基于经验模态分解的SO_2浓度检测信号处理方法

荧光法测量SO2浓度是大气监测中常用的检测手段.双光路技术可以消除光源和光路的噪音干扰,但光电转换器件在激发光照射下产生的背景噪音也会影响定量分析的准确度.本文采用经验模态分解滤波算法降低检测中存在的各种噪音,在实现有效降噪的基础上较好地保存了有用的原始信号.仿真结果表明,针对SO2浓度检测系统,利用经验模态分解去噪后信号的信噪比达到204.273 6,均方误差为0.007 0.与小波去噪法相比,经验模态分解检测效果更佳.最后将经两组不同方法处理后的信号应用于气体检测系统中,实验数据的线性关系更好地验证了经验模态分解方法应用到浓度检测系统的可行性.     

Thermal performance analysis of vacuum variable-temperature blackbody system

In this paper, the design and structure of a vacuum variable-temperature blackbody system were described, and the steady-state thermal analysis of a 3-D blackbody model was presented. Also, the thermal performance of the blackbody was evaluated using an infrared camera system. The blackbody system was constructed to operate under vacuum conditions (2.67 × 10⁻² Pa) to reduce its temperature uncertainty, which can be caused by vapor condensation at low temperatures usually below 273.15 K. A heat sink and heat shield including a cold shield were embedded around the radiator to maintain the heat balance of the blackbody. A simplified 3-D model of the blackbody including a radiator, heat sink, heat shield, cold shield, and heat source was thermophysically evaluated by performing finite elements analysis using the extended Stefan–Boltzmann’s rule, and the infrared radiating performance of the developed system was analyzed using an infrared camera system. On the basis of the results of measurements and simulations, we expect that the suggested blackbody system can serve as a highly stable reference source for the calibration and measurement of infrared optical systems within operational temperature ranges.     

Biaxial Q-shearing of 27Al 3QMAS NMR spectra: Insight into the structural disorder of framework aluminosilicates

In this contribution, we present the application potentiality of biaxial Q-shearing of ²⁷Al 3QMAS NMR spectra in the analysis of structural defects of aluminium units in aluminosilicates. This study demonstrates that the combination of various shearing transformations of the recorded ²⁷Al 3QMAS NMR spectra enables an understanding of the broadening processes of the correlation signals of disordered framework aluminosilicates, for which a wide distribution of ²⁷Al MAS NMR chemical shifts and quadrupolar parameters (i.e., second-order quadrupolar splitting and quadrupole-induced chemical shifts) can be expected. By combining the suitably selected shearing transformation procedures, the mechanisms of the formation of local defects in aluminosilicate frameworks, including Al/Si substitution effects in the next-nearest neighbouring T-sites, variations in bond angles, and/or variations in the physicochemical nature of charge-balancing counter-ions, can be identified. The proposed procedure has been extensively tested on a range of model aluminosilicate materials (kyanite, γ-alumina, metakaolin, analcime, chabazite, natrolite, phillipsite, mordenite, zeolite A, and zeolite Y).     

EEMD在土壤剖面反射光谱消噪中的应用

实测光谱常含有大量干扰信息, 消噪在光谱数据处理和分析中极为重要, 它直接影响后续的定量分析和信息挖掘。因此, 选择适当的消噪方法是改善光谱分析精度, 提升光谱分析能力的一个关键性突破。集合经验模态分解(EEMD)方法是一个以信号固有特征尺度为度量的时空滤波过程, 能充分保留信号本身的非线性和非平稳特征, 在信号的滤波和消噪中具有较大的优势。结合EEMD的多尺度滤波特性, 提出了一种新的EEMD阈值光谱消噪方法, 并应用于新疆塔里木河中游典型绿洲33个土壤剖面反射光谱数据的预处理。为探讨EEMD阈值法在土壤剖面反射光谱消噪中的效用, 对EEMD阈值法和小波阈值法的消噪结果进行了对比分析。结果表明: 与传统的小波阈值法相比, EEMD阈值法消噪结果的信噪比从14.836 6 dB提高到34.275 7 dB, 均方根误差由6.786 1×10-5降到7.240 6×10-6, 相关系数从0.982 5提高到0.999 8, EEMD阈值法的三个消噪效果衡量指标均优于小波阈值法。证明了EEMD阈值法可有效地去除土壤剖面光谱噪声, 较好地保留了光谱的细节信息, 提高了光谱的定量分析精度, 且与小波阈值消噪方法相比具有较强的可靠性和自适应优势, 作为光谱数据预处理的一种新方法, 其应用前景良好。     

