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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是281-290 订阅

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Vector -Quantization variational autoencoder Based Data Rate Reduction for Wireless Ultrasound Imaging Systems

Vector -Quantization Variational Autoencoder Based Data Rate...

引用

IEEE SoutheastCon Conference on Engineering the Future

作者： Bastola, Sulav Tekes, Coskun Kennesaw State Univ Elect & Comp Engn Marietta GA 30060 USA

ISBN: (纸本)9798350317107;9798350317114

Ultrasound imaging has become a preferred medical diagnostics tool for many applications due to its cost-effectiveness, non-ionizing nature, and real-time capabilities. There has been a significant progress in the development of new ultrasound probes and systems, particularly portable and wearable devices, incorporating new transducer technologies, sophisticated electronics integration, artificial intelligence and advanced beamforming strategies. Wearable ultrasound systems, equipped with wireless data transfer interfaces, offer unique advantages for continuous signal monitoring of the patients for their critical conditions both in and out-of-hospital settings. Many challenges specifically in data rate reduction for wireless real-time systems needs to be explored. To address this issue, in this paper, we present a vector quantized variational autoencoder model to effectively compress ultrasound RF signals without compromising image quality. We tested and evaluated the performance of the model on real ultrasound datasets. The experimental results demonstrate 92% of data reduction enabling achievable real-time imaging speeds over wireless channels.

关键词： Ultrasound imaging Data reduction variational autoencoder wearable ultrasound

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A multimodal variational autoencoder for estimating progression scores from imaging and microRNA data in rare neurodegenerative diseases

A multimodal variational autoencoder for estimating progress...

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Conference on Medical Imaging - Image Processing

作者： Kmetzsch, Virgilio Becker, Emmanuelle Saracino, Dario Anquetil, Vincent Rinaldi, Daisy Camuzat, Agnes Gareau, Thomas Le Ber, Isabelle Colliot, Olivier INRIA Aramis Project Team F-75013 Paris France Sorbonne Univ Hop Pitie Salpetriere AP HP Paris Brain InstInst Cerveau ICMInserm U1127CN Paris France Univ Rennes IRISA CNRS INRIA F-35000 Rennes France IM2A AP HP Ctr Reference Demences Rarer Precoces Paris France Hop La Pitie Salpetriere AP HP DMU Neurosci Paris France PSL Res Univ EPHE Paris France Paris Brain Inst FrontLab Inst Cerveau ICM Paris France

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Frontotemporal dementia (FTD) is a rare neurodegenerative disease, often of genetic origin, with no effective treatment. There is a substantial pathophysiological overlap with amyotrophic lateral sclerosis (ALS), mutations in the C9orf72 gene being their most common genetic cause. In these disorders, no single biomarker can accurately measure progression, thus it is crucial to combine complementary information from multiple modalities to evaluate new therapeutic interventions. In particular, neuroimaging and transcriptomic (microRNA) data have been shown to have value to track FTD and ALS progression. As these conditions are rare, large samples are not available, hence the need for methods to fuse multimodal data from small samples. In this paper, we propose a method for computing a disease progression score (DPS) from cross-sectional multimodal data, based on variational autoencoders (VAE). We show that unsupervised training leads to the estimation of meaningful latent spaces, where subjects with similar disease states are clustered together and from which a DPS may be inferred. Models were evaluated on 14 patients, 40 presymptomatic mutation carriers and 37 healthy controls from the PREV-DEMALS study. Since there is no ground truth for the DPS, we used the inferred scores to perform pairwise classification as a proxy metric. Presymptomatic subjects and patients were classified with an average area under the ROC curve of 0.83 and 0.94, respectively without and with feature selection. The proposed approach has the potential to leverage cross-sectional multimodal datasets with small sample sizes in order to objectively measure disease progression.

关键词： Multimodal neuroimaging transcriptomics microRNA variational autoencoder deep learning disease progression score neurodegenerative disease

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Optimization Algorithm of Time Synchronization Network Monitoring Based on variational autoencoder 5

Optimization Algorithm of Time Synchronization Network Monit...

