Lecture

Smart Sensors for the IoT: ADCs Fundamentals

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

In course

EE-594: Smart sensors for IoT

This lecture provides insights in the design and technologies of Internet-of-Things sensor nodes, with focus on low power technologies. The lectures alternate every two weeks between sensing technologies of various kinds (prof. Ionescu) and their integrate ...

Description

This lecture covers the fundamentals of Analog-to-Digital Converters (ADCs) for smart sensors in the Internet of Things (IoT). It explains how ADCs convert analog signals to digital, the role of Low Pass Filters in eliminating high-frequency signals, and the operation of Sampling circuits. The lecture also delves into Quantization, discussing quantization levels, errors, and noise. It explores ADC non-idealities like Offset and Gain errors, Differential and Integral Non-Linearity, and their impact on signal processing. Additionally, it covers ADC topologies, including Nyquist-rate and Oversampling ADCs, and the characteristics of SAR and Incremental ADCs.

Official source

Ontological neighbourhood

Information engineering

Signal processing: Sampling theory

Related concepts (32)

Quantization (signal processing)

Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.

Sampling (signal processing)

In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples". A sample is a value of the signal at a point in time and/or space; this definition differs from the term's usage in statistics, which refers to a set of such values. A sampler is a subsystem or operation that extracts samples from a continuous signal. A theoretical ideal sampler produces samples equivalent to the instantaneous value of the continuous signal at the desired points.

Digital-to-analog converter

In electronics, a digital-to-analog converter (DAC, D/A, D2A, or D-to-A) is a system that converts a digital signal into an analog signal. An analog-to-digital converter (ADC) performs the reverse function. There are several DAC architectures; the suitability of a DAC for a particular application is determined by figures of merit including: resolution, maximum sampling frequency and others. Digital-to-analog conversion can degrade a signal, so a DAC should be specified that has insignificant errors in terms of the application.

Digital signal processing

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.

Oversampling

In signal processing, oversampling is the process of sampling a signal at a sampling frequency significantly higher than the Nyquist rate. Theoretically, a bandwidth-limited signal can be perfectly reconstructed if sampled at the Nyquist rate or above it. The Nyquist rate is defined as twice the bandwidth of the signal. Oversampling is capable of improving resolution and signal-to-noise ratio, and can be helpful in avoiding aliasing and phase distortion by relaxing anti-aliasing filter performance requirements.

Related lectures (64)

Sampling and Reconstruction

Covers sampling, Fourier Transform, and reconstruction using low-pass filters in signal processing.

Electrical Metrology: Noise Reduction Techniques

Explores noise reduction techniques in electrical metrology, covering properties of charge, current, voltage, noise sources, and filtering methods.

Analog and Digital Conversion

Explores analog and digital signal processing, A/D and D/A conversion, resolution, settling time, and digital interfaces in electronic circuits.