Wood combustion is mainly used in cold regions as a primary or supplemental space heating source in residential areas. In several industrialized countries, there is a renewed interest in residential wood combustion (RWC) as an alternative to fossil fuel and nuclear power consumption. The main objective of this thesis was to investigate the impact of RWC on the air quality in urban areas. To this end, a field campaign was conducted in Northern Sweden during wintertime to characterize atmospheric aerosol particles and polycyclic aromatic hydrocarbons (PAH) and to determine their source apportionment. A large day-to-day and hour-to-hour variability in aerosol concentrations was observed during the intensive field campaign. On average, total carbon contributed a substantial fraction of PM10 mass concentrations (46%) and aerosol particles were mostly in the fine fraction (PM1 accounted for 76% of PM10). Evening aerosol concentrations were significantly higher on weekends than on weekdays which could be associated to the use of wood burning for recreational purposes or higher space heat demand when inhabitants spend longer time at home. It has been shown that continuous aerosol particle number size distribution measurements successfully provided source apportionment of atmospheric aerosol with high temporal resolution. The first compound-specific radiocarbon analysis (CSRA) of atmospheric PAH demonstrated its potential to provide quantitative information on the RWC contribution to individual PAH. RWC accounted for a large fraction of particle number concentrations in the size range 25-606 nm (44-57%), PM10 (36-82%), PM1 (31-83%), light-absorbing carbon (40-76%) and individual PAH (71-87%) mass concentrations. These studies have demonstrated that the impact of RWC on air quality in an urban location can be very important and largely exceed the contribution of vehicle emissions during winter, particularly under very stable atmospheric conditions.

This thesis considers the application of singular value decomposition (SVD) and semi-nonnegative matrix factorization (SNMF) within feedback control systems, called the SVD System and SNMF System, to control numerous subsystems with a reduced number of control inputs. The subsystems are coupled using a row-column structure to allow mn subsystems to be controlled using m+n inputs. Past techniques for controlling systems in this row-column structure have focused on scheduling procedures that offer limited performance. The SVD and SNMF Systems permit simultaneous control of every subsystem, which increases the convergence rate by an order of magnitude compared with previous methods. In addition to closed loop control, open loop procedures using the SVD and SNMF are compared with previous scheduling procedures, demonstrating significant performance improvements. This thesis presents theoretical results for the controllability of systems using the row-column structure and for the stability and performance of the SVD and SNMF Systems. Practical challenges to the implementation of the SVD and SNMF Systems are also examined. Numerous simulation examples are provided, in particular, a dynamic simulation of a pin array device, called Digital Clay, and two physical demonstrations are used to assess the feasibility of the SVD and SNMF Systems for specific applications.

Raman spectroscopy provides a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. The development of powerful, inexpensive lasers and sensitive charge-coupled device (CCD) detectors has led to widespread use of commercial and scientific Raman systems. However, relatively little work has been done developing physics-based probabilistic models for Raman measurement systems and crafting inference algorithms within the framework of statistical estimation and detection theory. The objective of this thesis is to develop algorithms and performance bounds for the identification of chemicals from their Raman spectra. First, a Poisson measurement model based on the physics of a dispersive Raman device is presented. The problem is then expressed as one of deterministic parameter estimation, and several methods are analyzed for computing the maximum-likelihood (ML) estimates of the mixing coefficients under our data model. The performance of these algorithms is compared against the Cramer-Rao lower bound (CRLB). Next, the Raman detection problem is formulated as one of multiple hypothesis detection (MHD), and an approximation to the optimal decision rule is presented. The resulting approximations are related to the minimum description length (MDL) approach to inference. In our simulations, this method is seen to outperform two common general detection approaches, the spectral unmixing approach and the generalized likelihood ratio test (GLRT). The MHD framework is applied naturally to both the detection of individual target chemicals and to the detection of chemicals from a given class. The common, yet vexing, scenario is then considered in which chemicals are present that are not in the known reference library. A novel variation of nonnegative matrix factorization (NMF) is developed to address this problem. Our simulations indicate that this algorithm gives better estimation performance than the standard two-stage NMF approach and the fully supervised approach when there are chemicals present that are not in the library. Finally, estimation algorithms are developed that take into account errors that may be present in the reference library. In particular, an algorithm is presented for ML estimation under a Poisson errors-in-variables (EIV) model. It is shown that this same basic approach can also be applied to the nonnegative total least squares (NNTLS) problem. Most of the techniques developed in this thesis are applicable to other problems in which an object is to be identified by comparing some measurement of it to a library of known constituent signatures.

