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The Escherichia coli chromosome is a dynamic molecule, exhibiting choreographed reorganizations across spatial scales. The chromosome is involved in a number of essential processes, such as DNA replication and chromosome segregation. These processes ensure that there are at least two copies of the genetic material at cell division, one for each daughter cell to inherit. However, maintaining a cycle-dependent chromosome organization is no small feat. To understand how the chromosome organization is regulated over the cell cycle and to understand the functional importance of the chromosome structure, we investigated the dynamics and intracellular positioning of various chromosomal loci in live E. coli using fluorescence microscopy. Our efforts to understand when and where in the cell chromosomal loci are replicated were based on fluorescently labeling a chromosomal locus and a subunit of the replisome in the same cell. With this labeling strategy, we followed the intracellular positioning of the replisome and various loci relative to each other, as well as their short-time-scale movements. We found that as loci were replicated their short-time-scale movements slowed down momentarily. Mapping the short-time-scale movements over different intracellular positions showed a clear repositioning of several loci towards the replisome to be replicated, which led us to conclude that the chromosome moves to the replisome during DNA replication. To investigate the three-dimensional positioning of chromosomal loci, we performed time-lapse imaging of E. coli strains with fluorescently labeled loci using a microscope with an astigmatic fluorescence emission path. To determine the 3D coordinates of the emitters, we developed a neural network-based algorithm trained on simulated images of E. coli cells with fluorescent foci. Applying this neural network to different loci showed distinct 3D localization patterns over the cell cycle. To study the 3D chromosome organization, we imaged

The Escherichia coli chromosome is a dynamic molecule, exhibiting choreographed reorganizations across spatial scales. The chromosome is involved in a number of essential processes, such as DNA replication and chromosome segregation. These processes ensure that there are at least two copies of the genetic material at cell division, one for each daughter cell to inherit. However, maintaining a cycle-dependent chromosome organization is no small feat. To understand how the chromosome organization is regulated over the cell cycle and to understand the functional importance of the chromosome structure, we investigated the dynamics and intracellular positioning of various chromosomal loci in live E. coli using fluorescence microscopy. Our efforts to understand when and where in the cell chromosomal loci are replicated were based on fluorescently labeling a chromosomal locus and a subunit of the replisome in the same cell. With this labeling strategy, we followed the intracellular positioning of the replisome and various loci relative to each other, as well as their short-time-scale movements. We found that as loci were replicated their short-time-scale movements slowed down momentarily. Mapping the short-time-scale movements over different intracellular positions showed a clear repositioning of several loci towards the replisome to be replicated, which led us to conclude that the chromosome moves to the replisome during DNA replication. To investigate the three-dimensional positioning of chromosomal loci, we performed time-lapse imaging of E. coli strains with fluorescently labeled loci using a microscope with an astigmatic fluorescence emission path. To determine the 3D coordinates of the emitters, we developed a neural network-based algorithm trained on simulated images of E. coli cells with fluorescent foci. Applying this neural network to different loci showed distinct 3D localization patterns over the cell cycle. To study the 3D chromosome organization, we imaged

Purpose: For tumor resection, surgeons need to localize the tumor. For this purpose, a magnetic seed can be inserted into the tumor by a radiologist and, during surgery, a magnetic detection probe informs the distance to the seed for localization. In this case, the surgeon still needs to mentally reconstruct the position of the tumor from the probe’s information. The purpose of this study is to develop and assess a method for 3D localization and visualization of the seed, facilitating the localization of the tumor. Methods: We propose a method for 3D localization of the magnetic seed by extending the magnetic detection probe with a tracking-based localization. We attach a position sensor (QR-code or optical marker) to the probe in order to track its 3D pose (respectively, using a head-mounted display with a camera or optical tracker). Following an acquisition protocol, the 3D probe tip and seed position are subsequently obtained by solving a system of equations based on the distances and the 3D probe poses. Results: The method was evaluated with an optical tracking system. An experimental setup using QR-code tracking (resp. using an optical marker) achieves an average of 1.6 mm (resp. 0.8 mm) 3D distance between the localized seed and the ground truth. Using a breast phantom setup, the average 3D distance is 4.7 mm with a QR-code and 2.1 mm with an optical marker. Conclusion: Tracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol., Computer Graphics and Visualisation, Medical Instruments & Bio-Inspired Technology

