Extraction and classification of self-consumable sport video highlights using generic HMM

This paper aims to automatically extract and classify self-consumable sport video highlights. For this purpose, we will emphasize the benefits of using play-break sequences as the effective inputs for HMMbased classifier. HMM is used to model the stochastic pattern of high-level states during specific sport highlights which correspond to the sequence of generic audio-visual measurements extracted from raw video data. This paper uses soccer as the domain study, focusing on the extraction and classification of goal, shot and foul highlights. The experiment work which uses183 play-break sequences from 6 soccer matches will be presented to demonstrate the performance of our proposed scheme.

An integrated method of feature extraction and objective evaluation of fabric pilling

A pilled fabric image consists of sub-images of different frequency components, and the fabric texture and the pilling information are in different frequency bands. Interference from fabric background texture affects the accuracy of computer-aided pilling ratings. A new approach for pilling evaluation based on the multi-scale two-dimensional dualtree complex wavelet transform (CWT) is presented in this paper to extract the pilling information from pilled fabric images. The CWT method can effectively decompose the pilled fabric image with six orientations at different scales and reconstruct fabric background texture and pilling sub-images. This study used an energy analysis method to search for an optimum image decomposition scale and dynamically discriminate pilling image from noise, fabric texture, fabric surface unevenness, and illuminative variation in the pilled fabric image. For pilling objective rating, six parameters were extracted from the pilling image to describe pill properties. A Levenberg-Marquardt backpropagation neural rule was used as a classifier to classify the pilling grade. The proposed method was evaluated using knitted, woven, and nonwoven pilled fabric images photographed with a digital camera.

An alternative for the extraction and storage of DNA from insects in forensic entomology

An important area of recent research in forensic entomology has been the use of insect DNA to provide identification of insects for fast and accurate estimation of time since death. This requires DNA to be extracted efficiently and in a state suitable for use in molecular procedures, and then stored on a long-term basis. In this study, Whatman FTA™ cards were tested for use with the Calliphoridae (Diptera). In particular, testing examined their ability to effectively extract DNA from specimens, and store and provide DNA template in a suitable condition for amplification using the polymerase chain reaction (PCR). The cards provided DNA that was able to be amplified from a variety of life stages, and thus appears to be of sufficient quality and quantity for use in subsequent procedures. FTA cards therefore appear suitable for use with calliphorids, and provide a new method of extraction that is simple and efficient and allows for storage and transportation without refrigeration, consequently simplifying the handling of DNA in forensic entomological cases.

Feature extraction and classification of metal detector signals using the wavelet transform and the fuzzy ARTMAP neural network

In this paper, the Fuzzy ARTMAP (FAM) neural network is used to classify metal detector signals into different categories for automated target discrimination. Feature extraction of the metal detector signals is conducted using a wavelet transform technique. The FAM neural network is then employed to classify the extracted features into different target groups. A series of experiments using individual FAM networks and a voting FAM network is conducted. Promising classification accuracy rates are obtained from using individual and voting FAM networks, respectively. The experimental outcomes positively demonstrate the effectiveness of the generated features, and of the FAM network in classifying metal detector signals for automated target discrimination tasks.

Patient and business rules extraction and formalisation using SVN and SBVR for automated healthcare

This paper describes advances in automated health service selection and composition in the Ambient Assisted Living (AAL) domain. We apply a Service Value Network (SVN) approach to automatically match medical practice recommendations to health services based on sensor readings in a home care context. Medical practice recommendations are extracted from National Health and Medical Research Council (NHMRC) guidelines. Service networks are derived from Medicare Benefits Schedule (MBS) listings. Service provider rules are further formalised using Semantics of Business Vocabulary and Business Rules (SBVR), which allows business participants to identify and define machine-readable rules. We demonstrate our work by applying an SVN composition process to patient profiles in the context of Type 2 Diabetes Management.

Analysis of residues of prometryne and acetochlor in soil-water system by solid-phase extraction and gas chromatography/mass spectrometry

A highly sensitive and simple analytical method was developed for analyzing the binary mixed pesticides of prometryne and acetochlor in soil–water system by gas chromatography/mass spectrometry (GC/MS). The sample solution was first purified by C18 solid-phase extraction column, which was leached by acetone. The leachate was enriched to 1.0 mL by pressure blowing concentrator and then analyzed by GC/MS. The linear calibration curves were showed in the range of 1–15 μg/mL with a correlation coefficient of 0.9991. The average recoveries (n = 5) were between 95.3 and 115.7%, with relative standard deviations ranged from 1.71 and 7.95%. The limits of detection of Prometryne/Acetochlor were up to 0.06 and 0.17 μg/mL, respectively. This method provides a reliable approach to examine and evaluate the residues of prometryne and acetochlor in the soil–water system.

Integrated RNA extraction and RT-PCR for semi-quantitative gene expression studies on a microfluidic device

This paper describes the development of a microfluidic methodology, using RNA extraction and reverse transcription PCR, for investigating expression levels of cytochrome P450 genes. Cytochrome P450 enzymes are involved in the metabolism of xenobiotics, including many commonly prescribed drugs, therefore information on their expression is useful in both pharmaceutical and clinical settings. RNA extraction, from rat liver tissue or primary rat hepatocytes, was performed using a silica-based solid-phase extraction technique. Following elution of the purified RNA, amplification of target sequences for the housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the cytochrome P450 gene CYP1A2, was carried out using a one-step reverse transcription PCR. Once the microfluidic methodology had been optimized, analysis of control and 3-methylcholanthrene-induced primary rat hepatocytes were used to evaluate the system. As expected, GAPDH was consistently expressed, whereas CYP1A2 levels were found to be raised in the drug-treated samples. The proposed system offers an initial platform for development of both rapid throughput analyzers for pharmaceutical drug screening and point-of-care diagnostic tests to aid provision of drug regimens, which can be tailor-made to the individual patient.

Integrated DNA extraction and amplification using electrokinetic pumping in a microfluidic device

An integrated system employing anion exchange for the extraction of DNA from biological samples prior to polymerase chain reaction DNA amplification has been developed, based on microfluidic methodology utilising electrokinetic pumping. In this system, the biological samples were added directly to chitosan-coated silica beads to facilitate DNA immobilisation. The purified, pre-concentrated DNA was then eluted using a combination of electro-osmotic flow enhanced with electrophoretic mobility, which enable DNA to be transported by both mechanisms into the DNA amplification chamber. Through optimisation of the DNA elution conditions, average DNA extraction efficiencies of 69.1% were achievable. Subsequent DNA amplification performed on the microfluidic system demonstrated not only the ability to use electrokinetic movement to integrate the two processes on a single device, but also that the quality and quantity of DNA eluted was suitable for downstream analysis. This work offers an attractive real-world to chip interface and a route to simpler Lab-on-a-Chip technology which eliminates the need for moving parts.

Solvent extraction and purification of sugars from hemicellulose hydrolysates using boronic acid carriers

Research was performed to determine whether it was technically feasible to use boronic acid extractants to purify and concentrate the sugars present in hemicellulose hydrolysates. Initially, five types of boronic acids (phenylboronic acid, 3,5-dimethylphenylboronic acid, 4-tert-butylphenylboronic acid, trans-β-styreneboronic acid or naphthalene-2-boronic acid) dissolved in an organic diluent (Shellsol® 2046 or Exxal® 10) containing the quaternary amine Aliquat® 336 were tested for their ability to extract sugars (fructose, glucose, sucrose and xylose) from a buffered, immiscible aqueous solution. Naphthalene- 2-boronic acid was found to give the greatest extraction of xylose regardless of which diluent was used. Trials were then conducted to extract xylose and glucose from solutions derived from the dilute acid hydrolysis of sugar cane bagasse and to then strip the loaded organic solutions using an aqueous solution containing hydrochloric acid. This produced a strip solution in which the xylose concentration had been increased over 7× that of the original hydrolysate while reducing the concentration of the undesirable acid-soluble lignin by over 90%. Hence, this process can be exploited to produce high concentration xylose solutions suitable for direct fermentation.

Data-mining twitter and the autism spectrum disorder: a pilot study

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.