幅度失配对SIC检测器误码性能影响的定量分析

基于幅度失配率,使用高斯近似方法给出有幅度失配的串行干扰消除检测器(SIC)的近似误码率递推公式。利用该公式,讨论了SIC中由于幅度失配而引起的误码率特性。通过引入正数来刻画有幅度失配SIC误码性能与其上限(无幅度失配误码性能)的损失度,使用Lagrange中值定理和近似分析方法,推导出幅度失配率与信噪比以及之间的定量分析公式。仿真表明,该定量分析公式揭示了幅度失配对系统性能的影响程度。     

Method of identifying burning material from its smoke using attenuation of light

In this study, it is verified that several materials can be accurately distinguished from their aerosols or from the smoke they emit when they are burnt individually. This is done by comparisons of transmitted and scattered light at various wavelengths using a Machine Learning Algorithm. Smoke was introduced in the paths of light of different wavelengths, simultaneously. The wavelengths were chosen from widest spectrum of radiation, for which LEDs and photodiodes were available commercially. These include UVC 275 nm, UVA 365 nm, Blue 405 nm, Red 620 nm and IR 960 nm. At least one photodiode was used to sense transmitted and at least one photodiode to sense scattered light from each wavelength of light. Each smoke or aerosol, from a single material, was tested many times to create large datasets. After a selection process, a Machine Learning Algorithm, namely Random Forest, was trained with the data from all materials burnt. It was found that a number of materials that are commonly involved in building fires can be identified with high accuracy using this model. The materials were identified with an accuracy of 99.6%–59%, which are N-Heptane, polyester carpet, Can smoke, PVC insulated wire, polyurethane foam, cotton fabric, cardboard, cigarette and polystyrene foam. The proposed method provides a model, whose accuracy is quantifiable, with easily trainable algorithm for new materials and can be tailored for certain materials of interest.