Best Voice Recognition Software for Raspberry Pi | DIY Hacking Best Voice Recognition Software for Raspberry Pi This tutorial is about the best voice recognition software for Raspberry Pi and how to use it. I was able to install and test three different voice recognition systems for Raspberry Pi. Two of these softwares were dependent on the internet and were online , however the third one was offline.The three softwares which were tested were : Out of these three, I rate the Voice Control software created by Steven Hickson to be the most precise and potent. The Jasper system, even though it works offline , it compromises on accuracy and speed. The softwares presented by Oscar and Steven use google voice APIs, they are very accurate and precise. Bill of Materials Hardware: Raspberry Pi model B with memory card preloaded with an OS.WiFi dongle (Optional) : Edimax EW 7811UN / LAN network cable.A USB webcam with microphone / USB microphone. You cannot use normal microphones with audio jacks because the raspberry pi does not have a sound card.
keynav - retire your mouse. :: semicomplete.com - Jordan Sissel What is keynav? Another episode in the revolution against mouse-requisite interfaces. It's one more step towards impulse-driven computing. Enough marketing jargon. keynav is a piece of an on-going experiment to make pointer-driven interfaces easier and faster for users to operate. What does it do? You select a piece of the screen. Why it is fast? keynav is geared towards selecting a piece of the screen very quickly. Watch the demo! Bored of reading? Hopefully the demo gives you a good idea of what keynav does. Mailing list The keynav users mailing list is: keynav-users@googlegroups.com I'll be announcing new versions on this mailing list. Supported Platforms keynav is currently written in C and only works in X11 (Unix graphics environment). Download it! keynav-0.20101014.3067.tar.gz Looking for an older version? Dependencies glib >= 2.0 libxdo (from xdotool) (or, if you build static, libX11 libXtst and libXext) Xinerama (comes with xorg) XSHAPE (comes with xorg) Build instructions start end warp
Getting Started : Alexa with Pi Getting Started With Raspberry Pi and Alexa Hardware you need Raspberry Pi 3 or Pi 2 (Model B) - Buy at Amazon - Pi 3 or Pi 2.Micro-USB power cable for Raspberry Pi.Micro SD Card - To get started you need an operating system. NOOBS (New Out Of the Box Software) is an easy-to-use operating system install manager for Raspberry Pi. The simplest way to get NOOBS is to buy an SD card with NOOBS pre-installed - Raspberry Pi 8GB Preloaded (NOOBS) Micro SD Card. Setting up the Raspberry Pi The first thing we’ll need to do is install Raspbian Jessie on our Pi. What is NOOBS? NOTE: If you already have Raspbian Jessie installed on your Pi, you may skip to Step 2 - Installing utilities - SSH, VNC Server, VLC, Node below. 0.1 - Downloading NOOBS If you have a micro SD card that came with NOOBS pre-installed, make sure it has Raspbian Jessie available as one of the install options. 0.2 - Installing Raspbian Jessie Insert your micro SD card with NOOBS pre-installed into the micro SD card slot on your Pi. .
Build a digital book with EPUB - Vimperator Before you start This tutorial guides you through creating eBooks in the EPUB format. EPUB is an XML-based, developer-friendly format that is emerging as the de facto standard for digital books. But EPUB isn't just for books: With it, you can: Bundle documentation for offline reading or easy distributionPackage blog posts or other Web-native contentBuild, search, and remix using common open source tools About this tutorial You start this tutorial by generating an EPUB book manually to help you learn all the components and required files. Then, it covers generating EPUB from DocBook XML—one of the most widely used standards for technical documentation—and how to use Python to completely automate EPUB creation with DocBook from end to end. Objectives In this tutorial, you: Prerequisites No particular operating system is assumed for this tutorial, although you should be familiar with the mechanics of creating files and directories. System requirements Back to top About the EPUB format What is EPUB?
