A Predictable Robust Fully Programmable Analog Gaussian Noise Source for Mixed-Signal/Digital ATE

2006 ◽  
Author(s):  
Sadok Aouini ◽  
Gordon Roberts
Keyword(s):  
Gaussian Noise ◽  
Noise Source ◽  
Mixed Signal ◽  
Programmable Analog ◽  
Digital Ate

Rapid prototyping of algorithmic A/D converters based on FPAA devices

2013 ◽  
Vol 61 (3) ◽  
pp. 691-696 ◽  
Author(s):  
R. Suszynski ◽  
K. Wawryn
Keyword(s):  
Rapid Prototyping ◽  
Sinusoidal Signal ◽  
Digital Converter ◽  
Mixed Signal ◽  
Analog Digital Converter ◽  
Signal Systems ◽  
Programmable Analog ◽  
Field Programmable ◽  
Field Programmable Analog Array

Abstract A rapid prototyping method for designing mixed signal systems has been presented in the paper. The method is based on implementation of the field programmable analog array (FPAA) to configure and reconfigure mixed signal systems. A serial algorithmic analog digital converter has been used as an example. Three converter architectures have been selected and implemented FPAA device. To verify and illustrate converters operation and prototyping capabilities, implemented converters have been excited by a sinusoidal signal. Analog sinusoidal excitations, digital responses and sinusoidal waveforms after reconstruction are presented.

scholarly journals The foraging brain: evidence of Lévy dynamics in brain networks

2016 ◽  
Author(s):  
Tommaso Costa ◽  
Giuseppe Boccignone ◽  
Franco Cauda ◽  
Mario Ferraro
Keyword(s):  
Gaussian Noise ◽  
Resting State ◽  
Noise Source ◽  
State Condition ◽  
Human Cognition ◽  
Lévy Noise ◽  
Characteristic Index ◽  
Lévy Motion ◽  
Levy Noise ◽  
Stable Noise

AbstractIn this research we have analyzed functional magnetic resonance imaging (fMRI) signals of different networks in the brain under resting state condition.To such end, the dynamics of signal variation, have been conceived as a stochastic motion, namely it has been modelled through a generalized Langevin stochastic differential equation, which combines a deterministic drift component with a stochastic component where the Gaussian noise source has been replaced with α-stable noise.The parameters of the deterministic and stochastic parts of the model have been fitted from fluctuating data. Results show that the deterministic part is characterized by a simple, linear decreasing trend, and, most important, the α-stable noise, at varying characteristic index α, is the source of a spectrum of activity modes across the networks, from those originated by classic Gaussian noise (α = 2), to longer tailed behaviors generated by the more general Lévy noise (1 ≤ α < 2).Lévy motion is a specific instance of scale-free behavior, it is a source of anomalous diffusion and it has been related to many aspects of human cognition, such as information foraging through memory retrieval or visual exploration.Finally, some conclusions have been drawn on the functional significance of the dynamics corresponding to different α values.Author SummaryIt has been argued, in the literature, that to gain intuition of brain fluctuations one can conceive brain activity as the motion of a random walker or, in the continuous limit, of a diffusing macroscopic particle.In this work we have substantiated such metaphor by modelling the dynamics of the fMRI signal of different brain regions, gathered under resting state condition, via a Langevin-like stochastic equation of motion where we have replaced the white Gaussian noise source with the more general α-stable noise.This way we have been able to show the existence of a spectrum of modes of activity in brain areas. Such modes can be related to the kind of “noise” driving the Langevin equation in a specific region. Further, such modes can be parsimoniously distinguished through the stable characteristic index α, from Gaussian noise (α = 2) to a range of sharply peaked, long tailed behaviors generated by Lévy noise (1 ≤ α < 2).Interestingly enough, random walkers undergoing Lévy motion have been widely used to model the foraging behaviour of a range of animal species and, remarkably, Lévy motion patterns have been related to many aspects of human cognition.

Mixed Noise Image De-Noising Based on EM Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4734-4741 ◽  
Author(s):  
Gui Cun Shi ◽  
Fei Xing Wang
Keyword(s):  
Applied Sciences ◽  
Wavelet Analysis ◽  
Em Algorithm ◽  
Gaussian Noise ◽  
Impulse Noise ◽  
Noise Source ◽  
High Quality ◽  
Noise Image ◽  
Mixed Noise ◽  
Non Gaussian

Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of the noise model’s parameters could affect the de-noising quality. In the case of mixed Gaussian noises, EM algorithm is an iterative algorithm, which simplifies the maximum likelihood equation. This thesis takes wavelet analysis and statistics theory as tools, studies on mixed noise image de-noising, provides two classes of algorithms for dealing with a special type of non-Gaussian noise, mixed Gaussian and Pepper & Salt noise.

scholarly journals A Native SPICE Implementation of Memristor Models for Simulation of Neuromorphic Analog Signal Processing Circuits

2022 ◽  
Vol 27 (1) ◽  
pp. 1-24
Author(s):  
Bo Li ◽  
Guoyong Shi
Keyword(s):  
Signal Processing ◽  
Analog Circuits ◽  
Large Scale ◽  
Circuit Simulation ◽  
Analog Signal Processing ◽  
Analog Signal ◽  
Storage Device ◽  
Simulation Accuracy ◽  
Mixed Signal ◽  
Programmable Analog

Since the memristor emerged as a programmable analog storage device, it has stimulated research on the design of analog/mixed-signal circuits with the memristor as the enabler of in-memory computation. Due to the difficulty in evaluating the circuit-level nonidealities of both memristors and CMOS devices, SPICE-accuracy simulation tools are necessary for perfecting the art of neuromorphic analog/mixed-signal circuit design. This article is dedicated to a native SPICE implementation of the memristor device models published in the open literature and develops case studies of applying such a circuit simulation with MOSFET models to study how device-level imperfections can make adversarial effects on the analog circuits that implement neuromorphic analog signal processing. Methods on memristor stamping in the framework of modified nodal analysis formulation are presented, and implementation results are reported. Furthermore, functional simulations on neuromorphic signal processing circuits including memristors and CMOS devices are carried out to validate the effectiveness of the native SPICE implementation of memristor models from the perspectives of simulation accuracy, efficiency, and convergence for large-scale simulation tasks.

Research of a Mixed-Signal Programmable SoC Based on FPAA

2014 ◽  
Vol 556-562 ◽  
pp. 1741-1744
Author(s):  
Jun Deng ◽  
Hua Yong Tan ◽  
Lun Cai Liu ◽  
Lin Tao Liu
Keyword(s):  
Digital Signal ◽  
Intermediate Frequency ◽  
Mixed Signal ◽  
Programmable Analog ◽  
Baseband Signal ◽  
Field Programmable ◽  
Baseband Signal Processing ◽  
Good Potential ◽  
On Chip ◽  
Field Programmable Analog Array

This paper presents a novel architecture for mixed-signal SoC, which integrates a Field Programmable Analog Array (FPAA) into a SoC based on 32-bit RISC CPU. The FPAA unit can be configured as Filter, Comparator, Gain Amplifier, and so on. The proposed mixed-signal SoC can transform the intermediate frequency (IF) analog signal to baseband digital signal and realize the real-time baseband signal processing, besides this, which can transmit the modulated IF signals which are converted from baseband signals by digital up-conversion (DUC). The proposed mixed-signal SoC is a transceiver on chip actually, due to the internal integrated IPs, such as ADC, DAC, DDC and DUC, which can provide smaller board area, lower power consumption and the system cost for the product development of transceiver. This design will have a good potential for wireless communication applications.

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