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5th International Conference on Computational Intelligence and Applications (ICCIA)

作者： Lv, Bo Pan, Feng Miao, Xinyu Hu, Changjun China Acad Informat & Commun Inst Technol & Stand Beijing Peoples R China

ISBN: (纸本)9781728160429

In this paper an optimization algorithm for time synchronization in telecommunication network is proposed based on VAE(variational Auto Encoder)framework. Firstly features are represented in latent space under proposed framework while performance of synchronization network is measured and evaluated. Secondly optimization algorithm is further designed with which feature of abnormal samples and benchmark are adaptively merged for smooth adjustment with low risk in practical network operation. Meanwhile considering the characteristics as domain knowledge of synchronization network, a novel metric is adopted to reduce the fluctuation of adjustment. The simulation results verified that performance of synchronization network is significantly improved by optimization templates reconstructed through decoding part of VAE model. It is implied that prior knowledge of synchronization in latent space is introduced with certain interpret-ability for assessment of monitoring performance while optimization adjustment can be properly operated through novel metric proposed in this algorithm.

关键词： telecommunication time synchronization machine learning variational autoencoder optimization algorithm

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AN INVARIANT REPRESENTATION OF COUPLER CURVES USING A variational autoencoder: APPLICATION TO PATH SYNTHESIS OF FOUR-BAR MECHANISMS

AN INVARIANT REPRESENTATION OF COUPLER CURVES USING A VARIAT...

引用

ASME International Design Engineering Technical Conferences / 43rd Annual Computers and Information in Engineering Conference (IDETC-CIE) / 47th Mechanisms and Robotics Conference (MR)

作者： Nurizada, Anar Purwar, Anurag SUNY Stony Brook Dept Mech Engn Comp Aided Design & Innovat Lab Stony Brook NY 11794 USA

ISBN: (纸本)9780791887363

This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called variational autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for automated conceptual design of mechanisms for robots and machines.

关键词： Planar four-bar linkage path synthesis Fourier descriptors Wavelets variational autoencoder Neural Networks Machine Learning Deep Learning

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Laser variational autoencoder for map construction and self-localization

Laser variational autoencoder for map construction and self-...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wakita, Shohei Nakamura, Takayuki Hachiya, Hirotaka Wakayama Univ Fac Syst Engn 930 Sakaedani Wakayama 6408510 Japan

ISBN: (纸本)9781538666500

For accurate global self-localization with small memory usage, researches for the compression of the laser-scan data have been actively conducted. Main approaches to the compression are to design feature extractor based on human knowledge regarding the specific environment, e.g., office and hallway. However, in real robot navigation tasks such as a security patrol robot, the robot would be applied to a variety of environments and it is expensive if the users need to tune the design at every environment. To alleviate such problem, we propose to extend the state-of-the-art variational auto-encoder (VAE) by introducing the step-edge detector, which detects non-continuous transition emerged frequently at the laser scan data due to the limitation of distance measurement. With our proposed method, called "laserVAE". the feature extractor of the laser scan is automatically tuned given unknown environments. Through experiments with a real self-localization with 2D laser scan, we demonstrate the effectiveness of the proposed method.

关键词： Feature description of scan data variational autoencoder Scan matching Global self-localization

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A variational autoencoder Approach for Speech Signal Separation 12th

A Variational Autoencoder Approach for Speech Signal Separat...

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12th International Conference on Computational Collective Intelligence (ICCCI)

作者： Do, Hao D. Tran, Son T. Chau, Duc T. Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam OLLI Technol JSC Ho Chi Minh City Vietnam

ISBN: (纸本)9783030630065;9783030630072

Speech separation plays an important role in a speech-related system since it can denoise, extract, and enhance speech signals. In recent years, many methods are proposed to separate the human voice of noise and other sounds. To separate the speech from a complicated signal, we propose a more powerful method by using a VAE model and then postprocessing with a bandpass filter. This combination can use to extract the original human speech in the mixture with not only high-frequency noise but also many different sounds. Our approach can be flexibly applied for the new background sounds.

关键词： Bandpass filter variational autoencoder Frequency domain Speech separation

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VAE-BRIDGE: variational autoencoder Filter for Bayesian Ridge Imputation of Missing Data

VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridg...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Pereira, Ricardo Cardoso Abreu, Pedro Henriques Rodrigues, Pedro Pereira Univ Coimbra Ctr Informat & Syst Dept Informat Engn Coimbra Portugal Univ Porto Ctr Hlth Technol & Serv Res Porto Portugal

ISBN: (纸本)9781728169262

The missing data issue is often found in real-world datasets and it is usually handled with imputation strategies that replace the missing values with new data. Recently, generative models such as variational autoencoders have been applied for this imputation task. However, they were always used to perform the entire imputation, which has presented limited results when comparing to other state-of-the-art methods. In this work, a new approach called variational autoencoder Filter for Bayesian Ridge Imputation is introduced. It uses a variational autoencoder at the beginning of the imputation pipeline to filter the instances that are later fitted to a Bayesian ridge regression used to predict the new values. The approach was compared to four state-of-the-art imputation methods using 10 datasets from the healthcare context covering clinical trials, all injected with missing values under different rates. The proposed approach significantly outperformed the remaining methods in all settings, achieving an overall improvement between 26% and 67%.

