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Fusion model of Modified Wavelet Transform and Neural Network for medical image compression 3

Fusion model of Modified Wavelet Transform and Neural Networ...

引用

3rd International conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Shyamala, N. Geetha, S. Nallamuthu Gounder Mahalingam College Ug Department of Computer Applications Pollachi642002 India Government Arts and Science College for Women Department of Computer Science Puliakulam CBE-641045 India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Medical imaging is one of emerging fields that has high impact on analysis and diagnosis of diseases. image compression is introduced in the medical image processing to solve memory and bandwidth requirement problems. In this paper, a hybrid image compression method by integrating Modified Wavelet Transform (MWT) and Neural Network (NN) is proposed for efficient storage as well as transmission of medical images. Primarily, medical images are preprocessed using median filter to suppress noisy data and then decomposed into approximation and detailed coefficients using wavelet transform. Subsequently, detailed coefficients are filtered with proposed thresholding technique. Both approximation and detailed coefficients are divided into sub matrix with the size of n x n and used as input to the NN to perform compression. The image compression performance of the proposed method is evaluated by computing peak signal to noise ratio, compression ratio and storage space and compared with prevailing methods to prove its effectiveness. The empirical findings reveal that the proposed method provides best performance compared to the former methods taken for comparison. © 2022 IEEE.

关键词： Wavelet transforms

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Speckle Reduction in SAR images using CNN 3

Speckle Reduction in SAR Images using CNN

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3rd International conference on signal processing and Communication (ICPSC)

作者： Santhi, V. Mohandass, Dheshan Jayanthi, J. Arulmozhivarman, P. Mehra, Raghav VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India ISRO Ctr Space Applicat Ahmadabad 380015 Gujarat India

ISBN: (纸本)9781665428644

The importance of remote sensing has ballooned in the recent decade, due to the increased need for its applications, from commercial and economic, to intelligence and military applications. Synthetic Aperture Radar (SAR), an important remote sensing instrument captures data that are inherently affected with speckle noise due to its coherent imaging, which makes working with SAR data complicated. Therefore, Synthetic Aperture Radar (SAR) images can benefit greatly from good speckle reduction or suppression techniques, making them easier to analyze and utilize their maximum potential. This paper gives a technical review of various speckle filtering approaches for Synthetic Aperture Radar images (SAR), discussing the drawbacks of the older conventional methods, and the improvements the newer convolutional Neural Networks (CNN) based solutions bring to the table.

关键词： Speckle Noise CNN Noise Suppression image Filtering SAR de-speckling

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CSIFA: A Configurable SRAM-based In-Memory FFT Accelerator

CSIFA: A Configurable SRAM-based In-Memory FFT Accelerator

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International conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Yiyang Lin Yi Zou Yanfeng Yang School of Microelectronic South China University of Technology Guangzhou China

ISBN: (数字)9798350349634

ISBN: (纸本)9798350349641

In the era of Artificial Intelligent (AI) and big data, the demand for signal processing algorithms for large datasets has grown across various fields, notably in the application of the Fast Fourier Transform (FFT). This algorithm is crucial in computation and storage-heavy applications like image and radar signal processing, where it has been integral for decades. In-memory computing (IMC), unlike traditional computing models, integrates computation and storage in the same unit, reducing data transfer time and energy costs. This paper presents CSIFA, a hardware accelerator for up to 1024-point FFT algorithm, utilizing SRAM and some other digital logic to enhance efficiency. Our evaluation indicates that CSIFA performing FFT is able to achieve an overall throughput 15MB/s, computational power 6.65mW, and 115.3GOPS, showcasing its high throughput and energy efficiency for edge application scenarios.

关键词： Program processors Fast Fourier transforms signal processing algorithms Systems architecture Random access memory Throughput Energy efficiency

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Stacking Ensemble Learning for Seabed Sediment Classification

Stacking Ensemble Learning for Seabed Sediment Classificatio...

