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A low sensitivity dual feedback active RC bandpass filterGoldman, Matthew, 1965- January 1989 (has links)
This thesis presents the analysis and characterization of a low sensitivity dual feedback active RC bandpass filter. Chapter 2 details the analysis of the network and a method of simplifying the resultant transfer function by a single pole/zero cancellation. Chapter 3 characterizes the simplified transfer function through an analysis of the quality factor and of the center frequency gain as functions of the individual variables of the circuit. It also details sensitivity analyses of these characteristic quantities and a stability analysis. Lastly, chapter 3 presents graphical representations of the equations developed so that they can be used as design tools. It then goes through the details of applying these graphs to an example network. Chapter 4 explains the differences between experimental data and predicted data by discussing some of the nonlinearities neglected in the original analysis. Finally chapter 5 restates the design technique in light of the predominant nonlinearities.
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Audio band integrated active RC filter with digital frequency tuning.January 2005 (has links)
Yeung Nang Ching. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 72-74). / Abstracts in English and Chinese. / ACKNOWLEDGMENTS --- p.I / ABSTRACT --- p.II / 摘要 --- p.III / TABLE OF CONTENTS --- p.IV / LIST OF FIGURES --- p.VII / LIST OF TABLES --- p.X / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Overview of filter --- p.1 / Chapter 1.1.1 --- History --- p.1 / Chapter 1.1.2 --- Application of analog filter --- p.2 / Chapter 1.1.3 --- Category of continuous time filters --- p.3 / Chapter 1.1.4 --- Problem issued from Active RC filter --- p.7 / Chapter 1.2 --- Motivation --- p.7 / Chapter 1.3 --- Outline --- p.8 / Chapter CHAPTER 2 --- FILTER FUNDAMENTAL --- p.9 / Chapter 2.1 --- Overview --- p.9 / Chapter 2.2 --- Terminology --- p.9 / Chapter 2.3 --- General Goals of Filter Design --- p.11 / Chapter 2.4 --- Standard Lowpass Filter Characteristic --- p.11 / Chapter 2.4.1 --- Butterworth --- p.11 / Chapter 2.4.2 --- Chebyshev --- p.12 / Chapter 2.4.3 --- Elliptic-Function --- p.13 / Chapter 2.5 --- Study on Different Tuning Approaches --- p.13 / Chapter CHAPTER 3 --- CURRENT DIVISION NETWORK (CDN) --- p.18 / Chapter 3.1 --- Overview of Current Division Technique --- p.18 / Chapter 3.2 --- Second Order Effects --- p.23 / Chapter 3.3 --- Working Principle of CDN --- p.23 / Chapter 3.4 --- Performances of CDN --- p.25 / Chapter 3.4.1 --- General Properties of CDN --- p.25 / Chapter 3.4.2 --- Input Resistances of CDN --- p.26 / Chapter 3.4.3 --- Noise Performance of CDN --- p.27 / Chapter CHAPTER 4 --- REALIZATION OF THE FILTER --- p.31 / Chapter 4.1 --- Overview --- p.31 / Chapter 4.2 --- Traditional Kerwin Huelsman Newcomb (KHN) Biquad --- p.31 / Chapter 4.2.1 --- State Variable Method --- p.31 / Chapter 4.2.2 --- KHN Biquad --- p.32 / Chapter 4.3 --- Proposed Filter --- p.33 / Chapter 4.3.1 --- Biquad with CDN --- p.33 / Chapter 4.3.2 --- A dvantages of Proposed Filter --- p.36 / Chapter 4.3.3 --- Schematic of Proposed Filter --- p.38 / Chapter CHAPTER 5 --- LAYOUT CONSIDERATION --- p.41 / Chapter 5.1 --- Overview --- p.41 / Chapter 5.2 --- Process Information --- p.41 / Chapter 5.3 --- Transistor Layout Techniques --- p.42 / Chapter 5.3.1 --- Multi-finger Layout Technique --- p.42 / Chapter 5.3.2 --- Common-Centroid Structure --- p.43 / Chapter 5.3.3 --- Guard Ring --- p.45 / Chapter 5.4 --- Passive Element Layout Techniques --- p.45 / Chapter 5.5 --- Layout of Whole Design --- p.47 / Chapter CHAPTER 6 --- SIMULATION RESULT --- p.49 / Chapter 6.1 --- Operational Amplifier --- p.49 / Chapter 6.2 --- Overall Performance of filter --- p.55 / Chapter CHAPTER 7 --- MEASUREMENT RESULT --- p.60 / Chapter 7.1 --- Measurement Setup --- p.60 / Chapter 7.2 --- Time Domain Measurement --- p.62 / Chapter 7.3 --- Frequency Domain Measurement --- p.63 / Chapter 7.4 --- Measurement of Non-Linearity --- p.66 / Chapter 7.5 --- Summary of the Performance --- p.69 / Chapter 7.6 --- Comparison on Tuning Ability --- p.70 / Chapter CHAPTER 8 --- CONCLUSION --- p.71 / BIBLIOGRAPHY --- p.72
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NEW MULTIFEEDBACK TYPE ACTIVE RC FILTERSSonu, Gene Ho, 1948- January 1978 (has links)
No description available.
