Min målsättning med denna uppsats har varit att forska kring hur kortvågsradion kan öka rörligheten på svenska förband i utlandsstyrkan. Inledningsvis har jag definerat vad jag menar med manöverkrigsföring, uppdragstaktik, de sex grundläggande förmågorna och radio. Jag har intervjuat individer som varit på enheter som rört sig i terrängen i Afghanistan och Kosovo. Jag har sedan diskuterat kring hur man kan öka rörligeheten genom att använd sig av uppdragstaktik och kortvågsradion. Jag har jämfört ledningsätt och radioanvändande mellan Afghanistan och Kosovo och dragit slutsatser från detta. En av slutsatserna som har dragits ur uppsatsen är att det är räckvidden som möjliggör en högre rörlighet genom att enheterna inte blir styrda till att hålla sig inom t.ex. en RAP-täckningskarta utan ger dem större möjlighet att själva välja framryckningsväg och möjliggöra att uppdragstaktik[1] kan utnyttjas på ett effektivare sätt. Denna slutsats beskriver hur rörlighet och uppdragstaktik blir effektivare genom att använda sig av kortvågsradion. Jag anser att kortvågsradion är ett radiosystem som försvarsmakten böra behålla och utveckla. Det som bör utvecklas är antenner och radio utrustningen som har tagit upp i uppsatsen. Förslag på förbättringar är att antennerna utvecklas så att man kan ha en antenn för alla frekvensområden så man inte behöver stanna fordonet och upprätta en deltaantenn[2] för att kunna sända på den lämpligaste frekvensen vid tillfället. Att det finns en individ vid campen som har som uppgift att uppdaterade frekvenspaketen varje vecka eftersom frekvenserna är väderberoende, för att mögligöra att radioförbindelsen blir så bra som mögligt. Radioapparaterna borde byggas med dagen elektronikkomponenter vilket skulle medföra mindre radioapparater, bättre batteritid och en mer lättmanövrerad radioapparat.  Jag kommer att avsluta med ett citat ur doktrin för markoperationer som jag anser styrker min uppsats: ”De ledningssystem som försvarsmakten använder ska medge en så stor flexibilitet som mögligt i valet av medel och metoder. Detta innebär att det bör finnas en inbyggd förmåga som möjliggör ledning från såväl en framskjuten plats inom ett operationsområde som från en stabsplats[3]”. / <p>Avdelning: ALB – Slutet Mag. 3 Hylla: Upps. YOP 06-09 // Avdelning:  Karlberg-filial - Karlberg Öppen hylla Hylla: K Upps. YOP REF</p>

Microtools that will be useful for the positioning and investigation microstructures must operate relevant environments, such as cell culture media or blood plasma. They must also be comparatively strong, and preferably allow a muscle like mode of movement. This is given by a novel family of actuators based on conjugated polymers (like polypyrrole, PPy). By miniaturising these structures using standard photolithographic techniques, the authors can reduce the size down to 10-micrometer dimensions and build mechanically active microdevices. These can be moved and positioned by applying a potential to dope or undope the PPy. These novel structures are now being developed as a unique microactuator technology, suitable for operation in applications coupled to cell biology and biomedicine

No description available.

Using in situ transmission electron microscopy, we investigated the kinetics of liquid Ga droplet decay on thin amorphous carbon films during annealing at 773 K. The transmission electron microscopy images reveal that liquid Ga forms spherical droplets and undergo coarsening/decay with increasing time. We find that the droplet volumes change non-linearly with time and the volume decay rates depend on their local environment. By comparing the late-stage decay behavior of the droplets with the classical mean-field theory model for Ostwald ripening, we determine that the decay of Ga droplets occurs in the surface diffusion limited regime. / <p>Funding Agencies|AFOSR|FA9550-10-1-0496|STINT||Swedish Foundation for International Cooperation in Research and Higher Education||Swedish Research Council||Austrian Science Fund FWF, START project|Y371|Knut and Alice Wallenberg Foundation||</p>

We report on the observation of intersubband absorption in GaN/AlN quantum well superlattices grown on (11<img src="http://jap.aip.org/math_images/JAPIAU/vol_113/iss_14/143109_1/143109_1-ft-d4e1233.gif" />2)-oriented GaN. The absorption is tuned in the 1.5–4.5 μm wavelength range by adjusting the well thickness. The semipolar samples are compared with polar samples with identical well thickness grown during the same run. The intersubband absorption of semipolar samples shows a significant red shift with respect to the polar ones due to the reduction of the internal electric field in the quantum wells. The experimental results are compared with simulations and confirm the reduction of the polarization discontinuity along the growth axis in the semipolar case. The absorption spectral shape depends on the sample growth direction: for polar quantum wells the intersubband spectrum is a sum of Lorentzian resonances, whereas a Gaussian shape is observed in the semipolar case. This dissimilarity is explained by different carrier localization in these two cases. / <p>Funding Agencies|EC FET-OPEN project Unitride|233950|EU ERC-StG under project TeraGaN|278428|French National Research Agency under project COSNI|ANR-08-BLAN-0298-01|</p>

