Moving Vehicle Detection for Automatic Traffic Monitoring

A video-based traffic monitoring system must be capable of working in various weather and illumination conditions. In this paper, we will propose an example-based algorithm for moving vehicle detection. Different from previous works, this algorithm learns from examples and does not rely on any a priori model for vehicles. First, a novel scheme for adaptive background estimation is introduced. Then, the image is divided into many small nonoverlapped blocks. The candidates of the vehicle part can be found from the blocks if there is some change in gray level between the current image and the background. A low-dimensional feature is produced by applying principal component analysis to two histograms of each candidate, and a classifier based on a support vector machine is designed to classify it as a part of a real vehicle or not. Finally, all classified results are combined, and a parallelogram is built to represent the shape of each vehicle. Experimental results show that our algorithm has a satisfying performance under varied conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination     

Monitoring Smartphones for Anomaly Detection

In this paper we demonstrate how to monitor a smartphone running Symbian operating system and Windows Mobile in order to extract features for anomaly detection. These features are sent to a remote server because running a complex intrusion detection system on this kind of mobile device still is not feasible due to capability and hardware limitations. We give examples on how to compute relevant features and introduce the top ten applications used by mobile phone users based on a study in 2005. The usage of these applications is recorded by a monitoring client and visualized. Additionally, monitoring results of public and self-written malwares are shown. For improving monitoring client performance, Principal Component Analysis was applied which lead to a decrease of about 80% of the amount of monitored features.     

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

Network-on-Chip (NoC) is a flexible and scalable solution to interconnect multi-cores, with a strong influence on the performance of the whole chip. On-chip network affects also the overall power consumption, thus requiring accurate early-stage estimation and optimization methodologies. In this scenario, the Dynamic Voltage Frequency Scaling (DVFS) technique have been proposed both for CPUs and NoCs. The promise is to be a flexible and scalable way to jointly optimize power-performance, addressing both static and dynamic power sources. Being simulation a de-facto prime solution to explore novel multi-core architectures, a reliable full system analysis requires to integrate in the toolchain accurate timing and power models for the DVFS block and for the resynchronization logic between different Voltage and Frequency Islands (VFIs). In such a way, a more accurate validation of novel optimization methodologies which exploit such actuator is possible, since both architectural and actuator overheads are considered at the same time. This work proposes a complete cycle accurate framework for multi-core design supporting Global Asynchronous Local Synchronous (GALS) NoC design and DVFS actuators for the NoC. Furthermore, static and dynamic frequency assignment is possible with or without the use of the voltage regulator. The proposed framework sits on accurate analytical timing model and SPICE-based power measures, providing accurate estimates of both timing and power overheads of the power control mechanisms.     

基于声信号处理的交通事故自动检测算法

建立交通事故应急救援系统具有很强的现实意义,关键技术是对交通事故进行自动检测。为了保证检测的实时性与准确性,提出一种基于声信号处理的方法,采集车辆周围的声音并进行预处理,使用Harr小波变换提取声信号的频域特征,采用单类支持向量机进行异常点检测,实现了分类判别。按上述方法对交通事故发生时的碰撞信号与正常行驶时的非碰撞信号做了分析,准确的识别出交通事故。仿真实验结果表明与常用的线性判别分析方法相比准确率有了显著提高,而且计算复杂度低,易于在DSP系统上实现,算法的判别性能达到了实用化的程度。     

FPGA-Based Real-Time Implementation of Detection Algorithm for Automatic Traffic Surveillance Sensor Network

This paper describes the FPGA-based hardware implementation of an algorithm for an automatic traffic surveillance sensor network. The aim of the algorithm is to extract moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed, using low power hardware of a sensor network node. A single, stationary, monochrome camera is used, mounted at a location high above the road. Occlusions are not detected, however simple shadow and highlight elimination is performed. The algorithm is designed for frame-rate efficiency and is specially suited for pipelined hardware implementation. The authors, apart from the careful selection of particular steps of the algorithm and the modifications towards parallel implementation, also proposed novel improvements such as backgrounds’ binary mask combination or non-linear functions in highlight detection, resulting in increasing the robustness and efficiency of hardware realization. The algorithm has been implemented in FPGA and tested on real-time video streams from an outdoor camera.     

