6853058
Markov chain correlation based clustering of gene expression data
An efficient Markov chain correlation based clustering method (MCC) has been proposed for clustering gene expression data. The gene expression data is first normalized and Markov chains (MC) are constructed from the dynamics of the gene expressions, in which the behavior of the genes at each step of the experiment can be taken into account. Based on the correlation of one-step Markov chain transition probabilities, an agglomerative method is employed to group the series that have similar behavior at each point. The proposed MCC clustering method has been applied to four gene expression datasets to obtain a number of clusters. The results show that the MCC method outperforms the commonly used K-means method and produces clusters that are more meaningful in terms of the similarity of the grouped genes. Another advantage of the proposed method over the existing clustering methods is that the knowledge of the group number is not required.
38318651
Parallel Virtual Screening for Prediction of Antimicrobial Activity Using Fourier Transforms
8628384
Fingerprint Indexing Based on LAS Registration
Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.
2141991
Spectral analysis for sampling image-based rendering data
Image-based rendering (IBR) has become a very active research area in recent years. The spectral analysis problem for IBR has not been completely solved. In this paper, we present a new method to parameterize the problem, which is applicable for general-purpose IBR spectral analysis. We notice that any plenoptic function is generated by light ray emitted/reflected/refracted from the object surface. We introduce the surface plenoptic function (SPF), which represents the light rays starting from the object surface. Given that radiance along a light ray does not change unless the light ray is blocked, SPF reduces the dimension of the original plenoptic function to 6D. We are then able to map or transform the SPF to IBR representations captured along any camera trajectory. Assuming some properties on the SPF, we can analyze the properties of IBR for generic scenes such as scenes with Lambertian or non-Lambertian surfaces and scenes with or without occlusions, and for different sampling strategies such as lightfield/concentric mosaic. We find that in most cases, even though the SPF may be band-limited, the frequency spectrum of IBR is not band-limited. We show that non-Lambertian reflections, depth variations and occlusions can all broaden the spectrum, with the latter two being more significant. SPF is defined for scenes with known geometry. When the geometry is unknown, spectral analysis is still possible. We show that with the "truncating windows" analysis and some conclusions obtained with SPF, the spectrum expansion caused by non-Lambertian reflections and occlusions can be quantatively estimated, even when the scene geometry is not explicitly known. Given the spectrum of IBR, we also study how to sample IBR data more efficiently. Our analysis is based on the generalized periodic sampling theory with arbitrary geometry. We show that the sampling efficiency can be up to twice of that when we use rectangular sampling. The advantages and disadvantages of generalized periodic sampling for IBR are also discussed.
16767212
A Robust Method for Biological Sequence Clustering
In this paper, we proposed a two-phase hybrid method for biological sequence clustering, which combines the strengths of the hierarchical agglomerative clustering methods and the partition clustering methods. In phase I, the hybrid method uses the hierarchical agglomerative clustering algorithm to pre-cluster the aligned sequences, while in the second phase it takes the pre-clustering result as the initial partition for the profile hidden Markov models (HMMs) based k-means partition clustering method. Such initial partitions (generated from phase I), as against random initial partitions, are usually more reasonable and thus can avoid the inconsistency problem in the partition clustering methods due to the randomness in initial partitions. In addition, the inaccuracy of the hierarchical agglomerative clustering methods can be compensated by the profile HMM based k-means partition clustering since the latter is model-based and can better describe the dynamic properties of the data in a cluster. Experiments on a molecular sequence dataset demonstrate the effectiveness and the efficiency of the proposed hybrid clustering algorithm
1874082
TCP-real: improving real-time capabilities of TCP over heterogeneous networks
We present a TCP-compatible and -friendly protocol which abolishes thr ee major shortfalls of TCP for reliable multimedia applications over heterogeneous networks: (i) ineffective bandwidth utilization, (ii) unnecessary congestion-oriented responses to wireless link errors (e.g., fading channels) and operations (e.g. handoffs), and (iii) wasteful window adjustments over asymmetric, low-bandwidth reverse paths. We propose TCP-Real, a high-throughput transport protocol that minimizes transmission-rate gaps, thereby enabling better performance and reasonable playback timers. In TCP-Real, the receiver decides with better accuracy about the appropriate size of the congestion window. Slow Start and timeout adjustments are used whenever congestion avoidance fails; however, rate and timeout adjustments are cancelled whenever the receiving rate indicates sufficient availability of bandwidth. We detail the protocol design and we report significant improvement on the performance of the protocol with time-constrained traffic, wireless link errors and asymmetric paths.
15177304
A new method of color image segmentation based on intensity and hue clustering
A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.