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With rapid growth in urbanization more number of people are attracting towards urban areas. Due to which density of human being in urban area is increasing day by day. To fulfill the demand of leaving area, people developing high rise buildings. With this there is increase in chances of mishaps that may occur due to earthquake, fire accident etc. These mishaps may lead dangerous to human beings as well as infrastructure. Fire is an unexpected event and it occurs very often but once it occurs it affects great manner to human and his infrastructure. Early detection of fire may assist to avoid unnecessary losses occurring fire mishaps. In the view of addressing this issue we are proposing hybrid architecture for building detection and smart evacuation system using LoRa and IoT.

Internet of Things with 8051 and ESP8266 provides a platform to get started with the Internet of Things (IoT) with 8051. This book describes programming basics and how devices interface within designed systems. It presents a unique combination of 8051 with ESP8266 and I/O devices for IoT applications supported by case studies to provide the solutions to real-time problems. The programs and circuits have been tested on real hardware and explore different areas in IoT applications. Divided into four sections, it explains the customized boards for IoT applications followed by the means by which 8051 and ESP8266 interface with I/O devices. It spans levels from basic to advanced interfacing with special devices, server design, and data logging with different platforms. Features: Covers how I/O devices interface with 8051 and ESP8266 Explains the basic concepts of interfacing complexity using applications with examples Provides hands-on practice exercises with 8051 and ESP8266 for IoT applications Discusses both case studies and programming tests on real hardware during industrial and student projects Reviews the integration of smart devices with IoT Internet of Things with 8051 and ESP8266 is intended for senior undergraduate and graduate students in electrical and electronics engineering, but anyone with an interest in the professional curriculum of electrical and electronics engineering will find this book a welcome addition to their collection.

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

Preeclampsia refers to a condition that occurs due to a sudden increase in Blood Pressure during 20 weeks of pregnancy. Preeclampsia may lead to the damage of some other organs of the body liver or kidneys or leads to the occurrence of brain stroke or seizures. It occurs in 15 to 20 women in 100 and becomes a significant reason for maternal mortality or infant loss. It is necessary to prevent the risks related to preeclampsia by monitoring the health parameters of the patient in a real-time environment. The risk assessment model, when amalgamating with IoT and other related technologies, offers intelligent solutions to monitor health parameters in a real-time environment. The present study is focusing on proposing an IoT based health assessment framework to capture the blood pressure and other health-related parameters of pregnant women continuously. The captured data is then processed and analyzed by fog nodes. These fog nodes then provide real-time suggestions to the patient for improving health conditions. Moreover, the fog nodes also send the processed data to cloud servers for further analysis by healthcare providers.

An investigation of auto-reconfiguration scheme for interchanging the electrical connections of photovoltaic (PV) array of 3 × 3 size, from series-parallel (SP) to total cross-tied (TCT) configuration under partial shading conditions (PSCs) is presented. The progressive increment shading patterns- 1, 2 and 3 are considered for the extensive comparative analysis for experimental and MATAB/Simulink system. The experimental system comprises three sections: under section I, four relays are used in 3 × 3 PV modules matrix, under section II, an Arduino based relay circuit operation control scheme is designed to modify the electrical connections of PV module and the wireless based User End Operating System (UEOS) is discussed in section III. Furthermore, the mathematical modeling of PV array is validated under the environment of MATLAB simulation to procured. The performance analysis of the system is successfully carried out in terms of power and voltage at global maximum power point (GMPP), power losses and fill factor (FF). Keywords: Photovoltaic array, Series-parallel, Total cross-tied, Power loss, Fill factor

BACKGROUND COVID-19 is chronic based disease which is spreading with rapid pace in the entire world. Present study addresses the situation of outbreak of the COVID-19 disease in India and estimate the rise of the cases in India. This study addresses the present health infrastructure, infected health workforce clearly with the statistics. Support Vector Machine and Linear Regression are implemented in this study for predicting the expected cases. For the purpose of modelling, the input data of number of cases is considered from the march 15th , 2020. With the input data, the two models are trained for prediction of the cases. In the end, the results show that support vector machine and linear regression are giving good accuracy for prediction. OBJECTIVE The current studies aim to analyze and estimate the developments in the near future with reference to COVID-19 in India. The research is also planned to look at the preparation level of Indian government for this outbreak. The scope of the study is narrowed to build prediction models for the Indian region and uses SVMs for prediction methods based on time series that are easily built and readable under these crucial conditions. The study does not cover coverage of a COVID-19 outbreak for any other country. METHODS Support Vector Machine and Linear Regression are implemented in this study for predicting the expected cases. For the purpose of modelling, the input data of number of cases is considered from the march 15th , 2020. With the input data, the two models are trained for prediction of the cases. In the end, the results show that support vector machine and linear regression are giving good accuracy for prediction. RESULTS 1.Considering the change, the change in slope of the both curves in the graph, it can be concluded that the trained model is giving a quite good range of accuracy. 2.The Graph shows the plot of the predicted values and actual values fed during the testing of model. Considering the change, the change in slope of the both curves in the graph, it can be concluded that the trained model is giving a quite good range of accuracy. CONCLUSIONS In conclusion, the present work emphasized on presenting observations and predictions about COVID-19 outbreaks in the Indian region. Although the rate of growth at world level is not equal to the rate of growth, the situation appears dangerous as India is heading towards exponential growth. The expected patients are reaching in millions in the next 30 days by means of two separate time series forecasting models. With regard to the poor health facilities, it is going to difficult to combat the outbreak of virus without government addressing the effective measurements. Contrast to strict lockdown, social distancing, isolation, patient testing and medical care need to implement with war base for combating the pandemic in India. The forecasting in this study are still in beginning phases as the historical data is limit for creating reliable model. That to the risen of cases in India followed from the last 10 days so the training for the model may not be accurate, however the prediction model would be enhanced from existing models, as the greater number of medical and demographic data is available.Furthermore, even if the predictions are 60-70 percent correct, then the nation will also encounter this quite hard days.

