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

The interconnection network is very influential in Multiprocessor system-on-chip (MPSoC) performance. Due to low power efficiency and high latency, conventional interconnects are replaced by electrical networks on-chip (ENoC). However, ENoCs cannot gratify the bandwidth, latency and loss requirements within the available power budget of optical integrated circuits. The reasons being restricted bandwidth, extended delay, and reasonably high power consumption. So, ENoC are being replaced by optical networks-on-chip (ONoC). ONoC has emerged as a promising substitute to provide greater bandwidth, low latency and low power ingestion in MPSoCs. Number of cores required on-chip is increasing which leads to demand of multilayer multi-core processors. Three dimensional (3D) ONoC has become essential for such multilayer multi-core processors. For 3D ONoC, in this paper we have proposed 6x6 non-blocking optical router using micro-ring resonator (MRR) with less number of waveguide crossings and waveguide bendings. Phoenix software is used to simulate proposed optical router. Insertion loss of proposed optical router is lowest among existing non-blocking optical routers of 3D ONoC. © Published under licence by IOP Publishing Ltd.

Engineering and Technology

International Conference on Electronics Circuits and Signaling Technologies

Optimized 6x6 Optical Router for Three Dimensional Optical Networks-on-Chip

With the advancements in the Internet of Things (IoT), machine-to-machine communication, big-data, and the associated environment, a new model of the Industrial Internet of Things (IIoT) has emerged. The IIoT brings sensors, intelligent machines and tools, instruments, and analytics together for applications like manufacturing, robotics, and many others. Most of these applications require adaptive and autonomous behavior, Quality of Service (QoS), efficient resource allocation, and reservation. One of these crucial challenges is to build and maintain a data communication schedule. Time Slotted Channel Hopping (TSCH) based network operation promises required QoS for low-power applications and enables high reliability. 6TiSCH layer is being developed by standardizing the protocol stack to achieve industrial performance requirements by using IPv6 over IEEE802.15.4e TSCH MAC. 6TiSCH aims to manage the schedule and configure it with the topology and traffic requirements in the industrial environment. This paper proposes a novel low latency autonomous scheduling scheme for the 6TiSCH networks. It generates a segmented schedule for the network where all source nodes can send application data packets to the root node in a single slotframe. The performance of the proposed technique is compared with existing scheduling techniques. Our scheme outperforms the other techniques. The result shows that the latency is reduced up to 41% in comparison with the other scheduling schemes. The proposed scheme has a lower radio duty cycle as the node's ON time is reduced, making it more energy efficient and reliable. © 2013 IEEE.

World University root node

IEEE

6TiSCH Low Latency Autonomous Scheduling for Industrial Internet of Things

Wireless low-power communication technologies play a vital role in building Industrial Internet of Things (IIoT) systems. The 6TiSCH layer is being developed for IIoT applications to use IPv6 upper stack over IEEE 802.15.4e TSCH MAC. 6TiSCH layer defines the scheduling scheme to achieve stringent industrial requirements and QoS advancement. This paper proposes a novel 6TiSCH traffic-aware autonomous scheduling technique for the IIoT network. In this technique, the schedule is created as per the traffic condition at the node, reducing the packet loss. It divides the slotframe into a number of segments and allocates a consecutive segment for the node as per its hop distance from the root. Supplementary cells are allocated to the nodes that are close to the root as these nodes have more traffic than the other nodes in the network. The proposed technique outperforms by improving the Packet Delivery Ratio (PDR) and reducing end-to-end delay compared to other scheduling techniques. The result shows that PDR is improved up to 23% and reduces delay up to 37%. © 2022 Informa UK Limited, trading as Taylor &amp; Francis Group.

International Journal of Computers and Applications

Traffic-aware autonomous scheduling for 6TiSCH networks

With the advent of the Internet of Things (IoT), machine to machine (M2M) communication, and the related ecosystem, recently a new paradigm of Industrial IoT (IIoT) has emerged. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SpringerBriefs in Applied Sciences and Technology

Optimal Network Configuration in Heterogeneous Industrial IoT

In an agricultural country like India farmer's contribution is very vital. Though a lot of work is going on to increase the yield quality, security of the crops is also the question on the front because if the crops are destroyed or damaged due to animals or thefts the farmers are helpless. Hence we propose a system to help a farmer to identify where an animal or thief has entered the field using Fuzzy Classification. In machine learning many classifiers are there like Neural network, Convolutional neural network, Probabilistic approach, K-nearest neighbor, SVM, etc. But little literature is found on Fuzzy classifiers. Hence we introduce here an approach to classify the images of farm at fencing with and without animals using Fuzzy systems. © Grenze Scientific Society, 2022.

