符合Real-time CORBA规范的传感器网络监控系统设计

传感器网络在解决底层数据源和I／O设备的异构性、处理连续查询的实时性、服务质量自适应等方面提出了许多新的挑战。提出一种基于实时CORBA的传感器网络数据流处理框架,说明了符合实时中间件规范的数据流处理系统设计的方法和特点,通过实验对该框架进行了性能测试,验证了其处理连续查询的实时性。     

Deadlock Resolution in Automated Manufacturing Systems With Robots

An automated manufacturing system (AMS) contains a number of versatile machines (or workstations), buffers, and an automated material handling system (MHS). The MHS can be an automated guide vehicle (AGV) system, and/or a system that consists of multiple robots. Deadlock resolution in AMS is an important issue. For the AMS with an AGV system as MHS, the problems of deadlock resolution for part processing process and AGV system as an integrated system has been studied. It is shown that AGVs can serve as both material handling devices and central buffers at the same time to help resolve deadlocks. For AMS with robots as MHS, the existing work treated the robots just as material handling devices and showed that the robots had contribution to deadlock. In this paper, such AMS is modeled by resource-oriented Petri nets. Contrary to the existing work, it is shown that the robots have no contribution to deadlock by adopting such nets to control AMS. More interestingly, they can be used to resolve deadlock by serving as temporary part storage devices. A new deadlock control policy is proposed by treating robots as both material handling devices and buffers. The new policy outperforms the existing ones.     

Real-time image and video processing in portable and mobile devices

Mobile handheld and battery-operated consumer electronic devices such as digital still cameras, personal media players, digital camcorders, camera phones, mobile video telephones, etc. have become very popular and their annual shipments are already in hunderds of millions of units. The ability to carry out image and video processing in real-time has been one of the major reasons for the widespread success of these devices. In this article, the typical components present in such devices and the image and video processing that are carried out are described first. Some of the emerging trends relevant to image and video processing algorithms used on these devices are highlighted next. A brief overview of system requirements and challenges for image and video processing on these devices is then provided. These requirements are driving image and video processing research in new directions.     

A method of two-handed gesture interactions with applications based on commodity devices

Recent progress on human–computer interface technology has enabled more intuitive and natural communications between humans and sensor-based devices in many fields such as games, e-learning, augmented reality (AR), and medical image processing. While traditional human–computer interface devices such as the keyboard and mouse still dominate in our daily life, we propose a new method of two-handed gesture interactions to control applications based on commodity devices such as Wiimote, the primary controller for Nintendo’s Wii game console. Our main goal is to construct a unified way to interact with computers and their application systems just like using home appliances. The proposed system allows users to track their fingers moving in three-dimensional space in real time and define each gesture’s function by using the commodity devices.     

Measuring the degree of eyestrain caused by watching LCD and PDP devices

In this paper, we propose a new way of measuring the degree of eyestrain caused by watching LCD (Liquid Crystal Display) and PDP (Plasma Display Panel) devices. In the experiments, we used a head-mounted eye capturing device and an illumination setup that was designed to avoid specular reflections caused by glasses or contact lenses. Using the captured eye images, we analyzed the eye blinking and changes of pupil sizes (pupil accommodation), using a real-time image processing algorithm. Then we analyzed the degree of eyestrain based on the calculated blinking rate and the pupil accommodation speed. The proposed method offers five improvements over previous methods. First, we perform a comparative analysis of LCD and PDP devices based on the degree of eyestrain. Second, to analyze the degree of eyestrain, we use quantitative data such as the blinking rate and the pupil accommodation speed. Third, we measure the accurate eye blinking and changes of pupil sizes by using high-resolution and zoomed eye image sequences. Fourth, since the camera and illuminative system are based on a specular reflective model, the proposed method can be used with subjects that wear glasses or contact lenses. Fifth, the proposed method is performed at real-time speed.Experimental results showed that the degree of eyestrain when watching LCD devices was greater than that when watching PDP devices.

Relevance to industry

In the large display industry, LCD and PDP devices have become more and more prevalent. In the past, LCD devices have been compared to PDP devices in terms of factors such as spatial resolution, brightness, contrast levels, etc. To perform more accurate comparisons based on human factor, we are proposing a new way of comparing LCD to PDP devices based on the degree of eyestrain.     

