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"This book provides a practical guide, complete with accompanying Matlab software, to many different types of polynomial and discrete splines and spline-based wavelets, multiwavelets and wavelet frames in signal and image processing applications. In self-contained form, it briefly outlines a broad range of polynomial and discrete splines with equidistant nodes and their signal-processing-relevant properties. In particular, interpolating, smoothing, and shift-orthogonal splines are presented. "

"This book contains interesting findings of some state-of-the-art research in the field of signal and image processing. It contains twenty one chapters covering a wide range of signal processing applications involving filtering, encoding, classification, segmentation, clustering, feature extraction, denoising, watermarking, object recognition, reconstruction and fractal analysis. Various types of signals including image, video, speech, non-speech audio, handwritten text, geometric diagram, ECG and EMG signals, MRI, PET and CT scan images, THz signals, solar wind speed signals (SWS) and photoplethysmogram (PPG) signals have been dealt with. It demonstrates how new paradigms of intelligent computing like quantum computing can be applied to process and analyze signals in a most precise and effective manner. Processing of high precision signals for real time target recognition by radar and processing of brain images, ECG and EMG signals that feature in this book have significant implications in defense mechanism and medical diagnosis. There are also applications of hybrid methods, algorithms and image filters which are proving to be better than the individual techniques or algorithms. Thus the present volume, enriched in depth and variety of techniques and algorithms concerning processing of various types of signals, is likely to be used as a compact yet handy reference for the young researchers, academicians and scientists working in the domain of signal and image processing and also to the post graduate students of computer science and information technology."

"As this monograph shows, the purpose of cardinal spline interpolation is to bridge the gap between the linear spline and the cardinal series. The author explains cardinal spline functions, the basic properties of B-splines, including B- splines with equidistant knots and cardinal splines represented in terms of B-splines, and exponential Euler splines, leading to the most important case and central problem of the book-- cardinal spline interpolation, with main results, proofs, and some applications. Other topics discussed include cardinal Hermite interpolation, semi-cardinal interpolation, finite spline interpolation problems, extremum and limit properties, equidistant spline interpolation applied to approximations of Fourier transforms, and the smoothing of histograms."

"This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice for the estimation of functions from noisy data on functionals. The simplest example is the estimation of a smooth curve, given noisy observations on a finite number of its values. The estimate is a polynomial smoothing spline. By placing this smoothing problem in the setting of reproducing kernel Hilbert spaces, a theory is developed which includes univariate smoothing splines, thin plate splines in d dimensions, splines on the sphere, additive splines, and interaction splines in a single framework. A straightforward generalization allows the theory to encompass the very important area of (Tikhonov) regularization methods for ill-posed inverse problems. Convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a wide variety of problems which fall within this framework. Methods for including side conditions and other prior information in solving ill-posed inverse problems are included. Data which involves samples of random variables with Gaussian, Poisson, binomial, and other distributions are treated in a unified optimization context. Experimental design questions, i.e., which functionals should be observed, are studied in a general context. Extensions to distributed parameter system identification problems are made by considering implicitly defined functionals."

"This book highlights cutting-edge research on various aspects of human–computer interaction (HCI). It includes selected research papers presented at the Third International Conference on Computing, Communication and Signal Processing (ICCASP 2018), organized by Dr. Babasaheb Ambedkar Technological University in Lonere-Raigad, India on January 26–27, 2018. It covers pioneering topics in the field of computer, electrical, and electronics engineering, e.g. signal and image processing, RF and microwave engineering, and emerging technologies such as IoT, cloud computing, HCI, and green computing. As such, the book offers a valuable guide for all scientists, engineers and research students in the areas of engineering and technology. "

"Synthesis of computational structures for analog signal processing focuses on analysis and design of analog signal processing circuits. The author presents a multitude of design techniques for improving the performances of analog signal processing circuits, and proposes specific implementation strategies that can be used in CMOS technology. The author's discussion proceeds from the perspective of signal processing as it relates to analog. Included are coverage of low-power design, portable equipment, wireless nano-sensors and medical implantable devices."

"This book presents the mathematical methodology for generic image analysis tasks. In the context of this book an image may be any m-dimensional empirical signal living on an n-dimensional smooth manifold. The existing literature on image methodology is rather scattered and often limited to either a deterministic or a statistical point of view. In contrast, this book brings together these seemingly different points of view in order to stress their conceptual relations and formal analogies. Furthermore, it does not focus on specific applications, although some are detailed for the sake of illustration, but on the methodological frameworks on which such applications are built. Covering many topics at the forefront of current research, including anisotropic diffusion filtering of tensor fields, this book will be of particular interest to graduate and postgraduate students and researchers in the fields of computer vision, medical imaging and visual perception."

"New approaches to knot insertion and deletion are presented in this unique, detailed approach to understanding, analyzing, and rendering B-spline curves and surfaces. Computer scientists, mechanical engineers, and programmers and analysts involved in CAD and CAGD will find innovative, practical applications using the blossoming approach to knot insertion, factored knot insertion, and knot deletion, as well as comparisons of many knot insertion algorithms. This book also serves as an excellent reference guide for graduate students involved in computer aided geometric design."

"ABSTRAKPenaksiran parameter dalam model regresi memiliki dua pendekatan yaitu pendekatan regresi parametrik dan pendekatan regresi nonparametrik. Dalam regresi parametrik bentuk dari kurva hubungan antara variabel respon dan variabel prediktor sudah ditentukan berdasarkan plot data, sedangkan dalam regresi nonparametrik bentuk dari kurva tidak diketahui. Salah satu regresi nonparametrik yang dapat digunakan adalah regresi spline. Regresi spline adalah suatu piecewise polynomial yang dihubungkan oleh titik-titik bersama yang disebut dengan knot. Regresi spline yang menggunakan fungsi basis B Spline disebut dengan regresi B Spline. Pada umumnya estimasi parameter regresi B Spline dilakukan dengan menggunakan metode OLS Ordinary Least Square. Namun, dengan metode OLS akan menyebabkan plot taksiran kurva regresi menjadi fluktuatif apabila pemilihan jumlah knot terlalu banyak. Untuk itu diperlukan suatu tambahan kendala berupa penalty yang didalamnya mengandung smoothing parameter sehingga diperoleh taksiran ideal. Metode estimasi parameter ini dikenal dengan metode PLS Penalized Least Square . Metode PLS dengan penalty yang merupakan integral kuadrat derivatif kedua dari taksiran kurva disebut juga dengan metode o rsquo;sullivan penalized spline. Pada penerapan contoh data, didapat 23 buah knot dan smoothing parameter sebesar 0.68.

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ABSTRACTParameter estimation of regression model has two approaches, that is parametric and nonparametric regression approach. In parametric regression, the shape of regression curve is determined based on scatterplot of dependent variable vs independent variable, whereas in the nonparametric regression, the shape of the curve is unknown. One of the nonparametric regression is spline regression. Spline regression is piecewise polynomials that connected by the knots. Spline regression using B Spline basis function is B Spline regression. In B spline regression, parameter estimation were fitted by OLS Ordinary Least Square method. However, the OLS method will lead the plot of estimated regression curve be fluctuative when using too much knots. Therefore, it needs additional constraint of penalty that contain smoothing parameter to obtain ideal fit result. This parameter estimation method known as PLS Penalized Least Square method. The estimate PLS method used penalty which is the integral of the square of second derivative of the estimate curve that called o 39 sullivan penalized spline method. In the application of sample data, 23 is used knots and the smoothing parameters is 0.68. "