Faculty of Criminology, Justice and Policy Studies

This thesis deals with focus of attention control in active vision systems. A framework for hierarchical gaze control in a robot vision system is presented, and an implementation for a simulated robot is described. The robot is equipped with a heterogeneously sampled imaging system, a fovea, resembling the spatially varying resolution of a human retina. The relation between foveas and multiresolution image processing as well as implications for image operations are discussed. A stereo algorithm based on local phase differences is presented both as a stand alone algorithm and as a part of a robot vergence control system. The algorithm is fast and can handle large disparities and maintaining subpixel accuracy. The method produces robust and accurate estimates of displacement on synthetic as well as real life stereo images. Disparity filter design is discussed and a number of filters are tested, e.g. Gabor filters and lognorm quadrature filters. A design method for disparity filters having precisely one phase cycle is also presented. A theory for sequentially defined data modified focus of attention is presented. The theory is applied to a preattentive gaze control system consisting of three cooperating control strategies. The first is an object finder that uses circular symmetries as indications for possible object and directs the fixation point accordingly. The second is an edge tracker that makes the fixation point follow structures in the scene. The third is a camera vergence control system which assures that both eyes are fixating on the same point. The coordination between the strategies is handled using potential fields in the robot parameter space. Finally, a new focus of attention method for disregarding filter responses from already modelled structures is presented. The method is based on a filtering method, normalized convolution, originally developed for filtering incomplete and uncertain data. By setting the certainty of the input data to zero in areas of known or predicted signals, a purposive removal of operator responses can be obtained. On succeeding levels, image features from these areas become 'invisible' and consequently do not attract the attention of the system. This technique also allows the system to effectively explore new events. By cancelling known, or modeled, signals the attention of the system is shifted to new events not yet described.

The subject of this dissertation is to show how learning can be used for multidimensional signal processing, in particular computer vision. Learning is a wide concept, but it can generally be defined as a system’s change of behaviour in order to improve its performance in some sense. Learning systems can be divided into three classes: supervised learning, reinforcement learning and unsupervised learning. Supervised learning requires a set of training data with correct answers and can be seen as a kind of function approximation. A reinforcement learning system does not require a set of answers. It learns by maximizing a scalar feedback signal indicating the system’s performance. Unsupervised learning can be seen as a way of finding a good representation of the input signals according to a given criterion. In learning and signal processing, the choice of signal representation is a central issue. For high-dimensional signals, dimensionality reduction is often necessary. It is then important not to discard useful information. For this reason, learning methods based on maximizing mutual information are particularly interesting. A properly chosen data representation allows local linear models to be used in learning systems. Such models have the advantage of having a small number of parameters and can for this reason be estimated by using relatively few samples. An interesting method that can be used to estimate local linear models is canonical correlation analysis (CCA). CCA is strongly related to mutual information. The relation between CCA and three other linear methods is discussed. These methods are principal component analysis (PCA), partial least squares (PLS) and multivariate linear regression (MLR). An iterative method for CCA, PCA, PLS and MLR, in particular low-rank versions of these methods, is presented. A novel method for learning filters for multidimensional signal processing using CCA is presented. By showing the system signals in pairs, the filters can be adapted to detect certain features and to be invariant to others. A new method for local orientation estimation has been developed using this principle. This method is significantly less sensitive to noise than previously used methods. Finally, a novel stereo algorithm is presented. This algorithm uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semi-transparent images.

Reinforcement learning is a general and powerful way to formulate complex learning problems and acquire good system behaviour. The goal of a reinforcement learning system is to maximize a long term sum of instantaneous rewards provided by a teacher. In its extremum form, reinforcement learning only requires that the teacher can provide a measure of success. This formulation does not require a training set with correct responses, and allows the system to become better than its teacher. In reinforcement learning much of the burden is moved from the teacher to the training algorithm. The exact and general algorithms that exist for these problems are based on dynamic programming (DP), and have a computational complexity that grows exponentially with the dimensionality of the state space. These algorithms can only be applied to real world problems if an efficient encoding of the state space can be found. To cope with these problems, heuristic algorithms and function approximation need to be incorporated. In this thesis it is argued that local models have the potential to help solving problems in high-dimensional spaces and that global models have not. This is motivated with the biasvariance dilemma, which is resolved with the assumption that the system is constrained to live on a low-dimensional manifold in the space of inputs and outputs. This observation leads to the introduction of bias in terms of continuity and locality. A linear approximation of the system dynamics and a quadratic function describing the long term reward are suggested to constitute a suitable local model. For problems involving one such model, i.e. linear quadratic regulation problems, novel convergence proofs for heuristic DP algorithms are presented. This is one of few available convergence proofs for reinforcement learning in continuous state spaces. Reinforcement learning is closely related to optimal control, where local models are commonly used. Relations to present methods are investigated, e.g. adaptive control, gain scheduling, fuzzy control, and jump linear systems. Ideas from these areas are compiled in a synergistic way to produce a new algorithm for heuristic dynamic programming where function parameters and locality, expressed as model applicability, are learned on-line. Both top-down and bottom-up versions are presented. The emerging local models and their applicability need to be memorized by the learning system. The binary tree is put forward as a suitable data structure for on-line storage and retrieval of these functions.

It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works. This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI). Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface. Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis. Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.