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

作者： Ting Zhao Yuxin Zhao Tian Zhou College of Underwater Acoustic Engineering Harbin Engineering University Harbin China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Seabed sediment information is extensively applied in marine exploration and management. Recent advancements in sonar technology and machine learning have introduced automated techniques for sediment classification. However, it still faces a series of challenges before acoustic sediment classification technology can be practically applied, due to the variability and complexity of sonar data. Developing more refined and reliable data interpretation techniques is crucial for accurately identifying sediment types from noise-contaminated data. Various machine learning methods have progressively been introduced into the field of acoustic sediment classification to handle predictive tasks. This study explores the application of stacking ensemble learning method for classifying seabed sediment using sonar images. A meta-learner is designed for integrating predictions of a number of base learners. Comparative analysis with traditionally used models demonstrates the effectiveness of stacking architecture in distinguishing between different sonar images. The improvements in classification performance suggests promising applications of ensemble learning for accurate seabed sediment classification.

关键词： image analysis Stacking Sonar applications signal processing Predictive models Marine engineering Acoustics Sediments Ensemble learning Reliability

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Performance Analysis of AIRBORNE SAR System

Performance Analysis of AIRBORNE SAR System

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Electronics, Communication and signal processing (ICECSP), International conference on

作者： Pranshu Upadhyay Sandeep Kumar Kritika Upadhyay Manisha Bharti Dept. of Electronics and Communication Engineering National Institute of Technology Delhi Delhi India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

In today's modern era, SYNTHETIC APERTURE RADAR (SAR) is used extensively for military purposes, especially AIRBORNE SAR. Traditional AIRBORNE SAR system is comparatively complex, costly and are of large volume. It cannot be installed again and again for different applications, so we have developed an optimum AIRBORNE SAR system operating in x band which have all system parameter like azimuth resolution, antenna aperture length etc. in prime state. In this paper, SAR sensor designing is put into practice in order to satisfy a set of performance parameters. The modeling of design parameters for Spotlight and Stripmap mode of operations are stimulated. This paper illustrates how one single SAR system may function in both Spotlight and Stripmap modes and it also yields different resolution levels based on requirements at the expense of other factors.

关键词： image resolution Defense industry Azimuth Atmospheric modeling Bandwidth signal processing Depression Radar polarimetry Performance analysis Synthetic aperture radar

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Label-free multiscale dynamic imaging using 3D phase contrast and deep UV microscopy 3

Label-free multiscale dynamic imaging using 3D phase contras...

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conference on Multiscale Imaging and Spectroscopy III

作者： Robles, Francisco E. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA Emory Univ Atlanta GA 30332 USA

ISBN: (纸本)9781510647602;9781510647596

In this work we present novel tools for label-free subcellular dynamic imaging. We explore dynamics using two orthogonal technologies: First, we look at dynamic information derived from refractive index fluctuations using an emerging 3D quantitative phase imaging methods called quantitative oblique back illumination microscopy (qOBM). Next, we look at dynamic information derived from the extinction coefficient of endogenous biomolecules in cells using multi-spectral deep-UV microscopy. Dynamics at multiple time-scales are explored and their unique attributes and applications in biomedicine are discussed.

关键词： Microscopy Deep ultraviolet Ultraviolet radiation 3D image processing signal processing Light sources Microscopes Phase contrast Refractive index Stereoscopy

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Improved Least-Square DV-Hop Algorithm for Localization in large scale wireless sensor network

Improved Least-Square DV-Hop Algorithm for Localization in l...

