Stochastic Signal Processing vs Polynomial Solver Step by Step

Speech, music, seismic vibrations, oil prices, and climate measurements are all examples of stochastic (random) signals. In this textbook—intended for individuals with prior training in introductory signal processing and introductory probability theory—we develop techniques to process such signals to extract useful information. We present case studies ranging from music to photographic images to oil prices to climate data to the motion of individual biomolecules. This textbook, as an interactive textbook ("iBook"), makes use of your device's ability to display dynamic information through films and animations and to hear the results of the techniques applied to music. At the end of every chapter there are homework problems ranging from easy to "olympic". We make use of your device's interactive capabilities to offer 59 laboratory experiments in signal processing. The experiments use the camera, the microphone, the speakers, and the graphical user interface. These experiments are not simulations; they are examples of real digital processing of signals in your device. In this time of at-home and online learning, this is the way to learn signal processing through study and experimentation.

Polynomial Solver Step by Step is a calculator, and, more importantly, a teaching tool. It is designed to help students of both basic and advanced levels. Basic Algebra functions: 1. Polynomial multiplication. 2. Polynomial long division with step by step explanation. 3. Algorithm for finding rational roots consisting of the following: a. Finding all possible rational roots. b. Eliminating the roots outside bounds. c. Performing polynomial long division for each of the roots. d. Adjusting bounds based on long division results if appropriate. Iterative polynomial solving algorithms for advanced-level students: 1. Durand-Kerner method. 2. Aberth method. 3. Newton method with both real and complex starting points. 4. Global (also known as "damped") Newton method with both real and complex starting points. 5. Laguerre method with both real and complex starting points. 6. Bairstow method. 7. Muller method. 8. Eigenvalues of ‘companion matrix’ (demonstration of QR algorithm for eigenvalue calculation is available in our “Matrix Solver Step by Step" app). Interactive graphs are available for: 1. Polynomial functions and their derivatives. 2. Aberth and Durand-Kerner method points on complex plane. 3. Newton, global Newton, Laguerre, and Muller algorithm steps. 4. Newton and global Newton method fractals / convergence images on complex plane 5. Laguerre method convergence images on complex plane 6 Bairstow method convergence images depending on initial approximation values. 7 Muller method convergence images depending on initial approximation values. (Images 4-7 have adjustable settings for area, resolution and shading). Links to online references are included. Please write to us if you like to have new features / chapters added! Developers welcome any feedback, please write to us at graphmath@aol.com.