VLSI architecture for an Underwater Robot Vision System

It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great number of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. For normalised correlation criteria, previous experiments shown that the result is not altered in presence of nonuniform illumination. Usually, hardware for motion estimation has been limited to simple correlation criteria. The main goal of this paper is to propose a VLSI architecture for motion estimation using a matching criteria more complex than Sum of Absolute Differences (SAD) criteria. Today hardware devices provide many facilities for the integration of more and more complex designs as well as the possibility to easily communicate with general purpose processors

An ATM distributed simulator for network management research

Due to the high cost of a large ATM network working up to full strength to apply our ideas about network management, i.e., dynamic virtual path (VP) management and fault restoration, we developed a distributed simulation platform for performing our experiments. This platform also had to be capable of other sorts of tests, such as connection admission control (CAC) algorithms, routing algorithms, and accounting and charging methods. The platform was posed as a very simple, event-oriented and scalable simulation. The main goal was the simulation of a working ATM backbone network with a potentially large number of nodes (hundreds). As research into control algorithms and low-level, or rather cell-level methods, was beyond the scope of this study, the simulation took place at a connection level, i.e., there was no real traffic of cells. The simulated network behaved like a real network accepting and rejecting SNMP ones, or experimental tools using the API node

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

An improved method for discovering link criticality in transport networks

Quantitatively assessing the importance or criticality of each link in a network is of practical value to operators, as that can help them to increase the network's resilience, provide more efficient services, or improve some other aspect of the service. Betweenness is a graph-theoretical measure of centrality that can be applied to communication networks to evaluate link importance. However, as we illustrate in this paper, the basic definition of betweenness centrality produces inaccurate estimations as it does not take into account some aspects relevant to networking, such as the heterogeneity in link capacity or the difference between node-pairs in their contribution to the total traffic. A new algorithm for discovering link centrality in transport networks is proposed in this paper. It requires only static or semi-static network and topology attributes, and yet produces estimations of good accuracy, as verified through extensive simulations. Its potential value is demonstrated by an example application. In the example, the simple shortest-path routing algorithm is improved in such a way that it outperforms other more advanced algorithms in terms of blocking ratio

Contrast ultrasound: a simple-to-use phase-shifting medium offers saline infusion sonography-like images.

OBJECTIVE: To test the ability of a novel phase-shifting medium (PSM) to provide sustained distension of the uterine cavity and produce saline infusion sonography (SIS)-like images in a simplified contrast ultrasound procedure. DESIGN: Prospective pilot feasibility trial of a new diagnostic procedure, contrast ultrasound. SETTING: Clinical reproductive endocrine and infertility unit of regional teaching hospital. PATIENT(S): Twenty-six asymptomatic infertile women (group I) and 27 women presenting with dysfunctional uterine bleeding (DUB) who were scheduled for exploratory surgery (group II). INTERVENTION(S): All women who were temporarily on oral contraceptive first had a regular pelvic ultrasound followed by the intrauterine instillation of up to 3 mL PSM, using a regular insemination catheter, after which all instruments were removed and a regular ultrasound was performed again. RESULT(S): In all 53 women, intrauterine instillation of 1-3 mL PSM resulted in a 3-7 mm uterine distension, sufficient to produce SIS-like images of the uterine cavity that lasted 7-10 min. Contrast ultrasound revealed an endometrial polyp in 3 asymptomatic women of group I. In group II. 12 of 14 women (86%) whose vaginal ultrasound were positive or dubious had positive findings with contrast ultrasound; 9 of 12 patients whose vaginal ultrasounds were negative also had positive contrast ultrasound findings. All the positive and negative findings of contrast ultrasound made in group II were confirmed anatomically (sensitivity and specificity of 100%), whereas the correlation for standard vaginal ultrasound was markedly lower at 57.1% and 85.7%, respectively. Most patients (46 of 53) reported no discomfort during or after the procedure, and 7 women described the procedure as mildly uncomfortable. CONCLUSION(S): Contrast ultrasound, a novel simple diagnostic procedure conducted after intrauterine instillation of 1-3 mL PSM using a simple plastic catheter, delivered SIS-quality images in asymptomatic (group I) and symptomatic (group II) patients while retaining the simplicity of standard ultrasound. We therefore foresee broad application of contrast ultrasound for sensitive and specific assessment for uterine pathologies in the physician's office.

Dermatophyte identification in skin and hair samples using a simple and reliable nested polymerase chain reaction assay.

