Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Instrumentation

The diagnosis and therapy of neurodegenerative diseases are highly indispensable. In particular, the definitive clinical diagnosis and therapy of Alzheimer's disease (AD) remains a challenge. Despite the use of amyloid beta (Aβ) positron emission tomography (PET) gold standard [11C]-PiB, other approved benzothiazole ([18F]-flutemetamol) and stilbene derivative ([18F]-florbetaben and [18F]-florbetapir) based probes have been extensively studied and advocated as potential early Aβ PET radioligands for AD. Recent years have witnessed a burgeoning research activity in the development of radiotracers for tau neurofibrillary tangles (NFTs) binding using PET imaging, while monitoring the progression of disease. Notably, several tau PET ligands (e.g. [18F]-THK5351, [18F]-MK-6240, and [18F]-AV-1451, [18F]-RO-6958948, [18F]-JNJ-64326067, [18F]-PI-2620) showed high affinity and selective binding to tau pathology. Although early detection and progression of AD have been studied extensively using PET imaging, therapeutic approaches to the disease are scarce. Recently, nanoparticles (NPs) based therapeutic approaches have emerged. Coordination of ligands to the surface of organic NPs (e.g., flavonoids: green tea polyphenol- EGCG, curcumin), and inorganic NPs (e.g. Au, ZnO, CeO2) have been explored to decrease/inhibit the amyloid aggregation and tau hyperphosphorylation. This review focuses on (i) the importance of coordination chemistry of ligands in the design of PET imaging probes with specific binding affinities to Aβ and tau NFTs in AD, and (ii) the role of surface ligands and their coordination to NPs and functional molecules for the rational design of novel anti-AD reagents for therapeutic interventions. © 2020 Elsevier B.V.

World University root node

School of Electrical Engineering

Vellore Campus

Coordination Chemistry Reviews

Coordination chemistry of ligands: Insights into the design of amyloid beta/tau-PET imaging probes and nanoparticles-based therapies for Alzheimer's disease

Good healthcare systems are crucial to the socio-economic growth of a country. In the current scenario, with a rapidly growing population, it is not always possible to find good hospitals nearby which has beds and doctors available, especially in times of emergencies. However, no reliable hospital recommendation system exists which compute these parameters and suggests hospitals. The proposed model utilizes neural networks to recommend hospitals based on hospital ratings, doctors available and distance to ensure that in time of emergencies, because crucial time is not wasted and the people can go to the best suited hospital. A comparative study among different neural network models were carried out in terms of accuracy and computing time. The most efficient model was chosen to be implemented in the final recommendation system. The dataset used to train the neural network models was the Hospital rating dataset provided by the Centers for Medicare and Medicaid services available on Kaggle. A real-time database has been made in Google Firebase, which contains information about hospitals like location, ratings, doctors and beds available. An attendance system for doctors has also been designed using Radio Frequency Identification (RFID) cards and NodeMCU. A web-application has also been designed where the user can obtain the name, address and phone number of the recommended hospital or hospitals. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

HealthSaver: a neural network based hospital recommendation system framework on flask webapplication with realtime database and RFID based attendance system

The keyword RISC stands for reduced instruction set computer. RISC architecture has become an important part of designing processors. This chapter discusses the designed processor using the Harvard architecture. This architecture has a different memory for data and instructions. Reduced complexity, a greater number of simpler instructions, pipelining and load–store architecture are the main advantages of this architecture. FPGAs have become of great importance to verify our proposed design. In this paper, Spartan 3E starter kit by Xilinx is used to perform the analysis of power and timing delays. The proposed processor has ALU unit, left barrel shifter, right barrel shifter and control unit. © 2021, Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Designing of General Purpose 8-Bit RISC Processor and Implementation on FPGA

Signal reliability of nano-scale interconnects is a major cause of concern at subthreshold voltages. This paper deals with the subthreshold operation of carbon nanotube (CNT) bundle interconnects of two different geometries viz. square CNT bundles (S-CNT) and triangular CNT (T-CNT) bundles. The output waveforms of the two interconnect geometries are studied to find out the effects of ON/OFF times and swing on the rising/falling propagation delays and power dissipated by the interconnects. Six cases of driver/load configurations comprising of inverter and transmission gates are considered in this study. Results show that the performance of CNT bundle interconnects is dependent on the timing and swing of the output waveforms. Also, triangular CNT bundle interconnects showed better performance in terms of propagation delay and power dissipated compared to traditionally proposed square CNT bundles. © 2021 Informa UK Limited, trading as Taylor &amp; Francis Group.

