Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Hydrogen production performance of a photovoltaic thermal system coupled with a proton exchange membrane electrolysis cell

As one of the cleanest energies, hydrogen has attracted much attention over the past decade. Hydrogen can be produced using water electrolysis in a Proton Exchange Membrane Electrolysis Cell (PEMEC). In the present study, the performance of the PEMEC, powered by the Photovoltaic-Thermal (PVT) system, is scrutinized. It is considered that the PVT system provides the required electrical power of the PEMEC and preheats the feedwater. A comprehensive numerical model of the coupled PVT-PEMEC system is developed. The model is used to investigate the effect of various operating parameters, including solar radiation intensity, inlet feedwater temperature, and feedwater mass flow rate, on the hydrogen production and operating voltage of the PEMEC at various Exchange Current Densities (ECDs). Furthermore, the effect of integration of Phase Change Material (PCM) and Thermoelectric Generator (TEG) on the hydrogen production of the system is evaluated. According to the obtained results, the PVT-TEG-PEMEC system outperforms other systems in hydrogen production. However, integration of the PVT-PEMEC system with PCM has a negligible effect on its hydrogen production.     

A survey on tracking control of unmanned underwater vehicles: Experiments-based approach

This paper aims to provide a review of the conceptual design and theoretical framework of the main control schemes proposed in the literature for unmanned underwater vehicles (UUVs). Additionally, the objective of the paper is not only to present an overview of the recent control architectures validated on UUVs but also to give detailed experimental-based comparative studies of the proposed control schemes. To this end, the main control schemes, including proportional–integral–derivative (PID) based, sliding mode control (SMC) based, adaptive based, observation-based, model predictive control (MPC) based, combined control techniques, are revisited in order to consolidate the principal efforts made in the last two decades by the automatic control community in the field. Besides implementing some key tracking control schemes from the classification mentioned above on Leonard UUV, several real-time experimental scenarios are tested, under different operating conditions, to evaluate and compare the efficiency of the selected tracking control schemes. Furthermore, we point out potential investigation gaps and future research trends at the end of this survey.     

A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks.     

Effects of alkali metal ion on imprinting GRIN microstructure in GeS2-Ga2S3-MCl (M=Na,K, Cs) glasses for visible to mid-infrared microgratings

Gradient refractive index (GRIN) micro-optics present unique opportunities for control of the chromatic properties, new degrees of freedom for optical design as well as the potential for use in new optical system applications. GRIN microgratings were imprinted in GeS₂-Ga₂S₃-MCl (M = Na, K, Cs) chalcohalide glasses by microthermal poling, and the effects of the type and concentration of alkali cations on their performance were investigated. Two effective imprinting formation regions of the GRIN microstructure based on the poling saturation voltage (U_s) and glass composition are observed at fixed poling time and temperature. The U_s increases from 140 to 750 and 2600 V in accordance with the activation energy (E_a) of alkali ions (Na⁺ to K⁺ and Cs⁺) increasing from 45.15 to 58.62 and 92.58 kJ/mol for studied samples. The saturated numbers of diffraction order (N_s) of the gratings in these samples are 7, 9 and 6, respectively, the highest number being provided by the K⁺-containing sample. This is in accordance with imprinting-induced phase differences (0.14λ, 0.19λ and 0.09λ) measured in the fabricated samples containing Na⁺, K⁺ and Cs⁺ ions. Furthermore, the U_s of samples decreases from 1500 to 300 V with four concentrations of K⁺ from 10 to 30%, associated with their E_a of K⁺ decreasing from 69.62 to 53.46 kJ/mol, while N_s increases from 8 to 14, which is attributed to the increase of the phase difference in the GRIN structures. The controllable GRIN microstructures are realized by adjusting the type and concentration of alkali cations in chalcohalide glasses, which is expected to drive the design of broadband GRIN microgratings.     

Spark plasma sintering of UO2 nanopowders: Pressure,heating rate and current effects

We analysed with different methods the densification of UO₂ nanopowders in SPS under constant heating rate (CHR) and isothermal sintering conditions. The apparent activation energy of densification in SPS (75 kJ/mol with CHR method) is significantly smaller than in conventional sintering. It is shown that this is likely not an effect of the applied current. We also observed a threshold stress at 64 MPa for the transition from pressure-insensitive sintering (stress exponent n≈0) to pressure-assisted sintering, suggesting that the contribution of the capillary stresses in such nanopowders is comparable with the typical stress applied in SPS.     

Molecular design for thermally stable SiZrBOC structure from single source precursor with high ceramic yield

A new type of high-temperature-resistant SiZrBOC ceramics was prepared by sol-gel method using polymethyl-hydro siloxane (PMHS), boric acid (B(OH)₃), and n-propyl zirconate (Zr(OPr)₄) as raw materials. After high-temperature pyrolysis, the SiZrBOC precursor was transformed into a crystalline ceramic material with a yield of 89.5 wt%. Fourier infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA) were applied to characterize the polymer-ceramic conversion process and thermal behavior of ceramic precursors. According to the results, the addition of boron elements led to the formation of Si-O-B links in the system. X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to study the phase composition and microstructure of SiZrBOC ceramics. Finally, the oxidation test at 1200 °C revealed that SiZrBOC ceramics with a boron/zirconium molar ratio of 2.5:1 exhibited the best oxidation resistance at a weight gain of 0.4 wt% only.     

Kinetic Monte Carlo simulation of hydrogen production from photocatalytic water splitting in the presence of methanol by 1 wt% Au/TiO2

In this research, using the kinetic Monte Carlo simulation (KMC), the hydrogen production from a water-methanol mixture using Au/TiO₂ photocatalyst is investigated. A mechanism is proposed, and the rate constants of the reaction steps are specified. The reaction rate constants of different steps and the concentration of the active sites on the photocatalyst surface were determined. An excellent match between simulated and experimental data confirms the results. The electron-hole pair production, methanol adsorption on the photocatalyst surface, and electron-hole recombination steps are considered the most critical steps. To study the effects of independent variables (initial concentration of methanol, photocatalyst dosage, pH, and time of reaction) on the produced hydrogen, a combination of KMC simulation and design of experiment was employed. The concentration of photocatalysis has the highest and pH has the lowest effect on the hydrogen production. The optimal conditions for photocatalytic hydrogen production are presented.     

格点QCD基础求解器及其异构计算实现的性能优化

格点量子色动力学(格点QCD)是研究夸克、胶子等微观粒子间相互作用的重要理论和方法. 通过将时空离散化为四维结构网格, 并将量子色动力学的基本场量定义在网格上, 让研究人员可以使用数值模拟方法, 从第一性原理出发研究强子间相互作用和性质, 但这个过程中的计算量极大, 需要进行大规模并行计算. 格点QCD计算的核心基础为格点QCD求解器, 是程序运行主要的计算热点模块. 本文研究在国产异构计算平台下格点QCD求解器的实现与优化, 提出一套格点QCD求解器的设计实现, 实现了BiCGSTAB求解器, 显著降低了迭代次数; 通过对奇偶预处理技术, 降低了所求问题的计算规模; 针对国产异构加速卡的特点, 优化了Dslash模块的访存操作. 实验测试表明, 相比优化前的求解器获得了约30倍的加速比, 为国产异构超算下格点QCD软件性能优化提供了有益的参考价值.