Daily aerosol samples were collected at the &Ccedil / andarli which is located on Aegean coast of Turkey. A rural site was selected to monitor atmospheric pollution by long range transport. Sampling was performed in both summer and winter seasons, and in total 151 samples were obtained. Concentrations of elements in the samples were measured in order to identify sources and possible source locations of pollutants. Measured concentrations of trace elements at the &Ccedil / andarli station were compared with those measured at various sites around the world and, also in Turkey. As a result of comparison, level of pollution at the Aegean Region was found to be lower than the Mediterranean Region and Black Sea Region. Air flow climatology at &Ccedil / andarli was investigated in order to determine potential source regions for pollutants. Frequency of air flows from Russia and Western Europe are higher suggesting that emissions from these industrial regions affect the chemical composition of particulate matter. Besides these, it was concluded that contributions from Central and Eastern European countries are significantly high because of frequent air mass transport. Concentrations of elements measured at &Ccedil / andarli station were found to show short and seasonal variations. Such variations in concentrations are explained by variations in the source strengths and transport patterns. Positive matrix factorization (PMF) was applied to determine sources of elements and contribution of sources to each element. This analysis revealed 5 sources, two local anthropogenic emissions factor, one soil factor, one sea salt factor and one long range transport factor. Distribution of Potential Source Contribution Function (PSCF) values showed that main sources of SO42- are observed in Bulgaria, Romania, Poland, Ukraine and central part of Aegean region.

In the past, we could rely on technology scaling and new micro-architectural techniques to improve the performance of processors. Nowadays, both of these methods are reaching their limits. The primary concern in future architectures with billions of transistors on a chip and limited power budgets is power/energy efficiency. Full-custom design of application-specific cores can yield up to two orders of magnitude better power efficiency over conventional general-purpose cores. However, a tremendous design effort is required in integrating a new accelerator for each new application. In this dissertation, we present the design of specialized compute fabrics that maintain the efficiency of full custom hardware while providing enough flexibility to execute a whole class of coarse-grain operations. The broad vision is to develop integrated and specialized hardware/software solutions that are co-optimized and co-designed across all layers ranging from the basic hardware foundations all the way to the application programming support through standard linear algebra libraries. We try to address these issues specifically in the context of dense linear algebra applications. In the process, we pursue the main questions that architects will face while designing such accelerators. How broad is this class of applications that the accelerator can support? What are the limiting factors that prevent utilization of these accelerators on the chip? What is the maximum achievable performance/efficiency? Answering these questions requires expertise and careful codesign of the algorithms and the architecture to select the best possible components, datapaths, and data movement patterns resulting in a more efficient hardware-software codesign. In some cases, codesign reduces complexities that are imposed on the algorithm side due to the initial limitations in the architectures. We design a specialized Linear Algebra Processor (LAP) architecture and discuss the details of mapping of matrix-matrix multiplication onto it. We further verify the flexibility of our design for computing a broad class of linear algebra kernels. We conclude that this architecture can perform a broad range of matrix-matrix operations as complex as matrix factorizations, and even Fast Fourier Transforms (FFTs), while maintaining its ASIC level efficiency. We present a power-performance model that compares state-of-the-art CPUs and GPUs with our design. Our power-performance model reveals sources of inefficiencies in CPUs and GPUs. We demonstrate how to overcome such inefficiencies in the process of designing our LAP. As we progress through this dissertation, we introduce modifications of the original matrix-matrix multiplication engine to facilitate the mapping of more complex operations. We observe the resulting performance and efficiencies on the modified engine using our power estimation methodology. When compared to other conventional architectures for linear algebra applications and FFT, our LAP is over an order of magnitude better in terms of power efficiency. Based on our estimations, up to 55 and 25 GFLOPS/W single- and double-precision efficiencies are achievable on a single chip in standard 45nm technology. / text

