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4th International Conference on Recent Innovations in computing, ICRIC 2021

作者： Rahi, Pankaj Sood, Sanjay P. Bajaj, Rohit University Institute of Computing Chandigarh University Punjab Gharuan Mohali India Health Informatics Division Centre for Development of Advanced Computing Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Gharuan Mohali India

ISBN: (纸本)9789811688911

Health issues caused by air pollution are the primary concern nowadays. The air quality monitoring system aids in monitoring the level of air pollutants by measuring the concentration of a particular pollutant in the environment. A systematic study of the existing systems has been done to analyze the limitations in the current scenario of these systems. This paper proposes a smart e-health air quality monitoring system that utilizes a meta-heuristic Firefly algorithm as well as a Cat Swarm Optimization algorithm to efficiently optimize the selected features to give better results in the feature selection process. Further, these features are classified by using a Support Vector Machine which predicts the index level of the air quality and gives better precision and recall. The combination of all these algorithms has true positive and false positive rates as 99% and 88.4%, respectively while the recall rate is 89.6%. Also, the proposed work predicts a high air quality index based on the input dataset. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1873-1886页

作者： Singh, Mrityunjay Pandey, Shivam Saxena, Rohaan Chaudhary, Maheep Lal, Niranjan Indian Institute of Information Technology Una Himachal Pradesh Una 177209 India C-DAC: Centre for Development of Advanced Computing Uttar Pradesh Noida 201010 India Navana Tech Karnataka Bengaluru 562130 India SoCSE Nanyang Technological University Singapore Singapore SRM Institute of Science and Technology Uttar Pradesh Modinagar 201204 India

Dataspace systems cope with the problem of integrating a variety of data based on its structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answers to their users. The existing works on query answering from a dataspace system are content-based and paid attention to return the best answers to the users without taking care of their preferences. This paper aims to consider not only the content-based information but also the users’ preferences while answering the users’ queries. Therefore, we present a framework, known as Collaborative Query-Answering Framework for a Research Article Dataspace, to answer the users’ queries in efficient manner and returns more prominent answers to the users. In this work, we present a collaborative approach that adopts the advantages of the existing content-based and users’ preferences-based approaches. To achieve the objectives, we use the Bidirectional Encoder Representations from Transformers model to represent our dataspace and users’ query. We have validated our proposed approach on the research papers dataset available on Kaggle. The experimental results show that our approach works fairly well to return relevant information to the users. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： BERT model Pay-as-you-go data integration Query-Answering model Recommendation system Research Article Dataspace Similarity measure Vector space model

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Modeling and Analysis for Drain Source Voltage Overshoot in the Synchronous Rectifier of SiC based PSFB converter and Passive Snubber Design

Modeling and Analysis for Drain Source Voltage Overshoot in ...

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International Conference on Power Electronic Drives

作者： Anoop Kumar N K Neeraja Udaya Sagar V P Ganesan V Chandrasekar Power Electronics Group Centre for Development of Advanced Computing Thiruvananthapuram India

A frequency domain approach for snubber design, for the secondary side of High Frequency Switched Isolated Phase Shifted Full Bridge (PSFB) converter with synchronous rectification, is presented in this paper. At the rectifier side of the converter, the synchronous MOSFETs witness huge overshoots in the drain-source voltage reaching more than twice its rated values with very poor damping of the subsequent oscillations during turn-off. In order to investigate and identify the significant elements in the converter that contributes to such large over-shoots with nearly sustained ringing, a study is presented in this paper with the help of converter modeling and frequency domain analysis. The drain-source voltage characteristics obtained using the model are verified through simulations by comparing the results with the experimental values. For the analytical study, the rectifier side of the PSFB is modeled using an equivalent circuit and a frequency domain approach is adopted to design a passive RC snubber for suppressing the voltage overshoot and ringing. Its performance is validated through simulation and experimental results.

关键词： Analytical models Silicon carbide Frequency-domain analysis Snubbers Rectifiers Transfer functions Voltage

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Reliable Quantum-dot Cellular Automata Coplanar Adder and Subtractor for Multi-bit Designs 13

Reliable Quantum-dot Cellular Automata Coplanar Adder and Su...

