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2020 Index IEEE Transactions on Computational Imaging Vol. 6
2020
IEEE Transactions on Computational Imaging
Preserved Fulltext
., TCI 2020 1297-1308 Novikov, A., see Moscoso, M., TCI 2020 87-94 ...
: A Tractable
Bayesian Approach. ...
., +, TCI 2020 1139-1152 Fast Pixelated Lithographic Source and Mask Joint Optimization Based on Compressive Sensing. ...
doi:10.1109/tci.2021.3054596
fatcat:puij7ztll5ai7alxrmqzsupcny
2020 Index IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4
2020
IEEE Transactions on Emerging Topics in Computational Intelligence
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Gong, M., +, TETCI April 2020 108-118
Compressed sensing
Soil PH Measurement Based on Compressive Sensing and Deep Image
Prior. ...
Framework for Many-Task Optimization. ...
doi:10.1109/tetci.2020.3042423
fatcat:qj6bpqfey5gpjhqe7zvgg644l4
2021 Index IEEE Transactions on Signal Processing Vol. 69
2021
IEEE Transactions on Signal Processing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TSP 2021 5175-5188 Data compression A Novel Framework for Combining Multiple Radar Waveforms Using Time Compression Overlap-Add. ...
., +, TSP 2021 4085-4101 A Novel Framework for the Analysis and Design of Heterogeneous Federated Learning. ...
doi:10.1109/tsp.2022.3162899
fatcat:kcubj566gzb4zkj7xb5r5we3ri
Guest Editorial: Special Section on Advanced Deep Learning Algorithms for Industrial Internet of Things
2021
IEEE Transactions on Industrial Informatics
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Wei is a Senior Member of CCF. ...
., the article "Efficient Monocular Depth Estimation for Edge Devices in Internet of Things" authored by Tu et al. designed a pruned and optimized MDE model for precise depth sensing on edge devices. ...
In view of this challenge, the paper entitled "Edge Computing-Enabled Deep Learning for Real-time Video Optimization in IIoT" authored by Dou et al. proposed a real-time video streaming optimization method ...
doi:10.1109/tii.2020.3026551
fatcat:drpgvjt6gnafliu3ch7zxebn7y
A SPATIOTEMPORAL FUSION NETWORK TO MULTI SOURCE HETEROGENEOUS DATA FOR LANDSLIDE RECOGNITION
2022
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of ...
This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. ...
A storage structure is designed in parameter passing to pass the parameters after the joint action of stochastic search and Bayesian optimization to the next layer of the network, completing the optimization ...
doi:10.5194/isprs-annals-x-3-w1-2022-77-2022
fatcat:muoyuqi62bca3jouaqw444sbje
IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI
2021
IEEE Access
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multiplications for a subset of kernel weights in a convolutional neural network (CNN) layer. ...
Another is how edge computing supports AI in a networking environment. For example, AI training and inference can be efficiently enabled by a multitude of computing resources from edge computing. ...
RdS-ImS can yield a data packet loss predictive model based on the compressed sensing theory, thus preventing data losses by finding alternative paths for uploading sensing data to the cloud. ...
doi:10.1109/access.2021.3090143
fatcat:mdgmfeph6zcrzc6z7w7xjg3iua
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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Wang, Y., +, Exploiting Block-Sparsity for Hyperspectral Kronecker Compressive Sensing: A Tensor-Based Bayesian Method. ...
., +, TIP 2020 971-985 Optimized Sensing Matrix for Single Pixel Multi-Resolution Compressive Spectral Imaging. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Table of Contents
2021
IEEE Transactions on Signal Processing
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Godsill Non-Bayesian Estimation Framework for Signal Recovery on Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. ...
A. Jaber and D. Massicotte Grade Prediction From Multi-Valued Click-Stream Traces via Bayesian-Regularized Deep Neural Networks . . . . . . . . . . ...
doi:10.1109/tsp.2021.3136798
fatcat:kzkdhzcz3fgx3jv6gfjofooseq
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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Perfusion and Fetal Blood Oxygen Saturation in Normal Pregnancy and Placental Insufficiency 330 Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI 333 Local and ...
: scaling up to large unlabelled medical datasets 342 AtlasNet: Multi-atlas non-linear deep networks for medical image segmentation 345 Multiple Instance Learning for Heterogeneous Images: Training a CNN ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
[article]
2021
arXiv
pre-print
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The data-driven methods have a stronger prior knowledge learning capability for huge data, especially for nonlinear statistical features; however, the interpretability of the networks is poor, and they ...
The typical existing and potential coupling methods for remote sensing image restoration and fusion are introduced with application examples. ...
Considering the non-i.i.d. noise of HSIs, a denoising framework called the deep spatio-spectral Bayesian posterior (DSSBP) method [36] for non-i.i.d. noise was designed based on the deep spatio-spectral ...
arXiv:2108.06073v1
fatcat:vdnabzwvvnbvnllrlvzvvqgolm
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
[article]
2017
arXiv
pre-print
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First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications ...
A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. ...
SYSTEM FRAMEWORK We introduce DeepIoT, a neural network structure compression framework for sensing applications. ...
arXiv:1706.01215v3
fatcat:pewnjp3byzb4nmyi5k32s5govm
Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems
2021
Sensors
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Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback ...
This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. ...
A CSI sensing and recovery framework is proposed in [81] . ...
doi:10.3390/s22010309
pmid:35009848
pmcid:PMC8749942
fatcat:2w4th63dtrdyboa6rmhr5rcvja
Table of Contents
2020
IEEE Transactions on Computational Imaging
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A. Evers and J. A. Jackson 291 A Unified Learning-Based Framework for Light Field Reconstruction From Coded
, and S. ...
Li 968 Fast Pixelated Lithographic Source and Mask Joint Optimization Based on Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tci.2021.3054278
fatcat:zlrqgncqrfatpbsybevq6iioli
2020 Index IEEE Transactions on Signal Processing Vol. 68
2020
IEEE Transactions on Signal Processing
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A Abdelaziz, M., see Brihuega, A., TSP 2020 3603-3618 Abdolee, R., see Ahmadi, M.J., TSP 2020 3808-3823 Abolhasani, M., and Rahmani, M., One-Step Prediction for Discrete Time-Varying Nonlinear Systems ...
Marnissi, Y., +, TSP 2020 2356-2369 Model-Based Deep Learning for One-Bit Compressive Sensing. ...
., +, TSP 2020 5441-5456
D
Data acquisition
Model-Based Deep Learning for One-Bit Compressive Sensing. ...
doi:10.1109/tsp.2021.3055469
fatcat:6uswtuxm5ba6zahdwh5atxhcsy
IEEE Access Special Section Editorial: Mission-Critical Sensors and Sensor Networks (MC-SSN)
2021
IEEE Access
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Algorithms are sought for fused and/or coherent cross-platform radio frequency (RF) sensing. ...
The response to the Call for Papers was overwhelming, with 216 articles submitted from worldwide. During the review ...
The article ''A Bayesian compressive data gathering scheme in wireless sensor networks with one mobile sink,'' by Gu et al., studies the compressive data gathering problem in terms of Bayesian theory for ...
doi:10.1109/access.2021.3068830
fatcat:mcmdtikg2vfqvokgu7pnm6xofq
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