Advances in Communication, Devices and Networking

Full adder and full subtractor are often a combinational logic circuit that performs three one-bit binary digits of addition and subtraction operations, respectively. Full adder is an important element for the development of different devices such as microprocessors and digital signal processors. The design of the area-efficient full adder is essential to build an area-efficient processor. In this paper, we have implemented an area-efficient full adder circuit and full subtractor circuit using quantum dot cellular automata (QCA). A better EX-OR gate configuration in terms of efficient area is chosen to create the full adder and full subtractor designs. The proposed full subtractor and full adder architecture requires 22 and 23 quantum cells, respectively, whose effective area is 0.0192 μm2 (micro-metre square) only. A tool called QCADesigner is used to implement the design and check its performance. The dissipation of energy is measured using the QCAD-E tool.

In this paper, an investigation on all-optical direction couplers has been demonstrated. A two-dimensional, holes in slab, photonic crystal structure has been chosen for designing the directional coupler and its working principle is based on linear optics. Moreover, the plane wave expansion and finite difference time domain methods have been employed to analyze the photonic band gap (PBG) and electrical field distribution of the structure, respectively. Different designs of directional couplers have been analyzed like a single waveguide, double waveguide also with obstacle holes in between the waveguides. The outcome of the constructive/destructive interference due to phase difference has also been assessed. Moreover, the impact of variation of hole radius with respect to coupling length and coupled power has also been investigated.

In the recent emerging research field, the reversible gate is very popular to researchers and they use resources to develop the reversible gate. It yields a huge impact on optical nanotechnology, quantum, and DNA computing. In the all-optical domain, reversible gates like Feynman gate, Fredkin gate, Peres gate, etc., are very demanding. Therefore, a design of frequency encoded Feynman gate using add/drop multiplexer (ADM) and reflective semiconductor optical amplifier (RSOA) is proposed. In long-range propagation, frequency encoding gives benefit in respect to the other encoding techniques. This encoding technique may improve the bit error problems. Due to the high-gain and low-noise property of RSOA, the proposed design performs the operation like computation, data processing, etc., at ultra-high speed. Here, to verify the proposed design, we have used the MATLAB Simulink (R2018a) software.

In the current semiconductor era, MOSFET gradually losses its performance to the short channel effect (SCE) due to decreasing channel length. Advanced FinFET technology introduces reduction in the SCEs. A tri-gate FinFET, where the range of the gate is 14 nm, is accomplished and presented here using Silvaco TCAD. The geometrical dimensions of the device are characterized based on the width and the height of the fin to attain an aspect ratio of 0.5, 1, and 2. It can be reformed in two ways where firstly width is constant with varying heights to get aspect ratio and vice versa. The electrical parameters such as Ion current, threshold voltage, sub threshold current, drain-induced barrier lowering, and Ion/Ioff current ratio are compared for the different aspect ratios to attain an optimized aspect ratio and also from the ID–VGS graph, the threshold voltage is determined with respect to VDS. The logarithmic ID–VGS graph depicts the leakage current. Thereby, the development of the TG FinFET structure is proposed, where the standardized schematic is at fixed height, width, and length of 10 nm.

Future generation communication and computation system are dealing with ultra-high-speed data transfer, systematic management as well as lower cost. So the researchers are motivated to implement the different logic gates, digital devices, combinational circuits, optical computers. In optical computing systems, the parity generator is familiar as an essential part. In the case of all-optical devices design, reflective semiconductor optical amplifiers (RSOAs) appear as a promising candidate. Due to the versatile gain medium of RSOA, it has various important applications in passive optical networks. In comparison with semiconductor optical amplifiers (SOAs), RSOAs exhibit better gain performance because of their double pass property. Therefore, a frequency encoded dibit-based 4-bit parity generator is designed using low-cost RSOA. It shows also better switching properties. This design operates at a high speed. This device is benefited with dibit-based logic as reduces bit error problem. MATLAB Simulink (R2018a) software is used to verify the proposed design.

Quantum dot cellular automata (QCA) is a current low-power nano-technology that is an effective replacement of popular CMOS technology in this recent nano-technical digital world. The QCA offers high-speed yet less complex digital circuitry. Further, QCA supports multilayer design with reversibility. In this paper, a novel nano-sized, high-speed temperature tolerance low-power multi-bit (4 bit and 8 bit) shift register with parallel-in parallel-out (PIPO) operation is designed in multilayer QCA platform using less complex reversible ‘D’ flip-flop. In this proposed design, the pipelined structure is not used up to 4 bit. Two 4-bit registers are placed in a pipelined manner to form an 8-bit structure. QCA designer software is mainly used in this research work to get the QCA-based designs and then check and calculate the required parameters. To establish the novelty of the proposed design, a parametric comparison among this proposed design and most optimized existing designs is shown in this paper based on the parameters like occupied area, cell complexity, cost, and delay.

The paper reports a 12-bit successive approximation register (SAR)-based analog to digital converter (ADC) with minimal power consumption, better accuracy, and high speed suitable for biomedical applications. The major role in ADC conversion operation is mainly played by the feedback. It is the feedback which drives the approximation of input signal samples in an analog form to the required digital form. Also, design and performance analyses are carried out for charge scaling DAC technique such as charge redistribution and two-stage open loop comparator. SAR control logic is designed using TG-based D flip-flop (DFF) for minimum power consumption. This proposed design operates at a supply voltage of 1 V. This SAR ADC architecture is designed and optimized for power area and speed using Cadence 45 nm gpdk and simulated to extract various parameters with sampling rate of 50MS/s, and power consumed by the design is 205.9 μW at 1 V operating supply with a delay of 249 μs.

