Efficient algorithms for subwindow search in object detection and localization

Recently, a simple yet powerful branch-and-bound method called Efficient Subwindow Search (ESS) was developed to speed up sliding window search in object detection. A major drawback of ESS is that its computational complexity varies widely from O(n²) to O(n⁴) for n × n matrices. Our experimental experience shows that the ESS's performance is highly related to the optimal confidence levels which indicate the probability of the object's presence. In particular, when the object is not in the image, the optimal subwindow scores low and ESS may take a large amount of iterations to converge to the optimal solution and so perform very slow. Addressing this problem, we present two significantly faster methods based on the linear-time Kadane's Algorithm for 1D maximum subarray search. The first algorithm is a novel, computationally superior branchand- bound method where the worst case complexity is reduced to O(n³). Experiments on the PASCAL VOC 2006 data set demonstrate that this method is significantly and consistently faster (approximately 30 times faster on average) than the original ESS. Our second algorithm is an approximate algorithm based on alternating search, whose computational complexity is typically O(n²). Experiments shows that (on average) it is 30 times faster again than our first algorithm, or 900 times faster than ESS. It is thus wellsuited for real time object detection.

Effectiveness of alternative organic solvents in field preservation of whole barnacles for PCR analyses

There are few reports of non-cryogenic preservation methods for marine invertebrates, so potable alcohol and acetone-based nail varnish remover (NVR) are for the first time evaluated against absolute ethanol as short-term preservatives of whole barnacles. Performance of ethanol and NVR-preserved material was comparable, but potable alcohol was significantly worse. These results are of practical importance for fieldwork in remote areas where laboratory chemicals are unattainable but potable alcohol or NVR are locally available. Of these, acetone-based NVR would be the solvent of preference.

An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection

Brain volume changes at structural level appear to have utmost importance in depression biomarkers studies. However, these brain volumetric findings have very minimal utilization in depression detection studies at individual level. Thus, this paper presents an evaluation of volumetric features to identify the relevant/optimal features for the detection of depression. An algorithm is presented for determination of rank and degree of contribution (DoC) of structural magnetic resonance imaging (sMRI) volumetric features. The algorithm is based on the frequencies of each feature contribution toward the desired accuracy limit. Forty-four volumetric features from various brain regions were adopted for evaluation. From DoC analysis, the DoC of each volumetric feature for depression detection is calculated and the features that dominate the contribution are determined.

Exploring connections between the in-field and on-campus components of a preservice teacher education program : a student perspective

This paper discusses the preservice teacher education practicum experience from the perspective of preservice teachers at a regional Australian university. It locates the practicum in the broader context of work integrated learning and associated principles of good practice. The paper argues that there are some perceived disconnections between the in-field and on–campus components of the teacher education program as well as an endorsement of some aspects of the practicum experience in closing the theory-practice gap. Our research adds to international debate about the balance between theory and practice and contributes a much needed student perspective on these issues. The paper concludes with suggestions on ways to improve the quality of the practicum experience.

An overview of recent advances in intrusion detection

The intrusion detection system is one of the security defense tools for computer networks. In recent years this research has lacked in direction and focus. In this paper we present a survey on the recent progression of multiagent intrusion detection systems. We survey the existing types, techniques and architectures of Intrusion Detection Systems in the literature. Finally we outline the present research challenges and issues

Exploring connections between the in-field and on-campus components of a preservice teacher education program : a student perspective

This paper discusses the preservice teacher education practicum experience from the perspective of preservice teachers at a regional Australian university. It locates the practicum in the broader context of work integrated learning and associated principles of good practice. The paper argues that there are some perceived disconnections between the in-field and on–campus components of the teacher education program as well as an endorsement of some aspects of the practicum experience in closing the theory-practice gap. Our research adds to international debate about the balance between theory and practice and contributes a much needed student perspective on these issues. The paper concludes with suggestions on ways to improve
the quality of the practicum.

A review of recent advances in chemiluminescence detection using nano-colloidal manganese(IV)

The application of 'soluble' (colloidal) manganese(IV) for chemiluminescence detection is reviewed, focussing on papers published since the last comprehensive review of the subject in 2008. Advances in this reagent system include: the on-line formation of manganese(IV); new insight into the light-producing pathway and selectivity of the reagent; its application to assess total antioxidants in plant derived samples and oxidative stress in biological fluids and tissues; and the replacement of the formaldehyde enhancer with ethanol.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.