关键词： missing data variational autoencoder bayesian ridge data imputation healthcare data

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TOWARD UNSUPERVISED 3D POINT CLOUD ANOMALY DETECTION USING variational autoencoder

TOWARD UNSUPERVISED 3D POINT CLOUD ANOMALY DETECTION USING V...

引用

IEEE International Conference on Image Processing (ICIP)

作者： Masuda, Mana Hachiuma, Ryo Fujii, Ryo Saito, Hideo Sekikawa, Yusuke Keio Univ Tokyo Japan Denso IT Lab Tokyo Japan

ISBN: (纸本)9781665441155

In this paper, we present an end-to-end unsupervised anomaly detection framework for 3D point clouds. To the best of our knowledge, this is the first work to tackle the anomaly detection task on a general object represented by a 3D point cloud. We propose a deep variational autoencoder based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds. To verify the effectiveness of the model, we conducted extensive experiments on ShapeNet dataset. Through quantitative and qualitative evaluation, we demonstrate that the proposed method outperforms the baseline method.

关键词： 3D point cloud anomaly detection unsupervised learning variational autoencoder

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Study on Attenuation Tomography Using Low-frequency Ultrasound Differential Imaging with variational autoencoder for Human Thorax

Study on Attenuation Tomography Using Low-frequency Ultrasou...

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2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium

作者： Zhang, Tong Shi, Haokang Guo, Rui Li, Maokun Yang, Fan Xu, Shenheng Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350371918;9798350371901

Ultrasound (US) is widely employed in medical imaging, but its use in the human thorax is constrained by high attenuation. Recently, researchers have addressed this limitation by utilizing low-frequency US (10 kHz to 750 kHz), which can penetrate the thorax. Studies indicate that low-frequency US can monitor the respiratory system through attenuation factor differential imaging, reconstructing variations in the thorax attenuation factor based on changes in transmitted US energy during respiration. In this paper, we propose a novel inversion method using a neural network structure, the variational autoencoder (VAE), to integrate prior knowledge of thorax structure. The VAE is trained to compress high-dimensional data into a low-dimensional code and subsequently reconstruct it. During inversion, the latent code of the domain of interest (DOI) is first reconstructed, followed by decoding to obtain the high-dimensional attenuation distribution, effectively incorporating structural prior information. Numerical simulations with both training and test set models demonstrate the superiority of the proposed method.

关键词： Respiratory system monitoring Low-frequency ultrasound Thorax imaging Attenuation imaging variational autoencoder

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Federated variational autoencoder for Collaborative Filtering

Federated Variational Autoencoder for Collaborative Filterin...

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International Joint Conference on Neural Networks (IJCNN)

作者： Polato, Mirko Univ Padua Dept Math Padua Italy

ISBN: (纸本)9780738133669

Recommender Systems (RSs) are valuable technologies that help users in their decision-making process. Generally, RSs are designed with the assumption that a central server stores and manages historical users' behaviors. However, users are nowadays more aware of privacy issues leading to a higher demand for privacy-preserving technologies. To cope with this issue, the Federated Learning (FL) paradigm can provide good performance without harming the users' privacy. Some efforts have been devoted to adapt standard collaborative filtering methods (e.g., matrix factorization) into the FL framework in recent years. In this paper, we present a Federated variational autoencoder for Collaborative Filtering (FedVAE), which extends the state-of-the-art MultVAE model. Additionally, we propose an adaptive learning rate schedule to accelerate learning. We also discuss the potential privacy-preserving capabilities of FedVAE. An extensive experimental evaluation on five benchmark data sets shows that our proposal can achieve performance close to MultVAE in a reasonable number of iterations. We also empirically demonstrate that the adaptive learning rate guarantees both accelerated learning and good stability.

关键词： federated learning variational autoencoder collaborative filtering recommender systems top-n item recommendation

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