Super-resolution (SR) of the degraded and real low-resolution (LR) video remains a challenging problem despite the development of deep learning-based SR models. Most existing state-of-the-art networks focus on getting...
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Super-resolution (SR) of the degraded and real low-resolution (LR) video remains a challenging problem despite the development of deep learning-based SR models. Most existing state-of-the-art networks focus on getting high-resolution (HR) videos from the corresponding down-sampled LR video but fail in scenarios with noisy or degraded low-resolution video. In this article, a novel real-world "zero-shot" video spatio-temporal SR model, i.e., 3D-Deep convolutional auto-encoder (3D-CAE) guided attention-based deep spatio-temporal back-projection network has been proposed. 3D-CAE is utilized for extracting noise-free features from real low-resolution video and used in the attention-based deep spatio-temporal back-projection network for clean, high-resolution video reconstruction. In the proposed framework, the denoising loss of low-resolution video with high-resolution video reconstruction loss is jointly used in an end-to-end manner with a zero-shot setting. Further, Meta-learning is used to initialize the weights of the proposed model to take advantage of learning on the external dataset with internal learning in a zero-shot environment. To maintain the temporal coherency, we have used the Motion Compensation Transformer (MCT) for motion estimation and the Sub-Pixel Motion Compensation (SPMC) layer for motion compensation. We have evaluated the performance of our proposed model on REDS and Vid4 Dataset. The PSNR value of our model is 25.13 dB for the RealVSR dataset, which is 0.72 dB more than the next-best performing model, EAVSR+. For MVSR4x, our model provides 24.61 db PSNR, 0.67 dB more than the EAVSR+ model. Experimental results demonstrate the effectiveness of the proposed framework on degraded and noisy real low-resolution video compared to the existing methods. Furthermore, an ablation study has been conducted to highlight the contribution of 3D-CAE and attention layer to the overall network performance.
In practical engineering environments, rolling bearing vibration signal is often interfered by strong noise, which negatively affects the diagnostic accuracy of intelligent diagnosis models. To solve the above problem...
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In practical engineering environments, rolling bearing vibration signal is often interfered by strong noise, which negatively affects the diagnostic accuracy of intelligent diagnosis models. To solve the above problem, a fault diagnosis model (MDCAE-CACNN) that fuses a multi-scale dilated convolutional auto-encoder (MDCAE) and a channel attention-based convolutional neural network (CACNN) is proposed. First, the MDCAE model is used to capture the feature information of different time scales in rolling bearing vibration signal by using convolutional kernels with different receptive field sizes. Through unsupervised learning, the noise is removed to obtain high-quality reconstructed signals. Then, the fault features in the reconstructed signal are effectively extracted by CACNN model and the fault type is accurately diagnosed. The experimental results show that the proposed MDCAE-CACNN model exhibits remarkable improvements in fault diagnosis accuracy and effectiveness. Additionally, it showcases high levels of precision, robustness, and generalization ability.
Handwriting is, eventually, a variation of the printed forms where the characters are little larger, smaller, angled and deformed than the printed forms. The small changes in handwriting define the parameters of the c...
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Handwriting is, eventually, a variation of the printed forms where the characters are little larger, smaller, angled and deformed than the printed forms. The small changes in handwriting define the parameters of the character to be recognized. Handwritten numeral recognition (HNR) poses significant challenges due to the deformations and other variations. This study proposes a new notion of HNR on the hypothesis that the handwritten numerals are distinct deformations of the printed forms, which leads to easier recogni-tion task with higher accuracy when superimposing handwritten numeral images onto the corresponding printed numeral images. In the proposed HNR, auto-encoder and convolutional auto-encoder have been adapted for the superimposing task that transform HNIs into PNIs, while neural network and convolu-tional neural network are employed for classification of PNIs. The superimposition method reduces the computational overhead. Moreover, this method employs simple pre-processing without feature extrac-tion whereas the traditional methods employ pre-processing, feature extraction, and recognition with machine learning tools, which add to the computational overhead. The performance of HNRSP has been evaluated for recognizing handwritten numerals of Bengali, Devanagari, and English on benchmark data -sets and the proposed system achieves 99.68%, 99.73%, and 99.62% recognition accuracy for Bengali, Devanagari, and English handwritten numerals, respectively.(c) 2022 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for th...
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The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for the distribution network based on a double convolutional neural network is proposed in this study. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained lowdimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The identification accuracy of the proposed model is higher than that of the traditional methods by the verification of the measured transient fault data of the power distribution network. The robustness study implies that the DCNN model can be applied in practical situations prone to using contaminated samples.
