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A New Noise-Tolerance Approach by using Sigma-Delta Modulation

Abstract

本論文以 Sigma-Delta 調變原理為基礎設計一雜訊容忍方法,可對受雜訊干擾的數位訊號進行還原,並可處理雜訊干擾時間為連續的情況,且具備不需要修改原始架構的優點。 Sigma-Delta 為一使用超取樣的調變法,擁有雜訊整形的能力。本論文設計一個四階單位元的 Sigma-Delta 調變器,並根據來源訊號設計相對應的 Butterworth 濾波器,用來對 Sigma-Delta 調變後訊號進行還原,並同時濾除訊號頻帶外的雜訊。 當系統的輸出訊號受到一能量上限為原始訊號 50% 的持續雜訊干擾,透過本方法可將訊號還原至與原始訊號平均誤差小於 2% 的還原訊號。In recent years, for increasing processor performance, the microprocessor industry is driving the scaling of feature sizes into nanometer regime. The CMOS transistor sizes become smaller, causing their working voltages and threshold voltages become lower too. Even though, the noise never been reduced, but affecting the circuit more serious instead. For this reason, we developed a noise tolerance approach by using sigma-delta modulation, which can recover the noisy signals. Beside most of the previously schemes based on solving Single Event Upset (SEU), our approach can get over the case of multiple faults, even the noise has been continued a long time. The other property is no need to change the original Digital Signal Processor (DSP). Sigma-delta is a modulation based on over sampling, and the ability of noise shaping. In this paper, a fourth-order single-bit sigma-delta modulator has been presented. According to the input signal, uses a Butterworth filter as the sigma-delta output filter to remove the modulated out-bands noise. Assuming that a DSP output signal is affected by noises, and the total noise energy is about 0~50% of the original signal, then using our approach can produce a recovery signal with a deviation under 2% of the original signal.摘 要 i Abstract ii Index iii List of Tables v List of Figures vi Chapter 1 Introduction - 1 - 1.1 General Background Information - 1 - 1.2 Noise - 1 - 1.2.1 Shot Noise - 2 - 1.2.2 Thermal Noise - 2 - 1.2.3 Flicker Noise[3] - 3 - 1.2.4 Burst Noise[3] - 5 - 1.2.5 Avalanche Noise[3] - 6 - 1.3 Research Background and Motivation - 6 - 1.4 Research Methods and Procedures - 7 - 1.5 Paper Framework - 8 - Chapter 2 Related Work - 9 - 2.1 Algorithmic Noise-Tolerance (ANT) - 9 - 2.1.1 Prediction-Based ANT[8] - 10 - 2.1.2 Forward-Backward Predictor[9] - 11 - 2.1.3 Reduced-Precision Redundancy(RPR)-Based ANT[8] - 12 - 2.1.4 Spatial Algorithmic Soft Error Tolerance(S-ASET)[10][11] - 13 - 2.1.5 Spatio-Temporal ASET(ST-ASET)[10][11] - 14 - 2.1.6 Temporal ASET(T-ASET)[10][11] - 15 - 2.1.7 ANT Simulation Results - 15 - 2.2 Sigma-Delta Modulated signals - 17 - 2.2.1 Adder using Sigma-Delta Signals[14] - 18 - 2.2.2 Multiplier using Sigma-Delta Modulated Signals[14] - 19 - 2.2.3 Functionally DSP using Sigma-Delta Modulated Signals[15] - 20 - 2.3 Principle of Sigma-Delta Modulation[17][18] - 23 - 2.3.1 Sampling Theory - 25 - 2.3.2 Over Sampling - 26 - 2.3.3 Noise Shaping - 27 - Chapter 3 System Design and Implementation - 30 - 3.1 System Architecture Introduction - 30 - 3.2 System Stability Analysis of Sigma-Delta Modulation - 31 - 3.2.1 Creating Equivalent Model - 31 - 3.2.2 Finding out Stability - 33 - 3.3 Stable Conditions of Sigma-Delta Modulation in System - 35 - 3.3.1 Stability in First State - 35 - 3.3.2 Stability in Other States - 37 - 3.4 Design Methods - 39 - 3.5 System Analysis - 43 - Chapter 4 Simulations and Experiment Results - 47 - 4.1 Simulation Environment - 47 - 4.2 Analysis of Over Sampling and Noise Shaping - 47 - 4.3 Analysis of Noises in Sigma-Delta Modulator - 48 - 4.4 Non-Ideal System [25] - 50 - 4.5 Simulations and Results - 51 - 4.5 Analysis of Choosing OSR - 59 - Chapter 5 Conclusions and Feature Works - 62 - 5.1 Conclusions - 62 - 5.2 Feature Works - 63 - References - 64

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Last time updated on 16/06/2016

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