Sign language is a worldwide gesture-based technique for communication in mute and hearing-impaired communities. It is rich in its own vocabulary and grammar. The ideas, emotions and information are conveyed only through physical and non-physical gestures of hands, facial muscular movements, and eye’s movement. Sign language recognition is broadly classified as vision-based and sensor-based techniques. However, vision-based technique is having issues like low illumination, bad camera setting, etc. We prefer here sensor-based technique for sign word prediction by using decision tree algorithm. In this study, we are using surface electromyography and accelerometer data of both fore arms of subject for classification and prediction of isolated sign words; this database is freely available. We have used decision tree classifier and found accuracy of 95% with k-fold validation. Sign language recognition will have grate impact on hearing-impaired community for expressing their views, emotions, thoughts with hearing individuals.

The study aims to highlight the bibliometric analysis of published literature on mobile healthcare in the past one decade. The data consist of literature indexed in Scopus database from 2011 to 2020. The keywords used for the search were m-healthcare, m-healthcare, mobile healthcare, mobile healthcare. Microsoft Excel version 2016 and VOS Viewer have been used for data analysis. Bibliometric analysis was done for co-authorship based on countries, co-occurrence of all keywords using network visualization and overlay visualization. The co-occurrence analysis of all keywords resulted in formation of four clusters. The most impactful keywords were human, health care, m-healthcare, telemedicine and m-health. The keywords cryptography, network security, cloud computing, Internet of Things, big data, wearable sensors and mobile application are being used more often in the recent years.

In this era of technology, we have seen many applications of data science around us, such as in medical science, business analytics, finance, and many more. But there is a huge room for the applications of data science in the agricultural sector also, but very few developments are seen in this sector. In view of the need and the potential of data science in agriculture, an application is developed with the objective to assist farmers toward smart farming practices. This application will be in the form of a mobile application and website. It serves as a platform to connect farmers with the market. It provides current market prices and the trend of prices for different crops in the local market. It also provides a better communication mode between farmers in form of forums. On the sidelines, relevant government schemes with the all-at-one-place idea. It will also be able to push notifications and alerts for market-rate peaks, weather alerts, and government schemes. This is achieved by taking data from various sources and government websites. ISRO satellites have collected humongous weather and soil data over the period. The app could serve as a crop and fertilizer guide by using the available data from the aforementioned sources and user input. Additionally, there are many available open data sources which could be used effectively to augment current functionalities as well as new ones. For example, Google Maps could be integrated which can locate buyers and agri shops in the vicinity. The farmers could make use of the web-based forum platforms which will link farmers with professional experts as well as other farmers all over India. This will open an incredible avenue for farmers to get the solution to their problems as well as get a sense of community with farmers across the states. Some many farmers and communities are still deprived of new techniques. This application will help them in changing their crop patterns and also to act smarter and more precisely.

The work involves designing and implementing conventional spectrum detection systems for the orthogonal frequency division multiplexing (OFDM) structure. The different detection algorithms are used to assess the performance of the system at the end of the receptor. The structure efficiency is explored by analysing Pd Vs SNR for various spectrum sensing techniques. The performance of the system was examined by simulating the energy detection (ED), matching filter (MF) and cyclostationary method in MATLAB 2014. The result of the projected work reveals that the cyclostationary exceeds the ED and MF technique and achieved a gain of 2 and 3.2 dB.

Pharmaceutical corporations provide promotional inputs to physicians and advertise their medicines and other products with the help of detailing. The objective of the study is to determine if preference of the physicians for traditional detailing is an outcome of select benefits sought. Seven benefits that are frequently requested by physicians were identified and their preference for traditional detailing was measured using a seven-point Likert scale with the help of a questionnaire. 384 samples were taken from non-allopathic practitioners. A regression analysis was done with traditional detailing as the dependent variable and the benefits sought as the independent variables. It was found that preference for traditional detailing had strong linkage with certain benefits, important to the physicians like social interaction with MR, possibility of technology failures, and personalized information.

It is illegal in India to promote prescription drugs to patients through social media. On the other hand, some medical social media pages have misused this law. For health-related information, patients are increasingly turning to the Internet, and especially pharmaceutical pages, for help. This article presents various points of view. Differences in perception can lead to more disagreement between patients and doctors, so this is important to address. Around 1500 people and 400 doctors were interviewed. The perspectives of patients and doctors differ significantly. For this reason, patients and doctors work as a team to find health solutions, and their perceptions for the disadvantages of SMP may be so divergent that the association may possibly fail.

The use of social media to promote prescription drugs is controlled by ethical and legal behavior and practice standards. Only two countries, the USA and New Zealand, have made it lawful to contact patients directly in order to sell medications. This reasonable approach is no longer applicable due to technological developments. The use of social media for health information, including drug marketing, is gaining popularity. This development has been lauded by a wide spectrum of organizations. This is owing to the fact that it provides a wide range of advantages. The advantages of this form of advertising are widely established. According to some studies, SMP is related to many factors such marginalized group can get lots of benefit using social media and patients’ party think that it is ethical and nothing wrong can happen while sharing personal information in social media. This study has significant implications for marketers, physicians, and policymakers who play critical roles in promoting the creation of safe systems to improve health systems. Future research should use an enhanced communication model tailored to the social media environment to ensure a thorough investigation.

