Electronics

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

School of Electronics Engineering

In this paper, we investigate the suitability of two commonly used rare earth ions viz. Erbium and Ytterbium for building a solar pumped fiber amplifier. Based on the solar spectrum and the absorption cross section for the rare earth ions we find that the Ytterbium is more suitable since it has a larger net absorption over the solar spectrum. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

Chennai Campus

Springer Proceedings in Physics

Investigation of Rare Earth Elements for Solar Pumped Fiber Amplifier

The technique of reversible logic can be applied on any digital, VLSI and quantum computing applications for optimization of power. A decoder and an encoder are the basic building blocks that can be used to realize any logic network. This pair can be used in communication system applications as transceivers. A new 4 × 4 reversible gate named FADE is proposed in this paper which is novel, unique and first of its kind as it performs the operation of three combinational logic circuits simultaneously, i.e., a full adder, a 2:4 decoder and a 4:2 encoder simultaneously. The design of the proposed reversible gate is coded using Verilog, synthesized and simulated using Xilinx software. It is implemented and tested on FPGA XC3S400-5PQ208. This gate would act as a threshold in designing complex arithmetic logic circuits and thus enhance the performance of computing systems. © 2021, Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Reversible Fade Gate as Decoder, Encoder and Full Adder

Literature reports that there is a large dependence of domestic and commercial activities on snow and glaciers due to the growing demand for freshwater for agriculture and industrial developments at higher latitudes of Indian Himalayan region. Therefore, precise mapping of snow cover is essential for planning and water resource management which includes water runoff, reservoir, flood forecasting, environmental surveillance, etc. Higher melt rates compared to winter accumulation are an impact of changes in precipitation and temperature. Present study focuses on utilization of remote sensing data to map dry/wet snow for five benchmark glaciers in Chandra basin, Western Himalayas. Landsat 7/8 optical satellite data is used to map and compute the dry/wet snow areas using NDSI and NIR thresholding algorithm for one hydrological year 2016–2017 (October 2016–September 2017). © 2021, Springer Nature Singapore Pte Ltd.

Mapping of Wet and Dry Snow for Himalayan Glaciers Using Band Ratioing and Thresholding Techniques

The resource constrained wireless sensor embedded devices are deployed in the edge of the Internet of Things (IoT) system for smart monitoring and control of large scale Wireless Sensor Network (WSN) applications. A joint hybrid corona based Opportunistic Routing (OR) with path-constrained Quasi-Mobile Sink (QMS) is designed to address the challenges for (i) providing long term sustainable operation and scalability of WSN (ii) also counteract the hot-spot problem near the sink. This hybrid routing design adopted opportunistic mode of forwarding as it’s first and last resort to increase transmission reliability, despite the time varying lossy radio links. The eligible relay-set based on the corona level it resides with respect to sink, collaborates for packet forwarding in a fully distributed online manner during the opportunistic mode. The priority order of relay(s) are determined on the fly based on multi-metrics fuzzy decision logic for timer based coordination and adopts a cross layered differentiated back off strategy for distributed priority based contention. The routing design switches to unicast mode of forwarding via the most trusted relay(s) for subsequent transmissions to increase the energy efficiency under stable link conditions. Simulation results shows that hybrid OR design achieves high end to end packet delivery ratio and minimizes the average energy consumed per node in comparison with existing routing protocol designs. The joint routing design with QMS is found to scale well and prevents the unbalanced energy consumption by minimizing the maximum energy dissipation and normalized energy consumption per packet compared to the Static Sink (SS) and Uncontrolled Mobility Model based Sink (UMM-S). © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Fulltext

Journal of Ambient Intelligence and Humanized Computing

A joint hybrid corona based opportunistic routing design with quasi mobile sink for IoT based wireless sensor network

Capnography, the measurement of the concentration of carbon dioxide in exhaled air has the potential to provide insight into the diagnosis of many cardiopulmonary diseases. Our objective is to develop an effective deep learning algorithm to analyze capnograph and thereby diagnose Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF). In this paper, we propose a novel 1D Deformable Convolutional Neural Network for the quantitative analysis of the capnograph. A learnable and deformable convolution layer and pooling layer are incorporated in 1D Convolutional Neural Network (CNN) to form a 1D Deformable CNN. The feature extraction and classification which are the main two steps in quantitative analysis of capnograph are fused into a single learning system in the proposed architecture. The learnable parameters are extracted by backpropagating the error. The proposed system classifies the capnographic signals extracted using a speed of sound-based sensor and the signal from a reference database into three classes namely, Healthy, COPD and CHF. The experimental results were clinically validated. Performance analysis yields an average classification accuracy of 92.9% and 92.16% for the signal from the reference database and the signal acquired using the sensor respectively. The performance depicts the feasibility of the proposed 1D Deformable CNN to replace existing methods for diagnosis using capnograph. The analysis shows that better learning is achieved in 1D Deformable CNN compared to state-of-the-art techniques. Moreover, no prior knowledge is required to use the system because the proposed method performs feature extraction and classification automatically. © 2001-2012 IEEE.

