MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Electronics & Communication Engineering

Printed Circuit Boards ('PCB') are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset. © 2020 IEEE.

World University root node

Engineering and Technology

MPCIT 2020 - Proceedings: IEEE 3rd International Conference on "Multimedia Processing, Communication and Information Technology"

PCB-Fire: Automated Classification and Fault Detection in PCB

Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls are often very irksome. The paper intends to give a simple intelligible solution to the problem with Automated Crop Field Surveillance using Computer Vision. The solution will significantly reduce the cost of crops destroyed annually and completely automate the security of the field. © 2020 IEEE.

2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings

Automated Crop Field Surveillance Using Computer Vision

With the emergence of the Internet of things (IoT) and machine-to-machine (M2M) communications, massive growth in the sensor node deployment is expected soon. According to IHS Markit forecast, the number of connected IoT devices would grow to 125 billion by 2030. The exponential growth in IoT is impacting virtually all stages of industry and nearly all market areas. It is redefining the ways to design, manage, and maintain the networks, data, clouds, and connections. To support the requirements of new applications, an innovative paradigm called low-power wide-area networks (LPWAN) is evolved. The LPWAN is a class of wireless IoT communication standards and solutions with characteristics such as large coverage areas, low transmission data rates with small packet data sizes, and long battery life operation. The LPWAN technologies are being deployed and have shown enormous potential for the vast range of applications in IoT and M2M, especially in constrained environments. This chapter focuses on a general introduction to LPWANs, innovative applications and services, requirements, wireless access, and characteristics. © 2020 Elsevier Inc. All rights reserved.

LPWAN Technologies for IoT and M2M Applications

Introduction to low-power wide-area networks

Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. This paper focuses on defining a systematic and powerful approach of identifying the key characteristics of such applications, translating them into explicit requirements, and then deriving the associated design considerations. LPWANs are resource-constrained networks and are primarily characterized by long battery life operation, extended coverage, high capacity, and low device and deployment costs. These characteristics translate into a key set of requirements including M2M traffic management, massive capacity, energy efficiency, low power operations, extended coverage, security, and interworking. The set of corresponding design considerations is identified in terms of two categories, desired or expected ones and enhanced ones, which reflect the wide range of characteristics associated with LPWAN-based applications. Prominent design constructs include admission and user traffic management, interference management, energy saving modes of operation, lightweight media access control (MAC) protocols, accurate location identification, security coverage techniques, and flexible software re-configurability. Topological and architectural options for interconnecting LPWAN entities are discussed. The major proprietary and standards-based LPWAN technology solutions available in the marketplace are presented. These include Sigfox, LoRaWAN, Narrowband IoT (NB-IoT), and long term evolution (LTE)-M, among others. The relevance of upcoming cellular 5G technology and its complementary relationship with LPWAN technology are also discussed. © 2020 by the authors.

Future Internet

LPWAN technologies: Emerging application characteristics, requirements, and design considerations

Texture segmentation is one of the popular researchdomains and researchers across the globe are working on texturesegmentation to enhance segmentation performance to address itsrequirements in many fields. Color texture segmentation has widespectrum of applications in diverse fields such as segmentation ofnatural images, medical image analysis, remote sensing, shapeextraction and inspection of products etc. This paper presentscolor texture segmentation algorithm which can satisfyrequirements for such applications. Proposed algorithm is basedon Markov Random Field (MRF) model eliminating the need ofmajor contributor viz. Gabor filter used in past four decades forfeature extraction and use only color as texture feature. Highlycrude segmentation results are produced using only color astexture features. Crude segmentation results are enhanced byusing Median filter with enlarged window size quantitativelydetermined by using parameters viz. structural similarity index(SSIM), mean square error (MSE) and peak signal to noise ratio(PSNR). Feature space dimensions are reduced by factor of 11 inproposed approach and this reduced computations by a factor of11. The experimentation is carried out on 80 multi-class colortexture benchmark images from Prague texture segmentationdataset and 4 benchmark images in Vistex dataset. Meansegmentation accuracy achieved for Prague texture dataset is87.55% and it is higher by 9.82% over the best performingalgorithm among 11 state-of-art algorithms suggested in mostrecent literature. Accuracy achieved for Vistex dataset is 98.21%.Average SSIM for Prague dataset is 0.91403 and Vistex dataset is0.9405.

