), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.Printed on acid-free paper PrefaceReceivers have been a basic block in telecommunication systems since the invention of the radio in the late 19th century, acquiring an essential role in what has been called the third Communication Revolution where information is transferred via controlled waves and electronic signals. Their main function is to recover the information from the transmitted wave and convert it to electronic signals that can be understood by the succeeding electronic processing signal systems. Since the Internet revolution, new receivers appeared to connect computers one to another or to the World Wide Web, such as wireless systems, have been gaining more and more popularity over the last few years. Thus, great investments in time, effort and money from both academia and industry have been made in the development of these receivers in order to achieve fully integrated solutions in form of ASICs meeting the demand for ever increasing high performance with low cost, low voltage supply, low power consumption and reduced surface area.The design of one of these receivers include different blocks such as filters, low noise amplifiers, gain controlled amplifiers, mixers and analog to digital converters. This book is precisely focused on the analysis and design of automatic gain control, AGC, circuits with wireless receivers as the main target application. In this context, the general function of the AGC circuitry is to automatically adjust the output signal of a variable gain amplifier to an optimal rated level, for different input signal strengths. This function is essential to guarantee that the system dynamic range is neither saturated with large signals nor makes the system fall below a tolerable noise level.Specifically, some wireless applications, such as WLAN or Bluetooth, must be able to handle packets-based data transmission and orthogonal frequency division multiplexing which introduce stringent settling-time constraints. Thus, fast AGCs are primordial in those systems. It is under these conditions that feedforward AGCs present their greatest advantages as an alternative to conventional feedback AGCs. Thus, all through this book we offer a detailed study about feedforward AGCs design-both at basic AGC cells and system level-, their main characteristics and performances. vi viThe starting point is a complete review and theoretical analysis of both feedforward and feedback configurations and their behavioural modelling, issues addressed in Chap. 2.Next, basic components in gain control func...

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An integrated machine-learning based adaptive circuit for sensor calibration implemented in standard 0.18µm CMOS technology with 1.8V power supply is presented in this paper. In addition to linearizing the device response, the proposed system is also capable to correct offset and gain errors. The building blocks conforming the adaptive system are designed and experimentally characterized to generate numerical high-level models which are used to verify the proper performance of each analog block within a defined multilayer perceptron architecture. The network weights, obtained from the learning phase, are stored in a microcontroller EEPROM memory, and then loaded into each of the registers of the proposed integrated prototype. In order to verify the proposed system performance, the non-linear characteristic of a thermistor is compensated as an application example, achieving a relative error e r below 3% within an input span of 130 • C, which is almost 6 times less than the uncorrected response. The power consumption of the whole system is 1.4mW and it has an active area of 0.86mm 2 . The digital programmability of the network weights provides flexibility when a sensor change is required.INDEX TERMS Adaptive signal processing, artificial neural networks, CMOS, sensor conditioning.

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Resumen. En este artículo se presenta el diseño electrónico en tecnología CMOS estándar de 0.18µm con alimentación VDD = 1,8V , de los principales bloques que conforman una neurona: el multiplicador y la función de activación no-lineal. De igual forma, se presentan los resultados por simulación eléctrica en CADENCE, así como el modelado matemático en MATLAB de su comportamiento. Una comparación de ambos modelos presenta errores relativos er < 1 % para las dos operaciones. Para su validación, los modelos matemáticos generados fueron aplicados a una estructura de red neuronal entrenada para resolver la operación lógica XOR.Abstract. The electronic design of the artificial neuron main characteristic blocks is presented in this paper. Both, the multiplier and the non-linear activation function, were designed in standard 0,18µm CMOS process with 1,8V power supply. CADENCE electrical simulation results and mathematical modeling in MATLAB are also presented. A comparison between the high level and electrical models shows a relative error below 1 % for both operations. In order to verify the correct operation, the generated models were applied to a trained neural network structure to solve the XOR logical operation.