Comparative Noise Analysis of Readout Circuit in Hemispherical Resonator Gyroscope.

In high-precision Hemispherical Resonator Gyroscope (HRG) control systems, readout circuit noise critically determines resonator displacement detection precision. Addressing noise issues, this paper compares the noise characteristics and contribution mechanisms of the Transimpedance Amplifier (TIA) and Charge-Sensitive Amplifier (CSA). By establishing a noise model and analyzing circuit bandwidth, the dominant role of feedback resistor thermal noise in the TIA is revealed.

Deep Ring-Block-Wise Network for Hyperspectral Image Classification.

Deep learning has achieved many successes in the field of the hyperspectral image (HSI) classification. Most of existing deep learning-based methods have no consideration of feature distribution, which may yield lowly separable and discriminative features. From the perspective of spatial geometry, one excellent feature distribution form requires to satisfy both properties, i.

Stability Boundary and Enhanced Solution of Dual-Mode Based Micro Gyroscope Mode Matching Technology.

During in-run mode matching under a dual-mode gyro scheme, the stability of the closed-loop control system has a boundary. This phenomenon will lead to the failure of the in-run frequency split calibration scheme when the initial mode mismatch is too severe to exceed the stability boundary. This paper gives a detailed analysis of this stability boundary through simulations and experiments.

Deep Network With Irregular Convolutional Kernels and Self-Expressive Property for Classification of Hyperspectral Images.

This article presents a novel deep network with irregular convolutional kernels and self-expressive property (DIKS) for the classification of hyperspectral images (HSIs). Specifically, we use the principal component analysis (PCA) and superpixel segmentation to obtain a series of irregular patches, which are regarded as convolutional kernels of our network. With such kernels, the feature maps of HSIs can be adaptively computed to well describe the characteristics of each object class.

Fusion of Infrared and Visible Images Using Fast Global Smoothing Decomposition and Target-Enhanced Parallel Gaussian Fuzzy Logic.

As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges.