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20th International Conference on Image Analysis and Processing (ICIAP)

作者： Plebe, Alice Da Lio, Mauro Univ Trento Dept Informat Engn & Comp Sci Trento Italy Univ Trento Dept Ind Engn Trento Italy

ISBN: (纸本)9783030306427;9783030306410

In the last decades, the research in autonomous vehicles has greatly improved thanks to the success of artificial neural models. Yet, self-driving cars are far from reaching human performances. It is our opinion that would be wise to reflect on why the human brain is so effective in learning tasks as complex as the one of driving, and to try to take inspiration for designing new artificial driving agents. For this aim, we consider two relevant and related neurocognitive theories: the Convergence-divergence Zones (CDZs) mechanism of mental simulation, and the predicting brain theory. Then, we propose an implementation of a semi-supervised variational autoencoder for visual perception, with an architecture that best approximates those two neurocognitive theories.

关键词： Mental imagery Deep learning Autonomous driving variational autoencoder Free energy

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variational autoencoder-Based Parameter Estimation in Beam-Space OFDM Integrated Sensing and Communication

Variational Autoencoder-Based Parameter Estimation in Beam-S...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Dehkordi, Saeid K. Hauffen, Jan C. Jaensch, Fabian Jung, Peter Caire, Giuseppe Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798350310900

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system employing a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture that uses Orthogonal Frequency Division Multiplexing (OFDM) digital modulation. This framework takes raw signals as input and utilizes a variational autoencoder (VAE) followed by a regression network to output the spatial extent and location of extended targets. Owing to the HDA setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. The proposed solution is motivated as a remedy for the increasing computational complexity associated with high-resolution extended target estimation in multi-carrier digital modulations such as OFDM. In addition, it is well known that off-grid delay-Doppler shifts which are present in the doubly-dispersive channels in the high mobility scenarios expected in ISAC applications, exhibit leakage effects that adversely affect the parameter estimation performance. Due to the data-centric nature of the proposed method, these effects can be learned by the network. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation.(1)

关键词： integrated sensing and communication OFDM variational autoencoder parameter estimation

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variational autoencoder-Based Deep Neural Network for Coronary Heart Disease Risk Prediction 1

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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Amarbayasgalan, Tsatsral Park, Kwang Ho Davagdorj, Khishigsuren Ryu, Keun Ho Theera-Umpon, Nipon Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chungbuk Natl Univ Dept Comp Sci Coll Elect & Comp Engn Cheongju 28644 South Korea Chiang Mai Univ Inst Biomed Engn Chiang Mai 50200 Thailand

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

Coronary heart disease (CHD) is one of the top causes of global mortality. Most patients cannot be diagnosed at the early stage because it does not give any symptoms for many years. If CHD gets worse, it will require advanced treatments, such as heart transplant and stent surgery. Therefore, it is useful in preventing CHD by predicting high-risk people who will suffer from CHD. In this study, we have proposed a variational autoencoder (VAE)-based deep neural network (DNN) model for predicting CHD risk. We improved the performance of DNN model by enriching training dataset using VAE-based data generation. The proposed method has been compared with machine learning algorithms on Korea National Health and Nutrition Examination Survey (KNHANES) dataset. As a result, the proposed method outperformed the compared classifiers. The performance measurements include accuracy, precision, recall, F1-score, and AUC score which reached 0.851, 0.882, 0.809%, 0.844, and 0.851, respectively.

关键词： Coronary heart disease Deep neural network Machine learning variational autoencoder

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variational autoencoder BASED UNSUPERVISED DOMAIN ADAPTATION FOR SEMANTIC SEGMENTATION

VARIATIONAL AUTOENCODER BASED UNSUPERVISED DOMAIN ADAPTATION...

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IEEE International Conference on Image Processing (ICIP)

作者： Li, Zongyao Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Educ & Res Ctr Math & Data Sci Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781728163956

Unsupervised domain adaptation, which transfers supervised knowledge from a labeled domain to an unlabeled domain, remains a tough problem in the field of computer vision, especially for semantic segmentation. Some methods inspired by adversarial learning and semi-supervised learning have been developed for unsupervised domain adaptation in semantic segmentation and achieved outstanding performances. In this paper, we propose a novel method for this task. Like adversarial learning-based methods using a discriminator to align the feature distributions from different domains, we employ a variational autoencoder to get to the same destination but in a non-adversarial manner. Since the two approaches are compatible, we also integrate an adversarial loss into our method. By further introducing pseudo labels, our method can achieve state-of-the-art performances on two benchmark adaptation scenarios, GTA5-to-CITYSCAPES and SYNTHIA-to-CITYSCAPES.

关键词： Unsupervised domain adaptation semantic segmentation variational autoencoder adversarial learning

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variational autoencoder for Channel Estimation: Real-World Measurement Insights 27

Variational Autoencoder for Channel Estimation: Real-World M...

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27th International Workshop on Smart Antennas (WSA)

作者： Baur, Michael Boeck, Benedikt Turan, Nurettin Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol Munich Germany

ISBN: (纸本)9798350362008;9798350361995

This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.

