Lecture

Metrology: Introduction

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In course

MICRO-428: Metrology

The course deals with the concept of measuring in different domains, particularly in the electrical, optical, and microscale domains. The course will end with a perspective on quantum measurements, which could trigger the ultimate revolution in metrology. ...

Description

This lecture introduces the concept of metrology, focusing on measuring in electrical, optical, and microscale domains, with a perspective on quantum measurements. Topics covered include elements of statistics, image sensors, opto-electrical non-idealities, and quantum metrology. The instructors, Edoardo Charbon and Claudio Bruschini, provide insights into the course structure, exercise sessions, and the use of Matlab. The lecture also delves into the history of metrology, the importance of units, and the evolution of the International System of Units (SI) based on physical constants.

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Instructors (3)

Claudio Bruschini

Claudio Bruschini holds an MSc in high energy physics from the University of Genova and a PhD in Applied Sciences from the Vrije Universiteit Brussel (VUB). He started his career with INFN (Italy, 1993), in the WA92 CERN collaboration (particle physics), and then moved to CERN as a Fellow in the European GP-MIMD2 project, attached to the NA48 collaboration (particle physics, parallel programming, 1994-1995). He then started his close collaboration with EPFL, first in the DeTeC (Demining Technology Center) project (sensors for landmine detection/humanitarian demining, 1996-1997). After DeTeC's end, he started the first of a series of fruitful collaborations with the Vrije Universiteit Brussel (VUB) on humanitarian demining related R&D (1998). This was followed by the EUDEM survey project (The European Union in Humanitarian Demining, 1998), the EUDEM2 three year EC sponsored support measure (www.eudem.info, 2001-2004), and the DELVE support action (www.delve.vub.ac.be, 2007). In parallel he started working within the EPFL's AQUA group (Advanced Quantum Architectures, Edoardo Charbon), on topics as diverse as ultrasonic sensors for in-air application, optical 3D and high speed 2D sensing, sensor networks, or tracking/motion capture systems, in particular for the preparation of research projects. This culminated in the European MEGAFRAME (www.megaframe.eu, FP6, 2006-2010, SPAD arrays and related in-pixel time stamping electronics in deep submicron CMOS technology) and SPADnet (www.spadnet.eu, FP7, 2010-2014, networked SPAD arrays for Positron Emission Tomography) projects, coordinated by EPFL-AQUA. As from 2009 he also worked with Dario Floreano on the management of the CURVACE Curved Artificial Compound Eyes FP7 project (www.curvace.org), coordinated by EPFL-LIS. He was also active with CHUV (Lausanne University Hospital) within EndoTOFPET-US (endoscopic PET) as well as on a CTI project devoted to the development of a new hand-held standalone tool for tracer-guided medical procedures. In 2014 he had also the pleasure of joining the EPFL ICLAB of Christian Enz during its ramp-up phase, collaborating on device related topics (SNF GigaRadMOST) and biomedical R&D (NanoTera WiseSkin). Claudio is now fully with EPFL’s Advanced Quantum Architecture (AQUA). He has also been active as independent scientific consultant, under the label CBR Scientific Consulting, on the preparation of (European) R&D project proposals and the execution of individual studies, and worked in 2006 for a local start-up as operations manager and R&D advisor.... but this is another story. An unauthorized early biography is available at http://lami.epfl.ch/team/claudiob/...

Official source

Related concepts (32)

Image sensor

An image sensor or imager is a sensor that detects and conveys information used to form an . It does so by converting the variable attenuation of light waves (as they pass through or reflect off objects) into signals, small bursts of current that convey the information. The waves can be light or other electromagnetic radiation. Image sensors are used in electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, optical mouse devices, medical imaging equipment, night vision equipment such as thermal imaging devices, radar, sonar, and others.

Active-pixel sensor

An active-pixel sensor (APS) is an , which was invented by Peter J.W. Noble in 1968, where each pixel sensor unit cell has a photodetector (typically a pinned photodiode) and one or more active transistors. In a metal–oxide–semiconductor (MOS) active-pixel sensor, MOS field-effect transistors (MOSFETs) are used as amplifiers. There are different types of APS, including the early NMOS APS and the now much more common complementary MOS (CMOS) APS, also known as the CMOS sensor.

Digital image

A digital image is an composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively. Depending on whether the is fixed, it may be of vector or raster type. Raster image Raster images have a finite set of digital values, called picture elements or pixels.

Digital imaging

Digital imaging or digital image acquisition is the creation of a digital representation of the visual characteristics of an object, such as a physical scene or the interior structure of an object. The term is often assumed to imply or include the , , , printing and display of such images. A key advantage of a , versus an analog image such as a film photograph, is the ability to digitally propagate copies of the original subject indefinitely without any loss of image quality.

Digital image processing

Digital image processing is the use of a digital computer to process s through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over . It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

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