3952600
TCP smoothness and window adjustment strategy
We observe that even when the system throughput is relatively stable,end users of media-streaming applications do not necessarily experience smooth throughput, due to the unsynchronized window adjustments triggered by random congestion indications. We analyze and evaluate the negative impact of random window adjustments on smoothness, short-term fairness, and long-term fairness. We further propose an experimental congestion avoidance mechanism, namely TCP(/spl alpha/, /spl beta/, /spl gamma/, /spl delta/), based on coordinated window adjustments. The flow-level smoothness is enhanced significantly for media-streaming applications, without a cost on fairness and responsiveness. Responsiveness is even boosted when bandwidth is underutilized.
3967435
Experimental Evaluation of Red in Heterogeneous Environments
1383951
Improving TCP smoothness by synchronized and measurement-based congestion avoidance
In this paper, we observe that although multiplicative decrease is necessary to accomplish fairness in congestion control, it does not inevitably sacrifice system throughput, as long as the system operates between the knee and the cliff, according to an equation. However, even when the system throughput is relatively stable, end users of real-time applications do not necessarily experience a smooth traffic, mainly caused by the unsynchronized window adjustments due to random congestion indications. We analyzed and evaluated the negative impact of random window adjustments on smoothness, short-term fairness, and even long-term fairness measured by a novel fairness metric defined in this paper. Therefore, we propose an experimental congestion avoidance mechanism to improve TCP smoothness for media-streaming applications. The mechanism relies on a fine-grained RTT estimation to measure the network condition, and coordinates the upward and backward window adjustments to abolish the damage of unsynchronized window control. Congestive packet drops are reduced by a new control parameter g, and the bottleneck queue length can also be controlled in an end-to-end way. Simulation results confirm that the new mechanism enhance significantly the smoothness and fairness, without a cost of responsiveness. In fact, by enabling a new parameter d, the responsiveness can be even enhanced when the bandwidth is under-utilized.
3978452
The Interaction Between Window Adjustment Strategies and Queue Management Schemes
In this paper, we investigate extensively the joint network dynamics with different AIMD window-adjustment parameters on end-hosts, and different queue management schemes (i.e. DropTail vs. RED) in routers. We reveal that with DropTail buffer, although smooth TCPs causes less queuing-delay jitter, its average queuing delay is significantly higher than that of responsive TCPs. The direct implication of this discovery is that when mobile users of media-streaming and short messages share a bottleneck link, the energy consumption for sending short messages can increase severely if media-streaming users adopt smooth TCPs. With RED, on the other hand, smooth TCPs not only lead to smaller queue oscillation, the average/max queue length is smaller as well.
33105882
CyberBridges A Model Collaboration Infrastructure for e-Science
The "CyberBridges" pilot project is an innovative model for creating a new generation of scientists and engineers who are capable of fully integrating cyberinfrastructure into the whole educational, professional, and creative process of their respective disciplines. CyberBridges augments graduate student education to include a foundation of understanding in advanced networking and grid infrastructure for high performance computing, and bridges the divide between the information technology community and diverse science and engineering disciplines. We demonstrate the effectiveness of CyberBridges by providing four case studies. Groundwork has begun to extend the outreach of CyberBridges for international research and education collaborations.
1830896
A Communication Virtual Machine
The convergence of data, voice and multimedia communication over digital networks, coupled with continuous improvement in network capacity and reliability has significantly enriched the ways we communicate. However, the stovepipe approach used to develop today's communication applications and tools results in rigid technology, limited utility, lengthy and costly development cycle, difficulty in integration, and hinders innovation. In this paper, we present a fundamentally different approach, which we call communication virtual machine (CVM) to address these problems. CVM provides a user-centric, model-driven approach for conceiving, synthesizing and delivering communication solutions across application domains. We argue that CVM represents a far more effective paradigm for engineering communication solutions. The concept, architecture, modeling language, prototypical design and implementation of CVM are discussed
8800642
Multimedia Data Mining for Traffic Video Sequences
In this paper, a multimedia data mining framework for discovering important but previously unknown knowledge such as vehicle identification, traffic flow, and the spatio-temporal relations of the vehicles at the intersections from traffic video sequences is proposed. The proposed multimedia data mining framework analyzes the traffic video sequences by using background subtraction, image/video segmentation, object tracking, and modeling with multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over an intersection. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data-mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-a-vis traffic operations. A real-life traffic video sequence is used to illustrate the effectiveness of the proposed multimedia data mining framework.
83568
Fingerprint Reference Point Detection Based on Local Axial Symmetry
Reference point detection is an important process for fingerprint analysis. In this paper, we propose a novel feature which is named local axial symmetry (LAS) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. Experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods
2688646
A unified framework for image database clustering and content-based retrieval
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called <i>Markov Model Mediators</i> (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.
45852164
An Agent-Based Hybrid Intelligent System for Financial Investment Planning
Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. however, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. this paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. this system currently consists of 13 different agents. the experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. the system is very flexible and robust. the success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.
30275744
Robust Active Shape Model Construction and Fitting for Facial Feature Localization
Active Shape Model (ASM) has proved to be a powerful tool for interpreting face images. However, it may fail in the presence of non-Gaussian noise, or outliers. In this paper, we present a framework for both automatic model construction and efficient model fitting with outliers. In model construction, the training face samples are automatically labeled by local image search using Gabor wavelet features. Then robust principle component analysis (RPCA) is applied to capture the statistics of shape variations. In model fitting, an error function is introduced to deal with the outlier problem, which provides a connection to robust M-estimation. Gauss-Newton algorithm is adopted to efficiently optimize the robust energy function. Extensive experiments demonstrate the efficiency and robustness of our approach over previous methods.