Application of the self-organising map to trajectory classification

This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world coordinates to provide trajectory information for the classifier. In this paper we show that analysis of trajectories may be carried out in a model-free fashion, using self-organising feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented using positional and first and second order motion information, with moving-average smoothing. This allows novelty detection to be applied on a point-by-point basis in real time, and permits both instantaneous motion and whole trajectory motion to be subjected to novelty detection

Novelty detection in video surveillance using hierarchical neural networks

Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state

A Neural System for Automated CCTV Surveillance

This paper overviews a new system, the “Owens Tracker,” for automated identification of suspicious pedestrian activity in a car-park. Centralized CCTV systems relay multiple video streams to a central point for monitoring by an operator. The operator receives a continuous stream of information, mostly related to normal activity, making it difficult to maintain concentration at a sufficiently high level. While it is difficult to place quantitative boundaries on the number of scenes and time period over which effective monitoring can be performed, Wallace and Diffley [1] give some guidance, based on empirical and anecdotal evidence, suggesting that the number of cameras monitored by an operator be no greater than 16, and that the period of effective monitoring may be as low as 30 minutes before recuperation is required. An intelligent video surveillance system should therefore act as a filter, censuring inactive scenes and scenes showing normal activity. By presenting the operator only with unusual activity his/her attention is effectively focussed, and the ratio of cameras to operators can be increased. The Owens Tracker learns to recognize environmentspecific normal behaviour, and refers sequences of unusual behaviour for operator attention. The system was developed using standard low-resolution CCTV cameras operating in the car-parks of Doxford Park Industrial Estate (Sunderland, Tyne and Wear), and targets unusual pedestrian behaviour. The modus operandi of the system is to highlight excursions from a learned model of normal behaviour in the monitored scene. The system tracks objects and extracts their centroids; behaviour is defined as the trajectory traced by an object centroid; normality as the trajectories typically encountered in the scene. The essential stages in the system are: segmentation of objects of interest; disambiguation and tracking of multiple contacts, including the handling of occlusion and noise, and successful tracking of objects that “merge” during motion; identification of unusual trajectories. These three stages are discussed in more detail in the following sections, and the system performance is then evaluated

Aging Bison Teeth with a GIS: A New Tooth Age Prediction Methodology and Its Archaeological and Ecological Implications

Archaeologists use teeth to estimate the age an animal died based on tooth eruption, growth, and wear. Animal age estimations then inform archaeologists about when and why archaeological sites were occupied. However, to date, no concise and repeatable practice exists to age estimate teeth. Therefore, we propose a new tooth age estimation methodology, in this case using bison teeth. The new tooth aging method uses GIS mapping software to draw tooth surfaces and then calculate tooth surface areas of known-age bison teeth. Then, this known-age tooth sample is used to derive algebraic equations that can estimate the age of prehistoric specimens. To test our age prediction models, we use the well-known Folsom, New Mexico bison tooth assemblage. Overall, the new method provides statistical insights to how often Folsom may have been occupied and which type of hunting behavior appears to have occurred. Most importantly, the new model has the potential to provide a wealth of information about past bison hunting behaviors and may greatly improve our understanding of prehistory

Rapid acute dose assessment using MCNP6

2017 Spring.Includes bibliographical references.Acute radiation doses due to physical contact with a high-activity radioactive source have proven to be an occupational hazard. Multiple radiation injuries have been reported due to manipulating a radioactive source with bare hands or by placing a radioactive source inside a shirt or pants pocket. An effort to reconstruct the radiation dose must be performed to properly assess and medically manage the potential biological effects from such doses. Using the reference computational phantoms defined by the International Commission on Radiological Protection (ICRP) and the Monte Carlo N-Particle transport code (MCNP6), dose rate coefficients are calculated to assess doses for common acute doses due to beta and photon radiation sources. The research investigates doses due to having a radioactive source in either a breast pocket or pants back pocket. The dose rate coefficients are calculated for discrete energies and can be used to interpolate for any given energy of photon or beta emission. The dose rate coefficients allow for quick calculation of whole-body dose, organ dose, and/or skin dose if the source, activity, and time of exposure are known. Doses are calculated with the dose rate coefficients and compared to results from the International Atomic Energy Agency (IAEA) reports from accidents that occurred in Gilan, Iran and Yanango, Peru. Skin and organ doses calculated with the dose rate coefficients appear to agree, but there is a large discrepancy when comparing whole-body doses assessed using biodosimetry and whole-body doses assessed using the dose rate coefficients

