contains a quadratic expression having square and modulus function whose direct digital hardware is not yet available in the literature. A novel algorithm has now been proposed which can provide low complexity hardware solution to implement the interleaver address generator. This paper describes VHDL model and timing simulation of the proposed address generator using ModelSim XE-III software. Due to absence of implementation results in the literature, comparison of this work is made by implementing conventional LUT-based technique on the same FPGA. Such comparison shows better FPGA resource utilization by 71.16% and improved operating speed by 82.26% in favour of the novel proposed technique.

Effect of Cognitive Task on the Central Nervous System

Abstract: Electroencephalography (EEG) signal analysis has received great acknowledgment in the domain of biomedical signal processing for the interpretation of human brain activities. There is a close bonding between the EEG signal and human brain activities. In the human brain, millions of neurons interact with one other and as a result, we obtain electrical signals by placing the electrodes on the scalp in a non-invasive way. The human behavior (polite, rude, whimsical, etc.), mood (happy, sad, anger, depressed, etc.), sensory states (movement of the eye, lip, hand, etc.), cognitive task ability (understanding, thinking, problem-solving, implementation, debugging, recalling) can be monitored, interpreted and analyzed with the exploitation of EEG signals. Moreover, to detect neurological diseases and for treatment purposes, EEG signals are countless boons in the field of biomedical signals. The central nervous system is responsible for controlling human behavior, mood, cognitive task motor, and imaginary task to some extent. To find evidence, we have focused on the effects of cognitive tasks on the central nervous system. Due to the non-linearity and non-stationarity nature of the EEG signals, we have investigated the signals using non-linear tools like the Surrogate data test and phase space plot. Moreover, we have explored the topological scalp map view to obtain the visual effects of the scalp.

FPGA-Based Efficient Implementation of CBNS Computational Circuits: A Modular Approach

Abstract: Complex binary number system (CBNS) finds extensive applications in the faster computation of various digital signal processing (DSP) algorithms. In this paper, an attempt has been undertaken to develop various computational circuits based on CBNS for implementation in Spartan XC3S700A FPGA platform. The circuits have been designed following a modular approach. The designed modules involve simple logic gates leading ultimately to efficient implementation on FPGA. The codes for the modules have been developed using verilog hardware description language (HDL). Structural-level designs of nibble size CBNS adder, multiplier, and subtractor have been exclusively accomplished involving these modules. In the design of multiplier and subtractor, a new concept of sub-block has been introduced to efficiently utilize the limited input capability of the designed modules. The proposed design involves less hardware complexity, silicon area, and path delay compared to existing works. Simulation results and performance metrics for all the three CBNS circuits have been included.

Prediction of Blended Fuel Characteristics Through Regression Modelling

Abstract: Blended fuel attracted considerable attention for the environmental sustainability, mitigation of scarcity of non-renewable fuels and enhancement of property modification for the last few decades. Jatropha Curcas oil (JCO), a non-edible vegetable oil, can be utilized for the preparation of non-conventional alternative energy sources like biodiesel which may be blended with diesel fuel for better environmental sustainability. Initially, biodiesel is prepared from JCO with methanol through transesterification reaction maintaining optimized reaction parameters in the presence of biocatalyst. After that mathematical relationships between fuel properties and blended fuel have been established through regression analysis method for the prediction of fuel properties like density, kinematic viscosity, cloud point and flash point. The blended samples are prepared ranging from 10 to 60% (B10 to B60) for biodiesel-diesel fuel. From the experimental results, graph of each fuel property has been plotted and mathematical equation of each fuel property for biodiesel-diesel blends are approximated with their respective coefficient of determination (R2). The results of estimation show that blended fuel properties have linear relationships regarding density, kinematic viscosity, cloud point and flash point. The equations identified for the properties of blended fuels are prerequisites as input data research findings. From the estimation of mathematical regression equation based on experimental findings, prediction can be done for any fuel properties for any ratios of biodiesel-diesel blends. So mathematical understanding contributes a better pathway for finding out the properties of blended fuels which may help to reduce the scarcity of conventional fuels.

