高光谱成像用高帧频CMOS图像传感器北大核心

针对高帧频、全局曝光和光谱平坦等成像应用需求,设计了一款高光谱成像用CMOS图像传感器。其光敏元采用PN型光电二极管,读出电路采用5T像素结构。采用列读出电路以及高速多通道模拟信号并行读出的设计方案来获得低像素固定图像噪声(FPN)和非均匀性抑制。芯片采用ASMC 0.35μm三层金属两层多晶硅标准CMOS工艺流片,为了抑制光电二极管的光谱干涉效应,后续进行了光谱平坦化VAE特殊工艺,并对器件的光电性能进行了测试评估。电路测试结果符合理论设计预期,成像效果良好,像素具备积分可调和全局快门功能,最终实现的像素规模为512×256,像元尺寸为30μm×30μm,最大满阱电子为400 ke^(-),FPN小于0.2%,动态范围为72 dB,帧频为450 f/s,相邻10 nm波段范围内量子效率相差小于10%,可满足高光谱成像系统对CMOS成像器件的要求。     

Synthesis and characterizations of highly responsive H2S sensor using p-type Co3O4 nanoparticles/nanorods mixed nanostructures

High-quality p-type semiconducting Co₃O₄ with mixed morphology of nanoparticles/nanorods are synthesized using a hydrothermal route for high response and selective hydrogen sulphide (H₂S) sensor application. XRD and Raman studies revealed the crystal structure and molecular bonding for obtained Co₃O_4, respectively. The nanoparticles/nanorods-like structures were confirmed for Co₃O₄ using FESEM and TEM analysis. The EDS and XPS spectra analysis were carried out for elemental composition and chemical atomic states of Co₃O₄. The Co₃O₄ sensor is investigated for gas sensing properties in dynamic conditions. The sensor exhibited the highest selectivity towards H₂S among various hydrogen-contained gases at 225 °C. The sensor revealed a high response of 357% and 44% for 100 and 10 ppm H₂S gas concentrations, respectively. The Co₃O₄ sensor exhibited a systematic dynamic resistance response for 100–10 ppm range H₂S gas. The excellent dynamic resistance repeatability of the sensor was shown towards 25 ppm H₂S gas. The response of Co₃O₄ sensor was investigated at different operating temperatures and H₂S concentrations. The sensor stability and H₂S sensing mechanism for the Co₃O₄ sensor have been reported. Highly uniform and mixed nanostructures of Co₃O₄ can be the potential sensor material for real-time high-performance H₂S sensor application.     

Thermal properties of hierarchical YSZ and LZO ceramic microspheres with multi-scaled voids investigated by using theoretical,experimental and simulation methods

Heat transfer within ceramic feedstock powders is still unclear, which impedes optimization of the thermal and mechanical properties of the thermal sprayed coatings. The microspheres (yttria-stabilized zirconia YSZ and lanthanum zirconate LZO) were prepared via the electro-spraying assisted phase inversion method (ESP). The thermal properties of the two ESP microspheres and a commercial hollow spherical powder (HOSP) were investigated by using theoretical, experimental, and simulation methods. Thermal conductivity of the single microsphere was estimated via a novel nest model that was derived from the Maxwell-Eucken 1 and the EMT model. Thermal conductivity of a single YSZ/LZO-ESP microsphere prepared at 1100–1200 °C was within 0.36–0.75 W/m K, which was ～ 20 % lower than that of a single YSZ-HOSP microsphere with a similar porosity. Heat flux simulation showed that high tortuosity around the multi-scaled voids of the ESP microsphere led to a more efficient decrease in thermal conductivity compared with total porosity.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Ammonia gas sensing and magnetic permeability of enhanced surface area and high porosity lanthanum substituted Co–Zn nano ferrites

This work reports magnetic permeability and ammonia gas sensing characteristics of La³⁺ substituted Co–Zn nano ferrites possessing chemical formula Co_0.7Zn_0.3La_xFe_2-2xO₄ (x = 0–0.1) synthesized by a sol-gel route. Refinement of X-ray diffraction (XRD) patterns of the ferrite powders by the Rietveld technique has revealed the creation of single-phase spinel structure. The tenancy of constituent cations at tetrahedral/octahedral sites was obtained from the refinement of XRD. The crystallite sizes calculated from the W–H method vary from 20 to 24 nm. The scanning electron microscope (SEM) profiles of the ferrite samples were analyzed for the morphological details. The energy dispersive X-ray analysis (EDAX) patterns of the samples were obtained to test the elemental purity of the ferrites within their stoichiometry. The transmission electron microscope (TEM) image of the ferrite (x = 0.1) exhibits the spherical and oval shaped particles with a mean size of 20 nm. Fourier transform infra-red (FTIR) spectra were analyzed to confirm the superseding of La³⁺ cations at octahedral sites. The Brunauer-Emmett-Teller (BET) analysis of nitrogen adsorption-desorption isotherms of the ferrites was performed to investigate the porous structure and to determine the surface area of the nanocrystalline ferrites. The oxidation states of the constituent ions were confirmed by means of X-ray photoelectron spectroscopy (XPS). The complex permeability as a function of frequency was studied to explore the effects of structural parameters on the magnetic behaviour of the ferrites. Analysis of gas sensing properties of the ferrites have proved that the Co–Zn–La ferrite with controlled La composition can be utilized as an effective ammonia gas sensing material in commercial gas sensors.     

混凝土结构的压电声波检测分析

本文介绍了混凝土结构的压电体波和表面波检测的主要进展,对两种压电声波检测的优缺点进行了总结。体波检测设备一般埋入混凝土内部,需要选择合理的检测部位,检测结果较为精确;声表面波检测无需选择特定的部位,但是检测深度有限。在实际检测工作过程中,可以联合两种方法相互验证。     

缺水传感器改进设计

针对一种煤矿用本安型缺水传感器在现场安装及维护难度大,以及抽采泵供水状态下管道内有水但传感器检测显示为无水,或抽采泵停止工作时传感器检测显示有水问题,分析原产品中磁体及探头感应装置结构。改进其结构形式,并优化安装,使检测稳定可靠。实现抽采泵管道用缺水传感器的可靠性设计和轻量化设计。     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.