11332751
A capacity acquisition protocol for channel reservation in CDMA networks
In this paper, a capacity acquisition protocol is proposed for channel reservation in CDMA networks. Under this protocol, a cell is virtually divided into three regions (i.e., inner region, forced handoff region, and active handoff region). A new call in the active handoff region works in soft handoff mode upon its admission, while a new call in the inner and forced handoff regions works in single mode. However, in the forced handoff region, calls working in single mode can be forced into soft handoff mode, when extra capacity is needed by soft handoff calls. As a result, no explicit channel reservation is required before a call enters soft handoff. By adjusting the size of the forced handoff region, the capacity acquisition can adapt to traffic load and guarantee a desired call dropping probability. To evaluate the capacity acquisition protocol, an analytical model is derived and is also validated through computer simulations. Numeric results illustrate that the capacity acquisition protocol significantly reduces the call dropping probability.
9104202
Rate-Adaptive Framing for Interfered Wireless Networks
The majority of existing wireless rate controls are based on the implicit assumption that frames are corrupted due to the random, arbitrary environmental and thermal noises. They generally reduce the channel rate on frame losses, trading lower efficiency in frequency band utilization for more robust modulation so that the current noise level may be tolerable. In highly interfered wireless networks where frames are lost mainly due to interference from other wireless transceivers, simply reducing the channel rate prolongs the frame transmission time and therefore aggravates frame loss ratio. This positive feedback in the rate control loop quickly diverges the interfered transceivers into a suboptimal channel rate and drives the network into a state with high interference. In the worst case, interfered transceivers can be starved. In this paper we present RAF, the rate-adaptive framing that jointly controls the channel rate and frame size according to the observed interference patterns and noise level at the receiver. Based on the inputs from physical layer carrier sense, the receiver derives the optimal channel rate and frame size that maximize throughput, and informs the transmitter of such optimal configuration in a few bits in the per-frame acknowledgement. Through intensive simulations we show that RAF consistently outperforms ARF, RBAR, and OAR in all simulated scenarios.
10922426
Language Model Information Retrieval with Document Expansion
Language model information retrieval depends on accurate estimation of document models. In this paper, we propose a document expansion technique to deal with the problem of insufficient sampling of documents. We construct a probabilistic neighborhood for each document, and expand the document with its neighborhood information. The expanded document provides a more accurate estimation of the document model, thus improves retrieval accuracy. Moreover, since document expansion and pseudo feedback exploit different corpus structures, they can be combined to further improve performance. The experiment results on several different data sets demonstrate the effectiveness of the proposed document expansion method.
12743894
Feature-based Displacement Mapping
Displacement mapping was originally created as a rendering tool to provide small-scale modulation of an underlying smooth surface. However, it has now emerged as a sculpting tool, to the extent that complex geometry can effectively be added to a scene at rendering time. The attendant complexity of displacement maps is placing increased demands on rendering systems, from quality, performance, and memory perspectives. While adequate solutions exist within scanline rendering architectures, good general solutions have been difficult to come by in ray-traced or hardware-based environments, or in situations in which a complete displaced surface is desired. We present an approach to the rendering of displacement mapped surfaces that scales with the complexity of the displacement map, with an eye to minimizing the amount of additional geometry generated by the mapping process. We perform a feature analysis of displacement maps, aggregate these features, and map them onto geometry in space. This approach affords a significant degree of complexity control, it permits feature-based tessellation of surfaces, and it is amenable to use in ray-traced, scanline, or hardware accelerated settings. This kind of feature analysis naturally applies to other classes of texture mapping as well.
15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.
26169252
Multicast Internet protocol
In this paper, we first review the existing IPv4 based multicast protocols and identify their shortcomings. We then proposed a new multicast protocol, called Multicast Internet Protocol (MIP), which is both scalable and flexible. The design principle of MIP is fundamentally different from the existing IPv4 based multicast protocols. The issues related to MIP routing and implementations are also studied in this paper.
2235439
Learn from web search logs to organize search results
Effective organization of search results is critical for improving the utility of any search engine. Clustering search results is an effective way to organize search results, which allows a user to navigate into relevant documents quickly. However, two deficiencies of this approach make it not always work well: (1) the clusters discovered do not necessarily correspond to the interesting aspects of a topic from the user's perspective; and (2) the cluster labels generated are not informative enough to allow a user to identify the right cluster. In this paper, we propose to address these two deficiencies by (1) learning "interesting aspects" of a topic from Web search logs and organizing search results accordingly; and (2) generating more meaningful cluster labels using past query words entered by users. We evaluate our proposed method on a commercial search engine log data. Compared with the traditional methods of clustering search results, our method can give better result organization and more meaningful labels.