In this paper, solar energy has been used to generate required maximum value of power from solar photovoltaic (PV) system under partial shading condition. A simulation study of Maximum Power Point Tracking (MPPT) controller for solar PV array under partial shading condition is shown in MATLAB using the two known techniques that is conventional Perturb and Observe (P&O) and a global search algorithm Grey Wolf Optimization (GWO). The result of both the techniques are compared to show effectiveness of GWO algorithm. The proposed GWO based MPPT controller extracts the maximum amount power from the solar photovoltaic (PV) system under partial shading condition by modifying the duty cycle of Boost Converter (DC-DC). It was observed that GWO gives oscillation free maximum value of power at a faster rate as compared to conventional P&O technique.

Agriculture provides employment to more than fifty percent of people in India. So, sustaining the variety of crop production is essential for the economy of India. Crop yield prediction involves an analytical study of Indian crop productivity based on different states, along with weather data of respective states. It would be beneficial if we could perform more analytical studies and produce results that are helpful for the agricultural sector to grow more. The crop prediction can be used by the sector to take necessary actions on the management of fields related to manpower, the number of crops to be taken, the type of crops to be selected, etc. In this work, a crop prediction based on linear regression, random forest, and support vector machine methods is applied to existing data to predict crop productivity. The dataset considered here has additional features added like annual precipitation, temperature, humidity, and surface pressure for more accuracy in yield prediction.

Regardless of the advancement in the science and medical field, diabetes still emerges as a big threat to humanity. The only medicament that will help in reducing the effects of this disease to minimum is early detection and taking prophylactic steps toward it. One of the complications that affect eyes is diabetic retinopathy (DR). A diabetic causes impairment in the blood vessels of the tissue that is light sensitive at the retina. As per survey conducted by International Diabetes Federation in the year 2015, it was mentioned that it affects approximately 410 million people, worldwide. India is a commorancy to approximately 70 million people with this disease. Diabetic eye disease or DR is the most common impediment of diabetes. Round about 2.6% of global blindness is caused by this disease. In unmitigated terms, just about 3–4.5 million individuals in India are anticipated to fall victim to vision threatening diabetic retinopathy (VTDR). The choices for treatment of VTDR demand costly devices and medications, and a regular follow-up from diagnosis is required to the last day of your life. In deliberation of the fact that 70% of the Indian citizenry depend on necessitous expenses for their healthcare benefit, one person with VTDR in a household is enough to drive a menage to below poverty line. Therefore, all undertakings should be initiated exigently to prevent individuals with diabetes to move into the vicious cycle of diabetes, blindness, and poverty. This proposed work will classify the stages of diabetic retinopathy with an approximate precision of 97.8% which is an improvement from the previous model with a margin of around 10%. We have used pretrained models in our ensemble learning process.

Simulink, a graphical programming framework based on MATLAB, can be used to model, simulate, and study multi-domain dynamical systems. Because of its drag-and-drop capabilities, easy graphic user interface features, and zero-coding settings, Simulink has become the most extensively used tool in industry and academy. In comparison with alternative software solutions, Simulink can minimize the development cycle time of any real-time system. The value of subsystems in biomedical signal and image processing, such as the enabled subsystem, the triggered subsystem, the triggered and enabled subsystem, and the control flow subsystem, is highlighted in this paper. Breast cancer picture and human voice signal are used to implement image segmentation using enabled subsystem, voiced/unvoiced classification using triggered subsystem, and calculating of root mean square (RMS) amplitude using If Action subsystem. In an experimental environment, the MATLAB 9.4 tool is used to simulate biomedical signals and images.

When it comes to discrete-time systems, whether it is an FIR or an IIR filter, it is important to understand the complexities of data representation, data operation, and so on. The system should be able to handle problems such as arithmetic overflow, truncation, rounding, and so on. With actual examples, this study demonstrates the impact of quantization on the filter coefficients. To investigate the relevance of finite word length effects, application examples such as hum noise reduction and breathing interference removal from an ECG signal are investigated. With the use of biomedical signal processing examples, the frequency responses, impulse responses, and pole-zero plots of the original and quantized systems are discussed. In coding with 5-bits, 6-bits, 7-bits, and 8-bits, quantization effects are used. Furthermore, an elliptic filter is taken into account while comparing the simulated outcomes. For experimental simulations with an ECG signal, the MATLAB 9.4 tool is employed.

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be “more expensive, difficult, and time-consuming”. Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes “comprehension reading work” is treated as fundamental in the proposal of disclosures. In case of any, “the programme appeared to positively affect contributing students”. Techniques of classification like “decision tree and Mixed Model Database Miner (MMDBM)” are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection.

Objectives To study and compare different types of biometric systems and to obtain the result for the same, this paper has discussed the need of biometrics, ways of performing it and the best type of biometrics that should be implemented. Methods and findings: We used various statistics, graphical and tabular representations for showing data studied from different papers and reviewing them. Instances of infringements of right to privacy and protection of personal data are proliferating. Biometric systems are identity authentication systems for information assurance and protection via biological traits since they are unique. Technology is rapidly advancing in every field and choosing passwords are not enough. Biometric systems are the best approach to keep data safe, and it also makes the accessing of data simple. Everything has its own pros and cons so; we will be discussing the pros and cons of various biometric technologies in order to determine the best among them.

