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7th International Conference on Image Information Processing, ICIIP 2023

作者： Uyyala, Ravi Bojjagani, Sriramulu Department of Computer Science And Engineering Hyderabad Gandipet India School of Engineering And Applied Sciences SRM-AP Department of Computer Science And Engineering Andhra Pradesh Amaravathi India

ISBN: (纸本)9798350371406

Predicting the pixel is crucial in prediction error expansion (PEE) based reversible data hiding (RDH). There are a number of methods for predicting pixels that can be found in research papers. The gradients are used to make a prediction about the current pixel. Gradients in the image can be used to make forecasts about the current pixel, and this has been the subject of extensive study. In this work, we suggest a three-by-three grid of surrounding pixels as a basis for prediction. Later, the histogram bin shifting technique was implemented to integrate more information with less distortion. In order to include more information, the suggested technique makes adaptive changes to the histogram based on local complexity. The experimental analysis demonstrates that When compared to the other, the proposed method is superior. © 2023 IEEE.

关键词： Pixels

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of Technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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CIFER: A Cache-Coherent 12-nm 16-mm2SoC With Four 64-Bit RISC-V Application Cores, 18 32-Bit RISC-V Compute Cores, and a 1541 LUT6/mm2Synthesizable eFPGA

IEEE Solid-State Circuits Letters

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IEEE Solid-State Circuits Letters 2023年 6卷 229-232页

作者： Li, Ang Chang, Ting-Jung Gao, Fei Ta, Tuan Tziantzioulis, Georgios Ou, Yanghui Wang, Moyang Tu, Jinzheng Xu, Kaifeng Jackson, Paul Ning, August Chirkov, Grigory Orenes-Vera, Marcelo Agwa, Shady Yan, Xiaoyu Tang, Eric Balkind, Jonathan Batten, Christopher Wentzlaff, David Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Princeton University Department of Computer Science PrincetonNJ08544 United States National Cheng Kung University Miin Wu School of Computing Tainan701 Taiwan Cornell University Department of Electrical and Computer Engineering IthacaNY14850 United States The University of Edinburgh School of Engineering EdinburghEH8 9YL United Kingdom University of California at Santa Barbara Department of Computer Science Santa BarbaraCA93106 United States

This letter presents CIFER, the world's first open-source, fully cache-coherent, heterogeneous many-core, CPU-FPGA system-on-chips. The 12 nm, 16-mm2 chip integrates four 64-bit, OS-capable, RISC-V application cores;three TinyCore clusters that each contain six 32-bit, RISC-V compute cores (18 in total);and an electronic design automation-synthesized, standard-cell-based eFPGA. CIFER enables the decomposition of real-world applications and tailored execution (parallelization or specialization) per decomposed task. Our evaluation shows that: 1) the TinyCore clusters increase the throughput and energy efficiency of data- and thread-parallel tasks by up to 7.95× and 7.75× over one 64-bit core, respectively;2) the eFPGA increases the throughput and energy efficiency of hardware-accelerable tasks by up to 9.29× and 10.62× , respectively;and 3) using coherent caches for data transfer between the processors and the eFPGA increases the throughput and energy efficiency by up to 11.1× and 10.5× , respectively. © 2018 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

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computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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Multi-Scale CNN Architectures for Enhanced Feature Extraction in Image Sentiment Analysis 3

Multi-Scale CNN Architectures for Enhanced Feature Extractio...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Indhumathi, S. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

Imagery sentiment analysis is a complex task, the aim of which is the interpretation of both local and global visual signs in order to correctly classify the emotions ordered by the image. CNNs old architectures commonly have trouble with the proper balance of the tip-oriented extraction and the overall understanding of the context. This research suggests a new multi-scale CNN architecture in order to improve the extraction of features by the hierarchical representations of the visual sentiment cues. The architecture operates by adding convolutional layers of nucleotide scale and pyramid pooling modules to take features from different spatial resolutions. Besides, the attention mechanism is applied to the sentimental areas, thus dealing with the noise from unimportant parts. That is modeling a CNN with multi-scale convolutional layers in the construction of an image where each kernel size will deliver what is a fine signal versus a contextual pattern. A pooling module based on a pyramid structure is collected to the resolution to obtain features from different spatial scales, so the model will have the view of the entire image at a context level. Then, in addition, the proposed attention mechanism that finds and highlights regions with more probability of emotional content is used. The suggested architecture is prepared and tested on a number of datasets, that reflect the real-world sentiment contexts and the evaluation metrics like accuracy, precision, recall, and F1-score, which show the effectiveness of the proposed architecture. The proposed approach seeks to give a more in-depth definition of visual sentiment which, in turn, will lead to more precise classification as well as allow for better understanding with attention-based visualizations. The results are anticipated to drive the image sentiment analysis field further, making it relevant for social media analytics, advertising, and human-computer interaction. © 2025 IEEE.

