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A parallel Serial Filtering Mixed Advanced 1D Interpolation Architecture for AVS

A Parallel Serial Filtering Mixed Advanced 1D Interpolation ...

引用

2007 7th international conference on ASIC Electronic Version(ASICON 2007)

作者： Kai Luo,Dongxiao Li,Member,IEEE,Yu Dai,Zheng Zhu,Ming Zhang Institute of Information and Communication Engineering,Zhejiang University,Hangzhou 310027,P.R.China

this paper presentsthis paper presents an advanced 1D interpolation architecture for AVS video coding *** approaches have demerit of low data *** proposed design parallel filters the horizontal data and serial filters the vertical data,thus greatly improve the *** result shows it outperforms conventional designs in processing cycle and chip *** prototype design costs 26.2K gates when synthesized at 108MHz using 0.18 μm CMOS *** this paper presents an advanced 1D interpolation architecture for AVS video coding *** approaches have demerit of low data *** proposed design parallel filters the horizontal data and serial filters the vertical data,thus greatly improve the *** result shows it outperforms conventional designs in processing cycle and chip *** prototype design costs 26.2K gates when synthesized at 108MHz using 0.18 μm CMOS technology. an advanced 1D interpolation architecture for AVS video coding *** approaches have demerit of low data *** proposed design parallel filters the horizontal data and serial filters the vertical data,thus greatly improve the *** result shows it outperforms conventional designs in processing cycle and chip *** prototype design costs 26.2K gates when synthesized at 108MHz using 0.18 μm CMOS technology.

关键词： A parallel Serial Filtering Mixed Advanced 1D Interpolation Architecture for AVS

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Dolphin:A Search Engine for OSS Based on Crowd Discussions across Communities

Dolphin:A Search Engine for OSS Based on Crowd Discussions a...

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2016 IEEE 7th international conference on Software Engineering and Service Science

作者： Yun Zhan Gang Yin Tao Wang Cheng Yang Zhixing Li Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology

the global open source software resources have become an Internet-scale repository,which provide abundant resources for software ***,how to locate the desired resource efficiently and accurately from such large amounts is quite a challenge *** of recent works barely focus on the semantic similarity by analyzing the software itself,which often result in inappropriate *** this paper,we propose Dolphin,a global OSS search engine,which leverage the crowd discussions around OSS across communities for optimizing software *** employs the crowd wisdom as an important factor and combines it with the semantic similarity to optimize search results *** implement an OSS search engine based on the proposed approach,which provides online service *** experiments and user studies suggest the effectiveness of our approach.

关键词： Software retrieval ranking community influence

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Performance Evaluation of Image Enhancement Techniques on Transfer Learning for Touchless Fingerprint Recognition

Performance Evaluation of Image Enhancement Techniques on Tr...

引用

international conference on Signal processing and Information Security (ICSPIS)

作者： S Sreehari P D Dilavar S M Anzar Alavikunhu Panthakkan Saad Ali Amin Department of Electronics and Communication TKM College of Engineering Kollam India APJ Abdul Kalam Kerala Technological University Thiruvanaanthapuram Center for Artificial Intelligence TKM College of Engineering Kollam India College of Engineering and IT University of Dubai Dubai U.A.E

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

Fingerprint recognition remains one of the most reliable biometric technologies due to its high accuracy and uniqueness. Traditional systems rely on contact-based scanners, which are prone to issues such as image degradation from surface contamination and inconsistent user interaction. To address these limitations, contactless fingerprint recognition has emerged as a promising alternative, providing non-intrusive and hygienic authentication. this study evaluates the impact of image enhancement techniques on the performance of pre-trained deep learning models using transfer learning for touchless fingerprint recognition. the IIT-Bombay Touchless and Touch-Based Fingerprint Database, containing data from 200 subjects, was employed to test the performance of deep learning architectures such as VGG-16, VGG19, Inception-V3, and ResNet-50. Experimental results reveal that transfer learning methods with fingerprint image enhancement (indirect method) significantly outperform those without enhancement (direct method). Specifically, VGG-16 achieved an accuracy of 98% in training and 93% in testing when using the enhanced images, demonstrating superior performance compared to the direct method. this paper provides a detailed comparison of the effectiveness of image enhancement in improving the accuracy of transfer learning models for touchless fingerprint recognition, offering key insights for developing more efficient biometric systems.

