Novel lateral moving tracking optics with the SMS design method

Lateral moving optics along straight path has already been studied in the past. However, their relative small angular range can be a limitation to potential applications. In this work, a new design concept of SMS moving optics is developed, in which the movement is no longer lateral but follows a curved trajectory, which is calculated in the design process. We have chosen an afocal system, which aim to direct the parallel rays of large incident angles to parallel output rays, and we have obtained that the RMS of the divergence angle of the output rays remains below 1 degree within a input angular range of ��45 output. Potential applications of this beam-steering device are: skylights to provide steerable natural illumination, building integrated CPV systems, and steerable LED illumination.

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

A packing computational method relating fractal particle size distribution and void fraction in granular media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

A Packing Computational Method Relating Fractal Particle Size Distribution and Void Fraction in Granular Media

The study of granular systems is of great interest to many fields of science and technology. The packing of particles affects to the physical properties of the granular system. In particular, the crucial influence of particle size distribution (PSD) on the random packing structure increase the interest in relating both, either theoretically or by computational methods. A packing computational method is developed in order to estimate the void fraction corresponding to a fractal-like particle size distribution.

Influence of gravity on heavy particle statistics in turbulent channel and pipe flow at matched K��rm��n number

In this work, we use large eddy simulations (LES) and Lagrangian tracking to study the influence of gravity on particle statistics in a fully developed turbulent upward/downward flow in a vertical channel and pipe at matched K��rm��n number. Only drag and gravity are considered in the equation of motion for solid particles, which are assumed to have no influence on the flow field. Particle interactions with the wall are fully elastic. Our findings obtained from the particle statistics confirm that: (i) the gravity seems to modify both the quantitative and qualitative behavior of the particle distribution and statistics of the particle velocity in wall normal direction; (ii) however, only the quantitative behavior of velocity particle in streamwise direction and the root mean square of velocity components is modified; (iii) the statistics of fluid and particles coincide very well near the wall in channel and pipe flow with equal K��rm��n number; (iv) pipe curvature seems to have quantitative and qualitative influence on the particle velocity and on the particle concentration in wall normal direction.

Efficient Model-based 3D Tracking by Using Direct Image Registration

This thesis deals with the problem of efficiently tracking 3D objects in sequences of images. We tackle the efficient 3D tracking problem by using direct image registration. This problem is posed as an iterative optimization procedure that minimizes a brightness error norm. We review the most popular iterative methods for image registration in the literature, turning our attention to those algorithms that use efficient optimization techniques. Two forms of efficient registration algorithms are investigated. The first type comprises the additive registration algorithms: these algorithms incrementally compute the motion parameters by linearly approximating the brightness error function. We centre our attention on Hager and Belhumeur���s factorization-based algorithm for image registration. We propose a fundamental requirement that factorization-based algorithms must satisfy to guarantee good convergence, and introduce a systematic procedure that automatically computes the factorization. Finally, we also bring out two warp functions to register rigid and nonrigid 3D targets that satisfy the requirement. The second type comprises the compositional registration algorithms, where the brightness function error is written by using function composition. We study the current approaches to compositional image alignment, and we emphasize the importance of the Inverse Compositional method, which is known to be the most efficient image registration algorithm. We introduce a new algorithm, the Efficient Forward Compositional image registration: this algorithm avoids the necessity of inverting the warping function, and provides a new interpretation of the working mechanisms of the inverse compositional alignment. By using this information, we propose two fundamental requirements that guarantee the convergence of compositional image registration methods. Finally, we support our claims by using extensive experimental testing with synthetic and real-world data. We propose a distinction between image registration and tracking when using efficient algorithms. We show that, depending whether the fundamental requirements are hold, some efficient algorithms are eligible for image registration but not for tracking.

