Fast implementation of dense stereo vision algorithms on a highly parallel SIMD architecture

In this paper, we present faster than real-time implementation of a class of dense stereo vision algorithms on a low-power massively parallel SIMD architecture, the CSX700. With two cores, each with 96 Processing Elements, this SIMD architecture provides a peak computation power of 96 GFLOPS while consuming only 9 Watts, making it an excellent candidate for embedded computing applications. Exploiting full features of this architecture, we have developed schemes for an efficient parallel implementation with minimum of overhead. For the sum of squared differences (SSD) algorithm and for VGA (640 × 480) images with disparity ranges of 16 and 32, we achieve a performance of 179 and 94 frames per second (fps), respectively. For the HDTV (1,280 × 720) images with disparity ranges of 16 and 32, we achieve a performance of 67 and 35 fps, respectively. We have also implemented more accurate, and hence more computationally expensive variants of the SSD, and for most cases, particularly for VGA images, we have achieved faster than real-time performance. Our results clearly demonstrate that, by developing careful parallelization schemes, the CSX architecture can provide excellent performance and flexibility for various embedded vision applications.     

Understanding post‐adoption IS usage stages: an empirical assessment of self‐service information systems

Organisations have invested in self‐service information systems (IS) to provide a direct interface for service delivery. Enriching the usage of these systems can provide organisations with immense benefits. However, limited research has been directed towards understanding post‐adoption IS usage behaviour in general and specifically in the context of self‐service IS. This study proposes post‐adoption IS usage behaviour as a broader concept constituting feature level usage of IS, integration of IS in the work system and exploration of new uses of IS. We evaluate how the new conceptualisation can be used to classify users at different stages of self‐service IS usage. Further, we examine user perceptions that differentiate among the users situated at different self‐service IS usage stages. Data were collected in the context of a self‐service Web‐based IS to validate the post‐adoption IS usage constructs and to examine the proposed thesis. The newly developed conceptual structure and measures for post‐adoption IS usage behaviour exhibit strong psychometric properties. The analysis shows three distinct post‐adoption IS usage stages and highlights that usefulness, user‐initiated learning, ease of use, satisfaction and voluntariness of use differentiate users at the different stages of post‐adoption IS usage. The results show that these variables aggregate into value confirmation and learning orientation as two higher‐level concepts. Further, we evaluate the predictive efficacy of the research model in classifying users into different post‐adoption self‐service IS usage stages. Implications are drawn for future research.     

A model-based survey of alert correlation techniques

As telecommunication networks evolve rapidly in terms of scalability, complexity, and heterogeneity, the efficiency of fault localization procedures and the accuracy in the detection of anomalous behaviors are becoming important factors that largely influence the decision making process in large management companies. For this reason, telecommunication companies are doing a big effort investing in new technologies and projects aimed at finding efficient management solutions. One of the challenging issues for network and system management operators is that of dealing with the huge amount of alerts generated by the managed systems and networks. In order to discover anomalous behaviors and speed up fault localization processes, alert correlation is one of the most popular resources. Although many different alert correlation techniques have been investigated, it is still an active research field. In this paper, a survey of the state of the art in alert correlation techniques is presented. Unlike other authors, we consider that the correlation process is a common problem for different fields in the industry. Thus, we focus on showing the broad influence of this problem. Additionally, we suggest an alert correlation architecture capable of modeling current and prospective proposals. Finally, we also review some of the most important commercial products currently available.     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

Effect of Ag‐NPs synthesised by Chamaemelum nobile extract on the inflammation and oxidative stress induced by carrageenan in mice paw

An environmentally friendly and rapid procedure was developed to synthesise silver nanoparticles (Ag‐NPs) by Chamaemelum nobile extract and to evaluate its in vivo anti‐inflammatory and antioxidant activities. The ultraviolet–visible absorption spectrum of the synthesised Ag‐NPs showed an absorbance peak at 422. The average size of spherical nanoparticles was 24 nm as revealed by transmission electron microscopy. Fourier transform infra‐red spectroscopy analysis supported the presence of biological active compounds involved in the reduction of Ag ion and X‐ray diffraction confirmed the crystalline structure of the metallic Ag. The anti‐inflammatory and antioxidant activity of the Ag‐NPs was investigated against carrageenan‐induced paw oedema in mice. The levels of malondialdehyde (MDA) and antioxidant enzymes superoxide dismutase, catalase, glutathione peroxidase and inflammatory cytokines tumour necrosis factor (TNF‐α), interferon gamma and interleukin (IL)‐6, IL‐1β were assessed in this respect. The results demonstrated that anti‐inflammatory activity of the Ag‐NPs might be due to the ability of the nanoparticles to reduce IL‐1β, IL‐6 and TNF‐α. Moreover, reduction of antioxidant enzymes along with an increase in MDA level shows that the anti‐inflammatory activity of the synthesised Ag‐NPs by C. nobile is attributed to its ameliorating effect on the oxidative damage.Inspec keywords: silver, nanoparticles, nanofabrication, ultraviolet spectra, visible spectra, particle size, transmission electron microscopy, Fourier transform infrared spectra, X‐ray diffraction, crystal structure, enzymes, molecular biophysics, tumours, biomedical materials, nanomedicineOther keywords: Chamaemelum nobile extract, oxidative stress, mice paw, silver nanoparticles, antiinflammatory activity, antioxidant activity, ultraviolet‐visible absorption spectrum, spherical nanoparticle size, transmission electron microscopy, Fourier transform infrared spectroscopy, biological active compounds, X‐ray diffraction, crystalline structure, carrageenan‐induced paw oedema, malondialdehyde, antioxidant enzymes, superoxide dismutase, catalase, glutathione peroxidase, inflammatory cytokines, tumour necrosis factor, interferon gamma, interleukin, IL‐1β, IL‐6, TNF‐α, MDA level, Ag     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.