In recent years, deploying multiple antennas at both transmitter and receiver has appeared as a very promising technology. By exploiting the spatial domain, multiple-input multiple-output (MIMO) systems can support extremely high data rates as long as the environments can provide sufficiently rich scattering. To design high performance MIMO wireless systems and predict system performance under various circumstances, it is of great interest to have accurate MIMO wireless channel models for different scenarios. In this thesis, we characterize and model MIMO radio propagation channels based on indoor MIMO channel measurements. The recent development on MIMO radio channel modeling is briefly reviewed in this thesis. The models are categorized into non-physical and physical models, and discussed respectively. The non-physical models primarily rely on the statistical characteristics of MIMO channels obtained from the measured data, while the physical models describe the MIMO channel (or its distribution) via some physical parameters. We also briefly mention the MIMO channel modeling work within the IEEE 802.11n and 3GPP/3GPP2 standardization work. For the narrowband case, a non line-of-sight (NLOS) indoor MIMO channel model is presented. The model is based on a Kronecker structure of the channel covariance matrix and the fact that the channel is complex Gaussian. It is extended to line-of-sight (LOS) scenario by estimating and modeling the dominant component separately. For the wideband case, two NLOS MIMO channel models are proposed. The first model uses the average power delay profile and the Kronecker structure of the second order moments of each channel tap to model the wideband MIMO channel, while the second model combines a simple single-input single-output (SISO) model with the same Kronecker structure of the second order moments. Monte-Carlo simulations are used to generate indoor MIMO channel realizations according to the above models. The results are compared with the measured data and good agreement has been observed. Under the assumption of spatial wide sense stationary, a lower bound of the maximum Kronecker model errors is obtained by employing a combination of grid search and semidefinite programming to explore the feasible region. Numerical examples show that the bound is tight for moderate number of grid points. By comparing the worst case model errors with the model errors obtained from the measured channels, we find that the channel correlation matrix in these measurements can, indeed, be well approximated by the Kronecker product of the correlation matrix at the transmitter and the receiver. To model wideband MIMO channels, it is important to investigate the angular statistics on both the tap and cluster levels. Based on 5~GHz indoor wireless channel measurements, a frequency domain space alternating generalized expectation maximization (FD-SAGE) algorithm is employed to estimate the multipath components from the measured data. We then manually identify the clusters of the multipaths and calculate the tap and cluster angular spreads (ASs) for each identified cluster. It is found that for the 100 MHz channels, the average tap AS is just a few degrees less than the cluster AS and the difference diminishes for small channel bandwidth. / <p>QC 20121003</p>

The demand for efficient communication and data storage is continuously increasing and signal representation and compression are important factors in digital communication and storage systems. This work deals with Frame based signal representation and compression. The emphasis is on the design of frames suited for efficient representation, or for low bit rate compression, of classes of signals. Traditional signal decompositions such as transforms, wavelets, and filter banks, generate expansions using an analysis-synthesis setting. In this thesis we concentrate on the synthesis or reconstruction part of the signal expansion, having a system with no explicit analysis stage. We want to investigate the use of an overcomplete set of vectors, a frame or an overcomplete dictionary, for signal representations and allow sparse representations. Effective signal representations are desirable in many applications, where signal compression is one example. Others can be signal analysis for different purposes, reconstruction of signals from a limited observation set, feature extraction in pattern recognition and so forth. The lack of an explicit analysis stage originates some questions on finding the optimal representation. Finding an optimal sparse representation from an overcomplete set of vectors is NP-complete, and suboptimal vector selection methods are more practical. We have used some existing methods like different variations of the Matching Pursuit (MP) [52] algorithm, and we developed a robust regularized FOCUSS to be able to use FOCUSS (FOCal Underdetermined System Solver [29]) under lossy conditions. In this work we develop techniques for frame design, the Method of Optimal Directions (MOD), and propose methods by which such frames can successfully be used in frame based signal representation and in compression schemes. A Multi Frame Compression (MFC) scheme is presented and experiments with several signal classes show that the MFC scheme works well at low bit rates using MOD designed frames. Reconstruction experiments provides complimentary evidence of the good properties of the MOD algorithm.

