Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

Investigation into the introduction of clonal strains of Pseudomonas aeruginosa to the cystic fibrosis population by consumption of raw salad vegetables

Most salad vegetables are eaten fresh by consumers. However, raw vegetables may pose a risk of transmitting opportunistic bacteria to immunocompromised people, including cystic fibrosis (CF) patients. In particular, CF patients are vulnerable to chronic Pseudomonas aeruginosa lung infections and this organism is the primary cause of morbidity and mortality in this group. Clonal variants of P. aeruginosa have been identified as emerging threats to people afflicted with CF; however it has not yet been proven from where these clones originate or how they are transmitted. Due to the organisms‟ aquatic environmental niche, it was hypothesised that vegetables may be a source of these clones. To test this hypothesis, lettuce, tomatoes, mushrooms and bean sprout packages (n = 150) were analysed from a green grocer, supermarket and farmers‟ market within the Brisbane region, availability permitting. The internal and external surfaces of the vegetables were separately analysed for the presence of clonal strains of P. aeruginosa using washings and homogenisation techniques, respectively. This separation was in an attempt to establish which surface was contaminated, so that recommendations could be made to decrease or eliminate P. aeruginosa from these foods prior to consumption. Soil and water samples (n = 17) from local farms were also analysed for the presence of P. aeruginosa. Presumptive identification of isolates recovered from these environmental samples was made based on growth on Cetrimide agar at 42°C, presence of the cytochrome-oxidase enzyme and inability to ferment lactose. P. aeruginosa duplex real-time polymerase chain reaction assay (PAduplex) was performed on all bacterial isolates presumptively identified as P. aeruginosa. Enterobacterial repetitive intergenic consensus strain typing PCR (ERIC-PCR) was subsequently performed on confirmed bacterial isolates. Although 72 P. aeruginosa were isolated, none of these proved to be clonal strains. The significance of these findings is that vegetables may pose a risk of transmitting sporadic strains of P. aeruginosa to people afflicted with CF and possibly, other immunocompromised people.

Fast content-based file type identification

Digital forensic examiners often need to identify the type of a file or file fragment based only on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds classification by sampling several blocks from the file. Experimental results demonstrate that up to a fifteen-fold reduction in file size analysis time can be achieved with limited impact on accuracy.

Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

Atmospheric deposition as a source of stormwater pollution in Gold Coast, Australia

Atmospheric deposition is one of the most important pollutant pathways for urban stormwater pollution. Atmospheric deposition can be in the form of dry and wet depositions which have distinct characteristics in terms of pollutant types, pollutant sources and influential parameters. This paper discusses the outcomes of a comprehensive study undertaken to identify the characteristics of wet and dry deposition of pollutants. Sample collection was undertaken at eight study sites with distinct characteristics. Four sites were close to road sites with varying traffic characteristics, whilst the other four sites had different land use characteristics. Dry deposition samples were collected for different antecedent dry days and wet deposition samples were collected immediately after rainfall events. The dry deposition was found to increase with the antecedent dry days and consisted of relatively coarser particles (greater than 1 µm) when compared to wet deposition. The wet deposition showed a strong affinity to rainfall depth, but was not related to the antecedent dry period. It was also found that smaller size particles (less than 1 µm) travel much longer distances from the source and deposit mainly with the wet deposition

The source of the good faith obligation in the performance and enforcement of commercial contracts

There are many issues associated with good faith that will ultimately confront the Australian High Court and a number of these have been well canvassed. However, one significant issue has attracted relatively little comment. To date, a number of Australian courts (lower in the judicial hierarchy) have been prepared to hold directly, tacitly accept or assume (without making a final determination) that good faith is implied (as a matter of law) in the performance and enforcement of a very broad class of contract, namely commercial contracts per se. This broad approach is demonstrated in decisions from the Federal Court, the New South Wales Court of Appeal, the Supreme Courts of Victoria and Western Australia and has crept into pleadings in commercial matters in Queensland

Assessment of corneal dynamics with high-speed swept source optical coherence tomography combined with an air puff system

We present a novel method and instrument for in vivo imaging and measurement of the human corneal dynamics during an air puff. The instrument is based on high-speed swept source optical coherence tomography (ssOCT) combined with a custom adapted air puff chamber from a non-contact tonometer, which uses an air stream to deform the cornea in a non-invasive manner. During the short period of time that the deformation takes place, the ssOCT acquires multiple A-scans in time (M-scan) at the center of the air puff, allowing observation of the dynamics of the anterior and posterior corneal surfaces as well as the anterior lens surface. The dynamics of the measurement are driven by the biomechanical properties of the human eye as well as its intraocular pressure. Thus, the analysis of the M-scan may provide useful information about the biomechanical behavior of the anterior segment during the applanation caused by the air puff. An initial set of controlled clinical experiments are shown to comprehend the performance of the instrument and its potential applicability to further understand the eye biomechanics and intraocular pressure measurements. Limitations and possibilities of the new apparatus are discussed.

Numerical simulation for the variable-order Galilei invariant advection diffusion equation with a nonlinear source term

In this paper, we consider the variable-order Galilei advection diffusion equation with a nonlinear source term. A numerical scheme with first order temporal accuracy and second order spatial accuracy is developed to simulate the equation. The stability and convergence of the numerical scheme are analyzed. Besides, another numerical scheme for improving temporal accuracy is also developed. Finally, some numerical examples are given and the results demonstrate the effectiveness of theoretical analysis. Keywords: The variable-order Galilei invariant advection diffusion equation with a nonlinear source term; The variable-order Riemann–Liouville fractional partial derivative; Stability; Convergence; Numerical scheme improving temporal accuracy

Identification of circulating tumour cells in early stage breast cancer patients using multi marker immunobead RT-PCR

Introduction The ability to screen blood of early stage operable breast cancer patients for circulating tumour cells is of potential importance for identifying patients at risk of developing distant relapse. We present the results of a study of the efficacy of the immunobead RT-PCR method in identifying patients with circulating tumour cells. Results Immunomagnetic enrichment of circulating tumour cells followed by RT-PCR (immunobead RT-PCR) with a panel of five epithelial specific markers (ELF3, EPHB4, EGFR, MGB1 and TACSTD1) was used to screen for circulating tumour cells in the peripheral blood of 56 breast cancer patients. Twenty patients were positive for two or more RT-PCR markers, including seven patients who were node negative by conventional techniques. Significant increases in the frequency of marker positivity was seen in lymph node positive patients, in patients with high grade tumours and in patients with lymphovascular invasion. A strong trend towards improved disease free survival was seen for marker negative patients although it did not reach significance (p = 0.08). Conclusion Multi-marker immunobead RT-PCR analysis of peripheral blood is a robust assay that is capable of detecting circulating tumour cells in early stage breast cancer patients.