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检索条件"任意字段=16th Image Processing: Algorithms and Systems Symposium 2018"

共 650 条记录，以下是51-60 订阅

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Exploring Parallelism in ZynqNet AI Engine to Accelerate image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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IEEE Latin American symposium on Circuits and systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

this research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. the C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. the AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. the research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks Artificial intelligence Hardware acceleration Engines Field programmable gate arrays image classification

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Radon-Fourier Transform for Timing Correction in Uncoupled Digital Radar Networks

Radon-Fourier Transform for Timing Correction in Uncoupled D...

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German Microwave Conference

作者： Julian Aguilar Lukas Paulus David Werbunat Alexander Grathwohl Christian Waldschmidt Institut of Microwave Engineering Ulm University Germany

ISBN: (数字)9783982039749

ISBN: (纸本)9798331521790

Uncoupled radar networks offer many desired features such as large virtual apertures and flexibility in sensor placement without costly radio frequency (RF) cables. However, they also introduce novel challenges, especially the recovery of coherency between individual radar sensors. the correction of timing errors due to sampling frequency offsets (SFO) between a transmitter’s digital-to-analog converter (DAC) and receiver’s analog-to-digital converter (ADC) is essential for the coherent processing of uncoupled digital radar network measurements. systems with different error estimation and correction approaches have been proposed recently, which utilize resampling-based algorithms. However, they are costly to implement, i.e. on an FPGA, and require additional signal processing steps. the herein presented Radon-Fourier Transform (RFT)-based algorithm is capable of directly calculating the corrected radar image without any resampling, negating the downside of additional signal processing steps required after resampling. It is shown that the RFT approach can almost completely restore erroneous radar data, and even outperforms the resampling-based algorithm. In this work, an experimental orthogonal frequency-division multiplexing (OFDM) demonstrator is utilized to verify the concepts, which are applicable to other modulation types typically used for digital radar.

关键词： OFDM Computational modeling Signal processing algorithms Radar Transforms Radar imaging Data models image restoration Frequency measurement Frequency division multiplexing

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Edge-detection method for low-quality color pictures in lossy compression image file formats

Edge-detection method for low-quality color pictures in loss...

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International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Laura-Christina Simionescu Dobrescu Florin Dobrescu Valentin-Alexandru Stan Angel Ciprian Cormos Electronics Department Transports Factory National University of Science and Technology “Politehnica” of Bucharest Romania Christiana Medical Center Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

the objective of this article is to propose a robust edge detection algorithm appropriate for the processing of color images originating from image acquisition systems with unknown parameters, with the extreme case of low-quality color pictures in lossy compression image file formats taken from the internet, and to assess the performance of the proposed solution in comparison with the Canny edge detection algorithm, using a proof-of concept software developed in Python. the output of the software is intended to feed unassisted AI processes related to artificial vision; thus, it also involves a method of establishing a hierarchy in the detected edges and an identification strategy for reestablishing the continuity of the curves that can be assimilated with mathematical primitives and to de-fractalize the noise-induced features.

关键词： image coding image color analysis image edge detection Software algorithms Detectors Color Hardware

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Hardware-Accelerated Hypergraph processing with Chain-Driven Scheduling 28

Hardware-Accelerated Hypergraph Processing with Chain-Driven...

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28th Annual IEEE International symposium on High-Performance Computer Architecture (HPCA)

作者： Wang, Qinggang Zheng, Long Yuan, Jingrui Huang, Yu Yao, Pengcheng Gui, Chuangyi Hu, Ao Liao, Xiaofei Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp Wuhan Peoples R China

