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Surfactant in marine environment has been recognized to influence the biogeochemical cycle in atmosphere. This study aims to determine the concentration of surfactants in sea-surface microlayer (SML) and atmospheric aerosol at selected coastal area of Peninsular Malaysia. The sampling activities has been carried out between February 2012 and January 2013 using rotation drum to collect SML samples and a High Volume Sampler (HVS) equipped with two cascade impactor to collect fine and coarse mode aerosols. The Colorimetric method has been applied to determine surfactants as MBAS and DBAS using UV-Vis Spectrophotometer. Trajectory analysis was done using online-based HYSPLIT model in order to determine the influence of wind direction on surfactants concentrations. Overall, anionic surfactant as MBAS was dominated in SML with a range 0.19-0.24 µmolL-1. Surfactants in atmospheric aerosol was found to be dominant in fine mode aerosol (MBAS = 62.79-171.30 pmolm-3, DBAS = 29.41-182.86 pmolm-3) influenced by northeast monsoon. [ABSTRACT FROM AUTHOR]

Recently, in the surveillance field, scene analysis research is a hot topic, and many useful algorithms are developed. They not only reduce human power, but also make surveillance in real time. In this paper, we propose a robust and efficient anomaly-detection algorithm in traffic surveillance system. The proposed method consists of two parts: (1) scene modeling part, and (2) anomaly detection part. First part, to systemically collect trajectories of moving objects, we apply the sparse optical flow method to foreground regions extracted by a conventional background modeling method. These collected trajectories are represented as 3-dimensional feature vectors whose components are x and y coordinates and moving direction, and they are clustered by k-means clustering method. After this process, all feature vectors are assigned clustering labels, and then we construct a trajectory histogram based on cells whose mean grid with a particular size to make the scene model. Then we apply the entropy concept to generated histograms in order to handle some regions where the uncertainty of motion pattern is high. In the anomaly detection part, we get features of objects in a image and track them with the same way in the scene modeling part. At this time, they are classified by nearest neighborhood method. From this result of classification, we can detect anomalies in the traffic video by comparing it with the scene model. Experimental results demonstrate that the anomaly detection rate of the proposed method is very high, and the processing speed is almost real time. [ABSTRACT FROM PUBLISHER]

The newly developed Big Data oriented distributed systems such as Apache HBase have been proven effective in storing and analyzing the exponentially growing volume of wide variety of data such as sensors data, customer generated media, web logs, and so forth. In this paper, Apache HBase, a distributed scalable big data store, is used to store, process, and analyze a large amount of spatiotemporal data generated by shipboard AIS transponders. The objective is to predict the behavior of ships navigating through heavily trafficked fairways around the gates of busy harbors. For that purpose, experiments were conducted using tens of gigabytes of real world AIS data. The data were processed to form historical ships' tracks and were classified based on ships attributes such as type, draught, voyage destination and country of origin. Finally, a simple algorithm was implemented to predict the target ships behavior based on its attributes and movement characteristic. As a result, an acceptable prediction of ships movement is achieved. Furthermore, the experimental result also indicated that in the case of data processing speed, this technique remarkably outperformed the traditional GIS application software. [ABSTRACT FROM PUBLISHER]

This paper presents an effective and efficient semantic event detection method for broadcast tennis videos. We proposed an effective player tracking and trajectory modification technique, and then event detection based on trajectory analysis is developed accordingly. In our work, the playfield and court line are extracted from a court view. Then, we detect player objects and utilize adaptive Kalman filter to improve the tracking accuracy. In event detection phase, we refine the unreasonable trajectory to avoid the noise interference especially in upper court, and eliminate the minor clip which can't be a meaningful event. Finally, we analyze the players' trajectory combined with tennis rules to extract semantic events. The experiments indicate that the proposed method achieves satisfactory performance with 80% of precision rate and 89% of recall rate. [ABSTRACT FROM PUBLISHER]

A novel method is presented for trackingmultiple deformable objects in a multiview scenario. We avoid applying fixed constraints of object sizes for object isolation, but guide the splitting/merging of objects by grouping partial motion trajectories extracted from the occupancy status of objects in the homography ground-plane. We treat this clustering task as solving a rooted arborescence problem, and investigate the performance of the proposed method by experimental results. [ABSTRACT FROM PUBLISHER]

Online evaluation of tracking algorithms has received attentions in computer vision community to detect failures and apply correction methods for achieving better performances. In this paper, a novel online evaluation framework is proposed for a multitarget feature points based object tracking. An online partial least square regression and correlation model is constructed from short trajectory histories for the tracks. The model allows to estimate the state of one track from the other track states. The core idea for the method is creating a virtual reference data for evaluation from the learned model. The proposed self-evaluation mechanism is presented as a Dynamic Bayesian Network. The method is evaluated on a simulation data for tracking feature points from a pedestrian. [ABSTRACT FROM PUBLISHER]