关键词： Training Performance evaluation Accuracy Surface contamination Transfer learning Fingerprint recognition Signal processing Reliability Image enhancement Testing

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IoT Revolutionization using Federated Learning and Decentralized AI on Edge Devices

IoT Revolutionization using Federated Learning and Decentral...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Diwakar Chaudhary S K Verma Sumit Pundir S Ranganathan V. Umadevi K Srinivasan Department of Management Mangalmay Institute of Management & Technology Greater Noida Uttar Pradesh India School of Business Management Noida International University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Nehru Memorial College (Autonomous) Affiliated to Bharathidasan University Trichy Tamil Nadu India Department of Commerce General Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

this research proposes a novel strategy for addressing the limitations of centralized architectures in IoT data processing. Traditional systems experience significant bandwidth use, privacy difficulties, and scalability issues. To address these difficulties, the proposed approach combines Federated Learning (FL) with Decentralized AI on Edge Devices (DAI-ED). By decentralizing processing duties and utilizing FL methods, data is kept local to edge devices, lowering latency and improving privacy. Deployment entails choosing AI-capable edge devices and configuring them for seamless integration. Secure communication techniques provide privacy throughout FL operations. Model optimization approaches help to improve efficiency even further. Compared to centralized systems, the results show higher model accuracy, decreased communication overhead, and increased resource utilization. the proposed system has an overall accuracy of $\mathbf{9 5 \%}$, with edge device accuracy at $\mathbf{9 4. 5 \%}$ and central server accuracy at $\mathbf{9 6 \%}$. Communication overhead during the training and inference phases is greatly minimized, with the proposed system requiring 150 MB per device against $\mathbf{4 0 0} \mathrm{MB}$ in centralized systems. the proposed system has low memory consumption and computational complexity, but it is very scalable.

关键词： Training Privacy Data privacy Accuracy Protocols Federated learning Data processing Artificial intelligence Optimization Load modeling

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A Fast VLSI Architecture for Two-Dimensional Discrete Wavelet Transform Based on Lifting Scheme

A Fast VLSI Architecture for Two-Dimensional Discrete Wavele...

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2004 7th international conference on Solid-State and Integrated Circuits Technology（ICSICT 2004）

作者： Chengyi Xiong~(1,2*),Jinwen Tian~1 and Jian Liu~1 1 Institute of Pattern Recognition Artificial Intelligence,Key Laboratory of Eduaction Ministry for Image processing 2/- and Intelligent Control,Huazhong University of Science Technology,Wuhan 430074,China College of Electronic Information Engineering,South-Center University for Nationalities,Wuhan 430074,China

this paper proposes a novel fast architecture for two-dimensional discrete wavelet transform by using lifting scheme. the parallel and embedded decimation techniques are employed to optimize the architecture, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. the architecture is designed to generate two outputs at one working clock cycle, with every two subbands coefficients alternately. the total time of computing J levels of decomposition for N×N image is approximately 2N（1-4）/3 clock cycles. In comparison with the other devices reported in previous literature, the design has many advantages including lower hardware complexity and area and power efficient. the design is also fast, regular and simple, as well as well suited for VLSI implementation.

