Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

The relation between speech recognition in noise and the speech-evoked brainstem response in normal-hearing and hearing-impaired individuals

Little is known about the way speech in noise is processed along the auditory pathway. The purpose of this study was to evaluate the relation between listening in noise using the R-Space system and the neurophysiologic response of the speech-evoked auditory brainstem when recorded in quiet and noise in adult participants with mild to moderate hearing loss and normal hearing.

Characterizations of juvenile stages of some semaeostome Scyphozoa (Cnidaria), with recognition of a new family (Phacellophoridae)

Phacellophora camtschatica has long been assigned to the semaeostome scyphozoan family Ulmaridae. Early stages (scyphistomae, strobilae, ephyrae, postephyrae, and young medusae) of the species were compared with those of several other semaeostomes currently assigned to Ulmaridae, Pelagiidae, and Cyaneidae. Juveniles of P. camtschatica did not strictly conform with characters of those of any of these families, and appeared intermediate between Cyaneidae and Ulmaridae. A new family, Phacellophoridae, is proposed to accommodate P. camtschatica.

Mycoplasmal lipid-associated membrane proteins and Mycoplasma arthritidis mitogen recognition by serum antibodies from patients with rheumatoid arthritis

Mycoplasmal lipid-associated membrane proteins (LAMPs) and Mycoplasma arthritidis mitogen (MAM superantigen) are potent stimulators of the immune system. The objective of this work was to detect antibodies to MAM and LAMPs of Mycoplasma hominis and M. fermentans in the sera of patients affected by rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) to identify mycoplasmal products that can be involved in the etiopathogenesis of these autoimmune diseases. Serum samples from female RA and SLE patients and controls, recombinant MAM, and LAMPs of M. hominis PG21 and M. fermentans PG18 were used in Western blot assays. A similar frequency of sera from patients and controls reactive to MAM was detected. A larger number of M. hominis and M. fermentans LAMPs were recognized by sera from RA patients than controls, but no differences were detected between sera from SLE patients and controls. Among the LAMPs recognized by IgG antibodies from RA patients, proteins of molecular masses in a range of < 49 and a parts per thousand yen20 KDa (M. hominis) and < 102 and a parts per thousand yen58 KDa (M. fermentans) were the most reactive. These preliminary results demonstrate the strong reactivity of antibodies of RA patients with some M. hominis and M. fermentans LAMPs. These LAMPs could be investigated as mycoplasmal antigens that can take part in the induction or amplification of human autoimmune responses.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

Traffic and Road Sign Recognition

This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

Motion cue analysis for parkinsonian gait recognition

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson's disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject's body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

Implications of innovation for inclusive growth: a study of Medellín, Colombia's metro system and integral urban projects

Medellín, Colombia continues to attract global recognition for its evolution from a crimesaturated and desegregated city to an award-winning paragon of innovation. Two innovations in particular, the Metro System & the Integral Urban Projects, have fostered and contributed to Medellín’s inclusive growth, as indicated by a corresponding increase in both social and economic capital. Through a mixed methodology analysis of these two experiences, including participant observation, in-depth interviews with different industry leaders, and household surveys, this thesis explores the extent to which inclusive innovation has contributed to inclusive growth in Medellín. The surveys were distributed to three sensitive neighborhoods of Medellín and apply a Synthesized Framework for measuring inclusive growth, one that includes five indicators for social capital and five indicators for economic capital, emphasizing the importance of progression in both dimensions. By drawing on concepts of inclusivity surfacing more frequently in business lexicon and the emergence of a newly branded Medellín, the findings of this thesis indicates that the implementation of innovations in association with a unified city vision practiced by the local government, corporate and non-profit sector has contributed to achieving inclusive growth, and has left civilians hungry for more.

Mating system of a population of Myracrodruon urundeuva F.F. & M.F. Allemão using the fAFLP molecular marker

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A low-cost 3D reconstruction system using a single-shot projection of a pattern matrix

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Leak detection in petroleum pipelines using a fuzzy system

A methodology for pipeline leakage detection using a combination of clustering and classification tools for fault detection is presented here. A fuzzy system is used to classify the running mode and identify the operational and process transients. The relationship between these transients and the mass balance deviation are discussed. This strategy allows for better identification of the leakage because the thresholds are adjusted by the fuzzy system as a function of the running mode and the classified transient level. The fuzzy system is initially off-line trained with a modified data set including simulated leakages. The methodology is applied to a small-scale LPG pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria for the detection system. The results are very encouraging with relatively low levels of false alarms, obtaining increased leakage detection with low computational costs. (c) 2005 Elsevier B.V. All rights reserved.

