On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.     

A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour

The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning.     

水泥生产线篦冷机料层厚度新型控制技术

新型干法水泥窑第三代篦冷机熟料料层厚度控制是保证水泥成品质量的重要环节。使用安装在篦冷机本体上的料层测厚仪,直接测量出篦冷机熟料的料层厚度,通过自动控制系统对篦冷机的料层厚度进行直接控制,以保证篦冷机内部热工参数的稳定,提高和稳定水泥产品的质量和产量。     

椭圆工件机器视觉尺寸检测系统设计与实现

本文介绍了椭圆工件机器视觉检测系统的构成和软件功能模块,重点阐述了统计聚类和Hough变换相结合检测椭圆像素尺寸的新算法。在降低Hough变换参数维度的情况下,利用椭圆自身特征减少无效数据统计存储量。     

九轴陀螺仪在AGV自主导航中的应用设计

AGV小车自主导航的应用研究,是利用MPU9250传感器集成的三轴加速度计、三轴陀螺仪、三轴磁力计。本文研究了地理坐标系,并给出车体坐标系和场地坐标系的定义,在此基础上,首先进行车体坐标与地理坐标的转换,确定小车从停车点到目标点的初始航向角,然后通过卡尔曼滤波进行数据融合得到融合后的航向角,随后通过PID控制算法控制小车运动过程的角度,最后通过手动控制引导小车到达目标点的停车板上。并且在此基础上形成了不同应用场所的导航图,经现场实现卡尔曼滤波与导航效果良好。     

基于STAF自动化测试框架的研究与应用

随着软件功能实现技术越来越复杂,软件测试领域迫切需要具有自动化、多平台、开销小的自动化测试框架,STAF的出现很好地解决了这个问题。提出一种基于STAF的自动化测试框架,并进行模拟实践,采用ATT工具设计自动测试引擎,通过模拟实验测试,构建基于STAF的自动化测试框架。测试结果显示：在不同环境下,该框架均具有软件测试自动化程度高、测试效率高,易于管理、维护和可扩展等优点。     

Application execution path analysis for the automatic parallelisation of binary codes in the Intel x86 platform

Traditionally, computer programs have been developed using the sequential programming paradigm. With the advent of parallel computing systems, such as multicore processors and distributed environments, the sequential paradigm became a barrier to the utilisation of the available resources, since the program is restricted to a single processing unit. To address this issue, we propose a transparent automatic parallelisation tool with a binary rewriter. The steps of our approach are: the disassembly of the Intel x86 application, its transformation into an intermediary language; the analysis of this intermediary representation to obtain the flow and dependency graphs; the partitioning of the application into parallel units, using the obtained graphs; and, finally, the reassembly of the application back into the original Intel x86 architecture. By transforming the compiled application software, we aim at obtaining a program which can better explore the parallel resources, with no extra effort required from users or developers.     

Adaptive picking of microseismic event arrival using a power spectrum envelope

An automatic, adaptive, spectrogram-based algorithm for picking the arrival time of microseismic data is proposed. The algorithm provides a significant improvement in the ratio of detected events to false triggers and in the resolution of the microseismic structure. It mainly addresses the problem of automatic picking when the signal-to-noise ratio (S/N) is low and so false and missed triggers frequently occur. It combines the short time average/long time average (STA/LTA) algorithm with an envelope algorithm. It also constructs an envelope from a time-frequency representation of the signal. The threshold is set dynamically, according to the existing noise level and the S/N ratio. The algorithm also uses the fixed LTA value to represent the noise level for a seismic record. It is applied to pick the arrival times of P-waves of local events recorded at eight stations.     

Automatic camera control in virtual environments augmented using multiple sparse videos

Automated virtual camera control has been widely used in animation and interactive virtual environments. We have developed a multiple sparse camera based free view video system prototype that allows users to control the position and orientation of a virtual camera, enabling the observation of a real scene in three dimensions (3D) from any desired viewpoint. Automatic camera control can be activated to follow selected objects by the user. Our method combines a simple geometric model of the scene composed of planes (virtual environment), augmented with visual information from the cameras and pre-computed tracking information of moving targets to generate novel perspective corrected 3D views of the virtual camera and moving objects. To achieve real-time rendering performance, view-dependent textured mapped billboards are used to render the moving objects at their correct locations and foreground masks are used to remove the moving objects from the projected video streams. The current prototype runs on a PC with a common graphics card and can generate virtual 2D views from three cameras of resolution 768×576 with several moving objects at about 11 fps.