In signal compression we distinguish between lossless and lossy compression. In lossless compression, the encoded signal is more bit efficient than the original signal and is exactly the same as the original one when decoded. In lossy compression, the encoded signal represents an approximation of the original signal, but it has less number of bits. In the latter situation, the major issue is to find the best possible rate-distortion (RD) tradeoff. The rate-distortion function (RDF) represents the theoretical lower bound of the distortion between the original and the reconstructed signal, subject to a given total bit rate for the compressed signal. This is with respect to any compression scheme. If the compression scheme is given, we can find its operational RDF (ORDF). The main contribution of this dissertation is the presentation of a method that finds the operational rate-distortion optimal solution for an overcomplete signal decomposition. The idea of using overcomplete dictionaries, or frames, is to get a sparse representation of the signal. Traditionally, suboptimal algorithms, such as Matching Pursuit (MP), are used for this purpose. Given the frame and the Variable Length Codeword (VLC) table embedded in the entropy coder, the solution of the problem of establishing the best RD trade-off has a very high complexity. The proposed method reduces this complexity significantly by structuring the solution approach such that the dependent quantizer allocation problem reduces into an independent one. In addition, the use of a solution tree further reduces the complexity. It is important to note that this large reduction in complexity is achieved without sacrificing optimality. The optimal rate-distortion solution depends on the frame selection and the VLC table embedded in the entropy coder. Thus, frame design and VLC optimization is part of this work. Extensive coding experiments are presented, where Gaussian AR(1) processes and various electrocardiogram (ECG) signals are used as input signals. The experiments demonstrate that the new approach outperforms Rate-Distortion Optimized (RDO) Matching Pursuit, previously proposed in [17], in the rate-distortion sense.

Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals. An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study. The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods. The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects. In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.

Extensive studies about neural mechanisms involved in insect flight control have been carried out. Adaptive control of locomotion requires integration of salient sensory cues with ongoing motor activity. During flight, inputs received by an organism through sensory organs are processed by the central nervous system (CNS) and the integrated output thus obtained plays a significant role in controlling the wing phase shifts and flight muscle depressor asymmetries associated with adaptive flight maneuvers. The resulting maneuvers, in turn, bring a change in the insects sensory environment, thereby closing the feedback loop. Research on insect flight has been carried out using immobile preparations (tethered) and mobile preparations (free flight untethered). There are pros and cons associated with the tethered and the untethered approach. The tethered approach, however, provides an easier way to study the CNS and its role in motor control of flight. Insects such as locusts and moths exhibit pertinent wing phase shifts and asymmetries in depressor muscles. For locusts constant wing phase shifts and m97 (forewing first basalar depressor muscle) depressor asymmetries have been observed during adaptive flight maneuvers making this a useful system for creation of behaviorally appropriate visual feedback. A method that utilizes asymmetrical timing of bilateral depressor muscles, the forewing first basalars (m97), of the locust to close a visual feedback loop in a computer-generated flight simulator is presented here. The method converts the time difference between left and right m97s to analog voltage values. Analog voltage values can be acquired using an open-loop experimental protocol (visual motion controlled by the experimenter), or can be used to control closed-loop experiments (muscle activity controls the visual stimuli) experiments. We recorded electromyographic (EMG) activity from right and left m97 muscles. On testing this circuit with real animals, we were able to detect the spike time difference and convert it to voltage values. These voltage values were utilized to control the presentation of a stimulus in a closed-loop environment. The feedback circuit presented here may be used in conjunction with the flight simulator(s) to understand the neural mechanisms involved in control of insect flight and provide further understanding of general mechanisms of neural control of behaviour.

Extensive studies about neural mechanisms involved in insect flight control have been carried out. Adaptive control of locomotion requires integration of salient sensory cues with ongoing motor activity. During flight, inputs received by an organism through sensory organs are processed by the central nervous system (CNS) and the integrated output thus obtained plays a significant role in controlling the wing phase shifts and flight muscle depressor asymmetries associated with adaptive flight maneuvers. The resulting maneuvers, in turn, bring a change in the insects sensory environment, thereby closing the feedback loop. Research on insect flight has been carried out using immobile preparations (tethered) and mobile preparations (free flight untethered). There are pros and cons associated with the tethered and the untethered approach. The tethered approach, however, provides an easier way to study the CNS and its role in motor control of flight. Insects such as locusts and moths exhibit pertinent wing phase shifts and asymmetries in depressor muscles. For locusts constant wing phase shifts and m97 (forewing first basalar depressor muscle) depressor asymmetries have been observed during adaptive flight maneuvers making this a useful system for creation of behaviorally appropriate visual feedback. A method that utilizes asymmetrical timing of bilateral depressor muscles, the forewing first basalars (m97), of the locust to close a visual feedback loop in a computer-generated flight simulator is presented here. The method converts the time difference between left and right m97s to analog voltage values. Analog voltage values can be acquired using an open-loop experimental protocol (visual motion controlled by the experimenter), or can be used to control closed-loop experiments (muscle activity controls the visual stimuli) experiments. We recorded electromyographic (EMG) activity from right and left m97 muscles. On testing this circuit with real animals, we were able to detect the spike time difference and convert it to voltage values. These voltage values were utilized to control the presentation of a stimulus in a closed-loop environment. The feedback circuit presented here may be used in conjunction with the flight simulator(s) to understand the neural mechanisms involved in control of insect flight and provide further understanding of general mechanisms of neural control of behaviour.

