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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Pavithra S Ravikumar S Sreesubha S Babu R M. Kalaiselvi School of Computer Science and Engineering Vellore Institute of Technology Chennai Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Alagappa chettiar government college of engineering and technology

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This study utilizes machine learning techniques to predict the presence of diabetes at an early stage. Diabetic complications, including kidney and heart disease, can worsen over time if not detected at an earlier stage. Making the correct changes to one's lifestyle at the right time is crucial for preventing diabetes and its complications. So, there is an increasing need for a tool that can help doctors to detect this potentially deadly disease. This study has proposed machine learning based early stage diabetes detection model. The proposed model's performance is evaluated by analyzing how well the random forest classifier, a model known for its high accuracy, and resistance to overfitting performs. Random forest is a great option for feature selection because they deal with feature-intensive datasets, even in high-dimensional settings, and give useful information regarding the importance of features.

关键词： Resistance Analytical models Accuracy Machine learning algorithms Medical services Robustness Diabetes Random forests Tuning Diseases

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Rectenna-Based Wireless Power Transfer for Implantable Bioelectronic Devices

Rectenna-Based Wireless Power Transfer for Implantable Bioel...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Salehin, Sultanus Islam, Akib Jayed Pranta, Swapnil Das, Nobel Paul, Shreya Ul Alam, Sayem Pavel, Tanvir Ahamed Uddin, Arafat The University of Alabama Department of Ece Tuscaloosa United States Norwegian University of Science and Technology Computational Colour & Spectral Imaging Gjφvik Norway Port City International University Electrical and Electronic Engineering Chattogram Bangladesh National Institute of Technology Electrical and Electronics Engineering Tamil Nadu Trichy India The University of Alabama Electrical and Computer Engineering Tuscaloosa United States

ISBN: (纸本)9798331535476

Implantable medical devices (IMDs), such as pacemakers, neurostimulators, and drug delivery systems, are being revolutionized by advancements in radio frequency (RF)-based wireless power transfer (WPT). This study focuses on the design and optimization of implantable rectennas, which convert electromagnetic radiation into DC power for IMDs. Key considerations include operating frequency, antenna dimensions, biocompatibility, and power conversion efficiency. A comprehensive human tissue model, incorporating safety parameters such as Specific Absorption Rate (SAR), is proposed to evaluate antenna performance in realistic conditions. The results highlight an impressive 88.65% RF-to-DC conversion efficiency, achieved through innovations in material science and advanced 3D printing techniques. These advancements not only improve the compactness and efficiency of rectennas but also open new possibilities for enhancing the reliability and safety of biomedical power systems. © 2024 IEEE.

关键词： Rectennas

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： S.V Ramanan M. Rajasekar G. Shanmugapriya Sujitha R Akshya. J Mani Deepak Choudhry Department of Electronics and Communication Engineering P PG Institute of Technology Coimbatore Department of Computer Science and Engineering VSB college of Engineering Technical Campus Coimbatore Department of Computer Science and Business Systems Dr.N G.P Institute of Technology Coimbatore Department of Information Technology Christ The King Engineering College Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance.

关键词： Accuracy Dynamics Euclidean distance Feature extraction Real-time systems Robustness Object tracking Noise measurement Vehicle dynamics Noise level

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Securing unmanned aerial vehicle—heterogeneous networks using functional encryption scheme

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Multimedia Tools and Applications 2025年 1-19页

作者： Gupta, Sachin Kumar Gupta, Parul Singh, Pawan Department of Electronics and Communication Engineering Central University of Jammu UT of Jammu and Kashmir Jammu181143 India Computer Science of Engineering J.B. Institute of Technology Uttarakhand Dehradun248197 India Department of Computer Science School of Mathematics Statistics and Computational Sciences Central University of Rajasthan Ajmer- 305817 Rajasthan India

