Search Results - complex-valued convolutional autoencoder

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification by Shang, Ronghua, Wang, Guangguang, A. Okoth, Michael, Jiao, Licheng

“… In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted…”
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An Attention Based Complex-valued Convolutional Autoencoder for GEO SA-Bi SAR Ship Target Refocusing by Lian, Meng, Bolic, Miodrag

“… Inspired by the recent success of artificial neural network (ANN) and deep learning (DL) in optical image processing, an improved complex-valued convolutional autoencoder (ICV-CAE…”
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Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes by Wang, Hongyan, Zhou, He, Guo, Qinghua, Li, Jun

“…, where the nonlinear characteristics of the received signal are effectively represented and corrected using a complex-valued convolutional autoencoder (CVCAE) network…”
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SAR Images Unsupervised Feature Abstraction with A Complex-valued Convolutional Autoencoder by He, Jinling, Jin, Yuhang, Tian, Sirui, Wang, Chao, Zhang, Hong

“… In order to solve these problems, this paper proposes a model for vehicle SAR image interpretation based on complex-valued convolutional auto-encoder (CV-CAE…”
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Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features by Ean-Gyu Han, Tae-Koo Kang, Myo-Taeg Lim

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Preserving polarimetric properties in PolSAR image reconstruction through Complex-Valued Auto-Encoders by Gabot, Quentin, Fix, Jeremy, Frontera-Pons, Joana, Ren, Chengfang, Ovarlez, Jean-Philippe

“… In this paper, we employ complex-valued neural networks and study the performance of complex-valued convolutional autoencoders…”
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Fast Full Wave Electromagnetic Forward Solver Based on Deep Conditional Convolutional Autoencoders by Zhang, Huan Huan, Yao, He Ming, Jiang, Lijun, Ng, Michael

“…), consisting of a complex-valued deep convolutional encoder network and its corresponding complex-valued deep convolutional decoder network…”
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Complex-Valued Autoencoder for Multi-Polarization SLC SAR Data Compression with Side Information by Asiyabi, Reza Mohammadi, Anghel, Andrei, Rizzoli, Paola, Martone, Michele, Datcu, Mihai

“… In order to tackle this problem, in this study, a Complex-Valued (CV) end-to-end deep learning-based architecture based on convolutional autoencoders is proposed to compress Single Look Complex (SLC) SAR data…”
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Capsule Network With Multiscale Feature Fusion for Hidden Human Activity Classification by Wang, Xiang, Wang, Yumiao, Guo, Shisheng, Kong, Lingjiang, Cui, Guolong

“…) block, and a capsule block. Specifically, the feature extractor with two complex-valued convolutional layers is designed to extract the deep features from the range profiles…”
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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics

“…The aim of this reprint is to give researchers the opportunity to present new tendencies, the latest achievements and research directions, and their current…”
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Denoising Method for Wireless Communication Signals Based on Convolutional AutoEncoder by Min, Woonggyu, Kim, Jongseok, Jo, Ohyun

“…In this paper, we propose a method to effectively remove noise from signals by utilizing convolutional autoen-coder…”
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Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features by Han, Ean-Gyu, Kang, Tae-Koo, Lim, Myo-Taeg

“…) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference…”
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End-to-End Learning of Beam Probing and RSSI-Based Multi-User Hybrid Precoding Design by Abdallah, Asmaa, Celik, Abdulkadir, Alkhateeb, Ahmed, Eltawil, Ahmed M.

“…) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected…”
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Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery by Gopalakrishnan, Anand, Stanić, Aleksandar, Schmidhuber, Jürgen, Michael Curtis Mozer

“…Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures…”
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Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle by Kuen, Jason, Lim, Kian Ming, Lee, Chin Poo

“… These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations…”
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Compressed Sensing MRI Reconstruction with Co-VeGAN: Complex-Valued Generative Adversarial Network by Vasudeva, Bhavya, Deora, Puneesh, Bhattacharya, Saumik, Pradhan, Pyari Mohan

“… However, they treat the inherently complex-valued MRI data as real-valued entities by extracting the magnitude content or concatenating the complex-valued data as two real-valued channels for processing…”
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Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks by Zhao, Zhongyuan, Vuran, Mehmet Can, Guo, Fujuan, Scott, Stephen D.

“… In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT…”
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FDC-TA-DSN Ship Classification Model and Dataset Construction Based on Complex-Valued SAR by Gao, Gui, He, Yucong, Zhao, Jinghao, Li, Sijie, Wang, Meixiang, Yang, Gang, Zhang, Xi

“… On the other hand, there is a lack of available complex-valued SAR databases for the purpose of classification…”
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GNSS Anomaly Detection with Complex-Valued LSTM Networks by Savolainen, Outi, Elango, Arul, Morrison, Aiden, Sokolova, Nadezda, Ruotsalainen, Laura

“… Previous GNSS anomaly detectors concentrate on one interference type only. Although methods based on deep learning are emerging, most work use convolutional neural…”
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Deep Learning-Aided Optical IM/DD OFDM Approaches the Throughput of RF-OFDM by Van Luong, Thien, Zhang, Xiaoyu, Xiang, Luping, Hoang, Tiep M., Xu, Chao, Petropoulos, Periklis, Hanzo, Lajos

“… In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver…”
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