Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on ...

Flexible-grid wavelength-division multiplexing is a powerful tool in lightwave communications for maximizing spectral efficiency. In the emerging field of quantum networking, need effective resource provisioning particularly acute, given generally lower power levels, higher sensitivity to loss, and inapplicability digital error correction. this Letter, we leverage flex-grid technology demonstra...

The visual analytics presented in this paper augment conditioned choropleth maps. In the conditioned map, dynamic sliders partition the map into a 3 x 3 grid of partial maps. Two different variables are attached to the two partitioning sliders. One slider controls row membership in the grid and the other controls column membership. The analyst’s visual impression and comparison of the partial m...

Optimal partitioning of structured adaptive mesh applications necessitates dynamically determining and optimizing for the most time-inhibiting factor, such as load imbalance and communication volume. However, any trivial monitoring of an application evaluates the current partitioning rather than the inherent properties of the grid hierarchy. We present an analytical model that given a structure...

In the domain of data preparation for supervised classification, filter methods for variable ranking are time efficient. However, their intrinsic univariate limitation prevents them from detecting redundancies or constructive interactions between variables. This paper introduces a new method to automatically, rapidly and reliably extract the classificatory information of a pair of input variabl...

Data Mining is the process to find out the data from large data sets and transform into the valuable information. In this paper we are presenting about the clustering techniques and the impact noise on clustering techniques. Clustering is important in data analysis and data mining applications. It is the task of grouping a set of objects so that objects in the same group are more similar to eac...

Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, and recent advances in clustering, such as subspace clustering, text clustering and data stream cluste...

Recent advances in virtualization technologies can be applied to different research areas, such as virtualized resource management in High Performance Computing (HPC) Grid. On the one hand, the HPC Grid hides to users the complexity of the virtual cluster management, but on the other hand, the Virtual Machines (VM) are managed in the execution domain of the node’s resource manager. Hence, local...

Fractal image coding is a relatively new technique for compact image representation. The basic coding scheme exploits selfsimilarities between parts of the image and other parts in it at a different resolution. The various parts are consequences of a partition grid obtained by applying a splitting criterion to the image. In this work, we present an algorithm for adaptive image partitioning, ach...

In this paper the problem of obtaining efficient hardware for Viterbi decoders for high rate convolutional encoders is addressed. It is first shown that the graphs describing the interconnection of the add-compare-select units required may be classified in terms of structures of which there are only a small number for a given code constraint length. They correspond to the assignment of individu...