In today's scenario, as the internet expands at a faster pace, the number of data consumers also increases at an exponential rate. As the range and number of users increased, a change from traditional homogeneous networks to heterogeneous networks is in demand. However, heterogeneous networks' primary focus is to give a higher quality of service criteria to users. Furthermore, in a heterogeneous network enabled by unmanned aerial vehicles, a large number of users and devices are active. In such a diverse environment, intruder nodes may efficiently operate and transmit numerous harmful actions throughout the network. As a result, the network's entire communication is open to many types of security threats. Therefore, to secure the communication, we used the functional encryption scheme, which allows us to protect the data against intrusion attacks. The whole procedure of establishing a functional encryption scheme in unmanned aerial vehicles integrated heterogeneous network is proposed in this study in two phases: the first phase between user equipment and micro base station, and the second phase between micro base station and unmanned aerial vehicles. The proposed approach is validated using the simulation tool namely Automated Validation of Internet Security Protocols and Applications. The outcome of the security validation ensures that the proposed methodology provides the desired security and robustness to the network. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Extremal Results on Conflict-free Coloring

arXiv

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arXiv 2023年

作者： Bhyravarapu, Sriram Gupta, Shiwali Kalyanasundaram, Subrahmanyam Mathew, Rogers The Institute of Mathematical Sciences HBNI Chennai India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India

A conflict-free open neighborhood coloring of a graph is an assignment of colors to the vertices such that for every vertex there is a color that appears exactly once in its open neighborhood. For a graph G, the smallest number of colors required for such a coloring is called the conflict-free open neighborhood (CFON) chromatic number and is denoted by χON (G). By considering closed neighborhood instead of open neighborhood, we obtain the analogous notions of conflict-free closed neighborhood (CFCN) coloring, and CFCN chromatic number (denoted by χCN (G)). The notion of conflict-free coloring was introduced in 2002, and has since received considerable attention. We study CFON and CFCN colorings and show the following results. In what follows, ∆ denotes the maximum degree of the graph. • We show that if G is a K1,k-free graph then χON (G) = O(k ln ∆). Dębski and Przybylo in [JGT 2021] had shown that if G is a line graph, then χCN (G) = O(ln ∆). As an open question, they had asked if their result could be extended to claw-free (K1,3-free) graphs, which is a superclass of line graphs. Since χCN (G) ≤ 2χON (G), our result answers their open question. It is known that there exist separate family of K1.k-free graphs with χON (G) = Ω(ln ∆) and χON (G) = Ω(k). • Let δ ≥ 0 be an integer. We define fCN(δ) as follows: fCN(δ) = max{χCN(G): G is a graph with minimum degree δ}. It is easy to see that fCN(δ′) ≥ fCN(δ) when δ′ CN(c∆) = Θ(ln ∆). In this paper, we show (Equation presented) where Ε is a constant such that 0 ≤ Ε ≤ 1 and (ii) fCN(c∆1−ǫ) = Ω(ln2 ∆), where ϵ is a constant such that 0 CN(G) = O(ln2 ∆), this implies that fCN(c∆1−Ε) = Θ(ln2 ∆). • For a K1,k-free graph G on n vertices, we show that χCN(G) = O(ln k ln n). This bound is asymptotically tight since there are graphs G with χCN(G) = Ω(ln2 n) [Glebov, Szabó, Tardos, CPC 2014].MSC Codes 05C15, 68R10 Copyright © 2023, The Authors. All rights reserved.

关键词： Coloring

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Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

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IEEE Conference on Wireless Communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

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Simple Hardware-Efficient Long Convolutions for Sequence Modeling

arXiv

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arXiv 2023年

作者： Fu, Daniel Y. Epstein, Elliot L. Nguyen, Eric Thomas, Armin W. Zhang, Michael Dao, Tri Rudra, Atri Ré, Christopher Department of Computer Science Stanford University United States Institute of Computational and Mathematical Engineering Stanford University United States Department of Bioengineering Stanford University United States Department of Psychology Stanford University United States Department of Computer Science and Engineering University at Buffalo SUNY United States

State space models (SSMs) have high performance on long sequence modeling but require sophisticated initialization techniques and specialized implementations for high quality and runtime performance. We study whether a simple alternative can match SSMs in performance and efficiency: directly learning long convolutions over the sequence. We find that a key requirement to achieving high performance is keeping the convolution kernels smooth. We find that simple interventions—such as squashing the kernel weights—result in smooth kernels and recover SSM performance on a range of tasks including the long range arena, image classification, language modeling, and brain data modeling. Next, we develop FlashButterfly, an IO-aware algorithm to improve the runtime performance of long convolutions. FlashButterfly appeals to classic Butterfly decompositions of the convolution to reduce GPU memory IO and increase FLOP utilization. FlashButterfly speeds up convolutions by 2.2×, and allows us to train on Path256, a challenging task with sequence length 64K, where we set state-of-the-art by 29.1 points while training 7.2× faster than prior work. Lastly, we introduce an extension to FlashButterfly that learns the coefficients of the Butterfly decomposition, increasing expressivity without increasing runtime. Using this extension, we outperform a Transformer on WikiText103 by 0.2 PPL with 30% fewer parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