Theoretical analysis and numerical verification of the consistency of viscous smoothed-particle-hydrodynamics formulations in simulating free-surface flows

The theoretical formulation of the smoothed particle hydrodynamics (SPH) method deserves great care because of some inconsistencies occurring when considering free-surface inviscid flows. Actually, in SPH formulations one usually assumes that (i) surface integral terms on the boundary of the interpolation kernel support are neglected, (ii) free-surface conditions are implicitly verified. These assumptions are studied in detail in the present work for free-surface Newtonian viscous flow. The consistency of classical viscous weakly compressible SPH formulations is investigated. In particular, the principle of virtual work is used to study the verification of the free-surface boundary conditions in a weak sense. The latter can be related to the global energy dissipation induced by the viscous term formulations and their consistency. Numerical verification of this theoretical analysis is provided on three free-surface test cases including a standing wave, with the three viscous term formulations investigated.

Soiling and other optical losses in solar-tracking PV plants in Navarra

Field data of soiling energy losses on PV plants are scarce. Furthermore, since dirt type and accumulation vary with the location characteristics (climate, surroundings, etc.), the available data on optical losses are, necessarily, site dependent. This paper presents field measurements of dirt energy losses (dust) and irradiance incidence angle losses along 2005 on a solar-tracking PV plant located south of Navarre (Spain). The paper proposes a method to calculate these losses based on the difference between irradiance measured by calibrated cells on several trackers of the PV plant and irradiance calculated from measurements by two pyranometers (one of them incorporating a shadow ring) regularly cleaned. The equivalent optical energy losses of an installation incorporating fixed horizontal modules at the same location have been calculated as well. The effect of dirt on both types of installations will accordingly be compared.

Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches.

A method for Kinematic Calibration of a Parallel Robot by using one camera in hand and a spherical object

The main purpose of robot calibration is the correction of the possible errors in the robot parameters. This paper presents a method for a kinematic calibration of a parallel robot that is equipped with one camera in hand. In order to preserve the mechanical configuration of the robot, the camera is utilized to acquire incremental positions of the end effector from a spherical object that is fixed in the word reference frame. The positions of the end effector are related to incremental positions of resolvers of the motors of the robot, and a kinematic model of the robot is used to find a new group of parameters which minimizes errors in the kinematic equations. Additionally, properties of the spherical object and intrinsic camera parameters are utilized to model the projection of the object in the image and improving spatial measurements. Finally, the robotic system is designed to carry out tracking tasks and the calibration of the robot is validated by means of integrating the errors of the visual controller.

Applications for wireless sensor networks : tracking with binary proximity sensors

