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Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance. Previous approaches for this guided super‐resolution task often model the intrinsic characteristic of the desired HR HS image using hand‐crafted priors. Recently, researchers pay more attention to deep learning methods with direct supervised or unsupervised learning, which exploit deep prior only from training dataset or testing data. In this article, an efficient convolutional neural network‐based method is presented to progressively super‐resolve HS image with RGB image guidance. Specifically, a progressive HS image super‐resolution network is proposed, which progressively super‐resolve the LR HS image with pixel shuffled HR RGB image guidance. Then, the super‐resolution network is progressively trained with supervised pre‐training and unsupervised adaption, where supervised pre‐training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing scenes. The proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral‐spatial constraint. It has a good generalisation capability, especially for blind HS image super‐resolution. Comprehensive experimental results show that the proposed deep progressive learning method outperforms the existing state‐of‐the‐art methods for HS image super‐resolution in non‐blind and blind cases. [ABSTRACT FROM AUTHOR]

Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention. The purpose of most now available cross modal retrieval methods is to find a common subspace and maximize the different modal correlation. To generate specific representations consistent with cross modal tasks, this paper proposes a novel cross modal retrieval framework, which integrates feature learning and latent space embedding. In detail, we proposed a deep CNN and a shallow CNN to extract the feature of the samples. The deep CNN is used to extract the representation of images, and the shallow CNN uses a multi‐dimensional kernel to extract multi‐level semantic representation of text. Meanwhile, we enhance the semantic manifold by constructing cross modal ranking and within‐modal discriminant loss to improve the division of semantic representation. Moreover, the most representative samples are selected by using online sampling strategy, so that the approach can be implemented on a large‐scale data. This approach not only increases the discriminative ability among different categories, but also maximizes the relativity between different modalities. Experiments on three real word datasets show that the proposed method is superior to the popular methods. [ABSTRACT FROM AUTHOR]

A background calibration technique is proposed to correct bit weights in pipelined-successive-approximation-register (SAR) analogue-to-digital converters (ADCs). By splitting the second stage, the input signal interference is mostly removed, thereby greatly enhancing the convergence speed of the algorithm. Besides, the dither signal assists to eliminate mismatch issues between the partially split ADCs, thus relaxing the analogue overheads. According to the simulation, after calibration, the spurious-free-dynamic-range and signal-to-noise-and-distortion-ratio are improved from 53.2 to 88.2 dB and 49.5 to 75.2 dB, respectively. The calibration algorithm converges with about only 600 K samples. [ABSTRACT FROM AUTHOR]

A shaping technique is proposed to remedy the attenuation‐factor error in the split capacitor‐digital‐to‐analogue converter (CDAC) of a successive‐approximation‐register (SAR) ADC. By feeding back the previous quantised value of the least significant bit part to the most significant bit part, the inaccurate attenuation‐factor error caused by mismatches and parasitic components is shaped to high frequency. The in‐band dynamic performance is significantly improved without any calibration. Simulation results show that the proposed attenuation‐factor shaping technique improves the spurious‐free‐dynamic‐range by 10 dB, whereas the number of capacitors increases only 1.2%. [ABSTRACT FROM AUTHOR]

A difference‐equalisation based method is presented to estimate timing skew adaptively in time‐interleaved analogue‐to‐digital converters (ADCs). By digital zero‐crossing detection and calculating the mean of the absolute difference between adjacent sub‐ADC's outputs, the timing mismatch information for each channel can be identified easily. Simulation results demonstrate that the proposed method can estimate timing skew accurately. Furthermore, the proposed method requires far fewer samples to estimation than the previous methods. [ABSTRACT FROM AUTHOR]

High‐efficiency video coding (HEVC) is the next generation standard of video coding. The deblocking filter (DF) constitutes a significant part of the HEVC decoder complexity. A three‐step parallel framework (TPF) is proposed for the H.264/AVC DF, which is also suitable for HEVC except the third step. The third step of the TPF is replaced with a directed acyclic graph‐based order. Experiments show that the proposed method dramatically accelerates more than the state‐of‐the‐art parallel method. [ABSTRACT FROM AUTHOR]