Detection of fingertips based on the combination of color information and circle detection

Fingertips of human hand play an important role in hand-based interaction with computers. Therefore, identification of fingertips' positions on hand image is vital for developing a human computer interaction system. All most all of the research works for fingertips detection, initially isolate hand image from the background image. Most of these techniques develop color based segmentation methods because human skin color possess an exceptional characterises that can be used to isolate hand from the rest of the image quite easily. Sometimes color image segmentation becomes difficult due to illumination and background variations. To make it simple and reliable, this paper proposes a robust method for detecting fingertips of a hand image based on the combination of color segmentation and circle detection. Due to the characteristics of circularity of fingertips regions of hand boundary, any existing circle detection algorithms can be applied to detect circles at fingertips region. It is difficult to detect fingertips solely based on the circle detection method. For this reason, initially the proposed method detects all the circular regions on the image applying Circle Hough Transformation (CHT) then the fingertips are selected based on the color characteristics of the fingertips regions. Experimental results show that the proposed approach is promising.

GUITAR: An ontology-based automated requirements analysis tool

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. However, current requirements engineering approaches generally lack reliable support for automated analysis of such modeled artifacts. To address this problem, we have developed GUITAR, a tool which delivers automated detection of incorrectness, incompleteness and inconsistency between artifacts. GUITAR is based on our goal-use case integration meta-model and ontologies of domain knowledge and semantics. GUITAR also provides comprehensive explanations for detected problems and can suggest resolution alternatives.

Effective and efficient detection of premature ventricular contractions based on variation of principal directions

Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use "Replacing" strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.

Preparation of graphene oxide decorated Fe3O4@SiO2 nanocomposites with superior adsorption capacity and SERS detection for organic dyes

The fast detection and removal of organic dyes from contaminated water has become an urgent environmental issue due to their high toxicity, chemical stability, and low biodegradability. In this paper, we have developed graphene oxide decorated Fe3O4@SiO2 (Fe3O4@SiO2-GO) as a novel adsorbent aiming at the rapid adsorption and trace analysis of organic dyes followed by surface enhanced Raman scattering (SERS). The structure and morphology of the nanocomposites were characterized by transmission electron microscopy (TEM), Fourier infrared spectrometry (FT-IR), X-ray diffraction (XRD), and vibrating sample magnetometer (VSM). The obtained nanocomposites were used to adsorb methylene blue (MB) in aqueous solution based on π-π stacking interaction and electrostatic attraction between MB and GO, and the adsorption behaviors of MB were investigated. Moreover, the obtained nanocomposites with adsorbed dyes were separated from the solution and loaded with silver nanoparticles for SERS detection. These nanocomposites showed superior SERS sensitivity and the lowest detectable concentration was 1.0 × 10-7 M.

Profiling pedestrian distribution and anomaly detection in a dynamic environment

Pedestrians movements have a major impact on the dynamics of cities and provide valuable guidance to city planners. In this paper we model the normal behaviours of pedestrian flows and detect anomalous events from pedestrian counting data of the City of Melbourne. Since the data spans an extended period, and pedestrian activities can change intermittently (e.g., activities in winter vs. summer), we applied an Ensemble Switching Model, which is a dynamic anomaly detection technique that can accommodate systems that switch between different states. The results are compared with those produced by a static clustering model (Hy-CARCE) and also cross-validated with known events. We found that the results from the Ensemble Switching Model are valid and more accurate than HyCARCE.

A visual-numeric approach to clustering and anomaly detection for trajectory data

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.

Topic transition detection using hierarchical hidden Markov and semi-Markov models

In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling.

Graphene quantum dots and Nafion composite as an ultrasensitive electrochemical sensor for the detection of dopamine

A novel electrochemical sensor for highly sensitive and selective detection of dopamine (DA) was developed based on a graphene quantum dots (GQDs) and Nafion composite modified glassy carbon electrode (GCE). GQDs were synthesized by a hydrothermal approach for cutting graphene sheets into GQDs and characterized by TEM, UV-vis, photoluminescence, and FT-IR spectra. The GQDs had carboxyl groups with a negative charge, which not only provided good stability, but also enabled interaction with amine functional groups in DA through electrostatic interaction to enhance the specificity of DA. The interaction and electron communication between GQDs and DA can be further strengthened via π-π stacking force. Nafion was used as an anchoring agent to increase the robustness of GQDs on the electrode surface and sensor stability and reproducibility. The GQDs-Nafion composite exhibits a good linear range of 5 nM to 100 μM and a limit of detection as low as 0.45 nM in the detection of DA. The proposed electrochemical sensor also displays good selectivity and high stability and could be used for the determination of DA in real samples with satisfactory results. The present study provides a powerful avenue for the design of an ultrasensitive detection method for clinical application.

