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Unsupervised Abnormal Sensor Signal Detection with Channelwise Reconstruction Errors

Mingu Kwak, Seoung Bum Kim
2021 IEEE Access  
INDEX TERMS Anomaly detection, convolutional autoencoder, deep learning, multichannel sensor signal data, unsupervised learning.  ...  The reconstruction errors of abnormal and normal channels are shown to be different; therefore, it can be considered as an appropriate feature for anomaly detection.  ...  The symmetric skip-connections are widely used in many CAE architectures for image restoration [42] .  ... 
doi:10.1109/access.2021.3064563 fatcat:wbovlgrnjvf5fpvodillyiwlxq

International Conference on Image Processing

1996 Proceedings of 3rd IEEE International Conference on Image Processing  
Shah, Avideh Zakhor Multichannel adaptive L-filters in color image filtering Constantine Kotropoulos, I. Pitas, M . Gabrani Multichannel filtering for color image processing Kostas Plataniotis, D.  ...  quantizer for image restoration Sanjeev Mehrotra, Navin Chaddha, R.  ... 
doi:10.1109/icip.1996.560353 fatcat:le3ysy6wxrfr7nq56ueropy7tu

NAS-Unet: Neural Architecture Search for Medical Image Segmentation

Yu Weng, Tianbao Zhou, Yuejie Li, Xiaoyu Qiu
2019 IEEE Access  
In this paper, we design three types of primitive operation set on search space to automatically find two cell architecture DownSC and UpSC for semantic image segmentation especially medical image segmentation  ...  Inspired by the U-net architecture and its variants successfully applied to various medical image segmentation, we propose NAS-Unet which is stacked by the same number of DownSC and UpSC on a U-like backbone  ...  a encoder architecture to extract the high-level context and then that context flow to a decoder architecture to restore the spatial information and pixel classification results.  ... 
doi:10.1109/access.2019.2908991 fatcat:cw5knncj3fecxhbyeatplyj4ry

Proceedings of 3rd IEEE International Conference on Image Processing

1996 Proceedings of 3rd IEEE International Conference on Image Processing ICIP-96  
Map decoding of gray-level images over binary channels with memory Restoration of lossy compressed astronomical images Demetrios Sampson, Demetrios V .  ...  for image restoration 757 761 765 Sebastien Guillon, P.  ... 
doi:10.1109/icip.1996.559416 fatcat:jb4cdydgf5edtdljfuzj423ozu

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

Thorsten Hoeser, Claudia Kuenzer
2020 Remote Sensing  
To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN).  ...  The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019.  ...  Acknowledgments: We would such as to thank David Marshall for final proofreading. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12101667 fatcat:vlqupucfrndexnyhrsgawshc2y

HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation

Qixuan Sun, Nianhua Fang, Zhuo Liu, Liang Zhao, Youpeng Wen, Hongxiang Lin, Jialin Peng
2021 Journal of Healthcare Engineering  
Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance.  ...  Multimodal medical image segmentation is always a critical problem in medical image segmentation.  ...  In these structures, an encoder is usually used to extract features while a decoder is to restore extracted features and output the final segmentation predictions. e U-Net [5] has been widely used for  ... 
doi:10.1155/2021/7467261 pmid:34630994 pmcid:PMC8500745 fatcat:zcohwv4p7bgkjke6vdf66jspyi

Deep-learning Image Reconstruction for Real-time Photoacoustic System [article]

MinWoo Kim, Geng-Shi Jeng, Ivan Pelivanov, Matthew O'Donnell
2020 arXiv   pre-print
Standard reconstruction methods for PA imaging are based on solving an inverse problem using appropriate signal and system models.  ...  For handheld scanners, however, the ill-posed conditions of limited detection view and bandwidth yield low image contrast and severe structure loss in most instances.  ...  Additional image quality improvements were demonstrated using multichannel data as the network input (upgUNET).  ... 
arXiv:2001.04631v2 fatcat:gmo7nsrllzaqjhldclnlaomfwm