One-pot facile synthesis of platinum nanoparticle decorated reduced graphene oxide composites and their application in electrochemical detection of rutin

We report on the synthesis of platinum nanoparticle-reduced graphene oxide (PtNP-rGO) composites and their application as a novel architecture in electrochemical detection of rutin. PtNPs anchored over rGO are synthesized through a facile one-pot synthesis method, where the reduction of GO and in situ generation of PtNPs occurred concurrently. The characterization results of transmission electron microscopy (TEM) demonstrate that PtNPs with small particle sizes are dispersed on the rGO matrix. Electrochemical measurements reveal that a PtNP-rGO modified glass carbon electrode (GCE) directly catalyzes rutin oxidation and displays an enhanced current response compared with a bare GCE. Under the optimal experimental conditions, the peak current was linear with rutin concentration in the range of 5 × 10^-8 to 1 × 10^-5 M with the detection limit of 1 × 10^-8 M (S/N = 3) by differential pulse voltammetry. The proposed method was successfully applied to determine rutin in tablet samples with satisfactory results. This journal is

Metabolic power method underestimates energy expenditure in field sport movements using a GPS tracking system

The purpose of this study was to assess the validity of a GPS tracking system to estimate energy expenditure (EE) during exercise and field sport locomotor movements. Twenty-seven participants each completed one 90 minute exercise session on an outdoor synthetic futsal pitch. During the exercise session participants wore a 5 Hz GPS unit interpolated to 15 Hz (SPI HPU, GPSports Pty Ltd, Australia) and a portable gas analyser (Metamax® 3B, Cortex Pty Ltd, Germany) which acted as the criterion measure of EE. The exercise session was comprised of alternating five minute exercise bouts of randomised walking, jogging, running or a field sport circuit (x3) followed by 10 minutes of recovery. One-way ANOVA showed significant (p<0.01) and very large underestimations between GPS metabolic power derived EE and VO2 derived EE for all field sport circuits (% difference ≈ -44%). No differences in EE were observed for the jog (7.8%) and run (4.8%) while very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90 minute session was significantly lower (p<0.01) than the VO2 EE, resulting in a moderate underestimation overall (-19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however is able to provide a reasonably accurate estimation of EE during continuous jogging and running.

Deployment of DGT units in marine waters to assess the environmental risk from a deep sea tailings outfall

Measurements of total, filterable and DGT-labile concentrations of nine metals (Al, Cd, Cr, Cu, Fe, Pb, Mn, Ni and Zn) have been made at five sites up to 4.2km from a deep sea tailings outfall operated by Lihir Gold Ltd. at Lihir Island, Papua New Guinea. At each site, pairs of DGT units (one containing a 0.4mm and the other a 0.8mm diffusive gel layer) were deployed at three depths (50–70; 105–130; 135–155m) for 4–7 days. Comparison of predicted water column DGT-labile metal concentrations in field deployments showed the 0.8mm DGT units were relatively enriched in metals, with the effect being greatest closer to the outfall for Pb and Mn and least for Fe, Cr, Ni and Zn. The most likely explanation for this is that in addition to simple ion diffusion, kinetic factors associated with ageing or desorption processes govern release of metals from iron and aluminium oxyhydroxide colloids which diffuse through the gels. The thicker gels have a longer residence time over which metals can be released for adsorption. This model explains why enrichment is most pronounced near the outfall; more distant sites have lower colloid concentrations because of the longer time for coagulation to increase particle sizes to the extent they cannot enter the gels. Total and filterable metal (FM) concentrations were frequently below the limits of detection (LOD) achievable by conventional ICP-AES (1–52gL⁻¹) and this limited their usefulness for assessing environmental risk and for metal speciation determination. Because of its pre-concentration step DGT gave metal concentrations above their LODs and these decreased exponentially with distance from the outfall. Concentrations of DGT–labile metal fell below Australian water quality guidelines for protection of 99% of marine organisms within 0.13km of the outfall for Cd, Cr and Ni and below that for protection of 95% of marine organisms within 0.4, 0.7 and 3.6km for lead, zinc and copper, respectively.

A genomic approach for improved detection of annual ryegrass toxicity

Annual Ryegrass Toxicity is a severe and constant threat to the Australian agricultural industry. Current diagnostic and detection strategies to predict and monitor ARGT are limited. This thesis utilised genomic-based technologies to develop improved strategies for detection of molecular indicators of toxicity in field and livestock to facilitate pre-clinical detection.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.