Raspberry Pi · alexa/alexa-avs-sample-app Wiki On this page Overview This guide provides step-by-step instructions for setting up AVS on a Raspberry Pi. It demonstrates how to access and test AVS using our Java sample app (running on a Raspberry Pi), a Node.js server, and a third-party wake word engine. You will use the Node.js server to obtain a Login with Amazon (LWA) authorization code by visiting a website using your Raspberry Pi's web browser. For instructions on how to set it up on Mac, Linux or Windows, please see our wiki. Required hardware Before you get started, let's review what you'll need. Raspberry Pi 3 (Recommended) or Pi 2 Model B (Supported) - Buy at Amazon - Pi 3 or Pi 2.Micro-USB power cable for Raspberry Pi.Micro SD Card (Minimum 8 GB) - You need an operating system to get started. For extra credit, we'll show you how to remote(SSH) into your device, eliminating the need for a monitor, keyboard and mouse - and how to tail logs for troubleshooting. Let's get started Step 1: Setting up your Pi Step 4: Clone the sample app
Alexa Voice Service What Is an Alexa Built-in Product? Alexa built-in is a category of devices created with the Alexa Voice Service (AVS) that have a microphone and speaker. You can talk to these products directly with the wake word “Alexa,” and receive voice responses and content instantly. Why Alexa Voice Service? The Alexa Voice Service enables you to access cloud-based Alexa capabilities with the support of AVS APIs, hardware kits, software tools, and documentation. Create a Developer Account »Get the AVS Device SDK » Domotique : pilotez une lampe grâce aux relais - Perfectionnez-vous dans la programmation Arduino L'intérêt de la domotique est de réaliser des montages qui permettent de piloter des matériels qui fonctionnent sous 220V. Comme je vous l’ai déjà répété, cela demande beaucoup de mesures de sécurité. Si vous êtes prêts à prendre vos précautions, nous allons faire un point sur le courant domestique. Le 220V alternatif Contrairement à votre Arduino qui fournit du courant continu, le courant de vos prises murales est alternatif, c'est-à-dire qu'il change de polarité (+ et -) très rapidement. Il ne change pas d'un coup, il passe de +220 V à -220 V en suivant une courbe sinusoïdale. (Lukas, même en allant chercher dans le dictionnaire, c'est un peu long à comprendre...) Voici une représentation d'une telle courbe : Le courant oscille donc entre la valeur maximum et la valeur minimum continuellement. En plus de cette oscillation, il faut savoir que le courant domestique (celui de votre prise) est à 220V, soit 44 fois supérieur à la tension délivrée par l'Arduino. (Et bien non Lukas !
MongoDB Connection Pooling with Node.js Modules by Wesley Tsai Aug 2, 2015 How to implement MongoClient connection pooling in a simple Node.js/MongoDB web app. Not So Good Here is a sample node.js app. On a GET request, the app will do something with the database (maybe fetch some data). The database operations are confined in the database.js module, which is great, but a connection with the MongoDB is opened/closed with each operation, which can be costly and inefficient. app.js var express = require('express');var app = express();var database = require('. database.js var mongodb = require('mongodb');var MongoClient = mongodb.MongoClient;var mongoUrl = ' exports.doSomethingWithDatabase = function(callback){ MongoClient.connect(mongoUrl, function(err, db) { if( err ) throw err; db.collection('db_name').find({}, function(err, docs) { callback(docs); db.close(); }); });}; Better This is the example on the mongodb github on connection pooling. Best var express = require('express');var app = express();var database = require('.
How to Improve Your Predictive Model: A Post-mortem Analysis Building predictive models with machine learning techniques can be very insightful and provide tremendous business value in optimizing resources that are simply impossible to replicate manually or by more traditional statistical methods. It can best add this value when coupled with good data and domain expertise in interpreting the data and the predictions. Predictive modeling is seldom a one-way street, where the first run through the cycle of data wrangling, feature engineering, model building, evaluation and predictions yields perfectly accurate results. Sometimes predictions may be out of whack despite best tuning efforts given available resources. We’d like to go over some best practices in doing so while conducting a post-mortem of our recent not-so-great Belmont Stakes predictions. We included the results of last weekend’s race in our original dataset and ran an anomaly detection task on the resulting post-race dataset. 1) More Data 2) More Features 3) Feature Selection 5) Bagging