John Lai

Airborne vision-based collision-detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real-time, vision-based collision-detection system suitable for fixed-wing aerial robotics. Using two fixed-wing unmanned aerial vehicles (UAVs) to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5-s response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms.

Vision-based detection and tracking of aerial targets for UAV collision avoidance

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8–10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by-768). Currently, integration in the final platform is under way.

Characterization of Sky-region Morphological-temporal Airborne Collision Detection

Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.

Relative Entropy Rate Based Multiple Hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimization problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterizing multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

Vision-Based Estimation of Airborne Target Pseudobearing Rate using Hidden Markov Model Filters

The problem of estimating pseudobearing rate information of an airborne target based on measurements from a vision sensor is considered. Novel image speed and heading angle estimators are presented that exploit image morphology, hidden Markov model (HMM) filtering, and relative entropy rate (RER) concepts to allow pseudobearing rate information to be determined before (or whilst) the target track is being estimated from vision information.

Detection versus False Alarm Characterisation of a Vision-Based Airborne Dim-Target Collision Detection System

This paper presents a preliminary flight test based detection range versus false alarm performance characterisation of a morphological-hidden Markov model filtering approach to vision-based airborne dim-target collision detection. On the basis of compelling in-flight collision scenario data, we calculate system operating characteristic (SOC) curves that concisely illustrate the detection range versus false alarm rate performance design trade-offs. These preliminary SOC curves provide a more complete dim-target detection performance description than previous studies (due to the experimental difficulties involved, previous studies have been limited to very short flight data sample sets and hence have not been able to quantify false alarm behaviour). The preliminary investigation here is based on data collected from 4 controlled collision encounters and supporting non-target flight data. This study suggests head-on detection ranges of approximately 2.22 km under blue sky background conditions (1.26 km in cluttered background conditions), whilst experiencing false alarms at a rate less than 1.7 false alarms/hour (ie. less than once every 36 minutes). Further data collection is currently in progress.

Erratum to: Fusion transcript loci share many genomic features with non-fusion loci

After publication of the original article [1], a reader noted that one reference cited in the main text had not been mentioned in the References section. The reference (Qin et al., [2]) was cited as Ref. 33 within the text, but mistakenly did not appear in the References. As such the total number of References was also incorrect – there should have been 36 in total. References 33 – 35 should have been numbered 34 – 36 in the main text and in the References section.

Association Analysis of a Microsatellite Repeat in the TRIB1 Gene With Prostate Cancer Risk, Aggressiveness and Survival

With an estimated 1.1 million men worldwide diagnosed with prostate cancer yearly, effective and more specific biomarkers for early diagnosis could lead to better patient outcome. As such, novel genetic markers are sought for this purpose. The tribbles homologue 1 gene (TRIB1) has recently shown to have a role in prostate tumorigenesis and data-mining of prostate cancer expression data confirmed clinical significance of TRIB1 in prostate cancer. For the first time, a polymorphic microsatellite in this gene was studied for its potential association with prostate cancer risk and aggressiveness. Genomic DNA was extracted from a cohort of 1,152 prostate cancer patients and 1,196 cancer-free controls and the TTTTG-TRIB1 microsatellite was genotyped. The socio-demographic and clinical characteristics were analyzed using the non-parametric t-test and two-way ANOVA. Association of the TTTTG-TRIB1 microsatellite and prostate cancer risk and aggressiveness were analyzed by binary logistic regression and confirmed by bootstrapping. Total and prostate cancer mortality was analyzed using the Kaplan Meier test. Genotype and allele correlation with TRIB1 mRNA levels was analyzed using the non-parametric Kolmogorov–Smirnov test. To predict the effect that the TTTTG-TRIB1 polymorphisms had on the mRNA structure, the in silico RNA folding predictor tool, mfold, was used. By analyzing the publicly available data, we confirmed a significant over-expression of TRIB1 in prostate cancer compared to other cancer types, and an over-expression in prostate cancerous tissue compared to adjacent benign. Three alleles (three–five repeats) were observed for TTTTG-TRIB1. The three-repeat allele was associated with prostate cancer risk at the allele (OR = 1.16; P = 0.044) and genotypic levels (OR = 1.70; P = 0.006) and this association was age-independent. The four-repeat allele was inversely associated with prosatet cancer risk (OR = 0.57; P < 0.0001). TRIB1 expression was upregulated in tumors when compared to adjacent cancer-free tissue but was not allele specific. In silico analysis suggested that the TTTTG-TRIB1 alleles may alter TRIB1 mRNA structure. In summary, the three-repeat allele was significantly associated with prostate cancer risk, suggesting a biomarker potential for this microsatellite to predict prostate cancer. Further studies are needed to elucidate the functional role of this microsatellite in regulating TRIB1 expression, perhaps by affecting the TRIB1 mRNA structure and stability.