Die Erforschung des anatomischen Aufbaus und der Abläufe in neuronalen Schaltkreisen ist für das Verständnis von Gehirnfunktionen von entscheidender Bedeutung. In vielen Labors ist die Untersuchung zweidimensionaler histologischer Maushirnschnitte nach wie vor das Standardverfahren zur Untersuchung dieser Schaltkreise. Die Zuordnung dieser Schnitte zu einem standardisierten Koordinatensystem beinhaltet jedoch zahlreiche Herausforderungen. Artefakte, Verformungen und unterschiedliche (Schnitt-)Winkel aus dem Aufbereitungs- bzw. Vorbereitungsprozess erschweren dies, und die traditionelle Analysemethode, die auf menschlichem Fachwissen beruht, führt oft zu Verzerrungen und Inkonsistenzen. Um diesen Herausforderungen zu begegnen, führt unsere Studie AMBIA (Accurate Mouse Brain Image Analysis) ein. Diese Methodik inkl. SW-Tool zielt darauf ab, 2D-Gehirnschnitte automatisch auf ein 3D-Modell abzubilden, wobei nur minimale menschliche Eingriffe erforderlich sind. AMBIA besteht aus zwei Kernkomponenten. Der Lokalisierungsteil nutzt Deep Learning Ansätze, um die genaue Position des 2D-Schnitts im umfassenden 3D-Allen-Brain-Atlas zu identifizieren. Diese Identifizierung erzeugt die Zuordnung zu einer optimalen Atlasebene für den experimentellen Schnitt. Anschließend synchronisiert das Registrierungsmodul den Gehirnschnitt mit dem entsprechenden Atlas und stellt so sicher, dass die anatomischen Strukturen des Schnitts möglichst genau identifiziert werden. Durch den Vergleich der Leistung von AMBIA bei der Lokalisierung und Registrierung mit menschlichen Bewertungen zeigen wir, dass die Leistung von AMBIA der Leistung menschlicher Experten ebenbürtig ist. Dieser Ansatz vereinfacht nicht nur die Registrierung von 2D-Bildern in einem digitalen 3D-Atlas, sondern minimiert auch menschliche Fehler. Zusätzlich verfügt unser Tool über eine intuitive grafische Benutzeroberfläche, die speziell für Forscher mit minimalen Programmierkenntnissen entwickelt wurde und eine bessere Zugänglich, The accurate exploration of anatomical organization in neural circuits is essential for understanding brain function. In many laboratories, the study of 2D histological mouse brain slices remains the standard procedure for investigating these circuits. However, mapping these slices to a standardized coordinate system presents challenges. Artifacts, deformations, and angles from the preparation process complicate this, and the traditional analysis method, reliant on human expertise, introduces potential bias and inconsistency. To address these challenges, our study introduces AMBIA (Accurate Mouse Brain Image Analysis). This proposed methodology including the computational tool aims to automatically map 2D brain slices onto a 3D model, requiring minimal human intervention. AMBIA has two core components. The localization module utilizes deep learning to identify the exact location of the 2D slice within the comprehensive 3D Allen Brain Atlas. This identification produces a matching atlas plane for the experimental slice. Subsequently, the registration module aligns the brain slice with its corresponding atlas, ensuring anatomical structures match precisely. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that our computational tool performs at a level comparable to human experts. This tool not only simplifies the registration of 2D images to a 3D digital atlas but also minimizes human errors. Furthermore, our tool features a graphical user interface specifically designed for researchers with minimal programming knowledge, allowing for greater accessibility and ease of use within the research community. By facilitating accurate and consistent mapping, our computational tool has the potential to contribute significantly to the broader understanding of neural circuits and their functional organization within the brain., Maryam Sadeghi, M.Sc., Kurzbeschreibung in deutscher Sprache, Dissertation Medical University Innsbruck 2023

The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

[no abstract available]