The rapid development of technology has empowered us to achieve resilient infrastructure to establish a sustainable ecosystem. The construction site is one of the highest risk jobs for accident-related fatalities and injuries globally. From the previous studies, it is concluded that untrained or inexperienced workers were responsible for 40% of work-related accidents and the Health and Safety Executive (HSE) report concludes that inadequate working experience, knowledge, and safety awareness were the key causes of fatal accidents in the construction industry. Moreover, it is identified from previous studies that digital technology such as IoT with the assistance of wireless sensors can enhance the safety of construction sites. Based on this advantage, this study has implemented the hybrid architecture with the integration of the 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi communication protocol to monitor the health status of workers and construction sites and also to identify workers’ equipment wearing status in real-time scenarios. The proposed architecture is realized by implementing customized hardware, based on 2.4 GHz Zigbee, 433 MHz long-range (LoRa), and Wi-Fi. Furthermore, in the analysis of the evaluation metrics of LoRa, it is concluded that the lowest sensitivity is observed for SF 12 at BW 41.7 kHz and the highest is observed for SF 7 at BW 500 kHz; the maximum value data rate is observed at BW 500 kHz at CR 1 for SF 7, and the minimum data rate is observed at BW 41.7 at CR 4 for SF 12. In the future, the customized hardware will be implemented in different construction environments resolving possible challenges that empower to implementation of the proposed architecture in wide extensions.

In the medical field, due to their economic and clinical benefits, there is a growing interest in minimally invasive surgeries and microscopic surgeries. These types of surgeries are often recorded during operations, and these recordings have become a key resource for education, patient disease analysis, surgical error analysis, and surgical skill assessment. However, manual searching in this collection of long-term surgical videos is an extremely labor-intensive and long-term task, requiring an effective content-based video analysis system. In this regard, previous methods for surgical video retrieval are based on handcrafted features which do not represent the video effectively. On the other hand, deep learning-based solutions were found to be effective in both surgical image and video analysis, where CNN-, LSTM- and CNN-LSTM-based methods were proposed in most surgical video analysis tasks. In this paper, we propose a hybrid spatiotemporal embedding method to enhance spatiotemporal representations using an adaptive fusion layer on top of the LSTM and temporal causal convolutional modules. To learn surgical video representations, we propose exploring the supervised contrastive learning approach to leverage label information in addition to augmented versions. By validating our approach to a video retrieval task on two datasets, Surgical Actions 160 and Cataract-101, we significantly improve on previous results in terms of mean average precision, 30.012 ± 1.778 vs. 22.54 ± 1.557 for Surgical Actions 160 and 81.134 ± 1.28 vs. 33.18 ± 1.311 for Cataract-101. We also validate the proposed method’s suitability for surgical phase recognition task using the benchmark Cholec80 surgical dataset, where our approach outperforms (with 90.2% accuracy) the state of the art.

Digitalization of human taste has been on the back burners of multi-sensory media until the beginning of the decade, with audio, video, and haptic input/output(I/O) taking over as the major sensory mechanisms. This article reviews the consolidated literature on augmented reality (AR) in the modulation and stimulation of the sensation of taste in humans using low-amplitude electrical signals. Describing multiple factors that combine to produce a single taste, various techniques to stimulate/modulate taste artificially are described. The article explores techniques from prominent research pools with an inclination towards taste modulation. The goal is to seamlessly integrate gustatory augmentation into the commercial market. It highlights core benefits and limitations and proposes feasible extensions to the already established technological architecture for taste stimulation and modulation, namely, from the Internet of Things, artificial intelligence, and machine learning. Past research on taste has had a more software-oriented approach, with a few trends getting exceptions presented as taste modulation hardware. Using modern technological extensions, the medium of taste has the potential to merge with audio and video data streams as a viable multichannel medium for the transfer of sensory information.

These days, the oil industrial industry is leaning toward employing smart field improvements to streamline various activities in the midstream area. Oil transportation over large distances via pipelines has a cheap cost and high efficiency in this sector. If pipelines are not properly maintained, they may fail, potentially causing catastrophic, long-term, and irreversible consequences on both natural and human conditions. Low power wide area networks (LPWANs) are without a doubt one of the domains that cause the most from industrial fields when it comes to realizing the vision of the internet of things (IoT). Long-range (LoRa) is an emerging LPWAN technology that is particularly useful for transmitting data over long distances. The goal of this work is to offer a methodology for managing oil pipelines over long distances utilizing the LoRa communication protocol and the installation of sensor nodes and LoRa gateways along the pipeline. We also used the optimized network engineering tools (OPNET) simulator to examine various simulation findings of LoRa performance.