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13th International Conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Thalaimalai Vanaraj, Anantharaj Raja Sekar, K. Marshal, R. Venkitachalam, Anand Lakshminarayanan, G. Research and Development Western Digital Technologies Inc. MilpitasCA United States Research and Development Centre for Development of Advanced Computing Bangalore India CERT-In New Delhi India Research and Development Western Digital Bangalore India NIT Tiruchirappalli Department of ECE Tamil Nadu India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Arithmetic and computational operations rely largely on addition and subtraction. Computational speed in many applications is determined by the speed of adders and subtractors. Quantum-dot Cellular Automata (QCA) nanotechnology can compute at extremely high speed due to its electron repulsion based communication and computation. Fault tolerance is a vital parameter of importance in QCA. Fault tolerance is improved by compensating the size and latency of the circuit. In this work, reliable designs of adder and subtractor with smaller Carry and Borrow latency are proposed. The faster generation of Carry and Borrow without compromising the reliability makes them an ideal choice for multi-bit operations as they will generate the outputs faster compared to reliable QCA adder and subtractor designs. Validation is carried out by using QCADesigner. © 2022 IEEE.

关键词： Fault tolerance

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Effective Security Testing of Mobile Applications for Building Trust in the Digital World

Effective Security Testing of Mobile Applications for Buildi...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Kapil Kant Kamal Padmaja Joshi Ankur Bang Kavita Bhatia Centre for Development of Advanced Computing (C-DAC) Mumbai India Ministry of Electronics and Information Technology (MeitY) Delhi India

In recent years, mobile applications have become increasingly important. These applications differ significantly from desktop and web applications, and being used increasingly in various enterprise domains. Accordingly, this motivates the mobile industries to increase the accessibility of mobile applications and their development across many platforms, including Android and iPhone. This research work discusses the security testing procedure and features of India's first indigenous AppStore “mSeva AppStore”. To ensure the quality of the hosted mobile applications, distinct techniques, and effective testing approaches are required. Before hosting the application on mSeva AppStore regressive testing is performed in all aspects like insecure permissions, data storage, secure logic, proper platform algorithms, etc. However, in the current mobile application testing scenario, other AppStore does not consider all factors before hosting the applications, which is quite necessary. This study highlights the key terminologies of mobile application testing and quality parameters. Further, demonstrates the actual results that are obtained during the security testing of various applications. Along with this, the first-ever Common Weakness Enumeration (CWE) based classification regarding common artifacts in mobile application testing is provided. The peculiarities discussed with respect to mobile applications and the testing requirements in the context of secure hosting will help the relevant researcher and stakeholders in this field.

关键词： Industries Terminology Operating systems Memory Market research Mobile applications Security

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Machine Learning Application for Classification of Brain Tumors Through MR Images to Detect Malignancy

Machine Learning Application for Classification of Brain Tum...

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International Conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Kunal Chanda Jay Verdhan Tiwari Souvik Banik Washef Ahmed Advanced Signal Processing Group Centre for Development of Advanced Computing Kolkata India Medical Electronics Ministry of Electronics and Information Technology New Delhi India

In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size, shape and spread of the tumour. The importance of classifying tumours based on texture is that there is correlation between malignancy of the tumour and the texture of the tumour. The other methods normally don't give this information. In this paper relation between texture of the tumour and it's malignancy has been explored. Four methods of feature extraction have been used, in order to obtain texture information of the tumour. They are, Gray Level Co-occurrence Matrix (GLCM), Gradient Matrix (GM), Laplacian Of Gaussian (LoG) and the Run-Length Matrix (RLM). Once the features are extracted, they are passed to Decision Tree classification algorithm. The classification algorithms classified the extracted features as having Fine, Medium or Coarse textures. The result obtained showed correlation between texture of the tumour and its malignancy. If the texture is predicted as Fine, then it is most likely a non-malignant, or benign tumour, and its diagnosis is most likely going to give a Complete response. A Medium texture would most likely give a Partial response, and is not very malignant. This is important, since knowing this information can benefit the patient and clinicians. This improves the diagnostic confidence of the clinicians and allows them to diagnose and report multiple patients that results in lower cost of the examination as well as access to diagnostics to people living in remote areas.

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HYDRA: Hybrid Data Multiplexing and Run-time Layer Configurable DNN Accelerator

arXiv

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arXiv 2024年

作者： Kumar, Sonu Gupta, Komal Raut, Gopal Lokhande, Mukul Vishvakarma, Santosh Kumar The Centre for Advanced Electronics IIT Indore India The Center for Development of Advanced Computing Bengaluru560100 India The NSDCS Research Group Department of Electrical Engineering IIT Indore Simrol453552 India

Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer configurable DNN accelerators to overcome the drawbacks. The work proposes a layer-multiplexed approach, which further reuses a single activation function within the execution of a single layer with improved Fused-Multiply-Accumulate (FMA). The proposed approach works in iterative mode to reuse the same hardware and execute different layers in a configurable fashion. The proposed architectures achieve over 90% of power consumption and resource utilisation improvements of state-of-the-art works, with 35.21 TOPSW. The proposed architecture reduces the area-overhead (N-1) times required in bandwidth, AF and layer architecture. This work shows HYDRA architecture supports optimal DNN computations while improving performance on resource-constrained edge devices. © 2024, CC BY.