We introduce the concept of diverse activation functions, and apply them into convolutional auto-encoder (CAE) to develop diverse activation CAE (DaCAE), which considerably reduces the reconstruction loss. In contrast...
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ISBN:
(纸本)9781538618295
We introduce the concept of diverse activation functions, and apply them into convolutional auto-encoder (CAE) to develop diverse activation CAE (DaCAE), which considerably reduces the reconstruction loss. In contrast to vanilla CAE only with activation functions of the same types, DaCAE incorporates diverse activations by considering their cooperation and location. In terms of the reconstruction capability, DaCAE significantly outperforms vanilla CAE and full connected auto-encoder, and we conclude rules of thumb on designing diverse activations networks. Based on the high quality of the latent bottleneck features extracted from Da-CAE, we demonstrate a satisfying advantage that fuzzy rules classifier performs better than softmax layer in supervised learning. These results could be seen as new research points in the attempts at using diverse activations to train deep neural networks and combining fuzzy inference systems with deep learning.
In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmi...
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ISBN:
(纸本)9781538677476
In the past few years, a rapid increase in the number of patients requiring constant monitoring, which inspires researchers to develop intelligent and sustainable remote smart healthcare services. However, the transmission of big real-time health data is a challenge since the current dynamic networks are limited by different aspects such as the bandwidth, end-to-end delay, and transmission energy. Due to this, a data reduction technique should be applied to the data before being transmitted based on the resources of the network. In this paper, we integrate efficient data reduction with wireless networking transmission to enable an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. convolutional auto-encoder (CAE) approach was used to implement an adaptive compression/reconstruction technique with the minimum distortion. Then, a resource allocation framework was implemented to minimize the transmission energy along with the distortion of the reconstructed signal while considering different network and applications constraints. A comparison between the results of the resource allocation framework considering both CAE and Discrete wavelet transforms (DWT) was also captured.
How to detect emotions of different people in time is significant in various areas, such as healthcare, VR/AR and etc. In this paper, an intelligent emotion detection method is proposed to transmit individual emotion ...
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ISBN:
(纸本)9781728173276
How to detect emotions of different people in time is significant in various areas, such as healthcare, VR/AR and etc. In this paper, an intelligent emotion detection method is proposed to transmit individual emotion data in real-time with as little energy as possible in a resource-limited mobile edge computing (MEC) networks. First, a data compression method is designed based on convolutional auto-encoder (CAE) to compress emotion data on the user side. Then, to guarantee the requirements of data transmission delay and the data distortion rate simultaneously, an optimal channel allocation algorithm is proposed. Thereafter, the emotion data is recovered and analyzed by the edge personal emotion model EEGNET on the edge computing server where the real-time emotions of users can be monitored. The effectiveness of the proposed method is finally verified by extensive simulation experiments.
Objective: Abdominal ECG (AECG) recorded at the maternal abdomen is significantly affected by the maternal ECG (MECG), making the extraction of FECG a challenging task. This paper presents a new MECG elimination metho...
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ISBN:
(纸本)9781728118673
Objective: Abdominal ECG (AECG) recorded at the maternal abdomen is significantly affected by the maternal ECG (MECG), making the extraction of FECG a challenging task. This paper presents a new MECG elimination method based on Short Time Fourier Transform (STFT) and convolutionalautoencoder (CAE). Methods: First, the STFT is used to transform the AECG from one-dimensional (1D) time domain into two-dimensional(2D) time-frequency domain. Next, the CAE model is applied to estimate the 2D-STFT coefficients of MECG. Finally, after the inverse STFT of MECG, we can extract the FECG by subtracting the MECG from the AECG in the time domain. Different from the methods estimated the MECG in the 1D time domain, the novelty of the proposed method relies on estimating the MECG in the 2D time-frequency domain. Specifically, the CAE model learns the end-to-end mappings from the 2D-STFT coefficients of AECG to the MECG. Results: Experimental results show that the proposed method is effective in removing the MECG. Significance: This work enhances the clinical applications of FECG in the early detection of fetal heart diseases.
This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different m...
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ISBN:
(纸本)9781509009817
This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in medieval studies. DIVA-HisDB and the ground truth can be used for training and evaluating DIA tasks, such as layout analysis, text line segmentation, binarization and writer identification. Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis. An optimized state-of-the-art convolutionalautoencoder (CAE) performs with around 95 % accuracy, demonstrating that for this challenging layout there is much room for improvement. Finally, we show that existing text line segmentation methods fail due to interlinear and marginal text elements.
Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions...
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ISBN:
(纸本)9781509060146
Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutionalautoencoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGCv2.0 and achieve competitive result on YTF. The source code has been made publicly available at https://***/shaoxuying/DeepDiscriminativeClusteringNetwork.
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