A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and multiple particle filters. First, feature points are detected by a Harris corner detector and tracked by a Lucas-Kanade tracker. Clusters of moving targets are then initialized by grouping spatially co-located po...

We describe a new 512-CPU Beowulf cluster with Teraflop performance dedicated to problems in computational astrophysics. The cluster incorporates a cubic network topology based on inexpensive commodity 24-port gigabit switches and point to point connections through the second gigabit port on each Linux server. This configuration has network performance competitive with more expensive cluster co...

A layered scheme for target tracking in binary sensor networks is proposed in this paper to improve the tracking precision with minimum resource allocation. In the physical layer, binary sensors are employed, reducing not only hardware configuration overhead but also communication cost. By adopting binary variational filtering algorithm, considerable accurate tracking is ensured even in case of...

Multicasting large amounts of data efficiently to all nodes of a PC cluster is an important operation. In the form of a partition cast it can be used to replicate entire software installations by cloning. Optimizing a partition cast for a given cluster of PCs reveals some interesting architectural tradeoffs, since the fastest solution does not only depend on the network speed and topology, but ...

A relative pose and target model estimation framework using calibrated multicamera clusters is presented. It is able to accurately track up-to-date relative motion, including scale, between the camera cluster and the (freemoving) completely unknown target object or environment using only image measurements from a set of perspective cameras. The cameras within the cluster may be arranged in any ...

Resumo—This paper describes a model which aims to estimate the size of a cluster running Hadoop framework for the processing of large datasets at a given timeframe. As main contributions it defines (i) a light layer of optimization for MapReduce jobs, (ii) presents a model to estimate the size cluster for a Hadoop framework and (iii) performs tests using a real environment the Amazon Elastic Ma...

Encephalitis is a brain inflammation disease. can yield to seizures, motor disability, or some loss of vision hearing. Sometimes, encephalitis be life-threatening and proper diagnosis in an early stage very crucial. Therefore, this paper, we are proposing deep learning model for computerized detection from the electroencephalogram data (EEG). Also, propose Density-Based Clustering classify dist...

This work describes construction of a source and optimisation of its parameters for production of cluster ion beams using material ablation by the second harmonic of a Nd:YAG laser (532 nm). The influence of different source parameters such as carrier gas pressure, laser power, delay time between gas, and laser pulses as well as nozzle configuration on the cluster formation are studied. For the...

In this paper, we present a simple aggregation of framelevel CNN features in a face track to produce a tracklevel feature representation for face clustering in movies or videos. The approach is invariant of the image sequence and the number of frames the track has. We demonstrate the effectiveness of this strategy on three challenging benchmark video face clustering datasets: Big Bang Theory, B...

A unified approach, incorporating architectural and packaging issues, is necessary in the design of high performance computer networks. Clustering enables us to exploit the physical hierarchy imposed by packaging. In [HsYe91], we examined the clustering of hypercube networks within the context of wiring constraints. Here, we extend our earlier work to compare the performance of hypercubes and m...