Summary: This tutorial covers the basic issues of metrology and measurement activity. The measurement concept is introduced, showing why a measurement result cannot ever be the “true” value of the measured quantity. The uncertainty concept is defined, and the way it can be expressed and estimated is analyzed. The recommendation of the present international reference standard (IEC ISO Guide to the Expression of Uncertainty in Measurement) is also discussed. Alessandro Ferrero is a professor of electrical and electronic measurements at the Dipartimento di Elettrotecnica of the Politecnico di Milano. His current research interests are concerned with the application of digital methods to electrical measurements and measurements on electric power systems under nonsinusoidal conditions, and with the metrological characterization of instruments based on complex DSP algorithms.

Summary: This tutorial discusses how measurement is a key to life and explores where we use measurements. It defines instrumentation and measurement and reviews basic principles. Case studies detail car, LOX tank, submarine data acquisition system, and medical device examples. This tutorial also explores where instrumentation is found (e.g. laboratory, field instruments, car engine control, aircraft avionics and flight control, bridges, factories, houses, appliances) and discuss systems of instruments. It reviews sensor types, sizes and systems and covers basic instrumentation with a look at general configurations focused on areas such as inputs, conditioning and transformation, analog pre-processing, analog-to-digital converters (ADCs), outputs and basic processing. Review system configurations and the evolution of system designs are also discussed. After completing this course you should be able to develop and understanding of: The need for measurement and basic principles of measurements; Basic components or subsystems of a measurement instrument; Various architectures that can define instrumentation.

Summary: This course will provide an introduction to and an overview of type-2 fuzzy sets (T2 FSs) and systems. It will locate type-2 fuzzy sets and systems in an educational taxonomy, so that the student will appreciate from the onset the importance of studying such fuzzy sets; explain what a T2 FS is, how it is different from a type-1 FS, and why it is needed; provide careful definitions and pictures of the new terminology of T2 FSs; explain the importance of interval type-2 fuzzy sets over more general T2 FSs; explain important representations for a T2 FS (one is very good for computing, and another is very good for quickly developing the structure of the solution to a new theoretical problem); explain how T2 FSs are used in a rule-based system (a fuzzy logic system-FLS); describe the detailed computations that are used for an interval T2 FLS, relying mostly on graphical pictures; compare those computations with their type-1 counterparts; explain the major obstacle to using a T2 FLS in a real-time application and how that obstacle has been overcome; and wrap up the course with a plug for the applications course and a short reading list.

Summary: There are rapidly emerging needs to deal with distributed sources of data (sensors and sensor networks, web sites, databases). While recognizing their limited accessibility at a global level (associated with technical constraints and/or privacy issues) and fully acknowledging benefits of collaborative processing, we propose a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. The variety of possible mechanisms of interaction is organized into a setting of the C³ interaction paradigm (communication - collaboration - consensus). This helps us offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, the role granular information in the establishing of the mechanisms of interaction plays a pivotal role.
We consider distributed fuzzy models and fuzzy modeling. In particular, we elaborate on the key design issues concerning fuzzy rule-based systems with local functional models occurring at their conclusion parts and show how the fundamental modes of interaction are exploited here. It will be demonstrated that more advanced constructs such as type-2 fuzzy sets emerge naturally in distributed fuzzy modeling and come with a well-defined semantics of their membership functions by being fully reflective of the character of the underlying distributed data.
In the context of collaborative fuzzy modeling, we bring forward a concept experience-consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Detailed algorithmic considerations embrace several design scenarios in which we apply the mechanism of experience consistency at the level of conditions and conclusions of the rules. We also show that a level of achieved experience-driven consistency can be quantified through fuzzy sets (fuzzy numbers) of the parameters of the local models standing in the conclusion parts of the rules this leading to the emergence of granular constructs of fuzzy modeling.

Summary: Early diagnostics of diseases is the key to treatment, cure, and fatality prevention. Various biomedical sensors are available or being developed to achieve early disease diagnostics with non-invasive or minimally invasive techniques, such as magnetic resonance imaging (MRI), ultrasonic imaging, X-ray imaging, CT scan, optical coherent tomography (OCT), endoscopy, microscopy, spectroscopy, etc. Among these techniques, optical technologies, including various microscopy and spectroscopy approaches, provide the possibility to observe a large range of objects, from organs, cells, to molecules, with fast (ideally real-time) response and high spatial and spectral resolutions. In addition, to make the diagnostic tests of diseases, such as cancers, more accessible to the general public it is important to provide easy early diagnostic tools packaged as portable information devices. Such early diagnostics portable information devices must be highly sensitive, disease specific, reliable, inexpensive, easy to fabricate, fast, and compact.

This course will provide an overview of various optical biomedical sensors, including both imaging and spectroscopic techniques, and introduce some recent developments in biomedical sensors, such as nanoparticle surface enhanced Raman scattering (SERS) and its application in compact molecular sensors. Specifically, the following topics will be discussed: interaction of light with tissues, cells, and molecules; bioimaging including optical microscopy, endoscopic imaging, fluorescence imaging, and optical tomography; spectroscopy including absorption spectroscopy, fluorescence spectroscopy, and Raman spectroscopy; optical fiber surface enhanced Raman probes for biomedical applications.

Summary: This course will have a large impact on a large audience as handling uncertainties will be a very important challenge to any real world application that operate in real world changing and dynamic environments. The course will present the theoretical aspects of type-2 FLCs and how to build a type-2 FLC. The course will also present many applications in different areas ranging from Control of Marine Diesel Engines, Autonomous Outdoor mobile Robots as well as Embedded Agents and Ambient Intelligent Environments which deals with how we can embed very efficient computational intelligence and type-2 techniques in small computing and memory platforms. The course will present a very clear description of type-2 Fuzzy Logic Controllers (FLCs), their design and their various application in handling the uncertainties in various real world applications. Different examples will be provided.