Fulltext

International Journal of Recent Technology and Engineering

Markov Random Field based Color Texture Segmentation

Real Time Object Detection Using Tensor Flow Lite system has been developed to help visually impaired people for navigation and surrounding objects detection. This system is based on raspberry pi, a single board compute model and Tensor Flow lite framework. The algorithm developed is tested for detecting objects like a table, a chair, a TV, a laptop, a mouse, a cell phone, a bottle etc. This system is capable of detecting people as well as objects. The detection accuracy of 70% is achieved. The testing is done in varying light, background, and distance in indoors as well as outdoor scenarios. This system uses Google based sample quantized SSDLite-MobileNet-v2 object detection model, which is trained of the MSCOCO dataset and converted to run on TensorFlow Lite. The information regarding the detected object is converted into audio for guiding the visually challenged person. © WCSE 2020.

WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering

Real time object detection for visually impaired person using tensor flow lite

Despite all significant advances in the detection of pedestrians transmitted by computer vision for driver assistance, it remains a challenging issue. Several studies have been conducted to develop the accuracy of pedestrian detection of intelligent surveillance systems. However, person detection in outdoor conditions is a difficult problem due to the variation in lighting, shadows and occlusions. In this paper we have proposed new methodology for pedestrian detection and tracking under varying light condition for advanced driver assistance system. The main aim of the proposed systems is to avoid collision of vehicles with the pedestrians while driving on roads. It is very difficult to detect a pedestrian due to the varying light conditions (night time, fog). With the proposed systems another way of driver provision is realized, which can pay off the weaknesses of the human visual system. The evaluation is done on firsthand dataset that constructed for this purpose as well as on the freely available KAIST multispectral dataset. Three kinds of databases were tested, taking into account various environmental factors with daytime, night time and fog. Experiments shows proposed system is strong and works successfully. © 2020, Springer Nature Singapore Pte Ltd.

ICDSMLA 2019

Effective Pedestrian Detection and Tracking in Varying Light Conditions

Although LoRa (long range) technology is designed to tackle interference and power consumption issues, interference from other LoRa and non-LoRa networks limits its performance. In practice, LoRa performance also gets constrained by multipath fading. This paper presents the study of LoRa transmission over Rayleigh fading. We considered different scenarios of LoRa networks along with simultaneous parallel transmissions, change in transmission bandwidths, and Doppler shifts. Results demonstrate deviation in the BER because of multipath and interference. It is also evident that even for a change in transmission bandwidth or Doppler shift for Rayleigh fading channel, LoRa performance gets impacted. © Springer Nature Singapore Pte Ltd. 2020.

Lecture Notes in Networks and Systems

LoRa Transmission Over Rayleigh Fading Channels in Presence of Interference

Low-power wide-area network (LPWAN) has become one of the prominent solutions for long-range and lower-power Internet of things and machine-to-machine applications. LPWANs enable communication with a large number of low-complex and low-cost remote nodes and objects with very low-power consumption, extending the lifetime of battery-operated nodes to months and years. It has a wide range of emerging applications in industry, smart cities, environment monitoring, agriculture, health care, and many other fields. There are several candidates for LPWAN technologies, and the selection of appropriate technology for a given application is a challenging task. This chapter presents the study of various LPWAN hardware and software platforms available in the market. It also describes various open-source tools available for simulations and research.

Hardware and software platforms for low-power wide-area networks

With digital TV switchover and using cognitive radio technology, a spectrum of unoccupied TV channels, called TV white spaces (TVWS), can be used for different applications. Radio signals in these bands travel farther and penetrate obstacles more effectively than those in cellular and 2.4&nbsp;GHz band owing to the lower frequency. With the growth in wireless services and higher data rate applications, the demand for spectrum is also increasing. Studies show that such licensed spectrum is underutilized. This chapter covers the study on TVWS, its architecture, and protocols, TVWS over low-power wide-area networks, along with its applications and future challenges and opportunities.