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Distributed exact Grover’s algorithm

Frontiers of physics 2023年第5期18卷 255-279页

作者： Xu Zhou Daowen Qiu Le Luo Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China School of Physics and Astronomy Sun Yat-sen UniversityZhuhai 519082China QUDOOR Co Ltd.Beijing 100089China

Distributed quantum computation has gained extensive *** this paper,we consider a search problem that includes only one target item in the unordered *** that,we propose a distributed exact Grover’s algorithm(DEGA),which decomposes the original search problem into■n/2■***,(i)our algorithm is as exact as the modified version of Grover’s algorithm by Long,which means the theoretical probability of finding the objective state is 100%;(ii)the actual depth of our circuit is 8(n mod 2)+9,which is less than the circuit depths of the original and modified Grover’s algorithms,1+8■π/4√2^(n)■and 9+8■π/4√2^(n)-1/2■,*** only depends on the parity of n,and it is not deepened as n increases;(iii)we provide particular situations of the DEGA on MindQuantum(a quantum software)to demonstrate the practicality and validity of our *** our circuit is shallower,it will be more resistant to the depolarization channel noise.

关键词： distributed quantum computation search problem distributed exact Grover’s algorithm(DEGA) MindQuantum the depolarization channel noise

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Enriching Image Security in Healthcare Based on Cryptography and Deep Learning Techniques

Enriching Image Security in Healthcare Based on Cryptography...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Rishav Gusain Ashwani Kumar Avinash Kumar Sharma Shitharth Selvarajan Department of Computer Science & Engineering School of Engineering & Technology Sharda University India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Greater Noida India School of Built Environment Engineering and Computing Leeds Beckett University U.K

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Securing electronic health records in the internet of medical things is a key interest in health-care due to the sector's different surroundings. As technology evolves, preserving the privacy, reliability, and accessibility of healthcare data becomes extremely challenging. Cryptographic techniques provide a possible solution for safeguarding confidential information about medical pictures while it is being transferred and stored. On the other hand, deep leaning has the potential to totally change cryptography by providing strong encryption, quality improvements, and detection potential for healthcare picture security. To increase the privacy of healthcare image information, this research analysed the fusion of deep leaning and cryptography methods. It gives a study of the current situation of deep learning-based image detection of anomalies approaches in working contexts, such as network typologies, supervision levels, along with evaluation norms. This study provides direction to future research techniques to overcome these problems, also the possibility and challenges of medical picture cryptography and picture detection of abnormality. This work bridges the gap between deep learning and encryption, paving the way for better privacy, integrity, and availability of key image data.

关键词： Deep learning Image segmentation Data privacy Privacy Neural networks Internet of Medical Things Encryption Reliability Electronic medical records Biomedical imaging

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Secure Data Embedding in Digital Images: Enhancing Covert Communication with LSB-Based Techniques

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Procedia computer science 2025年 258卷 2091-2100页

作者： Tina Babu Rekha R Nair Rajesh T M Kishore S Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore Karnataka India

Information security and the ability to convey secret messages have been necessities since the dawn of civilization. Various media forms, such as photos, audio files, and videos, offer a conducive environment for concealing information that is invisible to the human eye in the current digital era by taking advantage of superfluous or unused data bits. This hidden information can be used for secret communication or as a way to include more messages into a single file, which will minimize the quantity of data that needs to be sent. In order to encode and store messages within the RGB pixels of a cover image (canvas image) without causing visual distortion, two methods are shown in this study. Both approaches split the message into separate segments, which are then translated into 8-bit binary values. These values are then encrypted using a straightforward symmetric exclusive OR (XOR) encryption key, and the message is encoded in the canvas image by changing the pixel value’s least significant bit (LSB). The first approach encodes the secret message from top to bottom and left to right by sequentially storing it beginning with the top-left pixel. By encoding the message bits into the cover image pixels at random, the second technique reduces the susceptibility of the concealment to analysis. By using redundant data in digital media, these approaches can hide information while preserving the original visual integrity. This makes them an effective and safe way to communicate covertly or embed data into a single file, which maximizes data transmission.

关键词： Steganography Least Significant Bit (LSB) Data Encryption Covert Communication Digital Media XOR Encryption

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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

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computers, Materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid Masood Mahmoud M.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam Mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-Madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping 2

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Poorana Senthilkumar, S. Sutherlin Subitha, G. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600 025 India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies. © 2024 IEEE.

关键词： Brain mapping

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Data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis 2

Data-Driven with IoT Sensing and Deep Learning Model for Dyn...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumar, S. Mohan Thenmozhi, R. Balaji Damodhar, T.S. Epsiba, P. Malathi, N. Meenakshi, B. Cmr University Karnataka Bangalore India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chengalpattu India Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Sri Indu College of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions. © 2024 IEEE.