The efficiency of the photovoltaic (PV) system is directly proportional to the solar energy. The maximum efficiency from the PV systems can be achieved if the panel is kept perpendicular to the direction of the radiations of sun. Hence, in order to achieve maximum concentration, radiation and efficiency of the PV cell, tracking of the position of the sun with accuracy is extremely important. The various methods of sun tracking system have been discussed which includes two axes, one axis, polar axis, open loop, closed loop, hybrid model, azimuth and tilt roll mechanism. The comparison of existing solar tracking systems and methods has also been discussed. In this paper, various existing solar tracking systems in terms of the controller used like PLC, microcontroller, FPGA to design the system and their economic assessment have been studied. It is concluded that two axis solar tracking systems has more power gain with respect to fixed systems. The microcontroller based tracking system with same size, capacity and location as of PLC based systems are more economic.

Currently, a smart city is an emerging field in urban cities to improve the quality of life through information and communication technology (ICT). In general, the traditional solid waste management (SWM) approach taken by municipal authorities for waste collection in urban areas must be enhanced to achieve the green and smart city goals. This article is primarily focused on the progress of ICT technologies in solid waste management. With that aim, a thorough analysis is carried out in the article, and from the analysis, we have identified distinct ICT technologies that have been implemented in SWM. The function, application, and limitations of each technology are presented in the article. From the review, it is concluded that the implementation of the Internet of Things (IoT) plays a significant role in minimizing the negative impact of waste on the environment. It is also identified that selection of the appropriate wireless communication protocol is critical during the implementation of IoT-based system because the sensor node at the bins is battery-powered. In addition, it is analysed that blockchain technology plays an essential role in realizing the waste–money model, as this model includes transactions between users and recyclers. Finally, in this article, we propose that the waste-to-money model, local network and gateway architecture, vision node, and customized prototype improve solid waste management system in terms of communication, energy consumption, and real-time monitoring.

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

According to United Nations (UN) 2030 agenda, the transportation system needs to be enhanced for the establishment of access to safe, affordable, accessible, and sustainable transport systems along with enhanced road safety. The highway road transport system is one of the transport systems that enables to transits goods and humans from one location to another location. The agenda of UN 2030 for the transport system will be accomplished with the assistance of digital technologies like the internet of things (IoT) and artificial intelligence (AI). The implementation of these digital technologies on highways empowers to provide reliable, smarter, intelligent, and renewable energy sources experience to the users travelling along the highways. This study discusses the significance of the digitalization of highways that supporting and realizing a sustainable environment on the highways. To discuss the significance of digitalization, the study has categorized digitalization into five subcomponents namely smart highway lighting system, smart traffic and emergency management system, renewable energy sources on highways, smart display and AI in highways. An architecture-for smart highway lighting, smart traffic, and emergency management are proposed and discussed in the study. The significance of implementing smart display boards and renewable sources with real-time applications is also addressed in this study. Moreover, the integration of AI in highways is addressed with the perspective of enhancing road safety. The integration of deep learning (DL) in the edge-based vision node for predicting the patterns of traffic flow, highway road safety, and maintenance of quality roads have been addressed in the discussion section. Embedding the deep learning techniques in the vison node at the traffic junction and the highway lighting controller is able to deliver an intelligent system that provides sustained experience and management of the highways. Smart reflectors, adoption of renewable energy, developing vehicle-to-vehicle communication in vehicles, and smart lamppost are the few recommendations for the implementation of digitalizing highways.

This study explores the modernization of road-based technologies for the enhancement of mobility while also implementing safer transportation. Mobility plays a critical role in everyday life on a micro and a macro scale combined. Modernization in mobility would enable establishment of a sustainable, digitalized and informed society. The inclusion of AI/ML to enhance road environment, curbing driver distraction, adopting electric vehicles, and integrating low power computing units in vehicular networks are among the potential recommendations for strengthening the evolving digital road architecture. The current ecosystem surrounding road safety and mobility can be boosted even further upon integrating products of modern technology into the classical elements of transportation. Modern technologies are classified and perceptually investigated by realizing the current challenges and proposing seamless potential extensions to the existing infrastructure from each domain. Techno-administrative concepts like the regulation of individual risk profiles for achieving a safer road environment are addressed.

For better efficiency and performance of solar panels, they need to be monitored regularly. Power generated by panels depends on several factors such as sun availability, the time instant of the day, light intensity, fill factor and light-generated current. This paper tells about monitoring solar panels online at different time intervals by studying the I–V curve of a solar panel at various intervals of time. And by using the curve, power generated, efficiency and fill factor are calibrated at the required intervals, and after that, the average of all the above three is calculated. All this data is collected online using Internet of things and finally is compared with the standard data available to judge the performance of the solar panel.