Biometric relationship analysis of the wet weight by carapace width to the swimming crab Arenaeus-cribrarius (Lamarck, 1818) (Crustacea, Brachyura, Portunidae)

The biometric relationship between the weigth and the carapace width in the swimming crab A. cribrarius was compared with the results from other portunid crabs studied previously. During November/1988 to October/1989, a total of 403 specimens (189 males and 214 females) were collected with otter-trawl nets in north coast of the São Paulo State, Brazil. The animals were measured (carapace width excluding lateral spines = LC! and weighed (wet weight = PE). The empiric points of this relation were fit according to the power function (Y = a.X(b)) for each sex, maturation phases and total of individuals. The relation PE x LC indicates that the mole's growth changes during the ontogenesys from isometric (in juvenile phase) to allometric positive (in adult phase). For the females the growth is isometric in the two phases. The weight grows in a higher proportion than the carapace width variable (allometric positive growth). The data can be grouped in a single equation (PE = 7.85.10(-5).LC(3.14)) for the convertion between the variables there was a greater similarity between the equations obtained far each sex. In spite of this, the males present the fattening grade value (''a'') slightly higher than that of the females, possibly because of the greater size reached in its devellopment. The mean weight of the males is greater than the females one (p < 0.01). In the range 80 proves 90mm the males were more abundant, probably due to the females terminal ecdysis is near this size. The females only have the mean weight greater than the males in the 60 proves 70mm range (p < 0.01) when the puberty molt occurs and they present morphological changes in their reproductive system.

Modulation of Maternal Immune System During Pregnancy in the Cow

There is a molecular crosstalk between the trophoblast and maternal immune cells of bovine endometrium. The uterine cells are able to secrete cytokine/chemokines to either induce a suppressive environment for establishment of the pregnancy or to recruit immune cells to the endometrium to fight infections. Despite morphological differences between women and cows, mechanisms for immune tolerance during pregnancy seem to be conserved. Mechanisms for uterine immunesuppression in the cow include: reduced expression of major histocompatability proteins by the trophoblast; recruitment of macrophages to the pregnant endometrium; and modulation of immune-related genes in response to the presence of the conceptus. Recently, an eGFP transgenic cloned embryo model developed by our group showed that there is modulation of foetal proteins expressed at the site of syncytium formation, suggesting that foetal cell can regulate not only by the secretion of specific factors such as interferon-tau, but also by regulating their own protein expression to avoid excessive maternal recognition by the local immune system. Furthermore, foetal DNA can be detected in the maternal circulation; this may reflect the occurrence of an invasion of trophoblast cells and/or their fragment beyond the uterine basement membrane in the cow. In fact, the newly description of exosome release by the trophoblast cell suggests that could be a new fashion of maternal-foetal communication at the placental barrier. Additionally, recent global transcriptome studies on bovine endometrium suggested that the immune system is aware, from an immunological point of view, of the presence of the foetus in the cow during early pregnancy.

Extended Hierarchical Censored Production Rules (EHCPRs) system: An approach toward generalized knowledge representation

One common problem in all basic techniques of knowledge representation is the handling of the trade-off between precision of inferences and resource constraints, such as time and memory. Michalski and Winston (1986) suggested the Censored Production Rule (CPR) as an underlying representation and computational mechanism to enable logic based systems to exhibit variable precision in which certainty varies while specificity stays constant. As an extension of CPR, the Hierarchical Censored Production Rules (HCPRs) system of knowledge representation, proposed by Bharadwaj & Jain (1992), exhibits both variable certainty as well as variable specificity and offers mechanisms for handling the trade-off between the two. An HCPR has the form: Decision If(preconditions) Unless(censor) Generality(general_information) Specificity(specific_information). As an attempt towards evolving a generalized knowledge representation, an Extended Hierarchical Censored Production Rules (EHCPRs) system is suggested in this paper. With the inclusion of new operators, an Extended Hierarchical Censored Production Rule (EHCPR) takes the general form: Concept If (Preconditions) Unless (Exceptions) Generality (General-Concept) Specificity (Specific Concepts) Has_part (default: structural-parts) Has_property (default:characteristic-properties) Has_instance (instances). How semantic networks and frames are represented in terms of an EHCPRs is shown. Multiple inheritance, inheritance with and without cancellation, recognition with partial match, and a few default logic problems are shown to be tackled efficiently in the proposed system.