ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

Motion estimation using higher-order statistics

The objective of this paper is to introduce a fourth-order cost function of the displaced frame difference (DFD) capable of estimatingmotion even for small regions or blocks. Using higher than second-orderstatistics is appropriate in case the image sequence is severely corruptedby additive Gaussian noise. Some results are presented and compared to those obtained from the mean kurtosis and the mean square error of the DFD.

Quantile Estimation in Non-Stationary Streaming Data

We provide an incremental quantile estimator for Non-stationary Streaming Data. We propose a method for simultaneous estimation of multiple quantiles corresponding to the given probability levels from streaming data. Due to the limitations of the memory, it is not feasible to compute the quantiles by storing the data. So estimating the quantiles as the data pass by is the only possibility. This can be effective in network measurement. To provide the minimum of the mean-squared error of the estimation, we use parabolic approximation and for comparison we simulate the results for different number of runs and using both linear and parabolic approximations.

Unifying Time to Contact Estimation and Collision Avoidance across Species

The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework the corrected modified Tau function capable of predicting both -type ("") and -type ("") responses. The outstanding property of our new framework is its resilience to noise. We show that can be derived from a firing rate equation, and, as , serves to describe the response curves of collision sensitive neurons. Furthermore, we show that predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between -type and -type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain.

On Effort Estimation in Software Projects

Software engineering is criticized as not being engineering or 'well-developed' science at all. Software engineers seem not to know exactly how long their projects will last, what they will cost, and will the software work properly after release. Measurements have to be taken in software projects to improve this situation. It is of limited use to only collect metrics afterwards. The values of the relevant metrics have to be predicted, too. The predictions (i.e. estimates) form the basis for proper project management. One of the most painful problems in software projects is effort estimation. It has a clear and central effect on other project attributes like cost and schedule, and to product attributes like size and quality. Effort estimation can be used for several purposes. In this thesis only the effort estimation in software projects for project management purposes is discussed. There is a short introduction to the measurement issues, and some metrics relevantin estimation context are presented. Effort estimation methods are covered quite broadly. The main new contribution in this thesis is the new estimation model that has been created. It takes use of the basic concepts of Function Point Analysis, but avoids the problems and pitfalls found in the method. It is relativelyeasy to use and learn. Effort estimation accuracy has significantly improved after taking this model into use. A major innovation related to the new estimationmodel is the identified need for hierarchical software size measurement. The author of this thesis has developed a three level solution for the estimation model. All currently used size metrics are static in nature, but this new proposed metric is dynamic. It takes use of the increased understanding of the nature of the work as specification and design work proceeds. It thus 'grows up' along with software projects. The effort estimation model development is not possible without gathering and analyzing history data. However, there are many problems with data in software engineering. A major roadblock is the amount and quality of data available. This thesis shows some useful techniques that have been successful in gathering and analyzing the data needed. An estimation process is needed to ensure that methods are used in a proper way, estimates are stored, reported and analyzed properly, and they are used for project management activities. A higher mechanism called measurement framework is also introduced shortly. The purpose of the framework is to define and maintain a measurement or estimationprocess. Without a proper framework, the estimation capability of an organization declines. It requires effort even to maintain an achieved level of estimationaccuracy. Estimation results in several successive releases are analyzed. It isclearly seen that the new estimation model works and the estimation improvementactions have been successful. The calibration of the hierarchical model is a critical activity. An example is shown to shed more light on the calibration and the model itself. There are also remarks about the sensitivity of the model. Finally, an example of usage is shown.

Postnatal ontogenesis of the tibia. Implications for age and sex estimation

The growth of five variables of the tibia (diaphyseal length, diaphyseal length plus distal epiphysis, condylo-malleolar length, sagittal diameter of the proximal epiphysis, maximum breadth of the distal epiphysis) were analysed using polynomial regression in order to evaluate their significance and capacity for age and sex determination during and after growth. Data were collected from 181 (90♂ and 91♀) individuals ranging from birth to 25 years of age and belonging to three documented collections from Western Europe. Results indicate that all five variables exhibit linear behaviour during growth, which can be expressed by a first-degree polynomial function. Sexual significant differences were observed from age 15 onward in the two epiphysis measurements and condylo-malleolar length, suggesting that these three variables could be useful for sex determination in individuals older than 15 years. Strong correlation coefficients were identified between the five tibial variables and age. These results indicate that any of the studied tibial measurements is likely to serve as a useful source for estimating sub-adult age in both archaeological and forensic samples.

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI.

We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each q-space point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

Is the matching function Cobb-Douglas?

[cat] Estudiem les propietats teòriques que una funció d.emparellament ha de satisfer per tal de representar un mercat laboral amb friccions dins d'un model d'equilibri general amb emparellament aleatori. Analitzem el cas Cobb-Douglas, CES i altres formes funcionals per a la funció d.emparellament. Els nostres resultats estableixen restriccions sobre els paràmetres d'aquests formes funcionals per assegurar que l.equilibri és interior. Aquestes restriccions aporten raons teòriques per escollir entre diverses formes funcionals i permeten dissenyar tests d'error d'especificació de model en els treballs empírics.