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Active RC synthesis using right hand plane phantom zerosGross, William Harlos, 1949- January 1973 (has links)
No description available.
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RC distributed network modelingJohnson, Stephen P. (Stephen Paul), 1952- January 1967 (has links)
No description available.
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Equivalent circuit for transient conduction and convection systemsAbdelnabi, Bassam Sabry Mohammad, January 2007 (has links) (PDF)
Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 14, 2007) Includes bibliographical references.
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GAIN BANDWIDTH EFFECTS AND COMPENSATION IN TWO ACTIVE RC FILTERS.Chaille, John Sheridan. January 1983 (has links)
No description available.
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High power carbon-based supercapacitorsWade, Timothy Lawrence January 2006 (has links) (PDF)
Energy storage devices are generally evaluated on two main requirements; power and energy. In supercapacitors these two performance criteria are altered by the capacitance, resistance and voltage. (For complete abstract open document)
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A Simple Multicoupled Band-Pass Active Filter Design Employs Resistor Summing to Provide MulticouplingChatila, Talal K. 01 January 1978 (has links) (PDF)
The purpose of this paper is to present a new design configuration for multicoupled band-pass filter based on Mason's loop rule, together with the negative feedback topology and to compare it with Leap Frog (LF) and Follow the Leader Feedback (FLF) design examples.
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Short-horizon Prediction of Indoor Temperature using Low-Order Thermal Networks : A case study of thermal models for heat-system control applications / Kortsiktig Modellering av Inomhustemperatur med Termiska Nätverk : En fallstudie av termiska modeller för kontrollapplikationerCederberg, Jonas January 2023 (has links)
Optimizing and controlling the heating systems in buildings is one way to decrease their load on the power grid, as well as introduce load flexibility to be used in Demand Response (DR) applications. A requirement in occupied buildings is that the thermal comfort of the residents is guaranteed, making the optimization of heating systems a constrained problem with respect to indoor temperature. Thermal models capable of predicting indoor temperatures over short (24 hour) horizons are one way to guarantee this comfort. The accuracy and computational complexity of these models have the most significant impact on controller performance. The data requirements and the expert knowledge required for model implementation are also important factors, since they determine the development costs and, finally, whether a model is feasible to implement. First a literature study explores current modeling approaches that depend only on time-series sensor data and that are suited for control applications. A modeling type found to be fit for such purposes are grey-box models, specifically physically inspired inverse models whose parameters are estimated based on data, such as Resistance- Capacitance (RC) models. This modeling of a dynamical system approach uses prior information in the form of the assumed physical equations and has the potential to increase the performance on sparse data problems. The simple form of the model also has a low level of complexity, making it well suited for control applications. However, expert knowledge can be needed for choosing the model equations as well as initializing the parameters. Then the effects of varying RC model complexity, parameter initialization, and training data are investigated in the case study. The chosen models are 1R1C, 2R2C, and 3R2C. They are fitted using the Nelder-Mead algorithm and validated using the data collected from the RISE Research Villa. Parameter initializations are varied by two orders of magnitude and then fitted on different data sequences to avoid relying on expert knowledge in model creation. The initializations that converged with the