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

Engineering and Technology

Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognitionof emotions based on electroencephalogram (EEG) can predict human emo- tion. However, it is notable that data obtained from either nonverbal or ver- bal behaviors are indirect emotional signals suggesting brain activity. Unlikethe nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals hasbecome a very important research subject in current emotional brain- computer interfaces (BCIs) aimed at inferring human emotional states basedon the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic diseasefrom EEG signals. The signals have been processed by CNN for feature ex- traction from runtime environment while LSTM has used for classificationof entire data. Finally, system demonstrates each EEG data file as normal orepileptic disease. In this research to describes a state of art for effective epi- leptic disease detection prediction and classification using hybrid deep learn- ing algorithms. This research demonstrates a collaboration of CNN andLSTM for entire classification of EEG signals in numerous existing systems.

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School of Computer Engineering & Technology

IOS Press Journal

A Review on BCI Emotions Classification for EEG Signals Using Deep Learning

From the time when the electronic age started, soldering has been an significant joining technique in microelectronic packaging. Sn-Pb based solder alloys have continuously provided outstanding outcomes, offering numerous remarkable benefits, explicitly, easy handling, little melting temperature, decent operating environment, ductility, and extraordinary wetting on Cu and its alloys. Because of its lethal and dangerous effects on health and the ecosystem, the US Congress initiated the legislation on the limited use of Pb in 1990. Subsequently, substitutes to lead-free solders are being developed for the last three decades. Sn-0.7Cu is a cost-effective plumbing alloy having a high melting temperature and demonstrates a modest wetting that seems fairly adequate for numerous applications. In this study, lead-free Sn-0.7Cu solder joints on Cu Substrate were analysed. Mechanical properties such as tensile strength, bending/flexural strength, and microhardness were studied. Salt spray test (SST) was used for understanding the effect of corrosion on the mechanical response of commercial and Sn-0.7Cu CSI solder joints. Outcomes of the study confirmed that after corrosion, tensile strength, bending strength and microhardness for CSI solder joints reduced in all the cases. © 2021 Elsevier Ltd. All rights reserved.

School of Mechanical Engineering

Materials Today: Proceedings

Mechanical properties and corrosion analysis of lead-free Sn–0.7 Cu solder CSI joints on Cu substrate

Emotion analysis plays a part in understanding the feelings of human beings. People's actions and speech express various feelings, behaviors and emotions which can have various impacts. Emotion and sentiment analysis is a broad research area for finding emotion which helps getting useful insight through text and speech. In most of previous work, nearly all projects have focused on analyzing the expression based on positive, negative and neutral classification. This research work analyzes the proposed system by categorizing the text into emotion classes called joy, sadness, anger, fear, love and surprise. This work helps us to label text emotions into multiple classes and categorize the text for better accuracy. This research work represents the enhancement of a novel Deep Learning model scheme, Long Short-Term Memory and Recurrent Neural Network which discuss various categories distribution on knowledgeable data. This will also summarize the previous works done on textual emotional classification based on various sentiment models and approach with comparative survey analysis.

Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES

Study and Analysis of Emotion Classification on Textual Data

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

School of Electronics & Communication Engineering

Future Internet

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

A low power flip-flop is demanding delay element in digital sigma delta modulator of DAC system. This paper highlights power optimisation of single-phase clocked feedback D flip-flop using various optimisation techniques like clock gating, MTCMOS etc. In this scenario, clock gating using GDI and single-phase clocked inverter is invented to avoid unwanted no data transitions of flip-flop, also proposed type is compared with existing techniques. In addition to this, full swing GDI (gate-Diffusion Input) cell itself is modified by single phase clocked inverter to minimise threshold voltage drop and unwanted spikes at the output. There is trade off in area, speed and power. This work is based on power optimisation with favourable speed and area. Flip-flop is verified for clock frequency of 50 MHz as well as CDMA (Code Division Multiple Access) frequency of 1.6 MHz. Analysis is carried out in SPECTRE simulator of CADENCE environment with 180 nm technology supplied by AMS. Not only power but delay is also measured to check the performance of flip-flop. Proposed circuit gives 92.8% power and power delay product improvement. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Electronics Letters

Power optimisation of single phase clocked feedback D flip-flop for CDMA

A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of p-degrees of connectivity concerning objective functions' evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for p-disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway. © 2013 IEEE.

IEEE Access

A novel enhanced quantum PSO for optimal network configuration in heterogeneous industrial IoT

The primary objective of this research paper is to detect and quantify the necking defect and surface velocity profiles in high-speed polymer melt extrusion film casting (EFC) process using Matlab® based image processing techniques. Extrusion film casting is an industrially important manufacturing process and is used on an industrial scale to produce thousands of kilograms of polymer films/sheets and coated products. In this research, the necking defect in an EFC process has been studied experimentally and the effects of macromolecular architecture such as long chain branching (LCB) on the extent of necking have been determined using image processing methodology. The methodology is based on the analysis of a sequence of image frames taken with the help of a commercial CCD camera over a specific target area of the EFC process. The image sequence is then analyzed using Matlab® based image processing toolbox wherein a customized algorithm is written and executed to determine the edges of the extruded molten polymeric film to quantify the necking defect. Alongwith the necking defect, particle tracking velocimetry (PTV) technique is also used in conjunction with the Matlab® software to determine the centerline and transverse velocity profiles in the extruded molten film. It is concluded from this study that image processing techniques provide valuable insights into quantifying both the necking defect and the associated velocity profiles in the molten extruded film. © 2021 Kingfa SCI. &amp; TECH. CO., LTD.

Fulltext

Advanced Industrial and Engineering Polymer Research

Flow visualization by Matlab® based image analysis of high-speed polymer melt extrusion film casting process for determining necking defect and quantifying surface velocity profiles

Object Detection Using SSD MobileNet on Indian Dataset

Mobile ad hoc network (MANET) is a subset of computer wireless networks and in its simplest form is the collection of mobile stations connected with each other by radio links to form a network. MANET is a self-configured, self-organized network where the movement of the mobile stations is random during the network. The positions of the nodes are unpredictable and have direct impact over network efficiency and its performance. For MANET network monitoring, we need to trace the motion of the node and for controlling, we need to decide the optimal node movement. The focus for this paper is the study of three-way solutions of modeling node movement in MANET such as movement via trajectories, facilities for random mobility and movement via programming interface. This study is followed by routing protocols performance analysis. Three routing protocols DSR, AODV and OLSR are compared with segment-based trajectory as node movement model. OPNET is the simulation tool use and the performances of the three routing protocols are analyzed based on the network metrics: throughput, end-to-end delay and network load. The comparison analysis will be carried out and at last, the conclusion will be presented.