Linear Technology offers some of the highest performance RF and signal chain solutions for wireless and cellularinfrastructure. These products support worldwide standards including, LTE, WiMAX, GSM,W-CDMA, TD-SCDMA,CDMA, and CDMA2000. Other wireless systems include broadband microwave data links, secure communications,satellite receivers, broadband wireless access, wireless broadcast systems, RFID readers and cable infrastructure
The CoolRunner-II CPLD is a highly uniform family of fast, low-power devices. Theunderlying architecture is a traditional CPLD architecture, combining macrocells intofunction blocks interconnected with a global routing matrix, the Xilinx AdvancedInterconnect Matrix (AIM). The function blocks use a PLA configuration that allowsall product terms to be routed and shared among any of the macrocells of the functionblock.
There are many manufacturers of dot matrix LCD modules. However, most of these displaysare similar. They all have on-board controllers and drivers capable of displaying alpha numericsand a wide variety of other symbols (including Japanese "Katakana" characters). The internaloperation of LCD controller devices is determined by signals sent from a central processing unit(in this case, a CoolRunner-II CPLD).
yright 2002 Cygnal Integrated Products, Inc. // // Filename: LIION_BC_MAIN.c // Target Device: 8051F300 // Created: 11 SEP 2002 // Created By: DKC // Tool chain: KEIL Eval C51 // // This is a stand alone battery charger for a Lithium ION battery. // It utilizes a buck converter, controlled by the on-chip 8-bit PWM, // to provide constant current followed by constant voltage battery charge.
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.
For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix.
heuristics: descent.
Tests: 100 queens in less than 1 second and 67 iterations. 500 queens in 1 second and 257 iterations. 1000 queens in 11 seconds and 492 iterations.
heuristics: Simulated annealing.
Tests: 100 queens in less than 1 second and 47 iterations. 500 queens in 5 seconds and 243 iterations. 1000 queens in 13 seconds and 497 iterations.
heuristics: based on Simulated Annealing.
Tests: 100 queens in less than 1 second and 60 iterations. 500 queens in 1 second and 224 iterations. 1000 queens in 5 seconds and 459 iterations. 10 000 queens in 20 minutes 30 seconds and 4885 iterations.
The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
