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

Nowadays, developments in the Internet of Things based technologies are becoming an essential part of every aspect of human life. It has also become a norm for vehicular traffic management applications. Safe and smart ways to manage ever-increasing road traffic are essential requirements in smart cities or urban environments. Although there are various solutions for traffic management, most of them have limitations in energy efficiency, complexity, and cost. In this paper, a solution is proposed for the quick clearance of the traffic for emergency vehicles such as fire trucks, ambulances. Our approach having based on the LoRaWAN protocol, which is known for its long-range, low energy needs, low cost, scalable, and wide -area applications. LoRa devices and the TTN gateway are used to communicate with each other and to establish a green corridor for emergency vehicles. Once the green corridor is created, regular traffic is suspended for a short time to enable the emergency vehicle to cross the traffic junctions without delay. To track the location of the vehicle from the traffic signal a web page is also created. The developed system reduces the wait time for emergency vehicles and allows tracking the emergency vehicle. © 2020 IEEE.

World University root node

Engineering and Technology

Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

Development of LoRaWAN based Traffic Clearance System for Emergency Vehicles

The characteristics and requirements for low-power wide-area network (LPWAN) solutions vary from application to application. This chapter discusses the different key design considerations of LPWANs. It also focuses on network design and topological aspects to give an overall architectural and design framework. It also briefly describes major LPWAN technology solutions available in the marketplace and summarized as a segue to the upcoming chapters which cover the major solutions. © 2020 Elsevier Inc. All rights reserved.

LPWAN Technologies for IoT and M2M Applications

Design considerations and network architectures for low-power wide-area networks

Proceeding of International Conference on Computational Science and …

Performance Evaluation of Video Segmentation Metrics

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International Journal of Engineering Research &amp; Technology (IJERT)

Intelligents Pillbox: Automatic &amp; Programmable Assitive Technology Device

Texture segmentation is a well-known research domain and a large number of researchers are working on it across the globe due to its wide varieties of applications in various domains such as medical imaging, shape extraction, product inspection, remote sensing, and segmentation of natural images. Many times, images are corrupted by various types of noises such as Gaussian, speckle, and salt-pepper noise. Most often Gaussian noise is a source of corruption in many applications. Prague texture dataset is extensively used by researchers due to wide varieties of multi-class textures in it. The present study offers the restoration of Prague texture database benchmark images. These images are corrupted with Gaussian noise with different variance values. The state-of-the-art algorithm viz. Colour Block Matching 3D (C-BM3D) filter, which achieves the first rank among 15 algorithms reported in the most recent literature, is used for the restoration of noisy texture benchmark images. Image restoration performance metrics used are peak signal to noise ratio (PSNR) and structural similarity index (SSIM). © 2020 IEEE.

Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020

Quantitative Restoration of Noisy Colour Texture Segmentation Benchmark Images using State-of-the-Art Algorithm

In the medical field various motion tracking techniques like block matching, optical flow, and histogram of oriented optical flow (HOOF) are being experimented for the abnormality detection. The information furnished by the existing techniques is inadequate for medical diagnosis. This technique has an inherent drawback, as the entire image is considered for motion vector calculation, increasing the time complexity. Also, the motion vectors of unwanted objects are getting accounted during abnormality detection, leading to misidentification / misdiagnosis. In this research, our main objective is to focus more on the region of abnormality by avoiding the unwanted motion vectors from the rest of the portion of the heart, allowing better time complexity. Proposed a region-based HOOF (RHOOF) for blood motion tracking and estimation; after experimentation, it is observed that RHOOF is four times faster than HOOF. The performance of supervised machine learning techniques was evaluated based on accuracy, precision, sensitivity, specificity, and area under the curve. In the medical field more importance is given to the sensitivity than accuracy. Support vector machine (SVM) has outperformed other technique on sensitivity and time complexity, hence chosen for abnormality classification in this work. An algorithm has been devised to use combination of RHOOF and SVM for the detection of atrial septal defect (ASD). © 2020, Science and Information Organization.

