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Recently, deep learning methods have achieved excellent performance in single image super-resolution (SISR), but most existing methods suffer from heavy computational costs and memory storage. To address this problem, some lightweight SR methods have been proposed, among which convolutional neural network (CNN) with attention mechanisms has received increasing attention. However, the existing CNN-based lightweight network and attention mechanism still have redundancy in feature extraction and enhancement. This paper presents Efficient Self-calibrated and Hierarchical Refinement Network (ESHR) for Lightweight Super-Resolution that includes three effective designs. First, we propose a more effective feature extraction basic module called Self-calibrated Residual Block (SRB), which can achieve superior performance when replacing other common convolution modules such as traditional convolution, BSConv and DSConv. Second, we propose a more lightweight spatial attention mechanism called Squeeze-Excitation Spatial Attention (SESA), which can interact and enhance features more effectively in spatial dimensions to improve the representation ability of the model. Third, we design an Efficient Hierarchical Refinement Block (EHRB) to integrate and utilize multi-level features, which can achieve better performance than single-level refinement without increasing the number of parameters. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art CNN-based methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Residual learning is one of the most effective components in blind image denoising. It learns to estimate the noise instead of the clean image itself. A shortcoming of residual learning is that it cannot capture hierarchical features efficiently because there are no connections between layers to extract multi-level features. Therefore, residual dense network (RDN) adds dense connections among layers in the residual block to facilitate extraction of hierarchical features. Although RDN is a superior non-blind denoiser, it is only better than state-of-the-art methods in blind denoising when noise level is high. In this paper, we employ the concept of Wiener filters and add a non-residual network as a guidance for RDN. The non-residual network predicts the signal instead of the noise to assist RDN in achieving satisfactory performance across all noise levels. Then, a guidance network combines the outputs from both residual and non-residual networks to generate the final denoised output. Experimental results show that the new architecture performs better than state-of-the-art blind denoisers quantitatively and image quality of the new method is also better than existing methods. • Improve performance of residual dense network in blind image denoising by employing the concept of Wiener filters. • Use non-residual network as a guidance of residual dense network and assist in predicting the signal instead of the noise. • Multiplications of noisy input and outputs from networks are used for signal and noise power prediction. • A guidance network combines input, outputs from networks, and the above multiplications to generate final denoised output. • Experimental results show residual dense network with non-residual guidance performs better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Radar specific emitter identification (SEI) plays important roles in spectrum resource management and Internet of Things (IoT) security. This term refers to the identification of different individual devices of the same type by analyzing the unintentional modulation features caused by hardware imperfections (i.e., fingerprint features). Increasingly many researchers are using deep learning (DL)-based methods for SEI. However, DL-based methods still have many problems, including model generalization, recognition efficiency, and adaptability in open electromagnetic environments. This paper proposes a one-dimensional convolutional neural network (1D CNN)—called a selective kernel residual network (SKResNet)—capable of autonomously adjusting the receptive field, to improve the feature extraction capability. The transient part is cut efficiently from a radar signal sequence as the input of SKResNet using a universal preprocessing method. In addition, considering that most existing methods obtain incorrect results while identifying new emitters without any prior knowledge, SKResNet was improved using OpenMax—an open-set recognition method—to develop Open-SKResNet. As this network could identify unknown emitters effectively, generating reliable classification results of known emitters simultaneously, it could be of great importance and have broad application prospects. [ABSTRACT FROM AUTHOR]

• A deep-learning method for classification of the audio codecs is proposed. • The effect of variable bit-rates in the classification of fragments is analyzed. • Longest common substrings and subsequences can effectively classify audio codecs. • A large set of speech codecs with 20 codecs and 96 bit-rates is considered. A large portion of the Internet bandwidth is used for transmission of multimedia such as audio data. As the file sizes are usually much bigger than the maximum network packet size, the audio data are segmented into fragments. For eavesdropping or network surveillance purposes, the first step of a sniffer may be to determine the codec by which a fragment is generated. This problem is usually modeled as a multi-class classification problem. The basic methods for determining the codec type of each fragment rely on the metadata in the corresponding file header. However, in a non-cooperative context, the whole file is not available. Statistical features extracted from the fragments are generally used for solving this problem by employing machine learning methods. In this paper, we present an end to end scalable deep learning approach for the classification of audio codecs with variable bit-rates. This method is based on an efficient variant of convolutional neural networks, which learns hidden layer representations to encode input vectors. Moreover, by integrating the statistical features and Cascade Deep Forest method, we jointly optimize classification labels assignment and learn the features that are suitable for classification. In several experiments on a dataset of speech codecs, we demonstrate that our approach outperforms the state-of-the-art methods by a significant margin. [ABSTRACT FROM AUTHOR]

Specific emitter identification (SEI) is an important problem in the field of electronic intelligence. There are two major limitations in most existing SEI methods: First, the features should be artificially extracted, which requires specialized expertise; Second, various features are not merged effectively to improve performance. In this paper, an automatic multi-feature extraction and fusion method based on deep ensemble learning is proposed for SEI. This method extracts and fuses multiple features via data-driven strategies using convolutional neural networks (CNN). No additional constraints are required on the type and number of the features to be fused. Three typical characteristics of radar pulse signals, including amplitude, phase and spectrum asymmetry, are taken as example features for method description and performance verification. Experiment results demonstrate that, the proposed method performs well in automatic feature extraction, and it can significantly improve the SEI performance via multi-feature fusion. [ABSTRACT FROM AUTHOR]