Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

Ensemble-based data assimilation and the localisation problem

The “butterfly effect” is a popularly known paradigm; commonly it is said that when a butterfly flaps its wings in Brazil, it may cause a tornado in Texas. This essentially describes how weather forecasts can be extremely senstive to small changes in the given atmospheric data, or initial conditions, used in computer model simulations. In 1961 Edward Lorenz found, when running a weather model, that small changes in the initial conditions given to the model can, over time, lead to entriely different forecasts (Lorenz, 1963). This discovery highlights one of the major challenges in modern weather forecasting; that is to provide the computer model with the most accurately specified initial conditions possible. A process known as data assimilation seeks to minimize the errors in the given initial conditions and was, in 1911, described by Bjerkness as “the ultimate problem in meteorology” (Bjerkness, 1911).

The impact of ICT on the work patterns of managers and their organisations

Purpose – The main aim of this paper is to present the results of a study examining managers' attitudes towards the deployment and use of information and communications technology (ICT) in their organisations. The study comes at a time when ICT is being recognised as a major enabler of innovation and new business models, which have the potential to have major impact on western economies and jobs. Design/methodology/approach – A questionnaire was specially designed to collect data relating to three research questions. The questionnaire also included a number of open-ended questions. A total of 181 managers from a wide range of industries across a number of countries participated in the electronic survey. The quantitative responses to the survey were analysed using SPSS. Exploratory factor analysis using Varimax rotation was used and ANOVA to compare responses by different groups. Findings – The survey showed that many of the respondents appeared equipped to work “any place, any time”. However, it also highlighted the challenges managers face in working in a connected operation. Also, the data suggested that many managers were less than confident about their companies' policies and practices in relation to information management. Originality/value – A next step from this exploratory research could be the development of a model exploring the impact of ICT on management and organisational performance in terms of personal characteristics of the manager, the role performed, the context and the ICT provision. Also, further research could focus on examining in more detail differences between management levels.

Bioactive peptides from food proteins: new opportunities and challenges

Food proteins such as milk and soy are a rich source of bioactive peptides. In the last decade, research into this area has intensified and new bioactive peptide sequences have been discovered with a range of apparent biological functions; for example, antihypertensive, antioxidant, and antimicrobial effects and opiate-like qualities have been reported. These peptides could therefore lead to the development of important functional food products and ingredients for the prevention and even treatment of chronic diseases such as cardiovascular disease and cancer. Peptides can be produced by fermentation with dairy starters for instance, and by enzymatic hydrolysis with pancreatic and microbial enzymes. Further purification is typically carried out by membrane filtration and/or chromatographic methods. The production of novel bioactive peptides and their incorporation into functional food products poses several technological challenges as well as regulatory and marketing issues. Proof of efficacy is of paramount importance; this should be verified by conducting appropriate tests in vivo in animals and in humans. In addition, tests for cytotoxicity and allergenicity must be conducted. Despite all of these hurdles, scientific evidence is increasingly demonstrating the health benefits of diet-based disease prevention, and therefore new developments in this area are likely to continue both at the research and the commercialisation level.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

A model for self-directed problem-based learning for renal therapeutics

Objective To introduce a new approach to problem-based learning (PBL) for self-directed learning in renal therapeutics. Design This 5-week course, designed for large student cohorts using minimal teaching resources, was based on a series of case studies and subsequent pharmaceutical care plans, followed by intensive and regular feedback from the instructor. Assessment Assessment of achievement of the learning outcomes was based on weekly-graded care plans and peer review assessment, allowing each student to judge the contributions of each group member and their own, along with a written case-study based examination. The pharmaceutical care plan template, designed using a “tick-box” system, significantly reduced staff time for feedback and scoring. Conclusion The proposed instructional model achieved the desired learning outcomes with appropriate student feedback, while promoting skills that are essential for the students' future careers as health care professionals.

