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.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Comparison Of Demand Pattern Calibration In Water Distribution Networks With Geographic And Non-Geographic Parameterization

Demands are one of the most uncertain parameters in a water distribution network model. A good calibration of the model demands leads to better solutions when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behaviour of demand groups. Generally, the behaviours of demands in cities are mixed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighbourhoods, commercial zones and industrial sectors. Demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. For this reason, the demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were also non-geographic.

Cartel deterrence and settlements: the Brazilian experience

Settlements are an important part of a program of cartel deterrence, particularly when the likelihood of conviction and the litigation costs are higher. This type of negotiated procedure to reach finality is in essence complementary to the fully adversarial procedures associated to the trial by the administrative or judicial courts, and to other investigative instruments, such as the leniency agreement. The Brazilian experience provides some insights about the different models of direct settlement in cartel cases and the complex interaction among settlements, leniency agreements, and trial outcome. First, there is leeway for the complementary models of settlements, the first oriented mainly to increasing the likelihood of detection, and the second oriented to saving social costs of litigation. Second, the concern with the preservation of the demand for leniency agreements led the competition authority to restrict the use of settlements, which are effectively designed for the defendants that are likely guilty and give higher value to finality. The recent experience illustrates that the current settlement policy has not caused any adverse effect on leniency agreements, while reducing litigation costs and granting finality in some cases.

Direct determination of Cu, Cd, Ni and Pb in aquatic humic substances by graphite furnace atomic absorption spectrometry

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

Volatile compounds from organic and conventional passion fruit (Passiflora edulis F. Flavicarpa) pulp

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

EFFECT of GAMMA IRRADIATION on CAPROLACTAM MIGRATION FROM MULTILAYER POLYAMIDE 6 FILMS INTO WATER FOOD SIMULANT and VALIDATION of THE METHOD

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Development of practical HPLC methods for the separation and determination of eggplant steroidal glycoalkaloids and their aglycones

A practical set of HPLC methods was developed for the separation and determination of the eggplant steroidal glycoalkaloids, solanine, chaconine, solasonine, solamargine, and their aglycones, solasodine and solanidine. A gradient method was initially developed, but proved to be neither robust nor practical. Three separate isocratic methods using acetonitrile and ammonium dihydrogen phosphate were developed and shown to be more repeatable, less subject to fluctuations in mobile phase composition, and less time consuming. The effect of adjusting buffer pH, column temperature, and buffer type (triethylammonium phosphate vs. ammonium dihydrogen phosphate) were evaluated. It was also discovered that, by addition of 10% methanol to the acetonitrile portion of the mobile phase, more control over the separations was possible. The use of methanol as a mobile phase entrainer greatly improved separations in some cases and its effectiveness was also dependent upon column temperature. Assessments of the method recovery, limit of detection, and limit of quantitation were made using extracts from S. melongena and S. linnaeanum.

Development and Validation of a UV-Spectrophotometric Method for Determination of Flucloxacillin Sodium in Capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Development and validation of an analytical method by RP-HPLC for quantification of sibutramine hydrochloride in pharmaceutical capsules

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

Performance characteristics of bioassay UV-spectrophotometry and high perfomance liquid chromatographic determination of gatifloxacin in tablets

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Performance characteristics of high performance liquid chromatography, first order derivative UV spectrophotometry and bioassay for fluconazole determination in capsules

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

Correlation between the intensity of venous reflux in the saphenofemoral junction and morphological changes of the great saphenous vein by duplex scanning in patients with primary varicosis

Aim. One of the major causes of chronic venous disease is venous reflux, the identification and quantification of which are important for diagnosis. Duplex scanning allows for the detection and quantification of reflux in individual veins. Evaluation of the great saphenous vein in primary varicosis is necessary for its preservation. Objective of the study is to evaluate a possible correlation between the intensity of reflux at the saphenofemoral junction, diameter alterations of the incompetent great saphenous vein and the practical effect of such correlation. Also to compare the clinical severity of the CEAP classification with such parameters.Methods. Three hundred limbs were submitted to duplex evaluation of their insufficient saphenous veins. Vein diameter was measured on five different points. Velocity and flow at reflux peak and reflux time were determined. The saphenous vein's diameters were correlated with velocity, flow and time. The three latter parameters and diameters were compared with clinical severity according to CEAP.Results. Correlation was found between the saphenous vein's diameters, velocity and flow. No correlation was observed between time and diameter in the thigh's upper and middle thirds. When comparing diameter, velocity and flow with CEAP clinical severity classification, an association was observed. The correlation between reflux time with clinical severity was weak.Conclusion. Reflux time is a good parameter for identifying the presence of reflux, but not for quantifying it. Velocity and peak flow were better parameters for evaluating reflux intensity as they were correlated with great saphenous vein alterations, and were associated with the disease's clinical severity. [Int Angiol 2010;29:323-30]