关键词： Lifting wavelet transform embedded decimation parallel power efficient

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MF-SPNet: Dilated Convolutional Pyramid with Attention Mechanism for Semantic Segmentation of Street Scenes

MF-SPNet: Dilated Convolutional Pyramid with Attention Mecha...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Peng Yu Zhiyuan Yang Hangzhou Dianzi University Zhejiang China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In the traditional semantic segmentation of street scene, it is difficult to extract target features due to the different sizes of images, the occlusion of targets and the difficulty of recognizing small targets. Images are difficult to be semantically segmented efficiently by these models. To solve the above problems, a pyramid network using spatial attention mechanism based on atrous convolutional pyramid (AMF-SPNet) is proposed. Firstly, the feature pyramid fused with dilated convolution is used to extract the features of the target. In the original ASPP model, we change the pooling layer to atrous convolution, and change the interior of the pyramid to atrous convolution with expansion rate 1,3,6,12,18. this model can expand the receptive field of the convolution kernel without increasing the number of parameters. Secondly, the spatial attention mechanism is added to the network, so that the model can better pay attention to the street view target information. Finally, the cityspaces dataset is used for verification, and the MIoU reaches 88.3%, the MAP reaches 89.6%. Compared with other network models, the proposed model can extract the location information and spatial information of street view images more accurately, so that the accuracy of semantic segmentation is improved.

关键词： Attention mechanisms Convolution Fuses Target recognition Semantic segmentation Semantics Predictive models parallel processing Feature extraction Data mining

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Moving Average Filter Block Implementation Using USP-Awadhoot Divider

Moving Average Filter Block Implementation Using USP-Awadhoo...

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international Baltic Electronics conference, BEC

作者： Udayan S. Patankar Miguel E. Flores Ants Koel Sunil Patankar TJS Department of Electronics TalTech Tallinn University of Technology Tallinn Estonia School of Electronics Don Bosco University Soyapango El Salvador Kavi Kulguru Kalidas Sanskrit University Ramtek India

ISBN: (数字)9798350365115

ISBN: (纸本)9798350365122

In IoT systems, compact, battery-operated circuitry, and rapid data transmission are essential for reducing power consumption and extending battery life. Efficient data transmission requires robust local data processing capabilities supported by appropriate arithmetic and logical units (ALU). Modern processors or field-programmable gate arrays (FPGA) with a suitable ALU are well-suited for meeting these demands. To ensure data quality, effective signal processing before transmission is crucial. A moving average filter (MAF) is an excellent choice for this purpose, providing a balance of speed and precision while utilizing minimal hardware resources, thereby reducing the chip area needed for implementation. this article describes the design and implementation of the moving average low pass filter using the area-efficient novel USP-Awadhoot divider. the proposed USP-Awadhoot divider requires 266 LUTs and 146 FFs for 8-bit implementation using Xilinx FPGA. the proposed moving average filter based on a novel USP-Awadhoot divider is implemented on Nexys A7 XC7A100T-1CSG324C FPGA boards with Xilinx Vivado Design Suite. It is suitable for the cost-effective implementation of IoT and battery-operated applications.

关键词： Program processors Power demand Low-pass filters Signal processing Batteries Table lookup parallel architectures Mobile applications Data communication Field programmable gate arrays

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Fast RTL Implementation of A* Path Planning Algorithm

Fast RTL Implementation of A* Path Planning Algorithm

引用

international conference on Microelectronics, ICM

作者： Adham Osama Ahmed Mostafa Eslam Mamdouh Mohamed Gamal Usama Imam Mohamed Taha Ahmed Khalil Islam Ahmed Hassan Mostafa Cairo University Giza IC Verification Solutions Siemens EDA Cairo Egypt Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology University of Science and Technology 6th of October Giza Egypt

ISBN: (纸本)9781665408400

the conventional A* algorithm consumes a lot of time due to its large number of iterations. In every iteration, the memory is accessed for multiple data structures, functions are evaluated then sorted into queues which makes it sometimes not suitable for real-time applications. this paper proposes a fast implementation for the A* algorithm to meet requirements of real-time applications. the proposed implementation uses parallelism and caching to achieve better performance. We used Register Transfer Level (RTL) simulation and formal verification to do functional verification of the implemented *** design is implemented on Xilinx Virtex-7 to be evaluated. Experiments prove that this implementation achieves 100 times enhancement for low obstacle maps and 50 times for high ones relative to software implementation. the design is suitable for real-time applications.