ISBN: (纸本)9781665420273

Beyond ordinary graphs, hypergraphs are a graph representation to flexibly express complex multilateral relationships between entities. Hypergraph processing can be used to solve many real-world problems, e.g., machine learning, VLSI design, and image retrieval. Existing hypergraph processing systems handle a hypergraph in order of its hyperedge and vertex indices. this makes processing hypergraphs on general-purpose architectures suffer significantly from excessive off-chip memory accesses, most of which however are redundant in frequently accessing overlapped hyperedges and vertices, but the index-ordered scheduling destroys this potential locality. In this paper, we propose a novel Generate-Load-Apply (GLA) execution model to improve locality in hypergraph processing. the key insight of GLA is to use a concept of chain to characterize the overlapped feature of a hypergraph, exposing data reuse opportunities missed in existing hypergraph systems. the precondition of driving GLA model is to generate expected chains on the fly, but the software solution is so expensive that its overheads may outweigh the benefits achieved from the chain-driven scheduling. We further present ChGraph, the first hardware-accelerated hypergraph processing engine near each core. ChGraph is specialized in accelerating the chain generation and the chain-guided data loading (to hide memory access latency) while the general-purpose cores are responsible only for handling the apply operations of GLA. We evaluate ChGraph against a state-of-the-art hypergraph processing system Hygra on six hypergraph algorithms using five large real-world hypergraphs. Results on a simulated 16-core system show that ChGraph reduces the number of off-chip memory accesses by up to 4.56x and achieves up to 4.73x speedup while introducing only 0.26% area overhead.

关键词： hypergraph processing locality chain overlapped feature hardware-accelerated scheduling

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High-efficiency Reconfigurable Crypto Accelerator Utilizing Innovative Resource Sharing and Parallel processing

High-efficiency Reconfigurable Crypto Accelerator Utilizing ...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Vu Trung Duong Le Hoai Luan Pham thi Hong Tran thi Sang Duong Yasuhiko Nakashima Nara Institute of Science and Technology Nara Japan Osaka Metropolitan University Osaka Japan

In decentralized IoT ecosystems, four cryptographic algorithms, including SHA256, BLAKE256, BLAKE2s, and Chacha20, are principal to ensure data integrity and confidentiality. However, existing cryptographic hardware is often limited to supporting a single algorithm and suffers from low performance, which falls short of meeting the diverse requirements of these systems. To address these limitations, we introduce a reconfigurable crypto accelerator (RCA) that offers high flexibility, superior performance, and optimal hardware efficiency. Our RCA includes three novel optimizations, specifically, a homogeneous multi-core architecture, a register-adder sharing approach, and a multilevel pipeline scheduler. the RCA was successfully verified and implemented at the system-on-chip level on a ZCU102 FPGA. the real-time performance evaluation of the RCA, during the execution of various cryptographic algorithms, demonstrates an energy efficiency ranging from 94.3-160.4 Mbps/W, which is 3.1-10.5 times higher compared to modern CPUs. Experiments conducted on several FPGAs show that the RCA is higher flexibility while still outperforming previous works by 1.63-31.65 times in throughput and 1.04-2.76 times in area efficiency. Furthermore, in ASIC synthesis, the RCA exhibits exceptional throughput (48.79-92.16 Gbps), area efficiency (66.2-102.31 Gbps/mm 2 ), and energy efficiency (186.22-287.8 Gbps/W), surpassing other related ASIC-based works.

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Photograph enhancement via imitation-to-innovation training scheme 16

Photograph enhancement via imitation-to-innovation training ...

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16th IEEE International symposium on Broadband Multimedia systems and Broadcasting (IEEE BMSB)

作者： Feng, Yi Zhao, Jiying Univ Ottawa Sch Elect Engn & Comp Sci 800 King Edward Ave Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781665449083

Post processing is usually necessary for photographs captured in undesired situations to improve their visual quality. As photo retouching requires operators to receive professional training, automatic photograph enhancing system would benefit amateur photographers and also save experts from tedious retouching tasks. In this paper, we present a deep learning framework based on imitation-to-innovation training scheme. Our method integrates the bilateral grid data structure and Generative Adversarial Network (GAN) to achieve high time-efficiency and appealing retouching output. Besides, we present bilateral loss function to maintain piecewise smoothness. the experimental results and user study demonstrate that our method produces improved results than other state-of-the-art methods.

