Embedded system

Summary

An embedded system is a computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system. It is embedded as part of a complete device often including electrical or electronic hardware and mechanical parts. Because an embedded system typically controls physical operations of the machine that it is embedded within, it often has real-time computing constraints. Embedded systems control many devices in common use. , it was estimated that ninety-eight percent of all microprocessors manufactured were used in embedded systems. Modern embedded systems are often based on microcontrollers (i.e. microprocessors with integrated memory and peripheral interfaces), but ordinary microprocessors (using external chips for memory and peripheral interface circuits) are also common, especially in more complex systems. In either case, the processor(s) used may be types ra

Official source

https://en.wikipedia.org/wiki/Embedded_system

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (92)

Related publications

Related people (6)

Related people

Related units (7)

Related units

Related concepts (224)

An operating system (OS) is system software that manages computer hardware and software resources, and provides common services for computer programs. Time-sharing operating systems schedule tasks f

A microprocessor is a computer processor where the data processing logic and control is included on a single integrated circuit (IC), or a small number of ICs. The microprocessor contains the ar

A microcontroller (MCU for microcontroller unit, also MC, UC, or μC) is a small computer on a single VLSI integrated circuit (IC) chip. A microcontroller contains one or more CPUs (processor cores)

Related concepts

Related courses (70)

We introduce formal verification as an approach for developing highly reliable systems. Formal verification finds proofs that computer systems work under all relevant scenarios. We will learn how to use formal verification tools and explain the theory and the practice behind them.

The students will acquire a solid knowledge on the processes necessary to design, write and use scientific software. Software design techniques will be used to program a multi-usage particles code, aiming at providing the link between algorithmic/complexity, optimization and program designs.

This course consists of two parts:

architecture of automation systems, hands-on lab
handling of faults and failures in real-time systems, including fault-tolerant computing

Related courses

Related lectures (140)

Related lectures

, ,

Programmable Logic Controllers (PLCs) are embedded computers widely used in industrial control systems. Ensuring that a PLC software complies with its specification is a challenging task. Formal verification has become a recommended practice to ensure the correctness of safety-critical software but is still underused in industry due to the complexity of building and managing formal models of real applications. In this paper, we propose a general methodology to perform automated model checking of complex properties expressed in temporal logics (e.g., CTL, LTL) on PLC programs. This methodology is based on an Intermediate Model (IM), meant to transform PLC programs written in various standard languages (ST, SFC, etc.) to different modeling languages of verification tools. We present the syntax and semantics of the IM and the transformation rules of the ST and SFC languages to the nuXmv model checker passing through the intermediate model. Finally, two real cases studies of CERN PLC programs, written mainly in the ST language, are presented to illustrate and validate the proposed approach.

Institute of Electrical and Electronics Engineers2015

Joint Fusion Learning of Multiple Time Series Prediction

Orçun Gümüs

Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the transportation industry numerous possibilities to improve their operations using data analyses on traffic flow sensor data . However, even state-of-art algorithms for time series forecasting perform well on some transportation problems, they still fail to solve some critical tasks. In particular, existing traffic flow forecasting methods that are not utilising causality relations between different data sources are still unsatisfying for many real-world applications . In this report, we have focused on a new method named joint fusion learning that uses underlying causality in time series. We test our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we use our joint-fusion learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT) . We obtained better results on the short-term forecasts compared the widely-used benchmarks models that uses single time series to forecast the future.

2019

Design for Test With Unreliable Memories by Restoring the Beauty of Randomness

Andreas Peter Burg, Reza Ghanaatian Jahromi, Marco Widmer

This article presents a design-for-test methodology for embedded memories. The methodology relies on a fully random fault model of post-fabrication errors, which results in a low-overhead test strategy. The methodology's effectiveness is demonstrated on an embedded system with faulty memories.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022