Object detecting artificial retina

This paper describes the novel use of agent and cellular neural Hopfield network techniques in the design of a self-contained, object detecting retina. The agents, which are used to detect features within an image, are trained using the Hebbian method which has been modified for the cellular architecture. The success of each agent is communicated with adjacent agents in order to verify the detection of an object. Initial work used the method to process bipolar images. This has now been extended to handle grey scale images. Simulations have demonstrated the success of the method and further work is planned in which the device is to be implemented in hardware.

A simplified method for referring the energy and overheating performance of window design

This paper describes a simplified dynamic thermal model which simulates the energy and overheating performance of windows. To calculate artificial energy use within a room, the model employs the average illuminance method, which takes into account the daylight energy impacting upon the room by the use of hourly climate data. This tool describes the main thermal performance ( heating, cooling and overheating risk) resulting proposed a design of window. The inputs are fewer and simpler than that are required by complicated simulation programmes. The method is suited for the use of architects and engineers at the strategic phase of design, when little is available.

Development and validation of a qPCR-based method for quantifying Shiga toxin-encoding and other lambdoid bacteriophages

P>To address whether seasonal variability exists among Shiga toxin-encoding bacteriophage (Stx phage) numbers on a cattle farm, conventional plaque assay was performed on water samples collected over a 17 month period. Distinct seasonal variation in bacteriophage numbers was evident, peaking between June and August. Removal of cattle from the pasture precipitated a reduction in bacteriophage numbers, and during the winter months, no bacteriophage infecting Escherichia coli were detected, a surprising occurrence considering that 1031 tailed-bacteriophages are estimated to populate the globe. To address this discrepancy a culture-independent method based on quantitative PCR was developed. Primers targeting the Q gene and stx genes were designed that accurately and discriminately quantified artificial mixed lambdoid bacteriophage populations. Application of these primer sets to water samples possessing no detectable phages by plaque assay, demonstrated that the number of lambdoid bacteriophage ranged from 4.7 x 104 to 6.5 x 106 ml-1, with one in 103 free lambdoid bacteriophages carrying a Shiga toxin operon (stx). Specific molecular biological tools and discriminatory gene targets have enabled virus populations in the natural environment to be enumerated and similar strategies could replace existing propagation-dependent techniques, which grossly underestimate the abundance of viral entities.

New-on-line method for water isotope analysis of speleothem fluid inclusions using laser absorption spectroscopy (WS-CRDS)

A new online method to analyse water isotopes of speleothem fluid inclusions using a wavelength scanned cavity ring down spectroscopy (WS-CRDS) instrument is presented. This novel technique allows us simultaneously to measure hydrogen and oxygen isotopes for a released aliquot of water. To do so, we designed a new simple line that allows the online water extraction and isotope analysis of speleothem samples. The specificity of the method lies in the fact that fluid inclusions release is made on a standard water background, which mainly improves the δ D robustness. To saturate the line, a peristaltic pump continuously injects standard water into the line that is permanently heated to 140 °C and flushed with dry nitrogen gas. This permits instantaneous and complete vaporisation of the standard water, resulting in an artificial water background with well-known δ D and δ18O values. The speleothem sample is placed in a copper tube, attached to the line, and after system stabilisation it is crushed using a simple hydraulic device to liberate speleothem fluid inclusions water. The released water is carried by the nitrogen/standard water gas stream directly to a Picarro L1102-i for isotope determination. To test the accuracy and reproducibility of the line and to measure standard water during speleothem measurements, a syringe injection unit was added to the line. Peak evaluation is done similarly as in gas chromatography to obtain &delta D; and δ18O isotopic compositions of measured water aliquots. Precision is better than 1.5 ‰ for δ D and 0.4 ‰ for δ18O for water measurements for an extended range (−210 to 0 ‰ for δ D and −27 to 0 ‰ for δ18O) primarily dependent on the amount of water released from speleothem fluid inclusions and secondarily on the isotopic composition of the sample. The results show that WS-CRDS technology is suitable for speleothem fluid inclusion measurements and gives results that are comparable to the isotope ratio mass spectrometry (IRMS) technique.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Background Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. Results In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Conclusions Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

A non-parametric vessel detection method for complex vascular structures

Modern medical imaging techniques enable the acquisition of in vivo high resolution images of the vascular system. Most common methods for the detection of vessels in these images, such as multiscale Hessian-based operators and matched filters, rely on the assumption that at each voxel there is a single cylinder. Such an assumption is clearly violated at the multitude of branching points that are easily observed in all, but the Most focused vascular image studies. In this paper, we propose a novel method for detecting vessels in medical images that relaxes this single cylinder assumption. We directly exploit local neighborhood intensities and extract characteristics of the local intensity profile (in a spherical polar coordinate system) which we term as the polar neighborhood intensity profile. We present a new method to capture the common properties shared by polar neighborhood intensity profiles for all the types of vascular points belonging to the vascular system. The new method enables us to detect vessels even near complex extreme points, including branching points. Our method demonstrates improved performance over standard methods on both 2D synthetic images and 3D animal and clinical vascular images, particularly close to vessel branching regions. (C) 2008 Elsevier B.V. All rights reserved.

