In this letter, we propose a feedback reduction scheme based on autoencoder and deep reinforcement learning for frequency division duplex (FDD) overlay device-to-device (D2D) communication networks. All D2D receivers ...
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In this letter, we propose a feedback reduction scheme based on autoencoder and deep reinforcement learning for frequency division duplex (FDD) overlay device-to-device (D2D) communication networks. All D2D receivers and transmitters are equipped with an Encoder and Decoder, respectively, of the trained autoencoder. The D2D receivers compress the feedback information using the Encoder before transmitting while the transmitters decompress the received feedback information. We also employ a dueling deep Q network (DQN) to allow each D2D transmitter to autonomously determine whether to transmit data based on the decompressed feedback information. The performance of the proposed feedback reduction scheme is analyzed in terms of the average MSE of the autoencoder and the average sum-rate of a D2D communication network. Our numerical results show that the proposed feedback reduction scheme using the autoencoder can achieve 100%, 89%, and 86% of the average sum-rate of the perfect feedback scheme with no compression when the signal-to-noise ratio is 10dB, -5dB, and -20dB, respectively, while reducing the feedback by 50%.
High-resolution range profile (HRRP) is indispensable for modern radar automatic target recognition (RATR) systems and commonly has noisy background and misalignment along the range dimension. In this article, we prop...
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High-resolution range profile (HRRP) is indispensable for modern radar automatic target recognition (RATR) systems and commonly has noisy background and misalignment along the range dimension. In this article, we propose a novel algorithm for HRPP recognition with the improvement of recognition accuracy, stability against range translation, and robustness to noise background. This method divides the HRRP into patches and encodes the patches to latent feature vectors via a multihead attention mechanism, modeling the spatial dependence, eliminating the influence of range translation, and focusing on the target area. A decoder is adopted to reconstruct the HRRP and improve the robustness against the noise. Experiments on measured data prove that the proposed algorithm outperforms other existing methods with recognition accuracy increased significantly, sensitivity to the range translation eliminated, and noise robustness improved. This study provides a promising and effective approach for HRRP target recognition.
Image generation has seen huge leaps in the last few years. Less than 10 years ago we could not generate accurate images using deep learning at all, and now it is almost impossible for the average person to distinguis...
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ISBN:
(纸本)9798400701870
Image generation has seen huge leaps in the last few years. Less than 10 years ago we could not generate accurate images using deep learning at all, and now it is almost impossible for the average person to distinguish a real image from a generated one. In spite of the fact that image generation has some amazing use cases, it can also be used with ill intent. As an example, deepfakes have become more and more indistinguishable from real pictures and that poses a real threat to society. It is important for us to be vigilant and active against deepfakes, to ensure that the false information spread is kept under control. In this context, the need for good deepfake detectors feels more and more urgent. There is a constant battle between deepfake generators and deepfake detection algorithms, each one evolving at a rapid pace. But, there is a big problem with deepfake detectors: they can only be trained on so many data points and images generated by specific architectures. Therefore, while we can detect deepfakes on certain datasets with near 100% accuracy, it is sometimes very hard to generalize and catch all real-world instances. Our proposed solution is a way to augment deepfake detection datasets using deep learning architectures, such as autoencoders or U-Net. We show that augmenting deepfake detection datasets using deep learning improves generalization to other datasets. We test our algorithm using multiple architectures, with experimental validation being carried out on state-of-the-art datasets like CelebDF and DFDC Preview. The framework we propose can give flexibility to any model, helping to generalize to unseen datasets and manipulations.
Pansharpening refers to the fusion of a multispectral image (MS) and a panchromatic image (PAN) to obtain a new image with the same spatial resolution as the PAN image and the same spectral resolution as the MS image....
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Pansharpening refers to the fusion of a multispectral image (MS) and a panchromatic image (PAN) to obtain a new image with the same spatial resolution as the PAN image and the same spectral resolution as the MS image. This paper describes a new, efficient, and accurate pansharpening architecture. The Vector-Quantized Variational autoencoder (VQ-VAE) is the foundation of the proposed method. The VQ-VAE model is trained to learn the non-linear mapping of degraded panchromatic image patches to high-resolution patches. This approach ensures that high-resolution patches can be recovered from low-resolution ones. After training on PAN patches, the VQ-VAE estimates high-resolution multispectral patches for each band of the original multispectral image before reconstructing the high-resolution multispectral image from the patches. The original multispectral image, the panchromatic image, and the estimated high-resolution multispectral image are combined through a modified Component Substitution (CS) process to obtain the pansharpened image. Three large satellite datasets from urban areas with 4-band spectral resolution (blue, green, red, and near-infrared) were used to evaluate the proposed pansharpening method's performance. The effectiveness of the proposed method is demonstrated by the quantitative and visual results obtained compared to several literature approaches.
