结构化环境下基于结构单元软编码的3维激光雷达点云描述子

在同时定位与地图构建（SLAM）系统中,基于3维激光雷达点云数据的闭环检测由于描述子计算困难而极具挑战．为此,本文提出一种结构化环境下可用于闭环检测的基于结构单元软编码的新型3维激光雷达点云描述子．针对3维激光雷达点云的稀疏性和独立性导致的3维空间线段提取困难的问题,首先通过几何滤波的方法提取3维空间中垂直于地面的线段,用于保留3维空间的结构信息;然后,基于线段的空间几何关系构建结构单元集合,并通过软编码技术计算特征向量,作为3维激光雷达点云的描述子;最后,通过两帧点云描述子的匹配实现闭环检测．在KITTI公开数据集和自采数据集上的对比实验,验证了本文方法在时效性和鲁棒性等方面均优于主流的3维激光闭环检测方法．     

Simultaneous Achievement of Open and Closed Loop Diagonal Dominance Through Constant Feedback

This paper proposes a new and low complexity technique for the simultaneous achievement of open and closed loop diagonal dominance for uncertain plants. The method is based on the introduction of a static high gain inner feedback which modifies the plant to attain a diagonal dominant open loop compensated plant. The sufficient, easy to use, conditions derived for the open loop diagonal dominance also assure the simultaneous achievement of closed loop diagonal dominance under soft assumptions on the diagonal elements of the decoupled controller robustly stabilizing the plant. These assumptions agree with the usual closed‐loop performance specifications, so that closed‐loop diagonal dominance is achieved without any extra complication on the controller design procedure. The main merits of the present method are: applicability to uncertain plants, ease of implementation and low computational cost. Three examples taken from the relevant literature, are given to show the effectiveness of the proposed approach.     

Static analysis of JavaScript libraries in a scalable and precise way using loop sensitivity

Statically analyzing JavaScript applications often requires an analysis of JavaScript libraries because many JavaScript applications use libraries. However, static analysis techniques for JavaScript are not yet ready for analyzing libraries in a scalable and precise manner. Simply loading JavaScript libraries uses various dynamic features of JavaScript, which cause static analyzers to suffer from mutually intermingled problems of scalability and imprecision. In this paper, we present a loop‐sensitive analysis (LSA) technique, which can improve the analysis scalability when analyzing JavaScript libraries by enhancing the analysis precision of loops. The LSA technique distinguishes loop iterations when loop conditions can be determined to be either true or false precisely. We formalize LSA in the abstract interpretation framework in the presence of tricky language features such as exceptions and prove its soundness and precision theorems using Coq. We evaluate our LSA implementation with the analysis results of programs that use 5 JavaScript libraries and show that LSA significantly improves the analysis scalability and precision of an existing JavaScript static analyzer when analyzing JavaScript libraries. In addition, using the configurability of LSA, we experimentally show the correlation between scalability and precision in the analysis of JavaScript libraries. We found that even the analysis of simple programs that just load jQuery, which is the most popular JavaScript library, in a scalable way requires distinguishing not only the last 4 functions being called but also 40 iterations in each loop with 2‐level nested loops at least. Both the mechanization and implementation of LSA are publicly available.     

Droplet and Microchamber‐Based Digital Loop‐Mediated Isothermal Amplification (dLAMP)

Digital loop‐mediated isothermal amplification (dLAMP) refers to compartmentalizing nucleic acids and LAMP reagents into a large number of individual partitions, such as microchambers and droplets. This compartmentalization enables dLAMP to be an excellent platform to quantify the absolute number of the target nucleic acids. Owing to its low requirement for instrumentation complexity, high specificity, and strong tolerance to inhibitors in the nucleic acid samples, dLAMP has been recognized as a simple and accurate technique to quantify pathogenic nucleic acid. Herein, the general process of dLAMP techniques is summarized, the current dLAMP techniques are categorized, and a comprehensive discussion on different types of dLAMP techniques is presented. Also, the challenges of the current dLAMP are illustrated together with the possible strategies to address these challenges. In the end, the future directions of the dLAMP developments, including multitarget detection, multisample detection, and processing nucleic acid extraction are outlined. With recently significant advances in dLAMP, this technology has the potential to see more widespread use beyond the laboratory in the future.     

