Fifth issue: some of the great metrics (distances between kth-neighbors) might not have a simple mathematical formula. But we can use Monte-Carlo simulations to address this issue: simulate a random process, compute the distribution of distances (with confidence intervals) based on thousands of simulations, and compare with distances computed on your data. If distance distribution computed on the data set matches results from simulations, we are good, it means our data is probably random. However, we would have to make sure that distance distribution uniquely characterizes a Poisson process, and that no non-random processes could yield the same distance distribution. This exercise is known as goodness-of-fit testing: you try to see if your data support a specific hypothesis of randomness.
Pig Design Patterns is a comprehensive guide that will enable readers to readily use design patterns that simplify the creation of complex data pipelines in various stages of data management. This book focuses on using Pig in an enterprise context, bridging the gap between theoretical understanding and practical implementation. Each chapter contains a set of design patterns that pose and then solve technical challenges that are relevant to the enterprise use cases. The book covers the journey of Big Data from the time it enters the enterprise to its eventual use in analytics, in the form of a report or a predictive model. By the end of the book, readers will appreciate Pig's real power in addressing each and every problem encountered when creating an analytics-based data product.
Summary: A new artificial intelligence technique could speed up complex physics simulations and help create multilayered nanoparticles, researchers say. A new technique developed by MIT physicists could someday provide a way to custom-design multilayered nanoparticles with desired properties, potentially for use in displays, cloaking systems, or biomedical devices. It may also help physicists tackle a variety of thorny research problems, in ways that could in some cases be orders of magnitude faster than existing methods. The innovation uses computational neural networks, a form of artificial intelligence, to "learn" how a nanoparticle's structure affects its behavior, in this case the way it scatters different colors of light, based on thousands of training examples. Then, having learned the relationship, the program can essentially be run backward to design a particle with a desired set of light-scattering properties -- a process called inverse design.
Udemy Coupon ED MATLAB/Simulink for Power Electronics Simulations Power electronics simulation with Simulink lets you model complex topologies with multiple switching devices using standard circuit components. You can run fast simulations with average models or ideal switching behavior, or use detailed nonlinear switching models for parasitics and detailed design Get Coupon Bestseller What you'll learn How to simulate power electronics devices in MATLAB/Simulink Simulation of half-wave and full-wave rectifiers in MATLAB/Simulink Simulation of buck, boost, and buck/boost converters in MATLAB/Simulink Simulation of single-phase and three-phase inverters in MATLAB/Simulink How rectifiers, dc-to-dc converters, and inverters work How to determine the performance of power electronics devices How to design power electronics devices to meet certain design specifications MATLAB/Simulink models provided so you can follow along and use for your own designs How to implement a PID controller in MATLAB/Simulink Power Engineering and Electrical Engineering Simulations in MATLAB/Simulink Requirements MATLAB/Simulink software, free trial available online Description This course is designed to allow you to simulate any power electronics device in MATLAB/Simulink, including rectifiers, dc-to-dc converters, and inverters. This course not only gives a review of the theory of how rectifiers, dc-to-dc converters, and inverters work, but also gives several examples on how to simulate these devices using MATLAB/Simulink. The MATLAB/Simulink models for the power electronics devices created during the lectures are available for download with each lecture. The course is divided into the following sections: 1. Introduction to MATLAB/Simulink for Power Electronics: in section 2, we will begin by reviewing the theory behind the semiconductor devices that are used in power electronics, such as diodes, power BJTs, power MOSFETs, IGBTs, and Thyristors.
In this post, we share some recent promising results regarding the applications of Deep Learning in analog IC design. While this work targets a specific application, the proposed methods can be used in other black box optimization problems where the environment lacks a cheap/fast evaluation procedure. So let's break down how the analog IC design process is usually done, and then how we incorporated deep learning to ease the flow. The intent of analog IC design is to build a physical manufacturable circuit that processes electrical signals in the analog domain, despite all sorts of noise sources that may affect the fidelity of signals. Usually analog circuit design starts off with topology selection.