关键词： Recurrent neural networks

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Spatially Attentive Scale Invariant Feature Modelling for Alzheimer’s Disease Detection

Spatially Attentive Scale Invariant Feature Modelling for Al...

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International Conference for Convergence of Technology (I2CT)

作者： Santosh Kumar Tripathy Chandan Kumar Behera Pranjal Mohan Pandey Vedant Pandey Pratishtha Verma School of Computing Science and Engineering VIT Bhopal University Bhopal MP India Department of Computer Science and Engineering NIT Kurukshetra Haryana India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The prevalence of Alzheimer’s Disease (AD) patients has significantly increased recently, and early diagnosis is crucial. AI-aided solutions for diagnosing AD have been rising for decades. The majority of these solutions show the application of deep learning methods, both with and without transfer learning. However, a few issues have been noted that require attention in order to detect AD: a. there is a lack of scale-invariant feature modelling, and b. there is a lack of spatial attention paid to scale-invariant features. The suggested model addresses these issues in two ways: first, an efficient multiscale feature modelling module (EMFMM) is designed to exploit scale invariant features through multilayers each having multicolumn of dilated convolution layers. At each layer the multicolumn features are concatenated and forwarded to its subsequent layer. Secondly, the spatial convolution block attention (SCBA) is plugged in different layers of concatenated features to provide spatial attention over the multiscale features. The spatially attentive feature maps from several SCBAs are further concatenated and given to a multilayer neural network to employ disease forecasting. Two open-source datasets, OASIS and AD-Dataset, are used to demonstrate extensive experiment. The suggested model performs better than current best practices, with accuracy values on OASIS and AD-Dataset of 99.75% and 96.20%, respectively.

关键词： Deep learning Convolution Transfer learning Neural networks Nonhomogeneous media Feature extraction Convolutional neural networks

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TerEffic: Highly Efficient Ternary LLM Inference on FPGA

arXiv

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arXiv 2025年

作者： Yin, Chenyang Bai, Zhenyu Venkatram, Pranav Aggarval, Shivam Li, Zhaoying Mitra, Tulika School of Electronic Engineering and Computer Science Peking University China School of Computing National University of Singapore Singapore

Large Language Model (LLM) deployment on edge devices is typically constrained by the need for off-chip memory access, leading to high power consumption and limited throughput. Ternary quantization for LLMs is promising in maintaining model accuracy while reducing memory footprint. However, existing accelerators have not exploited this potential for on-chip inference. We present TerEffic, an FPGA-based accelerator that carefully co-designs memory architecture and computational units to unlock highly efficient LLM inference with fully on-chip execution. Through weight compression, custom computational units, and memory hierarchy optimization, we achieve unprecedented efficiency by eliminating off-chip memory bandwidth bottlenecks. We propose two architectural variants: a fully on-chip design for smaller models and an HBM-assisted design for larger ones. When evaluated on a 370M parameter model with single-batch inference, our on-chip design achieves 12,700 tokens/sec (149× higher than NVIDIA’s Jetson Orin Nano) with a power efficiency of 467 tokens/sec/W (19× better than Jetson Orin Nano). The HBM-assisted design provides 521 tokens/sec on a 2.7B parameter model (2× higher than NVIDIA’s A100) with 33W power consumption, achieving a power efficiency of 16 tokens/sec/W (8× better than A100). © 2025, CC BY.

关键词： Integrated circuit design

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Lossless Image Compression using K-Means Clustering in Color Pixel Domain

Lossless Image Compression using K-Means Clustering in Color...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Rimjhim Kumari Srinivasan Sriramulu Department of Computer Science and Engineering Galgotias University Greater Noida India School of computing Science and Engineering Galgotias University Greater Noida India

Compressing images is a method for shrinking the image’s dimensions using a particular algorithm. Image compression is a solution associated with transmitting and storing large amounts of data for digital images. While image storage is necessary for medical images, satellite images, documents, and pictures, image transmission includes a variety of applications like television broadcasting, remote sensing via satellite, and other long-distance communication. These kinds of applications deal with image compression. Lossy compression and lossless compression are two separate methods for compressing images. While unneeded metadata is deleted in lossless compression to minimize file size, lossy compression permanently removes part of the original data to reduce file size. The terms “supervised learning” and “unsupervised learning” refer to two different machine learning techniques. As opposed to a non-supervised learning algorithm, which uses unlabeled input data, supervised learning uses labeled data. While clustering is a subtype of unsupervised learning, classification, and regression are additional subcategories of supervised learning. This paper discusses the application of clustering using K-means. This methodology explores the concept of lossless picture compression is the process of reducing the size of an image without sacrificing its quality which is achieved using k-mean clustering. The proposed methodology achieves higher compression ratios than other existing approaches to image compression algorithms.

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