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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Change Detection in Heterogeneous Remote Sensing Images Based on Copula-Supervised Neural Networks 2

Change Detection in Heterogeneous Remote Sensing Images Base...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Li, Weiming Wang, Xueqian Li, Gang Geng, Baocheng Varshney, Pramod K. Tsinghua University Department of Electronic Engineering Beijing100084 China The University of Alabama at Birmingham Department of Computer Science Birmingham United States Syracuse University Department of Electrical Engineering and Computer Science Syracuse United States

ISBN: (纸本)9798331515669

Change detection (CD) in heterogeneous remote sensing images has been extensively applied in response to emergencies. There have been substantial improvements during the past decades in addressing heterogeneous CD challenges, driven by the rapid advancements in deep neural networks (DNNs). However, most DNN-based heterogeneous CD methods often exhibit black-box behavior, limiting their interpretability and controllability in many practical CD applications. To overcome these issues, we propose a neural copula change detection (NCCD) framework, an approach for the heterogeneous CD problem that leverages an interpretable neural network supervised by copula theory. In NCCD, copula's mathematical characteristics are incorporated as loss functions to supervise a neural network in modeling dependence between bi-temporal heterogeneous remote sensing images. The trained neural network works as a copula function to estimate the degrees of dependence that serve as the indicator for identifying changes. Experimental results on two real heterogeneous CD datasets substantiate the effectiveness of our NCCD. © 2024 IEEE.

关键词： Change detection

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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A Fine Grained Stochastic Geometry-Based Analysis on LEO Satellite Communication Systems

IEEE Networking Letters

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IEEE Networking Letters 2023年第4期5卷 237-240页

作者： Sun, Yanshi Ding, Zhiguo Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom

Recently, stochastic geometry has been applied to provide tractable performance analysis for low earth orbit (LEO) satellite networks. However, existing works mainly focus on analyzing the 'coverage probability', which provides limited information. To provide more insights, this letter provides a more fine grained analysis on LEO satellite networks modeled by a homogeneous Poisson point process (HPPP). Specifically, the distribution and moments of the conditional coverage probability given the point process are studied. The developed analytical results can provide characterizations on LEO satellite networks, which are not available in existing literature, such as 'user fairness' and 'what fraction of users can achieve a given transmission reliability'. Simulation results are provided to verify the developed analysis. Numerical results show that, in a dense satellite network, it is beneficial to deploy satellites at low altitude, for the sake of both coverage probability and user fairness. © 2019 IEEE.

关键词： Stochastic systems

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Causal Digital Twins for Physical System Control 59

Causal Digital Twins for Physical System Control

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Mohseni-Sehdeh, Saeed Saad, Walid Sakaguchi, Kei Yu, Tao Virginia Tech Bradley Department of Electrical and Computer Engineering AlexandriaVA22305 United States Institute of Science Tokyo Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9798331513269

Digital twins (DTs) are virtual representations of physical systems, replicating their behavior, dynamics, and interactions with the environment. Hosted on computational platforms, DTs have access to more extensive information and computational power than their physical counterparts (PTs), making them suitable for control applications. This paper proposes a novel DT-based control architecture that leverages causal learning to achieve autonomous control. In this framework, the DT employs causal inference to determine the desired system behavior and, subsequently, instructs the PT accordingly. By incorporating causal learning, a DT can gain self-training capabilities, enabling it to generate and analyze hypothetical scenarios based on cause-and-effect relationships. Simulation results show that DT-based control outperforms the traditional local control method by 49% in terms of the tracking performance measured through the mean squared error. Additionally, causal learning demonstrates 16% better tracking performance over statistical learning in a self-training scenario, as measured by the mean squared error. © 2025 IEEE.

