The least-mean-square (LMS) algorithm is an adaptation scheme widely used in practice due to its simplicity. In some applications the involved signals are continuous-time. Then, usually either a fully analog implementation of the LMS algorithm is applied or the input data are sampled by analog-to-digital (AD) converters to be processed digitally. A purely digital realization is most often the p...

The research of its convergence of Mean Shift algorithm is the foundation of its application. Comaniciu and Li Xiang-ru have respectively provided the proof for the convergence of Mean Shift but they both made a mistake in their proofs. In this paper, the imprecise proofs existing in some literatures are firstly pointed out. Then, the local convergence is proved in a new way and the condition o...

The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to av...

Clustering is associate automatic learning technique geared toward grouping a collection of objects into subsets or clusters. The goal is to form clusters that are coherent internally, however well completely different from one another. In plain words, objects within the same cluster ought to be as similar as potential, whereas objects in one cluster ought to be as dissimilar as potential from ...

This paper focuses on the resolution of the capacitated minimum cost flow problem on a network comprising n nodes and m arcs. We present a method that counts imperviousness to degeneracy among its strengths, namely the minimum mean cycle-canceling algorithm (MMCC). At each iteration, primal feasibility is maintained and the objective function strictly improves. The goal is to write a uniform an...

Object tracking algorithms, when it comes to implementing it on hardware ASIC, it becomes difficult task, due to certain limitations in hardware. This paper shows how mean-shift algorithm is implemented in HDL along with the description of ports and interfaces. Keywords— Object tracking, complexity in hardware ASIC, Mean Shift algorithm, Histogram, Bhattacharya coefficient

A Genetic Algorithm for K-Mean Clustering Varsha Singh Asst. Prof. JSSATE, Noida, Uttar Pradesh, India Prof A K Misra Professor, Deptt of CSE, MNNIT Allahabad, Uttar Pradesh, India _________________________________________________________________________________________ Abstract: Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the pro...

Statistical inference subject to nonnegativity constraints is a frequently occurring problem in signal processing. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush-Kuhn-Tucker conditions. It was shown to provide low variance estimates, but it however suffers f...

Recommender systems apply data analysis techniques to the problem of helping users find the items they would like to purchase at E-Commerce sites by producing a predicted likeliness score or a list of top-N recommended items for a given user. We apply Clustering algorithms for finding nearest similar item. To finding nearest item for this we use C++ language. We apply improved K-mean algorithms...