The power of programmability gives industrial automation designers a highly efficient， cost-effective alternative to traditional motor control units （MCUs）。 The parallel-processing power， fast Computational speeds， and connectivity versatility of Xilinx® FPGAs can accelerate the implementation of advanced motor control algorithms such as Field Oriented Control （FOC）。
Additionally， Xilinx devices lower costs with greater on-chip integration of system components and shorten latencies with high-performance digital signal processing （DSP） that can tackle compute-intensive functions such as PID Controller， Clark/Park transforms， and Space Vector PWM.
The Xilinx Spartan®-6 FPGA Motor Control Development Kit gives designers an ideal starting point for evaluating time-saving， proven， motor-control reference designs. The kit also shortens the process of developing custom control capabilities， with integrated peripheral functions （Ethernet， PowerLink， and PCI® Express）， a motor-control FPGA mezzanine card （FMC） with built-in Texas Instruments motor drivers and high-precision Delta-Sigma modulators， and prototyping support for evaluating alternative front-end circuitry.
CGAL is a collaborative effort of several sites in Europe and Israel. The goal is to make the most important of the solutions and methods developed in Computational geometry available to users in industry and academia in a C++ library. The goal is to provide easy access to useful, reliable geometric algorithms
Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the Computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The Computational complexity is N M^3, where N is the number of time samples and M the number of sources.
CGAL is a collaborative effort of several sites in Europe and Israel. The goal is to make the most important of the solutions and methods developed in Computational geometry available to users in industry and academia in a C++ library.
The tar file contains the following files:
ptfsf.c: heart of the perfect TFSF code
ptfsf.h: header file for same
ptfsf-demo.c: FDTD code which demonstrates use of perfect TFSF code. Essentially this program used to generate results shown in the paper
ptfsf-file-maker.c: code to generate an incident-field file using the "perfect" incident fields
ptfsf-demo-file.c: FDTD code which uses the perfect incident fields stored in a file
fdtdgen.h: defines macros used in much of my code
Makefile: simple make-file to compile programs
Also include are some simple script files to run the programs with reasonable values.
The code assumes a two-dimensional Computational domain with TMz polarization (i.e., non-zero field Ez, Hx, and Hy). The program is currently written so that the incident field always strikes the lower-left corner of the total-field region first. (If you want a different corner, that should be a fairly simple tweak to the code, but for now you ll have to make that tweak yourself.)
- XCS for Dynamic Environments
+ Continuous versions of XCS
+ Test problem: real multiplexer
+ Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts.
+Reference papers:
H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence
H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less Computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a Computational problem as soon as the dataset gets larger than a few hundred examples.
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle
Filters (PFs) that exploit conditional dependencies between
parts of the state to estimate. By doing so, RBPFs can
improve the estimation quality while also reducing the overall
Computational load in comparison to original PFs. However,
the Computational complexity is still too high for many
real-time applications. In this paper, we propose a modified
RBPF that requires a single Kalman Filter (KF) iteration per
input sample. Comparative experiments show that while good
convergence can still be obtained, Computational efficiency is
always drastically increased, making this algorithm an option
to consider for real-time implementations.
Abstract—Mobile devices performing video coding and
streaming over wireless and pervasive communication networks
are limited in energy supply. To prolong the operational lifetime of
these devices, an embedded video encoding system should be able
to adjust its Computational complexity and energy consumption
as demanded by the situation and its environment.
