5530667
On achieving optimized capacity utilization in application overlay networks with multiple competing sessions
In this paper, we examine the problem of large-volume data dissemination via overlay networks. A natural way to maximize the throughput of an overlay multicast session is to split the traffic and feed them into multiple trees. While in single-tree solutions, bandwidth of leaf nodes may remain largely under-utilized, multi-tree solutions increase the chances for a node to contribute its bandwidth by being a relaying node in at least one of the trees. We study the following problems: (1) What is the maximum capacity multi-tree solutions can exploit from overlay networks? (2) When multiple sessions compete within the same network, what is the relationship of two contradictory goals: achieving fairness and maximizing overall throughput? (3) What is the impact of IP routing in achieving at constraining the optimal performance of overlay multicast.We extend the multicommodity flow model to the case of overlay data dissemination, where each commodity is associated with an overlay session, rather than the traditional source-destination pair. We first prove that the problem is solvable in polynomial time, then propose an ε-approximation algorithm, assuming that each commodity can be split in arbitrary ways. The solution to this problem establishes the theoretical upper bound of overall throughput that any multi-tree solution could reach. We then study the same problem with the restriction that each commodity can only be split and fed into a limited number of trees. A randomized rounding algorithm and an online tree-construction algorithm are presented. All these algorithms are evaluated by extensive simulations.
5921080
Dynamic reconfiguration for complex multimedia applications
Current state-of-the-art distributed multimedia applications require an environment that provides stable quality of service (QoS). However, these applications typically run on best-effort heterogeneous platforms, and compete with other applications or connections for end system and network resources, hence suffer from instability and dynamic variations with regards to QoS. We present a dynamically reconfigurable middleware control architecture to enhance adaptation awareness of these applications. Our middleware architecture allows for detecting QoS fluctuations in the surrounding environment and signaling optimal control actions to the application. Furthermore, we discuss the design of a core component, the Configurator, that adopts a fuzzy control approach to compute optimal control actions. Preliminary experimental results with a distributed visual tracking application show that our approach is viable and effective in controlling adaptive applications.
8043231
QoS-Aware Adaptive Services in Mobile Ad-Hoc Networks
Ad-hoc wireless networks consist of mobile nodes interconnected by multi-hop wireless paths. Unlike conventional wireless networks, ad-hoc networks have no fixed network infrastructure or administrative support. Because of the dynamic nature of the network topology and limited bandwidth of wireless channels, Quality-of-Service (QoS) provisioning is an inherently complex and difficult issue. In this paper, we propose a fully distributed and adaptive algorithm to provide statistical QoS guarantees with respect to accessibility of services in an ad-hoc network. In this algorithm, we focus on the optimization of a new QoS parameter of interest, service efficiency, while keeping protocol overheads to the minimum. To achieve this goal, we first theoretically derive the lower and upper bounds of service efficiency based on a novel model for group mobility, followed by extensive simulation results to verify the effectiveness of our algorithm.
2274256
Overlay Networks with Linear Capacity Constraints
Overlay networks are virtual networks residing over the IP network; consequently, overlay links may share hidden lower level bottlenecks. Previous work has assumed an independent overlay model: a graph with independent link capacities. We introduce a model of overlays that incorporates correlated link capacities and linear capacity constraints (LCC) to formulate hidden shared bottlenecks; we refer to these as LCC-overlays. We define metrics to qualitatively measure overlay quality in terms of its accuracy (in representing the true network topology) and efficiency (that is, performance). Through analysis and simulations, we show that LCC-overlay is perfectly accurate and, hence, enjoys much higher efficiency than the inaccurate independent overlay. We discover that even a highly restricted LCC class-node-based LCC-yields near-optimal accuracy and significantly higher efficiency. We study two network flow problems in the context of LCC-graphs: widest path and maximum flow. We prove that Widest Path with LCC is NP-complete. We formulate Maximum Flow with LCC as a linear program and propose an efficient distributed algorithm to solve it. Based on the LCC model, we further study the problem of optimizing delay while still maintaining optimal or near-optimal bandwidth. We also outline a distributed algorithm to efficiently construct an overlay with node-based LCC.
3208295
Price-based resource allocation in wireless ad hoc networks
The shared-medium multi-hop nature of wireless ad hoc networks poses fundamental challenges to the design of an effective resource allocation algorithm to maximize the aggregated utility of flows, while maintaining basic fairness among multiple flows. When previously proposed scheduling algorithms have been shown to perform well in providing fair shares of bandwidth among single-hop wireless flows, they did not consider multi-hop flows with an end-to-end perspective. Moreover, the resource allocation strategies employed in the wireline network can not be applied directly in the context of ad hoc networks due to the unique characteristic of location dependent contention and spatial reuse of the shared wireless channel. In this paper, we propose a price-based resource allocation model to achieve maximized aggregated utility (i.e., social welfare) of flows. Our original contributions are: First, we propose to use maximal clique-associated shadow prices for wireless channel access coordination, rather than link-associated price for wireline link access arbitration. Second, we present a new pricing policy for end-to-end multi-hop flow. Using this model, different fairness goals can be realized in ad hoc networks for end-to-end flows. With a two-tier distributed and iterative algorithm, scarce channel capacity is allocated fairly among multi-hop flows from an end-to-end perspective, using shadow prices as the mechanism to arbitrate channel access. Through extensive analysis and simulation results, we show that our proposed algorithm is able to fairly distribute resources among multi-hop flows, while simultaneously maximizing the aggregated utility of flows globally.
