Computational Advancement in Communication, Circuits and Systems

Breast cancer is the most common form of invasive cancer and after lung cancer, it is the second leading cause of cancer death in women. Many statistical models have been used to predict the malignancy of the tumor. Therefore due to the violation of the proportional hazard assumption, a statistical model may fail to predict breast cancer accurately. In the current epoch, machine learning algorithms play a decisive role in predicting the malignancy of a tumor with high accuracy. The primary purpose of this paper is to compare the performance of eleven different machine learning classification techniques for breast cancer prediction. Wisconsin Diagnostic Breast Cancer dataset is utilized to compare these established algorithms based on the k-fold Cross-Validation accuracy score. Additionally, three different feature selection methods have been incorporated to reduce the number of features on the dataset. After the reduction of the features, the same methods are applied again to compare performance based on their accuracy score. It is found that all the algorithms perform very well with more than 93% accuracy score; among these Logistic Regression, Support Vector Classification and Multilayer Perceptron get an accuracy score of over 98%. It is also observed that even after a drastic reduction in the number of features, the result remains satisfactory, and the accuracy score is more than 90% for all the applied algorithms.

Speech recognition is a vast area for research. In speech recognition word boundary detection is an important part. Understanding and fixing the problems of efficiently detecting where the words are present in a signal is still challenging. In this paper, we have discussed our work on word boundary detection using two different approaches: (1) Convolutional Neural Network and (2) Decision Tree Method. CNN is efficiently used in areas like face recognition, object detection, image classification, etc. whereas a decision tree is broadly used in decision analysis. This study can further help with speech recognition.

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.

Amidst the current pandemic situation which has been going on for a significant period of time now, although vast numbers of people are safe in their homes, this crisis has rendered millions of people unemployed, mainly the daily wage labourers, etc., who are now struggling with not only the gravity of the coronavirus but also with hunger and unemployment. We are witnessing a rich and poor divide that has crossed boundaries that were never explored before. We are all aware of the hardships of the people who are daily struggling just to manage a day’s food for their family, and so as responsible engineers, we plan to develop an Android application which aims on directing financial aid to those who are actually needy by gathering funds from millions of donors across the nation who share the same ideology and wish to help these people out but are unable due to the pandemic related restrictions. People who are deserving of this financial aid and also the people who wish to donate will be able to register themselves on the application, post which verification will be conducted for both the parties after which a designated amount of money will transferred to the needful directly. All cyber security protocols are aimed to be implemented so as there is no presence of any anomaly.

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.

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.

Passive optical network (PON) is an ultimate solution for recent communication technology which accentuates on faster, less expensive and dependable communication system used as access network for optical fiber communication. The broadband carrier suppliers throughout the world are involved to innovate technique to pull down the complexity of network systems, required time of installation, and necessary skill set for installation which finally downsize the overall deployment expenses of the entire network structure. PON imparts one optical fiber to many end users by utilizing power splitters associated with various optical network unit (ONU) situated to customer's premises. One of the fundamental criteria of PON network planning is to design the path of optical link of a point-to-multipoint network which interfaces each end user through the central offices (COs) and power splitter(s), within a lower cost of deployment. This criterion needs to be executed considering the practical restrictions, such as conceivable fiber path, the splitting ratio of optical splitters, position of splitters, and presence of any obstacle on the deployment path. So, here, an algorithm is proposed to decrease the deployment cost by maintaining strategy of lower distance limit with avoiding the obstacle in the fiber path. So, ultimately, by this proposition, the network set-up cost will decrease, and the approximate deployment cost will be possible to compute.

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.

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.

Mukherjee, Madhumita Sanyal, Salil KumarComplex 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.

Banerjee, Asmita Behera, Bikash K. Das, Kunal Panigrahi, Prasanta K.Checking the graph state and coloring it with possible least number of colors (or chromatic number) has many useful applications such as coloring of maps, solving Sudoku, making schedules to name a few. Though classical algorithms are used to solve this problem, it is believed that with quantum approach, the space and time complexity can be reduced. Thus, here we present quantum algorithms and design quantum circuits to check the graph states having two- and three-vertices with given edges and color them accordingly. We also propose a quantum algorithm for coloring of trees. For each of the cases, we discuss the quantum costs and complexities and compare it with the classical approach.

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.

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.

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.

