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共 4118 条记录，以下是251-260 订阅

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Compressor-Based Approach for Power-Optimized Error-Tolerant High Accurate Approximate Multipliers Design and Implementation

Compressor-Based Approach for Power-Optimized Error-Tolerant...

引用

International conference on signal processing and Communication (ICSC)

作者： Lakshmi Sowjanya Koneru Shravya Manchala Prakash Kodali dept. Electronics and Communication NIT Warangal dept. Electronics and Communication NIT Andhra Pradesh

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

A common operation in many different applications is multiplication. The performance of a multiplier can be increased by using compressors. Approximate multipliers fall under Approximate Computing, a developing approach focused on en-hancing power efficiency, reducing area, and minimizing delay in applications that can tolerate acceptable errors. It also trades off the necessity of exact computation for improved performance. By using this approach, power usage can be reduced in applications in which precision is not given much importance. An efficient 8-bit approximate multiplier design is achieved by introducing a approximate compressor. The experimental results demonstrate substantial improvements in an 8-bit multiplier, reducing total power by up to 28.4% versus exact multipliers and 31.71% versus existing designs while decreasing delay by up to 54.93% and 72.15% respectively. The average accuracy is found to be 97.02%. The results not only reduce the power consumption but also maintain acceptable levels of precision, demonstrating that this is an effective solution for modern image processing and signal processing applications. The findings add to what we already know about approximate computing, which is important for future research and development in creating energy-efficient digital systems.

关键词： Accuracy Power demand image coding Approximate computing Compressors Hardware Delays Table lookup Error correction Research and development

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Malaysia Traffic Sign Recognition for Autonomous Vehicles with Textual Information Using Computer Vision

Malaysia Traffic Sign Recognition for Autonomous Vehicles wi...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Chear Li xian Usman Ullah Sheikh Syed Abdul Rahman Syed Abu Bakar Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face limitations such as differing traffic sign designs, language barriers in textual information, and varying environmental conditions. In this paper, we propose a traffic sign detection and recognition system tailored for Malaysia, utilizing Convolutional Neural Networks (CNNs) and Optical Character Recognition (OCR). In this paper, we propose a traffic sign detection and recognition system utilizing You Only Look Once (YOLO) V8 for object detection and EasyOCR to process textual information on selected traffic signs. Our system achieves a mean Average Precision (mAP) of 0.824 and an average processing time of 1.2 seconds per frame, which is comparable to existing literature. Furthermore, the complexity of our method is significantly reduced, enhancing its potential for real-time processing applications, as evidenced by its efficient processing time.

关键词： YOLO image recognition Accuracy Roads Face recognition Optical character recognition Assistive technologies

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A Fast image Matching Approach in Fatigue Testing of Wall Plate Structures

A Fast Image Matching Approach in Fatigue Testing of Wall Pl...

引用

IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Zhang Wendong Shi Baoquan Chen xianmin Lv Yuanbo National Key Laboratory of Strength and Structural Integrity AVIC Aircraft Strength Research Institute Xi'an China School of Mechano-Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

To address the challenges in motion vector estimation due to local non-uniform deformation of specimens during fatigue tests, this paper proposes an image matching method that combines template sub-image techniques with a Kalman filter. This method employs a mesh motion estimation model, initially using the Kalman filter to predict the matching point positions of mesh nodes. Subsequently, template matching is utilized to achieve precise matching of these nodes. The algorithm is accelerated through GPU-CPU heterogeneous parallel computing technology. Test results indicate that the GPU-accelerated image matching method attains the accuracy of the CPU-based algorithm while significantly reducing processing time, thereby fully meeting the real-time requirements of online image feature matching applications.

关键词： Accuracy Deformation image matching signal processing algorithms Fatigue Prediction algorithms Vectors Real-time systems Kalman filters Testing

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Hardware Parallel Structure for Convolution Computing in image processing

Hardware Parallel Structure for Convolution Computing in Ima...

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International conference on Telecommunications and signal processing (TSP)

作者： Reyes Perez Melesio Moises Arredondo-Velázquez Javier Diaz-Carmona Electronics Engineering Department Technological Institute of Celaya Celaya Gto Mexico Faculty of Physical and Mathematical Sciences Meritorious Autonomous University of Puebla Puebla Puebla Mexico

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

A proposed hardware structure for convolution computing with application in image processing is described in this paper. The solution is based on using parallel processing in the computing of the filtering and MAC operation between the convolution mask and one or various filters, as well as on a proposed structured and defragmented input data. According to the described proposal, a flexible feature state of the proposed structure is possible with the combination of different parallelism computing types. Such feature can be extrapolated to different types of hardware. The parallel computing allows reduced computing time mainly in the filtering and convolution computing stages. The obtained structure is suitable to be implemented in convolutional neuronal networks applications.

关键词： Filters Convolution image processing Parallel processing Hardware Telecommunications Performance analysis

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QUARTER LAPLACIAN FILTER FOR EDGE AWARE image processing

QUARTER LAPLACIAN FILTER FOR EDGE AWARE IMAGE PROCESSING

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IEEE International conference on image processing (ICIP)

作者： Gong, Yuanhao Tang, Wenming Zhou, Lebin Yu, Lantao Qiu, Guoping Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

ISBN: (纸本)9781665441155

This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing. Its support region is 2x2, which is smaller than the 3x3 support region of the classical Laplacian filter. Thus, it is more local. Moreover, this filter can be implemented via the classical box filter, leading to high performance for real time applications. Finally, we show its edge preserving property in several image processing tasks, including image smoothing, texture enhancement, and low-light image enhancement. The proposed filter can be adopted in a wide range of image processing applications.

