Distributed data association for multi-target tracking in sensor networks

Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more challenging in the context of sensor networks, since association is coupled across the network, yet centralized data processing is in general infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data

An efficient message passing algorithm for multi-target tracking

We propose a new approach for multi-sensor multi-target tracking by constructing statistical models on graphs with continuous-valued nodes for target states and discrete-valued nodes for data association hypotheses. These graphical representations lead to message-passing algorithms for the fusion of data across time, sensor, and target that are radically different than algorithms such as those found in state-of-the-art multiple hypothesis tracking (MHT) algorithms. Important differences include: (a) our message-passing algorithms explicitly compute different probabilities and estimates than MHT algorithms; (b) our algorithms propagate information from future data about past hypotheses via messages backward in time (rather than doing this via extending track hypothesis trees forward in time); and (c) the combinatorial complexity of the problem is manifested in a different way, one in which particle-like, approximated, messages are propagated forward and backward in time (rather than hypotheses being enumerated and truncated over time). A side benefit of this structure is that it automatically provides smoothed target trajectories using future data. A major advantage is the potential for low-order polynomial (and linear in some cases) dependency on the length of the tracking interval N, in contrast with the exponential complexity in N for so-called N-scan algorithms. We provide experimental results that support this potential. As a result, we can afford to use longer tracking intervals, allowing us to incorporate out-of-sequence data seamlessly and to conduct track-stitching when future data provide evidence that disambiguates tracks well into the past

Segmentation of the evolving left ventricle by learning the dynamics

We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences

Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object

Modeling of on-chip (bio)-particle separation and counting using 3D electrode structures

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.In lab-on-a-chip applications, manipulation and quantification of (bio)particles is required in a variety of biomedical applications such as drug screening, disease detection and treatment. For manipulation of particles, electrical techniques such as dielectrophoresis (DEP) is very suitable. For the quantification or counting of the bioparticles, flow cytometer, fluorescence-activated cell sorting (FACS) and magneticactivated cell sorting (MACS) are common techniques. In this study, modeling of microfluidic (bio)-particle separation based on dielectrophoresis and counting based on capacitance measurement using COMSOL Multiphysics have been presented. The device performance with planar and 3D electrode structures have been compared. Microfluidic devices with an asymmetric pair (for separation) and a symmetric pair (for counting) of electrodes are considered. The effects of the geometrical parameters, material properties, flow rate, particle size and applied voltage on the device performance have been discussed. The fabrication procedure of 3D electrode structures is also addressed

Why is there a gender segregation in choosing occupation?

Since the globe is developing century by century, the working environment also changes in every era. Currently, the working of women and also female occupations has reached great importance among society around the world. Women are enterprising, flexible and productive beings while working and in constructing new ideas and they want also be recognized by the community. From past to the present women worked in a variety of fields and some of them also did work in some village areas within carrying their small infants. But this area altered and everything turned to modernized and we see more women working in towns and cities. However, there are big differences, such as income and the working environment. Actually the reason why the occupations differ may have different factors. The considering reasons may due to social norms and cultural backgrounds. People want to make a career what social perception expects from them. The social values define whether you will work by your gender or by your skills or opposite of your gender such as women in male-dominated occupations. One of the significant points here is that professions differ according to gender. This paper aims to research about how gender segregation occurred in occupations. Additionally, the research will analyse the perceptions of gender roles in society and also the pay gaps and different factors which have an influence on women's condition at work

Incremental Distance Transforms (IDT)

A new generic scheme for incremental implementations of distance transforms (DT) is presented: Incremental Distance Transforms (IDT). This scheme is applied on the cityblock, Chamfer, and three recent exact Euclidean DT (E2DT). A benchmark shows that for all five DT, the incremental implementation results in a significant speedup: 3.4×−10×. However, significant differences (i.e., up to 12.5×) among the DT remain present. The FEED transform, one of the recent E2DT, even showed to be faster than both city-block and Chamfer DT. So, through a very efficient incremental processing scheme for DT, a relief is found for E2DT’s computational burden

İş güvencesi endeksi ve iş güvencesi memnuniyeti ölçeği: güvenirlik ve geçerlik analizi

İş güvencesi konusu şirketlerin yeniden yapılanma çabalarının yoğunlaştığı, çalışma süreçlerinde ve iş gücü piyasasında önemli değişikliklerin gerçekleştiği son yirmi yıl boyunca artan bir ilgi görmüştür. Bu dönemde araştırmacılar iş güvencesi kavramının geliştirilmesi ve bu kavramın ölçülmesi için gerekli araçların yaratılması üzerinde yoğunlaşmıştır. Bu çalışmada Probst’un (1998) geliştirdiği iki ölçeğin (İş Güvencesi Endeksi ve İş Güvencesi Memnuniyeti ölçeği) güvenirlik ve geçerlik analizi sunulmaktadır. Çalışmanın bulguları bu ölçeklerin Türkçe’de güvenirlik ve geçerliklerini desteklemektedir. Özellikle yakın dönemdeki mali kriz nedeniyle işten çıkarmaların yoğunlaştığı Türkiye’de iş güvencesi çalışmaları önem kazanmıştır. Geçerlik ve güvenirliğe sahip olduğu gösterilen, kısa ve anlaşılır olan İş Güvencesi Endeksi ve İş Güvencesi Memnuniyeti ölçeği bu çalışmalara katkıda bulunabilecektir

A Search for Vector Diquarks at the CERN LHC

Resonant production of the first generation vector diquarks at the CERN Large Hadron Collider (LHC) is investigated. It is shown that the LHC will be able to discover vector diquarks with masses up to 9 TeV for quark-diquark-quark coupling alpha_(D)=0.1 and 4 TeV for alpha_(D)=5x10^(-4).Comment: 9 pages, 4 tables, 4 figure