基于Windows Mobile平台的移动学习系统的设计与实现

近年来移动技术的高速发展以及移动终端设备的迅速普及,使得将现有学习系统扩展到移动端成为可能。这种新型学习模式称为移动学习,其旨在使学习者不受时间和空间的限制,能够在任何时间、任何地点自主地进行学习。文章介绍了一个基于Windows Mobile平台的移动学习系统,其使用web Service技术实现移动客户端和服务器的通信。针对移动终端设备资源受限的特点．对系统进行了合理的分层设计,并进一步分析了数据处理、界面展现及课件浏览等方面的相关技术。     

An efficient memory control method for video and image processing in digital TV

High definition (HD) and ultra-high definition (UHD) digital TV require high-resolution images and lots of data transfers between processors and memory devices often become the bottleneck of the system. Video and image signal processing usually require blocks of square or rectangular shaped pixel data for signal processing. It requires frequent precharging and activating new rows, and results in extra latencies for reading and writing pixel data in memory devices. This paper proposes an efficient memory controller for video and image processing to reduce the latencies for reading and writing blocks of pixel data. The controller stores a frame of pixel data by distributing contiguous lines of pixel data to multiple banks in sequence. Its efficiency is enhanced more with an interface protocol such as AMBA AXI in which outstanding transactions are allowed. Memory controllers according to the proposed scheme are designed and the performance and the efficiency are compared with the previous works.     

基于视觉的大鼠肢体运动自动分析系统

设计并实现了实验大鼠肢体运动自动分析系统,系统由通用视频设备和计算机硬件组成,采用基于颜色标记的运动捕捉技术,提出了一种RGB空间映射到HSV空间的快速转换方法,以及快速颜色分类算法结合轮廓搜索算法得到标记点坐标,提取肢体运动学特征参数,并应用于步态行为分类和识别.实际测试平均每帧处理时间小于10ns,识别正确率97％...     

安全域间信息资源访问的协议和方法

为了保护内部网络的安全，必须设置应用边界安全设备．Internet上不同的应用安全域间要实现信息资源的安全访问，首先需要认证．Kerberos是目前比较常用的认证协议，一般的应用边界安全设备（如SOcks5）中就应用了该认证协议，但应用该协议存在一定的缺陷：在应用边界安全设备链的认证过程中，资源域中的应用边界安全设备认证对象是主体域中的应用边界安全设备，而不是真正发起资源请求的客户端，因此资源域中的应用边界安全设备审计的对象是主体域中的应用边界安全设备，而不是真正的客户端．在Kerberos域间认证的基础上，给出了新的域间认证协议以及身份传递协议，使用新的协议不仅能够提供应用边界安全设备对用户访问请求的安全审计而且只需要两次域间的网络连接，这两次域间网络连接不需要主体和客体直接进行，而是通过应用边界安全设备完成的，提高了系统的通信效率，扩大了该系统的应用范围，适合于现有的企业网环境，能有效地解决企业网与企业网之间的信息安全传输．     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

提高智能设备应用系统性能的技术研究与设计

随着智能设备在各个领域的广泛应用,他们的应用系统在功耗、效率、实时性等方面的问题日益突出,这些问题已经成为智能设备可以持续发展的主要障碍. 如何能够提高智能设备应用系统的性能,已成为这一领域的一个重要课题. 从应用软件的核心算法、设计架构等方面研究了能够有效提高智能设备应用系统性能的技术. 首先,基于JNI、NDK等技术,对应用软件的核心算法、架构进行跨平台的移植;然后使用本地代码（C或C++语言）对原来的算法进行改进;最后,利用智能设备多核的运算处理能力,适当地增加任务数量,改善负载均衡. 实验结果表明：如果把这些技术适当地集成到原来的应用系统里面,就能够大幅提高智能设备的整体性能.     

Pervasive speech recognition

As mobile computing devices grow smaller and as in-car computing platforms become more common, we must augment traditional methods of human-computer interaction. Although speech interfaces have existed for years, the constrained system resources of pervasive devices, such as limited memory and processing capabilities, present new challenges. We provide an overview of embedded automatic speech recognition (ASR) on the pervasive device and discuss its ability to help us develop pervasive applications that meet today's marketplace needs. ASR recognizes spoken words and phrases. State-of-the-art ASR uses a phoneme-based approach for speech modeling: it gives each phoneme (or elementary speech sound) in the language under consideration a statistical representation expressing its acoustic properties.     

Surveillance and human–computer interaction applications of self-growing models

The aim of the work is to build self-growing based architectures to support visual surveillance and human–computer interaction systems. The objectives include: identifying and tracking persons or objects in the scene or the interpretation of user gestures for interaction with services, devices and systems implemented in the digital home. The system must address multiple vision tasks of various levels such as segmentation, representation or characterization, analysis and monitoring of the movement to allow the construction of a robust representation of their environment and interpret the elements of the scene.It is also necessary to integrate the vision module into a global system that operates in a complex environment by receiving images from acquisition devices at video frequency and offering results to higher level systems, monitors and take decisions in real time, and must accomplish a set of requirements such as: time constraints, high availability, robustness, high processing speed and re-configurability.Based on our previous work with neural models to represent objects, in particular the Growing Neural Gas (GNG) model and the study of the topology preservation as a function of the parameters election, it is proposed to extend the capabilities of this self-growing model to track objects and represent their motion in image sequences under temporal restrictions.These neural models have various interesting features such as: their ability to readjust to new input patterns without restarting the learning process, adaptability to represent deformable objects and even objects that are divided in different parts or the intrinsic resolution of the problem of matching features for the sequence analysis and monitoring of the movement. It is proposed to build an architecture based on the GNG that has been called GNG-Seq to represent and analyze the motion in image sequences. Several experiments are presented that demonstrate the validity of the architecture to solve problems of target tracking, motion analysis or human–computer interaction.     