In the recent time as well as the last decade cryptocurrency has been one of the most discussed topic among the researchers all around the world. Different economies across the globe have seen a lot of growth of cryptocurrency over the time and bitcoin especially has seen a growth of 1100% that is why time series analysis of cryptocurrency is of immense significance. Time series analysis can be referred to as the process of taking into consideration a sequence of different points which are observed over a specific time interval. A large number of people start investing into cryptocurrencies without having any knowledge or analyzing the cryptocurrency market because of the hype it has these days and suffer huge losses so designing a model which can predict accurately as to how different cryptocurrencies would behave on basis of previous record can be very helpful and it can help some people in making profit rather than suffering loss. This paper presents a comparative overview of different algorithms like RNN, Linear Regression, GARCH, and ARIMA which can be used for time series analysis and concludes as to which algorithm is best suitable for time series analysis by considering different parameters like RMSE, MAE, etc., Besides this, it also analyzes the different factors which affect the prices of cryptocurrency.

Internet of things (IoT) is a cloud-based “extensive global network” that connects numerous devices. Various devices are connected to the Internet for acquiring and storing the data. With the growth of technology, wireless networks, and affordable computer chips, everything can be integrated into the IoT. Wireless IoT can utilize a variety of different wireless communication technologies and protocols to connect various smart devices. Recent advancements in communication protocols and data processing permit the description of a new sort of restricted communication in terms of how objects communicate with one another on the Internet of things. This review paper presents a novel classification for the various traditional IoT network protocols and highlights different wireless IoT technologies along with their applications. In addition, comparative analysis of different features of IoT communication protocols is also presented.

Background The study was conducted in a large NABH accredited, 2000 bedded tertiary care teaching hospital which had footfall of 2500–3000 patients every day and 300 admissions. There was a robust system for filing of the medical records, and large manpower was employed to store, retrieve, and manage the logistics of transporting records from a central location to the respective clinics or OPDs. The clinics were spread in four building. A lot of paper usage used to occur as all the clinical notes, investigations, and prescription. Most important problem among all was that doctors not comfortable with the regular electronic medical record (EMR) solutions which involves keying in of data. All these issues called for a solution that was handwritten yet digitized. Objectives Identifying and designing an EMR solution that would match the requirements vis-a-vis doing a costing study. Methodology A standard costing study was done by researchers to identify direct labor costs, equipment costs, material costs between the two options—physical records and electronic records. The end-user challenges in using electronic records were analyzed by questionnaire. A phased implementation plan of digitization in specialty outpatient departments was coordinated. Results Cost of each paper file was INR 8.57/-using 156,223 files per year costed INR 1,338,831/-. Storage space rental value equivalent was INR 21,24,000/-. Direct salary costs of 73 employees in medical record department (MRD) is INR 13,406,844/- per year. The digitization was introduced in lesser crowded departments; first, their experiences were shared to customize the product with features in other departments. Conclusion It was found to be feasible to transform the hospital from Healthcare Information and Management Systems Society (HIMSS) level-1 digitization to HIMSS level-2 system.

With a recent development and phenomenal activity in the area of microwave, there is need to maintain quality of service and high data rate. Therefore, requirement to design an antenna which provide these facilities. This paper presents a double regular hexagonal radiating patch, defected ground, and small circular slot etched out from the middle part of radiating patch. It demonstrated the Ultra-Wide band (UWB) operation, which works efficiently in entire band from 2.52 to 12.91 GHz. The overall dimension of proposed antenna is 31 × 51.5 mm2 Simulated in HFSS and has almost stable radiation pattern of E- and H-plane, positive gain of 6 dBi, and 134% of bandwidth in the entire band. This microstrip antenna is simulated and fabricated to verify its result, the equivalent circuit model is also constructed to verify simulate a measured result, which works efficiently in S, C, and X band applications.

In this paper, six circular shorting columns combined with rectangular microstrip antenna are proposed to increase the polarization purity in the orthogonal plane with stable radiation pattern. The circular shorting columns have been placed along the length of the patch between the ground plane and patch. From the proposed structure, around 31 dB polarization purity over a wide elevation angle has been obtained as compared to conventional microstrip patch antenna on FR-4 substrate. The proposed structure takes care of the higher-order orthogonal mode at the non-radiating edge without affecting any fundamental (dominant) mode characteristics.

A dual-wideband performance monopole triangular patch antenna is designed, and for wireless communication, applications are presented. The proposed antenna consists of a triangular patch via a microstrip feed line on the substrate and a dumbbell-shaped defected ground structure (DGS) on the partial ground plane. The proposed antenna operates at a dual band of 2.9–3.3 GHz and 5.2–6.4 GHz, a partial ground with dumbbell-shaped DGS implementation. A DGS with a dumbbell shape is operated at 3.2 and 5.8 GHz. The simulated 10 dB bandwidth for return loss is achieved to be 12.90% (2–9–3.3 GHz) and 20.68% (5.2–6.4) in the lower and upper band, respectively, which covers the bandwidth requirements of 5.2/5.8 GHz WLAN and 5.5 GHz WiMAX application bands. The proposed antenna has a very simple planar structure and occupies a small area of 40 mm × 47 mm. The proposed antenna has a desirable VSWR level and radiation pattern which is suitable for wireless communication applications. The introduction of defected ground structure also improved the antenna performance. The parameter of the proposed antenna is varied and discussed in detail to observe its effect on the antenna performance.

