A Broadband Gain Amplifier Designed with the Models for Package and Diode Using 0.5 μm GaAs E-pHEMT Process

This paper presents a 50 MHz to 5 GHz broadband gain amplifier using a 0.5 μm gallium-arsenide pseudomorphic high-electron-mobility-transistor (GaAs pHEMT). For broadband design, a high gain cascode structure with a feedback network was used. To ensure the robustness of the design, the amplifier had to consider the effects of the Electrostatic Discharge (ESD)-protected diode and the package, which can degrade the broadband performance. Therefore, the equivalent circuit models of the package and the ESD-protected diode were analyzed and simulated in this paper. The designed broadband gain amplifier from 50 MHz to 5 GHz frequency band has a die size of 700 μm x 1000 μm and consumes 156 mW of dc power, and it was simulated with a gain of 18.7 dB to 20.6 dB, a P1dB of 15.3 to 16.9 dBm, and a OIP3 of 26.5 to 31 dBm. Furthermore, the excellent gain flatness exhibited within 18.7 dB ± 1.92 dB at the interest of the frequency band.

Multi-Scale Selective Feedback Network with Dual Loss for Real Image Denoising

The feedback mechanism in the human visual system extracts high-level semantics from noisy scenes. It then guides low-level noise removal, which has not been fully explored in image denoising networks based on deep learning. The commonly used fully-supervised network optimizes parameters through paired training data. However, unpaired images without noise-free labels are ubiquitous in the real world. Therefore, we proposed a multi-scale selective feedback network (MSFN) with the dual loss. We allow shallow layers to access valuable contextual information from the following deep layers selectively between two adjacent time steps. Iterative refinement mechanism can remove complex noise from coarse to fine. The dual regression is designed to reconstruct noisy images to establish closed-loop supervision that is training-friendly for unpaired data. We use the dual loss to optimize the primary clean-to-noisy task and the dual noisy-to-clean task simultaneously. Extensive experiments prove that our method achieves state-of-the-art results and shows better adaptability on real-world images than the existing methods.

A comprehensive review of deep learning-based single image super-resolution

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

Sensors In Buildings: Aadding Another Layer Of Expression In Architecture

Biological organisms, from microbes, to plants, to humans, survive in part because these bodies respond and react to changes within and outside their bodies. In order to function, these living things use sensors to read the environment and sense changes, and relay information back and forth. Sensors should no longer have one simple functional role but should be a node in an entire cybernetic network. By using sensors in a building, we can make systems more efficient, without losing effectiveness of their functions, allowing energy to be saved within an action and reaction feedback network, but more interestingly sensors can be used so that buildings can emerge with greater opportunity for expression and therefore user experiences.

Sensors In Buildings: Aadding Another Layer Of Expression In Architecture

Biological organisms, from microbes, to plants, to humans, survive in part because these bodies respond and react to changes within and outside their bodies. In order to function, these living things use sensors to read the environment and sense changes, and relay information back and forth. Sensors should no longer have one simple functional role but should be a node in an entire cybernetic network. By using sensors in a building, we can make systems more efficient, without losing effectiveness of their functions, allowing energy to be saved within an action and reaction feedback network, but more interestingly sensors can be used so that buildings can emerge with greater opportunity for expression and therefore user experiences.