基于层次分析法的智能手机恶意软件静态检测及评价模型

Mobile malware is rapidly increasing and its detection has become a critical issue. In this study, we summarize the common characteristics of this mali-cious software on Android platform. We design a de-tection engine consisting of six parts: decompile, grammar parsing, control flow and data flow analysis, safety analysis, and comprehensive evaluation. In the comprehensive evaluation, we obtain a weight vector of 29 evaluation indexes using the analytic hierarchy process. During this process, the detection engine ex-ports a list of suspicious API. On the basis of this list, the evaluation part of the engine performs a compre-hensive evaluation of the hazard assessment of soft-ware sample. Finally, hazard classification is given for the software. The false positive rate of our approach for detecting malware samples is 4. 7% and normal samples is 7.6% . The experimental results show that the accuracy rate of our approach is almost similar to the method based on virus signatures. Compared with the method based on virus signatures, our approach performs well in detecting unknown malware. This approach is promising for the application of malware detection.     

交通事故现场防入侵设备设计

为了提高交通事故现场防护水平和预防二次事故,利用C8051F020单片机以及MMA7260,MicroMag2等传感器设计了一种设备,对车辆入侵进行检测,迅速有效地判断车辆是否侵入防护现场,根据检测结果实现报警。     

带自检功能的智能型LED路灯

本文研究的带自检功能的智能型LED路灯是基于ZigBee和GPRS通讯,利用单片机和PLC实现对LED路灯的控制和监测,对每一盏灯的电压、电流、功率因数、开关状态、温度及防盗设备等进行监视、采集和统计,并可根据路面、光线和时段自动调节和控制路灯。     

网络流量与入侵检测研究

文章先介绍了入侵检测技术在国内外的发展和现状,进而针对目前入侵检测技术报警信息量大、响应速度慢的缺点,提出了一种新的网络流量分析应用于入侵检测的方法.利用ARIMA模型对网络的流量进行预测,通过实验结果表明对短期流量预测能够达到比较好的效果;这样在用ARIMA模型可以预测出异常网络流量的前提下,可以快速使用本文提到的入侵检测模型检测到已知和未知入侵并预警,预期该方法可以有效提高网络预警的速度.     

智能手机技术发展及展望

文章基于智能手机技术与业务部署方式的发展演化,对未来智能手机的发展进行了分析,论证了基于Web语言的WebOS是智能手机一个重要的发展趋势。文章认为未来智能手机必将是一种能够与云计算技术充分结合的Web化的平台,这种智能平台在业务服务扩展能力上具有巨大的优势,有可能成为未来颠覆现有产业格局的有利武器。     

利用NetFlow技术构建网络异常监测及分析系统

随着互联网的不断普及,网络已经跟人们的日常生活密不可分,使用者在享受网络所带来的便利的同时,网络上的许多问题也陆续衍生而出,运营商网络正面临着日益严重的安全威胁.作为网络管理者,如何迅速有效地监测网络异常情况,同时准确地掌握整个网络的业务流量模型,供市场部门作决策分析,已成为一项重要的课题.介绍了利用NetFlow技术,构建网络异常监测及分析系统,利用本系统可随时监测网络运行情况,以达到网络稳定顺畅运作及节省经费、人力的目的.     

Performance Analysis with Traffic Accident for Cooperative Active Safety Driving in VANET/ITS

Recently, by using vehicle-to-vehicle and vehicle-to-infrastructure communications for VANET/ITS, the cooperative active safety driving (ASD) providing vehicular traffic information sharing among vehicles significantly prevents accidents. Clearly, the performance analysis of ASD becomes difficult because of high vehicle mobility, diverse road topologies, and high wireless interference. An inaccurate analysis of packet connectivity probability significantly affects and degrades the VANET/ITS performance. Especially, most of related studies seldom concern the impact factors of vehicular accidents for the performance analyses of VANET/ITS. Thus, this paper proposes a two-phase approach to model a distributed VANET/ITS network with considering accidents happening on roads and to analyze the connectivity probability. Phase 1 proposes a reliable packet routing and then analyzes an analytical model of packet connectivity. Moreover, the analysis is extended to the cases with and without exhibiting transportation accidents. In phase 2, by applying the analysis results of phase 1 to phase 2, an adaptive vehicle routing, namely adaptive vehicle routing (AVR), is proposed for accomplishing dynamic vehicular navigation, in which the cost of a road link is defined in terms of several critical factors: traffic density, vehicle velocity, road class, etc. Finally, the path with the least path cost is selected as the optimal vehicle routing path. Numerical results demonstrate that the analytical packet connectivity probability and packet delay are close to that of simulations. The yielded supreme features justify the analytical model. In evaluations, the proposed approach outperforms the compared approaches in packet connectivity probability, average travel time, average exhausted gasoline. However, the proposed approach may lead to a longer travel distance because it enables the navigated vehicle to avoid traversing via the roads with a higher traffic density.     

