行驶车辆磁感应检测及智能道路传感系统

智能运输系统的前提条件之一是实现道路行驶车辆自动检测与交通信息实时采集。详细分析了磁感应式行驶车辆检测工作原理,建立了道路交通流信息计算模型,完成了基于DSP技术的智能检测系统设计与实现,采取了软硬件综合技术来对付现场各种干扰与噪声信号。对试验结果进行了分析,实际测试表明:系统性能稳定可靠。     

声表面波谐振器无源无线传感虚拟仪器系统

分析了在通用硬件平台上结合软件设计的声表面波谐振器无源无线传感虚拟仪器系统。该系统通过设计虚拟仪器实现部分硬件功能,控制闭环系统实现自动检测,并采用数字信号处理方法提高检测系统的精度,从而避免了通常的复杂硬件系统,降低硬件成本,并使检测更灵活。本文介绍了设计原理和系统构成,并对系统的误差进行了分析。实验表明:该仪器系统能够达到5m的无线检测距离。     

A new instrument for passive remote sensing: 2. Measurement of leaf and canopy reflectance changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence

A previously described passive remote sensing fluorimeter (see companion paper) was modified to detect changes in the reflectance of vegetation. The utility of this remote sensing technique to measure the Physiological Reflectance Index (PRI) is shown at both leaf level under laboratory conditions and at the canopy level in the field. PRI, defined as the relative changes in reflectance at 531 nm with respect to those at 570 nm (PRI=R531−R570/R531+R570), is related to xanthophyll-related, dynamic changes of non-photochemical quenching of chlorophyll fluorescence. The robustness of this relationship by simultaneous remote sensing of PRI and chlorophyll fluorescence is strengthened. At the leaf level, the existence of two kinetically distinct components of PRI is shown. A fast (within seconds) component that is partly attributed to ΔpH induced chloroplast shrinkage, and a slow (within minutes), main component that is related to xanthophyll de-epoxidation, as demonstrated by its disappearance in the presence of DTT. Overall, PRI correlated better with non-photochemical quenching of chlorophyll fluorescence (NPQ) than with any other measured parameter, including the photochemical efficiency of PSII. Finally, at the canopy level and under field conditions, it is shown that PRI can be a useful tool for remote sensing of water stress in grapevines.     

Wave propagation in delaminated beam

The wave propagation model investigated herein is based on the known fact that material discontinuities affect the propagation of elastic waves in solids. The change in certain material characteristics, such as a local change in stiffness or inertia caused by a crack or the presence of material damage, will affect the propagation of transmitted elastic waves and will modify the received signal. Wave frequencies associated with the highest detection sensitivity depend, among other things, on the type of structure, the type of material, and the type of damage. This paper presents a method of wave propagation, which can be further used to detect small delaminations in beam-like structures. The considered beam is modelled by spectral finite elements.     

Detection filter design for LPV systems—a geometric approach

This paper investigates fault detection and isolation of linear parameter-varying (LPV) systems by using parameter-varying (C,A)-invariant subspace and parameter-varying unobservability subspaces. The so called “detection filter” approach, formulated as the fundamental problem of residual generation (FPRG) for linear time-invariant (LTI) systems, is extended for a class of LPV systems. The question of stability is addressed in the terms of Lyapunov quadratic stability by using linear matrix inequalities. The results are applied to the model of a generic small commercial aircraft.     

A new algorithm for ball recognition using circle Hough transform and neural classifier

A large number of methods for circle detection have been studied in the last years for several image processing applications. The context application considered in this work is the soccer game. In the sequences of soccer images it is very important to identify the ball in order to verify the goal event. This domain is a challenging one as a great number of problems have to be faced, such as occlusions, shadows, objects similar to the ball, real-time processing and so on. In this work a visual framework trying to solve the above-stated problems, mainly considering real-time computational aspects, has been developed. The ball detection algorithm has to be very simple in terms of time processing and also has to be efficient in terms of false positive rate. Our framework consists of two sequential steps for solving the ball recognition problem: the first step uses a modified version of the directional circle Hough transform to detect the region of the image that is the best candidate to contain the ball; in the second step a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found. Some tricks like background subtraction and ball tracking have been applied in order to maintain the search of the ball only in limited areas of the image. Different light conditions have been considered as they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light, as the light source is strictly directional, the ball, due to self-shades, appears as a spherical cap; this case has been taken in account and the search of the ball has been modified in order to manage this situation. A large number of experiments have been carried out showing that the proposed method obtains a high detection score.     

Connectivity-based multiple-circle fitting

This paper proposes a connectivity-based method for circle fitting. The use of pixel connectivity effectively avoids false circle detection, improves the robustness against noise and significantly reduces the computational load. The desired circular models are extracted by searching for meaningful circular arcs. The algorithm does not require a good initial guess, and is effective for extracting an a priori unknown number of circles even when the number of outliers exceeds 50%. The experimental results demonstrate that the proposed method performs well in detecting multiple intersecting or occluded circles.     

Integrated condition monitoring and control of fed-batch fermentation processes

This paper investigates the benefits that the partial least squares (PLS) modelling approach offers engineers involved in the operation of fed-batch fermentation processes. It is shown that models developed using PLS can be used to provide accurate inference of quality variables that are difficult to measure on-line, such as biomass concentration. It is further shown that this model can be used to provide fault detection and isolation capabilities and that it can be integrated within a standard model predictive control framework to regulate the growth of biomass within the fermenter. This model predictive controller is shown to provide its own monitoring capabilities that can be used to identify faults within the process and also within the controller itself. Finally it is demonstrated that the performance of the controller can be maintained in the presence of fault conditions within the process.     

Text information extraction in images and video: a survey

Text data present in images and video contain useful information for automatic annotation, indexing, and structuring of images. Extraction of this information involves detection, localization, tracking, extraction, enhancement, and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. While comprehensive surveys of related problems such as face detection, document analysis, and image & video indexing can be found, the problem of text information extraction is not well surveyed. A large number of techniques have been proposed to address this problem, and the purpose of this paper is to classify and review these algorithms, discuss benchmark data and performance evaluation, and to point out promising directions for future research.     

Object detection using feature subset selection

Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using principal component analysis (PCA). Without using an effective scheme to select an appropriate set of features in this space, however, these methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper, we argue that feature selection is an important problem in object detection and demonstrate that genetic algorithms (GAs) provide a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. As a case study, we have considered PCA for feature extraction and support vector machines (SVMs) for classification. The goal is searching the PCA space using GAs to select a subset of eigenvectors encoding important information about the target concept of interest. This is in contrast to traditional methods selecting some percentage of the top eigenvectors to represent the target concept, independently of the classification task. We have tested the proposed framework on two challenging applications: vehicle detection and face detection. Our experimental results illustrate significant performance improvements in both cases.