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检索条件"机构=Computer Science Engineering Indian Institute of Technology"

共 143258 条记录，以下是151-160 订阅

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Point-To-Set Principle and Constructive Dimension Faithfulness 49

Point-To-Set Principle and Constructive Dimension Faithfulne...

引用

49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Nandakumar, Satyadev Pulari, Subin Akhil, S. Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9783959773355

Hausdorff Φ-dimension is a notion of Hausdorff dimension developed using a restricted class of coverings of a set. We introduce a constructive analogue of Φ-dimension using the notion of constructive Φ-s-supergales. We prove a Point-to-Set Principle for Φ-dimension, through which we get Point-to-Set Principles for Hausdorff dimension, continued-fraction dimension and dimension of Cantor coverings as special cases. We also provide a Kolmogorov complexity characterization of constructive Φ-dimension. A class of covering sets Φ is said to be "faithful" to Hausdorff dimension if the Φ-dimension and Hausdorff dimension coincide for every set. Similarly, Φ is said to be "faithful" to constructive dimension if the constructive Φ-dimension and constructive dimension coincide for every set. Using the Point-to-Set Principle for Cantor coverings and a new technique for the construction of sequences satisfying a certain Kolmogorov complexity condition, we show that the notions of "faithfulness" of Cantor coverings at the Hausdorff and constructive levels are equivalent. We adapt the result by Albeverio, Ivanenko, Lebid, and Torbin [1] to derive the necessary and sufficient conditions for the constructive dimension faithfulness of the coverings generated by the Cantor series expansion, based on the terms of the expansion. © Satyadev Nandakumar, Subin Pulari, and Akhil S.

关键词： Geometry

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Exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis 3rd

Exploring Transformer-Based Approaches for Hyperspectral Ima...

引用

3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Arya, Rajat Kumar Srivastava, Rajeev Department of Computer Science and Engineering Indian Institute of Technology-BHU Varanasi India

ISBN: (纸本)9789819743582

Hyperspectral imaging (HSI) is a technique that captures and analyzes multiple spectral bands for each pixel in an image. Convolutional neural networks (CNNs) effectively extract local features for HSI classification but struggle with capturing sequential spectral signatures. To address this, we conducted a comparative study to assess the performance of transformers, which can handle sequential information, in HSI classification. We reviewed a few models in the literature, analyzing their architecture and performance. Using the indian Pines dataset, we evaluated models like ViT, SSRN-ViT, SpectralFormer, and Double-ViT. Our results show that Double-ViT models outperform transformers for HSI classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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SecureHD: Designing Low-Cost Reliable and Security Aware Hardware Accelerators During High-Level Synthesis for Computationally Intensive Application Frameworks 10

SecureHD: Designing Low-Cost Reliable and Security Aware Har...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

Reliable and low-cost hardware accelerator design and its security countermeasures are crucial to safeguard not only the end consumer but also the intellectual property (IP) vendor and system-on-chip (SoC) integrator. The reusable hardware accelerator-based design paradigm has become popular for its usage in several consumer electronics (CE) and computing systems. This is because the usage of hardware accelerators enables higher performance and efficacy by accelerating the underlying computationally-intensive application frameworks. In the proposed work, secure hardware accelerators for digital signal processing (DSP), multimedia and machine learning frameworks are designed (at zero design overhead) during high-level synthesis (HLS) as it offers lesser design complexity than lower levels of chip design. Further, HLS facilitates to integration of modules for design space exploration and integrating multi-layer security. The proposed methodologies offer the following: (a) hardware accelerator design for various computationally intensive application frameworks (b) negligible design cost overhead (c) seamless detective control against counterfeiting (d) higher tamper tolerance (e) low probability of false positive (f) higher strength of obfuscation to thwart reverse engineering attack. © 2024 IEEE.

关键词： High level synthesis

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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HLS based Hardware Watermarking of Blur, Embossment and Sharpening Filters Using Fused Ocular Biometrics and Digital Signature 37

HLS based Hardware Watermarking of Blur, Embossment and Shar...

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Chourasia, Vishal Sengupta, Anirban Chaurasia, Rahul Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350377569

Securing reusable hardware intellectual property (IP) cores has become increasingly critical due to their widespread application in multimedia systems and modern consumer electronics. These IP cores, particularly digital image filter cores, play a vital role in executing data and computation-intensive tasks like image sharpening, edge detection, and blurring. Therefore, ensuring the security of these digital image filter IP cores is essential for any image processing-based applications. This paper proposes a novel hardware watermarking approach utilizing high-level synthesis (HLS) based design methodology. The watermarking process integrates the IP vendor's ocular biometrics and encoded digital signature for enhanced security and robustness of the design against potential threats of IP piracy and false claim of IP ownership. The proposed approach begins with the extraction of unique biometric features from ocular image, which are fused with a secure digital signature to generate a robust watermark. Through the proposed approach, the watermark is seamlessly integrated into the design in form of encoded hardware watermarking constraints (as digital evidence) during the register allocation phase of HLS. Experimental results demonstrate the effectiveness of the proposed watermarking technique in terms of the following: (a) achieves a low probability of false positive (watermark collision), signifying a low probability of coincidence (b) achieves higher robustness against tampering/brute force attacks signifying higher tamper tolerance ability and (c) ensures robust security of IP designs against piracy and false IP ownership at zero design cost overhead. © 2024 IEEE.

