This work deals with roaming issues in the WiFi network. Takes options from a 802.11 standard view. Factory setting options for fast roaming from CISCO and MikroTik. It proposes to measure and test these networks.

蓄電池は携帯機器，電気自動車をはじめ，自然エネルギー有効利用に至るまで広範囲に利用され，その重要性はますます高まっている。これら機器の使用時間や特性は蓄電池の特性に大きく依存することから，電池自体の特性改善に加え，劣化を診断してより効率的に電池を運用することが求められている。本論文は，非線形情報処理を得意とする人工知能を用いた2次電池の劣化診断法を開発し，エネルギーの有効利用に資する技術を確立した。機器動作時の電池電圧・電流波形と電池劣化特性との関連性を，人工知能を用い学習することにより，機器稼働時に電池の劣化を診断することができる。なお，この関連性は非線形で複雑であるが，非線形分析を得意とする人工知能は劣化診断に適している。学習には時間を要するものの，診断は短時間になし得ることから，提案法は稼働時劣化診断に適している。本論文では，この特徴を生かし，電池の等価回路（ECM）を導出し，充電率（SOC），容量維持率（SOH）を推定している。また，本論文では現在産業応用分野で用いられている，リチウムイオン電池，ニッケル水素電池，鉛蓄電池を対象とし，提案法はあらゆる電池使用機器に応用可能である。また，提案法を電池状態監視装置（BMU）や，マイコンなどを用いた組み込みシステムに応用可能とし，実証している。以上のことから，本論文は，新たな蓄電池の劣化診断法の確立し，その有効性を確認している。 / The importance of rechargeable batteries nowadays is increasing from the portable electronic devices and solar energy industry up to the development of new EV models. The rechargeable batteries have a crucial role in the storage system, mostly in mobile applications and transportation, because the period of its usage and the flexibility of the function are determined by the battery. Due to the black box approach of the ANN it is possible to connect the complex physical phenomenon with a specific physical meaning expressed with a nonlinear logic between inputs and output. Using specific input data to relate with the desired output, makes possible to create a pattern connection with input and output. This ability helps to estimate in real time the desired outputs, behaviors, phenomes and at the same time it can be used as a real time diagnosis method. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Magnetic Resonance Imaging (MRI) is a non-ionizing form of medical imaging which has practical uses in diagnosing, characterizing, and studying diseases in vivo. Current clinical practice utilizes a highly trained radiologist to view MR images and qualitatively diagnose, characterize, or study a disease. There is no easy way to compare qualitative data. That is why developing quantitative measures in MRI show promise. Quantitative measures of disease can be compared across a population, MRI sites, and over time. Osteoarthritis is one disease where those who have it may benefit from the development of quantitative MRI measures. Those benefits may include earlier diagnosis and treatment of the disease or treatment which may halt or even reverse the damage from the disease.The work presented in this dissertation focuses on analyzing and developing new methods of radiofrequency (B1) field mapping to improve quantitative MRI measures. The dissertation opens with an introduction and a brief primer on MRI physics, followed by an introduction to B1 and flip-angle mapping in MRI (Chapters 1-3). Chapter 4 presents a careful statistical analysis of a recent and popular B1 mapping method, the Bloch-Siegert shift (BSS) method, along with a comparison of the technique to other common B1 mapping methods. The statistical models developed in chapter 4 are verified using both Monte Carlo simulation and actual MRI experiments in phantoms. Chapter 5 analyzes and details the potential errors introduced in B1 mapping when a 3D slab-selective excitation is employed. A method for correcting errors introduced by 3D slab-selective B1 mapping is then introduced in chapter 6, along with metrics to quantify the error involved. The thesis closes with a summary of other scientific contributions made by the author in chapter 7. The chapters comprising the bulk of the presented research (4-7) are briefly summarized below. Chapter 4, the statistical analysis of B1 mapping methods, demonstrates the effectiveness of deriving the B1 estimate from the phase of the MR image. These techniques are shown to perform particularly well in low signal-to-noise ratio (SNR) applications. However, there are benefits and drawbacks of each B1 mapping technique. The BSS method deposits a significant amount of radiofrequency (RF) power into the patient, causing a concern that tissue heating may occur. The Phase-Sensitive (PS) method of B1 mapping outperforms the other techniques in many situations, but suffers from significant sensitivity to off-resonance. The Dual-Angle (DA) method is very simple to implement and the analysis is straightforward, but it can introduce significant mean bias in the estimate. No B1 mapping technique performs well for all situations. Therefore, the best B1 mapping method needs to be determined for each situation. The work in chapter 4 provides guidance for that choice. Many B1 mapping techniques rely on a linear relationship between flip angle and transmit voltage. That assumption breaks down when a 3D slab-selective excitation is used. 3D slab-selective excitation is a common technique used to reduce the field-of-view (FOV) in MRI, which can directly reduce scan time. The problem with slab-selective excitation in conjunction with B1 mapping has been documented, but the potential errors in B1 estimation have never been properly analyzed across different techniques. The analysis in chapter 5 demonstrates that the errors introduced in B1 mapping using a slab-selective excitation in conjunction with the ubiquitous DA B1 mapping method can be significant. It is then shown that another B1 mapping technique, the Actual Flip Angle Imaging (AFI) method, doesn't suffer from the same limitation. The analysis presented in Chapter 6 demonstrates that some errors introduced by 3D slab-selective B1 mapping may be modeled and corrected allowing the use of 3D slab-selective excitation to reduce field-of-view, and potentially reduce scan time. The errors are modeled and corrected with a general numerical method using Bloch simulations. The general method is applied to the DA method as an example, but is general and could easily be extended to other methods as well. Finally, a set of metrics are proposed and briefly explored that can be used to better understand the topology and severity of errors introduced into B1 mapping methods. With a better understanding of the errors introduced, the need for correction can be determined. Chapter 7 details other significant ancillary contributions made by the author including: (1) presentation of a new B1 mapping method, the decoupled RF-pulse phase-sensitive B1 mapping method, which has potential for parallel transmit MRI; (2) demonstration of an ultra-short TE method which has potential for imaging Alzheimers brain lesions in vivo; (3) introduction of a new steady-state diffusion tensor imaging technique; (4) phase-sensitive B1 mapping in sodium is demonstrated, a feat not previously demonstrated; (5) a comparison between a dual-tuned and single-tuned sodium coil; (6) introduction of a water- and fat-separation technique using multiple acquisition SSFP; (7) an inter-site and inter-vendor quantitative MRI study is introduced; (8) a relaxation and contrast optimization for laryngeal imaging at 3T is introduced; and (9) diffusion imaging with insert gradients is introduced.