A triple-band metamaterial absorber is proposed and discussed in paper for C-band application. The proposed absorber surface is simulated for FR4 substrate by CST microwave studio electromagnetic simulator. The unit cell of metamaterial structure resonance appears at frequency of 4 GHz (−14 dB), 4.7 GHz (−13.5 dB), and 4.9 GHz (−26 dB). Absorptivity for simulated design is 96% for 4 GHz, 95% for 4.7 GHz, and 99.8% for 4.9 GHz. Electromagnetic microwave radar/absorber plays a vital role in military and defense techniques as well. This type of absorber also works for cloaking and shielding of drones which are used in defense sectors.

The Global Navigation Satellite System (GNSS) has been a very integral part of our modern-day communications and navigation. Initially, it was limited for military systems only but now with involvement of both commercial and civilian access to the data, the possibility of development for new applications is endless. So far only there has been a very limited number of Global Systems and a few regional systems which are likely to change in the coming days as more countries are looking to make their presence in earth’s orbit. The applications of GNSS are helping enormously for mapping, surveying, precise positioning, ionosphere research, earth data monitoring and more. But with the newly emerging trends and innovations in the commercial and research and development by so many private enterprises, the need for GNSS will be greater than ever before. More devices will need the use of GNSS to integrate for various applications. The fast-paced innovation has led to the democratization of the system to a large extent. With more development and adoptions of next generation autonomous technology such as driverless cars, automatic traffic management system and more access to high-speed global network, GNSS will play and will have more impact on everyday life in the coming years. Thus, it is important to start finding use cases that can have a large-scale impact. This paper focuses specifically on the use case of GNSS for ground-based measurements that can be further developed for more niche applications. The GNSS receiver and the data used in the study are located at Sikkim Manipal Institute of Technology, Sikkim, India. The study presented here is to show that the system is viable and can be developed into products with very important use cases and applications that can be of interests for both commercial and military uses.

Estimation of Ku/Ka band radio signal scintillation is done over eight North East Indian locations using meteorological database of NASA Giovanni. A signal fade of at least one dB is found even for ten percent of time during the observation period of one year for rainy summer season as well as non-rainy winter season. The effect of elevation angle is quite significant while estimating Ka band signal fade. As frequency has a very strong correlation with scintillation intensity, the design of SATCOM link with the use of higher frequency band needs accurate estimation of signal fade depth due to scintillation.

In this paper, we present a modified printed monopole antenna for wireless applications. The proposed antenna consists of an inserted fed to radiating patch with a four L slots. The designed antenna was found to operate at three wireless frequency bands of 2.3–5.86, 5.8–6.37 and 9.3–10.5 GHz. This design achieves better impedance matching without any additional circuits. The parametric studies and radiation characteristics show the antenna has good characteristics and is applicable for mobile wireless applications.

One of the most important tasks for any electricity system is to balance supply and demand. This is a dynamic equilibrium. An amount is projected for power demand based on historical patterns. This demand estimation will assist the system operator in determining how much electricity to deliver to meet demand or decrease waste. Demand appears to fluctuate over time, requiring supply to be adjusted correspondingly. Traditional fossil-fired power plants have a far simpler time adjusting their output than wind power plants. The output of thermal power plants can be programmed to respond to variations in demand. Wind power, like most other forms of renewable energy, is subject to unpredictable swings. Wind electricity, unlike natural gas power plants, cannot be sent. The maximum wind power output is mostly determined by the current wind conditions. We have proposed a novel approach of deep learning for forecasting wind at the location of Dewas, Madhya Pradesh, India. The wind power has been forecasted using the machine learning regression and long short-term method for the comparative study on MATLAB and concluding the more accurate predictions than the conventional statistical and other conventional techniques.

Road accidents are common often leading to serious injuries and often death. There are several causes of road accidents. Fatigue and drowsiness in drivers being one of the prime causes. Being a potential cause for danger on the road, one of the best ways to prevent this is to implement a drowsiness detection system. The proposed work implements a driver drowsiness detection system based on computer vision. A webcam is used to capture the face of the driver, and eye aspect ratio of the driver is used to detect if the driver is sleepy. An alcohol alert module (based on Arduino ethanol gas sensor MQ3) is also included. The system will alert the driver with alarms if the driver is found in drowsy state, and a message will be sent to the owner of the vehicle in case driver is drowsy or drunk. The owner will also be able to remotely monitor the activities of the driver.

In this paper, the authors propose a novel deep learning (DL) approach for the prediction of blood glucose levels (BGL), non-invasively, analyzing photoplethysmography (PPG) signals. Features such as Kaiser–Teager energy, spectral entropy and zero crossing rate were extracted from the PPG signals, and a random forest (RF) model was used to predict the BGL. Later, a convolutional neural network (CNN) model with 25 layers was implemented and trained for the purpose using stochastic gradient descent method. A Bland–Altman (BA) plot and the Clarke error grid (CEG) were used as means to estimate the accuracy of the RF and CNN models. By comparing the CEG and BA plots, it was observed that the accuracy of BGL measurements was superior in the CNN model when compared to the RF model. The CNN model had 84% of the points lying in region-A of the CEG and a standard deviation of errors equal to 15.67 in the BA plot.