Is the matching function Cobb-Douglas?

[cat] Estudiem les propietats teòriques que una funció d.emparellament ha de satisfer per tal de representar un mercat laboral amb friccions dins d'un model d'equilibri general amb emparellament aleatori. Analitzem el cas Cobb-Douglas, CES i altres formes funcionals per a la funció d.emparellament. Els nostres resultats estableixen restriccions sobre els paràmetres d'aquests formes funcionals per assegurar que l.equilibri és interior. Aquestes restriccions aporten raons teòriques per escollir entre diverses formes funcionals i permeten dissenyar tests d'error d'especificació de model en els treballs empírics.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

Direct noninvasive estimation of myocardial tricarboxylic acid cycle flux in vivo using hyperpolarized (13)C magnetic resonance.

BACKGROUND: The heart relies on continuous energy production and imbalances herein impair cardiac function directly. The tricarboxylic acid (TCA) cycle is the primary means of energy generation in the healthy myocardium, but direct noninvasive quantification of metabolic fluxes is challenging due to the low concentration of most metabolites. Hyperpolarized (13)C magnetic resonance spectroscopy (MRS) provides the opportunity to measure cellular metabolism in real time in vivo. The aim of this work was to noninvasively measure myocardial TCA cycle flux (VTCA) in vivo within a single minute. METHODS AND RESULTS: Hyperpolarized [1-(13)C]acetate was administered at different concentrations in healthy rats. (13)C incorporation into [1-(13)C]acetylcarnitine and the TCA cycle intermediate [5-(13)C]citrate was dynamically detected in vivo with a time resolution of 3s. Different kinetic models were established and evaluated to determine the metabolic fluxes by simultaneously fitting the evolution of the (13)C labeling in acetate, acetylcarnitine, and citrate. VTCA was estimated to be 6.7±1.7μmol·g(-1)·min(-1) (dry weight), and was best estimated with a model using only the labeling in citrate and acetylcarnitine, independent of the precursor. The TCA cycle rate was not linear with the citrate-to-acetate metabolite ratio, and could thus not be quantified using a ratiometric approach. The (13)C signal evolution of citrate, i.e. citrate formation was independent of the amount of injected acetate, while the (13)C signal evolution of acetylcarnitine revealed a dose dependency with the injected acetate. The (13)C labeling of citrate did not correlate to that of acetylcarnitine, leading to the hypothesis that acetylcarnitine formation is not an indication of mitochondrial TCA cycle activity in the heart. CONCLUSIONS: Hyperpolarized [1-(13)C]acetate is a metabolic probe independent of pyruvate dehydrogenase (PDH) activity. It allows the direct estimation of VTCA in vivo, which was shown to be neither dependent on the administered acetate dose nor on the (13)C labeling of acetylcarnitine. Dynamic (13)C MRS coupled to the injection of hyperpolarized [1-(13)C]acetate can enable the measurement of metabolic changes during impaired heart function.

Unifying Time to Contact Estimation and Collision Avoidance across Species

The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework- the corrected modified Tau function- capable of predicting both -type ("") and -type ("") responses. The outstanding property of our new framework is its resilience to noise. We show that can be derived from a firing rate equation, and, as , serves to describe the response curves of collision sensitive neurons. Furthermore, we show that predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between -type and -type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional least-square method (LS). Among these the most important are: there are no restrictions on linearity or in the form which the parameters appears in the mathematical model describing the system, and it is not required that these parameters be time invariant. The EKF uses the statistical properties of the process and the observation noise, to produce estimates based on the mean square error of the estimates themselves. Differently, the LS minimizes a cost function based on the plant output behavior. Results for the estimation of some longitudinal aerodynamic derivatives from simulated data are presented.

Estimation procedures affect the center of pressure frequency analysis

Even though frequency analysis of body sway is widely applied in clinical studies, the lack of standardized procedures concerning power spectrum estimation may provide unreliable descriptors. Stabilometric tests were applied to 35 subjects (20-51 years, 54-95 kg, 1.6-1.9 m) and the power spectral density function was estimated for the anterior-posterior center of pressure time series. The median frequency was compared between power spectra estimated according to signal partitioning, sampling rate, test duration, and detrending methods. The median frequency reliability for different test durations was assessed using the intraclass correlation coefficient. When increasing number of segments, shortening test duration or applying linear detrending, the median frequency values increased significantly up to 137%. Even the shortest test duration provided reliable estimates as observed with the intraclass coefficient (0.74-0.89 confidence interval for a single 20-s test). Clinical assessment of balance may benefit from a standardized protocol for center of pressure spectral analysis that provides an adequate relationship between resolution and variance. An algorithm to estimate center of pressure power density spectrum is also proposed.