Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.

Multiple camera people detection and tracking using support integration

This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ""foreground mass"" above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS`06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method. (C) 2010 Elsevier B.V. All rights reserved.

A Robust, Fully Adaptive Hybrid Level-Set/Front-Tracking Method for Two-Phase Flows with an Accurate Surface Tension Computation

We present a variable time step, fully adaptive in space, hybrid method for the accurate simulation of incompressible two-phase flows in the presence of surface tension in two dimensions. The method is based on the hybrid level set/front-tracking approach proposed in [H. D. Ceniceros and A. M. Roma, J. Comput. Phys., 205, 391400, 2005]. Geometric, interfacial quantities are computed from front-tracking via the immersed-boundary setting while the signed distance (level set) function, which is evaluated fast and to machine precision, is used as a fluid indicator. The surface tension force is obtained by employing the mixed Eulerian/Lagrangian representation introduced in [S. Shin, S. I. Abdel-Khalik, V. Daru and D. Juric, J. Comput. Phys., 203, 493-516, 2005] whose success for greatly reducing parasitic currents has been demonstrated. The use of our accurate fluid indicator together with effective Lagrangian marker control enhance this parasitic current reduction by several orders of magnitude. To resolve accurately and efficiently sharp gradients and salient flow features we employ dynamic, adaptive mesh refinements. This spatial adaption is used in concert with a dynamic control of the distribution of the Lagrangian nodes along the fluid interface and a variable time step, linearly implicit time integration scheme. We present numerical examples designed to test the capabilities and performance of the proposed approach as well as three applications: the long-time evolution of a fluid interface undergoing Rayleigh-Taylor instability, an example of bubble ascending dynamics, and a drop impacting on a free interface whose dynamics we compare with both existing numerical and experimental data.

The reaction of methyl radical with nitrogen atom on the triplet potential energy surface: A CCSD(T)/CBS characterization

CCSD(T)/cc-pVnZ (n = D, T, Q) calculations followed by extrapolations to the CBS limit are used to characterize stationary states of species participating in the N((4)S) + CH(3) (2A ``) reaction on the triplet PES. A mechanistic model is investigated and reaction rates are computed for every step and the overall reaction. Our best CBS estimate (1.93 x 10(10) cm(3) molecule(1) s(1)) for the overall rate constant leading to the formation of H(2)CN + H compares well with the experimental values (8.5 x 10 (11) and 1.3 x 10(10) cm(3) molecule(1) s(1)), thus reducing significantly the discrepancy of a previous theoretical result (9.1 x 10(12) cm(3) molecule(1) s(1)). (C) 2008 Elsevier B.V. All rights reserved.

Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

On processing GPS tracking data of car-movements in Borlänge, Sweden

The advancement of GPS technology enables GPS devices not only to be used as orientation and navigation tools, but also to track travelled routes. GPS tracking data provides essential information for a broad range of urban planning applications such as transportation routing and planning, traffic management and environmental control. This paper describes on processing the data that was collected by tracking the cars of 316 volunteers over a seven-week period. The detailed information is extracted. The processed data is further connected to the underlying road network by means of maps. Geographical maps are applied to check how the car-movements match the road network. The maps capture the complexity of the car-movements in the urban area. The results show that 90% of the trips on the plane match the road network within a tolerance.

Tracking and tracing applications of 3G for SMEs

With dynamic growth and acceptance of mobile devices, many innovative business applications are beginning to emerge. Tracking and tracing seems to be one of the popular applications which many organisations have initiated, often facilitated by location based services provided by mobile network operators. However, there are many issues associated with the provisioning of this application with current technologies and business models. Small and Medium-size Enterprises (SMEs) that make up a significant segment of businesses worldwide do not yet seem able to benefit widely from these services. In this chapter, we initially review current technologies/ applications and the issues associated with them, drawing from research and the experiences of a long term ongoing action research project with SMEs in the trade sector. Subsequently, we explore the opportunities offered by 3G services/business applications to SMEs, and provide a broad critical outlook on future opportunities for SMEs to benefit from 3G services.

Tracking the nomadic life of the eductional researcher: what future for feminist public intellecturals and the performative university?

Is the idea of the liberal university dead, has the postmodern university any chance of being emancipatory, has the theory–practice divide merely collapsed in an era of 'new knowledge work', or has the university just become one aspect of market states and global capitalism? Knowledge-based economies locate universities as central to the commodification and management of knowledge, while at the same time the legitimacy of the university and the academic as knowledge producers is challenged by postmodernist, feminist, post-colonial and indigenous claims within a wider trend towards the 'democratisation of knowledge' and a new educational instrumentalism and opportunism. What becomes of the educational researcher, and indeed their professional organisations, in this changing socio-political and economic scenario? Is our role one of policy service, policy critique, technical expert or public intellectual? In particular what place is there for feminist public intellectuals in a so-called era of post feminism and public–private convergence? The paper draws on feminist and critical perspectives to mount a case for the importance of the public intellectual in the performative postmodern university.

An active stereo vision-based learning approach for robotic tracking, fixating and grasping control

In this paper, an active stereo vision-based learning approach is proposed for a robot to track, fixate and grasp an object in unknown environments. First, the functional mapping relationships between the joint angles of the active stereo vision system and the spatial representations of the object are derived and expressed in a three-dimensional workspace frame. Next, the self-adaptive resonance theory-based neural networks and the feedforward neural networks are used to learn the mapping relationships in a self-organized way. Then, the approach is verified by simulation using the models of an active stereo vision system which is installed in the end-effector of a robot. Finally, the simulation results confirm the effectiveness of the present approach.

Precision missile guidance for systems intercepting at small attack angles : a H‡ tracking approach

This paper uses the H1 tracking ideas in the missile guidance problem. The controller performs as a Precision guidance controller for small desired attack angles although theoretically the formulas are valid (when intercepting a maneuvering target in a precision guidance point of view) only for the case of head on collision. The controller essentially navigates the missile in a unique relative arc that is de ned by the missile and target location and the desired attack angle.

Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks

Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using robust extended Kalman filter (REKF) as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model and more effective in comparison with the standard Kalman filter.

Robust parallel filtering for mobile agent tracking

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

Robust parallel filtering design of a swarm tracking system

Swarming networks of mobile autonomous agents require inter-agent position information in order perform various tasks. The primary control input for the majority of current control strategies is inter-agent distance information. In this paper we provide a robust parallel filter based tracking scheme that allows a mobile agent to track other multiple mobile agents. The distance, angle, and relative position is given in a direct target tracking output. This allows the mobile agent to decide which information is best suited for the particular objective