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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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Blockchain inspired secure and reliable data exchange architecture for cyber-physical healthcare system 4.0

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 309-322页

作者： Kumar, Mohit Raj, Hritu Chaurasia, Nisha Gill, Sukhpal Singh Department of Information Technology National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Punjab Jalandhar India School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission. © 2023 The Authors

关键词： Blockchain

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A comparative analysis for deep-learning-based approaches for image forgery detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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STPM: Spatial-Temporal Token Pruning and Merging for Complex Activity Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2024年第6期35卷 5407-5418页

作者： Su, Yumeng Zhang, Jiachao Yan, Rui Li, Pengpeng Xie, Guosen Shu, Xiangbo Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing Institute of Technology Artificial Intelligence Industrial Technology Research Institute Nanjing211167 China Nanjing University Department of Computer Science and Technology Nanjing210023 China

Lightweight video representation techniques have advanced significantly for simple activity recognition, but they still encounter several issues when applied to complex activity recognition: (i) The presence of numerous individuals and varying spatial positions makes it difficult for traditional token pruning methods to maintain accuracy. (ii) Simply discarding entire frames may result in the loss of crucial clues. (iii) To maintain parallel computing, applying the same pruning rate to every frame leads to significant redundancy in frames with low information content. To this end, we propose a lightweight and novel Spatial-Temporal Token Pruning and Merging (STPM) framework, specifically designed for complex action videos where human actors occupy a small spatial resolution within video frames. Our framework considers two critical factors: semantic importance and spatial-temporal redundancy, to further reduce overhead. For semantic importance, STPM captures class-specific attention scores by learning multiple class tokens within the transformer to guide token pruning. For spatial-temporal redundancy, STPM employs an anchor graph and temporal attention to perform spatial and temporal token merging, preserving appearance and temporal cues while eliminating semantic duplication and redundancy. We conduct extensive experiments on JRDB-PAR primarily using recently introduced video transformer backbones, e.g., MViT and ViT. Our framework achieves similar results while requiring 40% less computation. © 1991-2012 IEEE.

关键词： Economic and social effects

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Centralized Platform for Sensor Integration and AI Models in Precision Agriculture 2

Centralized Platform for Sensor Integration and AI Models in...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Chowdary, Ch Srinivasa Akshith Likith, S. Maya, B.S. Lokesh, S.V. Manish, Y. Asha, T. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India Bangalore Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A centralized platform for integrating various sensors and AI models in precision agriculture. The platform connects soil moisture sensors with AI models to generate crop recommendations and irrigation alerts, aiming to enhance resource utilization and crop yield. Key outcomes include real-time monitoring, actionable insights, and data-driven decision support for farmers. The practical implications for farmers and the agricultural industry are profound, as this system enables them to adopt precision agriculture techniques, reducing waste, increasing efficiency, and ultimately improving profitability © 2024 IEEE.

关键词： Smart agriculture

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Labor Safety Analysis Using Object Recognition and Deep Learning 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Evangeline, D. Jatin, B. Manoj, S. Pannaga, N. Sanjeev, G. Parkavi, A. Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Occupational safety is a major concern in any industry or workplace, and the use of advanced technologies such as object recognition and deep learning can greatly enhance safety measures. This paper proposes a labor safety analysis system that utilizes object recognition and deep learning algorithms to detect potential hazards and ensure safe working conditions. The proposed system can recognize various types of safety gear and analyze their positions and movements to detect potential safety in real time. The system can also provide alerts and warnings to workers and supervisors in case of the absence of safety gear. Experimental results demonstrate the effectiveness and efficiency of the proposed work. This can significantly improve the safety and productivity of the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object recognition

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Pothole Sensing and Depth Estimation System using Deep Learning Technique

Pothole Sensing and Depth Estimation System using Deep Learn...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Singh, Angad Randhawa, Mannat Kalra, Nidhi Seth, Chandvi Gill, Anhad Chopra, Palika Kaur, Gurnoor Thapar Institute of Engineering & Technology Computer Science and Engineering Department Punjab Patiala India

ISBN: (纸本)9798350373783

Roads, serving as vital transportation routes, undergo wear due to heavy usage and environmental factors, necessitating regular maintenance. However, neglect or the impracticality of monitoring every location often results in the formation of potholes, contributing to traffic disruptions and accidents. This paper explores a real-time pothole detection and depth estimation system employing computer vision and deep learning models. Utilizing image processing techniques, the system algorithm capable of identifying potholes and estimating their depths using a smartphone's camera and an ultrasonic sensor. Implemented using a PyTorch library in Python, the system employs a fine-tuned ResNet-50 architecture on a specialized pothole classification dataset, training it effectively for pothole identification. Additionally, the MIDAS model, enables precise distance calculations for both potholes and their surroundings, thereby facilitating accurate depth estimation. © 2024 IEEE.

关键词： Transportation routes

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm

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International Journal of Information and Communication technology 2024年第3期24卷 289-304页

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjunarao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Applications Annamacharya Institute of Technology and Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets. © 2024 Inderscience Enterprises Ltd.

关键词： Database systems

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