Electronic textiles are a sound platform for wearable computing. Many applications have been devised that use sensors placed on these textiles for fields such as medical monitoring and military use or for display purposes. Most of these applications require that the sensors have known locations for accurate results. Activity recognition is one application that is highly dependent on knowledge of the sensor position. Therefore, this thesis presents the design and implementation of a method whereby the location of the sensors on the electronic textile garments can be automatically identified when the user is performing an appropriate activity. The software design incorporates principle component analysis using singular value decomposition to identify the location of the sensors. This thesis presents a method to overcome the problem of bilateral symmetry through sensor connector design and sensor orientation detection. The scalability of the solution is maintained through the use of culling techniques. This thesis presents a flexible solution that allows for the fine-tuning of the accuracy of the results versus the number of valid queries, depending on the constraints of the application. The resulting algorithm is successfully tested on both motion capture and sensor data from an electronic textile garment. / Master of Science

Spherical Harmonics (SPHARM) and Singular Value Decomposition (SVD) utilize the orthogonal relations of its parameters to represent and process images. The process involve mapping of the image from its original parameter domain to a new domain where the processing is performed. This process induces distortion and smoothing is required. The image now mapped to the new parameter domain is descripted using SPHARM and SVD using one at a time. The least significant values for the SPHARM coefficients and singular values of SVD are truncated which induces compression in the reconstructed image keeping the memory allocation in view. In this thesis, we have applied SPHARM and SVD tools to represent and reconstruct an image. The image is first mapped to the unit sphere (a sphere with unit radius). The image gets distorted that is maximum at the north and south poles, for which smoothing is approached by leaving 0.15*π space blank at each pole where no mapping is done. Sampling is performed for the θ and φ parameters and the image is represented using spherical harmonics and its coefficients are calculated. The same is then repeated for the SVD and singular values are computed. Reconstruction is performed using the calculated parameters, but defined over some finite domain, which is done by truncating the SPHARM coefficients and the singular values inducing image compression. Results are formulated for the various truncation choices and analyzed and finally it is concluded that SPHARM is better as compared with SVD as compression tool as there is not much difference in the quality of the reconstructed image with both tools, though SVD seem better quality wise, but with much higher memory allocation than SPHARM.

Digital information och kommunikation genom digitala medier är ett växande område. E-post och andra kommunikationsmedel används dagligen över hela världen. Parallellt med att området växer så växer även intresset av att hålla informationen säker. Transmission via antenner är inom signalbehandling ett välkänt område. Transmission från en sändare till en mottagare genom fri rymd är ett vanligt exempel. I en tuff miljö som till exempel ett rum med reflektioner och oberoende elektriska apparater kommer det att finnas en hel del distorsion i systemet och signalen som överförs kan, på grund av systemets egenskaper och buller förvrängas.Systemidentifiering är ett annat välkänt begrepp inom signalbehandling. Denna avhandling fokuserar på systemidentifiering i en tuff miljö med okända system. En presentation ges av matematiska verktyg från den linjära algebran samt en tillämpning inom signalbehandling. Denna avhandling grundar sig främst på en matrisfaktorisering känd som Singular Value Decomposition (SVD). SVD’n används här för att lösa komplicerade matrisinverser och identifiera system.Denna avhandling utförs i samarbete med Combitech AB. Deras expertis inom signalbehandling var till stor hjälp när teorin praktiserades. Med hjälp av ett välkänt programmeringsspråk känt som LabView praktiserades de matematiska verktygen och kunde synkroniseras med diverse instrument som användes för att generera signaler och system. / Digital information transmission is a growing field. Emails, videos and so on are transmitting around the world on a daily basis. Along the growth of using digital devises there is in some cases a great interest of keeping this information secure. In the field of signal processing a general concept is antenna transmission. Free space between an antenna transmitter and a receiver is an example of a system. In a rough environment such as a room with reflections and independent electrical devices there will be a lot of distortion in the system and the signal that is transmitted might, due to the system characteristics and noise be distorted. System identification is another well-known concept in signal processing. This thesis will focus on system identification in a rough environment and unknown systems. It will introduce mathematical tools from the field of linear algebra and applying them in signal processing. Mainly this thesis focus on a specific matrix factorization called Singular Value Decomposition (SVD). This is used to solve complicated inverses and identifying systems. This thesis is formed and accomplished in collaboration with Combitech AB. Their expertise in the field of signal processing was of great help when putting the algorithm in practice. Using a well-known programming script called LabView the mathematical tools were synchronized with the instruments that were used to generate the systems and signals.

The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.