关键词： Software algorithms parallel processing Shift registers Data structures Real-time systems Path planning Software

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Design and Analysis of Stochastic Query Optimizer for Biobank Databases

Design and Analysis of Stochastic Query Optimizer for Bioban...

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international conference on Computational Science and its Applications (ICCSA)

作者： Manik Sharma Gurvinder Singh Rajinder Singh Jasbir Singh Department of Computer Science and Applications DAV University Jalandhar India Department of Computer Science and Engineering Guru Nanak Dev University Amritsar India Department of Computer Science GGS Khalsa College Sarhali Tarn Taran India

ISBN: (纸本)9781467373685

the enduring revolution in the field of life sciences is producing biological data at phenomenal rate. the large volume of biological data is meaningful only when it is accessed and analyzed by different researchers. A flood of biological databases is creating a problem in finding an efficient query execution plan for the complex query as posed by bio-researchers. In this research paper, an effort has been made to effectively process the biological data collected in biobanks. the major objective of this research work is to find an optimal query execution plan for biobank database queries using the proposed Restricted Exhaustive Enumeration Approach (REA) and Entropy based Restricted Genetic Approach (ERGA). the result of different query optimization approaches viz. Exhaustive Enumeration, Restricted Exhaustive Enumeration, Simple Genetic Approach and Entropy Based Restricted Genetic Approach are compared with each other on the basis of usage of system resources. the results of Entropy Based Restricted Genetic Algorithm in finding query execution plan for Burbank queries are better than EA and SGAby 2-20% and 7-15% respectively. Furthermore, experimental results reveal that use of Inter-site parallel environment further optimized the results of ERGA by 0-4%.

关键词： Genetic algorithms Entropy Genetics Query processing Optimization

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An Overview of YOLO-Based Helmet Detection: Modifications and Advancements

An Overview of YOLO-Based Helmet Detection: Modifications an...

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international conference on Imaging, Signal processing and Communication (ICISPC)

作者： Seba Al Mokdad Manar Abu Talib Simon Zerisenay Ghebremeskel Fouad Lamghari Shaher Bano Mirza Ali Bou Nassif Department of Computer Science University of Sharjah United Arab Emirates Artificial Intelligence Department Fujairah Research Centre Fujairah United Arab Emirates Research Department Fujairah Research Centre Fujairah United Arab Emirates

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

Small objects detection is a major challenge in computer vision and one well-known example of this is helmet detection. Helmets have a small size, which limits typical YOLO (You Only Look Once) object detection models from efficiently detecting them. Recent studies have focused on modifying several YOLO architectures to propose a solution to this problem. this study provides a comprehensive evaluation of novel solutions that address the helmet detection problem which include modifying multiple YOLO versions. A comprehensive examination of 53 research publications, which were published the period between 2019 to 2023, out of which 30 were included investigates the contributions in terms of model modifications, datasets used for evaluation, loss function optimization, and resulting performance measures. Several YOLO versions are addressed including YOLOv3, YOLOv4, YOLOv5, YOLOv7, and YOLOX. the studies discuss various modifications to YOLO including architecture modifications, integration of attention mechanisms, addition of detection scales, optimization and regularization techniques, loss function Improvements and anchor box optimization. the findings provide insight into the evolution of helmet detection algorithms within the YOLO architecture and how these modifications helped in addressing the problem of small objects detection. this review aims to provide academics and practitioners with significant insights into cutting-edge techniques for overcoming the challenges of identifying small objects as helmets.

关键词： YOLO Head Reviews Imaging Computer architecture Signal processing Loss measurement

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