A new crossover operator in genetic programming for object classification.

The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.     

Using genetic programming for context-sensitive feature scoring in classification problems

Feature scoring is an avenue to feature selection that provides a measure of usefulness for the individual features of a classification task. Features are ranked based on their scores and selection is performed by choosing a small group of high-ranked features. Most existing feature scoring/ranking methods focus on the relevance of a single feature to the class labels regardless of the role of other features (context-insensitive). The paper proposes a genetic programming (GP)-based method to see how a set of features can contribute towards discriminating different classes. The features receive score in the context of other features participating in a GP program. The scoring mechanism is based on the frequency of appearance of each feature in a collection of GP programs and the fitness of those programs. Our results show that the proposed feature ranking method can detect important features of a problem. A variety of different classifiers restricted to just a few of these high-ranked features work well. The proposed scoring-ranking mechanism can also shrink the search space of size O(2 ⁿ ) of subsets of features to a search space of size O(n) in which there are points that are very likely to improve the classification performance.     

基于3D打印技术的宽带圆形微带天线

该文介绍了一款环形耦合的圆形贴片天线。采用圆形金属贴片作为天线的辐射贴片,通过在介质板上引入两个谐振环,产生两个新的工作谐振频段。通过调节圆形贴片和耦合环之间的高度,进一步拓宽了天线的工作频段。通过3D打印技术对不规则的介质基板进行加工制作,有效解决了传统加工方法加工难度大及生产周期长的问题。测试结果表明,该天线在3.8～5.8 GHz工作频段内,回波损耗小于-10 dB;在5.1 GHz时,天线增益达到8.4 dBi;天线相对带宽为42%,且具有良好的全向辐射特性。天线的测试结果与仿真结果基本吻合。     

应用于低频无源RFID的低成本2 kbit EEPROM

基于350 nm 2-poly 3-metal EEPROM工艺,设计了一种应用于低频无源RFID的低成本2 kbit EEPROM存储器。在保证存储容量能满足大多数使用场景的情况下,通过优化Dickson电荷泵和读出电路的结构,实现电路版图面积的最小化,从而对整体电路实现低成本设计。优化后的Dickson电荷泵能实现10μs内从3.3 V到16 V的稳定升压,且功耗为334μW;读出电路基于检测NCG器件阈值电压的方式实现存储逻辑值的判别,该方法不需要能提供高精度电流的基准电路和具有高增益的灵敏放大器,有效降低了整体电路的面积。低成本2 kbit EEPROM的工作电压为3.3 V,能实现32位并行输入和1位串行输出,芯片总面积仅为0.14 mm²,有效降低了低频无源RFID设计复杂度和制造成本。     

Nested planar array: configuration design,optimal array and DOA estimation

Nested array enables to enhance localisation resolution and achieve under-determined direction of arrival (DOA) estimation. In this paper, we improve the traditional nested planar array to achieve more degrees of freedom (DOFs) and better angle estimation performance. The closed-form expressions for sensor positions of the improved array are given and the optimal array configuration for largest available DOFs is derived. Meanwhile, a computationally efficient DOA estimation algorithm is proposed. Specifically, we utilise two dimensional Discrete Fourier Transform (2D DFT) method to obtain the coarse DOA estimates; Subsequently, we achieve the fine DOA estimates by 2D spatial smoothing multiple signals classification (SS-MUSIC) algorithm. The proposed algorithm enjoys the same estimation accuracy as SS-MUSIC algorithm but with lower complexity because the coarse DOA estimates enable to shrink the range of spectral search. In addition, estimation of the number of signals is not required by 2D DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm.     

一种永磁同步电机无模型超螺旋快速终端滑模控制方法

针对内置式永磁同步电机(interior permanent magnet synchronous motor, IPMSM)由于内部参数变化、外部扰动等各种不确定性因素导致控制性能不佳的问题,提出一种无模型超螺旋快速终端滑模控制方法。首先,建立考虑IPMSM不确定性的新型超局部模型,结合超螺旋算法和快速终端切换函数设计无模型超螺旋快速终端滑模控制器,确保系统状态有限时间收敛,并有效减小抖振。其次,设计扩展滑模扰动观测器精准估计超局部模型中的未知部分,并前馈补偿给设计的控制器,进一步提升系统的抗干扰能力和跟踪性能。最后,通过与PI控制和传统无模型滑模控制进行仿真实验对比,验证了该方法具有更快的收敛速度和更强的鲁棒性。     

SAC OCDMA系统FOC地址码设计及性能研究

设计了一种新的适合同步光谱幅度编码光码分变址(Spectral-amplitude coding optical code division multiple access,SAC OCDMA)系统的地址码,能够使多址干扰产生的误码率为0.且该地址码的互相关限为1,在给定码重和用户数的情况下能够得到较小码长,便于系统的编解码。利用OptiSys 7.0设计出仿真系统,分析比较用波分复用器和光纤光栅作为编解码器适用的不同系统,在考虑散弹噪声、相位引起的强度噪声(PIIN)和热噪声的情况下,根据该地址码的特点分析整个编解码系统的信噪比和误码率,并与现有的SAC地址码性能进行比较.结果表明:选用该地址码的SAC OCDMA系统得到的眼图更为理想,在系统误码率、编解码实现,及系统性能上都有了很大的提高。     

自适应多组分气体检测系统设计

针对传统气体检测方案检测气体种类少、类别固定、灵活性差等问题,本中提出了一种自适应多组分气体检测系统。该系统采用端到端的设计思想,分为下位机气体检测端和上位机数据显示端。下位机支持同时进行6路气体检测,最多可扩展至32路气体检测;文中提出的自适应气体检测算法支持根据应用场景的需要更换不同的气体传感器;采集到的数据基于Fatfs文件管理系统实现有效管理;为进一步增强系统实时性,采用FreeRTOS操作系统实现任务调度。上位机基于Qt开发,可远程实时查看气体数据和对应的地理位置。实际测试结果表明,气体数据检测误差低于1%,数据传输时延在秒级,可忽略不计。     

OCDMA系统中新型正交码的设计及性能研究

为了得到性能较为良好的正交码,采用没有重复数字的全间隔集,进行了地址码的设计和系统仿真验证,设计出的正交码具有理想的相关性和灵活的码字容量。分析整个系统,在考虑各种噪声和多址干扰的影响下,推导出正交码的误比特率公式。自行设计并搭建了采用光纤延时线作为编解码器的异步光码多分址系统。结果表明,该正交码能够适应更多用户的需求,通过误比特率公式计算和系统仿真得到的结果都较为理想,此研究对需要大容量的光码多分址系统的进一步发展具有一定的帮助。