Selective determination of amino acids using flow injection analysis coupled with chemiluminescence detection

The determination of the amino acids proline, histidine, tyrosine, arginine, phenylalanine and tryptophan using flow injection analysis (FIA) with chemiluminescence detection is described. Proline was the only amino acid to exhibit chemiluminescence with the tris(2,2-bipyridyl)ruthenium(III) reaction at pH 10. While, histidine was found to selectively enhance the reaction of luminol with Mn(II) salts in a basic medium. Acidic potassium permanganate chemiluminescence was able to selectively determine tyrosine at pH 6.75. Low pressure separations using a C18 guard column allowed the simultaneous determination of tyrosine and tryptophan or phenylalanine and tryptophan with acidic potassium permanganate and copper(II)–amino acid–hydrogen peroxide chemiluminescence, respectively. Precision for each method was less than 3.9% (R.S.D.) for five replicates of a standard (1×10−5 M) and the detection limits ranged between 4×10−9 and 7×10−6 M. Preliminary investigations revealed that the methodology developed was able to selectively determine the individual amino acids in an equimolar mixture of the 20 naturally occurring amino acids.

Determination of proline in wine using flow injection analysis with tris(2,2`-bipyridyl)ruthenium(II) chemiluminescence detection

Flow injection methodology is described for the determination of proline in red and white wines using tris(2,2′-bipyridyl)ruthenium(II) chemiluminescence detection. Selective conditions were achieved for proline at pH 10, while other amino acids and wine components did not interfere. The precision of the method was less than 1.00% (R.S.D.) for five replicates of a standard (4 × 10⁻⁶ M) and the detection limit was 1 × 10⁻⁸ M. The level of proline in white and sparkling wines using the developed methodology was equivalent to those achieved using HPLC-FMOC amino acid analysis. SPE removal of phenolic material was required for red wines to minimize Ru(bipy)₃³⁺ consumption and its associated effect on accuracy.

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.

Investigation into the temporal stability of aqueous standard solutions of psilocin and psilocybin using high performance liquid chromatography

This paper reports an investigation into the temporal stability of aqueous solutions of psilocin and psilocybin reference drug standards over a period of fourteen days. This study was performed using high performance liquid chromatography utilising a (955% vlv) methanol: 10 mM ammonium formate,
pH 3.5 mobile phase and absorption detection at 269 nm. It was found that the exclusion of light significantly prolonged the useful life of standards, with aqueous solutions of both psilocin and psilocybin being stable over a period of seven days.

A video-based real-time vehicle detection method by classified background learning

A new two-level real-time vehicle detection method is proposed in order to meet the robustness and efficiency requirements of real world applications. At the high level, pixels of the background image are classified into three categories according to the characteristics of Red, Green, Blue (RGB) curves. The robustness of the classification is further enhanced by using
line detection and pattern connectivity. At the lower level, an exponential forgetting algorithm with adaptive parameters for different categories is utilised to calculate the background and reduce the distortion by the small motion of video cameras. Scene tests show that the proposed method is more robust and faster than previous methods, which is very suitable for real-time vehicle detection in outdoor environments, especially concerning locations where the level of illumination changes frequently and speed detection is important.

Rules and resources: a structuration approach to understanding the coordination of childrens`s inpatient health care

Aim. This paper presents findings from a multi-method study exploring the process of care coordination in children's inpatient health care.

Background. Existing work on care coordination is typified by 'black-box' type studies that measure inputs to and outcomes of care coordination roles and practices, without addressing the process of coordination.

Method. Using questionnaires, interviews and observation to collect data in multiple sites in the United Kingdom and Denmark between 1999 and 2005, the study gathered the perceptions of staff and compared these with observed practice. Giddens' structuration theory was used to provide an analytical and explanatory framework.

Findings. Current care coordination practice is diverse and inconsistent. It involves a wide range of clinical and non-clinical staff, many of whom perceive a lack of clarity about who should perform specific coordination activities. Staff draw upon a wide range of different material and non-material resources in coordinating care, the use of which is governed by largely tacit and informal rules.

Conclusions. Care coordination can be usefully conceptualized as a 'structurated' process – one that is continually produced and reproduced by staff using rules and resources to 'instantiate' or bring about care coordination through action. Potentially negative implications of this are manifested in diversity and inconsistency in care coordination practice. However, positive aspects such as the opportunity this provides to tailor care to the needs of the individual patient can be realized.