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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Shyamali Chaudhury, Pamela Tripathy, Hrudaya Kumar Sillicon Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

Autism spectrum disorder (ASD) is a neurodevelopment condition that impacts a person's ability to communicate and interact socially. Diagnosing autism spectrum disorder (ASD) early and correctly is critical for providing appropriate help to those who need it. Transferring information or learned representations from one task or domain to another is possible using a computerized learning technique known as transfer learning. In this study, we proposed a method for classifying ASD using an Enhanced Layerwise Sequential Convolutional Neural Network (ELS-CNN) with transfer learning. Transfer learning involves leveraging pre-trained models on large-scale datasets used for the Kaggle platform and fine-tuning them for specific tasks. A dataset that tested these models was collected and consisted of 2,928 face images. These images are preprocessed using Adaptive Median Filtering to remove noise from an image and Histogram Equalization to improve image contrast. Otsu's Binary Threshold can be used for image segmentation. Extracting the features can be Principal Component Analysis (PCA) for image compression. We suggested the ELS-CNN algorithm, which has demonstrated good performance in a number of image identification applications. The output of the deep learning neural network models was assessed using common assessment measures like accuracy, precision, recall, and fl-score. The Enhanced Layerwise Sequential CNN algorithm achieved the highest accuracy. © 2024 IEEE.

关键词： Principal component analysis

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Emotional Synchronization for Audio-Driven Talking-Head Generation 24

Emotional Synchronization for Audio-Driven Talking-Head Gene...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Zhao Luo, Yan Zuo, Zhichao Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798331506681

Audio-driven talking-head synthesis has become a significant focus in the field of virtual human applications. However, existing methodologies face challenges in effectively synchronizing audio and video, especially in maintaining emotional consistency. Additionally, there is a notable inefficiency in leveraging emotional prompts to guide expression generation. To address these limitations, this paper introduces an Emotion Synchronized audio-driven Talking-head synthesis (EST) approach. The EST approach aims to enhance the emotion-agnostic talking-head models by enabling emotion control, and it incorporates a diffusion module to learn diverse latent rep-resentations. Furthermore, EST utilizes null-text embedding to align the latent code with emotional prompts. Additionally, a novel Sync Attention Block (SAB) is developed to broaden the spatial perceptual field, thus preventing the loss of critical information. Extensive experiments demonstrate the effectiveness of the EST method, showcasing state-of-the-art performance across widely-adopted datasets. Moreover, the EST approach exhibits exceptional generalization capabilities, even in scenarios where emotional training videos are unavailable. © 2024 IEEE.

关键词： Synchronization

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An Efficient Vehicle Detection and Shadow Removal Using Gaussian Mixture Models with Blob Analysis for Machine Vision Application

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SN computer Science 2023年第5期4卷 451页

作者： Rajkumar, S. Hariharan, A. Girish, S. Arulmurugan, Manoj School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Cities in the modern era often face a hectic challenge due to increased urbanization and industrialization. This problem arises as the number of cars on the road increases over time, and the need for traffic data becomes important. This increase is not foreseen with the creation of suitable new road sections. Accordingly, in this paper, we developed a novel algorithm to detect and count vehicles of each frame from a CCTV footage after enhancing it accordingly. Morphological filtering is used for removal of noise from each frame. The vehicles are detected using blob analysis and detected as well as counted by Gaussian mixture models. Finally, we compare our proposed method’s results against a suitable and a recent method’s YOLO (you only look once algorithm) result. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bilateral filtering Blob analysis CCTV Foreground extraction Gaussian mixture models Morphological filtering

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OFDM-structure based waveform designs for integrated sensing and communication

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Science China(Information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Crop Recommendation and Irrigation System Using Machine Learning with Integrated IoT Devices 1

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Khan, Mohammad Umair Dangi, Nikhil Kumar, Ankit Saproo, Vishwas Agrawal, Harsh Gaffar, H. Abdul School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9789819732425

ISBN: (纸本)9789819732418

In agriculture, timely and efficient irrigation is crucial to achieving maximum crop yield. However, traditional irrigation methods are often inefficient and wasteful, leading to water scarcity and abbreviated crop productivity. To solve these problems, we suggest an intelligent irrigation system that combines IoT devices and machine learning techniques to suggest the best crop for a particular area and deliver the best irrigation based on real-time weather data and soil moisture levels. The system accumulates data on soil nutrients, temperature, and sultriness utilizing IoT sensors and uses XGBoost, a popular machine learning algorithm, to recommend the most lucrative crop predicated on historical data. The system additionally incorporates authentic-time weather data from APIs and water level sensors to provide customized irrigation for each crop. Our system aims to improve crop productivity, minimize water waste, and avail farmers to make data-driven decisions to maximize their profits © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Blockchain Integration with Internet of Things (IoT)-Based Systems for Data Security: A Review