As the scale and complexity of data continue to grow at unprecedented rates, scientists are increasingly relying on Large, High-Resolution Displays to visualize and analyze scientific datasets. Recent studies have demonstrated the effectiveness of these displays in supporting cognitively demanding data analysis and sensemaking tasks. While there has been an abundance of research on rendering algorithms for large, high-resolution displays, far less effort has gone into designing interactive visual analytic interfaces to effectively leverage these displays in visual exploration and sensemaking scenarios involving large collections of data. In this paper, we present an interactive visual analytics application for the exploration of large trajectory datasets. Our application utilizes large, high-resolution 3D display environments to simultaneously visualize and juxtapose a large number of trajectories. It also integrates a scalable visual query technique, which can be used to quickly formulate and verify hypotheses, encouraging scientists to contemplate multiple competing theories before drawing conclusions. We evaluate our design within the context of a behavioral ecology case study. We also share our observations from a pilot user study to provide insights on how scientists might utilize large display environments in visual exploration and sensemaking scenarios. [ABSTRACT FROM PUBLISHER]

The present work introduces a new method for activity extraction from video. To achieve this, we focus on the modelling of context by developing an algorithm that automatically learns the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Automatically learning the context of the scene (activity zones) allows first to extract a knowledge on the occupancy of the different areas of the scene. In a second step, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones, in this way, the activity of a person can be summarised as the series of zones that the person has visited. For the analysis of the trajectory, a multiresolution analysis is set such that a trajectory is segmented into a series of tracklets based on changing speed points thus allowing differentiating when people stop to interact with elements of the scene or other persons. Tracklets allow thus to extract behavioural information. Starting and ending tracklet points are fed to a simple yet advantageous incremental clustering algorithm to create an initial partition of the scene. Similarity relations between resulting clusters are modeled employing fuzzy relations. These can then be aggregated with typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the final structure of the scene. To allow for incremental learning and update of activity zones (and thus people activities), fuzzy relations are defined with online learning terms. We present results obtained on real videos from different activity domains. [ABSTRACT FROM PUBLISHER]

Calabazas Creek Research, Inc. has completed initial development of an advanced, 3D program for modeling electron trajectories in electromagnetic fields. The code is being used to design complex guns and collectors. Beam Optics Analysis (BOA) is a fully relativistic, charged particle code using adaptive, finite element meshing. Geometrical input is imported from CAD programs generating ACIS-formatted files. Parametric data is inputted using an intuitive, graphical user interface (GUI), which also provides control of convergence, accuracy, and post processing. The program includes a magnetic field solver, and magnetic information can be imported from Maxwell 2D/3D and other programs. The program supports thermionic emission and injected beams. Secondary electron emission is also supported, including multiple generations. Work on field emission is in progress as well as implementation of computer optimization of both the geometry and operating parameters. The principle features of the program and its capabilities are presented. © 2005 American Institute of Physics [ABSTRACT FROM AUTHOR]

Contagion can be defined as a process by which a disease, behavior, or mood transmits from one person to the next. Following a few pioneering studies on the contagious nature of violence, prolific efforts have evolved to understand the temporal fluctuations or spatial spread of crime. Despite a growing interest in social contagion, existent research has been neglectful of the linkage between theoretical accounts of social contagion and empirical evidence supporting temporal fluctuations or spatial distribution of crime. The current paper examines one of the mechanisms by which social contagion of crime can be further elaborated. The main proposition of this study is that population density, ethnic homogeneity, and the concentration of social transactions will facilitate the process of social contagion, which, in turn, explains temporal change or spatial spread of crime. Utilizing trajectory modeling and spatial econometric modeling, the study presents a theoretical framework for and empirical evidence of the contagious nature of crime. [ABSTRACT FROM AUTHOR]

Heuristic analysis of interictal single spike and sharp wave (SSW) voltage topography in five cases of partial complex epilepsy of temporal lobe origin leads to the following preliminary conclusions. The SSWs examined revealed EEG voltage topographic evidence to suggest both local and distant involvement of temporal lobe structures. Limited local involvement was predominant in all the cases, but evidence of distant involvement including contralateral activation was seen in the most medically refractory case. Sophisticated EEG analysis can facilitate localization of human epileptic phenomena which may serve as a robust model of human neuronal networks [ABSTRACT FROM PUBLISHER]