Standard cell automated layout for a CMOS 50 MHz 8 × 8 bit pipelined parallel dadda multiplier

An 8 × 8 pipelined parallel multiplier which uses the Dadda scheme is presented. The multiplier has been implemented in a 3-μm n-well CMOS process with two layers of metal using a standard cell automatic placement and routing program. The design uses a form of pipelined carry look-ahead adder in the final stage of summation, thus providing a significant contribution to the high performance of the multiplier. The design is expected to operate at a clock frequency of at least 50 MHz and has a flush time of seven clock cycles. The design illustrates a possible method of implementing an irregular architecture in VLSI using multiple levels of low-resistance, low-capacitance interconnect and automated layout techniques.

Single sensor boost converter-based maximum power point tracking algorithms

Two new maximum power point tracking algorithms are presented: the input voltage sensor, and duty ratio maximum power point tracking algorithm (ViSD algorithm); and the output voltage sensor, and duty ratio maximum power point tracking algorithm (VoSD algorithm). The ViSD and VoSD algorithms have the features, characteristics and advantages of the incremental conductance algorithm (INC); but, unlike the incremental conductance algorithm which requires two sensors (the voltage sensor and current sensor), the two algorithms are more desirable because they require only one sensor: the voltage sensor. Moreover, the VoSD technique is less complex; hence, it requires less computational processing. Both the ViSD and the VoSD techniques operate by maximising power at the converter output, instead of the input. The ViSD algorithm uses a voltage sensor placed at the input of a boost converter, while the VoSD algorithm uses a voltage sensor placed at the output of a boost converter. © 2011 IEEE.

10-Gbit/s transmission over 300-m standard multimode fiber using multilevel coding and 2-channel WDM

A combination of multilevel coding schemes and simple two-channel wavelength division multiplexing (WDM) at 1300 and 1550 nm was used to transmit an aggregate of 10 Gbit/s over 300 m of multimode fiber that is typical of that employed in current Local Area Networks (LANs). It was shown that this technique could be a simple solution for achieving 10 Gigabit ethernet links over installed multimode fiber building backbones.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

Multiple object tracking using evolutionary MCMC-based particle algorithms

Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained. © 2009 IFAC.