Efficient load measurements using singular value decomposition

Various basic research was performed on efficient load measurement estimation techniques for aircraft structure analysis. An overview is presented of the load measurement problem. Two basic equivalent approaches to load measurement evaluations were considered. Under approach 1, the load values are modeled as depending linearly on the measured values. Under approach 2, the measured values depend linearly on the load values. By using the modern Singular Value Decomposition method, it was shown that under all conditions of the number of loads and number of gages, approach 1 is equivalent to approach 2. By using the conventional normal equation (linear regression) approach, approach 1 is only valid when the number of loads is equal to or greater than the number of gages, while approach 2 is the reverse. Furthermore, except for the case of the number of loads equals the number of gages, the load prediction formulas under the two approaches are not equivalent

Adaptively Determination of Model Order of SVD-based Harmonics and Interharmonics Estimation

The singular value decomposition (SVD) is one of the most popular methods in harmonics and interharmonics estimation. However, its accuracy strongly depends on the correctness of the selected model order. To this purpose, this work aims at contributing to the correct estimation of the model order. This is achieved by exploiting the energy of the singular values (SVs). Firstly, the relationship between one frequency component and its corresponding SVs is theoretically investigated. Secondly, a new indicator is proposed for determining the model order, which denotes the energy of the k-th pair of consecutive SVs. Thirdly, an adaptive threshold is defined for separating signal components from noise. This way, the number of components can be obtained for unknown noise levels. Finally, the effectiveness and robustness of the proposed method has been validated by simulations. They have been run implementing typical signals designed according to the harmonics and interharmonics measurements standard, the IEC standard 61000-4-7

Estimation du nombre de composantes d'un signal à l'aide du critère GDE amélioré

Parmi les nombreux critères d'estimation du nombre de sources ou de composantes fréquentielles disponibles dans la littérature, nous nous intéressons au critère GDE qui repose sur l'estimation des rayons des disques de Gerschgôrin. Ce critère requiert au préalable la modification de la matrice de covariance à l'aide d'une transformation unitaire, afin de séparer les disques de Gerschgôrin en deux jeux distincts, l'un associé aux sources du signal, l'autre associé au bruit. Notre contribution consiste à modifier ce critère en un nouveau critère appelé SGDE utilisant la somme des rayons des disques. De plus, nous proposons d'appliquer une technique de déflation à la matrice de covariance avant l'utilisation de ce nouveau critère, afin de le rendre moins sensible à des sources de puissance différente. Les résultats obtenus montrent que le critère présente de bonnes performances, notamment avec du bruit coloré, des signaux comprenant peu d'échantillons et des sources de puissance différente

