On processor sharing and its applications to cellular data network provisioning

To develop simple traffic engineering rules for the downlink of a cellular system using Proportional Fairness (PF) scheduling, we study the “strict” and “approximate” insensitivity of a Processor Sharing (PS) system, specifically for the Egalitarian (EPS) and Discriminatory (DPS) variants of PS. Assuming homogeneous channel conditions, all concurrent flows are allocated an equal share of downlink transmission slots regardless of flow types and locations. The cell system is modeled as an EPS queue. We prove the performance insensitivity of EPS in a relevant new case that has not been studied in the literature. Considering heterogenous channel conditions, the system is modeled as the DPS queue in which each traffic type is divided into subclasses with different assigned weights. Asymmetric weights among the subclasses model the unequal channel sharing that occurs with PF scheduling. Our results show that the first-order performance of the DPS is largely insensitive to the input traffic characteristics, as long as the weights among subclasses are not highly skewed. Our findings, confirmed by the simulation of a cellular system, imply reduced complexity for traffic provisioning procedures. However, our study also shows that the first-order performance is sensitive to the traffic details when there is discrimination among different traffic types. This observation implies that the introduction of differentiated services may pose a great challenge to network provisioning in future cellular systems.     

A recognition network model-based approach to dynamic image understanding

In this paper, we present definitions for a dynamic knowledge-based image understanding system. From a sequence of grey level images, the system produces a flow of image interpretations. We use a semantic network to represent the knowledge embodied in the system. Dynamic representation is achieved by ahypotheses network. This network is a graph in which nodes represent information and arcs relations. A control strategy performs a continuous update of this network. The originality of our work lies in the control strategy: it includes astructure tracking phase, using the representation structure obtained from previous images to reduce the computational complexity of understanding processes. We demonstrate that in our case the computational complexity, which is exponential if we only use a purely data-driven bottom-up scheme, is polynomial when using the hypotheses tracking mechanism. This is to say that gain improvement in computation time is a major reason for dynamic understanding. The proposed system is implemented; experimental results of road mark detection and tracking are given.     

Parallel natural language processing on a semantic network array processor

This paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, the Semantic Network Array Processor (SNAP). Our system uses a memory-based parsing approach in which parsing is viewed as a memory search process. Linguistic information is stored as phrasal patterns in a semantic network knowledge base distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking phrasal patterns that reflect a sentence interpretation. This is achieved by propagating markers over the distributed network. We have developed a system capable of processing newswire articles from a particular domain. The paper presents the structure of the system, the memory-based parsing method used, and the performance results obtained.<>     

Computational cost of image registration with a parallel binary array processor

The application of a simulated binary array processor (BAP) to the rapid analysis of a sequence of images has been studied. Several algorithms have been developed which may be implemented on many existing parallel processing machines. The characteristic operations of a BAP are discussed and analyzed. A set of preprocessing algorithms are described which are designed to register two images of TV-type video data in real time. These algorithms illustrate the potential uses of a BAP and their cost is analyzed in detail. The results of applying these algorithms to FLIR data and to noisy optical data are given. An analysis of these algorithms illustrates the importance of an efficient global feature extraction hardware for image understanding applications.     

Memristive crossbar array with applications in image processing

A memristor is a kind of nonlinear resistor with memory capacity.Its resistance changes with the amount of charge or flux passing through it.As the fourth fundamental circuit element,it has huge potential applications in many fields,and has been expected to drive a revolution in circuit theory.Through numerical simulations and circuitry modeling,the basic theory and properties of memristors are analyzed,and a memristor-based crossbar array is then proposed.The array can realize storage and output for binary,grayscale and color images.A series of computer simulations demonstrates the effectiveness of the proposed scheme.Owing to the advantage of the memristive crossbar array in parallel information processing,the proposed method is expected to be used in high-speed image processing.     

忆阻器交叉阵列及在图像处理中的应用

忆阻器是一种有记忆功能的非线性电阻,其阻值的变化依赖于流过它的电荷数量或磁通量.忆阻器作为第4个基本的电路元件,在众多领域中有巨大的应用潜力,有望推动整个电路理论的变革.文中利用数值仿真和电路建模,分析了忆阻器的理论基础和特性,提出了一种用于图像存储的忆阻器交叉阵列,可以实现黑白、灰度和彩色图像的存储和输出,一系列的计...     