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

This paper presents an effective methodology and algorithm to optimal schedule of the generators and load dispatch centres for economic integration of renewable energy sources adhering reactive power constraints of the network. The algorithm proposes optimal utilization of all the resources available in the smart grid and considers meticulously the important aspects of power system like voltage profile improvement and the power loss to develop an efficient optimal schedule for both generators and load dispatch centres. The methodology proposes the development of suitable indices which is helpful in monitoring and maintaining the system performance even in the worst possible contingency. For proper management of voltage profile in smart grid, the algorithm within the methodology identifies the weak generators and the load dispatch centres (LDCs) causing problems and alters their generation and active and reactive load schedule for desired operating conditions. It is also of great concern that how the intermittent energy sources will be scheduled with the available thermal power plants. The developed methodology maximises the conversion of renewable power into active power so that those sources are competent enough with the thermal power plants at their availability. The proposed algorithm also sheds the peak load demand in order to attain an optimal generation schedule of these sources. Simulations were carried out in the updated IEEE 30 bus system, and the results were found to be quite encouraging.

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.

Saha, Swarna Sarkar, Soumyadip Patra, Rituparna Bhattacharjee, SubhasreeSorting 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 $$((n/2)+1)$$ ( ( 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.

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.

Photovoltaic energy sources are the most reliable renewable energy sources. The major limitations of solar energy are its Weather-Dependency and availability at daytime only. As photovoltaic energy storage is very expensive hence it is smarter to use solar energy during the day and take energy from the grid during the night. So, it is a good solution to connect the solar power systems with the grid which is linked with an infinite bus. In this paper, a microgrid system, based on two photovoltaic generating stations and an infinite bus system that is capable to export or import power to the grid is simulated. The microgrid system investigated in this paper represents a study based on an eight-bus system. The output voltage of photovoltaic generation system and infinite bus with constant voltage is calculated in our study. Computation of bus voltage and power flow of the microgrid for green power importation or exportation to the local power grid is carried out using Newtown—Raphson algorithm.

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 Köppen 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.

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.

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.

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.

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.

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.

Roy, Kumar Kalpadiptya Mazumder, Ipsita Das, Arijit Das, SubhramClinical 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.

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.

The enhanced mobile broadband is one of the prime features of 3GPP specified 5G NR technology which defines a maximum broadband speed of 20Gbps. It is to be mentioned that data traffic is expected to grow to the tune of 590% for conventional applications, 770% for mobile video, 950% for mobile virtual reality (VR), and 1320% for M2M/IoT by year 2021 (CISCO Whitepaper, “CISCO Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021,” Feb. 2017.). To facilitate the need and consumption of high speed data by mobile users for various user applications, mm wave carrier is the only options to be exploited due to their huge available bandwidth. A feasibility study of 60 GHz mm wave link is conducted to be used typically as an access link for 5G eMBB APs in small cell deployment scenario like airports, shopping malls, stadiums, etc. Small cells like femto cells with a radius of 50 m are generally considered for the above mentioned scenarios. A 5G NR link, suitable for femto cell network, is simulated and the performance of the link is evaluated in terms of data throughput and BER in varying conditions of SNR. The results obtained are very promising. The post-decoder BER and throughput at varying received signal power are measured. With carrier aggregation (CA) of five component carriers (CC) and bandwidth 1950.703 MHz (~2 GHz), the combined throughput achieved is 7.17 Gbps. The BER obtained above SNR of 20 dB and received signal power of −60 dBm is almost zero. Our results suggest the potential use of 60 GHz signal as an access link for 5G eMBB APs or could be used as a back haul link in small cell networks.

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 $$\chi^{2}$$ χ 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.

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.

Human history is observing a very strange time fighting an invisible enemy; the novel COVID-19 is the greatest challenge to humankind since the Second World War. The current outbreak of COVID-19 coronavirus infection among humans in Wuhan (China) and its spreading around the globe is heavily impacting global health and mental health. Novel coronavirus (n-CoV) is a generic name given to severe acute respiratory syndrome coronavirus 2(SARs-CoV-2). It has rapidly spread around the world posing enormous mental, social, economic, and environmental challenges to the entire human population. This paper evolved from an overview of the coronavirus and its effect on public health and economics. The main focus of this paper is to survey the various species and types of COVs. The overall statistics of the count around the world and an inclusive survey of its impact on society is being discussed in this paper. In this paper, the linear regression analysis of different vaccines commissioned around the world in COVID-19 and manifold updated information across India has been analyzed in a statistical approach.

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.