International Journal of Advanced Computer Science and Applications

Facilitating the detection of ASD in ultrasound video using RHOOF and SVM

Due to the quickly aging population, the number of elderly persons is rapidly increasing, posing significant challenges for monitoring and assisting them in indoor and outdoor settings. Although some techniques are available for the indoor localization of elderly persons, in the coming years, outdoor localization will be an essential part of society. Different approaches such as GPS, range-based, and range-free have been developed for outdoor localization. However, the localization accuracy and precision is still a significant challenge. For accurate and low-cost localization, we propose a novel IoT-based range-based localization for smart city applications. Using the extreme learning machine (ELM), fuzzy system, and modified swarm intelligence, a hybrid optimized fuzzy threshold ELM (HOFTELM) algorithm is developed. The particle swarm grey wolf optimization is used to identify the direction of the moving sensor node. A fuzzy weighted centroid is used to optimize the consequences of irregular movement of the nodes. Lastly, an optimized threshold extreme learning machine and weighted mean are applied to localize the moving nodes accurately. Our algorithm outperforms the existing algorithms in terms of average location error ratio (ALER), the number of localized nodes, and the computational time. The results show that ALER reduces by at least 48.07% in comparison with the other algorithms. The proposed algorithm also localizes at least 7.25% additional nodes and has a computationally efficient operation. © 2021 Elsevier Ltd

Sensors (MDPI) - Accepted and in Proof Reading Stage

IoT-based hybrid optimized fuzzy threshold ELM model for localization of elderly persons

With the emergence of the Internet of Things (IoT) and machine to machine (M2M) communications, massive growth in the IoT-enabled wireless sensor node deployment is expected in the near future. The critical challenges for the sensor network include energy efficiency, optimum route calculation, and the overall transmission cost. To avoid the bias toward one of the objectives and also to facilitate ease of position updating, we propose a novel multi-objective optimisation (MOO) agent based on particle swarm grey wolf optimisation (PSGWO) and inverse fuzzy ranking. We initially developed an enhanced PSGWO model, and then it is utilised for the development of population and multi-criteria based soft computing algorithm, called fuzzy PSGWO. The performance of the proposed algorithm is validated and compared with the well-known techniques; for the proposed algorithm, residual energy of the nodes is much higher than that of other algorithms, and save up to 48% energy along with smaller variation in the standard deviation. The results also demonstrate the smaller average values of fitness function and computationally efficient capabilities of the proposed algorithm. Copyright © 2021 Inderscience Enterprises Ltd.

International Journal of Sensor Networks --- In Production Stage cuurently

Towards Green Computing: Intelligent Bio-Inspired Agent for IoT-enabled Wireless Sensor Networks

Extension of fast R-CNN (Recurrent Convolution Neural Network) [1] and Recurrent-CNN [2] is Faster Recurrent CNN detection techniques. All these three strategies use convolutional neural networks (CNN). The distinction between them is the way to select the regions to work and the way those regions are categorised. Recurrent CNN and fast R-CNN used algorithm proposed region as a pre-processing step before running CNN. Algorithms are usually technical proposals such as Borders [3] or Selective Search [4] boxes, which are independent of CNN. Fast Recurrent CNN, the use of these systems becomes bottleneck process with relevance to CNN’s operation. Fast R-CNN solves this downside by applying the projected mechanism to use the region and then CNN. An RPN is a fully convex network that predicts object boundaries and object scores at each location simultaneously. © 2020, Springer Nature Singapore Pte Ltd.

ICDSMLA 2019

Vehicle Detection Using Faster Recurrent Convolution Neural Network

There are around 16 million people who are blind, and this system is to support them for object detection and as well as person recognition in future. This is future of artificial intelligence. This system used Intel neural compute stick which has OpenVINO API and supports various frameworks like TensorFlow, Caffe, MXNet, ONNX, Kaldi, etc. This system can detect object from the frame correctly. For testing, implementation is done on raspberry pi 3B+ which uses Raspbian operating system, Intel’s neural compute stick, and Web camera. This can be artificial eye for blind people, and beauty of project is edge intelligence, without Internet and cloud. When data is analyzed and aggregated in a spot, where it is captured in a network is known as edge intelligence. There are many advantages of intelligence at the edge like it will minimize latency and help to reduce bandwidth, and cost reduction is also one of the important features addition to reducing threats which also minimizes duplication as well as improves reliability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.