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Ultrasound Beamforming: Exploring Cloud-Native and Edge Computing Solution

Ultrasound Beamforming: Exploring Cloud-Native and Edge Comp...

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Technological Advancements in Computational Sciences (ICTACS), International conference on

作者： Hamza Hadri Abderahhim Fail Mohamed Sadik Amine Essaken NEST Research Group LRI Lab ENSEM of Hassan II University Casablanca Morocco ENSEM of Hassan II University Casablanca Morocco

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Ultrasound imaging is pivotal in modern medicine for its non-invasive visualization of anatomical structures. How- ever, traditional systems face challenges in scalability, flexibility, and cost-effectiveness due to centralized processing units. this paper introduces a novel cloud-native architecture tailored for ultrasound imaging, focusing on the computationally intensive beamforming process. the architecture integrates on-premise edge-computing acquisition probes with cloud-based processing, leveraging edge computing to enhance efficiency and responsive- ness. Initial data preprocessing at the edge optimizes bandwidth use before secure transmission to the cloud via dedicated connections. In the cloud, real-time data ingestion through AWS Kinesis supports scalable processing using containerization technologies. High-performance computing languages ensure efficient beam- forming, improving diagnostic accuracy and operational efficacy. the architecture's scalability, cost-effectiveness, and integration potential for AI-driven diagnostics are discussed, highlighting its transformative impact on ultrasound imaging practices and healthcare delivery.

关键词： Cloud computing Ultrasonic imaging Array signal processing image edge detection Scalability Computer architecture Medical services Transforms Probes Edge computing

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Enhancing Facial image Resolution: Leveraging UNET++ for Super-Resolution using Deep Learning

Enhancing Facial Image Resolution: Leveraging UNET++ for Sup...

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International conference on Computer and Information Technology (ICCIT)

作者： Asef Jamil Ajwad Sk Tahmed Salim Rafid Saurov Podder Department of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh

this paper presents a novel and robust method for achieving 4X upscaling of facial images, leveraging the UNET++ architecture and applied to the widely recognized Flickr-Faces-HQ (FFHQ) dataset comprising high-resolution 512x512 images. the study systematically explores the performance of four distinct UNET++ model sizes, each offering a unique balance between computational efficiency and upscaling quality. through extensive experimentation, the proposed approach demonstrates remarkable prowess in preserving intricate facial features, enhancing texture details, and maintaining visual realism during the upscaling process. Notably, the best-performing model exhibits an impressive average Peak signal-to-Noise Ratio (PSNR) of 30.465 dB, a Structural Similarity Index (SSIM) of 0.859 and a Multiscale SSIM (MS-SSIM) of 0.976, establishing a new standard of excellence in facial image upscaling. these findings contribute to the advancement of image processing techniques, particularly in the context of facial imagery, and hold profound implications for a wide range of applications, including computer graphics, image enhancement, and face recognition systems.

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Low Power signal processing via Approximate Multiplier for Error-Resilient applications

Low Power Signal Processing via Approximate Multiplier for E...

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IEEE International conference on Industrial and Information Systems

作者： Bharat Garg G K Sharma ABV-Indian Institute of Information Technology and Management Gwalior India

the exponentially enlarging usability and mushrooming features in the portable hand-held devices such as cellphone appeal for the highly energy-efficient designs as the user cannot tolerate power famished devices rather can manage with tiny degraded quality. the multimedia applications such as image/video processing are compute-intensive and require efficient designs which can be achieved by approximate designs with the feeble accord in accuracy. As multiplication is the most frequently used and highly energy-consuming arithmetic operation, in this paper, a low power approximate multiplier architecture (AMA) is proposed that significantly improves the design metrics. We evaluate the proposed AMA with various quality metrics and design an AMA embedded Gaussian smoothing filter (GSF). the simulation results show that AMA reduces 81.5% power consumption over accurate and 19.17% energy over the well-known approximate multiplier architecture. Further, the AMA embedded GSF reduces 6.2% area and 7.53% power consumption over the best known approximate multiplier embedded GSF.

关键词： Adders Computer architecture Approximation algorithms Algorithm design and analysis Smoothing methods Delays Algorithm design and analysis smoothing methods adders Computer Architecture Approximation algorithms Multiplying circuits Approximate energy-efficient design delay hand-held equipment arithmetic operation

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Rate-perceptual-distortion optimization (RpDO) based picture coding — Issues and challenges

Rate-perceptual-distortion optimization (RpDO) based picture...

