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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials

Design and Implementation of Low Power Circuits Based on 5nm...

引用

IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Shrabanti Kundu Jyotsna Kumar. Mandal Dept. of Computer Science and Engineering University of Kalyani Kalyani Nadia WB India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications.

关键词： Performance evaluation MOSFET Electric potential Power demand Logic gates MOSFET circuits Threshold voltage Indium gallium arsenide Substrates Thermal stability

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Architectural Design for AdhaarAssist — A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist — A Forward-Looking L...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Sana Jafar Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why; parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar.

关键词： Multicore processing Signal processing algorithms Parallel processing System recovery Signal processing Data structures Throughput Supercomputers Software Testing

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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Networking and Communications (ICNWC), International Conference on

作者： Skandarsini R R Sangavi M S Ravitla Harika Varun Cm Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact.

关键词： Profitability Decision making Demand forecasting Pricing Machine learning Production Organizations

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MSCE-LT: Multi-Label Supervised Contrastive Enhancement for Long-Tailed Retinal Diseases Recognition

MSCE-LT: Multi-Label Supervised Contrastive Enhancement for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tingyao Li Bin Sheng Dept. Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Retinal diseases are leading causes of blindness globally. In real-world clinical practice, a patient may suffer from multiple retinal diseases, and these diseases are often under a long-tailed distribution, which poses significant challenges for accurate diagnosis. In this work, we propose a novel contrastive learning(CL)-based framework for multi-label retinal disease recognition. It consists of two parallel branches, a multi-label supervised contrastive learning branch and a classifier branch. The positive sets are created by the extent of proportional label overlap between samples and the anchor in calculating contrastive loss. For minority information enhancement, we design a hybrid-proxy model to generate class-dependent proxies, which are updated alongside the network. We capture rich relations samples, proxies, and labels by introducing the hybrid sample-proxy contrastive loss. Taking both label co-occurrence and data imbalance into consideration, we further utilize Distribution Balanced (DB) binary cross-entropy loss to guide the classifier branch learning. Experimental results on four public retinal disease datasets have demonstrated the superiority and effectiveness of our method.

关键词： Representation learning Heavily-tailed distribution Accuracy Contrastive learning Tail Blindness Retina Bioinformatics Diseases

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning

Reconnaissance Detection in Decentralised IoT System using M...

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International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Rajdeep Kumar Dutta Bishal Chhetry Rakesh Matam Ferdous Ahmed Barbhuiya Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model’s ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats.

关键词： Training Data privacy Federated learning Scalability Reconnaissance Data models Telecommunications Internet of Things Servers Faces

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Minimization of data centers in survivable dynamic SDM-EONs

Minimization of data centers in survivable dynamic SDM-EONs

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sourabh Chandra Khokan Mondal Dept. of Computer Science & Engineering Calcutta Institute of Technology Howrah India Dept. of Computer Science & Engineering Techno India University Kolkata India

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms.

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks

Design and Optimization of High-Performance Health Monitorin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Heena Mehta Mukesh Singla Dept. of Computer Science and Engineering Baba Mastnath University Rohtak Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution.

关键词： Wireless sensor networks Accuracy Protocols System performance Medical services Network architecture Real-time systems Biosensors Monitoring Optimization

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“Vulnerabilities in Smart Contracts: A Detailed Survey of Detection and Mitigation Methodologies”

“Vulnerabilities in Smart Contracts: A Detailed Survey of D...

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Emerging Technologies in computer science for Interdisciplinary applications (ICETCS), International Conference on

作者： Nayantara K Kumar Niranjan V Honnungar M Sharwari Prakash J J Lohith Dept. of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security.

关键词： Surveys Systematics Generative AI Smart contracts Ecosystems Static analysis Maintenance engineering

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices

Integrating Artificial Intelligence and Educational Technolo...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Lathika Ilayaraja Yukthika S S Subramani Prabakaran S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation.

关键词： Training Ethics Technological innovation Large language models Education Educational technology Learning (artificial intelligence) Internet of Things Artificial intelligence Guidelines

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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1_score of 97%, 96% and 98% respectively.

关键词： Support vector machines Sensitivity Simulation Epilepsy Signal processing algorithms Transforms Signal processing Electroencephalography Discrete wavelet transforms Prognostics and health management

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