With the development of consumer electronics, the circuitry structure become more complex, For this reason, it might cause numerical errors to be cumulated in the simulation using the numerical electromagnetic algorithm, and result in simulated divergence or error. The two reasons of numerical error are passivity and causality, which priginate from the defect in the numerical calculation. In this thesis, for this problem, investigate the numerical compensation method for passivity, The occurrence of passive will make the frequency point of power is negative, this will makes the system divergence, Improve this problem, passivity verification and enforcement by eigenvalue in the Y-parameter, in the S-parameter by the singular value, causality conditions must be match with the imaginary part and the real part relationship, such as the Hilbert transform or the Kramer-Kronig relation, can be used to make causal verification and enforcement. Through some numerical methods, used simulation software such as: HFSS, ADS simulation of the microwave circuit model extraction, modified singular value, eigenvalue, and reached to reduce the numerical error, let it satisfy the convergence and avoid incorrect results, and minimize the impact of the initial data, does not change the characteristics of the original module, but also to solve the passive and the issue of causality.

Since uncertainty exists inevitably in control systems, it is questionable whether the controller, designed to compensate a nominal plant well, still guarantees the criteria of robust stability and robust H¡Û performance for the perturbed plant. In this thesis, controller parameters tuning based on the sensitivity concept of structured singular value, called £g-sensitivity, will be adopted to do the parameter adjustment so that, when the influence of uncertainty is considered, the robust stability and robust performance properties of the nominal closed-loop system will be preserved. In view of the time consuming effect of numerical computation and the misjudgment due to discontinuity problem involved in the £g-sensitivity analysis, this thesis proposes the sensitivity concept of skewed structured singular value, called £h-sensitivity, to remedy these drawbacks. Finally, the feasibility of the £h-sensitivity based controller parameters tuning technique is verified by the simulation results of two examples.

This thesis presents a novel singular-value-based semi-fragile watermarking scheme for image content authentication with tampering localization. The proposed scheme first generates a secured watermark bit sequence by performing a logical "xor" operation on a content-based watermark and content-independent watermark, wherein the content-based watermark is generated by a singular-value-based watermark bit sequence that represents intrinsic algebraic image properties, and the content-independent watermark is generated by a private-key-based random watermark bit sequence. It next embeds the secure watermark in the approximation subband of each non-overlapping 4×4 block using the adaptive quantization method to generate the watermarked image. The image content authentication process starts with regenerating the secured watermark bit sequence following the same process mentioned in the secured watermark bit sequence generation. It then extracts a possibly embedded watermark using the parity of the quantization results from the probe image. Next, the authentication process constructs a binary error map, whose height and width are a quarter of those of the original image, using the absolute difference between the regenerated secured watermark and the extracted watermark. It finally computes two authentication measures (i.e., M1 and M2), with M1 measuring the overall similarity between the regenerated watermark and the extracted watermark, and M2 measuring the overall clustering level of the tampered error pixels. These two authentication measures are further seamlessly integrated in the authentication process to confirm the image content and localize any possible tampered areas. The extensive experimental results show that the proposed scheme outperforms four peer schemes and is capable of identifying intentional tampering, incidental modification, and localizing tampered regions.

Multiway data are becoming more and more common. While there are many approaches to extending principal component analysis (PCA) from usual data matrices to multiway arrays, their conceptual differences from the usual PCA, and the methodological implications of such differences remain largely unknown. This thesis aims to specifically address these questions. In particular, we clarify the subtle difference between PCA and singular value decomposition (SVD) for multiway data, and show that multiway principal components (PCs) can be estimated reliably in absence of the eigengaps required by the usual PCA, and in general much more efficiently than the usual PCs. Furthermore, the sample multiway PCs are asymptotically independent and hence allow for separate and more accurate inferences about the population PCs. The practical merits of multiway PCA are further demonstrated through numerical, both simulated and real data, examples.

The interfacial heat transfer coefficient is an important variable regarding the subject of metal castings. The error associated with the experimental temperature data must be dealt with appropriately so that they do not significantly affect the resulting interfacial heat transfer coefficient. The systematic and random errors are addressed using a combination of three-dimensional finite element modeling and the matrix transform method, respectively. Experimentally obtained A356 permanent mold casting data was used to estimate the interfacial heat transfer coefficient corresponding to common industrial mold coatings.

Electronic textiles provide an effective platform to contain wearable computing elements, especially components geared towards the application of activity recognition. An activity recogni tion system built into a wearable textile substrate can be utilized in a variety of areas including health monitoring, military applications, entertainment, and fashion. Many of the activity recognition and motion capture systems previously developed have several drawbacks and limitations with regard to their respective designs and implementations. Some such systems are often times expensive, not conducive to mass production, and may be difficult to calibrate. An effective system must also be scalable and should be deployable in a variety of environments and contexts. This thesis presents the design and implementation of a self-contained motion sensing wearable electronic textile system with an emphasis toward the application of activity recognition. The system is developed with scalability and deployability in mind, and as such, utilizes a two-tier hierarchical model combined with a network infrastructure and wireless connectivity. An example prototype system, in the form of a jumpsuit garment, is presented and is constructed from relatively inexpensive components and materials. / Master of Science