Real-time detection of concealed chemical hazards under ambient light conditions using Raman spectroscopy

Current concerns regarding terrorism and international crime highlight the need for new techniques for detecting unknown and hazardous substances. A novel Raman spectroscopy-based technique, spatially offset Raman spectroscopy (SORS), was recently devised for non-invasively probing the contents of diffusely scattering and opaque containers. Here, we demonstrate a modified portable SORS sensor for detecting concealed substances in-field under different background lighting conditions. Samples including explosive precursors, drugs and an organophosphate insecticide (chemical warfare agent surrogate) were concealed inside diffusely scattering packaging including plastic, paper and cloth. Measurements were carried out under incandescent and fluorescent light as well as under daylight to assess the suitability of the probe for different real-life conditions. In each case, it was possible to identify the substances against their reference Raman spectra in less than one minute. The developed sensor has potential for rapid detection of concealed hazardous substances in airports, mail distribution centers and customs checkpoints.

Spectrum sensing using two-stage detection to compensate for reduced primary user duty cycle

Spectrum sensing is considered to be one of the most important tasks in cognitive radio. One of the common assumption among current spectrum sensing detectors is the full presence or complete absence of the primary user within the sensing period. In reality, there are many situations where the primary user signal only occupies a portion of the observed signal and the assumption of primary user duty cycle not necessarily fulfilled. In this paper we show that the true detection performance can degrade from the assumed achievable values when the observed primary user exhibits a certain duty cycle. Therefore, a two-stage detection method incorporating primary user duty cycle that enhances the detection performance is proposed. The proposed detector can improve the probability of detection under low duty cycle at the expense of a small decrease in performance at high duty cycle.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Viruses of banana in East Africa

Bananas are one of the world's most important food crops, providing sustenance and income for millions of people in developing countries and supporting large export industries. Viruses are considered major constraints to banana production, germplasm multiplication and exchange, and to genetic improvement of banana through traditional breeding. In Africa, the two most important virus diseases are bunchy top, caused by Banana bunchy top virus (BBTV), and banana streak disease, caused by Banana streak virus (BSV). BBTV is a serious production constraint in a number of countries within/bordering East Africa, such as Burundi, Democratic Republic of Congo, Malawi, Mozambique, Rwanda and Zambia, but is not present in Kenya, Tanzania and Uganda. Additionally, epidemics of banana streak disease are occurring in Kenya and Uganda. The rapidly growing tissue culture (TC) industry within East Africa, aiming to provide planting material to banana farmers, has stimulated discussion about the need for virus indexing to certify planting material as virus-free. Diagnostic methods for BBTV and BSV have been reported and, for BBTV, PCR-based assays are reliable and relatively straightforward. However for BSV, high levels of serological and genetic variability and the presence of endogenous virus sequences within the banana genome complicate diagnosis. Uganda has been shown to contain the greatest diversity in BSV isolates found anywhere in the world. A broad-spectrum diagnostic test for BSV detection, which can discriminate between endogenous and episomal BSV sequences, is a priority. This PhD project aimed to establish diagnostic methods for banana viruses, with a particular focus on the development of novel methods for BSV detection, and to use these diagnostic methods for the detection and characterisation of banana viruses in East Africa. A novel rolling-circle amplification (RCA) method was developed for the detection of BSV. Using samples of Banana streak MY virus (BSMYV) and Banana streak OL virus (BSOLV) from Australia, this method was shown to distinguish between endogenous and episomal BSV sequences in banana plants. The RCA assay was used to screen a collection of 56 banana samples from south-west Uganda for BSV. RCA detected at least five distinct BSV isolates in these samples, including BSOLV and Banana streak GF virus (BSGFV) as well as three BSV isolates (Banana streak Uganda-I, -L and -M virus) for which only partial sequences had been previously reported. These latter three BSV had only been detected using immuno-capture (IC)-PCR and thus were possible endogenous sequences. In addition to its ability to detect BSV, the RCA protocol was also demonstrated to detect other viruses within the family Caulimoviridae, including Sugar cane bacilliform virus, and Cauliflower mosaic virus. Using the novel RCA method, three distinct BSV isolates from both Kenya and Uganda were identified and characterised. The complete genome of these isolates was sequenced and annotated. All six isolates were shown to have a characteristic badnavirus genome organisation with three open reading frames (ORFs) and the large polyprotein encoded by ORF 3 was shown to contain conserved amino acid motifs for movement, aspartic protease, reverse transcriptase and ribonuclease H activities. As well, several sequences important for expression and replication of the virus genome were identified including the conserved tRNAmet primer binding site present in the intergenic region of all badnaviruses. Based on the International Committee on Taxonomy of Viruses (ICTV) guidelines for species demarcation in the genus Badnavirus, these six isolates were proposed as distinct species, and named Banana streak UA virus (BSUAV), Banana streak UI virus (BSUIV), Banana streak UL virus (BSULV), Banana streak UM virus (BSUMV), Banana streak CA virus (BSCAV) and Banana streak IM virus (BSIMV). Using PCR with species-specific primers designed to each isolate, a genotypically diverse collection of 12 virus-free banana cultivars were tested for the presence of endogenous sequences. For five of the BSV no amplification was observed in any cultivar tested, while for BSIMV, four positive samples were identified in cultivars with a B-genome component. During field visits to Kenya, Tanzania and Uganda, 143 samples were collected and assayed for BSV. PCR using nine sets of species-specific primers, and RCA, were compared for BSV detection. For five BSV species with no known endogenous counterpart (namely BSCAV, BSUAV, BSUIV, BSULV and BSUMV), PCR was used to detect 30 infections from the 143 samples. Using RCA, 96.4% of these samples were considered positive, with one additional sample detected using RCA which was not positive using PCR. For these five BSV, PCR and RCA were both useful for identifying infected samples, irrespective of the host cultivar genotype (Musa A- or B-genome components). For four additional BSV with known endogenous counterparts in the M. balbisiana genome (BSOLV, BSGFV, BSMYV and BSIMV), PCR was shown to detect 75 infections from the 143 samples. In 30 samples from cultivars with an A-only genome component there was 96.3% agreement between PCR positive samples and detection using RCA, again demonstrating either PCR or RCA are suitable methods for detection. However, in 45 samples from cultivars with some B-genome component, the level of agreement between PCR positive samples and RCA positive samples was 70.5%. This suggests that, in cultivars with some B-genome component, many infections were detected using PCR which were the result of amplification of endogenous sequences. In these latter cases, RCA or another method which discriminates between endogenous and episomal sequences, such as immuno-capture PCR, is needed to diagnose episomal BSV infection. Field visits were made to Malawi and Rwanda to collect local isolates of BBTV for validation of a PCR-based diagnostic assay. The presence of BBTV in samples of bananas with bunchy top disease was confirmed in 28 out of 39 samples from Malawi and all nine samples collected in Rwanda, using PCR and RCA. For three isolates, one from Malawi and two from Rwanda, the complete nucleotide sequences were determined and shown to have a similar genome organisation to previously published BBTV isolates. The two isolates from Rwanda had at least 98.1% nucleotide sequence identity between each of the six DNA components, while the similarity between isolates from Rwanda and Malawi was between 96.2% and 99.4% depending on the DNA component. At the amino acid level, similarities in the putative proteins encoded by DNA-R, -S, -M, - C and -N were found to range between 98.8% to 100%. In a phylogenetic analysis, the three East African isolates clustered together within the South Pacific subgroup of BBTV isolates. Nucleotide sequence comparison to isolates of BBTV from outside Africa identified India as the possible origin of East African isolates of BBTV.