El inter��s cada vez mayor por las redes de sensores inal��mbricos pueden ser entendido simplemente pensando en lo que esencialmente son: un gran n��mero de peque��os nodos sensores autoalimentados que recogen informaci��n o detectan eventos especiales y se comunican de manera inal��mbrica, con el objetivo final de entregar sus datos procesados a una estaci��n base. Los nodos sensores est��n densamente desplegados dentro del ��rea de inter��s, se pueden desplegar al azar y tienen capacidad de cooperaci��n. Por lo general, estos dispositivos son peque��os y de bajo costo, de modo que pueden ser producidos y desplegados en gran numero aunque sus recursos en t��rminos de energ��a, memoria, velocidad de c��lculo y ancho de banda est��n enormemente limitados. Detecci��n, tratamiento y comunicaci��n son tres elementos clave cuya combinaci��n en un peque��o dispositivo permite lograr un gran n��mero de aplicaciones. Las redes de sensores proporcionan oportunidades sin fin, pero al mismo tiempo plantean retos formidables, tales como lograr el m��ximo rendimiento de una energ��a que es escasa y por lo general un recurso no renovable. Sin embargo, los recientes avances en la integraci��n a gran escala, integrado de hardware de computaci��n, comunicaciones, y en general, la convergencia de la inform��tica y las comunicaciones, est��n haciendo de esta tecnolog��a emergente una realidad. Del mismo modo, los avances en la nanotecnolog��a est��n empezando a hacer que todo gire entorno a las redes de peque��os sensores y actuadores distribuidos. Hay diferentes tipos de sensores tales como sensores de presi��n, aceler��metros, c��maras, sensores t��rmicos o un simple micr��fono. Supervisan las condiciones presentes en diferentes lugares tales como la temperatura, humedad, el movimiento, la luminosidad, presi��n, composici��n del suelo, los niveles de ruido, la presencia o ausencia de ciertos tipos de objetos, los niveles de tensi��n mec��nica sobre objetos adheridos y las caracter��sticas moment��neas tales como la velocidad , la direcci��n y el tama��o de un objeto, etc. Se comprobara el estado de las Redes Inal��mbricas de Sensores y se revisaran los protocolos m��s famosos. As�� mismo, se examinara la identificaci��n por radiofrecuencia (RFID) ya que se est�� convirtiendo en algo actual y su presencia importante. La RFID tiene un papel crucial que desempe��ar en el futuro en el mundo de los negocios y los individuos por igual. El impacto mundial que ha tenido la identificaci��n sin cables est�� ejerciendo fuertes presiones en la tecnolog��a RFID, los servicios de investigaci��n y desarrollo, desarrollo de normas, el cumplimiento de la seguridad y la privacidad y muchos m��s. Su potencial econ��mico se ha demostrado en algunos pa��ses mientras que otros est��n simplemente en etapas de planificaci��n o en etapas piloto, pero aun tiene que afianzarse o desarrollarse a trav��s de la modernizaci��n de los modelos de negocio y aplicaciones para poder tener un mayor impacto en la sociedad. Las posibles aplicaciones de redes de sensores son de inter��s para la mayor��a de campos. La monitorizaci��n ambiental, la guerra, la educaci��n infantil, la vigilancia, la micro-cirug��a y la agricultura son solo unos pocos ejemplos de los much��simos campos en los que tienen cabida las redes mencionadas anteriormente. Estados Unidos de Am��rica es probablemente el pa��s que m��s ha investigado en esta ��rea por lo que veremos muchas soluciones propuestas provenientes de ese pa��s. Universidades como Berkeley, UCLA (Universidad de California, Los ��ngeles) Harvard y empresas como Intel lideran dichas investigaciones. Pero no solo EE.UU. usa e investiga las redes de sensores inal��mbricos. La Universidad de Southampton, por ejemplo, est�� desarrollando una tecnolog��a para monitorear el comportamiento de los glaciares mediante redes de sensores que contribuyen a la investigaci��n fundamental en glaciolog��a y de las redes de sensores inal��mbricos. As�� mismo, Coalesenses GmbH (Alemania) y Zurich ETH est��n trabajando en diversas aplicaciones para redes de sensores inal��mbricos en numerosas ��reas. Una soluci��n espa��ola ser�� la elegida para ser examinada m��s a fondo por ser innovadora, adaptable y polivalente. Este estudio del sensor se ha centrado principalmente en aplicaciones de tr��fico, pero no se puede olvidar la lista de m��s de 50 aplicaciones diferentes que ha sido publicada por la firma creadora de este sensor espec��fico. En la actualidad hay muchas tecnolog��as de vigilancia de veh��culos, incluidos los sensores de bucle, c��maras de video, sensores de imagen, sensores infrarrojos, radares de microondas, GPS, etc. El rendimiento es aceptable, pero no suficiente, debido a su limitada cobertura y caros costos de implementaci��n y mantenimiento, especialmente este ultimo. Tienen defectos tales como: l��nea de visi��n, baja exactitud, dependen mucho del ambiente y del clima, no se puede realizar trabajos de