14934278
Optimal 2-D interleaving with latin rectangles
One of the key problems in the study of optimal interleaving schemes for correcting two-dimensional (2-D) cluster errors is how to place, say, n distinct symbols, each appearing m times, in an m/spl times/n array such that the resulting array has the maximum possible burst error-correcting power. In a previous paper, the authors have proved that for any given m, n, the maximum possible interleaving distance, and hence, the largest possible value t such that an arbitrary error burst of size t can be corrected in an m/spl times/n interleaved array, is given by t=/spl lfloor//spl radic/2n/spl rfloor/ for n = /spl les/ /spl lceil/m/sup 2//2/spl rceil/, and t=m+/spl lfloor/(n-/spl lceil/m/sup 2//2/spl rceil/)/m/spl rfloor/ for n /spl ges/ /spl lceil/m/sup 2//2/spl rceil/. In this work, we extend these results and show that for all m, n with n /spl ges/ m, an optimal m/spl times/n interleaving array can always be obtained by a Latin rectangle in which each row and each column contains each symbol at most once. This provides additional error-correcting power to the array in that all linear error bursts occupying a whole row or column can also be corrected.
18611881
Optimal Interleaving Schemes for Two-Dimensional Arrays
Given an mtimesn array of k single random error correction (or erasure) codewords, each having length l such that mn=kl, we construct optimal interleaving schemes that provide the maximum burst error correction power such that an arbitrarily shaped error burst of size t can be corrected for the largest possible value of t. We show that for all such mtimesn arrays, the maximum possible interleaving distance, or equivalently, the largest value of t such that an arbitrary error burst of size up to t can be corrected, is bounded by lfloorradic2krfloor if kleslceil(min{m,n})2/2rceil, and by min{m,n}+lfloor(k-lceil(min{m,n})2 /2rceil)/min{m,n}rfloor if kgeslceil(min{m,n})2/2rceil. We generalize the cyclic shifting algorithm developed by the authors in a previous paper and construct, in several special cases, optimal interleaving arrays achieving these upper bounds. Additionally, for codewords of variable lengths, we solve a related array coloring problem for which the same upper bounds hold and can be achieved
2237682
Document clustering based on non-negative matrix factorization
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.
14482286
Document clustering by concept factorization
In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.
14445257
Background Segmentation Using Spatial-Temporal Multi-Resolution MRF
Robust and accurate background segmentation is crucial for surveillance applications and is a key element in visual tracking, layer-based compression, and silhouette-based 3D reconstruction. In this paper, we present a novel spatial-temporal model that describes the appearance and dynamics of background scenes at multiple resolutions. We propose a time-dependent Markov Random Field (MRF) to represent the state of foreground and background at each pixel in the spatial-temporal pyramid. Pixels are linked spatially and temporally across frames. The probability of adding/deleting a foreground object is calculated by online learning algorithm and is used as prior information in computing foreground label. We use Gibbs Sampling to solve the MRF in a Maximum A Posterior (MAP) framework. Experimental results show that this real-time algorithm is able to segment the foreground object accurately from videos and more resilient to distractions such as imaging noise, illumination changes, camera shakes, and random motion in the scene.
2062974
A Conditional Random Field Model for Video Super-resolution
In this paper, we propose a learning-based method for video super-resolution. There are two main contributions of the proposed method. First, information from cameras with different spatial-temporal resolutions is combined in our framework. This is achieved by constructing training dictionary using the high resolution images captured by still camera and the low resolution video is enhanced via searching in this customized database. Second, we enforce the spatio-temporal constraints using the conditional random field (CRF) and the problem of video super-resolution is posed as finding the high resolution video that maximizes the conditional probability. We apply the algorithm to video sequences taken from different scenes using cameras with different qualities and promising results are presented
15841217
An energy-aware active smart card
Despite recent advances in smart card technology, most modern smart cards continue to rely on card readers for power and clocking, creating a potential security gap. In this paper, we present an energy-aware smart card architecture that operates using an embedded battery and crystal. This low-power VLSI system is continually active and provides enhanced security through periodic internal update when the card is detached from a reader. Our architecture achieves reduced power consumption by deactivating the majority of its circuitry, including an embedded microcontroller, for the vast majority of the card's lifetime. A proof-of-concept prototype implementation of the architecture has been developed including register-transfer-level and gate-level designs which have been synthesized to silicon. To permit extended operation for up to 18 months, critical design logic has been implemented using ultralow-power (adiabatic) circuit techniques.