With the remarkable development of the Internet over the last few decades, network security is one of the major issues in this century. With the increasing development of malicious software or malware as well as extensive use of the Internet, the destruction and unauthorized access of the network security are increasingly vital. An intrusion detection system is basically implemented to detect the intrusion in the system and identify the different types of unauthorized access of data and information over the networks. However, to understand the research work on IDS, the survey is made on 40 papers from 2010 to 2021. The survey paper includes a basic idea of machine learning, datasets and the different algorithm used in IDS. After studying different existing approaches of IDS techniques, limitations and complexities are mentioned here.

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being.

This paper aims to discuss the application of blockchain technology for achieving sustainability and moving toward the circular economy. The researchers have examined the role of blockchain technology in achieving the aims of sustainable production. While discussing the part of the blockchain for sustainability, the cases of IBM and Deloitte have been conferred to. Further, in the circular economy, the instances of Mitsui and Lablaco have been addressed. Overall, it can be concluded that the implementation of blockchain helps achieve sustainability by bringing transparency to the system and enhancing the market position. The emphasis on blockchain also helps take organizational practices toward a circular economy by generating new profit opportunities and focusing on green practices.

Stock market forecasting is viewed as one of the most interesting is of study for many researchers. The crucial data that can be accessed is thought to have predictive correlations with future stock performance which could provide information to investors so that they may make better decisions when purchasing equities. The paper tries to present a comparative analysis of four machine learning models to predict stock market price. The methods that we have considered are: support vector machine (SVM), artificial neural network (ANN), and hybrid models like PCA + SVM and PCA + ANN to predict stock market state. We have experimented using Vanguard Total Stock Market ETF (VTI) dataset for last 10 years which shows that SVM-based predictive model performed well among all the models for predicting the stock market status.

Bird audio identification is one of the challenging fine-grained tasks due to various complexity in the signal. In the current work, we present a new bird audio dataset from the Indian subcontinent and propose a novel hybrid features-based ensembled residual convolutional neural network to identify bird audios from the Indian subcontinent. We utilized mel-frequency cepstral coefficients (MFCC) and melspectrogram features to train the neural network. We compared the results of the proposed model with other machine learning and deep learning models. The results show that our proposed model achieved the best accuracy of 92% and best F1-score of 91% on using modified ResNet50 model. The dataset and the experiemental codes are available at GitHub ( https://github.com/Theivaprakasham/iccdn-2021-birdcall-id ).

The evolution of deep learning has enhanced the technique of identifying and classifying lung diseases into various categories using medical images. This project aims to build lung disease detection models using deep learning to identify future potential and thus to efficiently observe and visualize the recent and upcoming trends in this domain. Identifying and discovering lung disease at an early stage has become a vital part of the medical domain because this would facilitate patient’s subsequent clinical management. The project primarily focuses on pneumonia as well as considering the breathing problems of patients. Deep learning and machine learning have served the utmost significance in detecting such lung diseases at a prior stage. This enhancement has contributed much to the doctors and medical systems to provide early treatment to patients. In this project, convolutional neural network (CNN) is used to predict lung disease (pneumonia) from chest X-ray images using machine learning and deep learning frameworks.

Skin cancer is widely menacing forms of cancer in North America and South East Asia and some part of Australia also. The main reason of skin cancer is caused by damaged deoxyribonucleic acid (DNA) in skin cells of human body which is inherited from genetic disorder or mutations on the skins. Skin cancer is to gradually spreading over other body parts with acute pain and it is only curable in initial stages. That is always recommended to detect at early stages of cancer as we know that there are four stages of cancer. The skin cancer has high mortality rate all over the globe as compare to other types of cancer and its treatments are very expensive. This paper presents a detailed review of deep learning techniques like convolutional neural network for the early detection of skin cancer and their types. We have develop a 2-D CNN model and evaluated the model with different parameters and finally evaluated model with data augmentation and predict the incorrect probability for different types of Skin cancer for HAM 1000 datasets.

Nowadays, skin cancer is the widely recognized cancer all over the world. As the spreading rate of skin cancer is increasing day by day, so, there is a need to develop a technique that can detect skin cancer at an early stage. These days, deep learning has attained outstanding success for the detection and diagnosis of cancers. In this paper, a transfer learning-based EfficientNetB0 model is improved by adding one average pooling layer, one dropout layer, one batch normalization and one dense layer with softmax activation function. The proposed model has been simulated using the Kaggle database. The training and calculation are done with different hyper parameters such as batch size, optimizer and epochs. The data augmentation technique is applied to solve the problem of less amount of images. The proposed model has attained 87% accuracy on Adam optimizer with 32 batch size and 30 epochs.

Liver is a crucial interior organ of the human body whose primary tasks are to eliminate waste which is produced by the organs. In human body, most of the difficult tasks are being performed by the liver, and any abnormal activities happening due to malfunctioning of the liver will result into life-taking reason. Disease prediction in the human being has always been a long tenure procedure in early days. As the days have passed, computer-based diagnosis has become an important role in the medical world for prognosis, analyzing, and storing medical information with their related images. The liver disorder can cause various fatal and life-taking diseases, which also includes liver cancer. Early diagnosis and treating the patients beforehand can be helpful to reduce the risk of those lethal and fatal diseases. As the diagnosis of liver disease is quite expensive and sophisticated, numerous research have been performed using machine learning (ML) methods for classifying liver disorder cases. In this present work, we have categorized the liver patients on the basis of liver patient dataset using various machine learning techniques and approaches. In our work, we have classified our dataset with different classifiers such as k-nearest neighbor, random forest, support vector machine, and Extra Trees. After analysis, we concluded that after comparing all the accuracies, we found that Extra Trees classifiers gave the highest accuracy compared to other classifiers. We got the highest accuracy of 91.67%.