best R2 training fit on all sequences were deemed reasonable initializations for the problem and used in the training length comparison. The training length of the models varies from 24 to 384 hours. The results showed that increased training data length correlates positively with performance up to 192 hours for all models, but further increasing it gave inconclusive results. The higher order models evaluated struggled to beat the simplest model or even the constant prediction baseline in Mean Absolute Error (MAE) performance at all training lengths, indicating either that the models selected are unsuitable or that the data features chosen are unrepresentative of the indoor temperature dynamics. Regardless, the MAE errors presented here are comparable to the outcomes of related works. This is possibly an artifact of this dataset having a low variance in temperature and thus resulting in lower errors, which underlines the importance of the data used in case-studies. / Att optimera och styra värmesystemen i byggnader är ett sätt att minska belastningen på elnätet och införa flexibilitet i belastningen som kan användas i tillämpningar för efterfrågeflexibilitet (Demand Response, DR). Ett krav i bebodda byggnader är att de boendes termiska komfort garanteras, vilket gör optimeringen av värmesystemen till ett begränsat problem med avseende på inomhustemperaturen. Termiska modeller som kan förutsäga inomhustemperaturer på kort sikt (24 timmar) är ett sätt att garantera denna komfort. Dessa modellers noggrannhet och beräkningskomplexitet har störst inverkan på styrningens prestanda. Datakraven och den expertkunskap som krävs för att genomföra modellen är också viktiga faktorer, eftersom de avgör utvecklingskostnaderna och slutligen om en modell är möjlig att implementera. Först görs en litteraturstudie av nuvarande modelleringsmetoder som endast är beroende av tidsserier av sensordata och som lämpar sig för reglertillämpningar. En modelleringstyp som visat sig vara lämplig för sådana ändamål är grey-box-modeller, särskilt fysikaliskt inspirerade inversa modeller vars parametrar estimeras på basis av data, t.ex. RC-modeller (Resistance-Capacitance). Denna modell av ett dynamiskt system modellering använder förhandsinformation i form av de antagna fysiska ekvationerna och har potential att öka prestandan vid problem med begränsad data. Modellens enkla form har också en låg komplexitetsnivå, vilket gör den väl lämpad för kontrolltillämpningar. Expertkunskap kan dock behövas för att välja modellekvationer och initiera parametrarna. Därefter undersöks effekterna av att variera RC-modellens komplexitet, parameterinitialisering och träningsdata i fallstudien. De valda modellerna är 1R1C, 2R2C och 3R2C. De tränas med hjälp av Nelder-Mead-algoritmen och valideras med hjälp av data som samlats in från RISE Research Villa. Initialiseringarna av parametrarna varieras med två storleksordningar och anpassas sedan på olika dataserier för att undvika att förlita sig på expertkunskap vid skapandet av modellerna. De initialiseringar som konvergerade med den bästa träningsanpassningen R2 på alla sekvenser ansågs vara rimliga initialiseringar för problemet och användes i jämförelsen av träningslängden. Modellernas träningslängd varierar mellan 24 och 384 timmar. Resultaten visade att en ökad längd på träningsdata korrelerar positivt med prestanda upp till 192 timmar för alla modeller, men att ytterligare ökning inte ger några entydiga resultat. De utvärderade modellerna av högre ordning hade svårt att överträffa den enklaste modellen eller till och med referensmodellen med konstant prediktion i fråga om genomsnittligt absolut fel (MAE) vid alla träningslängder, vilket tyder antingen på att de valda modellerna är olämpliga eller att de valda datafunktionerna inte är representativa för inomhustemperaturens dynamik. Oavsett detta är de MAE-fel som presenteras här jämförbara med resultaten från relaterade studier. Detta är möjligen en artefakt av att detta dataset har en låg varians i temperaturen och därmed resulterar i lägre fel, vilket understryker vikten av de data som används i fallstudier.
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