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Automation of lung ultrasound imaging and image processing for bedside diagnostic examinations

Automation of lung ultrasound imaging and image processing f...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Róbert Zsolt Szabó Gábor Orosz Tamás Ungi Colton Barr Chris Yeung Roland Incze Gabor Fichtinger János Gál Tamás Haidegger Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Department of Anesthesiology and Intensive Therapy Semmelweis University Budapest Hungary Laboratory for Percutaneous Surgery Queen’s University Kingston ON Canada Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

The causes of acute respiratory failure can be difficult to identify for physicians. Experts can differentiate these causes using bedside lung ultrasound, but lung ultrasound has a considerable learning curve. We investigate if an automated decision-support system could help novices interpret lung ultrasound scans. The system utilizes medical ultrasound, data processing, and a neural network implementation to achieve this goal. The article details the steps taken in the data preparation, and the implementation of the neural network. The best model’s accuracy and error rate are presented, along with examples of its predictions. The paper concludes with an evaluation of the results, identification of limitations, and suggestions for future improvements.

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JPEG AI Compressed Domain Face Detection

JPEG AI Compressed Domain Face Detection

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ieee Workshop on Multimedia signal processing

作者： Ayman Alkhateeb Alessandro Gnutti Fabrizio Guerrini Riccardo Leonardi João Ascenso Fernando Pereira Dep. of Information Engineering CNIT - University of Brescia Italy Dep. of Electronics Engineering University of Rome Tor Vergata Italy Instituto Superior Técnico Universidade de Lisboa - Instituto de Telecomunicações Lisbon Portugal

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Learning-based image coding has achieved competitive performance in terms of compression efficiency, while also gaining a key advantage in the ability to carry out computer vision tasks directly in the compressed domain. In fact, the latent representation which is generated using deep learning techniques may natively encapsulate all visual features needed for processing tasks, thereby eliminating the need to perform the expensive synthesis transform process at the decoder side. In this paper, it is proposed to perform face detection using the latent code present in the JPEG AI architecture. First, some experiments show how decoded images can be efficiently processed for face detection without retraining, albeit with some performance degradation. Then, for the first time a compressed domain RetinaFace-based detector applied to JPEG AI latent representations is competitively proposed. The performance achieved is comparable to the performance of the original RetinaFace applied to the reconstructed JPEG AI images, while reducing computational complexity since it bypasses the image decoding process. It is expected that this approach might be extended to other vision tasks since the JPEG AI representation format is not tailored specifically for any computer vision task.

关键词： Visualization image coding Transform coding Transforms signal processing Decoding Face detection Artificial intelligence image reconstruction Faces

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Research on super-resolution reconstruction of single-frame image of infrared focal plane polarization imaging 8

Research on super-resolution reconstruction of single-frame ...

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8th symposium on Novel Photoelectronic Detection Technology and Applications

作者： Yuan, Hongwu Wang, Yiqing Xu, Guoming Wang, Feng School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China Key Laboratory of Polarization Imaging Detection Technology in Anhui Province Hefei China Institute of Intelligent Information Anhui Xinhua University Hefei China School of Internet Anhui University Hefei China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. The experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion. © 2022 SPIE

关键词： image fusion

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Ultrasound image Enhancement with the Variance of Diffusion Models

Ultrasound Image Enhancement with the Variance of Diffusion ...

引用

ieee International symposium on Applications of Ferroelectrics (ISAF)

作者： Yuxin Zhang Clément Huneau Jérôme Idier Diana Mateus École Centrale Nantes LS2N CNRS UMR 6004 Nantes Université F-44000 France

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound imaging, despite its widespread use in medicine, often suffers from various sources of noise and artifacts that impact the signal-to-noise ratio and overall image quality. Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging to address this challenge. By applying Eigenspace-Based Minimum Variance (EBMV) beamforming and employing a denoising diffusion model fine-tuned on ultrasound data, our method computes the variance across multiple diffusion-denoised samples to produce high-quality despeckled images. This approach leverages both the inherent multiplicative noise of ultrasound and the stochastic nature of diffusion models. Experimental results on a publicly available dataset demonstrate the effectiveness of our method in achieving superior image reconstructions from single plane-wave acquisitions. The code is available at: https://***/Yuxin-Zhang-Jasmine/IUS2024_Diffusion.

关键词： Adaptation models Ultrasonic imaging Array signal processing computational modeling Noise reduction Speckle Diffusion models Spatial resolution image reconstruction signal to noise ratio

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CUDA-GPU Implementation Strategies for non-linear Beamforming for Synthetic Transmit Aperture Technique: Initial Results

CUDA-GPU Implementation Strategies for non-linear Beamformin...

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South Asian Ultrasonics symposium (SAUS), ieee

作者： Lokesh Basavarajappa Arun K. Thittai Department of Bioscience and Biomedical Engineering Indian Institute of Technology Indore Madhya Pradesh India Department of Applied Mechanics and Biomedical Engineering Indian Institute of Technology Madras Tamil Nadu India

ISBN: (数字)9798350384017

ISBN: (纸本)9798350384024

Synthetic transmit aperture (STA) ultrasound imaging offers dynamic focusing during both transmission and reception, which provides high-resolution images. However, its implementation demands high data processing capability. Nonlinear beamforming techniques have been developed to improve ultrasound image quality; these methods make use of signal correlation to increase image contrast and resolution. Here again, the computational complexity in implementation poses a huge challenge for applicability to real-time applications. Numerous works utilized GPU programming to address large computations in advanced beamforming and STA imaging. This paper investigates the GPU implementation strategies of STA imaging with non-linear beamforming and suggests a method for realistic imaging with improved image quality. In particular, we studied approaches that utilized GPU tensor technology, beamforming methods, and beamforming grid sizes. To evaluate different strategies, experimental radiofrequency (RF) data obtained from a CIRS general purpose phantom was used. The results showed that the filtered delay multiply and sum (FDMAS) provides better quality images than delay and sum (DAS) beamforming for all combinations. Moreover, the results suggest that the 1024x128 grid size provided better-quality images at an acceptable beamforming time.