关键词： Channel estimation measurement data deep neural network generative model variational autoencoder

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variational autoencoder FOR SPEECH ENHANCEMENT WITH A NOISE-AWARE ENCODER

VARIATIONAL AUTOENCODER FOR SPEECH ENHANCEMENT WITH A NOISE-...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Fang, Huajian Carbajal, Guillaume Wermter, Stefan Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany Univ Hamburg Knowledge Technol WTM Hamburg Germany

ISBN: (纸本)9781728176055

Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics. However, this approach only uses clean speech in the training phase, making the estimation particularly sensitive to noise presence, especially in low signal-to-noise ratios (SNRs). To increase the robustness of the VAE, we propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. We evaluate our approach on real recordings of different noisy environments and acoustic conditions using two different noise datasets. We show that our proposed noise-aware VAE outperforms the standard VAE in terms of overall distortion without increasing the number of model parameters. At the same time, we demonstrate that our model is capable of generalizing to unseen noise conditions better than a supervised feedforward deep neural network (DNN). Furthermore, we demonstrate the robustness of the model performance to a reduction of the noisy-clean speech training data size.

关键词： speech enhancement generative model variational autoencoder semi-supervised learning

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variational autoencoder Leveraged MMSE Channel Estimation 56

Variational Autoencoder Leveraged MMSE Channel Estimation

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56th Asilomar Conference on Signals, Systems, and Computers

作者： Baur, Michael Fesl, Benedikt Koller, Michael Utschick, Wolfgang Tech Univ Munich Sch Computat Informat & Technol Munich Germany

ISBN: (纸本)9781665459068

We propose to utilize a variational autoencoder (VAE) for data-driven channel estimation. The underlying true and unknown channel distribution is modeled by the VAE as a conditional Gaussian distribution in a novel way, parameterized by the respective first and second order conditional moments. As a result, it can be observed that the linear minimum mean square error (LMMSE) estimator in its variant conditioned on the latent sample of the VAE approximates an optimal MSE estimator. Furthermore, we argue how a VAE-based channel estimator can approximate the MMSE channel estimator. We propose three variants of VAE estimators that differ in the data used during training and estimation. First, we show that given perfectly known channel state information at the input of the VAE during estimation, which is impractical, we obtain an estimator that can serve as a benchmark result for an estimation scenario. We then propose practically feasible approaches, where perfectly known channel state information is only necessary in the training phase or is not needed at all. Simulation results on 3GPP and QuaDRiGa channel data attest a small performance loss of the practical approaches and the superiority of our VAE approaches in comparison to other related channel estimation methods.

关键词： Channel estimation deep learning variational autoencoder MMSE estimator machine learning

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variational autoencoder for Early Stress Detection in Smart Agriculture: A Pilot Study

Variational Autoencoder for Early Stress Detection in Smart ...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Zancanaro, Alberto Cisotto, Giulia Tegegn, Dagmawi Delelegn Manzoni, Sara L. Reguzzoni, Ivan Lotti, Edoardo Zoppis, Italo Univ Padua Dept Informat Engn DEI Padua Italy Univ Milano Bicocca Dept Informat Syst & Commun DISCo Milan Italy SeleTech Engn Srl Milan Italy

ISBN: (数字)9781665469982

ISBN: (纸本)9781665469982

The digitalization of the agrifood market is increasingly demanding for new technologies to support its transition towards smart agriculture, a sustainable food industry, and efficient management of greenhouses and crop breeding. In this work, we aim to exploit two emerging and promising technologies with application to the early detection of stressful conditions in plants. Two high-resolution near-infrared spectrometers, spanning the range from 1350 nm to 2150 nm, were used to acquire the reflectance spectra from a pothos (Epipremnum aureum) in two different hydration conditions, i.e., normal and anomalous. Then, we trained a machine learning model, i.e., a beta-variational autoencoder (beta-VAE), to identify the anomalies in the hydration of the plant over three months of acquisition. We are able to show the feasibility of our proposed combination of near-infrared spectrometry and the beta-VAE to accurately identify anomalies, i.e., to detect stressful conditions in plants. This contributes to the recent and promising advancements in smart agriculture, by exploiting a new generation of high-resolution, portable, and non-destructive near-infrared sensing technology and powerful machine learning data analytics.

关键词： variational autoencoder deep learning anomaly detection near-infrared spectrometry smart agriculture

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variational autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition

Variational Autoencoder based Latent Factor Decoding of Mult...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li, Xiang Zhao, Zhigang Song, Dawei Zhang, Yazhou Niu, Chunyang Zhang, Junwei Huo, Jidong Li, Jing Qilu Univ Technol Shandong Acad Sci Natl Supercomp Ctr Jinan Key Lab Med Artificial IntelligenceShandong Comp Jinan Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Zhengzhou Univ Light Ind Softwarc Engn Coll Zhengzhou Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Jiuquan Satellite Launch Ctr Alxa League Peoples R China

ISBN: (纸本)9781728118673

Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multi-channel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the state-of-the-art performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.

关键词： Affective Computing Latent Factor Decoding Emotion Recognition EEG variational autoencoder

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variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator

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CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024年第1期5卷 19-28页

作者： Nishikimi, Ryo Nakano, Masahiro Kashino, Kunio Tsukada, Shingo NTT Commun Sci Labs Atsugi Japan NTT Basic Res Labs Atsugi Japan NTT Commun Sci Labs 3-1Morinosato Wakamiya Atsugi Kanagawa 2430198 Japan

BACKGROUND For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high -cost and limited environment using supercomputers owing to the massive computation. OBJECTIVE The purpose of this study is to develop an efficient method for synthesizing 12 -lead ECG signals from cardiac parameters. METHODS The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model. RESULTS Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. CONCLUSION The proposed method has the potential to become an alternative to computationally expensive FEMbased heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.

关键词： variational autoencoder Electrocardiogram Synthesis Cardiac parameters Finite element method

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