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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Yadav, Kareena Srivastava, Pawan Singh, Balwinder Kumar, Parveen Centre for Development of Advanced Computing Academic and Consultancy Division Mohali India

ISBN: (纸本)9798350376753

An operational-transconductance amplifier (OTA) is a type of amplifier that has the capability to convert an input voltage signal into an output current signal. Traditional Op-amp amplifies voltage signals directly while the OTA amplifies the current making it suitable for applications such as filters, voltage-controlled oscillators, and active filters. Similar to operational amplifiers, OTAs rely on both the supply voltage and gain. To maximize the advantage, the analog circuitry depends on various parameters such as bandwidth, common mode rejection ratio, gain, noise, and power consumption. High gain and less power consumption are preferred to fulfill the desired requirements. It is observed that the circuit which is based on the current mode is preferable as compared to the voltage mode. This paper analyzes and compares various operational-transconductance amplifiers while utilizing basic current mirrors, cascode, and cascade current mirrors. The OTA having a current mirror load and current sink shows better performance as it gives high gain and consumes less power. All three circuits are simulated in 90nm CMOS technology in Cadence Virtuoso. © 2024 IEEE.

关键词： Operational amplifiers

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Towards Scalability and Performance: Framework for Heterogeneous Cluster Integration in Deep Learning Accelerators 4

Towards Scalability and Performance: Framework for Heterogen...

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4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Centre For Development Of Advanced Computing Embedded Systems Division Noida India

ISBN: (纸本)9798350362268

This paper presents a streamlined approach to address the challenges of integrating heterogeneous clusters in deep learning(DL) accelerators. As the demand for scalable and high-performance computing in DL continues to grow, the proposed framework offers an easy implementation solution, ensuring efficiency without compromise. The method details core components and design principles, guiding the inclusion of heterogeneous clusters step by step. Extensive experiments show the framework's adaptability, handling diverse workloads and cluster architectures. Our exploration integrates various dataflows, dynamically selected by NN during runtime for optimal performance. Results indicate improved scalability and robustness compared to existing methods. This research provides a valuable resource for practitioners seeking efficient and accessible solutions for harnessing the power of heterogeneous clusters in DL acceleration, with implications for advancements in AI and computational efficiency. © 2024 IEEE.

关键词： Computational efficiency

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Federated Machine Learning Architecture for Heterogeneous Quantum Devices 1

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3rd International Conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Singh, Nagendra Tiwari, Abhishek Khaneja, Vivek Embedded Systems Division Centre for Development of Advanced Computing Noida India

ISBN: (数字)9789819939633

ISBN: (纸本)9789819939626

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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MNIST Fashion Classification Using Quantum Convolutional Neural Networks

MNIST Fashion Classification Using Quantum Convolutional Neu...

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2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Ali, Nasir Tiwari, Abhishek Neiwal, Rahul Kumar Centre for Development of Advanced Computing Embedded Systems Division Noida India Ministry of Electronics and Information Technology Electronics Division New Delhi India

ISBN: (纸本)9798350376876

This paper explores the innovative application of Quantum Convolutional Neural Networks (QCNN) in the classification of the MNIST fashion dataset. Quantum computing presents novel opportunities for enhancing machine learning algorithms, particularly in handling complex datasets. The MNIST fashion dataset is a large collection of Zalando's article images, serves as an ideal candidate for demonstrating the efficacy of QCNN. This paper dives into the implementation of QCNN, compares QCNN's performance with traditional neural networks, and further evaluates QCNN's potential in revolutionizing image classification tasks in the realm of Quantum Machine Learning. © 2024 IEEE.

关键词： Convolutional neural networks

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Quantum counterfactuality with identical particles

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Communications in Theoretical Physics 2023年第6期75卷 59-65页

作者： Vinod N Rao Anindita Banerjee R Srikanth Theoretical Sciences Division Poornaprajna Institute of Scientific ResearchBidalurBengaluru—562164India Graduate Studies Manipal Academy of Higher EducationManipal—576104India Centre of Development of Advanced Computing Corporate Research and DevelopmentPune—411008India

Quantum self-interference enables the counterfactual transmission of information,whereby the transmitted bits involve no particles traveling through the *** this work,we show how counterfactuality can be realized even when the self-interference is replaced by interference between identical ***,the facet of indistinguishability called forth here is associated with first-order coherence,and is different from the usual notion of indistinguishability associated with the(anti-)commutation relations of mode *** an experimental perspective,the simplest implementation of the proposed idea can be realized by slight modifications to existing protocols for differential-phase-shift quantum key distribution or interaction-free measurement.

