A Low-Complexity Optimal Switching Time-Modulated Model-Predictive Control for PMSM With Three-Level NPC Converter

Conventional finite control set model-predictive control (FCS-MPC) presents a high computational burden, especially in three-level neutral-point-clamped (NPC) converters. This article proposes a low-complexity optimal switching time-modulated model-predictive control (OST-M2PC) method for a three-level NPC converter. In the proposed OST-M2PC method, the optimal switching time is calculated using a cost function. Compared with the conventional FCS-MPC, the proposed OST-M2PC method has a fixed switching frequency as well as better power quality. The proposed OST-M2PC can operate at a 20-kHz sampling frequency, reducing the computational burden of the processor. Simulation and experimental results validate the operation of the proposed method

The Qitai Radio Telescope

This study presents a general outline of the Qitai radio telescope (QTT) project. Qitai, the site of the telescope, is a county of Xinjiang Uygur Autonomous Region of China, located in the east Tianshan Mountains at an elevation of about 1800 m. The QTT is a fully steerable, Gregorian type telescope with a standard parabolic main reflector of 110 m diameter. The QTT has adopted an um-brella support, homology-symmetric lightweight design. The main reflector is active so that the deformation caused by gravity can be corrected. The structural design aims to ultimately allow high-sensitivity observations from 150 MHz up to 115 GHz. To satisfy the requirements for early scientific goals, the QTT will be equipped with ultra-wideband receivers and large field-of-view mul-ti-beam receivers. A multi-function signal-processing system based on RFSoC and GPU processor chips will be developed. These will enable the QTT to operate in pulsar, spectral line, continuum and Very Long Baseline Interferometer (VLBI) observing modes. Electromagnetic compatibility (EMC) and radio frequency interference (RFI) control techniques are adopted throughout the system design. The QTT will form a world-class observational platform for the detection of low-frequency (nanoHertz) gravitational waves through pulsar timing array (PTA) techniques, pulsar surveys, the discovery of binary black-hole systems, and exploring dark matter and the origin of life in the universe.Comment: 12 pages, 11 figures, accepted for publication in Science China Physics, Mechanics & Astronom

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Unified Multiple Proxy Deep Metric Learning Framework Embedded With Distribution Optimization for Fine-Grained Ship Classification in Remote Sensing Images

Improving ship classification performance in remote sensing imagery by deep metric learning (DML) is a newly emerging research topic and has good application prospects. From the perspective of the use of metric loss (classification loss and pairwise loss) and the way of proxy learning (a single proxy or multiple proxies), this study summarizes the existing DML methods into four representative frameworks and proposes a novel framework, namely, a unified multiple proxy deep metric learning framework embedded with distribution optimization (UMP+D). Specifically, the UMP+D not only unifies the combination of classification loss and pairwise loss into a single loss function containing only pairwise representation but also fuses it with multiple proxy learning. In addition, a distribution loss branch is embedded in the UMP+D to refine the distribution of samples in the feature embedding space to further tighten the intraclass samples and pull apart the interclass samples. Extensive experiments on two optical remote sensing datasets and one synthetic aperture radar dataset demonstrate that the proposed UMP+D framework outperforms the existing frameworks and achieves state-of-the-art performance

Speech Enhancement Based on Fusion of Both Magnitude/Phase-Aware Features and Targets

Recently, supervised learning methods have shown promising performance, especially deep neural network-based (DNN) methods, in the application of single-channel speech enhancement. Generally, those approaches extract the acoustic features directly from the noisy speech to train a magnitude-aware target. In this paper, we propose to extract the acoustic features not only from the noisy speech but also from the pre-estimated speech, noise and phase separately, then fuse them into a new complementary feature for the purpose of obtaining more discriminative acoustic representation. In addition, on the basis of learning a magnitude-aware target, we also utilize the fusion feature to learn a phase-aware target, thereby further improving the accuracy of the recovered speech. We conduct extensive experiments, including performance comparison with some typical existing methods, generalization ability evaluation on unseen noise, ablation study, and subjective test by human listener, to demonstrate the feasibility and effectiveness of the proposed method. Experimental results prove that the proposed method has the ability to improve the quality and intelligibility of the reconstructed speech

Learning Ratio Mask with Cascaded Deep Neural Networks for Echo Cancellation in Laser Monitoring Signals

Laser monitoring has received more and more attention in many application fields thanks to its essential advantages. The analysis shows that the target speech in the laser monitoring signals is often interfered by the echoes, resulting in a decline in speech intelligibility and quality, which in turn affects the identification of useful information. The cancellation of echoes in laser monitoring signals is not a trivial task. In this article, we formulate it as a simple but effective additive echo noise model and propose a cascade deep neural networks (C-DNNs) as the mapping function from the acoustic feature of noisy speech to the ratio mask of clean signal. To validate the feasibility and effectiveness of the proposed method, we investigated the effect of echo intensity, echo delay, and training target on the performance. We also compared the proposed C-DNNs to some traditional and newly emerging DNN-based supervised learning methods. Extensive experiments demonstrated the proposed method can greatly improve the speech intelligibility and speech quality of the echo-cancelled signals and outperform the comparison methods

Enhancement of Decompressed Images at Low Bit Rates

Conference PaperTransform coding at low bit rates introduces artifacts associated with the basis functions of the transform. For example, decompressed images based on the DCT (discrete cosine transform)- like JPEG¹⁶ - exhibit blocking artifacts at low bit rates. This paper proposes a post-processing scheme to enhance decompressed images that is potentially applicable in several situations. In particular, the method works remarkable well in "deblocking" of DCT compressed images. The method is non-linear, computationally efficient, and spatially adaptive - and has the distint feature that it removes artifacts while yet retaining sharp features in the images. An important implication of this result is that images coded using the JPEG standard can be efficiently post-processed to give significantly improved visual quality in the images