mantenimiento sin interrumpir las mediciones, la noche puede condicionar muchos de ellos, tienen altos costos de instalaci��n y mantenimiento, etc. Por consiguiente, en las aplicaciones reales de circulaci��n, los datos recibidos son insuficientes o malos en t��rminos de tiempo real debido al escaso n��mero de detectores y su costo. Con el aumento de veh��culos en las redes viales urbanas las tecnolog��as de detecci��n de veh��culos se enfrentan a nuevas exigencias. Las redes de sensores inal��mbricos son actualmente una de las tecnolog��as m��s avanzadas y una revoluci��n en la detecci��n de informaci��n remota y en las aplicaciones de recogida. Las perspectivas de aplicaci��n en el sistema inteligente de transporte son muy amplias. Con este fin se ha desarrollado un programa de localizaci��n de objetivos y recuento utilizando una red de sensores binarios. Esto permite que el sensor necesite mucha menos energ��a durante la transmisi��n de informaci��n y que los dispositivos sean m��s independientes con el fin de tener un mejor control de tr��fico. La aplicaci��n se centra en la eficacia de la colaboraci��n de los sensores en el seguimiento m��s que en los protocolos de comunicaci��n utilizados por los nodos sensores. Las operaciones de salida y retorno en las vacaciones son un buen ejemplo de por qu�� es necesario llevar la cuenta de los coches en las carreteras. Para ello se ha desarrollado una simulaci��n en Matlab con el objetivo localizar objetivos y contarlos con una red de sensores binarios. Dicho programa se podr��a implementar en el sensor que Libelium, la empresa creadora del sensor que se examinara concienzudamente, ha desarrollado. Esto permitir��a que el aparato necesitase mucha menos energ��a durante la transmisi��n de informaci��n y los dispositivos sean m��s independientes. Los prometedores resultados obtenidos indican que los sensores de proximidad binarios pueden formar la base de una arquitectura robusta para la vigilancia de ��reas amplias y para el seguimiento de objetivos. Cuando el movimiento de dichos objetivos es suficientemente suave, no tiene cambios bruscos de trayectoria, el algoritmo ClusterTrack proporciona un rendimiento excelente en t��rminos de identificaci��n y seguimiento de trayectorias los objetos designados como blancos. Este algoritmo podr��a, por supuesto, ser utilizado para numerosas aplicaciones y se podr��a seguir esta l��nea de trabajo para futuras investigaciones. No es sorprendente que las redes de sensores de binarios de proximidad hayan atra��do mucha atenci��n ��ltimamente ya que, a pesar de la informaci��n m��nima de un sensor de proximidad binario proporciona, las redes de este tipo pueden realizar un seguimiento de todo tipo de objetivos con la precisi��n suficiente. Abstract The increasing interest in wireless sensor networks can be promptly understood simply by thinking about what they essentially are: a large number of small sensing self-powered nodes which gather information or detect special events and communicate in a wireless fashion, with the end goal of handing their processed data to a base station. The sensor nodes are densely deployed inside the phenomenon, they deploy random and have cooperative capabilities. Usually these devices are small and inexpensive, so that they can be produced and deployed in large numbers, and so their resources in terms of energy, memory, computational speed and bandwidth are severely constrained. Sensing, processing and communication are three key elements whose combination in one tiny device gives rise to a vast number of applications. Sensor networks provide endless opportunities, but at the same time pose formidable challenges, such as the fact that energy is a scarce and usually non-renewable resource. However, recent advances in low power Very Large Scale Integration, embedded computing, communication hardware, and in general, the convergence of computing and communications, are making this emerging technology a reality. Likewise, advances in nanotechnology and Micro Electro-Mechanical Systems are pushing toward networks of tiny distributed sensors and actuators. There are different sensors such as pressure, accelerometer, camera, thermal, and microphone. They monitor conditions at different locations, such as temperature, humidity, vehicular movement, lightning condition, pressure, soil makeup, noise levels, the presence or absence of certain kinds of objects, mechanical stress levels on attached objects, the current characteristics such as speed, direction and size of an object, etc. The state of Wireless Sensor Networks will be checked and the most famous protocols reviewed. As Radio Frequency Identification (RFID) is becoming extremely present and important nowadays, it will be examined as well. RFID has a crucial role to play in business and for individuals alike going forward. The impact of ���wireless��� identification is exerting strong pressures in RFID technology and services research and