Determination of sorbate and benzoate in beverage samples by capillary electrophoresis - Optimization of the method with inspection of ionic mobilities

The aim of this study was to develop a fast capillary electrophoresis method for the determination of benzoate and sorbate ions in commercial beverages. In the method development the pH and constituents of the background electrolyte were selected using the effective mobility versus pH curves. As the high resolution obtained experimentally for sorbate and benzoate in the studies presented in the literature is not in agreement with that expected from the ionic mobility values published, a procedure to determine these values was carried out. The salicylate ion was used as the internal standard. The background electrolyte was composed of 25 mmol L(-1) tris(hydroxymethyl)aminomethane and 12.5 mmol L(-1) 2-hydroxyisobutyric acid, atpH 8.1.Separation was conducted in a fused-silica capillary(32 cm total length and 8.5 cm effective length, 50 mu m I.D.), with short-end injection configuration and direct UV detection at 200 nm for benzoate and salicylate and 254 nm for sorbate ions. The run time was only 28 s. A few figures of merit of the proposed method include: good linearity (R(2) > 0.999), limit of detection of 0.9 and 0.3 mg L(-1) for benzoate and sorbate, respectively, inter-day precision better than 2.7% (n =9) and recovery in the range 97.9-105%. Beverage samples were prepared by simple dilution with deionized water (1:11, v/v). Concentrations in the range of 197-401 mg L(-1) for benzoate and 28-144 mg L(-1) for sorbate were found in soft drinks and tea. (c) 2008 Elsevier B.V. All rights reserved.

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.

Desenvolvimento de sistemas embarcados para redes de sensores e atuadores sem fio aplicadas em unidades de elevação de petróleo do tipo Plunger-Lift

The Wireless Sensor Networks (WSN) methods applied to the lifting of oil present as an area with growing demand technical and scientific in view of the optimizations that can be carried forward with existing processes. This dissertation has as main objective to present the development of embedded systems dedicated to a wireless sensor network based on IEEE 802.15.4, which applies the ZigBee protocol, between sensors, actuators and the PLC (Programmable Logic Controller), aiming to solve the present problems in the deployment and maintenance of the physical communication of current elevation oil units based on the method Plunger-Lift. Embedded systems developed for this application will be responsible for acquiring information from sensors and control actuators of the devices present at the well, and also, using the Modbus protocol to make this network becomes transparent to the PLC responsible for controlling the production and delivery information for supervisory SISAL

A new method to quantify macroalgae and a practical sampler for experimentation in lotic habitats

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influência de fotoperíodo artificial no comportamento de um primata neotropical diurno (Callithrix jacchus)

One of the main environmental cues for the adjustment of temporal organization of the animals is the light-dark cycle (LD), which undergoes changes in phase duration throughout the seasons. Photoperiod signaling by melatonin in mammals allows behavioral changes along the year, as in the activity-rest cycle, in mood states and in cognitive performance. The aim of this study was to investigate if common marmoset (Callithrix jacchus) exhibits behavioral changes under short and long photoperiods in a 24h cycle, assessing their individual behaviors, vocal repertoire, exploratory activity (EA), recognition memory (RM) and the circadian rhythm of locomotor activity (CRA). Eight adult marmosets were exposed to a light-dark cycle of 12:12; LD 08:16; LD 12:12 and LD 16:08, sequentially, for four weeks in each condition. Locomotor activity was recorded 24h/day by passive infrared motion detectors above the individual cages. A video camera system was programmed to record each animal, twice a week, on the first two light hours. From the videos, frequency of behaviors was registered as anxiety-like, grooming, alert, hanging position, staying in nest box and feeding using continuous focal animal sampling method. Simultaneously, the calls emitted in the experimental room were recorded by a single microphone centrally located and categorized as affiliative (whirr, chirp), contact (phee), long distance (loud shrill), agonistic (twitter) and alarm (tsik, seep, see). EA was assessed on the third hour after lights onset on the last week of each condition. In a first session, marmosets were exposed to one unfamiliar object during 15 min and 24h later, on the second session, a novel object was added to evaluate RM. Results showed that long days caused a decreased of amplitude and period variance of the CRA, but not short days. Short days decreased the total daily activity and active phase duration. On long days, active phase duration increased due to an advance of activity onset in relation to symmetric days. However, not all subjects started the activity earlier on long days. The activity offset was similar to symmetric days for the majority of marmosets. Results of EA showed that RM was not affected by short or long days, and that the marmosets exhibited a decreased in duration of EA on long days. Frequency and type of calls and frequency of anxiety-like behaviors, staying in nest box and grooming were lower on the first two light hours on long days. Considering the whole active phase of marmosets as we elucidate the results of vocalizations and behaviors, it is possible that these changes in the first two light hours are due to the shifting of temporal distribution of marmoset activities, since some animals did not advance the activity onset on long days. Consequently, the marmosets mean decreased because the sampling was not possible. In conclusion, marmosets synchronized the CRA to the tested photoperiods and as the phase angle varied a lot among marmosets it is suggested that they can use different strategies. Also, long days had an effect on activity-rest cycle and exploratory behaviors

Analytical and semi-analytical analysis of an artificial satellite's rotational motion

The rotational motion of an artificial satellite is studied by considering torques produced by gravity gradient and direct solar radiation pressure. A satellite of circular cylinder shape is considered here, and Andoyers variables are used to describe the rotational motion. Expressions for direct solar radiation torque are derived. When the earth's shadow is not considered, an analytical solution is obtained using Lagrange's method of variation of parameters. A semi-analytical procedure is proposed to predict the satellite's attitude under the influence of the earth's shadow. The analytical solution shows that angular variables are linear and periodic functions of time while their conjugates suffer only periodic variations. When compared, numerical and analytical solutions have a good agreement during the time range considered.