BACKGROUND: Dermatophyte identification in tinea capitis is essential for choosing the appropriate treatment and in tinea infections to identify the possible source. The failure of fungi to grow in cultures frequently occurs, especially in cases of previous antifungal therapy. OBJECTIVES: To develop a rapid polymerase chain reaction (PCR) sequencing assay for dermatophyte identification in tinea capitis and tinea corporis. MATERIAL AND METHODS: Fungal DNA was extracted from hair and skin samples that were confirmed to be positive by direct mycological examination. Dermatophytes were identified by the sequence of a 28S ribosomal DNA subunit amplicon generated by nested PCR. RESULTS: Nested PCR was found to be necessary to obtain amplicons in substantial amounts for dermatophyte identification by sequencing. The results agreed with those of classical mycological identification in 14 of 23, 6 of 10, and 20 of 23 cases of tinea capitis, tinea corporis and tinea pedis, respectively, from which a dermatophyte was obtained in culture. In seven of the 56 cases, another dermatophyte was identified, revealing previous misidentification. A dermatophyte was identified in 12 of 18, three of five, and four of nine cases of tinea capitis, tinea corporis and tinea pedis, respectively, in cases in which no dermatophyte grew in culture. CONCLUSIONS: Although the gold standard dermatophyte identification from clinical samples remains fungal cultures, the assay developed in the present study is especially suitable for tinea capitis. Improved sensitivity for the identification of dermatophyte species was obtained as it is possible to identify the dermatophyte when the fungus fails to grow in cultures.

Comparison of topography- and aperture-dependent motion compensation algorithms for airborne SAR

This letter presents a comparison between threeFourier-based motion compensation (MoCo) algorithms forairborne synthetic aperture radar (SAR) systems. These algorithmscircumvent the limitations of conventional MoCo, namelythe assumption of a reference height and the beam-center approximation.All these approaches rely on the inherent time–frequencyrelation in SAR systems but exploit it differently, with the consequentdifferences in accuracy and computational burden. Aftera brief overview of the three approaches, the performance ofeach algorithm is analyzed with respect to azimuthal topographyaccommodation, angle accommodation, and maximum frequencyof track deviations with which the algorithm can cope. Also, ananalysis on the computational complexity is presented. Quantitativeresults are shown using real data acquired by the ExperimentalSAR system of the German Aerospace Center (DLR).

Kinematics and dynamic complexity of postural transitions in frail elderly subjects.

The aim of this study was to propose a methodology allowing a detailed characterization of body sit-to-stand/stand-to-sit postural transition. Parameters characterizing the kinematics of the trunk movement during sit-to-stand (Si-St) postural transition were calculated using one initial sensor system fixed on the trunk and a data logger. Dynamic complexity of these postural transitions was estimated by fractal dimension of acceleration-angular velocity plot. We concluded that this method provides a simple and accurate tool for monitoring frail elderly and to objectively evaluate the efficacy of a rehabilitation program.

Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Relación de la susceptibilidad genética y de las características anatomopatológicas del cáncer de próstata con los polimorfismos de nucleótido simple

Actualmente no se disponen de marcadores biológicos específicos para la cáncer de próstata produciéndose en muchas ocasiones biopsias prostáticas innecesarias o un sobretratamientos para cánceres indolentes. Existen cada vez más un número mayor de publicaciones sobre cómo los polimorfismos de nucleótido simple (SNP) se relacionan con la susceptibilidad al cáncer de próstata o predecir con mayor precisión qué grado de agresividad adquiere la enfermedad. Se presenta una revisión bibliográfica de las investigaciones publicadas en PubMed desde el año 2000 hasta el 2012 que ponen de manifiesto la relación de los SNP con el riesgo a padecer cáncer de próstata y con sus características anatomopatológicas.

Genetic diversity and host plant preferences revealed by simple sequence repeat and mitochondrial markers in a population of the arbuscular mycorrhizal fungus Glomus intraradices.

Arbuscular mycorrhizal fungi (AMF) are important symbionts of plants that improve plant nutrient acquisition and promote plant diversity. Although within-species genetic differences among AMF have been shown to differentially affect plant growth, very little is actually known about the degree of genetic diversity in AMF populations. This is largely because of difficulties in isolation and cultivation of the fungi in a clean system allowing reliable genotyping to be performed. A population of the arbuscular mycorrhizal fungus Glomus intraradices growing in an in vitro cultivation system was studied using newly developed simple sequence repeat (SSR), nuclear gene intron and mitochondrial ribosomal gene intron markers. The markers revealed a strong differentiation at the nuclear and mitochondrial level among isolates. Genotypes were nonrandomly distributed among four plots showing genetic subdivisions in the field. Meanwhile, identical genotypes were found in geographically distant locations. AMF genotypes showed significant preferences to different host plant species (Glycine max, Helianthus annuus and Allium porrum) used before the fungal in vitro culture establishment. Host plants in a field could provide a heterogeneous environment favouring certain genotypes. Such preferences may partly explain within-population patterns of genetic diversity.

Multicast algorithms evaluation using an adaptative routing in ATM networks

This paper proposes a multicast implementation based on adaptive routing with anticipated calculation. Three different cost measures for a point-to-multipoint connection: bandwidth cost, connection establishment cost and switching cost can be considered. The application of the method based on pre-evaluated routing tables makes possible the reduction of bandwidth cost and connection establishment cost individually

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.