15776548
Cross-layer flow control in lightly-loaded multi-hop ad hoc networks
The throughput in multi-hop ad hoc networks (MANETs) is highly dependent on the sending rate and the route length from the source node to the destination. Sending packets at the optimal rate for a given route length maximizes throughput in the network, whereas slightly increasing the sending rate over the optimal value may decrease throughput by up to 55%. This paper presents a novel cross-layer technique for flow control in lightly-loaded MANETs. The technique allows applications to send packets at the rate that maximizes throughput for a given route length. To achieve this, the routing layer notifies interested applications about routing changes, and the applications adaptively modify their sending rates based on the new route length to the destination. In static and mobile networks, this technique outperforms UDP-based flows with a fixed sending rate and doubles the throughput of TCP for networks with up to 2 concurrent flows.
14151705
An adaptive k-nearest neighbor text categorization strategy
k is the most important parameter in a text categorization system based on the k-nearest neighbor algorithm (kNN). To classify a new document, the k-nearest documents in the training set are determined first. The prediction of categories for this document can then be made according to the category distribution among the k nearest neighbors. Generally speaking, the class distribution in a training set is not even; some classes may have more samples than others. The system's performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias for large categories, and will not make full use of the information in the training set. To deal with these problems, an improved kNN strategy, in which different numbers of nearest neighbors for different categories are used instead of a fixed number across all categories, is proposed in this article. More samples (nearest neighbors) will be used to decide whether a test document should be classified in a category that has more samples in the training set. The numbers of nearest neighbors selected for different categories are adaptive to their sample size in the training set. Experiments on two different datasets show that our methods are less sensitive to the parameter k than the traditional ones, and can properly classify documents belonging to smaller classes with a large k. The strategy is especially applicable and promising for cases where estimating the parameter k via cross-validation is not possible and the class distribution of a training set is skewed.
5536365
Simulation of Diabetic Retinopathy Neovascularization in Color Digital Fundus Images
Diabetic retinopathy (DR) has been identified as a leading cause of blindness. One type of lesion, neovascularization (NV), indicates that the disease has entered a vision-threatening phase. Early detection of NV is thus clinically significant. Efforts have been devoted to use computer-aided analyses of digital retina images to detect DR. However, developing reliable NV detection algorithms requires large numbers of digital retinal images to test and refine approaches. Computer simulation of NV offers the potential of developing lesion detection algorithms without the need for large image databases of real pathology. In this paper, we propose a systematic approach to simulating NV. Specifically, we propose two algorithms based on fractal models to simulate the main structure of NV and an adaptive color generation method to assign photorealistic pixel values to the structure. Moreover, we develop an interactive system that provides instant visual feedback to support NV simulation guided by an ophthalmologist. This enables us to combine the low level algorithms with high-level human feedback to simulate realistic lesions. Experiments suggest that our method is able to produce simulated NVs that are indistinguishable from real lesions.
14227223
Bridging the semantic gap in sports video retrieval and summarization
One of the major challenges facing current media management systems and related applications is the so-called ''semantic gap'' between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs, with a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. We implemented an MPEG-7 compliant browsing system for semantic retrieval and summarization of sports video using the proposed algorithms.
16949052
Automatic keystone correction for smart projectors with embedded camera
We propose embedded algorithms for enabling a smart self-adjusting projector that is capable of fully automatic keystone distortion detection and correction, plus zooming and centering the image to fit a screen. The projector has an embedded camera and it automatically detects the screen and a specific pattern during self-adjustment. Proposed algorithms meet severe computational requirements of a portable projector and they exploit the physical constraints in a projector-(embedded) camera pair in a novel way in order to not compromise image quality under different usage conditions. We will demonstrate our projector system during the conference.
1260337
An Improved k-Nearest Neighbor Algorithm for Text Categorization
k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is uneven. Some classes may have more samples than others. Therefore, the system performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias on large categories. To deal with these problems, we propose an improved kNN algorithm, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories. More samples (nearest neighbors) will be used for deciding whether a test document should be classified to a category, which has more samples in the training set. Preliminary experiments on Chinese text categorization show that our method is less sensitive to the parameter k than the traditional one, and it can properly classify documents belonging to smaller classes with a large k. The method is promising for some cases, where estimating the parameter k via cross-validation is not allowed.