scholarly journals High-frequency two-input CMOS OTA for continuous-time filter applications

2000 ◽  
Vol 147 (1) ◽  
pp. 13 ◽  
Author(s):  
J. Glinianowicz ◽  
J. Jakusz ◽  
S. Szczepanski ◽  
Y. Sun
Author(s):  
Eka Fitrah Pribadi ◽  
Rajeev Kumar Pandey ◽  
Paul C.-P. Chao

Abstract A high-resolution, low offset delta-sigma analog to digital converter for detecting photoplethysmography (PPG) signal is presented in this study. The PPG signal is a bio-optical signal incorporated with heart functionality and located in the range of 0.1–10 Hz. The location to get PPG signal is on a pulsating artery. Thus the delta-sigma analog-to-digital (DS ADC) converter is designed specifically in that range. However, the DS ADC circuitry suffers from 1/f noise under 10 Hz frequency range. A chopper based operational amplifier is implemented in DS ADC to push the 1/f noise into high-frequency noise. The dc offset of the operational amplifier is also pushed to the high-frequency region. The DS ADC circuitry consists of a second-order continuous-time delta-sigma modulator. The delta-sigma modulator circuitry is designed and simulated using TSMC 180 nm technology. The continuous-time delta-sigma modulator active area layout is 746μm × 399 μm and fabricated using TSMC 180 nm technology. It operates in 100 Hz bandwidth and 4096 over-sampling ratios. The SFDR of the circuit is above 70 dB. The power consumption of the delta-sigma modulator is 35.61μW. The simulation is performed in three different kinds of corner, SS, TT, and FF corner, to guarantee the circuitry works in different conditions.


2004 ◽  
Vol 07 (08) ◽  
pp. 997-1030 ◽  
Author(s):  
MASCIA BEDENDO ◽  
STEWART D. HODGES

In this paper we propose a continuous time model capable of describing the dynamics of futures equity index returns at different time frequencies. Unlike several related works in the literature, we avoid specifying a model a priori and we attempt, instead, to infer it from the analysis of a data set of 5-minute returns on the S&P500 futures contract. We start with a very general specification. First we model the seasonal pattern in intraday volatility. Once we correct for this component, we aggregate intraday data into a daily volatility measure to reduce the amount of noise and its distorting impact on the results. We then employ this measure to infer the structure of the stochastic volatility model and of the leverage component, as well as to obtain insights on the shape of the distribution of conditional returns. Our model is then refined at a high frequency level by means of a simple nonlinear filtering technique, which provides an intraday update of volatility and return density estimates on the basis of observed 5-minute returns. The results from a Monte Carlo experiment indicate that a sample of returns simulated according to our model successfully replicates the main features observed in market returns.


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