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

Farm Security with Fuzzy Classification - An Approach

Abstract—: “MODI lipi” is one of the Indian ancient scripts and is as yet unrecognized script. It is not in use today, but it has importance for historical researchers of not only the ancient Maratha history but history in different regions of India. The recognition of MODI demands transform invariant approach, as MODI documents are deformed severely. Invariant handwritten character recognition is accomplished in the past by employing feature extraction methods, yet there is a scope to improve the results under global transformations. At present, convolutional neural network exhibits only local transform invariance impulsively by convolution-pooling architecture and data augmentation. To achieve global invariance for MODI recognition, the proposed classification framework used CNN-based transfer learning and a global feature extractor histogram of oriented gradient. Additionally, the criterion based on principal component analysis and confusion matrix are introduced to choose the invariant feature and to find classes responsible for poor recognition rate. The proposed classifiers are trained on a self-created handwritten MODI character dataset and tested on transformed MODI dataset. The results showed that the proposed framework is effective to recognize MODI handwritten characters under transformations without data augmentation and network alteration. © 2022, Pleiades Publishing, Ltd.

Pattern Recognition and Image Analysis

Convolutional Neural Network and Histogram of Oriented Gradient Based Invariant Handwritten MODI Character Recognition

Data in Brief

DATS_2022: A versatile indian dataset for object detection in unstructured traffic conditions

This article proposes active learning strategies for delivering engineering courses to the students in order to make learning more engaging, interesting, accomplishing, and joyful. The education system was impacted by the unprecedented situations generated by the Coronavirus pandemic from the last two academic years. The student community is also going through difficult situations on many levels. The primary challenge for teachers was to engage the students passionately in learning while maintaining a stress-free, healthy and fruitful environment in the online classes. For a stress-free teaching-learning process, adopting a variability in course content delivery methods would be a smart choice for teachers. This paper discusses the most effective active learning strategies in higher education. The work also highlights examples of implementation and an analysis of the effectiveness of active learning methodologies in terms of learners understanding, competency, satisfaction and knowledge gain. The findings indicate that the active learning strategy has assisted students in being more comfortable and engaged in effective learning in the online mode. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

Journal of Engineering Education Transformations

Active Learning Techniques for Effective Online Teaching and Learning in Higher Education

The researchers face a difficult problem in detecting and identifying underwater fish species. Marine researchers and ecologists must evaluate the comparative profusion of fish species in their environments on a regular basis and track population trends. Researchers have presented a number of underwater computer vision, machine learning-based automatic systems for fish detection and classification. However, due of the changing undersea environment, it is extremely challenging to find the ideal system for detecting and classifying fish. Because light has such a strong influence in the aqueous medium, conducting research in this environment is difficult. The MobileNet model is utilised to detect and recognise the fish breed in the proposed work. The dataset is preprocessed before the model is implemented in order to obtain appropriate performance metrics. The work is based on the Kaggle dataset, which has nine different fish breeds in total. With a 99.74 percent accuracy, the model can detect and recognise nine different breeds. In comparison to other state of art methods, the model exhibits promising results. © 2022 IEEE.

2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

Underwater Fish Detection and Classification using Deep Learning

The quest for learning more about the minute intricacies of the universe has fascinated mankind since ages. From stars to planets and asteroids to exoplanets, each research endeavor in astronomy has supplemented our knowledge about the cosmos. NASA's Kepler Mission is one monumental step in this direction wherein, telescopes conduct a survey of the Milky Way galaxy and try to identify thousands of earth-size and other smaller planets in or near the habitable zone, so as to determine the thousands or even millions of stars in our galaxy that might have such orbiting planets. Exoplanet is any new planet outside the solar system which orbits a star. Identifying new exoplanets gives us a chance to precisely understand the planet formation processes. Earlier, it was a laborious endeavour to mine the mission data using traditional algorithms and churn out the possible exoplanets. Ever since the advent of various machine learning algorithms, this process has become quite seamless. However, not all algorithms give equal and promising results when it comes to analysing different types of data. A comparative study of algorithms helps in this regard, thereby identifying the pros and cons of different algorithms for analysing certain forms of data. In this paper, we initially focus on feature set reduction using principal component analysis and thereafter make a detailed comparative analysis of ML algorithms for the identification of exoplanets, in the NASA Kepler mission data. © 2022 Elsevier B.V.. All rights reserved.