Study of Micro-Strip Patch Antenna for Applications in Contact-less Door Bell Looking at the COVID-19 Pandemic Situation

Abstract: As the technology advances, the modern trend of lifestyle also advances. The doorbell has an important responsibility in home safety; it is one of the competent and steady systems needs to be developed for better safety which could be access at a low cost. In this era, there are many doorbells systems doing different operation. This paper focuses on touchless type automatic doorbell systems which will ring the bell automatically when a visitor approaches near the door. This system is intended to people, and due to the spread of COVID-19 pandemic situation, it would be one of the safety steps that can be taken against corona. People are now more careful about their everyday work and their family. In the year 2020, the whole world is trapped in unprecedented COVID-19 pandemic. The situation takes away all our normal lifestyle, and all the researches are going on in controlling the situation and finding a new way of life. In this work, the author is trying to establish a contactless door alarm for the household application. Motivation behind the work is that due to the Corona virus spread around the world, we have to take utmost care in every step of our life. If we use the normal door alarm, then there will be the issue of contact for every people who will arrive in. But if there will be a replacement of the conventional door alarm with the help of antenna technology, then it can solve the issue with a contactless alarm. In this paper, the author have used the HFSS software for the proposed antenna.

Different Sensors in Modern day Healthcare Service

Abstract: Wireless sensor network (WSN) has a lot of applications in a wide range of fields like games, sports, environment monitoring many other fields. It is also widely used in medical field. A lot of research works are done on this topic. In the present context, we describe applications of WSN in health domain. We described the most commonly used sensor nodes in our daily life for healthcare. In this paper, we want to provide detailed information on this emerging research field.

Detection of COVID-19 Using Deep Transfer Learning-Based Approach from X-Ray and Computed Tomography(CT) Images

Abstract: Clinical authorities need technological support aided with artificial intelligence for early diagnosis and slowing the spread of pandemic diseases. The outbreak of COVID-19 disease caused by the newly discovered SARS-CoV-2 virus was reported by the officials in Wuhan City, China, in December 2019. Since then the virus had a disrupting impact on the health of people accompanied by psychological, financial, and social distress. In this paper, a deep learning-based approach for early detection of COVID-19 has been proposed. Five deep neural network architectures have been trained through transfer learning based on the available X-ray and computed tomography image dataset. The chosen architectures have given quite promising results in terms of accuracy. Thus, the proposed experiment provides an efficient tool for the early detection of COVID-19.

JK Flip-Flop Design Using Layered T Logic: A Quantum-Dot Cellular Automata-Based Approach

Abstract: The Quantum-dot Cellular Automata (QCA) claims itself as a potential substitute for conventional transistors in multilevel digital circuit design. In this work, the Layered T Gate methodology is utilized in the JK flip flop design. The proposed QCA layout is compared with conventional designs in QCA metrics like effective area, delay, Costα. The QCA layout is generated, and simulation results are verified in a computer-aided design tool, namely QCA Designer 2.0.3.

Electrical Load Clustering Using K-Means Algorithm for Identification of Village Clusters to Draw Optimal Power from Distributed Solar Generating Plants

Abstract: In recent days, renewable energy Distributed Generating (DG) plants are becoming reality in village electrification projects, where village clusters are identified to draw electricity from installed solar power plants around group of villages. Clustering results can be exploited by various energy providers to identify load groups among the villages and tailor-make more attractive time-varying tariffs and power generation schemes for their customer needs. In this article, we explored a clustering algorithm to compare the typical load profiles of different villages are different days of the week. We found that better results are obtained if the clustering is not performed on the entire data, but on some subset of the extracted data. These clusters were thus identified based on their Vicinity and Total Power Load requirements of the villages. In particular, despite the relevant differences among the several compared countries, we obtained the interesting result of identifying a single feature, the Village Level Load, which is alone able to identify how much power is needed in a village. On identifying the clusters, if total load requirements are more than the generating capacities of the plants, the villages are re-clustered until the load requirements are met for a cluster. The task is accomplished based on unsupervised machine learning technique called K-Means Clustering, and has been implemented using Python programming language where positive results were observed.