development, standards development, security compliance and privacy, and many more. The economic value is proven in some countries while others are just on the verge of planning or in pilot stages, but the wider spread of usage has yet to take hold or unfold through the modernisation of business models and applications. Possible applications of sensor networks are of interest to the most diverse fields. Environmental monitoring, warfare, child education, surveillance, micro-surgery, and agriculture are only a few examples. Some real hardware applications in the United States of America will be checked as it is probably the country that has investigated most in this area. Universities like Berkeley, UCLA (University of California, Los Angeles) Harvard and enterprises such as Intel are leading those investigations. But not just USA has been using and investigating wireless sensor networks. University of Southampton e.g. is to develop technology to monitor glacier behaviour using sensor networks contributing to fundamental research in glaciology and wireless sensor networks. Coalesenses GmbH (Germany) and ETH Zurich are working in applying wireless sensor networks in many different areas too. A Spanish solution will be the one examined more thoroughly for being innovative, adaptable and multipurpose. This study of the sensor has been focused mainly to traffic applications but it cannot be forgotten the more than 50 different application compilation that has been published by this specific sensor���s firm. Currently there are many vehicle surveillance technologies including loop sensors, video cameras, image sensors, infrared sensors, microwave radar, GPS, etc. The performance is acceptable but not sufficient because of their limited coverage and expensive costs of implementation and maintenance, specially the last one. They have defects such as: line-ofsight, low exactness, depending on environment and weather, cannot perform no-stop work whether daytime or night, high costs for installation and maintenance, etc. Consequently, in actual traffic applications the received data is insufficient or bad in terms of real-time owed to detector quantity and cost. With the increase of vehicle in urban road networks, the vehicle detection technologies are confronted with new requirements. Wireless sensor network is the state of the art technology and a revolution in remote information sensing and collection applications. It has broad prospect of application in intelligent transportation system. An application for target tracking and counting using a network of binary sensors has been developed. This would allow the appliance to spend much less energy when transmitting information and to make more independent devices in order to have a better traffic control. The application is focused on the efficacy of collaborative tracking rather than on the communication protocols used by the sensor nodes. Holiday crowds are a good case in which it is necessary to keep count of the cars on the roads. To this end a Matlab simulation has been produced for target tracking and counting using a network of binary sensors that e.g. could be implemented in Libelium���s solution. Libelium is the enterprise that has developed the sensor that will be deeply examined. This would allow the appliance to spend much less energy when transmitting information and to make more independent devices. The promising results obtained indicate that binary proximity sensors can form the basis for a robust architecture for wide area surveillance and tracking. When the target paths are smooth enough ClusterTrack particle filter algorithm gives excellent performance in terms of identifying and tracking different target trajectories. This algorithm could, of course, be used for different applications and that could be done in future researches. It is not surprising that binary proximity sensor networks have attracted a lot of attention lately. Despite the minimal information a binary proximity sensor provides, networks of these sensing modalities can track all kinds of different targets classes accurate enough.

Bernstein polynomials in element-free Galerkin method

In the recent decades, meshless methods (MMs), like the element-free Galerkin method (EFGM), have been widely studied and interesting results have been reached when solving partial differential equations. However, such solutions show a problem around boundary conditions, where the accuracy is not adequately achieved. This is caused by the use of moving least squares or residual kernel particle method methods to obtain the shape functions needed in MM, since such methods are good enough in the inner of the integration domains, but not so accurate in boundaries. This way, Bernstein curves, which are a partition of unity themselves,can solve this problem with the same accuracy in the inner area of the domain and at their boundaries.

Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.