关键词： quantum cryptography counterfactuality indistinguishability

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Malicious program detection using distinct textural features and fine-tuned InceptionV3 model 5

Malicious program detection using distinct textural features...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Kumar, Sanjeev Sapru, Yudhishthira Chahal, Navdeep Singh Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India Indian Computer Emergency Response Team CERT-In Delhi India

ISBN: (纸本)9798350376753

Various prevalent malware detection methods employ static signature-based detection, dynamic analysis, and other techniques to identify malware. However, these traditional malware detection techniques have become less effective due to sophisticated evasion techniques employed by malware authors. In addition, malware analysis is a highly compute-intensive process. This research introduces a new malware detection architecture that deploys different textural features and transfer learning. In this approach, the entire binary executable program is first converted into a grayscale image before extracting the deep textural features by taking the whole image as input to the convolution neural network (CNN) model. Subsequently, a pre-trained InceptionV3 model is used to extract the textural characteristics of the visualized image. The dimension of feature maps is reduced by customizing the InceptionV3 model and adding fully connected (FC) dense layers. This study performs the feature correlation analysis to select only highly distinct textural features. Finally, the selected feature map trains a softmax classifier to predict unknown malware families. This study conducts the experiments using two well-known public benchmarked datasets-MalImg and Microsoft BIG. The proposed model reported a classification accuracy of 98.57 % using the MalIng data set and an accuracy of 97.78 % for the Microsoft data set. The proposed model is superior to similar approaches in the literature. The minimal computation cost of feature extraction makes it well-suited for malware threat detection in industrial environments. Index Terms-Cybersecurity, Deep learning, Image visualization, Malware detection, Transfer learning © 2024 IEEE.

关键词： Deep learning

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The Indian healthcare system turns to digital health: eSanjeevaniOPD as a national telemedicine service

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Journal of Information Technology Teaching Cases 2023年第1期13卷 67-76页

作者： Naithani, Charu Sood, Sanjay P Agrahari, Amit Information Technology & Systems Indian Institute of Management Lucknow India Health Informatics Division Centre for Development of Advanced Computing Mohali India

Healthcare infrastructure in the emerging economies is largely concentrated in their urban areas, and the burgeoning rural population is usually deprived of quality medical care. Telemedicine systems are expected to bridge this gap. This case study documents telemedicine’s significance and allows students to examine India’s national patient-to-doctor telemedicine service, eSanjeevaniOPD. The portal provides free and contactless consultation by a government doctor using video calls. COVID-19 hastened the adoption of eSanjeevaniOPD in the country, but its post-covid future would largely depend on feature innovation, architecture development, and digital strategies. With a total of three million teleconsultations in one year, eSanjeevaniOPD is one of the world’s largest digital healthcare delivery systems. This case provides insight into Indian health infrastructure, summarizes the journey of eSanjeevaniOPD, and raises questions on the digital transformation of the Indian healthcare delivery system. © Association for Information Technology Trust 2021.

关键词： COVID-19 pandemic Developing Economy Digital Health Digital Tranformation Healthcare Delivery System Indian Public Sector Teaching Case Telemedicine

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Machine learning-based malware detection using stacking of opcodes and bytecode sequences 7

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

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7th International Conference on Parallel, Distributed and Grid computing, PDGC 2022

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

ISBN: (纸本)9781665454018

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. © 2022 IEEE.

关键词： Viruses

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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest 7

Deep learning methods for malicious URL detection using embe...

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7th International Conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Thakur, Isha Panda, Kajal Kumar, Sanjeev Centre for Development of Advanced Computing Cyber Security Technology Division Mohali India

ISBN: (纸本)9781665454018

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language Processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection. © 2022 IEEE.

关键词： Inverse problems

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Breaking barrier of binding buffer in colorimetric aptasensing of tetracycline in food fish using peroxidase mimic gold NanoZyme

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Methods 2025年 240卷 145-153页

作者： Sarkar, Dhruba Jyoti Raja, Ramij Santhana Kumar, V. Bhattacharyya, Soumyadeb Pal, Souvik Mukherjee, Subhankar Das, Basanta Kumar Aquatic Environmental Biotechnology Division ICAR-Central Inland Fisheries Research Institute Barrackpore West Bengal Kolkata 700120 India Centre for Development of Advanced Computing-Kolkata West Bengal Kolkata 700091 India

Tetracycline is extensively used in aquaculture as a therapeutic agent that needs to be monitored due to food safety concerns. Aptasensing has been revealed as a suitable diagnostic platform for tetracycline sensing in food matrix due to its quick, low cost and robust nature. But, the colorimetric aptasensing of tetracycline employing the peroxidase activity of gold nanoparticles (AuNPs) to 3,3,5,5-tetramethylbenzidine (TMB) was unsuitable until now owing to the aptamer-specific alkaline binding buffer. The present study developed a method with an optimized reaction protocol diminishing the inhibitory effect of binding buffer on the sensor probe (AuNPs-aptamer + TMB + H₂O₂). The overall peroxidase activity of the sensor probe was only inhibited by tetracycline through selective adsorption on the AuNPs-aptamer complex. The peroxidase inhibition percentage in the test range of 0.01 to 0.5 mg L^-1 tetracycline gave a logarithmic response (R², 0.99) with a detection limit of 0.017 mg L^-1 which is less than the prescribed limit (0.1 mg L^-1) set by EU and FSSAI. The developed sensing system in fish muscle showed high recovery (111–115 %) with great potential for rapid detection of tetracycline in fish muscle. © 2025 Elsevier Inc.

关键词： Aptamer Fish Gold nanoparticle NanoZyme Tetracycline

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