Entropy-Based Intelligent Computation for Decision-Making Models of Pandemic Analytics

Abstract: Coronavirus disease is a zoonotic disease that originated in Wuhan city of China in December 2019. It spreads quite rapidly until it transits from epidemic to pandemic as declared by the World Health Organization (WHO) in February 2020. Literature study on the epidemiology of infectious disease revealed that the spread of this novel coronavirus disease (COVID-19) takes place by human transmission. Accordingly, intervention measures are social distancing (2 m), repeated sanitizing of individuals, wearing masks to protect individuals from susceptible/infected candidates, and disinfecting affected environments. Furthermore, finally, implementing periodic lockdown measures have been implemented worldwide to mitigate the COVID-19 pandemic spread. At one point of time till the arrival of a peak, many researchers have attempted their models to understand the dynamics of the disease spread and the effectiveness of the interventions, especially the impact of lockdown measures for predicting the future evolution of COVID-19. In order to have a scientific judgment about the dynamics of the disease, data analytics plays an important role. Data analytics uses data, information technology, statistical analysis, quantitative methods, and mathematical or computer-assisted models to help competent authorities gain improved insight about operations pertaining to target business and make better fact-based decisions. It possesses a machine learning technique that provides an intelligent method to achieve the target, as mentioned. We named data analytics that deals with data related to the COVID-19 pandemic as “Pandemic Analytics (PA).” Pandemics upend businesses, educations, and change lives. PA can help society to determine the number of people who can fit in the society space using social distancing and corresponding risk profiles. The risk profile can be evaluated using information entropy of the system pertaining to the COVID-19 pandemic. This paper presents the details of Pandemic Analytics where intelligent computation for decision-making models is focused on the basis of Shannon entropy. A pandemic response index is computed to rank various countries with respect to the severity of COVID-19. Stability of the dynamics has been investigated by computing Lyapunov exponent.

Price Sensitivity in a 30 Bus Congested Power System

Abstract: For the well being of a system, different parameters are needed to be tested by system operators, among which congestion management is of one prime importance. Different forms of economic parameters are required to signals the congestion management, and the most sensitive signal being the locational marginal price (LMP). These LMPs are the change of price of energy at each bus in the congested power market. LMPs are solved using of shift factor (SF) techniques on DC-OPF (DC-Optimal Power Flow). The LMPs are primarily comprises of three parts viz. marginal energy price (MEP), marginal congestion price (MCP) and marginal loss price (MLP). In this paper, nodal prices that are actually the LMPs are calculated in a thirty (30) bus test case system, and it shows that the LMPs vary from bus to bus when the system is congested.

Impact of Atmospheric Features for COVID-19 Prediction

Abstract: In the context of contagious diseases, recent advances in experimental techniques have not only generated a dramatic increase in the amount and diversity of data but also an ever increasing and complexifying molecular biology with context to meteorological parameters. To combat this probable inefficiency, decision tree-based methods have emerged to be one of the finest data ensembles showcasing excellent accuracy in combining interpretability. For past infectious diseases like influenza and severe acute respiratory syndrome (SARS), etc., direct correlations were spotted with respect to meteorological parameters including temperature, humidity and air pollution among others. The present study targets to explore the association between COVID-19 mortality rates and weather parameters for which the daily death numbers of corona virus disease 2019, meteorological parameters and air pollution data from March 28, 2020 to April 22, 2020 of different states of India were collected. To explore the effect of the minimum temperature, maximum temperature, minimum humidity and maximum humidity on the infection count of COVID-19, the gradient boosting model (GBM) has been implemented thereby achieving optimal performance by tuning its parameters. For prediction of active cases in Maharashtra, the GBM results stand at its best accuracy of R2 as 0.95. For the prediction of recovered cases of COVID-19 in Rajasthan and Kerala, R2 equals 0.98. The present study explores the correlation between atmospheric parameters and transmission rate of COVID-19 in different states of India thereby predicting the active and recovered cases of COVID-19 and establishing an efficient tree-based machine learning approach to explore the effect of temperature and humidity on the transmission rate of the said disease.