关键词： Radio frequency image quality Ultrasonic imaging Tensors Array signal processing Frequency division multiaccess Graphics processing units

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied computational Electromagnetics Society symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 ieee.

关键词： Classification (of information)

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Application of Tensor Completion for Reducing the Beamforming Time in Ultrafast Ultrasound Imaging: A Doppler Ultrasound Assessment

Application of Tensor Completion for Reducing the Beamformin...

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ieee International symposium on Applications of Ferroelectrics (ISAF)

作者： Sajjad Afrakhteh Federico Mento Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Doppler ultrasound is a non-invasive technique used to measure blood flow in blood vessels. However, this application requires a large number of temporal frames for precise blood flow measurements, posing challenges for beamforming time, even with a simple delay and sum (DAS) beamformer. Therefore, in this study, we aim to address this issue by proposing a post-processing solution and evaluating its effectiveness for Doppler ultrasound. In this study, We introduce sparse beamforming as a method to reduce beamforming time in preprocessing followed by applying tensor completion (TC) to reconstruct the missing information of the 3-dimensional (3D) beamforming data. We applied the proposed technique to the carotid artery data, demonstrating the feasiblity of improving the beamforming computational complexity by 4 to 5 times, while achieving comparable doppler velocity estimates to the original case (i.e., without sparse beamforming).

关键词： Ultrasonic imaging Tensors Array signal processing Ultrasonic variables measurement Time measurement Doppler effect computational complexity image reconstruction Carotid arteries Blood flow

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Benchmarking End-to-End Deep and Feature Engineered Learning on an Embedded Smart Access Control System

Benchmarking End-to-End Deep and Feature Engineered Learning...

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ieee International symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Jantana Panyavaraporn Virasit Keawkae Paramate Horkaew Department of Electrical Engineering Burapha University Chonburi Thailand School of Computer Engineering Suranaree University of Technology Nakorn Ratchasima Thailand

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

Artificial intelligence has been extensively applied to address a range of security concerns, especially in access control systems. The paradigm has shifted from analyzing features meticulously extracted from images to deep learning of global patterns present in them. This paper presents end-to-end systems based on both prevailing technologies. Facial recognition models were deployed on a Raspberry Pi 4 board, interfaced via the MQTT protocol with an ESP32 module, which was tasked with electromagnetic locking and lighting controls. Access to the demonstrated area was granted only to authorized faces previously stored in the system. Additionally, the system featured liveness detection, preventing the malicious use of photographed images to bypass access. The benchmarks showed that CNN marginally outperformed the HOG-based algorithm on the testing dataset, although at higher computational costs. It was thus chosen for deployment in an actual environment and left unattended for one week. Consequently, the authorization accuracy reached 98.06% under specified conditions.

关键词： Accuracy Protocols Face recognition signal processing algorithms Benchmark testing signal processing Feature extraction Cameras Liquid crystal displays Faces

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Differentiable Beamforming for Distributed Attenuation Estimation and Spatial Gain Compensation (SGC)

Differentiable Beamforming for Distributed Attenuation Estim...

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ieee International symposium on Applications of Ferroelectrics (ISAF)

作者： Benjamin N. Frey Dongwoon Hyun Walter Simson Thurston Brevett Louise Zhuang Jihye Baek Sergio J. Sanabria Jeremy J. Dahl Department of Applied Physics Stanford University Stanford CA Siemens Healthineers Palo Alto CA Department of Radiology Stanford University Stanford CA Department of Electrical Engineering Stanford University Stanford CA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound imaging with automatic differentiation has emerged as a technique to map many model-based approaches for estimating physical beamforming or tissue parameters onto a differentiable computational graph. Using a tomographic model, we adapt this approach to enable highly precise and accurate distributed attenuation estimation. We present a novel sub-aperture difference method for attenuation estimation on simulated and experimental ultrasound channel data. The presented attenuation estimation model estimates attenuation with less than 44 mdB/cm/MHz on simulated inclusion data, and 119 mdB/cm/MHz on simulated uniform attenuation data, but is challenged by experimental data with low signal-to-noise ratio and the selection of an a priori estimate of the attenuation.

关键词： Adaptation models Ultrasonic imaging Array signal processing computational modeling Estimation Tomography Attenuation Data models image reconstruction signal to noise ratio

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Effect comparative analysis of wavelet reconstruction and EMD data processing in continuous shock signal interference 14

Effect comparative analysis of wavelet reconstruction and EM...

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14th International symposium on computational intelligence and Design, ISCID 2021

作者： Zhang, Junbin Xu, Xiaobin Liu, Shiran Sun, Peng Lin, Jingzhou Zhang, Dewei Zou, Qiongfen Hypervelocity Aerodynamics Institute China Aerodynamics Research Development Center Mianyang China

ISBN: (纸本)9781665438568

When the mechanical structure is subjected to a very short interval of continuous shock, the shocked machine will oscillate, the shocked force value cannot be measured accurately, the later shock force value will be affected by the former shock force. In this paper, in order to eliminate the interference caused by mechanical self-oscillation in the shock force test, two signal processing algorithms were adopted, which conclude wavelet reconstruction algorithm and empirical mode decomposition (EMD). Through the analysis in time domain and frequency domain, it is found that both algorithms can eliminate the interference effectively, which caused by mechanical self-oscillation. These processing effects are the same. © 2021 ieee.

关键词： Empirical mode decomposition

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