International Journal of Electronics

Waveform analysis of carbon nanotube interconnects connected to various driver/load circuits

Disease is the conspicuous wellspring of mortality in around the world. The malignant growth which is cancer arises because of unrestrained varieties in a cell. Stringent diagnosis of cancer with explicit procedure is a prerequisite for retaining human lives. The relevant image transforms such as STFT and WT utilize a detection system where the cancerous image features are retained and localize the signal characteristics in different domains. The predetection of malignant growth prompts proper conclusion to either control or fix disease, as at later stages the patient may not react to the treatment. The suspicious tissues are examined and diagnosed with the help of MRI. These filtered pictures can be used to evaluate the destructive cells. The TFD yields appropriate information in the study of non-stationary images. The key objective of TFD representation is to figure out the variations in the frequency substance of images with respect to time. The TFD representation is proposed by STFT and WT to analyze, evaluate and transform the time differing category of images. © 2021, Springer Nature Singapore Pte Ltd.

An Efficient Approach for the Detection of Abnormalities in Different Cancerous Images Using TFD Techniques

Spray painting after vehicle body inspection is one of the most important aspects of automobile manufacturing and assembling industries. There are many methods available to identify the defects such as dents, scratches or some deformity containing both on a vehicle body using manual labor to closely look and identify the defects and then transfer to spray painting area which may be controlled either manually or semi-autonomously, but this method is not cost- effective as it require more labors and may result in slowing down the speed of manufacturing in a plant leading it to not achieving the target goals set. In this paper, we proposed a defect detection and inspection system using Faster RCNN approach for image processing on a car body to check type of defects apart from the ones which are planned on it and spray painting using a fully autonomous robotic arm having 6 degree of freedom with a sprayer attached to its end effectors and other robotic arm with a camera for inspection. The decision making is provided by classified neural networks through continuous monitoring the vehicle body by the camera. Here the case study takes up the spraying and defect detection in a car body. A similar setup can be used to detect deformity in any other vehicle. © 2021, Springer Nature Singapore Pte Ltd.

Autonomous Vehicle Spray Paint and Defect Detection Using Machine Vision

Journal of Electronic Materials

Crosstalk Analysis of Triangular CNT Bundle Interconnects

Design and Crosstalk Analysis in Carbon Nanotube Interconnects

Thermal Analysis of CNT Interconnects

Reducing Crosstalk in CNT Interconnects

This article presents two new hybrid swarm-human based meta-heuristic optimization algorithms benefitting from the synergy of whale optimization algorithm (WOA) and social group optimization (SGO) known as Hybrid Social Whale Optimization Algorithm (HS-WOA and HS-WOA+). HS-WOA and HS-WOA+ are hybridized combining the exploratory capabilities of WOA and convergence capabilities of SGO with a perfect balance between exploration and exploitation. A comparative analysis of the new proposed hybrid algorithm is performed through various benchmark functions. Various test cases to analyze the algorithm's performance like influence of population size, effect of dimensionality, effect of iterative count is performed and compared. The proposed algorithms are compared with modern-meta-heuristics and variants of WOA and SGO to justify its performance. The performance is evaluated statistically through the Wilcoxon's rank-sum test and Friedman's non-parametric test while the convergence curves and acceleration rates are provided to demonstrate the convergence capabilities of the proposed hybrid algorithms and the computational times are recorded to showcase the computational speeds of all the algorithms used in comparison. Composite benchmarking functions are considered to analyze the exploratory prowess and the algorithms’ capability to avoid local entrapment. To assess and evaluate the performance of the proposed algorithms with real world optimization tasks, four standard engineering problems with penalty constraints are added to the test bench. Further, a multi-unit production planning problem with correction constraints is deployed through the proposed algorithms. The benchmarking results prove that HS-WOA and HS-WOA+ s’ performance is competitive and better than the various algorithms tested against and had a statistically significant performance with lower computational times. The algorithms performed well for both standard engineering problems and the multi-unit production planning problem outperforming the various algorithms in the literature. © 2020 Elsevier B.V.