Table of contents

2018 2018 14th IEEE International Conference on Signal Processing (ICSP)  
Zhang 550 COMBINING CNN WITH HAND-CRAFTED FEATURES FOR IMAGE CLASSIFICATION Tianyu Zhou; Zhenjiang Miao; Jianhu Zhang 554 PREDICTING LYMPH NODE METASTASIS OF LUNG CANCER USING STACKED SPARSE  ...  IMAGES AND RESTORE THE CONTOURS OF OBJECTS OBTAINED IN THE INFRARED RANGE Vyacheslav Voronin; Evgenii Semenishchev; Oxana Balabaeva; Marina Pismenskova 430 UNDERWATER IMAGE ENHANCEMENT ALGORITHM BASED  ... 
doi:10.1109/icsp.2018.8652480 fatcat:elhxwghl3zbdflbxblxafw2f7i

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques.  ...  Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging  ...  Kamal Abu-Hassan for useful discussions during the early stage of the work. This work was supported by the ACSLab (www.acslab.info).  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
Chen, Y., +, LSP 2020 1505-1509 Sequential Poisson Regression in Diffusion Networks. Dedecius, K., +, LSP 2020 625-629 Stacked Bayesian Matching Pursuit for One-Bit Compressed Sensing.  ...  ., +, LSP 2020 261-265 Decode and forward communication Legitimate Surveillance via Jamming in Multichannel Relaying System.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information [article]

Jiahao Huang, Weiping Ding, Jun Lv, Jingwen Yang, Hao Dong, Javier Del Ser, Jun Xia, Tiaojuan Ren, Stephen Wong, Guang Yang
2021 arXiv   pre-print
One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information.  ...  However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space.  ...  All considered methods can restore image structure and edge information for low and medium noise levels (20-50%), with PIDD having strong advantages over PISD and nPIDD.  ... 
arXiv:2112.05758v1 fatcat:whzephzx5bgsvc6a6i6bwclg24

Speech synthesis from neural decoding of spoken sentences

Gopala K. Anumanchipalli, Josh Chartier, Edward F. Chang
2019 Nature  
These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication.  ...  Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments.  ...  Moses for comments on the manuscript and B. Speidel for his help reconstructing MRI images. This work was supported by grants from the NIH (DP2 OD008627 and U01 NS098971-01).  ... 
doi:10.1038/s41586-019-1119-1 pmid:31019317 fatcat:7taeckhko5fhnbk4gwio4y2ogy

VoiceFixer: Toward General Speech Restoration with Neural Vocoder [article]

Haohe Liu, Qiuqiang Kong, Qiao Tian, Yan Zhao, DeLiang Wang, Chuanzeng Huang, Yuxuan Wang
2021 arXiv   pre-print
Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping.  ...  However, SSR systems only focus on one task and do not address the general speech restoration problem.  ...  REPRODUCIBILITY STATEMENT We make our code and datasets downloadable for painless reproducibility.  ... 
arXiv:2109.13731v3 fatcat:m5yuo44k6bad7iyotzu2vtqkyq

Order statistics in digital image processing

I. Pitas, A.N. Venetsanopoulos
1992 Proceedings of the IEEE  
Such techniques are used in digital image filtering, image enhancement, and edge detection. One of the most important families of nonlinear image filters is based on order shztktics.  ...  In recent years significant advances have been made in the development of nonlinear image processing techniques.  ...  restoration.  ... 
doi:10.1109/5.192071 fatcat:nbazqqfjjjcm3p3biz2z36njbi

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments [article]

Zixing Zhang, Jürgen Geiger, Jouni Pohjalainen, Amr El-Desoky Mousa, Wenyu Jin, Björn Schuller
2018 arXiv   pre-print
In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for  ...  Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge.  ...  For image restoration and further image processing tasks, deep convolutional encoderdecoder networks were proposed in [76] and delivered promising performance.  ... 
arXiv:1705.10874v3 fatcat:evdhqnj7eraa5jiolakuf4mf3e
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