Double bridge circuit for self‐validated structural health monitoring strain measurements

In structural health monitoring (SHM) applications, sensor faults and structural damage need to be assuredly discriminated. A self‐diagnosis strain sensor operating in a continuous online SHM scenario is considered. The strain sensor is based on full electric resistance strain gauge Wheatstone bridges. The state of the art shows that such a sensor has not yet been developed. The loop current step response (LCSR) is a well‐known method to detect strain gauge debonding. However, applying the LCSR method to a full strain gauge Wheatstone bridge has some limitations analysed in this paper. To enable the use of the LCSR method in an online SHM scenario, the double bridge circuit is proposed in this work. Two new strain gauge debonding fault detection methods and a new debonding fault isolation method—based on the double bridge circuit measurements—are proposed and evaluated. Two new sensor fusion weighting approaches are also proposed and evaluated—to achieve strain gauge debonding fault tolerance on the double bridge circuit. The experimental results show that the proposed methods can detect, isolate, and tolerate a strain gauge grid debonding fault and can be applied in an online SHM self‐diagnosis sensor scenario.     

Particle swarm optimization procedure in determining parameters in Chaboche kinematic hardening model to assess ratcheting under uniaxial and biaxial loading cycles

As parameters in Chaboche model are difficult to be determined from experimental data, a single objective particle swarm optimization procedure was employed to obtain them. Hysteresis loop and uniaxial and biaxial ratcheting simulations were conducted to validate the determined models. Chaboche models determined by particle swarm optimization give more accurate simulation of ratcheting compared with the model determined by trial and error method. Chaboche models containing different backstress components were studied. Models determined considering uniaxial ratcheting can only predict uniaxial ratcheting precisely, while giving very bad simulation of biaxial ratcheting. The linear hardening rule in the N3L1 model clearly decreases the rate of the accumulation of ratcheting strain, and the N3L1 model gives the best simulations. For biaxial ratcheting, the fourth backstress component can decrease the rate of the accumulation apparently, while it has a little influence on prediction of uniaxial ratcheting.     

Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.     

基于光学锁相环的高稳定度激光稳频方法研究

报道了一种基于光学锁相环的高稳定度激光稳频方法,用于提高可调谐外腔半导体激光器(TECDL)的频率稳定度和准确度。自行研制的光学锁相环电路采用数字鉴相与差分运算相结合的方式获得高灵敏度的鉴频鉴相误差信号,并通过高速模拟PID实现整个系统的闭环锁定。利用该光学锁相环系统进行了TECDL偏频锁定至光学频率梳(OFC)的实验,实验结果表明环路锁定后拍频频率波动在±0.3Hz范围内,偏置频率为50MHz时,光学锁相环系统在1s和1000s积分时间的相对阿伦方差分别为1.5×10^-9和8.5×10^-13。系统锁定后,拍频线宽由500kHz压缩至2kHz。该研究表明采用基于光学锁相环的激光稳频方法可以实现亚Hz级的激光频差控制,通过将TECDL偏频锁定至高稳定度的参考激光源可显著提高其频率稳定度,使其能够满足超精密测量、冷原子/离子干涉测量等领域对激光频率稳定度和准确度的要求。     

用于高精度图像传感器的锁相环

为了提高图像传感器的探测精度,给像素中的传输管提供高精度时钟信号,设计了一款可编程式电荷泵锁相环(Phase-Locked Loop,PLL)模块。该模块使用分频器以输出可调控频率的时钟,增加了复用性;在电荷泵中加入单位增益放大器以消除毛刺,增大了锁相环精度;同时给出了针对整个模块的相位噪声分析。仿真结果表明,当输出200 MHz时钟时,信号的时钟抖动为28 ps,电路工作在1.5 V电压下的功耗<2 mW。该模块已用于一款高精度图像传感器中,在0.11μm CMOS工艺下进行了流片,测试结果表明其可以实现50 MHz到200 MHz的高精度时钟输出,满足了芯片对于时钟的需求。