In today’s world, the use of social networking websites is in the next level. People express their thoughts and opinions on any brand, product or any social events via these sites. Sentimental analysis often uses natural language processing (NLP) to obtain a sentiment behind the text, tweet or comments. In this modern world, sentiment analysis has become one of the most efficient way to mine the public emotions, opinions based on their particular topic of their interest. We have used datasets which consists of tweets from Twitter which contains tweets from various domains. This paper described an approach where a stream of tweets is pre-processed then classified based on the emotion within the text. We used sequential model, with long short-term memory (LSTM) as a layer to train our model. This trained model can be used to analyse any tweets or blogs to obtain sentiments behind that. We have achieved an accuracy of 81.5% from our developed model.

The project demonstrates PCB design for an Arduino-powered stun gun which transforms low current pulses into high voltage signals. Stun guns are interactive weapons that need direct contact with the subject to work efficiently. Generally, 3–5 s of contact can cause muscle disruption. This project involves a 12 V power supply, Arduino, power shield, 555 timers, a MOSFET transistor, a transformer, and a voltage doubler to model the stun gun. It paralyzes an attacker with fewer penalties and allows the user to pursue more safety precautions. Not all stun guns are created the same, they come in different sizes and voltages to perform differently.

In this paper the pacemaker is integrated with two different controllers, namely PID and FOPID, as they are widely used in today’s scenario, followed with an optimization technique, called Genetic Algorithm, to obtain a more tuned result, when the whole system is integrated with the heart. The aim of the project, is to do a comparative study on tuning PID and FOPID using Genetic Algorithm for heart rate control of a pacemaker, by doing analysis of time domain criterions of the control system.

Accurate parameter determination of any type of motor is an important aspect that further provides accurate motor performances. Equivalent circuits are widely used and are convenient means of computing the performances of three-phase induction motors. Amongst various equivalent circuit parameters, inductances are associated with the core material non-linear B-H curve, whose operating point shifts with the motor load. This results in motor inductances becoming non-linear with the variation of load on the motor The method presented in this work determines the non-linear inductances of the three-phase induction motor implemented on finite element method (FEM) developing the two-dimensional motor model in FEM package ANSYS Maxwell Electronics Desktop. Mainly performing two tests on FEM, all the motor non-linear inductances are determined. For the sake of comparison of the results obtained, constant values of motor inductances are also obtained from the conventional tests on the induction motor i.e. stator winding D.C. resistance test, no-load test, and the blocked rotor test. These conventional tests for determining the constant values of motor inductances are also performed on the FEM package, ANSYS Maxwell. Finally, all the results are presented.

This work presents the different approaches by which repetitive control can be implemented to achieve better rejection of periodic disturbances present in the system. An overall analysis and comparison have been performed together with various controllers. The servo system experimental results are included to illustrate the impact of RC acting with a real-time plant. Plug-in RC (RC-PD—Repetitive Controller with Proportional and Derivative controller, RC-PID—Repetitive Controller with Proportional, Integral and Derivative controller) architecture combines the features of both repetitive controller and the controllers it is used in combination. Real-world applications need a stable output in result to the provided input when subjected to unwanted disturbances, thus by usage of RC-PD and RC-PID the intention is to minimize those fluctuations present in signal and get the desired output with reference to the desired application.

Music production or signal processing is ignoring the hardware implementation and completely doing it on the software side. The main issue with this is the output speed and is reduced than the hardware, and it is dependent on the software processing speed. Also, we lose the original grunginess of the signal, an example of this is olden days retro songs in which you can hear and differentiate between all the different instruments used in the song. But in modern days, it is difficult to differentiate the instruments mainly because of the use of controlling and balancing tone on the software side and also the use of compression algorithms which completely reduces the quality of the original sound.

In our busy life, self-care can be one of the first things that are often neglected from our to-do list. Self-care is vital for our physical and mental health. Without adequate self-care, humans are less likely to be the best possible version of them which indeed affects their personal and professional life. A perfect way to recharge mind and body is to stay hydrated that safeguards our body from a minor health issue that is caused by not paying much attention to our self-care. In this paper, we have designed a multi-purpose robot which is a solution to all the small activities that are being ignored in our day-to-day life. The bot includes major domains like embedded systems, WeMoS D1 microcontroller, Internet of things (IOT) and image processing. It performs tasks such as cutting grass in the garden, acts as a dustbin using ultrasonic sensor, serving water when needed and it also reminds to carry umbrella using rain sensor, when it is about to rain. The bot uses Image processing through Haar cascade algorithms for face detection and sends information to the database and MIT app inverter. For the moment of robot, mechanical parts such as the center shaft, side shaft, spur and worm gear are used. It is a completely autonomous bot.

This paper gets an understanding of the challenges faced in implementing LPWAN technologies and the resources to solve the most challenging problems, which will be related to increasing range, increasing bandwidth and decreasing power consumption. This paper presents the latest versions of the documents required to get there. Understanding the network and security requirements while developing an engineering solution for industrial or domestic use is paramount. Each provides its own unique set of challenges that the developer and research have to adapt to. This paper presents a brief of these two core concepts and provides important references to delve deeper into the subject and implement the cheapest and most power-efficient system possible. This paper compares these technologies and presents to you in a way which will highlight important concepts while giving you the resources to find solutions for specific problems.