[ES] Dado que el mundo geopolítico ya no está polarizado, la competitividad del mercado aumenta como nunca antes para sobrevivir como organización industrial, es clave ser competitivo. Es decir, reduciendo costos y tiempos de producción entre otras necesidades. Los robots móviles son recursos que consiguen aliviar esas necesidades ya que pueden sustituir a los humanos y tener un mejor rendimiento. Esto provoca una caída casuística de los problemas humanos, la reasignación de recursos humanos a puestos de trabajo más creativos que no pueden ser reemplazados por robots, y una mayor eficiencia a largo plazo. El estado del arte del uso de los robots móviles se basa en el hecho de que estamos hablando no sólo de un único robot móvil, sino de una flota de ellos que funciona de forma inteligente y coordinada. Estos dispositivos pueden integrarse en la cadena de suministro para poder transportar cargas útiles sin necesidad de intervención humana. Además, esta integración permite una gran flexibilidad, ya que los robots móviles industriales inteligentes pueden adaptarse a nuevas condiciones, parámetros impuestos y obstáculos que no estaban previstos. Para cualquier robot móvil autónomo, es necesario un conocimiento previo de su entorno antes de realizar una navegación autónoma, es decir, tener un mapa previo. La cartografía suele ser una tarea intervenida por el hombre que lleva tiempo, especialmente en el caso de las grandes instalaciones. Este trabajo propone una forma de mapear autónomamente, de la manera más eficiente, un ambiente interior 2D utilizando el enfoque de Árboles aleatorios de exploración rápida, ya que está sesgado hacia regiones desconocidas. Además, este trabajo propone la discriminación de objetos durante el mapeo. Con el enfoque convencional, durante el proceso de mapeo, los escáneres láser leen la presencia de todos los obstáculos en el entorno. Este hecho es indeseable, ya que algunos de estos obstáculos escaneados son escaneados sólo por causalidad du, [EN] Since the geopolitical world is not polarized anymore, the market competitivity is increasing as never before so in order to survive as an industrial organization, it is key to be competitive. That is, reducing costs and production times among other needs. Mobile robots are resources that manage to get those needs relieved since they can substitute humans and perform better. This causes human issues casuistic drop, human resources re-allocation in more creative job positions which cannot replaced by robots, and more long-term efficiency. The state-of-the-art of the use of mobile robots remains on the fact that we are talking about not just a single mobile robot but a fleet of them which performs in a smart and coordinated way. These devices can be integrated in the supply-chain so that can transport payloads without the need of any human intervention. In addition, such integration allows a huge flexibility since smart industrial mobile robots can adapt to new conditions, imposed parameters and obstacles that were not predicted. For any autonomous mobile robot, a prior knowledge about its environment is necessary before performing autonomous navigation, that is to have a previous map. Mapping usually is a human intervened task which takes time, especially for large facilities. This work proposes a way to map autonomously, in the most efficient way, an indoor 2D environment by using the Rapidly-exploring Random Trees approach since it is biased towards unknown regions. In addition, this work proposes object discrimination during mapping. With the conventional approach, during the mapping process laser scanners read the presence of all the obstacles in the environment. This fact is undesired since some of such scanned obstacles are scanned just by causality during the exploration (e.g. personnel, industrial mobile equipment ). Such undesired registered data in the map suppose noise and does not represent the actual long-term environment. The implementation of remo

