Positioning an underwater vehicle through image mosaicking

Mosaics have been commonly used as visual maps for undersea exploration and navigation. The position and orientation of an underwater vehicle can be calculated by integrating the apparent motion of the images which form the mosaic. A feature-based mosaicking method is proposed in this paper. The creation of the mosaic is accomplished in four stages: feature selection and matching, detection of points describing the dominant motion, homography computation and mosaic construction. In this work we demonstrate that the use of color and textures as discriminative properties of the image can improve, to a large extent, the accuracy of the constructed mosaic. The system is able to provide 3D metric information concerning the vehicle motion using the knowledge of the intrinsic parameters of the camera while integrating the measurements of an ultrasonic sensor. The experimental results of real images have been tested on the GARBI underwater vehicle

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

Proposal of a parallel architecture for a motion detection algorithm

This paper proposes a parallel architecture for estimation of the motion of an underwater robot. 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. In a motion estimation algorithm, correspondences between two images have to be solved at the low level. In the underwater imaging, normalised correlation can be a solution in the presence of non-uniform illumination. Due to its regular processing scheme, parallel implementation of the correspondence problem can be an adequate approach to reduce the computation time. Taking into consideration the complexity of the normalised correlation criteria, a new approach using parallel organisation of every processor from the architecture is proposed

Dam wall detection and tracking using a Mechanically Scanned Imaging Sonar

In dam inspection tasks, an underwater robot has to grab images while surveying the wall meanwhile maintaining a certain distance and relative orientation. This paper proposes the use of an MSIS (mechanically scanned imaging sonar) for relative positioning of a robot with respect to the wall. An imaging sonar gathers polar image scans from which depth images (range & bearing) are generated. Depth scans are first processed to extract a line corresponding to the wall (with the Hough transform), which is then tracked by means of an EKF (Extended Kalman Filter) using a static motion model and an implicit measurement equation associating the sensed points to the candidate line. The line estimate is referenced to the robot fixed frame and represented in polar coordinates (rho&thetas) which directly corresponds to the actual distance and relative orientation of the robot with respect to the wall. The proposed system has been tested in simulation as well as in water tank conditions

Underwater SLAM in a marina environment

This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach

Enantioselective detection of L-serine

An acoustic wave sensor coated with an artificial biomimetic recognition element has been developed to selectively detect the amino acid L-serine. A highly specific non-covalently imprinted polymer was cast on one electrode of a quartz crystal microbalance (QCM) as a thin permeable film. Selective rebinding of the L-serine was observed as a frequency shift in the QCM with a detection limit of 2 ppb and for concentrations up to 0.4 ppm. The sensor binding is shown to be capable of discrimination between L- and D-stereoisomers of serine as a result of the enantioselectivity of the imprinted binding sites. (C) 2002 Elsevier Science B.V. All rights reserved.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

Benchmark datasets for detection and tracking

There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

Detection of lower tropospheric responses to solar energetic particles at midlatitudes

Solar energetic particles (SEPs) occasionally contribute additional atmospheric ionization beyond that arising from the usual galactic cosmic ray background. During an SEP event associated with a solar flare on April 11, 2013, the vertical ionization rate profile obtained using a balloon-borne detector showed enhanced ionization with a 26% increase at 20 km, over Reading, United Kingdom. Fluctuations in atmospheric electrical parameters were also detected at the surface, beneath the balloon’s trajectory. As no coincident changes in geomagnetism occurred, the electrical fluctuations are very likely to be associated with increased ionization, as observed by the balloon measurements. The lack of response of surface neutron monitors during this event indicates that energetic particles that are not detected at the surface by neutron monitors can nevertheless enter and influence the atmosphere’s weather-generating regions.

Detection of the phosphorylation of the estrogen receptor alpha as an outcome of GPR30 activation

Phosphorylation of the serine residues in estrogen receptor (ER) α is important in transcriptional activation. Hence, methods to detect such posttranslational modifi cation events are valuable. We describe, in detail, the analysis of the phosphorylated ERα by electrophoretic separation of proteins and subsequent immuno-blotting techniques. In particular, phosphorylation of the ERα is one possible outcome of activation of the putative membrane estrogen receptor (mER), GPR30. Hence, phosphorylation represents a cross talk event between GPR30 and ERα and may be important in estrogen-regulated physiology.

