Operator context scanning to support high segmentation rates for real time license plate recognition

Introducing high definition videos and images in object recognition has provided new possibilities in the field of intelligent image processing and pattern recognition. However, due to the large amount of information that needs to be processed, the computational costs are high, making the HD systems slow. To this end, a novel algorithm applied to sliding window analysis, namely Operator Context Scanning (OCS), is proposed and tested on the license plate detection module of a License Plate Recognition (LPR) system. In the LPR system, the OCS algorithm is applied on the Sliding Concentric Windows pixel operator and has been found to improve the LPR system’s performance in terms of speed by rapidly scanning input images focusing only on regions of interest, while at the same time it does not reduce the system effectiveness. Additionally, a novel characteristic is presented, namely, the context of the image based on a sliding windows operator. This characteristic helps to quickly categorize the environmental conditions upon which the input image was taken. The algorithm is tested on a data set that includes images of various resolutions, acquired under a variety of environmental conditions.     

基于高分辨率遥感影像的城市建筑目标提取研究

基于Geoeye-1高分辨率遥感影像,根据机场、大型公共建筑、污水处理厂3类建筑目标的光谱特征和形状特征,采用面向对象分类识别方法,并结合城市总体规划专题数据,对上述3类建筑目标进行提取实验。实验表明,本文提出的技术路线对上述3类城市建筑目标提取具有较好的应用效果。     

基于SNMP的校园网ARP攻击检测方法研究

防范地址解析协议ARP(Address Resolution Protocol)欺骗攻击的难点是:攻击源可以隐藏在网段内任何一个主机中,即使发现了攻击的存在,也难以迅速定位攻击源。结合校园网络的特点,提出了一种新的ARP攻击检测方案:检测服务器通过SNMP协议定期读取核心交换机的ARP、VLAN等信息和接入交换机的MAC-PORT信息,利用综合检测算法,及时发现攻击现象,迅速定位攻击源,并根据网络实际情况采用不同的技术措施进行处理。实践证明,该方法能够有效保障校园网络安全。     

A strategy for allowing meaningful and comparable scores in approximate matching

Approximate data matching aims at assessing whether two distinct instances of data represent the same real-world object. The comparison between data values is usually done by applying a similarity function which returns a similarity score. If this score surpasses a given threshold, both data instances are considered as representing the same real-world object. These score values depend on the algorithm that implements the function and have no meaning to the user. In addition, score values generated by different functions are not comparable. This will potentially lead to problems when the scores returned by different similarity functions need to be combined for computing the similarity between records. In this article, we propose that thresholds should be defined in terms of the precision that is expected from the matching process rather than in terms of the raw scores returned by the similarity function. Precision is a widely known similarity metric and has a clear interpretation from the user's point of view. Our approach defines mappings from score values to precision values, which we call adjusted scores. In order to obtain such mappings, our approach requires training over a small dataset. Experiments show that training can be reused for different datasets on the same domain. Our results also demonstrate that existing methods for combining scores for computing the similarity between records may be enhanced if adjusted scores are used.     

The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas

Remote sensing hyperspectral sensors are important and powerful instruments for addressing classification problems in complex forest scenarios, as they allow one a detailed characterization of the spectral behavior of the considered information classes. However, the processing of hyperspectral data is particularly complex both from a theoretical viewpoint [e.g. problems related to the Hughes phenomenon (Hughes, 1968) and from a computational perspective. Despite many previous investigations that have been presented in the literature on feature reduction and feature extraction in hyperspectral data, only a few studies have analyzed the role of spectral resolution on the classification accuracy in different application domains. In this paper, we present an empirical study aimed at understanding the relationship among spectral resolution, classifier complexity, and classification accuracy obtained with hyperspectral sensors for the classification of forest areas. We considered two different test sets characterized by images acquired by an AISA Eagle sensor over 126 bands with a spectral resolution of 4.6 nm, and we subsequently degraded its spectral resolution to 9.2, 13.8, 18.4, 23, 27.6, 32.2 and 36.8 nm. A series of classification experiments were carried out with bands at each of the degraded spectral resolutions, and bands selected with a feature selection algorithm at the highest spectral resolution (4.6 nm). The classification experiments were carried out with three different classifiers: Support Vector Machine, Gaussian Maximum Likelihood with Leave-One-Out-Covariance estimator, and Linear Discriminant Analysis. From the experimental results, important conclusions can be made about the choice of the spectral resolution of hyperspectral sensors as applied to forest areas, also in relation to the complexity of the adopted classification methodology. The outcome of these experiments are also applicable in terms of directing the user towards a more efficient use of the current instruments (e.g. programming of the spectral channels to be acquired) and classification techniques in forest applications, as well as in the design of future hyperspectral sensors.     

Using new scheduling heuristics based on resource consumption information for increasing throughput on rule‐based spam filtering systems

The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Development of active matrix LCD for use in high‐resolution adaptive headlights

Conventional adaptive driving beam headlamps are limited in achieving still higher quantities of switchable pixels by the number of LEDs and movable elements needed. In this paper, it is shown that by integrating an active matrix liquid crystal display module, it is possible to realize fully adaptive high‐resolution headlights without mechanical elements and a finite number of LED with 30 k switchable pixels.     

一种基于XML的非结构化数据转换方法

XML作为半结构化的语言,因其能预先定义标记等优势被普遍应用于非结构化到结构化信息的转换中。利用POI技术把网络上繁杂的非结构化数据转化为XML半结构化数据,把半结构化数据转化为结构化数据,使用户能够简便地查询所需信息。通过实验对SAX,DOM的解析效率进行了对比,实验表明解析相同大小的XML文件,SAX比DOM效率更高,而且此种差距会随着XML文件的增大而逐渐增大。     

高精度智能质子磁力仪的最新发展

本文从质子磁力仪核心技术的角度对高精度智能质子磁力仪的最新发展进行了科学的论述,总结了我国及当今该仪器领域的最新成果,为我国广大地磁勘探工作者选购高质量的质子磁力仪从事物探工作提供科学指南。     

小型水电站扩容增效技改中同期点和同期方式选择

本文针对目前每个小型水电站扩容增效技改中微机监控设计和改造中必须解决的同期点选择和同期方式确定问题结合相关理论、规范,在总结常规监控方式下同类问题的解决办法基础上结合工程实际提出了切实可行解决问题的方法和依据。