The most common method for securing components to each other during manufacturing of products is by joining these using screws, nuts and bolts. The benefit of using this method is that it is cheap and makes it easy to join and separate components quickly. The clamping force in the threaded joint is critical to the quality and in some respect the life length of the product, which makes it important to have good control of the clamping force. There are two main tightening strategies used when tightening a threaded joint – torque controlled tightening and angle controlled tightening. The first method monitors the applied torque during the entire tightening and halts when the target torque is reached. The second method, angle controlled tightening, measures the rotation of the threaded fastener in the joint. This method generally produces more accurate results with less scatter in the final clamping force. In order to apply angle controlled tightening using a hand-held tool it is required to not only control the output angle of the tool, but also how the tool moves in relation to the joint. This is to ensure that the control signal from the motor actually translates to clamping force in the joint and not to rotation of the tool itself. This thesis project aims to analyze data from an IMU (Inertial Measurement Unit) built into a hand-held tightening tool in order to estimate tool movement and thereby react to undesired tool movement. An analysis has been performed to evaluate how the two sensor fusion methods – Kalman filter and Particle filter – perform in terms of estimating the orientation of the tool by combining measurements from the IMU’s accelerometers and gyroscopes. Data was collected from the tool IMU during a number of angle tightening sequences with varying setups. Test were performed both for when the tool was kept still during the entire tightening and for when the tools was allowed to move freely. Tests were also carried out for a couple of different tool orientations to better understand the behavior of the two sensor fusion models. The results from the tests showed that the Kalman Filter was able to better estimate the tool orientation. Especially in terms of accuracy, repeatability and reliability. / Den vanligaste metoden för att fästa komponenter till varandra vid tillverkning av produkter är genom att sammanfoga komponenterna med hjälp av skruvar, bultar och muttrar. Fördelen med denna metod är att den är billig och gör det enkelt att sammanfoga och lossa komponenter snabbt. Klämkraften i skruvförbandet är avgörande för hur väl en produkt är ihopsatt och påverkar därmed dess kvalitet, samt i viss mån livslängd. Det finns i huvudsak två olika strategier vid åtdragning i ett skruvförband – momentåtdragning och vinkelåtdragning. Den första metoden bygger på att man kontinuerligt mäter momentet under åtdragning och avbryter åtdragningen när rätt moment uppnåtts. Den andra metoden, vinkelåtdragning, mäter hur många grader fästelementet roterat i förbandet. Metoden producerar i regel högre precision med mindre spridning av den slutgiltiga klämraften. För att kunna tillämpa vinkelåtdragning med ett handhållet verktyg räcker det inte att kontroll över rotationen av verktygets utgående axel, utan även hur verktyget rör sig i förhållande till förbandet under åtdragning. Detta för att säkerställa att verktygets motorstyrning resulterar i önskad klämkraft i förbandet och inte rotation av själva verktyget. Detta examensarbete ämnar analysera data från en IMU (Inertial Measurement Unit) integrerad i ett handhållet åtdragningsverktyg för att estimera verktygets rörelse under vinkelåtdragning och därmed kompensera för oönskade rörelser. En analys har gjorts för hur väl de två olika sensorfusions-modellerna - Kalmanfilter och Partikelfilter – presterar när det kommer till att uppskatta orientering för verktyget genom att kombinera data från IMU-enhetens accelerometrar och gyroskop. Data samlades in från verktygets IMU från ett antal dragningar med varierande uppställning. Tester genomfördes dels då verktyget hölls stilla under hela åtdragningen och dels då det tilläts röra sig fritt. Tester genomfördes även för flera olika orienteringar av verktyget för att i större utsträckning kunna säga hur de olika sensorfusions-modellerna presterade. Resultatet av testerna visade att Kalmanfiltret kunde producera bättre estimeringar av verktygets orientering, speciellt i avseende precision, repeterbarhet och tillförlitlighet.

This thesis presents the integration of an acoustic tracking system within an autonomous underwater AUV (AUV) to enable real-time tracking of sharks tagged with artificial acoustic sources. The tracking system consists of two hydrophones and a receiver unit that outputs a measurement of the relative angle to the tagged shark. Since only two hydrophones are used, the sign of the relative angle measurement is unknown. To overcome this ambiguity, a particle filter algorithm was developed to estimate the position of the acoustic source. When combined with an active control system that drives vehicle to obtain different orientations with respect to the acoustic source, real-time autonomous localization, tracking, and following of a tagged shark is shown to be possible. Four types of ocean experiments were used to validate the system including: 1) AUV tracking of a stationary tag, 2) AUV tracking of a tagged kayak, 3) AUV tracking of a tagged AUV, and 4) AUV tracking of a tagged shark. These experiments were analyzed with respect to the localization error, associated error variance, and distance between the AUV and the tag. The final shark tracking experiments took place in SeaPlane Lagoon, Los Angeles, CA, where the AUV was able to autonomously track and follow a tagged Leopard Shark for several hours.

In recent years, the particle filter has gained prominence in the area of target tracking because it is robust to non-linear target motion and non-Gaussian additive noise. Traditional track filters, such as the Kalman filter, have been well studied for linear tracking applications, but perform poorly for non-linear applications. The particle filter has been shown to perform well in non-linear applications. The particle filter method is computationally intensive and advances in processor speed and computational power have allowed this method to be implemented in real-time tracking applications. This thesis explores the use of particle filters to detect and track stealthy targets in noisy imagery. Simulated point targets are applied to noisy image data to create an image sequence. A particle filter method known as Track-Before-Detect is developed and used to provide detection and position tracking estimates of a single target as it moves in the image sequence. This method is then extended to track multiple moving targets. The method is analyzed to determine its performance for targets of varying signal-to-noise ratio and for varying particle set sizes. The simulation results show that the Track-Before-Detect method offers a reliable solution for tracking stealthy targets in noisy imagery. The analysis shows that the proper selection of particle set size and algorithm improvements will yield a filter that can track targets in low signal-to-noise environments. The multi-target simulation results show that the method can be extended successfully to multi-target tracking applications. This thesis is a continuation of automatic target recognition and target tracking research at Cal Poly under Dr. John Saghri and is sponsored by Raytheon Space and Airborne Systems.

A road lane detection and tracking algorithm is developed, especially tailored to run on high-performance heterogeneous hardware like GPUs and FPGAs in autonomous road vehicles. The algorithm was initially developed in C/C++ and was ported to OpenCL which supports computation on heterogeneous hardware.A novel road lane detection algorithm is proposed using random sampling of particles modeled as straight lines. Weights are assigned to these particles based on their location in the gradient image. To improve the computation efficiency of the lane detection algorithm, lane tracking is introduced in the form of a Particle Filter. Creation of the particles in lane detection step and prediction, measurement updates in lane tracking step are computed parellelly on GPU/FPGA using OpenCL code, while the rest of the code runs on a host CPU. The software was tested on two GPUs - NVIDIA GeForce GTX 660 Ti &amp; NVIDIA GeForce GTX 285 and an FPGA - Altera Stratix-V, which gave a computational frame rate of up to 104 Hz, 79 Hz and 27 Hz respectively. The code was tested on video streams from five different datasets with different scenarios of varying lighting conditions on the road, strong shadows and the presence of light to moderate traffic and was found to be robust in all the situations for detecting a single lane. / <p>Validerat; 20140128 (global_studentproject_submitter)</p>

No description available.

Maritime navigation heaviliy relies on GNSS and related technologies for positioning and navigiation. Since these technologies are vulnerable to external threats such as signal spoofing, alternatives are needed for backup purposes. Our proposal is to use radar to construct polar amplitude gridmaps tailored for the intended route, and using a particle filter for position estimation. The proposed approach has been successfully demonstrated on data from a surface vessel in the harbor of Helsinki. / Navigation i marina miljöer är idag mycket beroende av GNSS och relaterad teknologier. Eftersom dessa GNSS teknologier är föremål för terrorism och sabotage finns behov av alternativ. I detta examenarbete föreslås att använda radar ombord på ett fartyg för att konstruera amplitudrutnätskartor av omgivningen, och därefter använda ett partikelfilter för estimering av fartygets position.Fartygets position kunde framgångsrikt estimeras med data från ett fartyg i Helsingfors hamn.

No description available.

Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the sharks' behavior, and behavioral interaction between them. Our method uses particle filters, a computational Bayesian technique designed to sequentially model a dynamic system. We discuss how resampling is used to approximate arbitrary densities, and illustrate its use in a simple example of a particle filter implementation of a state-space model. We then introduce a particular model formulation that uses conditioning to introduce unobserved parameters for the shark's behaviors. We show how the irregularly-observed shark locations can be modeled by interpolation as a set of movements at constant-length time intervals. We use a spline method for generating approximations of the ground truth at these intervals for comparison with our model. Finally, we demonstrate our model's estimates of the sharks' behavioral and ecological parameters of interest on a subset of the observed data. / Statistics

The majority of aircraft structures are designed to be damage-tolerant such that safe operation can continue in the presence of minor damage. It is necessary to schedule inspections so that minor damage can be found and repaired. It is generally not possible to perform structural inspections prior to every flight. The scheduling is traditionally accomplished through a deterministic set of methods referred to as Damage Tolerance Analysis (DTA). DTA has proven to produce safe aircraft but does not provide estimates of the probability of failure of future flights or the probability of repair of future inspections. Without these estimates maintenance costs cannot be accurately predicted. Also, estimation of failure probabilities is now a regulatory requirement for some aircraft. The set of methods concerned with the probabilistic formulation of this problem are collectively referred to as Probabilistic Damage Tolerance Analysis (PDTA). The goal of PDTA is to control the failure probability while holding maintenance costs to a reasonable level. This work focuses specifically on PDTA for fatigue cracking of metallic aircraft structures. The growth of a crack (or cracks) must be modeled using all available data and engineering knowledge. The length of a crack can be assessed only indirectly through evidence such as non-destructive inspection results, failures or lack of failures, and the observed severity of usage of the structure. The current set of industry PDTA tools are lacking in several ways: they may in some cases yield poor estimates of failure probabilities, they cannot realistically represent the variety of possible failure and maintenance scenarios, and they do not allow for model updates which incorporate observed evidence. A PDTA modeling methodology must be flexible enough to estimate accurately the failure and repair probabilities under a variety of maintenance scenarios, and be capable of incorporating observed evidence as it becomes available. This dissertation describes and develops new PDTA methodologies that directly address the deficiencies of the currently used tools. The new methods are implemented as a free, publicly licensed and open source R software package that can be downloaded from the Comprehensive R Archive Network. The tools consist of two main components. First, an explicit (and expensive) Monte Carlo approach is presented which simulates the life of an aircraft structural component flight-by-flight. This straightforward MC routine can be used to provide defensible estimates of the failure probabilities for future flights and repair probabilities for future inspections under a variety of failure and maintenance scenarios. This routine is intended to provide baseline estimates against which to compare the results of other, more efficient approaches. Second, an original approach is described which models the fatigue process and future scheduled inspections as a hidden Markov model. This model is solved using a particle-based approximation and the sequential importance sampling algorithm, which provides an efficient solution to the PDTA problem. Sequential importance sampling is an extension of importance sampling to a Markov process, allowing for efficient Bayesian updating of model parameters. This model updating capability, the benefit of which is demonstrated, is lacking in other PDTA approaches. The results of this approach are shown to agree with the results of the explicit Monte Carlo routine for a number of PDTA problems. Extensions to the typical PDTA problem, which cannot be solved using currently available tools, are presented and solved in this work. These extensions include incorporating observed evidence (such as non-destructive inspection results), more realistic treatment of possible future repairs, and the modeling of failure involving more than one crack (the so-called continuing damage problem). The described hidden Markov model / sequential importance sampling approach to PDTA has the potential to improve aerospace structural safety and reduce maintenance costs by providing a more accurate assessment of the risk of failure and the likelihood of repairs throughout the life of an aircraft. / Statistics

The Blood Oxygen Level Dependent (BOLD) signal provides indirect estimates of neural activity. The parameters of this BOLD signal can give information about the pathophysiological state of the brain. Most of the models for the BOLD signal are overparameterized which makes the unique identification of these parameters difficult. In this work, we use information from multiple neu- roimaging sources to get better estimates of these parameters instead of relying on the information from the BOLD signal only. The mulitmodal neuroimaging setup consisted of the information from Cerebral Blood Volume (CBV) ( VASO-Fluid-Attenuation-Inversion-Recovery (VASO-FLAIR)), and Cerebral Blood Flow (CBF) (from Arterial Spin Labelling (ASL)) in addition to the BOLD signal and the fusion of this information is achieved in a Particle Filter (PF) framework. The trace plots and the correlation coefficients of the parameter estimates from the PF reflect ill-posedness of the BOLD model. The means of the parameter estimates are much closer to the ground truth compared to the estimates obtained using only the BOLD information. These parameter estimates were also found to be more robust to noise and influence of the prior. / Master of Science