Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework.

Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR).

Application of time-reversal in electromagnetic power synthesis under distributed motion platform.

Space-time adaptive focusing is the most prominent feature of time-reversal electromagnetic waves. This paper studies the spatial power synthesis technology of distributed motion platforms based on time-reversal electromagnetic waves. Firstly, the spatial power synthesis process based on time-reversal on a distributed fixed platform is modeled.

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars.

With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not only the type or individual of the emitter but also its current state. Existing methods identify MFR states through hierarchical modeling, but most of them rely heavily on prior information.

Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network.

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise.

Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation.

To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise.

The application of polysaccharide-based nanogels in peptides/proteins and anticancer drugs delivery.

Finding adequate carriers for proteins/peptides and anticancer drugs delivery has become an urgent need, owing to the growing number of therapeutic macromolecules and the increasing amount of cancer incidence. Polysaccharide-based nanogels have attracted interest as carriers for proteins/peptides and anticancer drugs because of their characteristic properties like biodegradability, biocompatibility, stimuli-responsive behaviour, softness and swelling to help achieve a controlled, triggered response at the target site. In addition, the groups of the polysaccharide backbone are able to be modified to develop functional nanogels.