Advanced sensor systems ET8008 Fall 2009 http://www.hh.se/et8008. Course examiner: Stefan Byttner (Stefan.Byttner@hh.se) Room E505. Lecture schedule: Thursday 3 sep: Introductory lecture (Stefan Byttner) Tuesday 8 sep: Camera and vision (Josef Bigun)
Thursday 3 sep: Introductory lecture (Stefan Byttner)
Tuesday 8 sep: Camera and vision (Josef Bigun)
Thursday 10 sep: State of the art in physics, nano sensors (Håkan Petterson)
Thursday 17 sep: Virtual sensors for control (Ulf Holmberg)
Tuesday 22 sep: Virtual sensors for monitoring (Antanas Verikas)
press “Schema för flera veckor”. Select “In english” on the top right. Then you can search for the course and see the schedule details.
Deeper study of a particular field of sensors/application. Give some experience in writing a research report. Possible project examples;
Each project will be done by student groups of 2 students each. Any deviations from this groupsize will be exceptions and must be approved by examiner. They must be approved before project work starts.
Seminar with oral presentation of the project work + presentation of the related paper:
22 Oct 13.15-17 Room D415
Written report sent to course examiner email adress as a PDF file no later than:
1 Nov 23:59 (Stefan.Byttner@hh.se)
Has compiled a satisfactory list of references.
Knows which references are important and which are less important for the
Can summarize the most important references.
Knows the contents of the remaining references in “abstract” form.
Understands the problem and can formulate subproblems.
Can relate the project to the references.
Can relate the references to each other and combine information from them and
come to conclusions.
Can use the references to sharpen the project plan, i.e. decide to omit some
studies or concentrate on some subproblem.
Is able to criticize and find weak and strong parts in reference articles as well
as the own work.
The result is acceptable, but can point at several things that,
with a reasonable effort, would have improved it.
Can identify and formulate significant strong and weak points in the result.
The result is good (i.e. matches well to the anticipated results in the, possibly
revised, project plan) and can point at only a few things that
could have been done better or that are missing.
Can, with minor supervision, come to some conclusions on how the result
could have been improved.
Can, with minor supervision, formulate some future directions for the project.
The result is excellent and the supervisor can point at only very few minor
improvements which could have been done.
The result is publishable in a scientific journal or at a conference.
Can, without supervision, evaluate the result in relation to other work done
in the field.
Presents the problem and proposed solution in a clear way.
Presents a clear analysis of the problem.
Can answer fundamental questions on the subject.
Presents the project in an attractive way with e.g. well chosen illustrations.
Can discuss different aspects of the problem.
An excellent presentation, which engages the audience and generates interested questions. All questions are answered in a relevant way.
The report is complete (i.e. with ”background”, ”methodology”,
”results”, ”conclusion”, ”summary” etc.).
All references, figures & tables are referred to in the text.
The report is, with significant supervision, well written. (”Well written” means
that: The English is correct and the text flows smoothly. Figures are relevant
and add value to the text. Similarly with tables. The result and conclusion is
The report is, with minor supervision, very well written.
The report is written such that it would be publishable,
provided the result is good enough.
Electrical, optical and magnetic properties of nanoscale devices
Performed in co-operation with the Nanometer Structure Consortium at Lund University
Real time quality measurements (soft sensors) for paper making
Machine learning, optics, spectroscopy
Shrinking, deinking, …
Machine learning and image analysis to measure color reproduction in printed media. Now for real-time feedback control
Used by (e.g.) Hallandsposten and Bank of England.
Under the hood of our highway
laboratory, a SAAB 9000 2.3 T
Information communicated using wireless technology
Vehicle DataVehicle Health
Fleet behavior can provide a norm in which an “unhealthy” bus can be detected as deviating from the norm. The RDM project investigates how this norm can be found and used on a fleet of vehicles to improve up-time management.
Autonomous robots for agriculture (weeding, sowing, etc.)
MultipleAutonomous Forklifts for Loading and Transportation Applications