Aligning Semantic Web applications with network access controls

Access controls for Semantic Web applications are commonly considered at the level of the application-domain and do not necessarily consider the security controls of the underlying infrastructure to any great extent. Low-level network access controls such as firewalls and proxies are considered part of providing a generic network infrastructure that hosts a variety of Semantic Web applications and is independent of the application-level access control services. For example, it is unusual to include firewall policy rules in an application policy that constrain the kinds of application information different principals may access. As a consequence, an improperly configured infrastructure may unintentionally hinder the normal operation of a Semantic Web application. Simply opening a firewall for HTTP and HTTPS services does not necessarily result in a proper configuration. Taking an ontology-based approach, this paper considers how a firewall configuration should be analyzed with respect to the Semantic Web application(s) that it hosts.     

Architectural barriers to array processor efficiency and their analysis

An analysis of the variation in efficiencies of the AP-120B and the MAP-6400 processors is made. The pitfalls in using peak MFLOPS as a measure of this is efficiency is studying an example involving the inversion of a 100×100 matrix.     

Design and construction of an array processor

Design and construction of an array processor that performs autocorrelation functions is presented. The architecture of this system offers speed and avoids complexity. Random access memory with shifting across zero techniques and a high speed address generator are used. Performance is measured for different sizes of arrays and compared with required time of processing the same arrays using software.     

面向网络处理器的软件组件模式

网络处理器通常由多个异构的处理和内存单元通过片上网络连接构成,其目标应用需要在Gbit/s到几十Gbit/s的网络环境中以线速处理数据包,具有实时、资源受限和异构的特点,目前的系统设计和开发方法难以达到性能和效率兼顾.通过分析硬件和应用,总结在基于网络处理器的系统中应用组件技术的基本原则,可应用组件技术来解决传统方法的问题.提出的组件模式抽取了网络处理器在硬件体系结构和目标应用方面的特征,可以简化系统建立并能够对系统行为进行预测.     

Object density-based image segmentation and its applications in biomedical image analysis

In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing.     

Mutation Hopfield neural network and its applications

In this paper, a new operator is proposed to optimize the traditional Hopfield neural network (HNN). The key idea is to incorporate the global search capability of the Estimation of Distribution Algorithms (EDAs) into the HNN, which typically has a powerful local search capability and fast operation. On account of this property of the EDA, our proposed algorithm also exhibits a powerful global search capability. In addition, the possible infeasible solutions generated during the re-sampling period of the EDA are eliminated by the HNN. Therefore, the merits of both these methods are combined in a unified framework. The proposed model is tested on a numerical example, the max-cut problem. The new and optimized model yielded a better performance than certain traditional intelligent optimization methods, such as HNN, genetic algorithm (GA). The proposed mutation Hopfield neural network (MHNN) is also used to solve a practical problem, aircraft landing scheduling (ALS). Compared with first-come-first-served sequence, MHNN sequence reduces both total landing time and total delay.     

Cellular topology and its applications in image processing

In this paper the interaction between Minkowski algebra, nondiscrete cellular topologies and some well known basic cellular image processing operations is investigated. It is shown that some useful topological measures can be extracted from these basic image operations and that these operations can be viewed from a nonalgebraic and purely topological point of view.     

Motion history image: its variants and applications

The motion history image (MHI) approach is a view-based temporal template method which is simple but robust in representing movements and is widely employed by various research groups for action recognition, motion analysis and other related applications. In this paper, we provide an overview of MHI-based human motion recognition techniques and applications. Since the inception of the MHI template for motion representation, various approaches have been adopted to improve this basic MHI technique. We present all important variants of the MHI method. This paper points some areas for further research based on the MHI method and its variants.     

A novel image decomposition approach and its applications

The current state-of-the-art edge-preserving decomposition techniques may not be able to fully separate textures while preserving edges. This may generate artifacts in some applications, e.g., edge detection, texture transfer, etc. To solve this problem, a novel image decomposition approach based on explicit texture separation from large scale components of an image is presented. We first apply a Gaussian structure-texture decomposition, to separate the majority of textures out of the input image. However, residual textures are still visible around the strong edges. To remove these residuals, an asymmetric sampling operator is proposed and followed by a joint bilateral correction to remove an excessive blur effect. We demonstrate that our approach is well suited for the tasks such as texture transfer, edge detection, non-photorealistic rendering, and tone mapping. The results show our approach outperforms existing state-of-the-art image decomposition approaches.     

A new k-winners-take-all neural network and its array architecture

In this paper, a new neural-network model called WINSTRON and its novel array architecture are proposed. Based on a competitive learning algorithm that is originated from the coarse-fine competition, WINSTRON can identify the k larger elements or the k smaller ones in a data set. We will then prove that WINSTRON converges to the correct state in any situation. In addition, the convergence rates of WINSTRON for three special data distributions will be derived. In order to realize WINSTRON, its array architecture with low hardware complexity and high computing speed is also detailed. Finally, simulation results are included to demonstrate its effectiveness and its advantages over three existing networks.