太阳对成像型天基激光告警系统的影响

针对成像型天基激光告警系统中的太阳背景辐射问题,通过理论分析和数值计算研究了太阳与系统探测器不同空间位置关系时直射太阳光和地球反射太阳光对系统成像信噪比、威胁激光检测、探测器自身安全的影响.结果表明:太阳光是天基激光告警系统的主要背景辐射,地球反射太阳光是系统红外探测器成像的主要背景光|反射太阳光辐照在探测器像面上的最大能量密度为0.97 mJ/cm2,反射太阳光背景下,攻击激光成像信噪比在103以上,信标激光信噪比小于1,反射太阳光对攻击激光检测没有影响,对信标激光成像是强背景干扰|直射太阳光辐照探测器是系统不可避免的情况,太阳成像光斑功率密度最大时约20 W/cm2,将导致探测器像元饱和甚至损坏|太阳成像光斑特征与入射激光相似,会造成系统严重虚警.     

聚光型粗细调互补阳光信号采集器的研制

高准确度自动跟踪系统是太阳能聚光器必不可少的组成部分,而信号采集器能否精确可靠地采集到阳光照射方向的信号又是自动跟踪准确度的关键.本文提出了一种粗细调互补信号采集器的设计方案,在正常工作期间通过软件能够根据需要不断地自动调整选择二组光电传感器中其中一组输出的差模信号作为有效信号,从而有效地解决了大范围寻找太阳和高准确度跟踪之间的矛盾|而在此基础上改进为对聚光后的阳光信号进行采集的新结构,又彻底解决了光电传感器本身在光照强度很大时进入饱和区与光照强度很弱时输出的差模信号太小的问题,有效延长了聚光器在早晚时段的正常工作时间.据此原理制作的聚光型粗细调互补信号采集器应用于某公司的CPV型1200W砷化镓发电系统中,取得了很好的跟踪效果,其实际跟踪误差≤0.1°（立体角）.     

Multi-task learning approach for modulation and wireless signal classification for 5G and beyond: Edge deployment via model compression

Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness on the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks based multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks while considering heterogeneous wireless signals such as radar and communication waveforms in the electromagnetic spectrum. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. We additionally include experimental evaluations of the model with over-the-air collected samples and demonstrate first-hand insight on model compression along with deep learning pipeline for deployment on resource-constrained edge devices. We demonstrate significant computational, memory, and accuracy improvement of the proposed model over two reference architectures. In addition to modeling a lightweight MTL model suitable for resource-constrained embedded radio platforms, we provide a comprehensive heterogeneous wireless signals dataset for public use.     

Accurate determination of extra-column band broadening using peak summation

The present contribution addresses the problem of the accurate determination of the extra-column band-broadening dispersion by applying the method of moments (MOM) on pulse response experiments. The MOM provides the only mathematically rigorous way to determine the variance of a pulse response, but suffers from the fact that the obtained variance values usually depend very strongly and unpredictably on the selected integration boundaries. In the present study, it is investigated whether the MOM cannot be made more accurate by repeating the pulse response experiment a number of times, then add all measured peaks (after retention time alignment) and subsequently perform the integration on this summed peak. Testing this approach for a number of different integration boundary detection methods consistently more accurate results are obtained than when the different repeat response pulses are integrated individually and the variances are averaged afterwards. It was also found that adding five repeats already leads to a significant improvement of the variance estimate, whereas the addition of 10-20 repeats is needed before the variance estimate converges to a steady value.