关键词： Digital elevation model

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Liver Ailment Prediction Using Random Forest Model

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computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Reliable and secure memristor-based chaotic communication against eavesdroppers and untrusted foundries

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Discover Internet of Things 2023年第1期3卷 2页

作者： Vishwakarma, Rahul Monani, Ravi Hedayatipour, Ava Rezaei, Amin Department of Computer Engineering & amp Computer Science California State University Long Beach Long BeachCA United States Department of Electrical Engineering California State University Long Beach Long BeachCA United States

Chaos is a deterministic phenomenon that occurs in a non-linear dynamic system under specific condition when the trajectories of the state vector become periodic and extremely sensitive to the initial conditions. While traditional resistor-based chaotic communications are primarily concerned with the safe transfer of information across networks, the transceivers themselves can be compromised due to outsource manufacturing. With the growth of wireless sensors in resource-constrained implantable and wearable devices, chaotic communication may be a good fit if the information transmitted is reliable and the transmitter devices are secure. We believe that memristor, as the fourth fundamental two-terminal circuit element, can close the gap between reliable communication and secure manufacturing since its resistance can be programmed and saved by the designer and not the foundry. Thus, in this paper, we propose a memristor-based Chua’s chaotic transceiver that is both reliable in the presence of eavesdroppers and secure against untrusted foundries. Specifically, we consider the pair of transmitter and receiver under the same memristor value to show the possibility of uninterrupted communication as well as cases where different values of memristors are used to find out the possible range in which the message can still be meaningfully decoded. Experimental results confirm that both reliable communication and secure design can be achieved via our proposed memristor-based chaos transceivers. © The Author(s) 2023.

关键词： Wearable technology

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STDG: Fast and Lightweight SNN Training Technique Using Spike Temporal Locality

STDG: Fast and Lightweight SNN Training Technique Using Spik...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Cai, Zhengyu Kalatehbali, Hamid Rahimian Walters, Ben Azghadi, Mostafa Rahimi Amirsoleimani, Amirali Genov, Roman University of Toronto Department of Electrical and Computer Engineering Toronto Canada York University Department of Electrical Engineering and Computer Science Toronto Canada James Cook University College of Science and Engineering Townsville Australia

ISBN: (纸本)9798350300260

Spiking neural networks (SNNs) possess biological plausibility and energy efficiency as they communicate using asynchronous and mostly sparse spikes. These features make them an ideal choice for efficient neuromorphic computing. The non-differentiable, discrete binary spike events transmitted in SNNs pose a challenge for applying gradient-based optimization algorithms directly to these networks. Therefore, efficient techniques are necessary to enhance energy efficiency without sacrificing accuracy. In this work, we propose Spike Timing Dependent Gradient (STDG), a fast and lightweight learning scheme that uses temporal locality among spikes to avoid non-differentiable derivatives. Our experiments show that STDG reaches the state-of-the-art accuracy of 99.5% and 98.2% on the Caltech101 face/motorbike and the MNIST datasets, respectively. © 2023 IEEE.

关键词： Neuromorphic Computing Spike Timing Dependent Gradient Spike-timing-dependent Learning Spiking Neural Networks

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In-Context Learning with Representations: Contextual Generalization of Trained Transformers 38

In-Context Learning with Representations: Contextual General...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Tong Huang, Yu Liang, Yingbin Chi, Yuejie Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Statistics and Data Science Wharton School University of Pennsylvania United States Department of Electrical and Computer Engineering The Ohio State University United States

In-context learning (ICL) refers to a remarkable capability of pretrained large language models, which can learn a new task given a few examples during inference. However, theoretical understanding of ICL is largely under-explored, particularly whether transformers can be trained to generalize to unseen examples in a prompt, which will require the model to acquire contextual knowledge of the prompt for generalization. This paper investigates the training dynamics of transformers by gradient descent through the lens of non-linear regression tasks. The contextual generalization here can be attained via learning the template function for each task in-context, where all template functions lie in a linear space with m basis functions. We analyze the training dynamics of one-layer multi-head transformers to in-contextly predict unlabeled inputs given partially labeled prompts, where the labels contain Gaussian noise and the number of examples in each prompt are not sufficient to determine the template. Under mild assumptions, we show that the training loss for a one-layer multi-head transformer converges linearly to a global minimum. Moreover, the transformer effectively learns to perform ridge regression over the basis functions. To our knowledge, this study is the first provable demonstration that transformers can learn contextual (i.e., template) information to generalize to both unseen examples and tasks when prompts contain only a small number of query-answer pairs. © 2024 Neural information processing systems foundation. All rights reserved.

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