Design of the 12-bit Delta-Sigma Modulator using SC Technique for Vibration Sensor Output Processing

The work deals with the design of the 12-bit Delta-Sigma modulator using switched capacitors (SC) technique. The modulator serves to vibration sensor output processing. The first part describes the Delta-Sigma modulator parameters definition. Results of the proposed topology ideal model were presented as well. Next, the Delta-Sigma modulator circuitry on the transistor level was done. The ONSemiconductor I2T100 0.7 um CMOS technology was used for design. Then, the Delta-Sigma modulator nonidealities were simulated and implemented into the MATLAB ideal model of the modulator. The model of real Delta-Sigma modulator was derived. Consequently, modulator coefficients were optimized. Finally, the corner analysis of the Delta-Sigma modulator with the optimized coefficients was simulated. The value of SNDR = 82.2 dB (ENOB = 13.4 bits) was achieved

Inertial Coupling Method for particles in an incompressible fluctuating fluid

We develop an inertial coupling method for modeling the dynamics of point-like 'blob' particles immersed in an incompressible fluid, generalizing previous work for compressible fluids. The coupling consistently includes excess (positive or negative) inertia of the particles relative to the displaced fluid, and accounts for thermal fluctuations in the fluid momentum equation. The coupling between the fluid and the blob is based on a no-slip constraint equating the particle velocity with the local average of the fluid velocity, and conserves momentum and energy. We demonstrate that the formulation obeys a fluctuation-dissipation balance, owing to the non-dissipative nature of the no-slip coupling. We develop a spatio-temporal discretization that preserves, as best as possible, these properties of the continuum formulation. In the spatial discretization, the local averaging and spreading operations are accomplished using compact kernels commonly used in immersed boundary methods. We find that the special properties of these kernels make the discrete blob a particle with surprisingly physically-consistent volume, mass, and hydrodynamic properties. We develop a second-order semi-implicit temporal integrator that maintains discrete fluctuation-dissipation balance, and is not limited in stability by viscosity. Furthermore, the temporal scheme requires only constant-coefficient Poisson and Helmholtz linear solvers, enabling a very efficient and simple FFT-based implementation on GPUs. We numerically investigate the performance of the method on several standard test problems...Comment: Contains a number of corrections and an additional Figure 7 (and associated discussion) relative to published versio

Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks

This paper presents a unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNN) using CMOS current-mode analog techniques. The net input signals are currents instead of voltages as presented in previous approaches, thus avoiding the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploitation of current mirror properties for the efficient implementation of both linear and nonlinear analog operators. These cells are simpler and easier to design than those found in previously reported CT and DT-CNN devices. Basic design issues are covered, together with discussions on the influence of nonidealities and advanced circuit design issues as well as design for manufacturability considerations associated with statistical analysis. Three prototypes have been designed for l.6-pm n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. Experimental results are given illustrating performance of these prototypes

High Performance Direct Gravitational N-body Simulations on Graphics Processing Units

We present the results of gravitational direct $N$-body simulations using the commercial graphics processing units (GPU) NVIDIA Quadro FX1400 and GeForce 8800GTX, and compare the results with GRAPE-6Af special purpose hardware. The force evaluation of the $N$-body problem was implemented in Cg using the GPU directly to speed-up the calculations. The integration of the equations of motions were, running on the host computer, implemented in C using the 4th order predictor-corrector Hermite integrator with block time steps. We find that for a large number of particles (N \apgt 10^4) modern graphics processing units offer an attractive low cost alternative to GRAPE special purpose hardware. A modern GPU continues to give a relatively flat scaling with the number of particles, comparable to that of the GRAPE. Using the same time step criterion the total energy of the $N$-body system was conserved better than to one in $10^6$ on the GPU, which is only about an order of magnitude worse than obtained with GRAPE. For N\apgt 10^6 the GeForce 8800GTX was about 20 times faster than the host computer. Though still about an order of magnitude slower than GRAPE, modern GPU's outperform GRAPE in their low cost, long mean time between failure and the much larger onboard memory; the GRAPE-6Af holds at most 256k particles whereas the GeForce 8800GTF can hold 9 million particles in memory.Comment: Submitted to New Astronom

Delta-Sigma Modulator based Compact Sensor Signal Acquisition Front-end System

The proposed delta-sigma modulator ($\Delta\Sigma$M) based signal acquisition architecture uses a differential difference amplifier (DDA) customized for dual purpose roles, namely as instrumentation amplifier and as integrator of $\Delta\Sigma$M. The DDA also provides balanced high input impedance for signal from sensors. Further, programmable input amplification is obtained by adjustment of $\Delta\Sigma$M feedback voltage. Implementation of other functionalities, such as filtering and digitization have also been incorporated. At circuit level, a difference of transconductance of DDA input pairs has been proposed to reduce the effect of input resistor thermal noise of front-end R-C integrator of the $\Delta\Sigma$M. Besides, chopping has been used for minimizing effect of Flicker noise. The resulting architecture is an aggregation of functions of entire signal acquisition system within the single block of $\Delta\Sigma$M, and is useful for a multitude of dc-to-medium frequency sensing and similar applications that require high precision at reduced size and power. An implementation of this in 0.18-$\mu$m CMOS process has been presented, yielding a simulated peak signal-to-noise ratio of 80 dB and dynamic range of 109dBFS in an input signal band of 1 kHz while consuming 100 $\mu$W of power; with the measured signal-to-noise ratio being lower by about 9 dB.Comment: 13 pages, 16 figure