Doctor of Philosophy

dissertationThe effective diagnosis of infectious diseases, like tuberculosis (TB), through the detection of biomarkers indicative of active infection, continues to challenge the scientific community. Due to the consistently high burden of disease in low-income economies, there has been a renewed interest to transition the capabilities of the diagnostic tests from the research laboratory to point-of-need (PON) applications. To identify the characteristics of these PON tests, the World Health Organization has established the ASSURED ( affordable, sensitive, specific, user-friendly, rapid, equipment-free, and delivered to those in need) guidelines. Using the detection of mannose-capped lipoarabinomannan (ManLAM), a TB biomarker, as a model system, the research presented herein focuses on approaches to meet the challenges faced by modern infectious disease diagnostics with an emphasis on transitioning state-of-the-art surface-enhanced Raman scattering (SERS) immunoassays toward PON applications within the framework of the ASSURED guidelines. First, we build on previous work investigating the underpinnings of an acid treatment method to improve the detection of ManLAM in the serum of infected individuals. This work demonstrates that while acid treatment improves the detection of ManLAM, assay performance is hindered because of the acid-induced degradation of ManLAM and the incomplete decomplexation of endogenous serum proteins. Through the application of an enzymatic sample treatment process, this work also shows that improved ManLAM recoveries lead to improved clinical accuracies. To increase assay performance, we developed, charactered, and validated a novel surface-enhanced resonance Raman scattering (SERRS) immunoassay. This improves the limit of detection (∼10x) and analytical sensitivity (∼39x) for ManLAM measurements compared to an analogous SERS immunoassay. The remainder of the work validates the use of a handheld Raman spectrometer for the detection of phospho-myo-inositol-capped LAM (PILAM), a ManLAM simulant. This work demonstrates the ability to achieve low limits of detection (∼0.2 ng/mL) for PILAM in human serum and document the impact of excitation wavelength and the plasmonic coupling between the labels and planar gold substrates as a basis for further improvements in SERS immunoassays. Taken together, this work begins to establish approaches for improved methodologies to combat the burden of infectious diseases, and to demonstrate the applicability of SERS detection beyond the research laboratory

The Effect of Aeration Rate and Free-Floating Carrier Media on the Emission of \u3ci\u3eBacillus globigii\u3c/i\u3e in Bioaerosols

Aerosols produced by turbulent mechanical mixing and bubble aeration at Waste Water Treatment Plants (WWTPs) become bioaerosols with the entrainment of biological materials. Bioaerosols become a public health risk when human pathogens are present. This study evaluated bioaerosols containing Bacillus globigii (BG) spores, and the effects that aeration rate and the addition of Free-Floating Carrier Media (FFCM) had on the amount of BG spores collected following aerosolization. A series of laboratory-scale experiments investigated two different sizes of floating polystyrene spheres as FFCM and four different aeration rates. When the differences in compared aeration rates were sufficiently large, a positive correlation was observed between increasing aeration rate and increasing bioaerosol production. The maximum increase from 0.50 to 1.00 L/min resulted in a 97.58% increase in the percent of starting BG spores captured after aerosolization. The addition of FFCM of both sizes reduced the amount of BG spores captured when compared to the control. Smaller spheres (0.42 cm diameter) consistently attenuated BG bioaerosol emissions more effectively than those with larger (1.91 cm) diameters, with a mean control efficiency of 93.03% compared to 83.95%. Statistical analysis showed a significant increase in the ability of smaller diameter FFCM to attenuate bioaerosol production at the two higher investigated aeration rates. This study was the first, to the author’s knowledge, to investigate multiple effects on bioaerosol production where the aerosol contained strictly bacterial endospores. As a part of a larger investigation including laboratory scale and pilot-scale WWTP research, this study is the first in a series of studies intended to investigate the effect of experimental scale on bioaerosol production. Results related to effects due to scale can be applied to better predict bioaerosol behaviors in operating treatment plants

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

Edge Colorings of Complete Multipartite Graphs Forbidding Rainbow Cycles

It is well known that if the edges of a finite simple connected graph on n vertices are colored so that no cycle is rainbow, then no more than n-1 colors can appear on the edges. In previous work it has been shown that the essentially different rainbow-cycle-forbidding edge colorings of Kn with n-1 colors appearing are in 1-1 correspondence with (can be encoded by) the (isomorphism classes of) full binary trees with n leafs. In the encoding, the natural Huffman labeling of each tree arising from the assignment of 1 to each leaf plays a role. Very recently it has been shown that a similar encoding holds for rainbow-cycle-forbidding edge colorings of Ka,b with a+b-1 colors appearing. In this case the binary trees are given Huffman labelings arising from certain assignments of (0,1) or (1,0) to the leafs. (Sibling leafs are not allowed to be assigned the same label.) In this paper we prove the analogous result for complete r-partite graphs, for r \u3e 2