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International conference on Digital signal processing (DSP)

作者： Hong Ren Wu Weisi Lin King Ngi Ngan School of Elec. & Comp. Engineering RMIT University Melbourne Australia School of Computer Engineering Nanyang Technological University Singapore Department of Electronic Engineering Chinese University of Hong Kong Hong Kong P.R. China

Perceptual image and video coders have been reported extensively in the literature with a recent survey listing sixteen representative PVCs (perceptual video coders) for single-view video coding, ten of which conform to H.263, MPEG-4 or H.264/AVC international standard. Successful approaches to perceptual picture coding are classified as perceptual predictive coding, perceptual quantization, rate-perceptual-distortion optimization (RpDO) and perception-based pre-, loop- and post-filtering. this contribution analyzes and examines the performance and tradeoffs of representative perceptual as well as traditional waveform based picture coding methods in terms of their effectiveness in quality constrained coding and RpDO criterion in the context of visual quality controlled or regulated visual communication, broadcasting, entertainment and consumer electronic products, systems and services or applications. Fundamental flaws of performance benchmarking using a rate-driven coding strategy are interrogated for visual quality assured services or user experience. It is revealed that while existing perceptual distortion measures (PDMs) successfully grade distortions corresponding to HVS (human visual system) perception, their quantitative correspondence, or lack of it, to discernible levels by human visual perception in terms of just-not-noticeable-difference (JNND), just-noticeable-difference level 1 (JND 1 ), JND level 2 (JND 2 ), etc. has not been thoroughly investigated nor established, and this motivates the related initial work reported in this paper. Issues and constraints of existing picture coding standards are discussed in the context of developing a future picture coding standard framework for constant visual picture quality systems and services.

关键词： Visualization Distortion measurement Channel coding Bit rate image coding Standards

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MOCEM V4 - radar simulation of ship at sea for SAR and ISAR applications

MOCEM V4 - radar simulation of ship at sea for SAR and ISAR ...

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EUSAR 2014;10th European conference on Synthetic Aperture Radar

作者： Christian Cochin Jean-Christophe Louvigne Ronan Fabbri Corentin Le Barbu Laurent Ferro-Famil Atle-Onar Knapskog Nina Odegaard

Known as a useful tool to simulate SAR image, the MOCEM software has been recently extended. Now, its V4 version includes new features to simulate radar raw data of a maritime scene composed of a ship above a dynamic sea surface and land or infrastructures behind the coastline. Moreover, the software is now able to compute raw data, not only for SAR purposes but, more generally, for ISAR applications considering fixed or mobile carriers. this paper describes the latest developments that are focused on real time computation of SAR and ISAR images. It is now possible to render the delocalization, defocusing and deformations encountered in images when dealing with complex and realistic ship motions. the GPU technique has been used for intensive computation. As an example, a range profile (IQ signal with Doppler in phase) can be produced in less than 10 ms. If it is not yet real time compared to a real radar acquisition rate, it opens the way to stimulate real processing and, in a near future, it may be possible to produce signals in real time for HWIL - Hardware In the Loop - testbeds.

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A Joint Dynamic Domain Adaptation Module and Multi-incentive-aware Based Model for Sentiment Analysis of Autism Paintings

A Joint Dynamic Domain Adaptation Module and Multi-incentive...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jiayin Min The Experimental High School Attached to Beijing Normal University Beijing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the development of society, the number of autistic children is increasing, and there is growing concern about their education and development. Autistic children often have difficulties in language expression, social interaction and emotional understanding. In this context, finding new ways to understand the emotions and needs of autistic children has become particularly important. By analyzing the drawings of autistic children and using deep learning models to mine the emotions and information embedded in them, we can provide educators and parents with a more intuitive and in-depth way to understand the inner world of autistic children. As a non-verbal form of expression, paintings can help autistic children release emotions and express their inner thoughts and feelings. Firstly, based on psychological research results, we propose a new image emotion classification network based on multi-incentive awareness, which consists of three phases, i.e., incentive selection (S), feature extraction (O) and emotion prediction (R). In the first phase, in this paper, we select special emotion excitations (color, object, face) by combining existing target detection methods in deep learning. Subsequently, we design three specific networks to extract different emotion features from multiple incentives synchronously, i.e., global network, semantic network, and expression network. Finally, the model combines all the above features for image emotion prediction. Comparative experiments demonstrate that the accuracy of our constructed sentiment analysis model outperforms existing algorithms. After training the above multi-incentive-aware image sentiment analysis model, we design a transfer learning model to map the features learned by the model on the regular image dataset to the drawing dataset of autistic patients. We perform multi-representation feature extraction using a hybrid structure, which consists of multiple sub-structural feature extraction units. At the same time we us

关键词： Deep learning Analytical models Adaptation models Sentiment analysis image color analysis Transfer learning Psychology Predictive models Feature extraction Painting

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4×4 Array Multiplier Using Transmission Gate Full Adder

4×4 Array Multiplier Using Transmission Gate Full Adder

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International conference for Convergence of Technology (I2CT)

