The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are Generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several Generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
The sample software includes, common library, peripheral APIs, and test modules
for the APIs. The common library include startup file, standard definition and
header files, processor specific setup module, Generic interrupt related APIs,
timer routine, and scatter loading file. The peripheral directories include,
GPIO, PWM, Real-time clock, timer, SPI, I2C, Watchdog timer, UART, external
interrupt, etc.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important Generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
The widespread use of embedded systems mandates the development of industrial software design methods, i.e. computer-aided design and engineering of embedded applications using formal models (frameworks) and standardized prefabricated components, much in the same way as in other mature areas of engineering such as mechanical engineering and electronics. These guidelines have been used to develop Component-based Design of Software for Embedded Systems (COMDES). The paper gives an overview of the COMDES framework, followed by a presentation of a Generic component types, such as function blocks, activities and function units. The execution of function units is discussed in the context of a newly developed execution model, i.e. timed-multitasking, which has been extended to distributed embedded systems.
The VISA probes for LabVIEW 7.0 greatly extend the amount of relevant
information displayed when using LabVIEW probes on VISA refnums. They are used
automatically by LabVIEW in place of the Generic probe when they are
installed.
The System Management BIOS Reference Specification addresses how motherboard and system vendors present
management information about their products in a standard format by extending the BIOS interface on Intel
architecture systems. The information is intended to allow Generic instrumentation to deliver this data to
management applications that use CIM (the WBEM data model) or direct access and eliminates the need for error
prone operations like probing system hardware for presence detection.