关键词： quarter Laplacian edge preserve box

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Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT

Evaluating the Posterior Sampling Ability of Plug&Play Diffu...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liam Moroy Guillaume Bourmaud Frédéric Champagnat Jean-François Giovannelli DTIS/ONERA Univ. Paris Saclay France IMS (Univ. Bordeaux – CNRS – BINP) France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer concentrated or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation properties. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.

关键词： Measurement image coding Computed tomography signal processing Diffusion models Acoustics Speech processing image reconstruction

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SPA 2022 - signal processing: Algorithms, Architectures, Arrangements, and applications, Proceedings

SPA 2022 - Signal Processing: Algorithms, Architectures, Arr...

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25th IEEE signal processing: Algorithms, Architectures, Arrangements, and applications, SPA 2022

ISBN: (纸本)9788362065424

The proceedings contain 28 papers. The topics discussed include: hardware-in-the-loop simulation of a UAV autonomous landing algorithm implemented in SoC FPGA;accuracy of smartphone depth cameras in stoma shape extraction for wafer fitting;impact of sampling rate and eye-condition on resting quantitative EEG;improving rehabilitative assessment with statistical and shape preserving surrogate data and singular spectrum analysis;image encryption through Ca, chaos and Lucas sequence based S-box;identification by recurrent neural networks applied to a pressure swing adsorption process for ethanol purification;and gender assessment from vertebra’s cortical bone x-ray images: texture analysis vs. deep learning approach.

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Optimizing Multimodal image Fusion: A Novel Approach with Nystrom Attention Mechanisms in Transformer Models

Optimizing Multimodal Image Fusion: A Novel Approach with Ny...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuqin Zeng Ze Wen Shuqian Fan Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing Key Laboratory of Additive Manufacturing Technology (CIGIT CAS) Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This work proposed a new model based on transformers for multimodal image fusion, with explicit attention paid to fusing infrared and visible images toward enhanced detail and information content. This method, which incorporates Nystrom attention and skip connections, optimizes the fusion process so that important features of both modalities are preserved. Traditional models find it hard to keep details in the content and introduce artifacts. On the contrary, this model can efficiently cope with these challenges, as it makes use of an attention mechanism that is computationally highly effective. This will efficiently fuse large-scale image data and may be useful in those fields where very detailed and comprehensive visual information is demanded. The detailed evaluations prove that our model has superior performance over the current state-of-the-art methods in texture detail preservation, thermal differentiation, and overall image quality, which possesses great potential for advanced imaging applications.

关键词： image quality Visualization Attention mechanisms Fuses Computational modeling Imaging signal processing Transformers Speech processing image fusion

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Matrix Completion via Local Density Loss Optimization 13

Matrix Completion via Local Density Loss Optimization

引用

13th International conference on Graphics and image processing (ICGIP)

作者： Fang, Hui Wang, Yunbin Chen, Zhaojiong Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

One of the main methods for dealing with low-rank problems is the matrix completion, which attempts to recover incomplete values in an incomplete matrix. It has been employed in a large number of real-world applications like recommender systems and image recovery. When the data is highly sparse, many practical problems of recommender systems and image recovery are hard to solve. Therefore, we use the fully connected neural networks for the matrix completion to avoid problems caused by sparse matrices. In this paper, we utilize the local density loss function to measure the difference of the matrix completion results, where trainable parameters are updated via calculating the derivatives according to the influence function. The local density loss function effectively measures the deviation between the predicted value and the real value, and the convergence of the model is guaranteed. So as to validate the effectiveness of the proposed method, we conduct substantial experiments in terms of image recovery and recommender systems. In addition, we employ three metrics, including root mean square error and peak signal-to-noise ratio and structure similarity, to measure the recovery accuracy of missing matrix. Experimental results demonstrate that this framework is superior to other state-of-the-art methods in running time and learning performance.

关键词： Matrix completion density loss recommender systems image recovery neural networks back propagation

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Digital Multitone image Reconstruction using Deep Generative Adversarial Nets

Digital Multitone Image Reconstruction using Deep Generative...

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Asia-Pacific-signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Guo, Jing-Ming Sankarasrinivasan, S. Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (纸本)9789881476890

Digital multitoning is a latest version of the halftone technology which is widely used in printing applications. The method transforms the original gray scale image into its appropriate printable version using more tones than conventional halftones. To be specific, the gray scale or color image is converted to its 2 or 3-bit version. The multitone image results in enhanced homogeneity, smooth texture and less halftone artifacts. On the other hand, many applications require reconstruction of printed images for digital processing and in most cases source image may not be available. In this paper, a deep learning model is proposed based on the conditional general adversarial nets (cGAN's) to perform effective reconstruction of multitone images. For experimentation, a digital multitone dataset is developed comprising of 10,000 images. The proposed model is optimized to perform with minimal layers and reduced parameters. The discriminator is constructed as Patch-GAN architechture and is implemented over the patch size of 64x64. From the overall experimental results, it has been validated that the proposed model achieves consistent and best reconstruction quality.

关键词： Digital multitoning GAN image reconstruction Inverse halftoning Printing

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