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In this article, an interval estimation problem is investigated for a class of discrete-time nonlinear networked systems under stealthy attacks. An improved event-triggered protocol with the time-varying threshold is adopted to govern the received signals of interval observer so as to reduce unnecessary data communication burden.

Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000.

This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples.

The recent success of Deep Convolutional Neural Network (DCNN) for various computer vision tasks such as image recognition has already demonstrated its robust feature representation ability. However, the limitation of training database on small scale vein recognition tasks restricts its performance because the recognition result of DCNN depends heavily on the number of trainsets.

Fusion based hyperspectral image (HSI) super-resolution method, which obtains a spatially high-resolution (HR) HSI by fusing a low-resolution (LR) HSI and an HR conventional image, has been a prevalent method for HSI super-resolution. One effective fusion based method is to cast HSI super-resolution into a unified optimization problem, where handcrafted priors such as sparse prior or low rank prior are always adopted to regularize the latent HR HSI to be optimized. 

The filtered-x least-mean-square (FxLMS) algorithm has been widely used for the active noise control. A fundamental analysis of the convergence behavior of the FxLMS algorithm, including the transient and steady-state performance, could provide some new insights into the algorithm and can be also helpful for its practical applications, e.g., the choice of the step size.

Previous research methods on wake-up word detection (WWD) have been proposed with focus on finding a decent word representation that can well express the characteristics of a word. However, there are various obstacles such as noise and reverberation which make it difficult in real-world environments where WWD works.