作者： Nitesh Kumar Rathore Ankur Beohar Pallabi Sarkar Kavita Khare Pooran Singh Dept. of Electrical and Computer Engineering Mahindra University Hyderabad India School of Electrical and Electronics Engineering (SEEE) VIT Bhopal University Madhya Pradesh India Dept. of Electronic and Communication Engineering MANIT Bhopal Madhya Pradesh India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Multiplication is the most essential function carried out by the arithmetic and logic unit. the multiplier specifically has important applications in signal processing analysis, image processing analysis, and so on. the design of multipliers with high-performance properties such as fast speed, low power, small size, and minimal latency is of great interest in the research field. In this research, a 4×4 array multiplier using a transmission gate full adder has been designed. the full adder used in the design is a conventional CMOS full adder implemented using XOR, AND, OR gates with a load capacitance of 32F. In the architecture, the same 4×4 multiplier has been designed using semi-dynamic logic and has carried out different design parameters calculations and observations respectively along with process, voltage, and temperature variation (PVT) of 10%. When compared to Full Adder implementation using a semi-dynamic logic gate, Full Adder implementation utilizing a transmission gate imparts less latency. Each of the mentioned logic blocks is designed using LT-Spice software for 180nm technology.

关键词： image processing Voltage Logic gates Software Power dissipation Delays Adders

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Autonomous Driving Technology through image Classfication and Object Recognition based on CNN 2020

Autonomous Driving Technology through Image Classfication an...

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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Hyeongi Yeo Paul Math School Cheonan South Korea

ISBN: (纸本)9781450389532

the time has come for humans that the cars themselves think and judge rather than driving by humans, and has already reached a certain level. It hasn't reached the commercialization stage yet, but it's not a story of the future anymore, and even now, a great deal of technological development is taking place every day. However, since this autonomous driving technology does not have a driver when an accident occurs, it can cause problems in terms of responsibility and ethics, so it has to be possible to make the best choice at anytime and anywhere without accident. It requires a high level of technology on the software and the hardware. Most autonomous vehicles currently on the market do not stay or exceed the level of 2 to 2.5. the level of 2~2.5 is a partial automation step, and in a stable environment, autonomous driving is possible in part, but it must be possible for the driver to immediately take over control of the driving of the *** CNN-based YOLOv3 implements object recognition and image classification, which are the core technologies of autonomous driving technology and Based on the data, it is implemented through the AI autonomous vehicle kit, so that it is possible to make suggestion or ideas for development of Autonomous driving technology.

关键词： image Classification Obstacle Recognition Autonomous Driving CNN YOLOv3

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An Adaptive Fusion Feature Extraction Algorithm Based on CNN 22

An Adaptive Fusion Feature Extraction Algorithm Based on CNN

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Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Jiangxia Wei Zhifei Wang Xinying Xu Zhe Zhang Lan Cheng College of Electrical and Power Engineering Taiyuan University of Technology China

ISBN: (纸本)9781450397810

Keypoint feature extraction algorithms based on manual feature rely on the professional domain knowledge of the designer, and usually perform well in depicting the detail of an image. However, these manual feature-based feature extraction algorithms tend to fail in obtaining the abstract information of the image, which results in low robustness of the algorithms. In view of this, this paper proposes an image feature extraction algorithm that adaptively integrates the low layer feature of CNN and the high layer feature of CNN. the proposed algorithm makes use of fact that the low layer feature can detect the detail of an image and the high layer feature can detect the abstract information of the image. Meanwhile, the similarity between the area descriptors of two feature points is employed to adaptively determine the weight of the high layer feature and that of the low layer feature in the integrated feature, so as to realize the trade-off between distinguishability and invariance of features, and make the integrated feature more robust. Experiments on HPatches, Oxford, and RDNIM datasets show that the proposed algorithm is not only robust to illumination changes, but also shows superior performance when it comes to challenging scenes such as large viewing angle changes and day and night matching.

关键词： CNN image feature extraction Feature matching Feature fusion

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Filter Performance in Lenke Scoliosis Type 1 X-Ray image Pre-processing

Filter Performance in Lenke Scoliosis Type 1 X-Ray Image Pre...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), International conference on

作者： Anna Nur Nazilah C Hasimah Ali Yessi Jusman Muhammad Ariffudin Azizul Akram Bin Salim Asy-Syifa Febya Ananta Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Mechatronic Engineering Faculty of Electrical Engineering Technology University Malaysia Perlis Perlis Malaysia Department of Medical Faculty of Medicine and Health Sciences Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Orthopaedic School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

Currently, scoliosis examinations based on the Lenke classification are carried out by doctors using a manual process, namely looking at X-Ray images visually. this method requires quite a long time to be able to diagnose the type of scoliosis. this can cause differences in interpretation and eye fatigue, which can lead to incorrect diagnosis and examination. Pre-processing techniques have the potential to improve the quality of X-ray images for better machine learning analysis. Machine Learning allows doctors to increase speed and avoid diagnostic errors. In this study, pre-processing filter methods, namely Salt and Pepper, Gaussian and Speckle, will be applied to 15 Lenke Type 1 scoliosis images. the results of pre-processing filters are evaluated using the metrics Peak signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). the research results show that the best pre-processing filter method is using Gaussian with an MSE value of 16.28, PSNR of 36.91, and SSIM of 0.94.

关键词： image quality Salt PSNR Scoliosis Noise Medical services Machine learning Speckle X-ray imaging Standards

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