En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal. / In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.

Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.

This thesis introduces a multilinear algebraic framework for computer graphics, computer vision, and machine learning, particularly for the fundamental purposes of image synthesis, analysis, and recognition. Natural images result from the multifactor interaction between the imaging process, the scene illumination, and the scene geometry. We assert that a principled mathematical approach to disentangling and explicitly representing these causal factors, which are essential to image formation, is through numerical multilinear algebra, the algebra of higher-order tensors. Our new image modeling framework is based on(i) a multilinear generalization of principal components analysis (PCA), (ii) a novel multilinear generalization of independent components analysis (ICA), and (iii) a multilinear projection for use in recognition that maps images to the multiple causal factor spaces associated with their formation. Multilinear PCA employs a tensor extension of the conventional matrix singular value decomposition (SVD), known as the M-mode SVD, while our multilinear ICA method involves an analogous M-mode ICA algorithm. As applications of our tensor framework, we tackle important problems in computer graphics, computer vision, and pattern recognition; in particular, (i) image-based rendering, specifically introducing the multilinear synthesis of images of textured surfaces under varying view and illumination conditions, a new technique that we call ``TensorTextures'', as well as (ii) the multilinear analysis and recognition of facial images under variable face shape, view, and illumination conditions, a new technique that we call ``TensorFaces''. In developing these applications, we introduce a multilinear image-based rendering algorithm and a multilinear appearance-based recognition algorithm. As a final, non-image-based application of our framework, we consider the analysis, synthesis and recognition of human motion data using multilinear methods, introducing a new technique that we call ``Human Motion Signatures''.