15896075
Effective bandwidth-based QoS provisioning for real-time audio/video streaming over MIMO-OFDM wireless networks
We propose a cross-layer design scheme to support the real-time quality of service (QoS) guarantees for heterogeneous mobile users over downlink MIMO-OFDM wireless networks. Inspired by the effective bandwidth theory originally established for wired networks, we develop the approach to apply the theory into wireless networks. Based on MIMO-OFDM infrastructure employed at the physical (PHY)-layer, we analytically derive the medium access control (MAC)-layer admission-control and time-slot allocation algorithms to guarantee the statistical delay-bound for different real-time users. Simulation results show that the proposed scheme can efficiently support the wireless QoS requirement for diverse real-time mobile users.
79892
Resource Allocation and Quality of Service Evaluation for Wireless Communication Systems Using Fluid Models
Wireless systems offer a unique mixture of connectivity, flexibility, and freedom. It is therefore not surprising that wireless technology is being embraced with increasing vigor. For real-time applications, user satisfaction is closely linked to quantities such as queue length, packet loss probability, and delay. System performance is therefore related to, not only Shannon capacity, but also quality of service (QoS) requirements. This work studies the problem of resource allocation in the context of stringent QoS constraints. The joint impact of spectral bandwidth, power, and code rate is considered. Analytical expressions for the probability of buffer overflow, its associated exponential decay rate, and the effective capacity are obtained. Fundamental performance limits for Markov wireless channel models are identified. It is found that, even with an unlimited power and spectral bandwidth budget, only a finite arrival rate can be supported for a QoS constraint defined in terms of exponential decay rate
1649407
Rhythm modeling, visualizations and applications
People use their awareness of others' temporal patterns to plan work activities and communication. This paper presents algorithms for programatically detecting and modeling temporal patterns from a record of online presence data. We describe analytic and end-user visualizations of rhythmic patterns and the tradeoffs between them. We conducted a design study that explored the accuracy of the derived rhythm models compared to user perceptions, user preference among the visualization alternatives, and users' privacy preferences. We also present a prototype application based on the rhythm model that detects when a person is "away" for an extended period and predicts their return. We discuss the implications of this technology on the design of computer-mediated communication.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
5173411
Email data cleaning
Addressed in this paper is the issue of 'email data cleaning' for text mining. Many text mining applications need take emails as input. Email data is usually noisy and thus it is necessary to clean it before mining. Several products offer email cleaning features, however, the types of noises that can be eliminated are restricted. Despite the importance of the problem, email cleaning has received little attention in the research community. A thorough and systematic investigation on the issue is thus needed. In this paper, email cleaning is formalized as a problem of non-text filtering and text normalization. In this way, email cleaning becomes independent from any specific text mining processing. A cascaded approach is proposed, which cleans up an email in four passes including non-text filtering, paragraph normalization, sentence normalization, and word normalization. As far as we know, non-text filtering and paragraph normalization have not been investigated previously. Methods for performing the tasks on the basis of Support Vector Machines (SVM) have also been proposed in this paper. Features in the models have been defined. Experimental results indicate that the proposed SVM based methods can significantly outperform the baseline methods for email cleaning. The proposed method has been applied to term extraction, a typical text mining processing. Experimental results show that the accuracy of term extraction can be significantly improved by using the data cleaning method.
1134742
Improving Tree Search in Phylogenetic Reconstruction from Genome Rearrangement Data
A major task in evolutionary biology is to determine the ancestral relationships among the known species, a process generally referred as phylogenetic reconstruction. In the past decade, a new type of data based on genome rearrangements has attracted increasing attention from both biologists and computer scientists. Methods for reconstructing phylogeny based on genome rearrangement data include distance-based methods, direct optimization methods (GRAPPA and MGR), and Markov Chain Monte Carlo (MCMC) methods (Badger). Extensive testing on simulated and biological datasets showed that the latter three methods are currently the best methods for genome rearrangement phylogeny. However, all these tools are dealing with extremely large searching spaces; the total number of possible trees grows exponentially when the number of genomes increases and makes it computationally very expensive. Various heuristics are used to explore the tree space but with no guarantee of optimum being found. In this paper, we present a new method to efficiently search the large tree space. This method is motivated by the concept of particle filtration (also known as Sequential Monte Carlo), which was originally proposed to boost the efficiency of MCMC methods on massive data. We tested and compared this new method on simulated datasets in different scenarios. The results show that the new method achieves a significant improvement in efficiency, while still retains very high topological accuracy.
207188648
Reliable Ad Hoc Routing Based on Mobility Prediction
Reliability is a very important issue in Mobile Ad hoc NETworks (MANETs). Shortest paths are usually used to route packets in MANETs. However, a shortest path may fail quickly, because some of the wireless links along a shortest path may be broken shortly after the path is established due to mobility of mobile nodes. Rediscovering routes may result in substantial data loss and message exchange overhead. In this paper, we study reliable ad hoc routing in the urban environment. Specifically, we formulate and study two optimization problems. In the minimum Cost Duration-bounded Path (CDP) routing problem, we seek a minimum cost source to destination path with duration no less than a given threshold. In the maximum Duration Cost-bounded Path (DCP) routing problem, we seek a maximum duration source to destination path with cost no greater than a given threshold. We use a waypoint graph to model the working area of a MANET and present an offline algorithm to compute a duration prediction table for the given waypoint graph. An entry in the duration prediction table contains the guaranteed worst-case duration of the corresponding wireless link. We then present an efficient algorithm which computes a minimum cost duration-bounded path, using the information provided in the duration prediction table. We also present a heuristic algorithm for the DCP routing problem. In addition, we show that the proposed prediction and routing schemes can be easily applied for designing reliable ad hoc routing protocols. Simulation results show that our mobility prediction based routing algorithms lead to higher network throughput and longer average path duration, compared with the shortest path routing.