Approximate stationary density of the nonlinear dynamical systems excited with white noise

IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005; Kobe; Japan; 23 May 2005 through 26 May 2005In this paper, obtaining approximate solution of Fokker-Planck-Kolmogorov (FPK) Equation using compactly supported functions has been discussed. With specific choice of such functions as piecewise multivariable polynomials which are supported on ellipsoidal regions, the parameters to be determined can be considerably decreased compared to Multi- Gaussian Closure scheme [1]. An example commonly considered in the literature has been analyzed and the proposed method has been compared with the Multi-Gaussian Closure scheme. The simulation results indicate that the new scheme is quite successful even if the driving noise is not white Gaussian, but has an exponential correlation function with small correlation time

Saliency on a chip: a digital approach with an FPGA

Selective-visual-attention algorithms have been successfully implemented in analog VLSI circuits.1 However, in addition to the usual issues of analog VLSI—such as the need to fi ne-tune a large number of biases— these implementations lack the spatial resolution and pre-processing capabilities to be truly useful for image-processing applications. Here we take an alternative approach and implement a neuro-mimetic algorithm for selective visual attention in digital hardware

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation

Parametric, Secure and Compact Implementation of RSA on FPGA

We present a fast, efficient, and parameterized modular multiplier and a secure exponentiation circuit especially intended for FPGAs on the low end of the price range. The design utilizes dedicated block multipliers as the main functional unit and Block-RAM as storage unit for the operands. The adopted design methodology allows adjusting the number of multipliers, the radix used in the multipliers, and number of words to meet the system requirements such as available resources, precision and timing constraints. The architecture, based on the Montgomery modular multiplication algorithm, utilizes a pipelining technique that allows concurrent operation of hardwired multipliers. Our design completes 1020-bit and 2040-bit modular multiplications in 7.62 μs and 27.0 μs, respectively. The multiplier uses a moderate amount of system resources while achieving the best area-time product in literature. 2040-bit modular exponentiation engine can easily fit into Xilinx Spartan-3E 500; moreover the exponentiation circuit withstands known side channel attacks

Glitch-free discretely programmable clock generation on chip

In this paper we describe a solution for a glitch-free discretely programmable clock generation unit (DPGC). The scheme is compatible with a GALS communication scheme in the sense that clock gating and clock pausing are possible. Besides, the proposed scheme does not require waiting for a new clock as the frequency change is seen as almost instantaneously. A prototype has been designed for a 0.13µm triple-well CMOS process technology to also study the properties of the scheme with respect to voltage scaling

A Power-Efficient Bio-Potential Acquisition Device with DS-MDE Sensors for Long-Term Healthcare Monitoring Applications

This work describes a power-efficient bio-potential acquisition device for long-term healthcare applications that is implemented using novel microelectromechanical dry electrodes (MDE) and a low power bio-potential processing chip. Using micromachining technology, an attempt is also made to enhance the sensing reliability and stability by fabricating a diamond-shaped MDE (DS-MDE) that has a satisfactory self-stability capability and superior electric conductivity when attached onto skin without any extra skin tissue injury technology. To acquire differential bio-potentials such as ECG signals, the proposed processing chip fabricated in a standard CMOS process has a high common mode rejection ratio (C.M.R.R.) differential amplifier and a 12-bit analog-to-digital converter (ADC). Use of the proposed system and integrate simple peripheral commercial devices can obtain the ECG signal efficiently without additional skin tissue injury and ensure continuous monitoring more than 70 hours with a 400 mAh battery