Articles

As I’m becoming a senior developer in terms of age, I’ve transitioned from one language to another. One of my main interests has always been clean, easy-to-understand UIs (User Interface). That journey started for me with Director (to create multimedia CD-ROMs), Flash website animation, and Flex Rich Internet Applications (= « Flash on steroids »). When I started developing with Java over 10 years ago, we had some projects with the early versions of Vaadin and JavaFX. As I went on with serverside applications, I only continued with JavaFX for some personal and side projects and loved the way you can create a UI both with XML (FXML actually) and code, exactly the same approach I loved with Flex. Since then, my love for Java and JavaFX only grew and it’s still my major programming environment.

But JavaFX has one missing piece: running it in the browser… Yes, JPRO can do this, but it needs a license and a dedicated server. And yes, there are some projects ongoing to bring JavaFX fully to the browser, but they are ongoing and not mature yet… Let’s look at another approach: Vaadin Flow and run it on a Raspberry Pi to control a LED and show the state of a button.

Source de l’article sur DZONE

In the first part of this series, we have seen all the pre-requisites that are needed to proceed with the hands-on. We will extend the EdgeX Foundry tutorial by Jonas Werner and deploy the EdgeX Foundry services on K3s. We have already learned that K3s will be a good lightweight solution to manage and orchestrate the EdgeX microservices. We will use the Geneva version of EdgeX Foundry.

The scope of this post is to demonstrate an Edge use case that will consume the sensor data e.g. ambient temperature. This sensor data will then be processed by EdgeXFoundry services hosted on K3s. This sensor data will be pushed to a cloud-based MQTT broker called HiveMQ. From here, the data can be stored and processed in the cloud. The configurations and manifests used in these posts are available in this repository.

Source de l’article sur DZONE


Agile 

AI

Big Data

Cloud

Database

DevOps

Integration

  • Mulesoft 4: Continuous Delivery/Deployment With Maven by Ashok S — This article is a great example of what we want every tutorial to look like on DZone. The main aim of this article is to provide a standard mechanism to release project artifacts and deploy to Anypoint Platform, from the local machine or configure in continuous delivery pipelines.
  • Integration With Social Media Platforms Series (Part 1) by Sravan Lingam — This article helps you to build a RESTful API through MuleSoft that integrates with LinkedIn and shares a post on behalf of one’s personal account. I like this article because, in the age of social media, it’s so important for businesses to be connected and integrated!

IoT

Java

Microservices

Open Source

Performance

  • What Is Big O Notation? by Huyen Pham — Aside from a silly name, this article is an example of an in-depth analysis on a little-spoken-about concept. In this article, take a look at a short guide to get to know Big O Notation and its usages.
  • Is Python the Future of Programming? by Shormisthsa Chatterjee — Where is programming going? This article attempts to answer this question in a well-rounded way. The author writes, "Python will be the language of the future. Testers will have to upgrade their skills and learn these languages to tame the AI and ML tools".

Security

Web Dev

  • A Better Way to Learn Python by Manas Dash: There’s so many resources available for learning Python — so many that it’s difficult to find a good and flexible place to start. Check out Manas’ curated list of courses, articles, projects, etc. to get your Python journey started today. 
  • Discovering Rust by Joaquin Caro: I’m a sucker for good Rust content, as there’s still so many gaps in what’s available. Joaquin does a great job of giving readers his perspective of the language’s features in a way that traditional docs just 

Source de l’article sur DZONE


Overview 

A neural network, trained to recognize images that include a fire or flames, can make fire-detection systems more reliable and cost-effective. This tutorial shows how to use the newly released Python APIs for Arm NN inference engine to classify images as “Fire” versus “Non-Fire.”

What Is Arm NN and PyArmNN? 

Arm NN is an inference engine for CPUs, GPUs, and NPUs. It executes ML models on-device in order to make predictions based on input data. Arm NN enables efficient translation of existing neural network frameworks, such as TensorFlow Lite, TensorFlow, ONNX, and Caffe, allowing them to run efficiently and without modification across Arm Cortex-A CPUs, Arm Mali GPUs, and Arm Ethos NPUs.

Source de l’article sur DZONE