IEEE Sensors Journal

A 1-D Deformable Convolutional Neural Network for the Quantitative Analysis of Capnographic Sensor

This paper examines about the power decrease system in a memory cell. It affords a low power high stability i8T Static Random Access Memory (SRAM) cell. Two typically used SRAM cells are analyzed in phrases of their stability and power. It presents improved performance as analyzed with traditional 6T SRAM cell in iterms iof leakage power and static noise margin. The scheme of ilow power i8T SRAM is executed along enforcing power gating approach. Power gating is executed with the aid of putting ia itransistor iin ibetween the i8T SRAM cell and VDD ior iground. However, this avoids ithe idirect VDD and ground path and forming an indirect VDD and indirect ground path. The static noise margin of 8T SRAM is computed to decide higher stability whilst compared with 6T SRAM. It is inferred that the power of theinewly programmed 8TiSRAMicell is diminished close to 1.5% ias contrasted iwith that of the traditionali8T SRAM cell and ithe stability is improved close to 8.19%. © 2021 Institute of Physics Publishing. All rights reserved.

Journal of Physics: Conference Series

Design and implementation of low power high stability 8T SRAM

The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion from radiological images for effective treatment planning. Manual delineation of the lesion by radiological experts is generally laborious and time-consuming. Sometimes, it is prone to intra-observer and inter-observer variability. State of the art deep architectures based on Fully Convolutional Networks (FCN) and cascaded CNNs have shown good results in automated lesion segmentation. This work proposes a series of enhancements over the learning paradigm in the existing methods, by focusing on learning meticulous feature representations through the CNN layers for accurate ischemic lesion segmentation from multimodal MRI. Multiple levels of losses, integration of features from multiple scales, an ensemble of prediction maps from sub-networks are employed to enable the CNN to correlate between features seen from different receptive fields. To allow for progressive refinement of features from block to block, a custom dropout module has been proposed that suppresses noisy features. Multi-branch residual connections and attention mechanisms were also included in the CNN blocks to enable the integration of information from multiple receptive fields and selectively weigh significant features. Also, to tackle data imbalance both at voxel and sample level, patch-based modeling and separation of concerns into classification &amp; segmentation functional branches are proposed. By incorporating the above mentioned architectural enhancements, the proposed deep architecture was able to achieve better segmentation performance against the existing models. The proposed approach was evaluated on the ISLES 2015 SISS dataset, and it achieved a mean dice coefficient of 0.775. By combining sample classification and lesion segmentation into a fully automated framework, the proposed approach has yielded better results compared to most of the existing works. © 2020 Elsevier B.V.

Computer Methods and Programs in Biomedicine

Ischemic Lesion Segmentation using Ensemble of Multi-Scale Region Aligned CNN

School of Computer Science and Engineering

Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems

Medical Image Watermarking: A Review on Wavelet-Based Methods

In the last decade, light has been increasingly used as an innovative optical stimulation tool for therapeutic and diagnostic neuronal applications. Light-emitting diodes (LEDs) have emerged as a suitable light source. Such implantable systems include small-dimensional diodes as light sources for nerve stimulation in tandem with neural electrodes. All such devices generate a huge amount of heat which may limit their functionality and characteristic behaviour. Heat generated, if not properly engineered to dissipate through the design, may cause serious damages to biological tissue especially in the case of brain implants. The main purpose of the proposed project is to analyse the thermal behaviour of Optrodes operating in the brain environment and thermal management techniques for such a device. The work started with the analysis of heat dissipation from an LED and methods to insulate the brain tissues. Finite element method simulation is done to investigate thermal characteristics of implantable Optrodes. In order to verify the simulated results, an Optrode structure is developed on a standard substrate. The temperature of the Optrode is measured experimentally in operating mode in environmental conditions similar to brain tissues. The results from simulation, experiment, and analytical calculations are compared for validation. Finally, optical characteristics of LED are measured to correlate the thermal behaviour of the system. From the results obtained, a technically feasible thermal management system for such an Optrode design is suggested. © 2020, © 2020 IETE.

VIT Unitversity

IETE Journal of Research

Thermal Analysis of Light Source for Optogenetics Experiments

A family of 12 triazolo-pyridazine-6-yl-substituted piperazines (5a-l) was synthesized and evaluated for their Dipeptidyl peptidase-4 (DPP-4) inhibition potentials in order to develop them as anti-diabetic medications. In the two-step synthesis process, 6-chloro-3-(m-tolyl)-[1,2,4]triazolo[4,3-b]pyridazine was synthesized with one-pot mode using pyridine, 3,6-dichloropyridazine 5-(3-methyl-phenyl)tetrazole in toluene. Conjugating corresponding 2° amines with 6-chloro-3-(m-tolyl)-[1,2,4]triazolo[4,3-b]pyridazine afforded the target triazolo-pyridazine-6-yl-substituted piperazines (5a-l). DPP-4 inhibition potential of these compounds was testified in silico and in nitro along with their insulinotropic activities in 832/13 INS-1 cells. H2O2 radical scavenging assay and MTT assay were conducted to assess the antioxidant and cytotoxicity of these compounds respectively. Molecular docking and ELISA based enzyme inhibition assay results revealed the strong inhibition potential of the target compounds. MTT assay results indicated a maximum dose of 2.5 nM (IC50 1.25 nM) could be used and above this level vital for the cells. Compounds 5a, 5c, 5g and 5i were found with excellent antioxidant and insulinotropic activity up to 99%.

European Journal of Medicinal Chemistry

Synthesis and discovery of triazolo-pyridazine-6-yl-substituted piperazines as effective anti-diabetic drugs; evaluated over dipeptidyl peptidase-4 inhibition mechanism and insulinotropic activities