Mirroring Smartphones for Good: A Feasibility Study

An increasing number of applications and functions are being introduced into smartphones, but smartphones have limited computation ability and battery resources. To enhance smartphone capacity, cloud computing and virtualization techniques can be used to shift the workload from smartphone to computational infrastructure. In this paper, we propose a new framework in which a mirror is kept for each smartphone on a computing infrastructure in the telecom network. With mirrors, the workload can be greatly reduced, and smartphone resources can be virtually expanded. The feasibility of deploying this framework in telecom networks is demonstrated in the protocol design, a synchronization study, and a scalability test. Two applications are introduced to show how computational workload on the smartphone and traffic in the telecom network are significantly reduced using our techniques.     

非法VoIP行为检测与管控方法的实现

由于IP网络的开放性和自由性,以及VoIP接入技术的简单易行,非法VoIP业务的管控工作具有重要意义。对VoIP业务监控主要可通过实时监测并分析网络协议,结合流量分析技术的方法,对采集来的流量进行VoIP业务的连接控制协议分析和数据流分析,检测出所有的VoIP业务流量,并判断是否合法。     

加密流量检测与态势预警平台研究

网络流量检测是实现网络整体安全态势感知的主要手段,通过采集网络流量、脆弱性、安全事件和威胁情报等数据,利用大数据和机器学习技术,分析网络行为及用户行为等因素构成的整个网络当前状态和变化趋势,并预测网络安全状态发展趋势。随着密码技术的广泛应用,网络中存在着越来越多的加密流量,如HTTPS、VPN流量;由于加密技术的使用,破坏了明文数据的统计特点、数据格式等,用通用的流量检测方法很难有效检测加密流量,基于加密技术的随机性、网络上下文等,结合人工智能技术和机器学习方法,研究和设计了网络加密流量检测体系框架、方法和关键技术,对加密流量的检测具有较强的指导意义。     

一种鲁棒的交通标志检测方法

提出一种鲁棒的交通标志检测与分类方法。根据交通标志的颜色与形状特点进行突出颜色转换,以提取交通标志中的显著颜色,并利用基于仿射不变矩的形状测度有效地度量区域形状。先根据颜色转换特征进行粗分割,再利用形态学方法去除面积较小的区域,并填补区域中的孔洞,然后根据形状测度,检测交通标志,并对其进行粗分类。实拍图像的实验结果表明,该方法在不同尺寸、光照变化、旋转变形的情况下,依然能够表现出良好的交通标志检测和粗分类性能。     

一种具有自检功能的压电角速率陀螺

介绍了一种具有自我检测功能的压电角速率陀螺以及基本原理和实验结果。     

P2P流量检测技术与分析

P2P技术及其应用的快速发展增加了网络的负载,影响了网络的性能,因此,对P2P流量进行分析及控制十分必要。在介绍P2P流量检测技术的基础上,对目前几种主流P2P流量识别技术进行比较和分析,指出了各种技术的优势和不足,阐明了流量检测技术的发展方向,提出了一种将不同检测方法相结合以满足一定准确率和效率的思路。     

智能交通感知新范式：面向元宇宙的交通标志检测架构

交通标志检测对智能交通系统和智能驾驶的安全稳定运行具有重要作用。数据分布不平衡、场景单一会对模型性能造成较大影响,而建立一个完备的真实交通场景数据集需要昂贵的时间成本和人工成本。基于此,该文提出一个面向元宇宙的交通标志检测新范式以缓解现有方法对真实数据的依赖。首先,通过建立元宇宙和物理世界之间的场景映射和模型映射,实现检测算法在虚实世界之间的高效运行。元宇宙作为一个虚拟化的数字世界,能够基于物理世界完成自定义场景构建,为模型提供海量多样的虚拟场景数据。同时,该文结合知识蒸馏和均值教师模型建立模型映射,应对元宇宙和物理世界之间存在的数据差异问题。其次,为进一步提高元宇宙下的训练模型对真实驾驶环境的适应性,该文提出启发式注意力机制,通过对特征的定位和学习来提高检测模型的泛化能力。所提架构在CURE-TSD, KITTI, VKITTI数据集上进行实验验证。实验结果表明,所提面向元宇宙的交通标志检测器在物理世界具有优异的检测效果而不依赖大量真实场景,检测准确率达到89.7%,高于近年来其他检测方法。