Search results for "sulautettu tietotekniikka"

IoT/Embedded vs. Security : Learn from the Past, Apply to the Present, Prepare for the Future

2018

It is expected there will be 50 billion IoT/embedded connected devices by 2020. At the same time, multiple recent studies revealed that IoT/embedded devices and their software/firmware is plagued with weaknesses and vulnerabilities. Moreover, various recent and prominent attacks, such as the Mirai botnet targeting Commercial Off-The-Shelf (COTS) IoT/embedded devices, and the ROCA attack targeting secure embedded hardware chips (in their many form-factors), clearly demonstrate the need to secure the many layers and components of the highly fragmented and heterogeneous ecosystem of IoT/embedded devices. In this paper we aim to explore, discuss and exemplify some research aspects and direction…

Automatisoitu regressiotestaus sulautetun järjestelmän kehitystyössä

2005

Digital processing of environmental noise samples

2014

Designing and Evaluating Ubicomp Characteristics of Intelligent In-Car Systems

2022

In this paper, we present a preliminary taxonomy for designing and evaluating intelligent systems from the viewpoint of ubiquitous computing (ubicomp). As an example of intelligent systems, we examine a few novel in-car systems, which are already commercially available. We also discuss some ergonomics issues related to the design of in-car systems and argue that rationale for solving these issues can be found from the ideal qualities of ubicomp systems. The five characteristics we define to be ideal for genuine ubicomp systems are context-awareness, natural interaction methods, invisibility, support for everyday tasks, and interconnectivity. Based on these characteristics, we create a frame…

Tiny Machine Learning for Resource-Constrained Microcontrollers

2022

We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed. These are exactly the same resources that play a key role in teaching and running a machine learning model with a basic computer. However, in a microcontroller environment, constrained resources make a critical difference. Therefore, a new paradigm known as tiny machine learning had to be created to meet the constrained requirements of the embedded devices. In this review, we discuss the resource optimization challenges of …

Sääsatelliittien datan vastaanotto tietokoneen avulla

2005

Distance Learning with Hands-on Exercises : Physical Device vs. Simulator

2022

This Research to Practice Full Paper presents a comparison between a physical device and a simulator in a distance learning context. Programming embedded devices is very commonly taught using embedded hardware. One of the most used solutions is the Arduino microcontroller platform, which allows small embedded applications to be built and commanded in a programming language. However, there are some challenges in using physical devices for educational purposes. These challenges are particularly acute in distance learning or when the course needs to be scalable to a varying number of students. To address these challenges, we explored the potential of a simulator as a replacement for a physical…