Search

In the field of remote sensing, due to memory consumption and computational burden, the single-image super-resolution (SISR) methods based on deep convolution neural networks (CNNs) are limited in practical application. To address this problem, we propose a lightweight feature enhancement network (FeNet) for accurate remote-sensing image super-resolution (SR). Considering the existence of equipment with extremely poor hardware facilities, we further design a lighter FeNet-baseline with about 158K parameters. Specifically, inspired by lattice structure, we construct a lightweight lattice block (LLB) as a nonlinear feature extraction function to improve the expression ability. Here, channel separation operation makes the upper and lower branches of the LLB only responsible for half of the features, and the weight coefficients calculated through the attention mechanism enable the upper and lower branches to communicate efficiently. Based on LLB, the feature enhancement block (FEB) is designed in a nested manner to obtain expressive features, where different layers are responsible for the features with different texture richness, and then features from different layers are sequentially fused from deep to shallow. Model parameters and multi-adds operations are used to evaluate network complexity, and extensive experiments on two remote-sensing and four SR benchmark test datasets show that our methods can achieve a good tradeoff between complexity and performance. Our code will be available at https://github.com/wangzheyuan-666/FeNet. [ABSTRACT FROM AUTHOR]

Due to the limitations of hardware technology and budget constraints, there always exists a tradeoff between spatial and spectral resolutions in a hyperspectral image (HSI). Because of the limited spatial resolution, mixed pixels are a common issue in HSIs, and consequently, some targets appear as subpixels. The effectiveness of hyperspectral target detection is affected greatly by the subpixel targets, especially when the size of the targets is small. In this article, we proposed a double dictionary-based nonlinear representation model for hyperspectral subpixel target detection (DDNRTD). DDNRTD represents HSIs with a nonlinear model based on background and target dictionaries, which fully considers the spatial property of background and targets and can separate background and targets reliably, especially for small-sized subpixel targets. In addition, we designed an over-completed background dictionary construction strategy to represent the background part more effectively, which integrates spectral angle distance (SAD) with sparse representation. Experiments on two simulated and five real HSI datasets showed that the proposed DDNRTD method produced more accurate detection results than six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This letter presents the design of a Dual-mode $W$ -band eight-way power-combining power amplifier (PA) implemented in Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm CMOS technology ($f_{t} / f_{\mathrm {max}}$ : 210 GHz/240 GHz). A configurable parallel–series-based eight-way power combine technique is proposed to achieve high output power with high power efficiency near half of $f_{t}$. The eight-way power-combiner is consisted of two transformer-based parallel two-way differential combiners and a coupled-line-based series combiner to achieve high passive efficiency and compact layout. The proposed PA achieves a measured peak gain of 30.9 dB with a bandwidth (BW) of 7.5 GHz. The PA has $P_{\mathrm {sat}}$ of +18 dBm with an 1dB output compression point (OP1dB) of +13.8 dBm and a peak PAE of 15.1% in high-power mode. In the low-power mode (LPM), the measured $P_{\mathrm {sat}}$ , OP1dB, and peak PAE are +13.6, +9.1 dBm, and 10.3%, respectively. The PAE is enhanced by 3.4% points at a 6-dB back-off in LPM. The core area of the proposed PA is 0.3 mm2. [ABSTRACT FROM AUTHOR]

Liquid-cooled garment (LCG) is a promising personal thermal management (PTM) technique that satisfies the individual thermal comfort requirement by creating a microclimate around the human body. Thermal sensation, which is defined as “the wearer’s sense of temperature between hot and cold,” is a helpful target for designing an LCG with good thermal comfort. There are just a few methods to predict the thermal sensation of the LCGs. In addition, the previous prediction methods related to the conventional heating, ventilation, and air conditioning (HVAC) systems cannot be directly applied to the LCG, due to the lack of consideration of some dominating characteristics of the LCG and the human body. To solve this problem, a neural network model was proposed to predict the thermal sensation of wearers of LCGs, taking physiological parameters [heart rate (HR), skin temperatures, and tympanic temperature] and physical parameters [ambient temperature (AT) and relative humidity (RH)] including water inlet temperature of LCGs into consideration. Experiments were carried out to obtain the model training data under various conditions. After optimization, the neural network model performs excellently, which shows the potential to predict the thermal sensation for LCGs. The correlation analysis indicates that water inlet temperature is the most correlated parameter to thermal sensation in LCGs. [ABSTRACT FROM AUTHOR]

An improved method is presented to study the efficient numerical modeling of stray-field loss in magnetic shielding excited by a 3-D harmonic magnetic field. The experimental platform is established to obtain the magnetic properties and loss model of the silicon steel lamination under harmonic magnetizations. Experimental determination is improved for more accurate stray-field loss based on the engineering-oriented model TEAM Problem 21c-M1. Prediction and evaluation of stray-field loss and additional iron loss due to harmonic excitations are achieved by special treatment of material properties in the numerical calculation of the harmonic magnetic field. [ABSTRACT FROM AUTHOR]

A two-stage algorithm is proposed to compute switching angles for selective harmonic elimination or selective harmonic control. In the first offline computing stage, the multivariate nonlinear equations are equivalently converted to a group of univariate linear equations by using the theories of symmetric polynomials and Gröbner bases, which are then stored into the target microcontrollers (MCUs) and solved online. In the second online computing stage, the linear equations are solved, and their solutions are used to construct a polynomial whose real roots are actually the final solutions; then, an algebraic-numerical hybrid method is developed to solve the constructed polynomial, which uses the Sturm's theorem to bracket each real root into an individual interval, and then, the bisection method is used to find all the exact real roots. Compared to the traditional numerical methods, the executing efficiency is increased significantly. This method is implemented on several commonly used MCUs and shows very good real-time performance. Experiments verify the correctness of the solved switching angles and demonstrate the feasibility of applying this method in the real control system. [ABSTRACT FROM AUTHOR]

A high-power dual-wideband GaN power amplifier (PA), which features a filter-based input diplexer and a transformer-combined load, has been proposed in this study. The diplexer realized by low-/high-pass filters presents a high dual-band isolation (>15 dB) and can arbitrarily select the location of the two widely spaced bands. Instead of using a diplexer in the output matching network (OMN), a transformer-combined load design has been proposed to simplify the OMN design. The transformer can effectively minimize the loading effect from each other branch, but does not obviously influence the optimal output impedance. The designed PA can operate in either $Ku$ -or $Ka$ -band mode. In the $Ku$ -band mode, an output power more than 40 dBm and a power-added efficiency (PAE) more than 35% are obtained at 13.5–18 GHz. In the $Ka$ -band mode, the output power more than 30 dBm and the PAE more than 17.5% are obtained at 33–38 GHz. The gain in the $Ku$ - and $Ka$ -band modes is more than 30 and 13 dB, respectively. [ABSTRACT FROM AUTHOR]