<p>The problem of path planning occurs in many areas, such as computational biology, computer animations and computer-aided design. It is of particular importance in the field of robotics. Here, the task is to find a feasible path/trajectory that the robot can follow from a start to a goal configuration. For the basic path planning problem it is often assumed that a perfect model of the world surrounding the robot is known. In industrial robotics, such models are often based on, for example, CAD models. However, in applications of autonomous service robotics less knowledge about the environment is available. Efficient and robust path planning algorithms are here of major importance. To be truly autonomous, a robot should be able to plan all motions on its own. Furthermore, it has to be able to plan and re-plan in real time, which puts hard constraints on the acceptable computation time.</p><p>This thesis presents a novel path planning method called Probabilistic Cell Decomposition (PCD). This approach combines the underlying method of cell decomposition with the concept of probabilistic sampling. The cell decomposition is iteratively refined until a collision-free path is found. In each immediate step the current cell decomposition is used to guide probabilistic sampling to important areas.</p><p>The basic PCD algorithm can be decomposed into a number of components such as graph search, local planning, cell splitting and probabilistic sampling. For each component different approaches are discussed. The performance of PCD is then tested on a set of benchmark problems. The results are compared to those obtained by one of the most commonly used probabilistic path planning methods, namely Rapidly-exploring Random Trees. It is shown that PCD efficiently solves various kinds of path planning problems.</p><p>Planning for autonomous manipulation often involves additional path constraints beyond collision avoidance. This thesis presents an application of PCD to path planning for a mobile manipulator. The robot has to fetch a carton of milk from the refrigerator and place it on the kitchen table. Here, opening the refrigerator involves motion with a pre-specified end-effector path. The results show that planning the different motions for the high-level task takes less time than actually executing them. The whole series of subtasks takes about 1.5 seconds to compute.</p>

No description available.

<p>Multiuser diversity can be used to significantly increase system throughput in wireless communication systems. The idea is to schedule users when they experience good channel conditions and let them wait when the channels are weak. In this thesis, several aspects on multiuser diversity OFDMA systems are investigated. An adaptive reduced feedback scheme for multiuser diversity OFDMA is proposed. It significantly reduces the total feedback overhead while maintaining a multiuser diversity gain. The scheme uses clusters of sub-carriers as feedback units and only feeds back information about the fading peaks. Furthermore, an opportunistic beamforming scheme for clustered OFDM is presented and evaluated. A key aspect of the opportunistic beamforming scheme is that it increases the frequency fading of users with relatively flat channels, which increases the likelihood of being scheduled. Scheduling is an important aspect of multiuser diversity. A modified proportional fair scheduler is proposed in this thesis. It incorporates user individual target bit-rates and delays and a tunable fairness level. These features makes the scheduler more attractive for future mixed service wireless systems. The use of the feedback information in the opportunistic beamforming process is discussed and evaluated. This extra information can help to increase the performance of unfairly treated users in the system. Several aspects of the proposed system are evaluated by means of simulation, using the 3GPP spatial channel model. In the simulations, the clustered beamforming performs better than three comparison systems. The modified proportional fair scheduler manages to divide the resources according to the user targets, while at the same time exploiting the multiuser diversity as well as the standard proportional fair algorithm. The thesis also includes results on coded packet error rate estimation from a channel realization by means of a two dimensional table. This can be useful in large network simulations as well as in designing adaptive modulation schemes.</p>

<p>This thesis deals with modelling, analysis, and design of interactions between subsystems in chemical process plants and intracellular biochemical processes. In the first part, the focus is on the selection of decentralized feedback control structures for plants in the chemical process industry, with the aim of achieving a desired performance in the presence of interactions. The second part focuses on modelling and analysis of complex biochemical networks, with the aim of unravelling the impact of interactions between genes, proteins, and metabolites on cell functions. </p><p>Decentralized control is almost the de-facto standard for control of large-scale systems, and in particular for systems in the process industry. An important task in the design of a decentralized control system is the selection of the control configuration, the so-called input-output pairing, which effectively decides the subsystems. Previous research addressing this problem has primarily focused on the effect of interactions on stability. In this thesis, the problem of selecting control configurations that can deliver a desired control performance is addressed. It is shown that existing measures of interactions, such as the relative gain array (RGA), are poor for selecting configurations for performance due to their inherent assumption of perfect control. Furthermore, several model based tools for the selection of control configurations based on performance considerations are proposed. </p><p>Central functions in the cell are often linked to complex dynamic behaviors, such as sustained oscillations and multistability, in a biochemical reaction network. Determination of the specific interactions underlying such behaviors is important, for example, to determine sensitivity, robustness, and modelling requirements of given cell functions. A method for identifying the feedback connections and involved subsystems, within a biochemical network, that are the main sources of a complex dynamic behavior is proposed. The effectiveness of the method is illustrated on examples involving cell cycle control, circadian rhythms and glycolytic oscillations. Also, a method for identifying structured dynamic models of biochemical networks, based on experimental data, is proposed. The method is based on results from system identification theory, using time-series measurement data of expression profiles and concentrations of the involved biochemical components. Finally, in order to reduce the complexity of obtained network models, a method for decomposing large-scale networks into biologically meaningful subnetworks is proposed.</p>