Microcontroller-Based Heart Rate Monitor

Abstract: This project is used to measure heartbeat rate by using an embedded technology. This project can measure and monitor the patient’s condition simultaneously. This project described the design of a simple, low-cost wireless patient monitoring system. Heartbeat rate of the patient is measured through fingertip using infrared device sensor. The pulse counting sensor is used to check whether the heart rate is normal or not. In case of abnormal condition, a SMS is sent to the mobile number using GSM module. The heart rate can be measured by monitoring one’s pulse using medical devices such as an electrocardiograph [ECG], portable device. The heartbeat monitoring systems is the wrist strap watch or any other commercial heart rate monitors.

A Comparative Study of Parametric Spectrum Estimation Techniques for Cognitive Radio Using Testbed Prototyping

Abstract: Cognitive radio (CR) has become an emerging field to rescue wireless communication applications from the spectrum scarcity problem. Spectrum estimation (SE) has been a key ingredient for faster and efficient network implementations using the concept of CR. In this work, we have performed a comparative study of SE technique for the CR systems by employing the null hypothesis approach. Autoregressive (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) modelling based on optimal data length and goodness of fit (GoF) has been utilized for optimal spectrum modelling. The optimization of the modelling has been achieved through the Akaike information criteria (AIC) and Bayesian information criteria (BIC). Validation and optimization of the time-series data samples have been accomplished using Fit (%) along with \(\chi^{2}\) test GoF. The entire process of SE along with the validation of data samples has been verified on the RICE University’s FPGA-based WARP radio testbed in association with MATLAB. A thorough statistical analysis of variance and Standard Error (SER) of the received samples has been carried out for the optimization of sample time-series data length for optimal performance of the receiver or users. It is noteworthy that, we could achieve a much accurate and frugal SE with a data length of 250 only using ARIMA (3,1,2) model of the data samples with a significant improvement in power spectral density (PSD) compared to other conventional approaches. Extensive experimental work has been incorporated to establish the work.

New Sorting Algorithm—RevWay Sort

Abstract: Sorting provides a method of rearrangement of elements in ascending or descending order. In this paper, we are introducing a new sorting algorithm called RevWay sort in which the two consecutive numbers are compared from left and then from right. This process is repeated \(((n/2)+1)\) times, where n is the total number of elements. We have compared running time of the proposed algorithm with other sorting algorithms. We run the algorithm starting from 10,000 to 50,000 elements. We found that the newly proposed RevWay sort yields lesser running time compared to bubble and selection sort. For 10,000 elements, RevWay sort takes 203.636 ms, whereas bubble sort takes 364.8243ms and selection sort consumes 337.5543 ms.

Contingency Analysis and Ranking for a 30 Bus System to Maintain Its Stability and Reliability

Abstract: Contingency analysis is performed to maintain secured operation of a power system. In this analysis technique, probabilistic prediction is made for the outage of each transmission line components and to take necessary actions to regain the system security, reliability and stability. Contingencies can be broadly classified into two types, viz. simple type where outage of a single component takes place or complex type where outage of multiple components has taken place. The process of determining the severity of these contingency, contingency sorting is done, i.e. to calculate the performance indices (PI) for each case and sort them according to their performances. The main objective of this paper is to carry out contingency analysis of a 30-Bus system by calculating the performance indices for single generator with single loadline outage, double generator outage and double loadline outage with help of Newton–Raphson load flow contingency analysis on MATLAB environment and ranking of the contingency according to their respective performance indices (PI) . This type of ranking provides an effective mean to rank the different abnormal cases according to their severity and take necessary actions beforehand to prevent total system failure