Blockchain Integration with Internet of Things (IoT)-Based S...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kaur, Gagandeep Shrivastava, Rajesh Gupta, Umesh Department of Computer Science and Engineering Madhav Institute of Technology and Science Gwalior India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819965434

The blockchain technology offers a secure channel for communicating between entities without the role of any third party. It is a digital ledger of transactions in a computer network that makes it hard for hackers to attack or alter the information. Banking, supply chain, precision agriculture, smart city, cyber-physical systems, industrial IoT, and health care are the various sectors in which blockchain technology has been adopted to enhance security. In recent times, these sectors are being revolutionized by digital transformation using sensor-aided physical devices forming Internet of Things (IoT) systems. Blockchain-based IoT system plays a vital role in replacing the conventional methods of storage and sharing data with a more reliable method. The integration of the two technology results in a secure, reliable, and smart system. This paper exhibits the background and working principle of blockchain technology. Also, it discusses the need of security and security challenges in IoT-based systems. Furthermore, it discusses briefly about smart contracts and motivation behind integrating blockchain technology with IoT-based systems. Finally, it proposes a secure IoT-based land registry architecture. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Exploring emotion detection in Kashmiri audio reviews using the fusion model of CNN, LSTM, and RNN: gender-specific speech patterns and performance analysis

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International Journal of Information technology (Singapore) 2024年 1-19页

作者： Dar, Gh. Mohmad Delhibabu, Radhakrishnan School of Advanced Sciences and School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The research examines the challenge of emotion detection in Kashmiri language utilizing audio reviews. It proposes a fusion model integrating convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The dataset is segmented into combined, male, and female subsets, and the fusion model’s efficacy is rigorously assessed. Results demonstrate promising performance, achieving an overall accuracy of 87% on the combined dataset, albeit facing difficulties in accurately categorizing certain emotions such as sadness and passive anger. Gender-specific speech characteristics significantly impact the model’s performance, with heightened accuracy in discerning emotional nuances in female speech (91%) compared to male speech (86%). The study highlights the necessity of incorporating gender-specific speech patterns into emotion detection frameworks and underscores the efficacy of fusion models in analyzing underrepresented linguistic data. These findings advance the development of emotion detection systems tailored for Kashmiri language applications and have implications for sentiment analysis and human–computer interaction domains. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： AI Convolutional neural network (CNN) Fusion model Kashmiri Kashmiri language Long short-term memory (LSTM) Mel-frequency cepstral coefficients (MFCC) RNN

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Optimizing Monitoring Utility of Unmanned Aerial Vehicles Considering Adverse Effects

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5996-6013页

作者： Zhang, Haihan Dai, Haipeng Qiu, Yu Yu, Enze Zhou, Ruiben Wang, Weijun Wang, Jingwu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China South China University of Technology School of Computer Science and Engineering Guangzhou China Beijing China

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact that UAV monitoring may cause a huge risk/annoyance on living objects. In this paper, we investigate the novel problem of oPtimizing unmanned aErial vehicles plAcement by Considering both monitoring utility and adverse Effects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a 1-1/e- approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a 1-1/e approximation and O(n\log n) time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Selective sampling and temporal positional encoding for monocular video based 3D human pose and shape estimation 5

Selective sampling and temporal positional encoding for mono...

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5th International Conference on Signal Processing and computer Science, SPCS 2024

作者： Hou, Yupeng Wen, Han Zeng, Guangping School of Computer & Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This study introduces a novel framework for video-based 3D human pose and shape estimation, termed Selective sampling and Temporal Positional Encoding (STPE). Our method leverages selective sampling and advanced positional encoding to tackle the temporal complexities of video data and the high cost and scarcity of annotated datasets. Inspired by the Masked Autoencoder (MAE), our approach adopts a selective sampling strategy that efficiently captures the essential dynamics of human motion from partial views, significantly reducing reliance on continuous frames. The framework incorporates Rotary Position Embedding (RoPE), using rotational angles to simplify positional encoding. This innovation decreases model complexity and boosts learning effectiveness. We also introduce randomized index positions during training, introducing variability and enhancing generalization across various datasets and motion patterns. Our model, validated on standard datasets like 3DPW, MPI-INF-3DHP, and Human3.6M, shows enhanced performance in accurate and robust 3D pose and shape capture compared to existing methods. Our results demonstrate that strategic frame sampling and sophisticated positional encoding can significantly improve accuracy and robustness of video-based pose estimation systems. © 2025 SPIE.

关键词： Video recording

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