关键词： Multimedia signal processing Synthetic imaging and rendering image enhancement GAN Bilateral grid

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image processing and Pattern Recognition-Based Car Classification for Intelligent Transportation System 6

Image Processing and Pattern Recognition-Based Car Classific...

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6th Smart Cities symposium, SCS 2022

作者： Dange, Varsha Pophale, Sayali Raut, Nitin Shadrach, Finney Daniel Chowdhury, Subrata Ramya, G. Dutta, Pushan Vishwakarma Institute of Technology Pune India Dept. of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India AP Chittoor India SITE School VIT Vellore India School of Engineering and Technology Amity University Kolkata India

ISBN: (纸本)9781839538544

the combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, VGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Convolutional neural networks

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Fire Alert System using Shape and Color Analysis through image processing via Mobile Application

Fire Alert System using Shape and Color Analysis through Ima...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Divina A. Chua Carla Louie H. Leandicho Leo Angelo C. Magtibay Jerome T. Ortiz Analyn N. Yumang Omar Mukhtar Y. Julkipli School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

Fire is a common safety problem both in the industry and household environments. this paper presents research on the development and implementation of a fire detection system designed for both industrial and household environments. the proposed system utilizes covariance matrix image processing in conjunction with Shape analysis and color analysis algorithms. the device is capable of detecting fire based on its color, leveraging an enrollment process conducted on our web server. To enhance user accessibility, a mobile phone application has been created to serve as the interface for receiving alarms generated by the device. Additionally, a mobile camera facilitates live video streaming of the monitored area. Upon detecting a fire, the device triggers an alarm to be sent to the user's Android mobile phone. the mobile application not only displays real-time footage but also offers users the option to contact the fire station directly.

关键词： Industries image color analysis Shape Streaming media Web servers Real-time systems Mobile applications

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Real-time Low Vision Simulation in Mixed Reality

Real-time Low Vision Simulation in Mixed Reality

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International IEEE Conference on Signal-image Technologies and Internet-Based System

作者： Valeria Acevedo Philippe Colantoni Éric Dinet Alain Trémeau Université de Lyon Université Jean Monnet Laboratoire Hubert Curien UMR

Visual impairments are a global health issue with profound socioeconomic ramifications in both the developing and the developed world. there exist ongoing research projects, that aim to investigate the influence of light in the perception of low vision individuals. But as of today, there is neither clear knowledge nor extensive data regarding the influence of light in low vision situations. this research will address these issues by introducing a methodology and a system to simulate visual impairments. A pipeline based on eye anatomy coupled with real-time image processing algorithms allows to dynamically simulate low vision specific characteristics of selected impairments in mixed reality. An original new approach based on massively parallelized processing combined with an efficient modeling of eye refractive errors aims to improve the accuracy of the low vision simulation.

关键词： image processing Heuristic algorithms Visual impairment Pipelines Mixed reality Real-time systems

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AI-based Inter-Tower Communication Networks: Challenges and Benefits 16

AI-based Inter-Tower Communication Networks: Challenges and ...

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16th IEEE International symposium on Broadband Multimedia systems and Broadcasting (IEEE BMSB)

作者： Bilbao, I Iradier, E. Montalban, J. Angueira, P. Wu, Y. Zhang, L. Univ Basque Country Dept Commun Engn Bilbao Spain Commun Res Ctr Canada Ottawa ON Canada

ISBN: (纸本)9781665449083

In this paper, the current Artificial Intelligence-based Signal processing landscape has been exposed and analyzed. Artificial Intelligence seems to be a promising tool to perform Signal processing in wireless communications. More concretely, in this study, a proposal of how these techniques could be implemented in Inter Tower Communication Networks, which involve In-Band Full-Duplex, has been made. Moreover, a first simulation test of Artificial Intelligence used in this context has been carried out. the analysis conducted shows a promising study area that will be further explored in the following works.

关键词： ATSC 3.0 AI IBFD IDL ITCN LDM MLP DNN Signal processing

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