关键词： Intellectual property core

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A Multi-Functional Web Tool for Comprehensive Threat Detection Through IP Address Analysis

A Multi-Functional Web Tool for Comprehensive Threat Detecti...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Tanan, Cebajel Kulkarni, Sameer G. Das, Tamal Hanawal, Manjesh K. Indian Institute of Technology Computer Science and Engineering Department Dharwad India

ISBN: (纸本)9798331505776

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we have noticed the lack of one-stop, free, and open-source tools that can analyse IP addresses. Hence, this work introduces a comprehensive web tool for advanced IP address characterisation. Our tool offers a wide range of features, including geolocation, blocklist check, VPN detection, proxy detection, bot detection, Tor detection, port scan, and accurate domain statistics that include the details about the name servers and registrar information. In addition, our tool calculates a confidence score based on a weighted sum of publicly accessible online results from different reliable sources to give users a dependable measure of accuracy. Further, to improve performance, our tool also incorporates a local database for caching the results, to enable fast content retrieval with minimal external Web API calls. Our tool supports domain names and IPv4 addresses, making it a multi-functional and powerful IP analyser tool for threat intelligence. © 2024 IEEE.

关键词： Internet protocols

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An Enhanced Network Density Based Centrality for Influential Node Detection in Complex Networks

An Enhanced Network Density Based Centrality for Influential...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Mitra, Sourav Kumar Chakraborty, Susanta Kule, Malay Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Shibpur Howrah India

ISBN: (纸本)9798350350821

Comprehending the dynamics of disease or information diffusion requires influential node detection in complex networks. Numerous centrality measures were developed for calculating the spreading capacity of a node. However, they fail to produce a high monotonic ranking of spreaders, which is a highly desired property. Two new measures, local density and extended local density are introduced in this paper to calculate the spreading impact of every node using the density of edges in the neighborhood of the node and the node degree. Our experimental results demonstrate that both of our proposed metrics offer a more consistent monotonic result for node ranking and better rank distribution than other centrality measures. © 2024 IEEE.

关键词： Complex networks

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pFedGame - Decentralized Federated Learning Using Game Theory in Dynamic Topology 16

pFedGame - Decentralized Federated Learning Using Game Theor...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Behera, Monik Raj Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798350383119

Conventional federated learning frameworks suffer from several challenges including performance bottlenecks at the central aggregation server, data bias, poor model convergence, and exposure to model poisoning attacks, and limited trust in the centralized infrastructure. In the current paper, a novel game theory-based approach called 'pFedGame' is proposed for decentralized federated learning, best suitable for temporally dynamic networks. The proposed algorithm works without any centralized server for aggregation and incorporates the problem of vanishing gradients and poor convergence over temporally dynamic topology among federated learning participants. The solution comprises two sequential steps in every federated learning round, for every participant. First, it selects suitable peers for collaboration in federated learning. Secondly, it executes a two-player constant sum cooperative game to reach convergence by applying an optimal federated learning aggregation strategy. Experiments performed to assess the performance of pFedGame in comparison to existing methods in decentralized federated learning have shown promising results with accuracy higher than 70% for heterogeneous data. © 2024 IEEE.

关键词： decentralized dynamic network federated learning game theory

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HLS Based Hardware Watermarking Using IP Seller's Superimposed Facial Anthropometric Features 10

HLS Based Hardware Watermarking Using IP Seller's Superimpos...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sengupta, Anirban Anshul, Aditya Chourasia, Vishal Computer Science and Engineering Indian Institute of Technology Indore Indore India

ISBN: (纸本)9798331533229

Ensuring the security of hardware intellectual property (IP) designs is crucial for IP sellers/vendors due to the exposure of the global design chain process to security risks like IP piracy and false IP ownership claim. A potential threat within system-on-chip (SoC) or foundry house may involve piracy or fraudulent claim of ownership rights by an attacker. Therefore, ensuring the security of IP designs is paramount from an IP seller's perspective. This paper presents a novel high level synthesis (HLS) based hardware watermarking by exploiting IP seller's superimposed facial anthropometric features as imperceptible embedded watermark. The watermark is embedded into the hardware IP design during HLS as covert security constraints which serves as digital evidence against external threats. The proposed approach offers enhanced security (i.e., stronger tamper tolerance and lower probability of coincidence) compared to prior works at negligible design cost overhead. © 2024 IEEE.

关键词： System-on-chip

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NeuroSense: Smartwatch-based Early Detection Framework for Alzheimer’s Disease 22

NeuroSense: Smartwatch-based Early Detection Framework for A...

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Dixit, Aarju Das, Debasis Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331510183

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with an annual global economic impact of approximately $1 trillion. Early diagnosis is crucial to mitigate disease progression, yet current detection methodologies lack consistency and accuracy in delineating disease stages. To address these challenges, we introduce NeuroSense, a novel smartwatch-based early detection framework for AD. NeuroSense employs advanced activity recognition algorithms to passively monitor ambulatory patterns, behaviors, and sleep cycles via Inertial Measurement Unit (IMU) and photoplethysmogram (PPG) sensors from smartwatches (i.e., Apple Watch and Fitbit), including metrics like walking speed, sleep posture, and heart rate variability. NeuroSense leverages enhanced shallow neural networks integrating cross-sectional entropy to dynamically adjust feature weights, improving sensitivity to subtle variations in AD biomarkers. Additionally, the SqueezeNet architecture has been optimized by introducing deeper fire modules and expanding filter sizes, enabling high-resolution feature extraction with reduced computational overhead. These models are further refined with a cloud computing infrastructure, allowing model updation, ensuring adaptive learning based on continuous data influx. To validate its efficacy, NeuroSense was tested on AI-generated datasets, achieving a detection accuracy of 94.4%, substantially higher than existing approaches. This novel framework provides a robust, scalable, and non-invasive solution for continuous AD monitoring, representing a significant advancement in the early detection and management of neurodegenerative disorders. ©2024 IEEE.

关键词： Sleep research

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