Could a satellite-based navigation system (GPS) be used to assess the physical activity of individuals on earth?

OBJECTIVES: To test whether the Global Positioning System (GPS) could be potentially useful to assess the velocity of walking and running in humans. SUBJECT: A young man was equipped with a GPS receptor while walking running and cycling at various velocity on an athletic track. The speed of displacement assessed by GPS, was compared to that directly measured by chronometry (76 tests). RESULTS: In walking and running conditions (from 2-20 km/h) as well as cycling conditions (from 20-40 km/h), there was a significant relationship between the speed assessed by GPS and that actually measured (r = 0.99, P < 0.0001) with little bias in the prediction of velocity. The overall error of prediction (s.d. of difference) averaged +/-0.8 km/h. CONCLUSION: The GPS technique appears very promising for speed assessment although the relative accuracy at walking speed is still insufficient for research purposes. It may be improved by using differential GPS measurement.

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory

Lumbar-sacral pedicle screw insertion with preoperative CT-based navigation

Objectif: Nous avons effectué une étude chez 135 patients ayant subis une chirurgie lombo-sacrée avec vissage pédiculaire sous navigation par tomographie axiale. Nous avons évalué la précision des vis pédiculaires et les résultats cliniques. Méthodes: Cette étude comporte 44 hommes et 91 femmes (âge moyen=61, intervalle 24-90 ans). Les diamètres, longueurs et trajectoires des 836 vis ont été planifiés en préopératoire avec un système de navigation (SNN, Surgical Navigation Network, Mississauga). Les patients ont subi une fusion lombaire (55), lombo-sacrée (73) et thoraco-lombo-sacrée (7). La perforation pédiculaire, la longueur des vis et les spondylolisthesis sont évalués par tomographies axiales postopératoires. Le niveau de douleur est mesuré par autoévaluations, échelles visuelles analogues et questionnaires (Oswestry et SF-36). La fusion osseuse a été évaluée par l’examen des radiographies postopératoires. Résultats: Une perforation des pédicules est présente pour 49/836 (5.9%) des vis (2.4% latéral, 1.7% inférieur, 1.1% supérieur, 0.7% médial). Les erreurs ont été mineures (0.1-2mm, 46/49) ou intermédiaires (2.1 - 4mm, 3/49 en latéral). Il y a aucune erreur majeure (≥ 4.1mm). Certaines vis ont été jugées trop longues (66/836, 8%). Le temps moyen pour insérer une vis en navigation a été de 19.1 minutes de l΄application au retrait du cadre de référence. Un an postopératoire on note une amélioration de la douleur des jambes et lombaire de 72% et 48% en moyenne respectivement. L’amélioration reste stable après 2 ans. La dégénérescence radiologique au dessus et sous la fusion a été retrouvée chez 44 patients (33%) and 3 patients respectivement (2%). Elle est survenue en moyenne 22.2 ± 2.6 mois après la chirurgie. Les fusions se terminant à L2 ont été associées à plus de dégénération (14/25, 56%). Conclusion: La navigation spinale basée sur des images tomographiques préopératoires est une technique sécuritaire et précise. Elle donne de bons résultats à court terme justifiant l’investissement de temps chirurgical. La dégénérescence segmentaire peut avoir un impact négatif sur les résultats radiologique et cliniques.

Propagation of Microwaves in the Troposphere with Potential Application to GPS based Navigation and Meteorology with emphasis on Indian region

Global Positioning System (GPS), with its high integrity, continuous availability and reliability, revolutionized the navigation system based on radio ranging. With four or more GPS satellites in view, a GPS receiver can find its location anywhere over the globe with accuracy of few meters. High accuracy - within centimeters, or even millimeters is achievable by correcting the GPS signal with external augmentation system. The use of satellite for critical application like navigation has become a reality through the development of these augmentation systems (like W AAS, SDCM, and EGNOS, etc.) with a primary objective of providing essential integrity information needed for navigation service in their respective regions. Apart from these, many countries have initiated developing space-based regional augmentation systems like GAGAN and IRNSS of India, MSAS and QZSS of Japan, COMPASS of China, etc. In future, these regional systems will operate simultaneously and emerge as a Global Navigation Satellite System or GNSS to support a broad range of activities in the global navigation sector.Among different types of error sources in the GPS precise positioning, the propagation delay due to the atmospheric refraction is a limiting factor on the achievable accuracy using this system. The WADGPS, aimed for accurate positioning over a large area though broadcasts different errors involved in GPS ranging including ionosphere and troposphere errors, due to the large temporal and spatial variations in different atmospheric parameters especially in lower atmosphere (troposphere), the use of these broadcasted tropospheric corrections are not sufficiently accurate. This necessitated the estimation of tropospheric error based on realistic values of tropospheric refractivity. Presently available methodologies for the estimation of tropospheric delay are mostly based on the atmospheric data and GPS measurements from the mid-latitude regions, where the atmospheric conditions are significantly different from that over the tropics. No such attempts were made over the tropics. In a practical approach when the measured atmospheric parameters are not available analytical models evolved using data from mid-latitudes for this purpose alone can be used. The major drawback of these existing models is that it neglects the seasonal variation of the atmospheric parameters at stations near the equator. At tropics the model underestimates the delay in quite a few occasions. In this context, the present study is afirst and major step towards the development of models for tropospheric delay over the Indian region which is a prime requisite for future space based navigation program (GAGAN and IRNSS). Apart from the models based on the measured surface parameters, a region specific model which does not require any measured atmospheric parameter as input, but depends on latitude and day of the year was developed for the tropical region with emphasis on Indian sector.Large variability of atmospheric water vapor content in short spatial and/or temporal scales makes its measurement rather involved and expensive. A local network of GPS receivers is an effective tool for water vapor remote sensing over the land. This recently developed technique proves to be an effective tool for measuring PW. The potential of using GPS to estimate water vapor in the atmosphere at all-weather condition and with high temporal resolution is attempted. This will be useful for retrieving columnar water vapor from ground based GPS data. A good network of GPS could be a major source of water vapor information for Numerical Weather Prediction models and could act as surrogate to the data gap in microwave remote sensing for water vapor over land.

