Random Acquisition in Compressive Sensing

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

Radar Emitter Individual Identification Based on Convolutional Neural Network Learning

Radar Emitter Individual Identification is a key technology in modern electronic radar systems. This paper will focus on Radar Emitter Individual Identification (REII). Based on the advantages of Empirical Mode Decomposition (EMD) and bispectrum in signal processing, we propose an REII method based on the CNN. Firstly, the radar emitter signal is preprocessed. Secondly, the Hilbert–Huang Transform (HHT) spectrum and bispectrum are combined to form an image of the signal. Finally, in order to avoid loss of information and achieve the potential identification performance improvement, the signal image obtained is identified by the optimized CNN. Experimental results based on the measured signals show that the proposed method has high identification accuracy and is capable of meeting real-time identification requirements. The deep-learning-based identification method proposed in this paper has strong generalization ability and adaptability, which provides a new way for REII.

Image-based mains signal disaggregation and load recognition

The mains signal is a complex fusion of various electrical equipment load signals in a building. In the non-intrusive load monitoring recognition, our main aim is to be able to extract as much load features as possible from the complex aggregate mains signal in a simpler way through a computer vision-based approach as opposed to the powers series signal approach. Power series methods, which are one dimensional in nature, suffer from poor aggregate and load signal feature localization necessitating a larger training dataset spanning very long time periods and normally require signal formatting and pre-processing. We use Gramian angular summation fields to transform the power series into a reduced image dataset that contains a rich set of localized signal features. A computer vision approach allows us to capture as much information as possible, and then propose an image-based mains load recognition system with high performance. In this paper for the entire recognition system, we use convolutional neural networks that very well adapted to vision recognition. The load signal image disaggregation is achieved through the powerful stacked denoising autoencoder noise extraction network. To test the proposed system, some simulations and comparisons are carried out and the results show that our easier to handle method can achieve acceptable performance.

Digital Signal, Image and Video Processing for Emerging Multimedia Technology

Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology [...]