Project วิชา Numerical Methods for Computer Engineers (2564-2)

Finishing up the work In the last post, we've seen the frontend of our Interpolation visualizer iPhone app. In order to make the app functional, we'll now write the backend portion of the app: the code that will perform computations of the interpolation functions. We'll also show how we can call the Neural Engine in modern iPhone to help accelerate this process. The code block(s) The first section of the code initialize these following libraries.   1. CoreGraphics libraries 2. Accelerated Vector Computation libraries 3. SIMD (Single Instruction Multiple Data) libraries. In which the focus point is the Accelerate.vecLib libraries, this is the libraries that allow the Neural Engine in the iPhone to help assist in interpolation computation. And, SIMD libraries allows for parallelization of the computation processes, further improving the application performance. We can also sees that the backend also initialize the interpolated graph Axes and what algorithm to use, in this cas...

Getting started with Swift Programming Swift is an Objective-C based programing language, designed by Apple inc. specially for Apple computer devices. Originally a proprietary specification, After 2015 with the introduction of Swift 2.2, it is now an open-source standards. Swift are designed to be use to write an application for the Apple devices, in which the performance are optimized for, but it also contains many powerful mathematical operations, in which if use with modern Apple devices which contains an dedicated Neural Engine to help accelerate mathematical operations, it allows the apps to perform complex mathematical operation on the fly and provide user with a more seamless app user experience. To keep in line with our theme of using computer programming to help visualize interpolation, we'll demonstrate how we can use Swift to write a simple iPhone application to help visualize linear and polynomial interpolations. Overview of our iPhone app with Swift. Even though Swift ...

  Julia in Visual Studio Code Julia programming language เป็นภาษาระดับสูงและไดนามิกที่สร้างขึ้นเพื่อความรวดเร็วและความเรียบง่าย  Julia  มักใช้ในด้านต่างๆ   เช่น  data science, machine learning, scientific computing,  แต่ยังคงเป็น  general purpose language ที่สามารถจัดการกับกรณีการใช้งานโปรแกรมส่วนใหญ่ได้ Julia extension สำหรับ  Visual Studio Code  ประกอบด้วย built-in dynamic autocompletion, inline results, plot pane, integrated REPL, variable view, code navigation  และคุณลักษณะภาษาขั้นสูงอื่นๆ   อีกมากมายฟีเจอร์เหล่านี้ส่วนใหญ่ใช้งานได้ทันที   ในขณะที่บางฟีเจอร์อาจต้องใช้การกำหนดค่าพื้นฐานเพื่อให้ได้ประสบการณ์ที่ดีที่สุด   หน้านี้สรุปคุณสมบัติของ  Julia  ที่รวมอยู่ในส่วนขยาย  Julia VS Code  สำหรับคำแนะนำเชิงลึกเกี่ยวกับวิธีการทำงานของคุณลักษณะเหล่านี้และสามารถกำหนดค่าได้   โปรดดูเอกสาร  Julia in VS Code Getting started ติดตั้ง  Julia  สำหรับแพลตฟอร์มดาวโหลด :  https://julialang....

 What is Julia? Julia is a high-level programming language, it is considered to be a general purpose programming language in which anyone can write applications using Julia, but its main features are specializes in the Mathematical and Scientific computing. This makes Julia an attractive programming language to perform complex mathematical calculations and scientific analysis. Julia has many of the mathematical computing modules built into the software bundle, making it very easy to get started on mathematical visualization coding. The one in which we'll be focusing on today is the "Interpolations" modules. Using Julia's Interpolation modules. The included Julia Interpolation module has the 4 main interpolation algorithms. 1. Constant 2. Linear 3. Quadratic 4. Cubic Which corresponds to B-Splines interpolation degrees of 0 to 3, respectively. The accuracy of each B-Spline degree interpolation is represented in the following picture. We'll now demonstrate the use o...

 What is Interpolation? Interpolation, to simply explain, is a mathematical process in which we attempt to "generate" a continuous datasets from the limited discrete data points existed. To better explain the process of interpolation, consider the following situation. We have these following data points which are a discrete digital samples from an unknown waveform. Using the most simple interpolation algorithm, the linear interpolation. In which we simply "connect the dots" of the digital sample points, we observe that the "interpolated" waveform is similar to the sine wave. There are several interpolation algorithms that has been introduced over the year, some of these are... 1. Polynomial Interpolations 2. Splines Interpolation 3. Gaussian Interpolation Interpolations are widely uses in many applications, the example of these application that we may sees is the reconstruction of a digital audio files that has been overdriven to a "clipping" In ...