Effect of dairy-based protein sources and temperature on growth, acidification and exopolysaccharide production of Bifidobacterium strains in skim milk

The aim of the present study was to find out the best growing conditions for exopolysaccharide (EPS) producing bifidobacteria, which improve their functionality in yoghurt-like products. Two Bifidobacterium strains were used in this study, Bifidobacterium longum subsp. infantis CCUG 52486 and Bifidobacterium infantis NCIMB 702205. In the first part of the study the effect of casein hydrolysate, lactalbumin hydrolysate, whey protein concentrate and whey protein isolate, added at 1.5% w/v in skim milk, was evaluated in terms of cell growth and EPS production; skim milk supplemented with yeast extract served as the control. Among the various nitrogen sources, casein hydrolysate (CH) showed the highest cell growth and EPS production for both strains after 18 h incubation and therefore it was selected for subsequent work. Based on fermentation experiments using different levels of CH (from 0.5 to 2.5% w/v) it was deduced that 1.5% (w/v) CH resulted in the highest EPS production, yielding 102 and 285 mg L− 1 for B. infantis NCIMB 702205 and B. longum subsp. infantis CCUG 52486, respectively. The influence of temperature on growth and EPS production of both strains was further evaluated at 25, 30, 37 and 42 °C for up to 48 h in milk supplemented with 1.5% (w/v) CH. The temperature had a significant effect on growth, acidification and EPS production. The maximum growth and EPS production were recorded at 37 °C for both strains, whereas no EPS production was observed at 25 °C. Lower EPS production for both strains were observed at 42 °C, which is the common temperature used in yoghurt manufacturing compared to that at 37 °C. The results showed that the culture conditions have a clear effect on the growth, acidification and EPS production, and more specifically, that skim milk supplemented with 1.5% (w/v) CH could be used as a substrate for the growth of EPS-producing bifidobacteria, at 37 °C for 24 h, resulting in the production of a low fat yoghurt-like product with improved functionality.

Mulsemedia: state of the art, perspectives, and challenges

Mulsemedia—multiple sensorial media—captures a wide variety of research efforts and applications. This article presents a historic perspective on mulsemedia work and reviews current developments in the area. These take place across the traditional multimedia spectrum—from virtual reality applications to computer games—as well as efforts in the arts, gastronomy, and therapy, to mention a few. We also describe standardization efforts, via the MPEG-V standard, and identify future developments and exciting challenges the community needs to overcome.

A new hybrid BCI paradigm based on P300 and SSVEP

Background: P300 and steady-state visual evoked potential(SSVEP) approaches have been widely used for brain–computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. New method: The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. Result: The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. Comparison with existing method: A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm.Allthe paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. Conclusions: The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.

Sialic acids in biological and therapeutic processes: opportunities and challenges

It is now well documented that carbohydrates play multiple roles in biological processes, and hence are interesting targets for chemical biology and medicinal chemistry programmes. This review focuses on a subset of carbohydrates, specifically sialic acid containing carbohydrates. It highlights their occurrence and diversity, and presents evidence for their roles in a range of biological pathways. It illustrates that they are targets for novel medicinal chemistry strategies for a range of therapeutic areas, including cancer and immunity. Case studies highlight opportunities and challenges in this area, and sialic acid based drugs that have entered clinical practice, and are promising candidates for future disease intervention schemes, are discussed. The review concludes by highlighting perspectives and emerging roles for these targets.

Filter for Car Tracking Based on Acceleration and Steering Angle

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking.

Understanding El Niño in ocean-atmosphere general circulation models: progress and challenges

Determining how El Niño and its impacts may change over the next 10 to 100 years remains a difficult scientific challenge. Ocean–atmosphere coupled general circulation models (CGCMs) are routinely used both to analyze El Niño mechanisms and teleconnections and to predict its evolution on a broad range of time scales, from seasonal to centennial. The ability to simulate El Niño as an emergent property of these models has largely improved over the last few years. Nevertheless, the diversity of model simulations of present-day El Niño indicates current limitations in our ability to model this climate phenomenon and to anticipate changes in its characteristics. A review of the several factors that contribute to this diversity, as well as potential means to improve the simulation of El Niño, is presented.