Fusion of morphological images for airborne target detection

Several track-before-detection approaches for image based aircraft detection have recently been examined in an important automated aircraft collision detection application. A particularly popular approach is a two stage processing paradigm which involves: a morphological spatial filter stage (which aims to emphasize the visual characteristics of targets) followed by a temporal or track filter stage (which aims to emphasize the temporal characteristics of targets). In this paper, we proposed new spot detection techniques for this two stage processing paradigm that fuse together raw and morphological images or fuse together various different morphological images (we call these approaches morphological reinforcement). On the basis of flight test data, the proposed morphological reinforcement operations are shown to offer superior signal to-noise characteristics when compared to standard spatial filter options (such as the close-minus-open and adaptive contour morphological operations). However, system operation characterised curves, which examine detection verses false alarm characteristics after both processing stages, illustrate that system performance is very data dependent.

HMM triangle relative entropy concepts in sequential change detection applied to vision-based dim target manoeuvre detection

The quick detection of abrupt (unknown) parameter changes in an observed hidden Markov model (HMM) is important in several applications. Motivated by the recent application of relative entropy concepts in the robust sequential change detection problem (and the related model selection problem), this paper proposes a sequential unknown change detection algorithm based on a relative entropy based HMM parameter estimator. Our proposed approach is able to overcome the lack of knowledge of post-change parameters, and is illustrated to have similar performance to the popular cumulative sum (CUSUM) algorithm (which requires knowledge of the post-change parameter values) when examined, on both simulated and real data, in a vision-based aircraft manoeuvre detection problem.

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Global flash multifocal electroretinogram : early detection of local functional changes and its correlations with optical coherence tomography and visual field loss in diabetic eyes

Purpose: To investigate the correlations of the global flash multifocal electroretinogram (MOFO mfERG) with common clinical visual assessments – Humphrey perimetry and Stratus circumpapillary retinal nerve fiber layer (RNFL) thickness measurement in type II diabetic patients. Methods: Forty-two diabetic patients participated in the study: ten were free from diabetic retinopathy (DR) while the remainder suffered from mild to moderate non-proliferative diabetic retinopathy (NPDR). Fourteen age-matched controls were recruited for comparison. MOFO mfERG measurements were made under high and low contrast conditions. Humphrey central 30-2 perimetry and Stratus OCT circumpapillary RNFL thickness measurements were also performed. Correlations between local values of implicit time and amplitude of the mfERG components (direct component (DC) and induced component (IC)), and perimetric sensitivity and RNFL thickness were evaluated by mapping the localized responses for the three subject groups. Results: MOFO mfERG was superior to perimetry and RNFL assessments in showing differences between the diabetic groups (with and without DR) and the controls. All the MOFO mfERG amplitudes (except IC amplitude at high contrast) correlated better with perimetry findings (Pearson’s r ranged from 0.23 to 0.36, p<0.01) than did the mfERG implicit time at both high and low contrasts across all subject groups. No consistent correlation was found between the mfERG and RNFL assessments for any group or contrast conditions. The responses of the local MOFO mfERG correlated with local perimetric sensitivity but not with RNFL thickness. Conclusion: Early functional changes in the diabetic retina seem to occur before morphological changes in the RNFL.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Fast power line detection and localization using steerable filter for active UAV guidance

In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.