关键词： Emotion Recognition

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Object Detection Its Progress and Principles 12

Object Detection Its Progress and Principles

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Maji, Srabanti Kriti Narayan Shukla, Ajay Arya, Vishakha Gupta, Pooja School of Computing Dit University Dehradun India Computer Science & Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350369861

Locating objects in images or videos is the major task accomplished by object detection. It has widely been used in various applications related to computer vision for the identification and localization of objects. Over the years, different techniques have been used for object detection ranging from conventional methods to deep learning-based methods. The present work describes the basic methodology of object detection algorithms and gives a broad classification of the techniques utilized for object detection. The work then further sheds light on the basic principle involved behind each technique and also reviews the different studies that have been carried out by utilizing the said object detection technique, highlighting the issue being addressed and the performance. © 2023 IEEE.

关键词： Object detection

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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A Design of IoT based Integrated Ambient Air Pollutants Measuring System for Non-Attainment Locations 3

A Design of IoT based Integrated Ambient Air Pollutants Meas...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Rahman, A. Abdul Manivannan, P. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India SASTRA Deemed to Be University School of Computing Tamilnadu Thanjavur India

ISBN: (纸本)9798350343632

Global warming is one of prominent issues in developing nations like India. As stated by World Health Organization (WHO), there are seven million people losing their life around the world due to air pollution. The report also states that nine out of ten people breathe air that exceeds Air Quality Index (AQI) level prescribed by WHO. The Sulfur dioxide (SO2), Carbon monoxide (CO), Nitrogen dioxide (NO2), Particulate Matter (PM) 2.5, PM10, Ozone (O3), and Ammonia (NH3) are more challenging pollutants, which harms the ambient air quality due to many social activities. However, the State Pollution Control Board (SPCB) and Central Pollution Control Board (CPCB) are extremely active in controlling and reducing air pollution through various national and state pollution programs. But one of the primary challenging tasks is to measure pollutants in non-attainment areas are which are away from state/central pollution control board stations. So it is difficult to measure air quality, particularly, the people living in forest and hills areas are becoming victims of the problem. The aim of the proposed work is to measure the ambient air quality in non-attainment areas using various sensors. It is an IoT based Integrated Ambient Air Pollutants Device (IAAPD), which is to measure the above said 7 different pollutants in air, The result of system can help to local administration/ authorities to plan for remedial and precaution measures to avoid, control and manages the air pollution in non-attainment locations. © 2023 IEEE.

关键词： Air pollutants Air Quality Index Ambient measure Internet of Things Non-attainment

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Impact of Antenna Beam Patterns on the Performance of Satellite Communication Links

Impact of Antenna Beam Patterns on the Performance of Satell...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Ganesh, D. Upadhyay, Ramakant Saxena, Ashendra Kumar Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

the overall performance of satellite communication links is extraordinarily established upon the spatial distribution of transmitted radiation. This distribution is managed by means of the antenna beam sample, which is a measure of the relative power emitted in one-of-a-kind directions from the antenna. The shape of the beam drastically impacts the efficiency of the hyperlink in addition to its robustness against fading. Numerous beam sample configurations are typically employed in satellite verbal exchange packages;every with it's set of advantages and disadvantages. By characterizing the variations among beam patterns, it is miles feasible to determine the most suitable design for a given utility. The most commonly used beam sample in satellite TV for PC communications is the pencil beam. This has a highly slim fundamental lobe width and presents exact gain traits inside the desired guidelines on the fee of extended aspect lobe power. This may cause better interference levels and fading outcomes, in particular in tremendously Asymmetric Circularly Polarized (HACP) structures. Then again, wider beam styles, which include fan or zone beams, provide decreased aspect lobe electricity on the fee of reduced gain. Broadening the sample will additionally increase the complexity of the link finances and antenna directivity calculations. Currently, extra complex antenna beam styles, including stepped-tapered, nested, and adaptive, have been hired so one can optimize the satellite TV for PC link overall performance. © 2024 IEEE.

关键词： Communication Extraordinarily Numerous Performance Satellite Stablished

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