HYBRID INTERNET TRAFFIC CLASSIFICATION TECHNIQUE1

Accurate and real-time classification of network traffic is significant to network operation and management such as QoS differentiation, traffic shaping and security surveillance. However, with many newly emerged P2P applications using dynamic port numbers, masquerading techniques, and payload encryption to avoid detection, traditional classification approaches turn to be ineffective. In this paper, we present a layered hybrid system to classify current Internet traffic, motivated by variety of network activities and their requirements of traffic classification. The proposed method could achieve fast and accurate traffic classification with low overheads and robustness to accommodate both known and unknown/encrypted applications. Furthermore, it is feasible to be used in the context of real-time traffic classification. Our experimental results show the distinct advantages of the proposed classification system, compared with the one-step Machine Learning (ML) approach.     

Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in the Internet of Things (IoT) network. The followings are the major contributions: i) An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network; ii) An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space; iii) Finally, the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency. Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.     

CTU depth decision algorithms for HEVC: A survey

High Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1(AV1).     

一种新的直方图核函数及在图像分类中的应用

基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用,运用支持向量机的关键是设计有效的核函数。为克服传统核函数较少融合先验知识的弱点,该文提出基于数据驱动的核函数构建方法;并结合词包(BOW)模型,设计了一种基于TF-IDF规则的加权二次卡方(Weighted Quadritic Chisquared, WQC)距离的直方图核函数;在计算直方图之间距离时充分考虑到不同量化区间的不同区分性能,从而增强核函数对不同类别的区分能力。在Caltech101/256等多个经典图像数据集上的分类实验结果验证了该文方法的有效性。     

DPMR协议呼叫控制层的研究和设计

数字专用无线电对讲机（DPMR,Digital Private Mobile Radio）协议是欧洲电信标准化协会推出的一种适用于民用数字对讲机系统的通信协议。首先,按照简化的开放系统互连（OSI,Open System Interconnection）分层模型对DPMR标准进行了简单的介绍。其次,以DPMR数字对讲机系统的呼叫控制功能为研究对象,详细叙述了系统的基本呼叫流程,并以有限状态机为基础设计出协议的状态转换过程;最后,以Visual C＋＋为平台,实现该呼叫控制方案的设计过程,并给出相关设计结果。通过结果分析,进一步验证了该呼叫控制方案的可行性。     

机器视觉检测系统在半导体工业切筋成型机上的应用

介绍了机器视觉检测系统在半导体工业后道关键设备（切筋成型机）上的应用,对产品的自动视觉检测功能。包含检测的基本原理、使用方法和镜头、照明等关键要素的选择,以及视觉系统对产品的常见检测等。     

机器类通信中基于NOMA短编码块传输的高可靠低迟延无线资源分配优化方案

针对机器类通信(MTC)应用场景的业务特征和服务质量(QoS)要求,该文考虑基于非正交多址(NOMA)的MTC中短分组/短编码块传输,探讨MTC中基于NOMA的高可靠低迟延无线资源优化问题。首先,上行传输是基于NOMA的MTC通信的瓶颈,考虑无线蜂窝网络中支持NOMA和高可靠低迟延性能要求,该文建立了上行无线资源优化的系统模型;然后,分析上行传输迟延,导出基于距离的链路可靠性函数;进一步,以迟延、可靠性和带宽为约束下条件,提出一种最大化中心用户和速率的无线资源分配算法,并给出算法的收敛性证明和复杂度分析;最后,实验仿真验证了所提算法的性能优势。     

CPFinder: Finding an unknown caller's profession from anonymized mobile phone data

Identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Owing to the limited data protection of various popular online services in some countries, such as taxi hailing and ordering takeouts, many users presently encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, threatening the user individuals as well as the society. In addition, numerous people experience excessive digital marketing and fraudulent phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, which does not work for the identification of multiple professions. We observed that web service requests issued from users' mobile phones might exhibit their application preferences, spatial and temporal patterns, and other profession-related information. This offers researchers and engineers a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g., General Data Protection Regulation). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the types of mobile phone callers without privacy concerns. In this paper, we develop CPFinder —- a system that exploits privacy-preserving mobile data to automatically identify callers who are divided into four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation of an anonymized dataset of 1,282 users over a period of 3 months in Shanghai City shows that the CPFinder can achieve accuracies of more than 75.0% and 92.4% for multiclass and binary classifications, respectively.     

复眼式UV-LED面光源的曝光机平行光系统

印制电路板生产设备紫外线曝光机的光源系统,其能耗大、使用维护成本高、含汞等缺陷,在倡导节能环保的当今,业界一直致力于改进。UV-LED面光源作为新一代绿色环保紫外线光源,为印制电路板曝光领域带来革命性的变化。文章通过分析传统平行光紫外曝光机光学系统特点,设计了一种平行光紫外线曝光机适用的UV-LED面光源系统,UV-LED灯珠光功率利用率大幅提高,光照均匀性优良,并且不改变曝光机结构的情况下,可直接安装使用。实验表明,采用了复眼式阵列光学透镜结构设计的UV-LED面光源系统,完全可替代传统平行光紫外曝光机光源。     

NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM

A new method called RS-MSVM （Rough Set and Multi-class Support Vector Machine） is proposed for network intrusion detection. This method is based on rough set followed by MSVM for attribute reduction and classification respectively, The number of attributes of the network data used in this paper is reduced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF （Heterogeneous Value Difference Metric Radial Basis Function）, based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are applied to build MSVM. DARPA （Defense Advanced Research Projects Agency） intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods mentioned in this paper on six aspects： detection accuracy, number of support vectors, false positive rate, falsc negative rate, training time and testing time.