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Physical Review Applied 2024年第2期22卷 024063页

作者： Henri Tertilt Jonas Mensing Marlon Becker Wilfred G. van der Wiel Peter A. Bobbert Andreas Heuer Institute for Physical Chemistry Department of Physics NanoElectronics Group Faculty of Electrical Engineering Mathematics and Computer Science MESA+ Institute for Nanotechnology and Center for Brain-Inspired Nano Systems (BRAINS) Department of Applied Physics Center for Computational Energy Research (CCER)

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinear aspects of variable-range hopping transport for realizing Boolean logic gates in these devices on three levels. First, we quantify the occurrence of individual gates for random choices of control voltages. We find that linearly inseparable gates such as the xor gate are less likely to occur than linearly separable gates such as the and gate, despite the fact that the number of different regions in the multidimensional control voltage space for which and or xor gates occur is comparable. Second, we use principal-component analysis to characterize the distribution of the output current vectors for the (00,10,01,11) logic input combinations in terms of eigenvectors and eigenvalues of the output covariance matrix. This allows a simple and direct comparison of the behavior of different simulated devices and a comparison to experimental devices. Third, we quantify the nonlinearity in the distribution of the output current vectors necessary for realizing Boolean functionality by introducing three nonlinearity indicators. The analysis provides a physical interpretation of the effects of changing the hopping distance and temperature and is used in a comparison with data generated by a deep neural network trained on a physical device.

关键词： Carrier dynamics Dynamics of networks Electrical conductivity Fitness Hopping transport Network optimization Optimization problems Temperature

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Evaluating Negative Sampling Approaches for Neural Topic Models

arXiv

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arXiv 2025年

作者： Adhya, Suman Lahiri, Avishek Sanyal, Debarshi Kumar Das, Partha Pratim School of Mathematical and Computational Sciences Indian Association for the Cultivation of Science 2A & 2B Raja S C Mullick Road Kolkata700032 India Department of Computer Science Ashoka University Haryana Sonipat131029 India Department of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India

Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of "learn-to-compare". The goal of this approach is to add robustness to deep learning models to learn better representation by comparing the positive samples against the negative ones. Despite its numerous demonstrations in various areas of computer vision and natural language processing, a comprehensive study of the effect of negative sampling in an unsupervised domain like topic modeling has not been well explored. In this paper, we present a comprehensive analysis of the impact of different negative sampling strategies on neural topic models. We compare the performance of several popular neural topic models by incorporating a negative sampling technique in the decoder of variational autoencoder based neural topic models. Experiments on four publicly available datasets demonstrate that integrating negative sampling into topic models results in significant enhancements across multiple aspects, including improved topic coherence, richer topic diversity, and more accurate document classification. Manual evaluations also indicate that the inclusion of negative sampling into neural topic models enhances the quality of the generated topics. These findings highlight the potential of negative sampling as a valuable tool for advancing the effectiveness of neural topic models. © 2025, CC BY.

关键词： Contrastive Learning

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Next Decade’s AI-Based Drug Development Features Tight Integration of Data and Computation

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Health Data science 2022年第1期2022卷 41-43页

作者： Yunan Luo Jian Peng Jianzhu Ma Department of Computer Science University of Illinois at Urbana-ChampaignUrbanaIL 61801USA School of Computational Science and Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Institute for Artificial Intelligence Peking UniversityBeijing 100871China

Traditional drug development heavily relies on humanderived rational and effort to detect the functional mechanisms of diseases,identify druggable targets,and design lead compounds to hit the *** our progress in understanding human diseases and the advances in biotechnology,the search for novel therapeutics remains a timeconsuming and costly *** the recent tremendous success of artificial intelligence(AI)in various domains,AIbased drug development is poised to become a revolutionary force in the pharmaceutical sector and is expected to fundamentally change the traditional trial-and-error design process(Figure 1(a)).

关键词： Figure consuming rational

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