[ES] Dado que el mundo geopolítico ya no está polarizado, la competitividad del mercado aumenta como nunca antes para sobrevivir como organización industrial, es clave ser competitivo. Es decir, reduciendo costos y tiempos de producción entre otras necesidades. Los robots móviles son recursos que consiguen aliviar esas necesidades ya que pueden sustituir a los humanos y tener un mejor rendimiento. Esto provoca una caída casuística de los problemas humanos, la reasignación de recursos humanos a puestos de trabajo más creativos que no pueden ser reemplazados por robots, y una mayor eficiencia a largo plazo. El estado del arte del uso de los robots móviles se basa en el hecho de que estamos hablando no sólo de un único robot móvil, sino de una flota de ellos que funciona de forma inteligente y coordinada. Estos dispositivos pueden integrarse en la cadena de suministro para poder transportar cargas útiles sin necesidad de intervención humana. Además, esta integración permite una gran flexibilidad, ya que los robots móviles industriales inteligentes pueden adaptarse a nuevas condiciones, parámetros impuestos y obstáculos que no estaban previstos. Para cualquier robot móvil autónomo, es necesario un conocimiento previo de su entorno antes de realizar una navegación autónoma, es decir, tener un mapa previo. La cartografía suele ser una tarea intervenida por el hombre que lleva tiempo, especialmente en el caso de las grandes instalaciones. Este trabajo propone una forma de mapear autónomamente, de la manera más eficiente, un ambiente interior 2D utilizando el enfoque de Árboles aleatorios de exploración rápida, ya que está sesgado hacia regiones desconocidas. Además, este trabajo propone la discriminación de objetos durante el mapeo. Con el enfoque convencional, durante el proceso de mapeo, los escáneres láser leen la presencia de todos los obstáculos en el entorno. Este hecho es indeseable, ya que algunos de estos obstáculos escaneados son escaneados sólo por causalidad du, [EN] Since the geopolitical world is not polarized anymore, the market competitivity is increasing as never before so in order to survive as an industrial organization, it is key to be competitive. That is, reducing costs and production times among other needs. Mobile robots are resources that manage to get those needs relieved since they can substitute humans and perform better. This causes human issues casuistic drop, human resources re-allocation in more creative job positions which cannot replaced by robots, and more long-term efficiency. The state-of-the-art of the use of mobile robots remains on the fact that we are talking about not just a single mobile robot but a fleet of them which performs in a smart and coordinated way. These devices can be integrated in the supply-chain so that can transport payloads without the need of any human intervention. In addition, such integration allows a huge flexibility since smart industrial mobile robots can adapt to new conditions, imposed parameters and obstacles that were not predicted. For any autonomous mobile robot, a prior knowledge about its environment is necessary before performing autonomous navigation, that is to have a previous map. Mapping usually is a human intervened task which takes time, especially for large facilities. This work proposes a way to map autonomously, in the most efficient way, an indoor 2D environment by using the Rapidly-exploring Random Trees approach since it is biased towards unknown regions. In addition, this work proposes object discrimination during mapping. With the conventional approach, during the mapping process laser scanners read the presence of all the obstacles in the environment. This fact is undesired since some of such scanned obstacles are scanned just by causality during the exploration (e.g. personnel, industrial mobile equipment ). Such undesired registered data in the map suppose noise and does not represent the actual long-term environment. The implementation of remo

[ES] Dado que el mundo geopolítico ya no está polarizado, la competitividad del mercado aumenta como nunca antes para sobrevivir como organización industrial, es clave ser competitivo. Es decir, reduciendo costos y tiempos de producción entre otras necesidades. Los robots móviles son recursos que consiguen aliviar esas necesidades ya que pueden sustituir a los humanos y tener un mejor rendimiento. Esto provoca una caída casuística de los problemas humanos, la reasignación de recursos humanos a puestos de trabajo más creativos que no pueden ser reemplazados por robots, y una mayor eficiencia a largo plazo. El estado del arte del uso de los robots móviles se basa en el hecho de que estamos hablando no sólo de un único robot móvil, sino de una flota de ellos que funciona de forma inteligente y coordinada. Estos dispositivos pueden integrarse en la cadena de suministro para poder transportar cargas útiles sin necesidad de intervención humana. Además, esta integración permite una gran flexibilidad, ya que los robots móviles industriales inteligentes pueden adaptarse a nuevas condiciones, parámetros impuestos y obstáculos que no estaban previstos. Para cualquier robot móvil autónomo, es necesario un conocimiento previo de su entorno antes de realizar una navegación autónoma, es decir, tener un mapa previo. La cartografía suele ser una tarea intervenida por el hombre que lleva tiempo, especialmente en el caso de las grandes instalaciones. Este trabajo propone una forma de mapear autónomamente, de la manera más eficiente, un ambiente interior 2D utilizando el enfoque de Árboles aleatorios de exploración rápida, ya que está sesgado hacia regiones desconocidas. Además, este trabajo propone la discriminación de objetos durante el mapeo. Con el enfoque convencional, durante el proceso de mapeo, los escáneres láser leen la presencia de todos los obstáculos en el entorno. Este hecho es indeseable, ya que algunos de estos obstáculos escaneados son escaneados sólo por causalidad du, [EN] Since the geopolitical world is not polarized anymore, the market competitivity is increasing as never before so in order to survive as an industrial organization, it is key to be competitive. That is, reducing costs and production times among other needs. Mobile robots are resources that manage to get those needs relieved since they can substitute humans and perform better. This causes human issues casuistic drop, human resources re-allocation in more creative job positions which cannot replaced by robots, and more long-term efficiency. The state-of-the-art of the use of mobile robots remains on the fact that we are talking about not just a single mobile robot but a fleet of them which performs in a smart and coordinated way. These devices can be integrated in the supply-chain so that can transport payloads without the need of any human intervention. In addition, such integration allows a huge flexibility since smart industrial mobile robots can adapt to new conditions, imposed parameters and obstacles that were not predicted. For any autonomous mobile robot, a prior knowledge about its environment is necessary before performing autonomous navigation, that is to have a previous map. Mapping usually is a human intervened task which takes time, especially for large facilities. This work proposes a way to map autonomously, in the most efficient way, an indoor 2D environment by using the Rapidly-exploring Random Trees approach since it is biased towards unknown regions. In addition, this work proposes object discrimination during mapping. With the conventional approach, during the mapping process laser scanners read the presence of all the obstacles in the environment. This fact is undesired since some of such scanned obstacles are scanned just by causality during the exploration (e.g. personnel, industrial mobile equipment ). Such undesired registered data in the map suppose noise and does not represent the actual long-term environment. The implementation of remo

[ES] Dado que el mundo geopolítico ya no está polarizado, la competitividad del mercado aumenta como nunca antes para sobrevivir como organización industrial, es clave ser competitivo. Es decir, reduciendo costos y tiempos de producción entre otras necesidades. Los robots móviles son recursos que consiguen aliviar esas necesidades ya que pueden sustituir a los humanos y tener un mejor rendimiento. Esto provoca una caída casuística de los problemas humanos, la reasignación de recursos humanos a puestos de trabajo más creativos que no pueden ser reemplazados por robots, y una mayor eficiencia a largo plazo. El estado del arte del uso de los robots móviles se basa en el hecho de que estamos hablando no sólo de un único robot móvil, sino de una flota de ellos que funciona de forma inteligente y coordinada. Estos dispositivos pueden integrarse en la cadena de suministro para poder transportar cargas útiles sin necesidad de intervención humana. Además, esta integración permite una gran flexibilidad, ya que los robots móviles industriales inteligentes pueden adaptarse a nuevas condiciones, parámetros impuestos y obstáculos que no estaban previstos. Para cualquier robot móvil autónomo, es necesario un conocimiento previo de su entorno antes de realizar una navegación autónoma, es decir, tener un mapa previo. La cartografía suele ser una tarea intervenida por el hombre que lleva tiempo, especialmente en el caso de las grandes instalaciones. Este trabajo propone una forma de mapear autónomamente, de la manera más eficiente, un ambiente interior 2D utilizando el enfoque de Árboles aleatorios de exploración rápida, ya que está sesgado hacia regiones desconocidas. Además, este trabajo propone la discriminación de objetos durante el mapeo. Con el enfoque convencional, durante el proceso de mapeo, los escáneres láser leen la presencia de todos los obstáculos en el entorno. Este hecho es indeseable, ya que algunos de estos obstáculos escaneados son escaneados sólo por causalidad du, [EN] Since the geopolitical world is not polarized anymore, the market competitivity is increasing as never before so in order to survive as an industrial organization, it is key to be competitive. That is, reducing costs and production times among other needs. Mobile robots are resources that manage to get those needs relieved since they can substitute humans and perform better. This causes human issues casuistic drop, human resources re-allocation in more creative job positions which cannot replaced by robots, and more long-term efficiency. The state-of-the-art of the use of mobile robots remains on the fact that we are talking about not just a single mobile robot but a fleet of them which performs in a smart and coordinated way. These devices can be integrated in the supply-chain so that can transport payloads without the need of any human intervention. In addition, such integration allows a huge flexibility since smart industrial mobile robots can adapt to new conditions, imposed parameters and obstacles that were not predicted. For any autonomous mobile robot, a prior knowledge about its environment is necessary before performing autonomous navigation, that is to have a previous map. Mapping usually is a human intervened task which takes time, especially for large facilities. This work proposes a way to map autonomously, in the most efficient way, an indoor 2D environment by using the Rapidly-exploring Random Trees approach since it is biased towards unknown regions. In addition, this work proposes object discrimination during mapping. With the conventional approach, during the mapping process laser scanners read the presence of all the obstacles in the environment. This fact is undesired since some of such scanned obstacles are scanned just by causality during the exploration (e.g. personnel, industrial mobile equipment ). Such undesired registered data in the map suppose noise and does not represent the actual long-term environment. The implementation of remo

The time taken to generate a super-resolution image and the quality of the final synthetic image depends on the performance of the localization algorithm which is used in the localization microscopy pipeline. The most precise and accurate algorithms are mostly iterative and they take a long time to generate the localization list while the faster ‘one-shot algorithms’ are not very accurate and precise. A deep learning method smNet (single-molecule Net) was developed by Zhang et al which was claimed to perform one-shot localization with precision close to the theoretical limit and very accurately, along with performing aberration estimation and dipole-emitter orientation angle estimation. The deep learning model smNet was trained either by augmenting experimental data or using simulated data generated with an erroneously simplified simulation model and a phase retrieval method. The purpose of this work was to characterize the performance of smNet when it was trained with simulated images generated using an accurate vector model for a range of physical conditions. Along with the characterization of smNet’s performance in doing 3D localization and aberration estimation with the accurate vector model, a pipeline was also designed which made the training process of smNet more efficient and computationally cheaper while performing accurate and precise 3D localization and aberration estimation. The pipeline was designed to implement the concept of simulator learning where a smNet model could be trained on simulated data and used to perform 3D localization and aberration estimation directly on experimental data without any retraining or domain adaptation techniques., BioMedical Engineering | Medical Physics

The time taken to generate a super-resolution image and the quality of the final synthetic image depends on the performance of the localization algorithm which is used in the localization microscopy pipeline. The most precise and accurate algorithms are mostly iterative and they take a long time to generate the localization list while the faster ‘one-shot algorithms’ are not very accurate and precise. A deep learning method smNet (single-molecule Net) was developed by Zhang et al which was claimed to perform one-shot localization with precision close to the theoretical limit and very accurately, along with performing aberration estimation and dipole-emitter orientation angle estimation. The deep learning model smNet was trained either by augmenting experimental data or using simulated data generated with an erroneously simplified simulation model and a phase retrieval method. The purpose of this work was to characterize the performance of smNet when it was trained with simulated images generated using an accurate vector model for a range of physical conditions. Along with the characterization of smNet’s performance in doing 3D localization and aberration estimation with the accurate vector model, a pipeline was also designed which made the training process of smNet more efficient and computationally cheaper while performing accurate and precise 3D localization and aberration estimation. The pipeline was designed to implement the concept of simulator learning where a smNet model could be trained on simulated data and used to perform 3D localization and aberration estimation directly on experimental data without any retraining or domain adaptation techniques., Biomedical Engineering | Medical Physics

Many algorithms today require a number of parameters to be set in order to perform well in a given application. The tuning of these parameters is often difficult and tedious to do manually, especially when the number of parameters is large. It is also unlikely that a human can find the best possible solution for difficult problems. To be able to automatically find good sets of parameters could both provide better results and save a lot of time. The prominent methods Bayesian optimization and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are evaluated for automatic parameter tuning in localization algorithms in this work. Both methods are evaluated using a localization algorithm on different datasets and compared in terms of computational time and the precision and recall of the final solutions. This study shows that it is feasible to automatically tune the parameters of localization algorithms using the evaluated methods. In all experiments performed in this work, Bayesian optimization was shown to make the biggest improvements early in the optimization but CMA-ES always passed it and proceeded to reach the best final solutions after some time. This study also shows that automatic parameter tuning is feasible even when using noisy real-world data collected from 3D cameras.

Many algorithms today require a number of parameters to be set in order to perform well in a given application. The tuning of these parameters is often difficult and tedious to do manually, especially when the number of parameters is large. It is also unlikely that a human can find the best possible solution for difficult problems. To be able to automatically find good sets of parameters could both provide better results and save a lot of time. The prominent methods Bayesian optimization and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are evaluated for automatic parameter tuning in localization algorithms in this work. Both methods are evaluated using a localization algorithm on different datasets and compared in terms of computational time and the precision and recall of the final solutions. This study shows that it is feasible to automatically tune the parameters of localization algorithms using the evaluated methods. In all experiments performed in this work, Bayesian optimization was shown to make the biggest improvements early in the optimization but CMA-ES always passed it and proceeded to reach the best final solutions after some time. This study also shows that automatic parameter tuning is feasible even when using noisy real-world data collected from 3D cameras.

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of UAVs. We consider two spatial configuration modes of UAVs: distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs cannot only be used for coverage improvement but also for capacity boosting in IAB cellular networks.

[ES] En este trabajo final de grado, se muestra una solución para localizar un objeto dentro de un espacio y cogerlo con un brazo robot. Para resolver este problema se ha utilizado el robot IRB 140 de ABB y un sistema de visión artificial. En este TFG contiene los siguientes puntos de interés; La calibración del sistema de visión artificial utilizada para resolver el problema y un breve estudio de alternativas. Programa utilizado para localizar el objeto de estudio y la programación en RAPID1 . Comunicación entre los dos equipos de trabajo. Problemática de localización de objetos en el laboratorio, la utilización de brazos robot para la interacción con humanos y equipos del laboratorio., [EN] The project aims at the location of an object in 3D space for further handling with robot ABB IRB indsutrial 140. The intention is that it can be used for a robot interaction with people.

Algorithms for 3D localization, mapping, and reconstruction are getting increasingly mature. It is time to also make the datasets on which they are tested more realistic to reflect the conditions in the homes of real people. Today algorithms are tested on data gathered in the lab or at best in a few places, and almost always by the people that designed the algorithm. In this paper, we present the first RGB-D dataset from the crowd sourced data collection project Kinect@Home and perform an initial analysis of it. The dataset contains 54 recordings with a total of approximately 45 min of RGB-D video. We present a comparison of two different pose estimation methods, the Kinfu algorithm and a key point-based method, to show how this dataset can be used even though it is lacking ground truth. In addition, the analysis highlights the different characteristics and error modes of the two methods and shows how challenging data from the real world is., QC 20160208

[ES] En este trabajo final de grado, se muestra una solución para localizar un objeto dentro de un espacio y cogerlo con un brazo robot. Para resolver este problema se ha utilizado el robot IRB 140 de ABB y un sistema de visión artificial. En este TFG contiene los siguientes puntos de interés; La calibración del sistema de visión artificial utilizada para resolver el problema y un breve estudio de alternativas. Programa utilizado para localizar el objeto de estudio y la programación en RAPID1 . Comunicación entre los dos equipos de trabajo. Problemática de localización de objetos en el laboratorio, la utilización de brazos robot para la interacción con humanos y equipos del laboratorio., [EN] The project aims at the location of an object in 3D space for further handling with robot ABB IRB indsutrial 140. The intention is that it can be used for a robot interaction with people.

[ES] En este trabajo final de grado, se muestra una solución para localizar un objeto dentro de un espacio y cogerlo con un brazo robot. Para resolver este problema se ha utilizado el robot IRB 140 de ABB y un sistema de visión artificial. En este TFG contiene los siguientes puntos de interés; La calibración del sistema de visión artificial utilizada para resolver el problema y un breve estudio de alternativas. Programa utilizado para localizar el objeto de estudio y la programación en RAPID1 . Comunicación entre los dos equipos de trabajo. Problemática de localización de objetos en el laboratorio, la utilización de brazos robot para la interacción con humanos y equipos del laboratorio., [EN] The project aims at the location of an object in 3D space for further handling with robot ABB IRB indsutrial 140. The intention is that it can be used for a robot interaction with people.

[ES] En este trabajo final de grado, se muestra una solución para localizar un objeto dentro de un espacio y cogerlo con un brazo robot. Para resolver este problema se ha utilizado el robot IRB 140 de ABB y un sistema de visión artificial. En este TFG contiene los siguientes puntos de interés; La calibración del sistema de visión artificial utilizada para resolver el problema y un breve estudio de alternativas. Programa utilizado para localizar el objeto de estudio y la programación en RAPID1 . Comunicación entre los dos equipos de trabajo. Problemática de localización de objetos en el laboratorio, la utilización de brazos robot para la interacción con humanos y equipos del laboratorio., [EN] The project aims at the location of an object in 3D space for further handling with robot ABB IRB indsutrial 140. The intention is that it can be used for a robot interaction with people.

This paper provides a detailed description of a set of algorithms to efficiently manipulate 3D geometric models to compute physical constraints and range observation models, data that is usually required in real-time mobile robotics or simulation. Our approach uses a standard file format to describe the environment and processes the model using the openGL library, a widely-used programming interface for 3D scene manipulation. The paper also presents results on a test model for benchmarking, and on a model of a real urban environment, where the algorithms have been effectively used for real-time localization in a large urban setting.

This paper provides a detailed description of a set of algorithms to efficiently manipulate 3D geometric models to compute physical constraints and range observation models, data that is usually required in real-time mobile robotics or simulation. Our approach uses a standard file format to describe the environment and processes the model using the openGL library, a widely-used programming interface for 3D scene manipulation. The paper also presents results on a test model for benchmarking, and on a model of a real urban environment, where the algorithms have been effectively used for real-time localization in a large urban setting., Postprint (author’s final draft)

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.

Rozvojom technológií strojového videnia sa neustále rozširujú aplikácie, ktoré by nám zvýšili produktivitu, univerzálnosť alebo jednoduchosť výrobných systémov. Táto práca sa zaoberá práve využitím strojového videnia pre navádzanie robota. Úloha pozostáva z vytvorenia postupu a praktickej realizácie, kde sa overia navrhnuté predpoklady. Hlavným cieľom je určenie 3D polohy a natočenia plechového dielu alebo podzostavy dielov hlavnej karosérie automobilu ležiaceho v dosahu robota vzhľadom k jeho základnému súradnému systému. Navrhnutý postup je vhodný pre viaceré typy a rozmery dielov, ktoré spĺňajú určité požiadavky. Zameranie dielu je realizované snímaním významných bodov na diele pomocou 3D skeneru umiestneného na prírube robota a následným spracovaním v navrhnutom programe. Teoretická časť práce sa venuje prieskumu v oblasti strojového videnia, presnosti priemyselných robotov, kompenzáciám ich chýb a manipulácii a montáži plechových výliskov v automobilovom prie-mysle. Na záver bolo poskytnuté zhodnotenie a odporúčania do praxe., With the development of machine vision technologies, new applications that can increase production, versatility or simplicity of production systems are widely spread. This thesis deals with usage of machine vision for robot guidance. The task consists of creating a technique and its practical realization, where the proposed assumptions are verified. The main objective is to determine 3D position and orientation of a sheet metal part or subassembly of the body-in-white, which is lying within the reach of an industrial robot with respect to its base coordinate system. The proposed method is suitable for several types and dimensions of components, which meet certain requirements. Targeting the component is carried out by scanning significant points on the component with the help of the 3D scanner attached to the robot flange. Afterwards, gained data are processed in a designed programme. The theoretical part is focused on research in the field of machine vision, accuracy of industrial robots, compensation of their errors and manipulation and assembly of the sheet metal parts in automotive. Finally, an evaluation and recommendations for practice are provided.