17024418
GRAIL: general real-time adaptable indoor localization
Yingying Chen†, Eiman Elnahrawy‡, John-Austen Francisco†, Konstantinos Kleisouris†, Xiaoyan Li∗, Hongyi Xue†, Richard P. Martin†,‡ †{yingche,deymious,kkonst,xuehy58,rmartin}@cs.rutgers.edu ‡eiman@kordinate.com, ∗xili@cs.lafayette.edu †Dept. of Computer Science ‡Kordinate LLC ∗Dept. of Computer Science Rutgers University 200 Centennial Ave., Ste 200 Lafayette College Piscataway, NJ 08854 Piscataway, NJ 08854 Easton, PA 18042
16418153
The limits of localization using RSS
We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10ft and 97th percentile of 30ft. We present strong evidence that these limitations are fundamental and that they are unlikely to be transcended without fundamentally more complex environmental models or additional localization infrastructure.
11674907
An Analytic Model Predicting the Optimal Range for Maximizing 1-Hop Broadcast Coverage in Dense Wireless Networks
We present an analytic model to predict the optimal range for maximizing 1-hop broadcast coverage in dense ad-hoc wireless networks, using information like network density and node sending rate. We first derive a geometric-based, probabilistic model that describes the expected coverage as a function of range, sending rate and density. Because we can only solve the resulting equations numerically, we next develop extrapolations that find the optimum range for any rate and density given a single precomputed optimum. Finally using simulation, we show that in spite of many simplifications in the model, our extrapolation is able to predict the optimal range to within 16%.
1979432
Using Fault Injection and Modeling to Evaluate the Performability of Cluster-Based Services
We propose a two-phase methodology for quantifying the performability (performance and availability) of cluster-based Internet services. In the first phase, evaluators use a fault-injection infrastructure to measure the impact of faults on the server's performance. In the second phase, evaluators use an analytical model to combine an expected fault load with measurements from the first phase to assess the server's performability. Using this model, evaluators can study the server's sensitivity to different design decisions, fault rates, and environmental factors. To demonstrate our methodology, we study the performability of 4 versions of the PRESS Web server against 5 classes of faults, quantifying the effects of different design decisions on performance and availability. Finally, to further show the utility of our model, we also quantify the impact of two hypothetical changes, reduced human operator response time and the use of RAIDs.
8799846
Syntactic features in question answering
Syntactic information potentially plays a much more important role in question answering than it does in information retrieval. Although many people have used syntactic evidence in Question Answering, there haven't been many detailed experiments reported in the literature. The aim of the experiment described in this paper is to study the impact of a particular approach for using syntactic information on question answering effectiveness. Our results indicate that a combination of syntactic information with heuristics for ranking potential answers can perform better than the ranking heuristics on their own.
35636017
A Novel Pixel-Level and Feature-Level Combined Multisensor Image Fusion Scheme
This paper proposes a novel image fusion scheme, which combines the merits of pixel-level and feature-level fusion algorithms. It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise and misregistration. The algorithm first segments images into several regions, then extract features from each segmented region to get the fused image. Two typical image segmentation methods, region growing and edge detection based are both presented in this paper. Experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the multiscale decompositions (MSD) based fusion approaches, both in visual effect and objective evaluation criteria.
13418116
An information-pattern-based approach to novelty detection
In this paper, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, ''novelty'' is redefined based on the proposed information patterns, and several different types of information patterns are given corresponding to different types of users' information needs. Second, a thorough analysis of sentence level information patterns is elaborated using data from the TREC novelty tracks, including sentence lengths, named entities (NEs), and sentence level opinion patterns. Finally, a unified information-pattern-based approach to novelty detection (ip-BAND) is presented for both specific NE topics and more general topics. Experiments on novelty detection on data from the TREC 2002, 2003 and 2004 novelty tracks show that the proposed approach significantly improves the performance of novelty detection in terms of precision at top ranks. Future research directions are suggested.
15133148
Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases
In this paper, we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the "semantic gap" by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: we design novel data structures, namely, a lexical hierarchy, an image-semantic hierarchy, and a number of atomic semantic domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective in achieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.
29798457
Adaptive Image Segmentation Using Modified Pulse Coupled Neural Network
This paper proposes a novel image segmentation algorithm based on Pulse Coupled Neural Network (PCNN).Unlike the traditional PCNN image segmentation methods, the presented algorithm can achieve the optimum parameters automatically. Experimental results show its good performance and robustness. The research fruits have great importance both on the theory research and practical application of PCNN.