关键词： Deep neural networks

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Parametric evaluation of carbon nano-tube based piezoresistive pressure sensor

Parametric evaluation of carbon nano-tube based piezoresisti...

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作者： Devi, Rekha Singh Gill, Sandeep Singh, Balwinder IKG Punjab Technical University Jalandhar India Chandigarh University Mohali India National Institute for Technical Teachers Training & Research Chandigarh India Centre for Development of Advanced Computing Mohali India

The MEMS-based piezoresistive pressure sensor is very important nowadays and is used in many applications such as barometry applications, aerospace applications, automobiles application, industries applications, and biomedical applications. When the mechanical stress is applied to semiconductors or metal, there is a change in electrical resistivity of the material is the piezo-resistive effect. Recently pressure sensors are developed using high-end materials like CNTs, Because of their greater gauge factor (1000), and strong mechanical and electrical properties-based MEMS sensors are designed with higher performance parameters. In this study squared structured pressure sensor with dimensions of 800 µm in length and 10µmin thickness is designed using two different materials (silicon and CNTs). Stress and deformation of the square diaphragm pressure sensor for the range of pressure 0 to 3 kPa are calculated using ANSYS. It is found from the simulations results that diaphragm with CNTs materials induced more stress and deformation as compared to silicon diaphragm and hence more sensitivity was obtained. © 2022

关键词： Carbon nanotubes

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Machine learning-based malware detection using stacking of opcodes and bytecode sequences

Machine learning-based malware detection using stacking of o...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Manoj Sai Aakansha Tyagi Kajal Panda Sanjeev Kumar Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9781665454025

Malware detection is a complex problem. The commonly used signature-based technique cannot detect unknown or zero-day malware. Traditional machine learning-based methods can identify unknown malicious programs but require high domain expertise for feature engineering. This research study presents a new method of malware detection using the stacking of static opcode and bytecode features. At first, bytecode and opcode sequences of executables files are extracted using a disassembler. After that, an NLP technique, TF-IDF, is employed to vectorize the extracted feature map. The extracted features as opcodes and bytecodes are concatenated to combine a single feature map, which serves as input to train machine learning classifiers: SVM, k-NN, and Random Forest (RF). The RF classifier obtained the best accuracy, precision, recall, and F-score results as 98.47%, 98.60%,98.46%, and 98.47% even using small data counts of bench-marked Microsoft BIG data set. The accuracy of the best model is comparatively better than the accuracy of using bytecodes and opcodes as individual feature vectors. The empirical results signify that the proposed method is helpful to the security industries with the benefit of being less dependent on manual feature engineering and can reduce the burden on virus databases by handling large-scale malware.

关键词： Support vector machines Learning systems Stacking Manuals Feature extraction Malware Security

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High Performance computing for Power Flow Analysis: A Way Forward for Indian Power Sector

High Performance Computing for Power Flow Analysis: A Way Fo...

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National Power Systems Conference (NPSC)

作者： Jainendra Jain Palem Benny Sudhakar Katta Jagan Mohan R Senthil Kumar S Bindhumadhava Bapu Real Time Systems Group Centre for Development of Advanced Computing Bengaluru India

With the advent of renewable energy, smart grids, and cutting-edge measurement technologies, modern power systems are becoming more complex. As a result, analyzing modern power systems requires more computational power. High Performance computing (HPC) is the most viable option for meeting this demand. In India’s power sector, the use of HPC is minimal. Hence, we are introducing HPC-based power flow analysis, which is the highly used application to analysis the system. The paper demonstrates the importance of HPC for power flow analysis. This paper also discusses a modified Gaussian Elimination method to utilize the sparse nature of Jacobian matrix to speedup the computation. Open-Multi Processing (OpenMP) is used to implement parallel computing. Parallel power flow analysis is simulated on the Central Processing Unit (CPU) and Graphics Processing Unit (GPU) nodes of the C-DAC’s PARAM Utkarsh supercomputer for various power system networks. The speedup obtained with HPC for the Polish 9241 bus network is 216.14 times the sequential computation.

关键词： Renewable energy sources Power measurement High performance computing Simulation Graphics processing units Supercomputers Power systems

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