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

CT data-based navigation for post-mortem biopsy-a feasibility study

INTRODUCTION: Recent advances in medical imaging have brought post-mortem minimally invasive computed tomography (CT) guided percutaneous biopsy to public attention. AIMS: The goal of the following study was to facilitate and automate post-mortem biopsy, to suppress radiation exposure to the investigator, as may occur when tissue sampling under computer tomographic guidance, and to minimize the number of needle insertion attempts for each target for a single puncture. METHODS AND MATERIALS: Clinically approved and post-mortem tested ACN-III biopsy core needles (14 gauge x 160 mm) with an automatic pistol device (Bard Magnum, Medical Device Technologies, Denmark) were used for probe sampling. The needles were navigated in gelatine/peas phantom, ex vivo porcine model and subsequently in two human bodies using a navigation system (MEM centre/ISTB Medical Application Framework, Marvin, Bern, Switzerland) with guidance frame and a CT (Emotion 6, Siemens, Germany). RESULTS: Biopsy of all peas could be performed within a single attempt. The average distance between the inserted needle tip and the pea centre was 1.4mm (n=10; SD 0.065 mm; range 0-2.3 mm). The targets in the porcine liver were also accurately punctured. The average of the distance between the needle tip and the target was 0.5 mm (range 0-1 mm). Biopsies of brain, heart, lung, liver, pancreas, spleen, and kidney were performed on human corpses. For each target the biopsy needle was only inserted once. The examination of one body with sampling of tissue probes at the above-mentioned locations took approximately 45 min. CONCLUSIONS: Post-mortem navigated biopsy can reliably provide tissue samples from different body locations. Since the continuous update of positional data of the body and the biopsy needle is performed using optical tracking, no control CT images verifying the positional data are necessary and no radiation exposure to the investigator need be taken into account. Furthermore, the number of needle insertions for each target can be minimized to a single one with the ex vivo proven adequate accuracy and, in contrast to conventional CT guided biopsy, the insertion angle may be oblique. Navigation for minimally invasive tissue sampling is a useful addition to post-mortem CT guided biopsy.

Does spatial locative comprehension predict landmark-based navigation?

In the present study we investigated the role of spatial locative comprehension in learning and retrieving pathways when landmarks were available and when they were absent in a sample of typically developing 6- to 11-year-old children. Our results show that the more proficient children are in understanding spatial locatives the more they are able to learn pathways, retrieve them after a delay and represent them on a map when landmarks are present in the environment. These findings suggest that spatial language is crucial when individuals rely on sequences of landmarks to drive their navigation towards a given goal but that it is not involved when navigational representations based on the geometrical shape of the environment or the coding of body movements are sufficient for memorizing and recalling short pathways.

Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices

This project proposes an approach for supporting Indoor Navigation Systems using Pedestrian Dead Reckoning-based methods and by analyzing motion sensor data available in most modern smartphones. Processes suggested in this investigation are able to calculate the distance traveled by a user while he or she is walking. WLAN fingerprint- based navigation systems benefit from the processes followed in this research and results achieved to reduce its workload and improve its positioning estimations.

Surface based hypothesis verification in intensity images using geometric and appearance data

In this paper we discuss current work concerning Appearance-based and CAD-based vision; two opposing vision strategies. CAD-based vision is geometry based, reliant on having complete object centred models. Appearance-based vision builds view dependent models from training images. Existing CAD-based vision systems that work with intensity images have all used one and zero dimensional features, for example lines, arcs, points and corners. We describe a system we have developed for combining these two strategies. Geometric models are extracted from a commercial CAD library of industry standard parts. Surface appearance characteristics are then learnt automatically by observing actual object instances. This information is combined with geometric information and is used in hypothesis evaluation. This augmented description improves the systems robustness to texture, specularities and other artifacts which are hard to model with geometry alone, whilst maintaining the advantages of a geometric description.

Fly by data link: feasibility of a relative navigation solution for aviation relying on a future L-band data link

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm