Big Eye Dolly Eye Blue Contact Lenses

diameter: 14,5 mm baskurva : 8,5 mm-Objektiv material : HEMA / Silikon hydrogel Lins effekt : 0-800 Använda tid: 12 månader Paketet : 1 par （2 st ) kontaktlinserTekniken som används i dessa linser garanterar utmärkt andningsbarhet och säkerhet för ögonen, eftersom det inte finns någon kontakt mellan linsfärgen och ögat självt. Har en fråga till. Vad betyder ”förfallodatum åtminstone 11 månader?” Om man har köpt 2 förpackningar och förfallodatum va åtminstone 11 månader, kan man inte använda dem efter förfallodatum? Kan du förklara detta? Tack På ditt linsrecept står det ett värde som heter sfär eller styrka. Var uppmärksam på om det står ett plus- (+) eller minustecken (-) framför. Minustecknet ersätts ibland med likhetstecknet (=). Det är en positiv styrka (+) om du har problem med att se på kort distans (långsynthet) och en negativ styrka (-) om du har problem med att se på långt håll (närsynthet).Detta är diametern (eller storleken) på linsen. Om du har linser med en specifik diameter som passar dig bra så bör du använda samma diameter. Det är inte den viktigaste parametern, men att välja en annan storlek på diametern är en risktagning.

Okej :/ Finns ju inga färgade linser med min baskurva och diameter (14.50). Letar efter några linser att ha på halloween, så inget jag kommer använda mer än några gånger under en månad, men är rädd att skada ögat.

Hej! Förut när jag köpte linser från fyndiq så fick jag en allergisk reaktion eller något så att ögonen började klia jätte mycket och att de svullnade upp när man kliade. Vet ni vad de kan bero på? Sen undrar jag vad som är skillnaden mellan dioptriska och icke dioptriska linser?Jag köpte Pretty Hazel. De är jätte bekvam att använda. De ger mina ögon större, men fortfarande ser naturligt ut. Jag gillar dem mycket. Rekommendera om någon fundera att köpa.

Kan man återanvända linserna? Om jag använder dem en dag och sedan tar av mig dem under natten, kan jag sätta på mig dem igen nästa dag? Hur många gånger kan man göra det isåfall?

ColourVUE Big Eyes färgade kontaktlinser från MaxVUE Vision kommer att överraska dig trevligt med deras distinkta sätt att blanda dina naturliga ögonfärger, samtidigt som de får fram och definierar iris. Med Big Eyes kontaktlinser och deras vackra nyanser kan du uppnå otroligt intressanta resultat, eftersom det slutliga utseendet delvis påverkas av den naturliga färgen i dina ögon. Vi använder cookies på våra webbplatser. Du kan när som helst hantera detta via webbläsarens inställningar. För mer information, se vår integritetspolicy. Vi gör vårt bästa för att säkerställa att produkterna du beställer levereras kompletta och enligt dina specifikationer. Om du däremot skulle ta emot en ofullständig beställning, eller andra artiklar än de du beställt, eller om det finns någon annan anledning till att du inte är nöjd med din beställning, kan du returnera beställningen, eller valfria produkter som ingår i beställningen, och få fullständig ersättning för artiklarna. Visa fullständig returpolicy ColourVUE Big Eyes effect lenses make your eyes look bigger and brighter with a doll like appearance. The thrick outer ring creates a defined eye with naturally blending colour tones, making them suitable for dark and light eye colours. Högkvalitativ färgad lins för att blända dina vänner. De trendigaste färgerna till ett riktigt vinnande pris. Behöver du verkligt fascinerande ögon för din nästa erövring? Vi kan hjälpa dig. Medan resten av världen står och gapar kommer du att kunna göra en välförtjänt succé med en självsäker blick.Vi har den här typen av kontaktlinser med noll dioptri (0,00, Plan) i lager i Budapest i vissa kvantiteter, tillgängliga för omedelbart köp. Linserna finns även tillgängliga i dioptriker och den förväntade ledtiden är upp till 3 veckor.

ColourVUE Big Eyes is a range of coloured opaque hidrogel contact lenses, with excellent oxygen permeability. Besides, are suitable for a daily use and should be replaced every three months.Om du inte är en erfaren kontaktlinsbärare bör du endast köpa linser efter en ögonundersökning och anpassning av en ögonläkare eller kontaktlinsspecialist.Produkten är en 3-månaders kontaktlins, dvs. den kan användas i 3 månader med rätt skötsel. Detta gör den mycket mer ekonomisk än de vanliga dagliga, 2 vecko- eller månadslinser som säljs av andra tillverkare. Innehåller ett par linser (2 linser) per låda!Highlighting colour: används för att framhäva den grundläggande ögonfärgen utan att helt täcka den ursprungliga färgen. Den kan endast användas för att framhäva den mörka ögonfärgen.

Hos Linsexpress beställer du enkelt och framförallt säkert dina kontaktlinser på nätet till bästa möjliga pris. Ett av Linsexpress viktigaste mål är att erbjuda dig Sveriges bästa kundservice. Linsexpress lagerhåller kontaktlinser av högsta kvalitet från alla välkända tillverkare. Då Linsexpress levererar betydligt större volymer än din lokala optiker kan de erbjuda ett lågt pris och en snabb service till sina kunder i Sverige. Leveranstider varierar beroende på om kontaktlinsen finns i lager eller ej. Information om leveranstid kan fås vid varje beställning genom att använda knappen ”Leveranstid” på ordersidan. Linsexpress erbjuder full returrätt inom 14 dagar från och med den dag du har mottagit varan, förutsatt att den returneras i oskadad, omärkt och obruten originalförpackning.

Generally, your eye doctor will use a keratometer to measure the curve of your cornea, which is the front surface of the eyes – where contacts rest. These numbers help to determine the lens diameter and base curve that appear on your contact lenses prescription.The range of contact lens options is vast and the more information you share, the easier it will be for your eye doctor to recommend the best contact lens to satisfy your requirements. After your contact lens fitting, the best way to assess a perfect fit is with a pair of trial lenses. Once these contacts are on your eyes, your optometrist will check with a slit lamp to see that your lenses move properly when you blink and look in all directions. At this point, patient feedback is very important; tell your doctor if the lenses are comfortable or if they hurt. Depending upon your eyes, various technology may be used during your fitting. An improper contact lens fit can lead to pain or damage your eye, which is why it’s so important to have a precise and thorough fitting.DIA: The Diameter of your contact lens is the distance across the lens surface, also measured in millimeters. Usually, the number is between 13 and 15. This value determines how the contact lens sits on your eye, and when it isn’t appropriate for you – the contact lens will be uncomfortable.

Also, you need a current vision prescription for contact lenses, so vision testing is a critical part of this exam. Once your vision condition and ocular health are determined suitable for contact lenses (nowadays, there’s a contact lens for almost everyone!), the next step is a precise contact lens fitting.PWR: Refers to refractive power, which is the value of correction that you need in order to have 20/20 vision. The number is measured in diopters; if it has a minus sign before it, it means you are nearsighted, and if it has a plus sign before it, it means you are farsighted. It is typical to have a different power for each eye.

Once a trial pair is found to be suitable, you will be issued a contact lens prescription. Your eye care provider will also instruct you on proper insertion, cleansing, wearing schedule and basic care of your contacts.

Corneal topography is an advanced technology that maps the shape of your cornea. This test is extremely helpful for ensuring a precise measurement of your corneal curvature, especially when you have an irregular shape. There are numerous specialty contact lenses that can be fit on an irregular cornea. For example, people with astigmatism may do best with specialized toric lenses or if you have keratoconus, your eye doctor may prescribe scleral lenses.To begin, your eye doctor will perform an examination that is essentially the same as a regular eye health exam. This is done in order to make sure you don’t have any pre-existing health conditions that may interfere with comfortable and healthy contact lens wear. Your optometrist will administer a slit lamp examination to inspect the inner tissues of your eyes carefully.

BC: This is the Base Curve (measured in millimeters), which describes the back curvature of the contact lens. The number matches your corneal curvature. Some brands of contact lenses only come in one base curve.Contact lenses are more comfortable to wear when you have enough lubricating tears to keep both your cornea and your lenses moist. Your eye doctor may evaluate your tear film by placing a thin strip of filter paper under your lower lid. Another procedure that may be done involves placing liquid dye on your eye and assessing your tear film with the aid of a slit lamp. If you have dry eyes, you will require specific types and materials of contact lenses.Now that there are so many types of contact lenses on the market, your eye doctor needs to know about your lifestyle and activities in order to determine which lenses are most appropriate.To ensure that your contact lenses continue to fit properly and provide you with sharp vision, you will need to return for follow-up visits. Your eye doctor will also examine the health of your eyes to confirm that your eyes can tolerate contacts with no problems. Sometimes, a change in the material of your contacts, brand or wearing schedule is necessary.

Contact lenses are not one-size-fits-all. When we visit our optometrist for a new pair of glasses, we’re used to having our glasses meticulously fit. The frames are adjusted to the bridge of our nose, the lenses are centralized to the middle of our eyes, and the arms of the frame are adjusted to sit properly on our ears. A similar type of precision is needed to properly fit contact lenses as well.An eye exam is not the same as a contact lens fitting. If you currently wear contacts, or would like to start, you need both an eye exam (a complete assessment of your refractive status, binocular system and eye health) and a contact lens fitting.Even those of us that are full time glasses wearers, we do still occasionally rely on contacts — whether it be for sports, convenience, lifestyle, or otherwise. For all contact lens wearers, a contact lens fitting is vital to ensure that the lenses fit each eye properly, your vision is good for distance and near, and your eye health is maintained.

If you can constantly feel your contact lenses, also known as “lens awareness”, it could mean they are poorly fitted. Lenses come in all shapes, sizes, diameters and curvatures. If you feel like there is always something in your eye or your eyes are always red with contact lenses on, the lens diameter or base curve could be incorrect and need to be adjusted.If your eye has a curvature imperfection (or more oval shaped), light is angled more in one direction than another and this provides only partial focus on an object. This curvature and non-ideal refraction of light, causes objects to look blurry, wavy or distorted. In order for your contacts to be able to counteract this imperfect curvature, you need a contact lens fitting to ensure an appropriate fit.

Your eye doctor will measure the curvature of the cornea (the front surface of the eye) to check that the curve of the contact lens properly fits the curve of your eye. A keratometer, topographer or autorefractor can be used to measure the curvature to determine what the appropriate curve is for your contact lenses. 2. Seek Professional Help. Remove your lenses, put on your glasses and immediately seek help from your optometrist to avoid risk of infection and any permanent damage to your vision and eyes. The great news is that most contact lens fittings can be combined with your comprehensive eye exam and it only adds about 10 minutes to your appointment. More time will be needed for first time wearers or those being fit in multifocal contacts.All of the above are considered refractive errors which are classified as very common eye disorders that are a result of your eyes shape, power and genetic composition. In fact, astigmatism is one of the most common vision problems that impacts more than 150 million Americans. Yes! Imagine getting a new pair of glasses but the frame is too wide so it continuously slips off the bridge of your nose. Or the arms are too short to reach behind your ear, skewing how the glasses fit on your face and hindering your vision. The same can happen with improperly fitted contact lenses. Poorly fit contact lenses can lead to a myriad of issues such as blurry vision, eye strain, headaches, red or dry eyes and infection. En del av kontaktlinser och tillbehör är förmånligare via oss än direkt från webbbutiken i fråga. Du får ett rabatterat pris genom att gå till butiken via länkarna som finns på vår sida.

* Priset inkluderar alla leverans- och bearbetningskostnader när säljarens billigaste leveransmetod används. I priset beaktas även om totala inköp är tillräckliga för gratis frakt när du köper ett (1) stycke av produkten i fråga. Observera, att du kan få gratis frakt när du beställer fler produkter samtidigt. ColourVUE Big Eyes är en populär färgad heltäckande s.k. opaque-lins, som gör dina ögon större och dockliknande. Dessa linser passar både ljusa och mörka ögon, och de finns att få med eller utan synkorrigering. Linserna är tillverkade av det patenterade hydrogel materialet och deras tillverkare Maxvue Vision är en ISO9001 certifierad organisation. Prisinformation uppdateras en gång om dagen, vanligtvis under natten, men priserna kan ha ändrats efter den senaste uppdateringen. Kontrollera det slutliga priset från försäljarens webbplats.

Mitä se tarkoittaa? Jos löydät meillä myynnissä olevan tuotteen muualta halvemmalla, teemme sinulle kilpailijan hintaa vielä 2 % edullisemman tarjouksen.By submitting your question, you agree to be answered by email. Your email address will only be used to answer your question unless you are an Academy member or are subscribed to Academy newsletters.

I have been wearing gas permeable contacts for years. I lost one of my gas permeable contacts and for two days I had to wear my back-up pair. My back-up pair had a much weaker prescription and I did not see very well with them. Everything looked blurry and out of focus. After those two days I started developing massive headaches and was worried I was causing damage to my eyes by wearing the old prescription. My question is, did wearing my old prescription for those two days cause any permanent damage to my eyes? Did the muscles get overstressed due to the poor vision I had while wearing them? If so, are these effects permanent?

Wearing the wrong prescription is very unlikely to cause any temporary or permanent damage to the eyes. It can, however, cause symptoms which are called asthenopia and include blurry vision, headache, nausea, eye pain, brow ache and others. These symptoms should resolve when you get your correct prescription.In the last decades, the development of self-driving vehicles has rapidly increased. Improvements in algorithms, as well as sensor and computing hardware have led to self-driving technologies becoming a reality. It is a technology with the potential to radically change how society interacts with transportation. One crucial part of a self-driving vehicle is control schemes that can safely control the vehicle during evasive maneuvers. This work investigates the modeling and lateral control of tractor-trailer vehicles during aggressive maneuvers. Models of various complexity are used, ranging from simple kinematic models to complex dynamic models, which model tire slip and suspension dynamics. The models are evaluated in simulations using TruckMaker, which is a high fidelity vehicle simulator. Several lateral controllers are proposed based on Model predictive control (MPC) and linear-quadratic (LQ) control techniques. The controllers use different complex prediction models and are designed to minimize the path-following error with respect to a geometric reference path. Their performance is evaluated on double lane change maneuvers of various lengths and with different longitudinal speeds. Additionally, the controllers’ robustness against changes in trailer mass, weight distribution, and road traction is investigated. Extensive simulations show that dynamic prediction models are necessary to keep the control errors small when performing maneuvers that result in large lateral accelerations. Furthermore, to safely control the tractor-trailer vehicle during high speeds, it is a necessity to include a model of the trailer dynamics. The simulation study also shows that the proposed LQ controllers have trouble to evenly balance tractor and trailer deviation from the path, while the MPC controllers handle it much better. Additionally, a method for approximately weighting the trailer deviation is shown to improve the performance of both the LQ and MPC controllers. Finally, it is concluded that an MPC controller with a dynamic tractor-trailer model is robust against model errors, and can become even more robust by tuning the controller weights conservatively.

To optimally compensate for time-varying phase aberrations with adaptive optics, a model of the dynamics of the aberrations is required to predict the phase aberration at the next time step. We model the time-varying behavior of a phase aberration, expressed in Zernike modes, by assuming that the temporal dynamics of the Zernike coefficients can be described by a vector-valued autoregressive (VAR) model. We propose an iterative method based on a convex heuristic for a rank-constrained optimization problem, to jointly estimate the parameters of the VAR model and the Zernike coefficients from a time series of measurements of the point-spread function (PSF) of the optical system. By assuming the phase aberration is small, the relation between aberration and PSF measurements can be approximated by a quadratic function. As such, our method is a blind identification method for linear dynamics in a stochastic Wiener system with a quadratic nonlinearity at the output and a phase retrieval method that uses a time-evolution-model constraint and a single image at every time step. (c) 2019 Optical Society of America.Main Results: The linear models had an average attended vs ignored speech classification accuracy of 95.87% and 50% for ∼30 second and 8 seconds long time windows, respectively. A DNN model designed for AAD resulted in an average classification accuracy of 82.32% and 58.03% for ∼30 second and 8 seconds long time windows, respectively, when trained only on the real EEG data. The results show that GANs generated relatively realistic speech-evoked EEG signals. A DNN trained with GAN-generated data resulted in an average accuracy 90.25% for 8 seconds long time windows. On shorter trials the GAN-generated EEG data have shown to significantly improve classification performances, when compared to models only trained on real EEG data.Evaluation shows that errors in velocity and the uncertainty of all the states are significantly reduced using an unscented Rauch-Tung-Striebel smoother. For the evaluated scenarios it can be concluded that the choice of motion model depends on scenarios and the motion of the tracked vehicle but are roughly the same. Further the results show that assuming fix width of a vehicle do not work and measurements using non-causal estimation of topography can significantly reduce the error in position, but further studies are recommended to verify this.For several years, there has been a remarkable increase in efforts to develop an autonomous car. Autonomous car systems combine various techniques of recognizing the environment with the help of the sensors and could drastically bring down the number of accidents on road by removing human conduct errors related to driver inattention and poor driving choices.

A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.

In this paper, we propose a distributed algorithm for solving coupled problems with chordal sparsity or an inherent tree structure which relies on primal–dual interior-point methods. We achieve this by distributing the computations at each iteration, using message-passing. In comparison to existing distributed algorithms for solving such problems, this algorithm requires far fewer iterations to converge to a solution with high accuracy. Furthermore, it is possible to compute an upper-bound for the number of required iterations which, unlike existing methods, only depends on the coupling structure in the problem. We illustrate the performance of our proposed method using a set of numerical examples.The ICP algorithm used in the system has a lot of flaws. The worst is that it easily converges to incorrect solutions, in other words that it estimates the wrong position of the vehicle. How this risk can be decreased is also investigated in this thesis. A method that decreases this risk drastically, and makes the viable performance of the system possible, is developed. The approach of the method is to exclude incorrect positions by removing a large amount of points from the point clouds, and keeping the most informative. By only utilizing the most informative data points in the point cloud, global positions with high accuracy are achieved.

We present an algorithm to estimate and
quantify the uncertainty of the accelerometers relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.

Search-And-Rescue (SAR) is one of manyfields with applications benefiting from the increasingavailability of Unmanned Aerial Systems (UASs). Most UAS applications rely on the UAS’s capabilityto carry a camera and stream video data for manualor automated processing. However, this relies onunobstructed views of the target, which limits the applicability of these systems. In this paper, we instead describe the development and initial application testing of a system with a UAS-carried harmonic radar. This sensor is designed to detect the presence of Recco radar reflectors, commonly found integrated into alpine clothes and gear. The reflectors can be detected through vegetation and snow and is independent of many external factors such as lighting conditions. The paper describesthe system design and provides initial real-world results. The initial tests show fruitful results and opens up several avenues of continued research and development.Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians’ position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other.Design: The proposed methods were tested in a dataset of 34 participants who performed an auditory attention task. They were instructed to attend to one of the two talkers in the front and ignore the talker on the other side and back-ground noise behind them, while high density EEG was recorded.The control-theoretic notion of controllability captures the ability to guide a systems behavior toward a desired state with a suitable choice of inputs. Controllability of complex networks such as traffic networks, gene regulatory networks, power grids etc. brings many opportunities. It could for instance enable improved efficiency in the functioning of a network or lead to that entirely new applicative possibilities emerge. However, when control theory is applied to complex networks like these, several challenges arise. This thesis consider some of these challenges, in particular we investigate how control inputs should be placed in order to render a given network controllable at a minimum cost, taking as cost function either the number of control inputs or the energy that they must exert. We assume that each control input targets only one node (called a driver node) and is either unconstrained or unilateral. To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics. The system being modelled is a multiple input multiple output system with acomplex internal structure. The modelling can be divided into several steps. Firstly, data has to be acquired from the system. Secondly, the data is analysed and processed. Thirdly, models are estimated based on the collected data. Different model structures such as state-space, ARX, ARMAX, Output Error, Box Jenkins and grey-box models are examined and compared to each other. Finally, the different derived models are validated and it turns out the ARMAX model yields the best prediction capabilities. However, when the controllers were tested on the actual system the controllers that are based on the grey-box model yield the best results.In this report, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed by Reece and Nicholson [1] is revisited. It turns out that the general results presented in [1] are incorrect, as some expectations appearing in the information matrix recursions are missing. This report presents the corrected results and it is argued that the missing expectations are only zero in a number of special cases. A nonlinear toy example is used to illustrate when this is not the case.Lastly, a network-based approach is used to integrate DNA methylation and RNA-seq in a case-control study centered around multiple sclerosis, in order to identify common regulatory patterns in DNA methylation and gene expression during the course of pregnancy. The strategy is based on the rationale that proteins that are interconnected in the protein-protein network are more likely to be involved in similar cellular functions. Indeed, the analysis highlights that similar pathways are altered at epigenetic and transcriptomic level, leading to a set of genes that are likely involved in the modification of the disease symptoms that is observed during pregnancy.

In this paper, we study the problem of controlling complex networks with unilateral controls, i.e., controls which can assume only positive or negative values, not both. Given a network with linear dynamics represented by the adjacency matrix A, we seek to understand the minimal number of unilateral controls that renders the network controllable. This problem has structural properties that for instance allows us to establish theoretical bounds and identify key topological properties that makes a network relatively easy to control with unilateral controls as compared to unrestricted controls. We find that the structure of the left null space of A is particularly important to this end. In a computational study we find that the network topology largely determines the number of unilateral controls and that the derived lower bounds often are achieved with heuristic methods. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

A robust positioning algorithm is able to handle interference well and to decrease its impact on the positioning accuracy. The cost, in terms of frequency resources, of using more orthogonal signals may not be worth the small improvement in accuracy and availability.

This thesis studies the possibility to replace the global navigation satellite system (GNSS) with a phased array radio system (PARS) for positioning and navigation of an unmanned aerial vehicle (UAV). With the increase of UAVs in both civilian and military applications, the need for a robust and accurate navigation solution has increased. The GNSS is the main solution of today for UAV navigation and positioning. However, the GNSS can be disturbed by malicious sources, the signal can either be blocked by jamming or modified to give the wrong position by spoofing. Studies have been conducted to replace or support the GNSS measurements with other drift free measurements, e.g. camera or radar systems. Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path-planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented. The unknown parameters of the grey box model were estimated using flight data and tuned to minimize the mean square of the prediction error. The numerical optimization of the parameters was performed using the Matlab implementations of the BFGS and SQP algorithms. An extended Kalman filter based on the model was also implemented.Motion planning is central to the efficient operation and autonomy of robots in the industry. Generally, motion planning of industrial robots is treated in a two-step approach. First, a geometric path between the start and goal position is planned where the objective is to achieve as short path as possible together with avoiding obstacles. Alternatively, a pre-defined geometric path is provided by the end user. Second, the velocity profile along the geometric path is calculated accounting for system dynamics together with other constraints. This approach is computationally efficient, but yield sub-optimal solutions as the system dynamics is not considered in the first step when the geometric path is planned.

Another approach is to jointly estimate any unknown model parameters together with the states, i.e., while estimating the state of the system, the parameters of the model are also estimated (learned). This can be done either offline or it can be done online, i.e., the parameters are learned after the state estimation procedure is “deployed” in practice. A challenge with online parameter estimation, is that it complicates the estimation procedures and typically increases the computational burden, which limits the applicability of such methods to models with only a handful of parameters.A maximum likelihood estimator for the determination of the position and orientation of a permanent magnet using an array of magnetometers is presented. To reduce the complexity and increase the robustness of the estimator, the likelihood function is concentrated and an iterative solution method for the resulting low-dimensional optimization problem is presented. The performance of the estimator is experimentally evaluated with a miniaturized sensor array that consists of 32 magnetometer triads. The results are compared to the Cramér-Rao bound for the estimation problem at hand. The comparisons show that the performance of the estimator is close to the Cramer-Rao bound; hence, the estimator is close to optimal. Further, the results illustrate that even with a matchbox-sized array and a small magnet with a dipole moment that has a magnitude of 7 2 · 10 -3 Am 2 the position and orientation of the magnet can, within a 80×80×80 mm volume, be estimated with a root mean square error of less than 10 mm and 15 deg, respectively.In a second track of this thesis, a trajectory is created in two steps. The first is path planning to find a shortest geometric path between two points. In the second step, the path is converted to a trajectory and is optimized to become dynamically feasible. For this purpose, a roadmap is generated from a modified version of the generalized Voronoi diagram. To find an initial path in the roadmap, the A-star algorithm is utilized and to connect start and goal position to the map a few different methods are examined. An initial trajectory is created by mapping a straight-line trajectory to the initial path, thus connecting time, position and velocity. The final trajectory is found by solving a discrete OCP initialized with the initial trajectory. The OCP contains spatial constraints that ensures that the vessel does not collide with static obstacles.

The problem is studied using SOO sources with either known or unknown locations. An extended Kalman filter (EKF) based solution is proposed for the first case which is shown to significantly improve the localisation performance compared to an unassisted INS in common scenarios. Yet, a number of factors affect this performance, including the measurement noise variance, the signal rate and the availability of known source locations. An outlier rejection mechanism is developed which is shown to increase the robustness of the suggested method. A numerical evaluation indicates that statistical consistency can be maintained in many situations even with the above-mentioned challenges.

The task of maneuvering ships in confined environments is a difficult task for a human operator. One major reason is due to the complex and slow dynamics of the ship which need to be accounted for in order to successfully steer the vehicle. In this work, a two-step optimization-based motion planner is proposed for autonomous maneuvering of ships in constrained environments such as harbors. A lattice-based motion planner is used in a first step to compute a feasible, but suboptimal solution to a discretized version of the motion planning problem. This solution is then used to enable efficient warm-start and as a terminal manifold for a second recedinghorizon improvement step. Both steps of the algorithm use a high-fidelity model of the ship to plan feasible and energy-efficient trajectories. Moreover, a novel algorithm is proposed for automatic computation of spatial safety envelopes around the trajectory computed by the lattice-based planner. These safety envelopes are used in the second improvement step to obtain collision-avoidance constraints which complexity scales very well with an increased number of surrounding obstacles. The proposed optimization-based motion planner is evaluated with successful results in a simulation study for autonomous docking problems in a model of the Cape Town harbor.

As the greenhouse effect is an imminent concern, motivation for the development of energy efficient systems has grown fast. Today heavy-duty vehicles (HDVs) account for a growing part of the emissions from the vehicular transport sector. One way to reduce those emissions is by driving at short intervehicular distances in so called platoons, mainly on highways. In such formations, the aerodynamic drag is decreased which allows for more fuel efficient driving, meanwhile the roads are used more efficiently. This thesis deals with the question of how those platoons can be controlled without using communications between the involved HDVs.

Mean square error optimal estimation requires the full correlation structure to be available. Unfortunately, it is not always possible to maintain full knowledge about the correlations. One example is decentralized data fusion where the cross-correlations between estimates are unknown, partly due to information sharing. To avoid underestimating the covariance of an estimate in such situations, conservative estimation is one option. In this paper the conservative linear unbiased estimator is formalized including optimality criteria. Fundamental bounds of the optimal conservative linear unbiased estimator are derived. A main contribution is a general approach for computing the proposed estimator based on robust optimization. Furthermore, it is shown that several existing estimation algorithms are special cases of the optimal conservative linear unbiased estimator. An evaluation verifies the theoretical considerations and shows that the optimization based approach performs better than existing conservative estimation methods in certain cases.

To solve this a model-based control design approach was used and a nonlineargrey-box model was derived, implemented and validated. The model parameterswere estimated using a nonlinear least-squares optimisation problem. The resulting model captures most of the system dynamics and the model fit is higher than 70% which was deemed good enough to use for control design. A PID controller was designed based on the estimated model and the controllerparameters were optimised. Furthermore, the controller was evaluated in simulations and implemented in a real forklift. The proposed controller was compared to the original controller for various scenarios. The results reveal improvedsteady state behaviour with enhanced temperature robustness compared to theoriginal controller.This thesis provides general insights in lidar data processing and state estimation in changing environments. For the underground mine application specifically, different methods presented in this thesis target different aspects of the higher goal of achieving robust and accurate position estimates. Together they present a collective view of how to design localization systems that produce reliable estimates for underground mining environments.

We study the fundamental problem of fusing one round trip time (RTT) observation associated with a serving base station with one time-difference of arrival (TDOA) observation associated to the serving base station and a neighbor base station to localize a 2-D mobile station (MS
). This situation can arise in 3GPP Long Term Evolution (LTE) when the number of reported cells of the mobile station is reduced to a minimum in order to minimize the signaling costs and to support a large number of devices. The studied problem corresponds geometrically to computing the intersection of a circle with a hyperbola, both with measurement uncertainty, which generally has two equally likely solutions. We derive an analytical representation of these two solutions that fits a filter bank framework that can keep track of different hypothesis until potential ambiguities have been resolved. Further, a performance bound for the filter bank is derived. The proposed filter bank is first evaluated in a simulated scenario, where the set of serving and neighbor base stations is changing in a challenging way. The filter bank is then evaluated on real data from a field test, where the result shows a precision better than 40 m 95% of the time.

Amaximumlikelihood estimator is presented for self-calibrating both accelerometers and gyroscopes in an inertial sensor array, including scale factors, misalignments, biases, and sensor positions. By simultaneous estimation of the calibration parameters and the motion dynamics of the array, external equipment is not required for the method. A computational efficient iterative optimizationmethod is proposed where the calibration problem is divided into smaller subproblems. Further, an identifiability analysis of the calibration problem is presented. The analysis shows that it is sufficient to know the magnitude of the local gravity vector and the average scale factor gain of the gyroscopes, and that the array is exposed to two types ofmotions for the calibration problemto bewell defined. The proposedestimator is evaluatedby real-worldexperimentsand byMonteCarlo simulations. The results show that the parameters can be consistently estimated and that the calibration significantly improves the accuracy of the motion estimation. This enables on-the-fly calibration of small inertial sensors arrays by simply twisting them by hand.In Contribution I, sparse Gaussian processes are proposed to model behaviours of targets that are caused by influences from the environment, such as wind or obstacles. The influences are learned online as a part of the state estimation using an extended Kalman filter. The method is also adapted to handle time-varying influences and to identify dynamic systems. It is shown to improve accuracy over the nearly constant velocity and acceleration models in simulation. The method is also evaluated in a sea ice tracking application using data from a radar on Svalbard.

A second class of collective decision-making models discussed in the thesis is obtained by replacing the saturations with sigmoidal nonlinearities. This nonlinear interconnected model is first investigated in the collaborative case and then in the antagonistic case, represented as a signed graph of interactions. In both cases, it is shown that the behavior of the model can be described by means of bifurcation analysis, with the equilibria of the system encoding the possible decisions for the community. A scalar positive parameter, denoted ”social effort”, is added to the model to represent the strength of commitment between the agents, and plays the role of bifurcation parameter in the analysis. It is shown that if the social effort is small, then the community is in a deadlock situation (i.e., no decision is taken), while if the agents have the ”right” amount of commitment two alternative consensus decision states for the community are achieved. However, by further increasing the social effort, the agents may fall in a situation of ”overcommitment” where multiple (more than 2) decisions are possible. When antagonistic interactions between the agents are taken into account, they may lead to conflicts or social tensions during the decision-making process, which can be quantified by the notion of ”frustration” of the signed network representing the community. The aim is to understand how the presence of antagonism (represented by the amount of frustration of the signed network) influences the collective decision-making process. It is shown that, while the qualitative behavior of the system does not change, the value of social effort required from the agents to break the deadlock (i.e., the value for which the bifurcation is crossed) increases with the frustration of the signed network: the higher the frustration, the higher the required social commitment.

The second contribution is a methodology for generating landmark densities from prior data for a forest scenario. These densities were generated from publicly available aerial data used in the Swedish forest industry.The detection works successfully with an average precision of 0.714. The filter using 2D-bounding boxes can not differentiate between a clockwise and counterclockwise rotation, but the performance is improved when a measurement of rotation is included. Using ARA* in the motion planner, the solution sufficiently avoids the obstacles. Inertial navigation systems (INS ) rely on integrating inertial sensor measurements. INS as a standalone system is known to have a cubic drift in the position error, and it needs supporting sensor information, for instance, position fixes from GNSS whenever available. For pedestrians, special tricks such as parametric gait models and step detections can be used to limit the drift. In general, the more accurate gait parameters, the better position estimation accuracy. An improved pedestrian dead reckoning (PDR) algorithm is developed that learns gait parameters in time intervals when direct position measurements (such as GNSS positions) are available. We present a multi-rate filtering solution that leads to improved estimates of both gait parameters and position. To further extend the algorithm to more realistic scenarios, a joint classifier of the user’s motion and the device’s carrying mode is developed. Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in backpack) provides information that can assist in the gait learning process and hence improve the position estimation. The algorithms are applied to collected data and promising results are reported. Furthermore, one of the most extensive datasets for personal navigation systems using both rigid body motion trackers and smartphones is presented, and this dataset has also been made publicly available. This thesis addresses the problem of achieving accurate, robust and consistent position estimates for long-term autonomy of vehicles operating in an underground mining environment. The focus is on onboard positioning solutions utilizing sensor fusion within the probabilistic filtering framework, with extra emphasis on the characteristics of lidar data. Contributions are in the areas of improved state estimation algorithms, more efficient lidar data processing and development of models for changing environments. The problem descriptions and ideas in this thesis are sprung from underground localization issues, but many of the resulting solutions and methods are valid beyond this application.

The best method is the pitch angle relationship while the other two proposed methods have potential but would need higher sampling frequencies and additional signals to fully perform satisfactorily. With additional information such as position of the robotic lawn mower the estimation of the global velocities could be significantly improved which in turn would improve the odometry method and serve as a complement to the current pitch angle relation. The frequency methods would also be valid if the sampling frequencies were much higher, some-thing that might not be as cost efficient as needed to make the method profitable.Robotic manipulators are used for industrial automation and play an important role in manufacturing industry. Increasing performance requirements such as high operating speed and motion accuracy conflict with demands on heavy pay-loads and light-weight design with reduced structural stiffness. The motion control system is a key factor for dealing with these requirements, particularly for increasing the robot performance, improving safety and reducing power consumption. Most industrial robot control systems rely on current and angular position measurements from the motors, meaning that the actual controlled variable, that is the position of the robot’s end-effector, needs to be calculated using a model. Therefore, the mathematical model used for motion control must accurately describe the system’s dynamic behavior. Based on physics equations, the model contains unknown parameters that are usually identified from experimental data. This identification is a challenging problem, since the equations are nonlinear in the parameters, the system is highly resonant and experiments can only be done in closed loop with a controller.

An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering.Collaborative behavior of autonomous vehicles requires highly accurate position estimates. In this thesis RTK is investigated and its accuracy and precision evaluated for the positioning of autonomous UAVs and UGVs through a series of experiments. The experiments range from stationary and dynamic accuracy to investigation of the consistency of the positioning estimates. The results are promising, RTK outperforms standard GNSS and can be used for centimeter-level accuracy when positioning a UAV in-flight.Maritime navigation heavily relies on global navigation satellite systems and related technologies for positioning. Since these technologies are vulnerable to external threats such as signal spoofing, alternatives are needed to increase reliability and ensure safe navigation. 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. In this paper, we study the asymptotic properties of the generalized cross validation (GCV) hyperparameter estimator and establish its connection with the Steins unbiased risk estimators (SURE) as well as the mean squared error (MSE). It is shown that as the number of data goes to infinity, the GCV has the same asymptotic property as the SURE does and both of them converge to the best hyperparameter in the MSE sense. We illustrate the efficacy of the result by Monte Carlo simulations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. In a sensor-to-sensor modeling approach, it is sometimes not obvious which signals to select as input and output. In this case, several common methods give different results when estimating the forward and inverse models. However, it is shown that the IV method will give identical results when estimating the forward and inverse models of a single-input single-output (SISO) system using finite data. Furthermore, this result is illustrated experimentally when the goal is to determine the center of gravity of a quadcopter. RESULTS: The results show that it is possible to detect sound from HeartMate 3 and the sound spectrum is clear. Pump frequency and frequency of the pulsatile mode are easily determined. Frequency spectra from in vitro and in vivo recordings have the same pattern, and the major proportion (96.7%) of signal power is located at the pump speed frequency ±40 Hz. The recordings from the patients show low inter-individual differences except from location of peaks originating from pump speed and harmonics. Electronic stethoscopes could be used for sound recordings, but the dedicated equipment showed a clearer sound spectrum. DISCUSSION: The results show that acoustic analysis can also be performed with the HeartMate 3 and that in vivo and in vitro sound spectrum is similar. The frequency spectra are different from previous devices, and methods for assessing pump function or thrombosis need further evaluation.

Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept-reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.Some controllability aspects for iterative learning control (ILC) are discussed. Via a batch (lifted) description of the problem a state space model of the system to be controlled is formulated in the iteration domain. This model provides insight and enables analysis of the conditions for and relationships between controllability, output controllability and target path controllability. In addition, the property miminum lead target path controllability is introduced. This property, which is connected to the number of time delays, plays an important role in the design of ILC algorithms. The properties are illustrated by a numerical example.

The methods developed are evaluated using experimental data from a prototype photon counting lidar system. The results show that the voxel discretization need to be at least as large as the range quantization in the lidar. No significant difference between using registration and SLAM in the third step is observed, but both methods outperform the odometric method.

The main idea in this paper is to implement a distributed primal-dual interior-point algorithm for loosely coupled Quadratic Programming problems. We implement this in Julia and show how can we exploit parallelism in order to increase the computational speed. We investigate the performance of the algorithm on a Model Predictive Control problem.The first contribution concerns a narrow-band standard in lte intended for internet of things (iot) devices. This lte standard includes a special position reference signal sent synchronized by all base stations (bs) to all iot devices. Each device can then compute several pair-wise time differences that corresponds to hyperbolic functions. Using multilateration methods the intersection of a set of such hyperbolas can be computed. An extensive performance study using a professional simulation environment with realistic user models is presented, indicating that a decent position accuracy can be achieved despite the narrow bandwidth of the channel.

Devising the planar routes of minimal length that are required to pass through predefined neighborhoods of target points plays an important role in reducing the missions operating cost. Two versions of the problem are considered. The first one assumes that the ordering of the targets is fixed a priori. In such a case, the optimal route is devised by solving a convex optimization problem formulated either as a second-order cone program or as a sum-of-squares optimization problem. Additional route properties, such as continuity and minimal curvature, are considered as well. The second version allows the ordering of the targets to be optimized to further reduce the route length. We show that such a problem can be solved by introducing additional binary variables, which allows the route to be designed using off-the-shelf mixed-integer solvers. A case study that shows that the proposed strategy is computationally tractable is presented.

This master’s thesis aims to make the BRIO Labyrinth Game autonomous and the main focus is on the development of a path following controller. A test-bench system is built using a modern edition of the classic game with the addition of a Raspberry Pi, a camera and two servos. A mathematical model of the ball and plate system is derived to be used in model based controllers. A method of using path projection on a cubic spline interpolated path to derive the reference states is explained. After that, three path following controllers are presented, a modified LQR, a Gain Scheduled LQR and a Gain Scheduled LQR with obstacle avoidance. The performances of these controllers are compared on an easy and a hard labyrinth level, both with respect to the ability of following the reference path and with respect to success rate of controlling the ball from start to finish without falling into any hole. All three controllers achieved a success rate over 90 % on the easy level. On the hard level the Gain Scheduled LQR achieved the highest success rate, 78.7 %, while the modified LQR achieved the lowest deviation from the reference path. The Gain Scheduled LQR with obstacle avoidance performed the worst in both regards. Overall, the results are promising and some insights gained when designing the controllers can possibly be useful for development of controllers in other applications as well.For accurate control of industrial robots, a fast and easy-to-use method to estimate the model parameters based on experimental data is desired. This publication is about optimal experiment design in terms of short experiment times and an accurate parameter estimate. An optimization problem that is based on information matrices is solved for finding the optimal robot configurations for the identification experiment. A simulation study shows that the experiment time can be reduced significantly and the accuracy of the parameter estimate can be increased if experiments are conducted only in the optimal manipulator configurations. Furthermore, it is shown that a realistic estimate of the uncertainty in the frequency response function is crucial for successful experiment design.

As marine vessels are becoming increasingly automated, having accurate simulation models available is turning into an absolute necessity. This holds both for the facilitation of development and for achieving satisfactory model-based control. Such models can be obtained through system identification, and in this thesis, particular emphasis is given to experiment design and parameter estimation, which constitute two central steps in the system identification process. The analysis is carried out for a special class of nonlinear regression models called second-order modulus models, which is a type of model that is often used for describing nonlinear hydrodynamic effects in greybox identification of ships.In this article we use high-throughput epigenomics, transcriptomics, and proteomics data to construct fine-graded models of the ”protein-coding units”gathering all transcript isoforms and chromatin accessibility peaks associated with more than 4000 genes in humans. Each protein-coding unit has the structure of a directed acyclic graph (DAG) and can be represented as a Bayesian network. The factorization of the joint probability distribution induced by the DAGs imposes a number of conditional independence relationships among the variables forming a protein-coding unit, corresponding to the missing edges in the DAGs. We show that a large fraction of these conditional independencies are indeed verified by the data. Factors driving this verification appear to be the structural and functional annotation of the transcript isoforms, as well as a notion of structural balance (or frustration-free) of the corresponding sample correlation graph, which naturally leads to reduction of correlation (and hence to independence) upon conditioning.

This thesis studies a class of sensor management problems called informative path planning (IPP). Sensor management refers to the problem of optimizing control inputs for sensor systems in dynamic environments in order to achieve operational objectives. The problems are commonly formulated as stochastic optimal control problems, where to objective is to maximize the information gained from future measurements. In IPP, the control inputs affect the movement of the sensor platforms, and the goal is to compute trajectories from where the sensors can obtain measurements that maximize the estimation performance. The core challenge lies in making decisions based on the predicted utility of future measurements.

Accordingly, the first contribution of this work will be to present transfer functions between these quantities that resemble those observed in traditional steering systems. The steering feel/feedback is then achieved by an electric motor which can be controlled by different control strategies. In this thesis three different control strategies are investigated.