The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a *** several advantages,the...
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The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a *** several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and *** overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection *** proposed approach learns the normal and anomalous data patterns to identify the various types of network *** most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of *** optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection *** efficacy of the suggested framework is evaluated via two standard and open-source *** proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class *** performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection *** outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.
Graph anomaly detection, aimed at identifying anomalous patterns that significantly differ from other nodes, has drawn widespread attention in recent years. Due to the complex topological structures and attribute info...
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ISBN:
(纸本)9789819755714;9789819755721
Graph anomaly detection, aimed at identifying anomalous patterns that significantly differ from other nodes, has drawn widespread attention in recent years. Due to the complex topological structures and attribute information inherent in graphs, conventional methods often struggle to effectively identify anomalies. Deep anomaly detection methods based on Graph Neural Networks (GNNs) have achieved significant success. However, they face the challenge of not only obtaining limited neighborhood information but over-smoothing. Over-smoothing is the phenomenon where the representations of nodes gradually become similar and flattened across multiple convolutional layers, thereby limiting the comprehensive learning of neighborhood information. Therefore, we propose a novel anomaly detection framework, TransGAD, to address these challenges. Inspired by the Graph Transformer, we introduce a Transformer-based autoencoder. Treating each node as a sequence and its neighborhood as tokens in the sequence, this autoencoder captures both local and global information. We incorporate cosine positional encoding and masking strategy to obtain more informative node representations and leverage reconstruction error for improved anomaly detection. Experimental results on seven datasets demonstrate that our approach outperforms the state-of-the-art methods.
Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many p...
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Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail. In a CAD model of overall size 10 x 10 x 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality. (c) 2023 The Authors. Published by Elsevier B.V.
Zero-shot learning aims to learn a visual classifier for a category which has no training samples leveraging its semantic information and its relationship to other categories. It is common, yet vital, in practical vis...
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Zero-shot learning aims to learn a visual classifier for a category which has no training samples leveraging its semantic information and its relationship to other categories. It is common, yet vital, in practical visual scenarios, and particularly prominent in the uncharted ocean field. Phytoplankton plays an important part in the marine ecological environment. It is common to encounter the zero-shot recognition problem during the in situ observation. Therefore, we propose a dual autoencoder model, which contains two similar encoder-decoder structures, to tackle the zero-shot recognition problem. The first one is used for the projection from the visual feature space to a latent space, then to the semantic space. Inversely, the second one projects from the semantic space to another latent space, then back to the visual feature space. This structure guarantees the projection from the visual feature space to the semantic space to be more effective, through the stable mutual mapping. Experimental results on four benchmarks demonstrate that the proposed dual autoencoder model achieves competitive performance compared with six recent state-of-the-art methods. Furthermore, we apply our algorithm to phytoplankton classification. We manually annotated phytoplankton attributes to develop a practical dataset for this real and special domain application, i.e., Zero-shot learning dataset for PHYtoplankton (ZeroPHY). Experiment results show that our method achieves the best performance on this real-world application.
In the satellite operation domain, the accurate pre-diction of the Remaining Useful Life (RUL) of satellite subsystems and components is fundamental for an effective management of the mission. The accuracy of the RUL ...
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In this paper, we analyze the capabilities of several multiscale convolutional autoencoder architectures for reduced-order modeling of two-dimensional unsteady turbulent flow over a cylinder and the collapse of the wa...
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In this paper, we analyze the capabilities of several multiscale convolutional autoencoder architectures for reduced-order modeling of two-dimensional unsteady turbulent flow over a cylinder and the collapse of the water column. The results demonstrate the significance of multiscale convolution design for precision. Multiscale convolution, on the other hand, leads to the creation of millions of training parameters that require a large amount of memory. This results in an increase in the computational cost of the system. As a solution to this problem, we propose using modified convolution to reduce the number of training parameters within the model. As far as accuracy and computational efficiency are concerned, separable convolution yields the most efficient results in terms of accuracy. Moreover, the encoder component of the architecture is responsible for encoding high-dimensional data into a latent space with a low dimension. The latent spaces are transferred to the recurrent neural-type network and decoder section for the temporal evolution of the latent spaces and reconstruction of the flow field. In addition, the results demonstrate that GRU has fewer parameters than LSTM while maintaining the same accuracy.
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