SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

iView: a feature clustering framework for suggesting informative views in volume visualization

The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.     

Adaptive predistortion using threshold decomposition‐based piecewise linear modeling

A new adaptive predistortion approach for nonlinear power amplifiers based on the piecewise linear (PWL) approximation of nonlinear functions is presented. The PWL model is obtained using threshold decomposition (TD) of the input‐output characteristics. The TD based PWL model is used to develop a baseband predistorter using both the direct and the indirect learning architectures. The proposed predistorter is proved to provide a better performance in suppressing nonlinear distortion than conventional polynomial predistorters and a reduced hardware complexity as compared to lookup table (LUT) approach. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

High accuracy peak location and amplitude spectral estimation via tuning APES method

In this paper, we introduce a new approach to the method of non-parametric adaptive spectral analysis by using the Amplitude and Phase Estimation (APES) method, and taking into account the small sample errors of the sample covariance matrix. This approach is referred to as Adaptive Tuning Amplitude and Phase Estimation method (ATAPES). The main advantage of the ATAPES algorithm is its elimination of biased estimation exists with APES method, which is a biased peak location and corresponding problem of the biased amplitude estimation. The ATAPES method provides more accurate peak location and amplitude estimation with higher resolution than APES method.     

A CDMA/TDD approach for wireless mesh networks

Wide band mesh or star oriented networks have recently become a subject of greater interest. Providing wideband multimedia access for a variety of applications has led to the inception of mesh networks. Classic access techniques such as FDMA and TDMA have been the norm for such networks. CDMA maximum transmitter power is much less than TDMA and FDMA counter parts, which is an important asset for mobile operation. In this paper we introduce a code division multiple access/time division duplex technique CDMA/TDD for such networks. The CDMA approach is an almost play and plug technology for wireless access, making it amenable for implementation by the mesh network service station, SS. Further it inherently allows mesh network service stations to use a combination of turbo coding and dynamic parallel orthogonal transmission to improve network efficiency. We outline briefly the new transmitter and receiver structures then evaluate the efficiency, delay and delay jitter. By analysis we show the advantages over classic counter parts with respect to the total network efficiency achievable especially for larger number of hops.     

Combining Bayesian optimization and Lipschitz optimization

Machine Learning - Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the...