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM).  The contributions of this dissertation can be divided into three main (overlapping) parts. First, we propose NMF-based enhancement approaches that use temporal dependencies of the speech signals. In a standard NMF, the important temporal correlations between consecutive short-time frames are ignored. We propose both continuous and discrete state-space nonnegative dynamical models. These approaches are used to describe the dynamics of the NMF coefficients or activations. We derive optimal minimum mean squared error (MMSE) or linear MMSE estimates of the speech signal using the probabilistic formulations of NMF. Our experiments show that using temporal dynamics in the NMF-based denoising systems improves the performance greatly. Additionally, this dissertation proposes an approach to learn the noise basis matrix online from the noisy observations. This relaxes the assumption of an a-priori specified noise type and enables us to use the NMF-based denoising method in an unsupervised manner. Our experiments show that the proposed approach with online noise basis learning considerably outperforms state-of-the-art methods in different noise conditions.  Second, this thesis proposes two methods for NMF-based separation of sources with similar dictionaries. We suggest a nonnegative HMM (NHMM) for babble noise that is derived from a speech HMM. In this approach, speech and babble signals share the same basis vectors, whereas the activation of the basis vectors are different for the two signals over time. We derive an MMSE estimator for the clean speech signal using the proposed NHMM. The objective evaluations and performed subjective listening test show that the proposed babble model and the final noise reduction algorithm outperform the conventional methods noticeably. Moreover, the dissertation proposes another solution to separate a desired source from a mixture with arbitrarily low artifacts.  Third, an HMM-based algorithm to enhance the speech spectra using super-Gaussian priors is proposed. Our experiments show that speech discrete Fourier transform (DFT) coefficients have super-Gaussian rather than Gaussian distributions even if we limit the speech data to come from a specific phoneme. We derive a new MMSE estimator for the speech spectra that uses super-Gaussian priors. The results of our evaluations using the developed noise reduction algorithm support the super-Gaussianity hypothesis. / <p>QC 20130916</p>

Concentrations of elements and ions measured in samples collected between February 1993 and December 2000 at a rural site in central Anatolia were investigated to evaluate the chemical composition of atmosphere at central Anatolia, to determine pollution level of the region, to study temporal variability of the pollutants and to investigate the sources and source regions of air pollutants in the region. Level of pollution at central Anatolia was found to be lower than the pollution level at other European countries and Mediterranean and Black Sea regions of Turkey. Enrichment factor calculations revealed that SO42-, Pb and Ca are highly enriched in the aerosol / whereas, soil component has dominating contribution on observed concentrations of V, Mg, Ca and K. SO42-/(SO2+SO42-) ratio observed in &Ccedil / ubuk station indicates that contribution of distant sources is more important than the contribution of local sources on observed SO42- levels. SO42-/NO3- ratio calculations showed that Central Anatolia is receipt of SO42- from Eastern European countries. Positive Matrix Factorization (PMF) analysis revealed 6 source groups, namely motor vehicle source, mixed urban factor, long range transport factor, soil factor, NO3- factor and Cd factor. Distribution of Potential Source Contribution Function (PSCF) values showed that main source areas of SO42-, NH4+ and Cd are western parts of Turkey, Balkan countries, central and western Europe, central Russian Federation and north of Sweden and Finland / NO3- are the regions located around the Mediterranean Sea / and there is no very strong potential source area observed for NH3 and Pb.

Measurement of chemical composition of precipitation is important both to understand acidification of terrestrial and aquatic ecosystems and neutralization process in the atmosphere. Such data are scarce in the Mediterranean region. In this study, chemical composition of daily, wet-only, 387 number of rain water samples collected between 1991 and 1999 were investigated to determine levels, temporal variation and long-term trends in concentrations of major ions and trace elements between 1991 and 1999. Samples had already been collected and some of the analysis had been completed. The anions SO42-, NO3- and Cl- were analyzed by HPLC coupled with UV-VIS detector, NH4+ was analyzed by colorimetry and H+ ion was analyzed by pH meter. The major ions and trace metals were analyzed by using Atomic Absorption Spectrometry (AAS) and Graphite Furnace Atomic Absorption Spectrometry (GFAAS). In this study complete data set were generated by analyzing samples that had not been previously analyzed for major ions and trace elements with Inductively Coupled Plasma with Optical Emission Spectrometry (ICP-OES). Statistical tools were used to determine the distribution of the pollutants. The rain water data tends to be log-normally distributed since data show large variations due to meteorological conditions, physical and chemical transformations and air mass transport patterns. The median pH of the rain water was found to be 5.29, which indicates that the rain water is not strongly acidic. This case is not a result of lacking of acidic compounds but rather indicates extended neutralization process in rain water. Eastern Mediterranean atmosphere is under the influence of three general source types: (1) anthropogenic sources, which are located to the north and northwest of the basin brings low pH values to the region (SO42-, NO3- ions) / (2) a strong crustal source, which is dried and suspended local soil and air masses transported from North Africa transport which have high pH values (Ca2+, Al, Fe ions) and (3) a marine source, which is the Mediterranean Sea itself (Na+, Cl- ions). In the region, the main acid forming compounds are H2SO4 and HNO3 whereas / CaCO3 and NH3 are responsible for the neutralization process. To describe the level of pollutant concentrations and the factors that affect their variations in rain water / ion compositions, neutralization of acidity, short and long-term variability of ions and elements, their time trend analysis and wet deposition fluxes were investigated briefly. Positive matrix factorization (PMF) was used to determine components of ionic mass in the precipitation. In Antalya Station the rain water has five factors: free acidity factor, crustal factor, marine factor, NO3- factor and SO42- factor. Potential Source Contribution Function (PSCF) and trajectory statistics were used to determine source regions generating these components. NO3- has potential source regions of Western Mediterranean countries and North Africa, whereas SO42- has additional southeasterly trajectory components of Israel and south east of Turkey.