무선 센서 네트워크 상에서의 효율적인 위치 추정 알고리즘 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김성철.In this dissertation, efficient localization algorithms for wireless sensor networks are represented. Localization algorithms are widely used in commercial systems and application. The localization techniques are anticipated to be developed for various environments and reduce the localization error for accurate location information because the user demands for more accurate positioning systems for medical care, home networks, and monitoring applications in personal range environments. A well-known localization system is GPS, with applications such as mobile navigation. The GPS shows good performance on road or roughly finding location system in outdoor environments but limited in indoor environments. Due to the development of handsets like smart phone, the users can easily receive the GPS signals and other RF signals including 3G/4G/5G signals, WLAN (Wireless Local Area Networks) signals, and the signals from other sensors. Thus, the various systems using localization schemes are developed, especially, the WSNs (Wireless Sensor Networks) localization system is actively studied in indoor environment without GPS. In this dissertation, the range-free localization algorithm and the range-based localization algorithm are reported for WSNs localization system. The range-free localization algorithms are proposed before to estimate location using signal database, called signal map, or the anchor nodes of antenna patterns, or ID configuration of the linked anchor nodes, etc. These algorithms generally need to additional hardware or have low accuracy due to low information for location estimation. The range-based algorithms, equal to distance-based algorithms, are based on received signal strength, RSSI, or time delay, TOA and TDOA, between the anchor nodes and a target node. Although the TOA and TDOA are very accurate distance estimation schemes, these scheme have the critical problem, the time synchronization. Although RSSI is very simple to setup the localization system with tiny sensors, the signal variation causes severe distance estimation error. The angle estimation, AOA, provides additional information to estimation the location. However, AOA needs additional hardware, the antenna arrays, which is not suitable for tiny sensors. In this dissertation, range-free and range-based localization algorithms are analyzed and summarized for WSNs with tiny sensors. The WSNs localization systems are generally used range-based algorithm. The range-based algorithms have major source of distance estimation error, and the distance estimation error causes severe localization error. In this dissertation, the localization error mitigation algorithms are proposed in two dimensional environments and three dimensional environments for WSNs. The mitigation algorithms in two dimensional environments consist of several steps, which are distance error mitigation algorithm, location error mitigation algorithm, and bad condition detection algorithm. The each algorithm is effective to reduce the localization error, but the accuracy of location estimation is the best when they are combined. The performance of proposed algorithms is examined with variation of received signal strength and it is confirmed that the combined proposed algorithm has the best performance rather than that of conventional scheme and each proposed algorithms. The three dimensional localization uses Herons formula of tetrahedron to calculate the target height, then transforms a two dimensional location computed by LLSE into a three dimensional estimated location. Simulation results validate the accuracy of the proposed scheme.Contents Chapter 1 Introduction...........................................................1 Chapter 2 Location estimation for wireless sensor networks.................................................................................................4 2.1 Introduction..................................................................................4 2.2 Range-free location estimation ...................................................7 2.2.1 Cell-ID location estimation .........................................................7 2.2.2 Fingerprint location estimation ...................................................8 2.2.3 Other range-free location estimation.........................................10 2.3 Range-based location estimation ..............................................12 2.3.1 Time delay based distance estimation.......................................12 2.3.2 Received signal strength based distance estimation .................16 2.3.3 Angle of arrival based location estimation................................18 2.4 Summary.......................................................................................20 Chapter 3 Two dimensional location estimation for wireless sensor networks......................................................................22 3.1 Introduction................................................................................22 3.2 Tri-lateration ..................................................................24 3.2.1 Linear least square estimation ..................................................24 3.2.2 The cases of tri-lateration .........................................................26 3.3 Geometric mitigation algorithm …............................................27 3.3.1 Motivation .................................................................................27 3.3.2 Algorithm explanation ..............................................................28 3.3.3 Simulation .................................................................................29 3.3.4 Conclusion ................................................................................34 3.4 Coordinate shift algorithm ..........................................................35 3.4.1 Motivation .................................................................................35 3.4.2 Algorithm explanation...............................................................36 3.4.3 Simulation .................................................................................41 3.4.4 Conclusion ................................................................................43 3.5 Bad condition detection algorithm ...............................................44 3.5.1 Motivation .................................................................................44 3.5.2 Algorithm explanation...............................................................45 3.5.3 Simulation .................................................................................51 3.5.4 Conclusion ................................................................................54 3.6 Conclusion..................................................................................55 Chapter 4 Three dimensional location estimation for wireless sensor networks .....................................................................56 4.1 Introduction................................................................................56 4.2 Motivation.....................................................................................57 4.2.1 Singular matrix problem…........................................................57 4.2.2 Short range location estimation.................................................59 4.3 Algorithm explanation....................................................................60 4.4 Simulation........................................................................................68 4.5 Conclusion..................................................................................72 Bibliography....................................................................................73 Abstract in Korean.....................................................................................78Docto

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered

Real-time Three-dimensional Photoacoustic Imaging

Photoacoustic imaging is a modality that combines the benefits of two prominent imaging techniques; the strong contrast inherent to optical imaging techniques with the enhanced penetration depth and resolution of ultrasound imaging. PA waves are generated by illuminating a light-absorbing object with a short laser pulse. The deposited energy causes a pressure change in the object and, consequently, an outwardly propagating acoustic wave. Images are produced by using characteristic optical information contained within the waves. We have developed a 3D PA imaging system by using a staring, sparse array approach to produce real-time PA images. The technique employs the use of a limited number of transducers and by solving a linear system model, 3D PA images are rendered. In this thesis, the development of an omni-directional PA source is introduced as a method to characterize the shift-variant system response. From this foundation, a technique is presented to generate an experimental estimate of the imaging operator for a PA system. This allows further characterization of the object space by two techniques; the crosstalk matrix and singular value decomposition. Finally, the results of the singular value decomposition analysis coupled with the linear system model approach to image reconstruction, 3D PA images are produced at a frame rate of 0.7 Hz. This approach to 3D PA imaging has provided the foundation for 3D PA images to be produced at frame rates limited only by the laser repetition rate, as straightforward system improvements could see the imaging process reduced to tens of milliseconds