24,646 research outputs found
Tracking interacting dust: comparison of tracking and state estimation techniques for dusty plasmas
When tracking a target particle that is interacting with nearest neighbors in
a known way, positional data of the neighbors can be used to improve the state
estimate. Effects of the accuracy of such positional data on the target track
accuracy are investigated in this paper, in the context of dusty plasmas. In
kinematic simulations, notable improvement in the target track accuracy was
found when including all nearest neighbors in the state estimation filter and
tracking algorithm, whereas the track accuracy was not significantly improved
by higher-accuracy measurement techniques. The state estimation algorithm,
involving an extended Kalman filter, was shown to either remove or
significantly reduce errors due to "pixel locking". It is concluded that the
significant extra complexity and computational expense to achieve these
relatively small improvements are likely to be unwarranted for many situations.
For the purposes of determining the precise particle locations, it is concluded
that the simplified state estimation algorithm can be a viable alternative to
using more computationally-intensive measurement techniques.Comment: 11 pages, 6 figures, Conference paper: Signal and Data Processing of
Small Targets 2010 (SPIE
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Exploring the Potential of 3D Visualization Techniques for Usage in Collaborative Design
Best practice for collaborative design demands good interaction between its collaborators. The capacity to share common knowledge about design models at hand is a basic requirement. With current advancing technologies gathering collective knowledge is more straightforward, as the dialog between experts can be supported better. The potential for 3D visualization techniques to become the right support tool for collaborative design is explored. Special attention is put on the possible usage for remote collaboration. The opportunities for current state-of-the-art visualization techniques from stereoscopic vision to holographic displays are researched. A classification of the various systems is explored with respect to their tangible usage for augmented reality. Appropriate interaction methods can be selected based on the usage scenario
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
The likelihood calculation of a vast number of particles is the computational
bottleneck for the particle filter in applications where the observation
information is rich. For fast computing the likelihood of particles, a
numerical fitting approach is proposed to construct the Likelihood Probability
Density Function (Li-PDF) by using a comparably small number of so-called
fulcrums. The likelihood of particles is thereby analytically inferred,
explicitly or implicitly, based on the Li-PDF instead of directly computed by
utilizing the observation, which can significantly reduce the computation and
enables real time filtering. The proposed approach guarantees the estimation
quality when an appropriate fitting function and properly distributed fulcrums
are used. The details for construction of the fitting function and fulcrums are
addressed respectively in detail. In particular, to deal with multivariate
fitting, the nonparametric kernel density estimator is presented which is
flexible and convenient for implicit Li-PDF implementation. Simulation
comparison with a variety of existing approaches on a benchmark 1-dimensional
model and multi-dimensional robot localization and visual tracking demonstrate
the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a
draft/preprint of one paper submitted to the IEEE Transaction
A multimodal approach to blind source separation of moving sources
A novel multimodal approach is proposed to solve the
problem of blind source separation (BSS) of moving sources. The
challenge of BSS for moving sources is that the mixing filters are
time varying; thus, the unmixing filters should also be time varying,
which are difficult to calculate in real time. In the proposed approach,
the visual modality is utilized to facilitate the separation for
both stationary and moving sources. The movement of the sources
is detected by a 3-D tracker based on video cameras. Positions
and velocities of the sources are obtained from the 3-D tracker
based on a Markov Chain Monte Carlo particle filter (MCMC-PF),
which results in high sampling efficiency. The full BSS solution
is formed by integrating a frequency domain blind source separation
algorithm and beamforming: if the sources are identified
as stationary for a certain minimum period, a frequency domain
BSS algorithm is implemented with an initialization derived from
the positions of the source signals. Once the sources are moving, a
beamforming algorithm which requires no prior statistical knowledge
is used to perform real time speech enhancement and provide
separation of the sources. Experimental results confirm that
by utilizing the visual modality, the proposed algorithm not only
improves the performance of the BSS algorithm and mitigates the
permutation problem for stationary sources, but also provides a
good BSS performance for moving sources in a low reverberant
environment
Advanced Fluorescence Microscopy Techniques-FRAP, FLIP, FLAP, FRET and FLIM
Fluorescence microscopy provides an efficient and unique approach to study fixed and living cells because of its versatility, specificity, and high sensitivity. Fluorescence microscopes can both detect the fluorescence emitted from labeled molecules in biological samples as images or photometric data from which intensities and emission spectra can be deduced. By exploiting the characteristics of fluorescence, various techniques have been developed that enable the visualization and analysis of complex dynamic events in cells, organelles, and sub-organelle components within the biological specimen. The techniques described here are fluorescence recovery after photobleaching (FRAP), the related fluorescence loss in photobleaching (FLIP), fluorescence localization after photobleaching (FLAP), Forster or fluorescence resonance energy transfer (FRET) and the different ways how to measure FRET, such as acceptor bleaching, sensitized emission, polarization anisotropy, and fluorescence lifetime imaging microscopy (FLIM). First, a brief introduction into the mechanisms underlying fluorescence as a physical phenomenon and fluorescence, confocal, and multiphoton microscopy is given. Subsequently, these advanced microscopy techniques are introduced in more detail, with a description of how these techniques are performed, what needs to be considered, and what practical advantages they can bring to cell biological research
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Evidence for DNA-mediated nuclear compartmentalization distinct from phase separation.
RNA Polymerase II (Pol II) and transcription factors form concentrated hubs in cells via multivalent protein-protein interactions, often mediated by proteins with intrinsically disordered regions. During Herpes Simplex Virus infection, viral replication compartments (RCs) efficiently enrich host Pol II into membraneless domains, reminiscent of liquid-liquid phase separation. Despite sharing several properties with phase-separated condensates, we show that RCs operate via a distinct mechanism wherein unrestricted nonspecific protein-DNA interactions efficiently outcompete host chromatin, profoundly influencing the way DNA-binding proteins explore RCs. We find that the viral genome remains largely nucleosome-free, and this increase in accessibility allows Pol II and other DNA-binding proteins to repeatedly visit nearby DNA binding sites. This anisotropic behavior creates local accumulations of protein factors despite their unrestricted diffusion across RC boundaries. Our results reveal underappreciated consequences of nonspecific DNA binding in shaping gene activity, and suggest additional roles for chromatin in modulating nuclear function and organization
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