Carbon nanotubes in nanostructured films: Potential application as amperometric and potentiometric field-effect (bio-)chemical sensors

The assembly of carbon nanotubes (CNTs) into nanostructured films is attractive for producing functionalized hybrid materials and (bio-)chemical sensors, but this requires experimental methods that allow for control of molecular architecturcs. In this study, we exploit the layer-by-layer (LbL) technique to obtain two types of sensors incorporating CNTs. In the first, LbL films of alternating layers of multi-walled carbon nanotubes (MWNTs) dispersed in polyarninoamide (PAMAM) dendrimers and nickel phthalocyanine (NiTsPc) were used in amperometric detection of the neurotransmitter dopamine (DA). The electrochemical properties evaluated with cyclic voltammetry indicated that the incorporation of MWNTs in the PAMAM-NT/NiTsPc LbL films led to a 3-fold increase in the peak current, in addition to a decrease of 50 mV in the oxidation potential of DA. The latter allowed detection of DA even in the presence of ascorbic acid (AA), a typical interferent for DA. Another LbL film was obtained with layers of PAMAM and single-walled carbon nanotubes (SWNTs) employed in field-effect-devices using a capacitive electrolyte-insulator-semiconductor structure (EIS). The adsorption of the film components was monitored by measuring the flat-band voltage shift in capacitance-voltage (C-P) curves, caused by the charges from the components. Constant capacitance (ConCap) measurements showed that the EISPAMAM/SWNT film displayed a high pH sensitivity (ca. 54.5 mV/pH), being capable of detecting penicillin G between 10(-4) mol L(-1) and 10(-2) mol L-1, when a layer of penicillinase was adsorbed atop the PAMAM/SWNT film. (C) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Designing single event upset mitigation techniques for large SRAM-Based FPGA components

This thesis presents the study and development of fault-tolerant techniques for programmable architectures, the well-known Field Programmable Gate Arrays (FPGAs), customizable by SRAM. FPGAs are becoming more valuable for space applications because of the high density, high performance, reduced development cost and re-programmability. In particular, SRAM-based FPGAs are very valuable for remote missions because of the possibility of being reprogrammed by the user as many times as necessary in a very short period. SRAM-based FPGA and micro-controllers represent a wide range of components in space applications, and as a result will be the focus of this work, more specifically the Virtex® family from Xilinx and the architecture of the 8051 micro-controller from Intel. The Triple Modular Redundancy (TMR) with voters is a common high-level technique to protect ASICs against single event upset (SEU) and it can also be applied to FPGAs. The TMR technique was first tested in the Virtex® FPGA architecture by using a small design based on counters. Faults were injected in all sensitive parts of the FPGA and a detailed analysis of the effect of a fault in a TMR design synthesized in the Virtex® platform was performed. Results from fault injection and from a radiation ground test facility showed the efficiency of the TMR for the related case study circuit. Although TMR has showed a high reliability, this technique presents some limitations, such as area overhead, three times more input and output pins and, consequently, a significant increase in power dissipation. Aiming to reduce TMR costs and improve reliability, an innovative high-level technique for designing fault-tolerant systems in SRAM-based FPGAs was developed, without modification in the FPGA architecture. This technique combines time and hardware redundancy to reduce overhead and to ensure reliability. It is based on duplication with comparison and concurrent error detection. The new technique proposed in this work was specifically developed for FPGAs to cope with transient faults in the user combinational and sequential logic, while also reducing pin count, area and power dissipation. The methodology was validated by fault injection experiments in an emulation board. The thesis presents comparison results in fault coverage, area and performance between the discussed techniques.

Método multirresíduo para monitoramento de contaminação ambiental de pesticidas na região de Bauru (SP) usando mel como bio-indicador

The presence of residues of the major groups of pesticides (organohalogen, organophosphorous, pyrethroids and organonitrogen) in representative samples of honey produced in Bauru (state of São Paulo, Brazil) was investigated from 1999 through2004. A multiresidue method was applied to honey samples to determine 48 pesticides with recoveries ranging from 76 to 95%. The limits of detection found were lower than 10 µg/kg for GC-MS-SIM. The results indicated that most pesticides found in the samples belonged to the organohalogen and organonitrogen groups. Residues of malathion were detected in almost all of the samples in high concentration.

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

DNA methylation patterns in bladder cancer and washing cell sediments: a perspective for tumor recurrence detection

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