Lecture

Analog-Digital Conversion: Fundamentals

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In course

EE-296: Electrical systems and electronics II

Les concepts de base permettant de comprendre et d'analyser les systèmes électroniques dédiés à l'acquisition et au traitement des signaux (signaux physiologique, bio-capteurs) seront abordés en théorie et en pratique. Cela englobe l'amplification, le filt ...

Description

This lecture covers the fundamentals of analog-to-digital and digital-to-analog conversion. It explains the process of converting analog signals into digital data and vice versa, highlighting concepts such as sampling, quantization, and coding. The lecture also delves into the implementation of converters, including flash and successive approximation methods, and the importance of resolution and precision in the conversion process.

Instructors (2)

Adil Koukab

Cédric Meinen

Teaching & PhD Courses Electrical systems and electronics I EE-295 The course has the aim to provide the fundamental knowledge and experiences to understand basic electrical systems. Electrical systems and electronics II EE-296 The basic concepts enabling to understand and analysis the electronic systems dedicated to signals acquisition and processing (e.g. biosensors, physiological signals, ECG) are addressed. This includes the acquisition, amplification and filtering. El

Official source

Related concepts (32)

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.

Convenience sampling

Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. Convenience sampling is not often recommended for research due to the possibility of sampling error and lack of representation of the population. But it can be handy depending on the situation. In some situations, convenience sampling is the only possible option.

Sampling error

In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. It can produced biased results. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.

Cluster sampling

In statistics, cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. The elements in each cluster are then sampled. If all elements in each sampled cluster are sampled, then this is referred to as a "one-stage" cluster sampling plan.

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