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2022 International conference on Control, Automation and Diagnosis, ICCAD 2022

作者： Mani, Rahma Sevillano, Jose Luis Liouane, Noureddine Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Monastir Tunisia Architecture and Computer Technology Departement Universidad de Sevilla Seville Spain

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

Certain applications of wireless sensor networks require that the sensor nodes should be aware of their position relative to the sensor environment. Generally in the applications of positioning in the internet of things (IoT), there is a deficiency of localization precision and concrete results. It is really important to maintain high-efficient localization schemes for the Internet of things, especially for wireless sensor networks. For that, an improved DV-hop algorithm is proposed in this paper to move for more accurate results based on the least square method. Therefore, the weight coefficient is calculated between an anchor node and the other anchor nodes using the mean square method. Then, this weighting coefficient, the hop size, will be applied between the unknown nodes and the anchor nodes in order to determine the distances. The computed hop-size average significantly enhances the positioning accuracy which is approved by the experiments that explain how suitable this improved Last-Square DV-hop Algorithm is for localization in WSN. © 2022 IEEE.

关键词： Internet of things

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Proceedings - 2022 IEEE International conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Proceedings - 2022 IEEE International Conference on Consumer...

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2022 IEEE International conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

ISBN: (纸本)9781665470506

The proceedings contain 302 papers. The topics discussed include: study on energy saving and carbon reduction in tourism: a case of electric boats;investigation of stray light threshold for VR head-mounted display;simulation on ultrasonic tomography for optimum resonance frequency and amplitude input signal;quadrature oscillator employing two current conveyors and grounded capacitors;single image dehazing based on dark channel prior with optimal scaling factors;design and analysis of 7x7 median filter with 8-step low-complexity fast searching approach for undersea image processing applications;design methodology of queue-based fast classification for sequential minimal optimization in SVM ML-training;gate voltages impacting on latch-up measurements;deep learning for base station association in cellular-connected unmanned aerial vehicle systems;a hypergraph convolutional neural network for node classification of citation network data;a convex optimization method for edge signal denoising over graph;sharpening the spectral response of polynomial graph filter by using amplitude change function;and temperature data denoising based on directed Laplacian matrix and heat kernel smoothing.

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DAASPP: Depth-Wise Asymmetric Atrous Spatial Pyramid Pooling for COVID-19 Lesion Segmentation

DAASPP: Depth-Wise Asymmetric Atrous Spatial Pyramid Pooling...

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

作者： Siti Raihanah Abdani Syed Mohd Zahid Syed Zainal Ariffin Nursuriati Jamil Shafaf Ibrahim School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Medical image segmentation plays a vital role in automated disease screening and diagnosing systems. One of the diseases that depend on image segmentation output is COVID-19, in which the diseased lesion is informative in order to determine the disease severity. The task of segmenting COVID-19 lesions is a challenging process, not only due to computational costs and hardware limitations but also because of the varying sizes and shapes of the lesions. In this study, we proposed to integrate a multiscale unit, specifically the ASPP, into a base DABNet, resulting in a lightweight module called DAASPP. The addition of a multiscale unit is meant to further improve the network capability in extracting lesion features of various scales so that small-and large-scale lesions can be segmented accurately. There are two variations of DAASPP have been proposed: one with a single-unit ASPP and another network with a dual-unit ASPP. Both variations are placed at the last and second last DABNet blocks due to the availability of complex features at these two layers. Four different scales are explored for each network variation, specifically the 2, 3, 4, and 5 dilated convolution layers. The results showed that DAASPP with a single-unit ASPP and four parallel scale branches demonstrated the best segmentation performance, achieving mean IoU, mean Dice coefficient, and mean accuracy scores of 0.6629, 0.7461, and 0.9991, respectively. This improvement in extracting the lesion regions will help in producing a better diagnosis system of the COVID-19 severity level.

关键词： COVID-19 image segmentation Shape Convolution Feature extraction Hardware Computational efficiency

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Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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

作者： Shiyu Miao Delong Chen Fan Liu Chuanyi Zhang Yanhui Gu Shengjie Guo Jun Zhou College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong SAR School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University Queensland Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction—such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks.

关键词： Training image segmentation Roads Semantics Optical fiber networks Optical imaging Optical sensors Speech processing Optical signal processing Remote sensing

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