Non-orthogonal multiple access (NOMA) proves to be a prominent solution for huge device connectivity and enhances spectral efficacy. NOMA can handle several users in a single time and frequency slot. The superimposed signal transmitted from the transmitter to the receiver will be applied to successive interference cancelation (SIC) technique at the user to extract the desired user’s signal. However in cognitive radio (CR) networks, the users in the secondary network will only be assigned with the unused spectrum not being used by the primary network users. Though the secondary users utilize the idle spectrum, there is a minimum probability of primary users being affected by interference from secondary users. On the other hand, reconfigurable intelligent surfaces (RIS) can control the propagation of electromagnetic waves in the radio environment, according to the position of the user. In this paper, we suggest a simple CR-enabled NOMA-assisted RIS system to examine the performance of the users in the secondary network, consisting of a base station, RIS device, and two NOMA users. We have derived the closed-form expressions for outage probability (OP) at both the users and compared their performance in the presence of CR networks. The results show that, in the presence of CR, the number of meta-surfaces in RIS can significantly enhance the outage performance of the users. The comparison of both users’ performance has manifested that near users will have better outage performance compared to the far users.

Providing data connectivity seamlessly without any kind of interruption is one of the compulsory requirements of sensor-based remote health monitoring system. It is therefore an important topic for research in modern times. It should support mobility handling as well as patients’ movement equally as patient may move to different places at different times. Different cases are required to be considered to provide this service. A health monitoring framework should support all such movements and should ensure that proper connectivity is maintained even when the patient is moving in a car, and there may not be proper infrastructure along the road side for providing uninterrupted data connectivity. Ideally, a health monitoring framework should support such connectivity at all times. In our present work, we considered different types of patients’ mobility and worked on how to provide seamless connectivity using intelligent vehicular communication-based architecture. This work is motivated based on the concept of Internet of vehicles and vehicular movement. In this paper, we have designed a mobility handling protocol which will handle patients’ movement and provide seamless data connectivity at all times.

IEEE 802.11ah is implemented for Internet of things (IoT) applications. The important feature in IEEE 802.11ah MAC layer is restricted access window (RAW) mechanism, which divides stations (STAs) into groups and every group designate with a RAW slot. In IEEE 802.11ah, uniform grouping (UG) is the default scheme having distinct rate STAs in each group is the root cause of anomaly. Due to distinct rate STAs, the high rate STAs are penalized by low rate STAs which consume more channel time for transmission. In this paper, we present a RAW slot allocation (RSA) scheme based on achievable data rates to overcome the anomaly. We also present an analytical model to compute the throughput and energy efficiency of IEEE 802.11ah network. From results, the RSA scheme resolves the anomaly and upgrades the IEEE 802.11ah network’s performance. The analytical results validated with simulations.

This paper presents optimal allocation of Micro-Phasor Measurement Units (M-PMU) in distribution network using Binary Integer Linear Programming (BILP) method considering security constraints. Placement of PMUs at every node of feeder network is infeasible as it is highly economical. So, to minimize the number of M-PMU's and to locate at optimal places, observability constraints are considered in one case, and allocation of M-PMUs at critical nodes in priority is considered in another case as security constraints. M-PMU allocation in case of single line outage or PMU loss is formulated. A node observability index (NOI) is proposed to check the observability of every node in the network. Complete feeder network observability (CFNO) is proposed to check performance of observability of complete network. MATLAB simulations are considered for IEEE-13, 33, 37, 69 feeder network, and obtained results are compared with standard methods to show its efficacy.

With growing technological advancements, every process is becoming easier. Everything is being automated. One of the most popular automation processes include home automation. A smart home not only results in a comfortable living, but also reduces a lot of manual tedious tasks. In this paper, a smart home system is simulated with the help of Cisco packet tracer software. Cisco packet tracer allows various network elements, setup options, wireless and smart components and a testing environment. Since this project is based on Internet of Things (IoT), the IoT command is used in the Cisco packet tracer. The software provides a means for us to create a network virtually and configure the devices and later implement it in real world. Besides smart devices, Cisco packet tracer also provides sensors, actuators and microcontrollers which can be used to create the smart home automatic system.

This paper presents analysis of wireless communication using the OFDM scheme. This OFDM is expecting skilled use of the obtainable band of frequencies for permitting to extend over and cover a part of between the carriers. Observing from various sides that the OFDM is the robustness modulation method which is susceptible to the higher data rate; accordingly, it will be capable of removing intersymbol interference. By using FFT or IFFT technique, we can calculate effective computation and operate. We have analyzed it in wireless communication using 16-QAM. From the simulation results, we have got this analytical result. The orthogonality among sub-carrier’s frequency components can be adequately preserved. We likewise noted from the BER calculation that minimum bit error rate showed the least intersymbol interference (ISI) and gives an excellent performance for wireless communication, but the bit error rate calculation tool calculates the error rate every moment that depends on time. It does not fix the error rate.

In this paper, we focus on the ergodic capacity of RIS-assisted NOMA in wireless communication systems. The closed upper bound of ergodic capacity analysis is obtained for the proposed technique by using both Rayleigh and Rician fading channel environments for multiple users. Compared to simulations, numerical results demonstrate the tightness and effectiveness of our closed-form expressions.