Service-oriented mobile malware detection system based on mining strategies

The large number of mobile internet users has highlighted the importance of privacy protection. Traditional malware detection systems that run within mobile devices have numerous disadvantages, such as overconsumption of processing resources, delayed updating, and difficulty in intersection. This study proposed a novel detection system based on cloud computing and packet analysis. The system detects the malicious behavior of the mobile malwares through their packets with the use of data mining methods. This approach completely avoids the defects of traditional methods. The system is service-oriented and can be deployed by mobile operators to send alarms to users who have malwares on their devices. To improve system performance, a new clustering strategy called contraction clustering was created. This strategy uses prior knowledge to reduce dataset size. Moreover, a multi-module detection scheme was introduced to enhance system accuracy. The results of this scheme are produced by integrating the detection results of several algorithms, including Naive Bayes and Decision Tree.     

Processing capacity-based decision mechanism edge computing model for IoT applications

The handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity-based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi-criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM-AI, PCDM-SI, PCDM-AN, and PCDM-SN are developed and compared against the five baseline existing strategies namely IoT-P, Edge-P, Cloud-P, Random-P, and Probabilistic-P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.     

基于演化母板的通用演化硬件平台研究

作为一种新型的理论研究和实践技术,演化硬件(EHW)在机器学习、人工神经网络、自适应控制、信号处理与识别、容错系统和机器人等方面已经受到了广泛的重视。本文针对FPGA等商用可编程逻辑器件在演化硬件研究中出现的种种问题,提出一个基于演化母板的新型演化平台,并介绍了它的系统结构及工作原理。     

The cascading neural network: building the Internet of Smart Things

Most of the research on deep neural networks so far has been focused on obtaining higher accuracy levels by building increasingly large and deep architectures. Training and evaluating these models is only feasible when large amounts of resources such as processing power and memory are available. Typical applications that could benefit from these models are, however, executed on resource-constrained devices. Mobile devices such as smartphones already use deep learning techniques, but they often have to perform all processing on a remote cloud. We propose a new architecture called a cascading network that is capable of distributing a deep neural network between a local device and the cloud while keeping the required communication network traffic to a minimum. The network begins processing on the constrained device, and only relies on the remote part when the local part does not provide an accurate enough result. The cascading network allows for an early-stopping mechanism during the recall phase of the network. We evaluated our approach in an Internet of Things context where a deep neural network adds intelligence to a large amount of heterogeneous connected devices. This technique enables a whole variety of autonomous systems where sensors, actuators and computing nodes can work together. We show that the cascading architecture allows for a substantial improvement in evaluation speed on constrained devices while the loss in accuracy is kept to a minimum.     

多媒体视、音频信号处理系统的集成与优化

在多媒体视、音频信号处理系统中,主要涉及数据通信、音频处理,复合视频信号（ＰＡＬ、ＮＴＳＣ）的变换、合成视频信号（Ｒ、Ｇ、Ｂ）的传输４大模块。讨论了如何结合新技术和新器件来实现各模块的高度集成和优化设计,从而使系统在性能指标、功能、结构体系等方面体现出良好的品质。     

基于SOC架构的智能图像处理和外设控制系统设计

针对当前采用的DSP+FPGA实时图像处理和控制系统的不足,提出了一种基于SOC架构的智能图像处理和控制系统,该系统硬件上使用SOC架构进行算法电路实现和外设控制,能够对所获取的图像进行跟踪、识别和匹配等图像处理,同时根据软件配置对外部器件进行控制和交互;通过对大尺寸卷积运算的验证试验,结果表明所设计的SOC架构具有通用性好、可靠性高、处理速度快和控制精准的特点,能够完全适应高复杂的卷积运算。     

Adaptive inverse halftoning for scanned document images through multiresolution and multiscale analysis

This paper describes an efficient algorithm for inverse halftoning of scanned document images to resolve problems with interference patterns such as moiré and graininess when the images are displayed or printed out. The algorithm is suitable for software implementation and useful for high quality printing or display of scanned document images delivered via networks from unknown scanners. A multi-resolution approach is used to achieve practical processing speed under software implementation. Through data-driven, adaptive, multi-scale processing, the algorithm can cope with a variety of input devices and requires no information on the halftoning method or properties (such as coefficients in dither matrices, filter coefficients of error diffusion kernels, screen angles, or dot frequencies). Effectiveness of the new algorithm is demonstrated through real examples of scanned document images, as well as quantitative evaluations with synthetic data.