GSM网络优化中接通率的分析

接通率是衡量GSM网络运行质量重要的指标之一,本文根据GSM网络优化的基本理论,针对GSM网络中低接通率现象,对影响GSM网络优化中接通率的因素进行了细致的分析与研究,提出了一套有效可行的优化方法,逐个排除故障原因,从而达到了提高接通率、优化GSM网络的目的,并针对提高GSM网络接通率设计开发了GSM网络优化软件。最后,对实例安阳市区GSM网络中存在的低接通率等问题进行了分析及其优化,并证实了优化软件中提高接通率方法的可行性、实效性。     

基于多源数据融合的注射坯密度分布控制算法

自动化和智能化是未来粉末注射成形技术的重要发展方向,本文通过对注射模具温度基于滑动窗口技术的数据挖掘处理以及对注射坯CT切片密度分布的分析修正、阈值预处理后,采用基于神经网络数据融合的方式对多源数据进行融合,实现对注射坯密度分布的有效控制。     

一种改进的地图匹配算法及其在移动通信巡检系统中的应用

巡检系统是对人员设备实施量化和动态管理的现代化手段,是促进企业及生产部门实行科学化和信息化管理的重要步骤,目前在石油行业管线和井位巡查管理、电力行业线路和设备巡查管理和通讯行业线路和设备巡查管理、铁路及安全设备巡查管理等领域都有广泛的应用。结合移动通信巡检工作的实际情况,本文提出了一种改进的基于最短路径的地图匹配(Map Matching)算法。在全球定位系统(GPS)数据的采样时间间隔较长的情况下能较准确地对定位点进行地图匹配,能够绘制出与电子地图道路信息相符的定位轨迹,并在长沙移动基站线路巡视巡检系统中得到实现。     

基础软件技术的发展趋势

从分析基础软件的基本概念和特性出发,对基础软件的内容、需求、技术、产品等方面进行了系统论述。探讨了现有的主流开发技术及未来的发展趋势。通过分析现有部分国产软件系统在一些典型领域的应用,揭示我国基础软件发展的现状和难题,为现有国产基础软件产业的发展提供决策参考。     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Texture and shape information fusion for facial expression and facial action unit recognition

A novel method based on fusion of texture and shape information is proposed for facial expression and Facial Action Unit (FAU) recognition from video sequences. Regarding facial expression recognition, a subspace method based on Discriminant Non-negative Matrix Factorization (DNMF) is applied to the images, thus extracting the texture information. In order to extract the shape information, the system firstly extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. A Support Vector Machine (SVM) system designed on an Euclidean space, defined over a novel metric between grids, is used for the classification of the shape information. Regarding FAU recognition, the texture extraction method (DNMF) is applied on the differences images of the video sequence, calculated taking under consideration the neutral and the expressive frame. An SVM system is used for FAU classification from the shape information. This time, the shape information consists of the grid node coordinate displacements between the neutral and the expressed facial expression frame. The fusion of texture and shape information is performed using various approaches, among which are SVMs and Median Radial Basis Functions (MRBFs), in order to detect the facial expression and the set of present FAUs. The accuracy achieved using the Cohn–Kanade database is 92.3% when recognizing the seven basic facial expressions (anger, disgust, fear, happiness, sadness, surprise and neutral), and 92.1% when recognizing the 17 FAUs that are responsible for facial expression development.     

Binarization and cleanup of handwritten text from carbon copy medical form images

This paper presents a methodology for separating handwritten foreground pixels, from background pixels, in carbon copied medical forms. Comparisons between prior and proposed techniques are illustrated. This study involves the analysis of the New York State (NYS) Department of Health (DoH) Pre-Hospital Care Report (PCR) [Western Regional Emergency Medical Services, Bureau of Emergency Medical Services, New York State (NYS) Department of Health (DoH), Prehospital Care Report v4.] which is a standard form used in New York by all Basic and Advanced Life Support pre-hospital health care professionals to document patient status in the emergency environment. The forms suffer from extreme carbon mesh noise, varying handwriting pressure sensitivity issues, and smudging which are further complicated by the writing environment. Extraction of handwriting from these medical forms is a vital step in automating emergency medical health surveillance systems.     

Automatic image authentication and recovery using fractal code embedding and image inpainting

In this paper, we propose a novel scheme of automatic image authentication and recovery, in which the altered region of the image can be detected and recovered in a fully automated fashion. To achieve high-quality image recovery, we choose the region of importance (ROI) in the image and perform fractal encoding for the blocks of ROI. And then we insert the fractal codes of ROI and the watermark into the original image to obtain the watermarked image. By watermark extraction, we can determine whether the watermarked image has been tampered or not. If the watermarked image has been tampered, we can automatically localize the altered region, and apply either fractal decoding or image inpainting for image recovery. Finally, experimental results are given to show the effectiveness of the proposed scheme.     

Cancellable biometrics and annotations on BioHash

Lately, the once powerful one-factor authentication which is based solely on either password, token or biometric approach, appears to be insufficient in addressing the challenges of identity frauds. For example, the sole biometric approach suffers from the privacy invasion and non-revocable issues. Passwords and tokens are easily forgotten and lost. To address these issues, the notion of cancellable biometrics was introduced to denote biometric templates that can be cancelled and replaced with the inclusion of another independent authentication factor. BioHash is a form of cancellable biometrics which mixes a set of user-specific random vectors with biometric features. In verification setting, BioHash is able to deliver extremely low error rates as compared to the sole biometric approach when a genuine token is used. However, this raises the possibility of two identity theft scenarios: (i) stolen-biometrics, in which an impostor possesses intercepted biometric data of sufficient high quality to be considered genuine and (ii) stolen-token, in which an impostor has access to the genuine token and used by the impostor to claim as the genuine user. We found that the recognition rate for the latter case is poorer. In this paper, the quantised random projection ensemble based on the Johnson–Lindenstrauss Lemma is used to establish the mathematical foundation of BioHash. Based on this model, we elucidate the characteristics of BioHash in pattern recognition as well as security view points and propose new methods to rectify the stolen-token problem.