En imagerie nucléaire (scintigraphie, TEMP, TEP), les diagnostics sont fréquemment faits à l'aide des courbes d'activité temporelles des différents organes et tissus étudiés. Ces courbes représentent l'évolution de la distribution d'un traceur radioactif injecté dans le patient. Leur obtention est compliquée par la superposition des organes et des tissus dans les séquences d'images 2D, et il convient donc de séparer les différentes contributions présentes dans les pixels. Le problème de séparation de sources sous-jacent étant sous-déterminé, nous proposons d'y faire face dans cette thèse en exploitant différentes connaissances a priori d'ordre spatial et temporel sur les sources. Les principales connaissances intégrées ici sont les régions d'intérêt (ROI) des sources qui apportent des informations spatiales riches. Contrairement aux travaux antérieurs qui ont une approche binaire, nous intégrons cette connaissance de manière robuste à la méthode de séparation, afin que cette dernière ne soit pas sensible aux variations inter et intra-utilisateurs dans la sélection des ROI. La méthode de séparation générique proposée prend la forme d'une fonctionnelle à minimiser, constituée d'un terme d'attache aux données ainsi que de pénalisations et de relâchements de contraintes exprimant les connaissances a priori. L'étude sur des images de synthèse montrent les bons résultats de notre approche par rapport à l'état de l'art. Deux applications, l'une sur les reins, l'autre sur le cœur illustrent les résultats sur des données cliniques réelles. / In nuclear imaging (scintigraphy, SPECT, PET), diagnostics are often made with time activity curves (TAC) of organs and tissues. These TACs represent the dynamic evolution of tracer distribution inside patient's body. Extraction of TACs can be complicated by overlapping in the 2D image sequences, hence source separation methods must be used in order to extract TAC properly. However, the underlying separation problem is underdetermined. We propose to overcome this difficulty by adding some spatial and temporal prior knowledge about sources on the separation process. The main knowledge used in this work is region of interest (ROI) of organs and tissues. Unlike state of the art methods, ROI are integrated in a robust way in our method, in order to face user-dependancy in their selection. The proposed method is generic and minimize an objective function composed with a data fidelity criterion, penalizations and relaxations expressing prior knowledge. Results on synthetic datasets show the efficiency of the proposed method compare to state of the art methods. Two clinical applications on the kidney and on the heart are also adressed.

Bundle adjustment is the process of simultaneously optimizing camera poses and 3D structure given image point tracks. In structure-from-motion, it is typically used as the final refinement step due to the nonlinearity of the problem, meaning that it requires sufficiently good initialization. Contrary to this belief, recent literature showed that useful solutions can be obtained even from arbitrary initialization for fixed-rank matrix factorization problems, including bundle adjustment with affine cameras. This property of wide convergence basin of high quality optima is desirable for any nonlinear optimization algorithm since obtaining good initial values can often be non-trivial. The aim of this thesis is to find the key factor behind the success of these recent matrix factorization algorithms and explore the potential applicability of the findings to bundle adjustment, which is closely related to matrix factorization. The thesis begins by unifying a handful of matrix factorization algorithms and comparing similarities and differences between them. The theoretical analysis shows that the set of successful algorithms actually stems from the same root of the optimization method called variable projection (VarPro). The investigation then extends to address why VarPro outperforms the joint optimization technique, which is widely used in computer vision. This algorithmic comparison of these methods yields a larger unification, leading to a conclusion that VarPro benefits from an unequal trust region assumption between two matrix factors. The thesis then explores ways to incorporate VarPro to bundle adjustment problems using projective and perspective cameras. Unfortunately, the added nonlinearity causes a substantial decrease in the convergence basin of VarPro, and therefore a bootstrapping strategy is proposed to bypass this issue. Experimental results show that it is possible to yield feasible metric reconstructions and pose estimations from arbitrary initialization given relatively clean point tracks, taking one step towards initialization-free structure-from-motion.