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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13605 条记录，以下是91-100 订阅

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Wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

引用

Digital Signal processing and its Applications (DSPA), International conference on

作者： Sinan V. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms Signal processing algorithms Electrocardiography Wavelet analysis Distortion

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FPGA Based Neural Processor Design For image Detection of Traffic Signs

FPGA Based Neural Processor Design For Image Detection of Tr...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Mohammad Sakib Bin Selim Priom Ghosh Tuton Chandra Mallick Department of Electrical and Electronic Engineering Premier University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

With the increasing complexity of traffic management systems, there is a critical need for efficient, high-performance solutions capable of processing visual data in real time to improve traffic control and safety. Traditional processors often face computational bottlenecks and latency issues when deploying neural networks for image recognition tasks. To address these limitations, this work proposes a dedicated neural processor implemented on the Cyclone iv FPGA, optimized for traffic signal image processing. The design leverages the parallelism and reconfigurability of FPGAs, enabling significant acceleration of computations while maintaining low power consumption. At its core, the processor integrates a neural network architecture with 4 layers tailored for detecting and classifying traffic signal colors, such as red, yellow, and green. This neural network is implemented using VHDL, ensuring efficient use of FPGA resources while achieving high computational performance. Key features of the design include real-time data processing, a focus on speed and accuracy, and a compact implementation that balances resource utilization. The effectiveness of the FPGA-based neural processor is evaluated using real-world traffic signal image datasets, demonstrating robust performance with 92% classification accuracy. Comprehensive evaluations of processing speed, classification accuracy, and resource utilization affirm the efficacy of the proposed neural network-based FPGA solution in addressing the complexities of real-time traffic signal processing and modern traffic management challenges.

关键词： Program processors Accuracy VHDL Scalability Neural networks Parallel processing Real-time systems Cyclones Resource management Field programmable gate arrays

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Robust Calibration for Enhanced Imaging in Near-Field D-Band MIMO Radar systems

Robust Calibration for Enhanced Imaging in Near-Field D-Band...

引用

European conference on Antennas and Propagation, EuCAP

作者： Lorena Perez-Eijo Marcos Arias Borja Gonzalez-Valdes José Vázquez-Cabo Ignacio Sardinero–Meirás Jesus Grajal AtlanTTic Research Center Universidade de Vigo Vigo Spain Information Processing and Telecommunications Center Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This work introduces a calibration algorithm designed to improve high-precision imaging in real-time MIMO radar systems. The algorithm applies the Inverse Fourier Transform (IFFT) and advanced signal processing techniques to efficiently extract and process key data from raw measurements, focusing on critical Regions Of Interest (ROI) while filtering out unnecessary information. A robust phase correction mechanism ensures precise calibration and alignment, preventing phase errors that could otherwise lower image quality. The algorithm provides an effective balance between computational efficiency and the high accuracy required by modern MIMO radars, paving the way for further improvements in real-time imaging.

关键词： MIMO radar Signal processing algorithms Coherence Radar imaging Signal processing Real-time systems Hardware Calibration Spatial resolution image reconstruction

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Automatic Identification of the American Sign Language Using Machine Learning

Automatic Identification of the American Sign Language Using...

引用

Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： Ravi Kishore Kodali Arindam Pattanayak Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Communication is essential, providing a means to express thoughts, emotions, and ideas. Sign language is a fundamental form of communication for those who are deaf, have hearing impairments, or are non-verbal. This research uses machine learning to develop an effective algorithm for identifying the alphabet in American Sign Language (ASL) through natural hand movements. The system combines image processing, machine learning, and artificial intelligence, especially a convolutional neural network (CNN), to translate ASL gestures into accurate and understandable output. After training the model on a dataset that includes sign language images, the results demonstrate the system's ability to identify hand gestures related to the English alphabet accurately.

关键词： Hands Training Deep learning Sign language Translation Accuracy Machine learning algorithms image processing Predictive models Convolutional neural networks

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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

引用

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

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning image quality Convolution Surveillance Redundancy Signal processing algorithms Feature extraction Real-time systems Speech processing Remote sensing

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

引用

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

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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Design of AXI Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

引用

12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Fast Sparse Learning from Streaming Data with LASSO

Fast Sparse Learning from Streaming Data with LASSO

引用

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

作者： Marija Iloska Petar M. Djurić Mónica F. Bugallo Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entire dataset upfront and repeated fitting. The main contribution of this work is in finding an easy and principled choice for the penalty parameter for every incoming data point, in cases where the input features are uncorrelated. The proposed Online LASSO has several benefits: i) it is memory and time efficient ii) it is easy to implement, iii) it does not require an initial batch of data to start, iv) it does not require any tuning (e.g., step size or tolerance), and finally v) it converges to the performance of the optimal predictor and correct selection of features. We demonstrate these capabilities and compare Online LASSO with standard LASSO as well as other adaptive LASSO variations and provide a discussion on their performances.

关键词： Simulation Noise Memory management Signal processing algorithms Prediction algorithms Iterative methods Speech processing Tuning Standards Time-varying systems

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A Cloud-Edge Integrated Water Extraction Using Superpixel Segmentation 18th

A Cloud-Edge Integrated Water Extraction Using Superpixel Se...

引用

18th International conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Zhang, Qingmin Chen, Chen Zhou, Yang Wang, Zhiyi Jin, Fan Lu, Haitao Wan, Shaohua School of Communication Engineering Xidian University Xi’an China Xidian Hangzhou Institute of Technology Hangzhou311231 China The State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun130000 China National Key Laboratory of Science and Technology on Space Microwave CAST Xi’an710100 China Ningbo Joynext Technology Corporation Ningbo361000 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

ISBN: (纸本)9783031714665

With the development of the Industrial Internet of Things (IIoT) and the proposal of smart water conservancy, the integration of the Internet of Things (IoT), edge computing, and computer vision for hydrological information monitoring has become a trend in water conservancy. Difficult to provide comprehensive hydrological data from hydrological observation stations, hydrological forecasting in uninformative basins has become one of the research hotspots. water extraction can provide support for many hydrological monitoring. However, The water is difficult to extract when the water in the near-water image has reflection and water surface ripples. We propose a multi-condition water extraction method based on superpixel segmentation under cloud-edge integration architecture. Firstly, based on the color characteristics of the water and the surrounding vegetation, we propose a reflection processing mechanism. Secondly, we propose a mechanism based on superpixel segmentation for the effect of reflection on water extraction. Then, we propose a contour correction mechanism. Finally, the flood-filling algorithm is used to extract the water. We compare the traditional image segmentation algorithm with our proposed segmentation method. The results show that the proposed method outperforms other algorithms in all evaluation metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion

On Momentum Acceleration for Randomized Coordinate Descent i...

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

作者： Matthew Callahan Trung Vu Raviv Raich School of EECS Oregon State University Corvallis OR USA Department of CSEE University of Maryland Baltimore County MD USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.

关键词： Closed-form solutions Shape Signal processing algorithms Machine learning Signal processing Computational efficiency Computational complexity Speech processing Recommender systems Convergence

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