The Phase-Locked Loop (PLL), and many of the devices used for frequency and phase tracking, carrier and symbol synchronization, demodulation, and frequency synthesis, are fundamental building blocks in today's complex communications systems. It is therefore essential for both students and practicing communications engineers interested in the design and implementation of modern communication systems to understand and have insight into the behavior of these important and ubiquitous devices. Since the PLL behaves as a nonlinear device (at least during acquisition), computer simulation can be used to great advantage in gaining insight into the behavior of the PLL and the devices derived from the PLL. The purpose of this Synthesis Lecture is to provide basic theoretical analyses of the PLL and devices derived from the PLL and simulation models suitable for supplementing undergraduate and graduate courses in communications. The Synthesis Lecture is also suitable for self study by practicing engineers. A significant component of this book is a set of basic MATLAB-based simulations that illustrate the operating characteristics of PLL-based devices and enable the reader to investigate the impact of varying system parameters. Rather than providing a comprehensive treatment of the underlying theory of phase-locked loops, theoretical analyses are provided in sufficient detail in order to explain how simulations are developed. The references point to sources currently available that treat this subject in considerable technical depth and are suitable for additional study. Download MATLAB codes (.zip) Table of Contents: Introduction / Basic PLL Theory / Structures Developed From The Basic PLL / Simulation Models / MATLAB Simulations / Noise Performance Analysis
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
This book provides a comprehensive tutorial to signal classification, an important signal processing technique that enables cognitive radios to identify the kind of users present in a frequency band, helping a radio to characterize the environment in which it is operating. Engineers involved in the design and implementation of cognitive radio receivers will benefit from this book’s coverage of a newly developed, multiuser signal classification algorithm, as well as techniques for design of other components to work in conjunction with the signal classifier, such as blind equalizers, blind channel estimators, and cognitive engines.
The Phase-Locked Loop (PLL), and many of the devices used for frequency and phase tracking, carrier and symbol synchronization, demodulation, and frequency synthesis, are fundamental building blocks in today's complex communications systems. It is therefore essential for both students and practicing communications engineers interested in the design and implementation of modern communication systems to understand and have insight into the behavior of these important and ubiquitous devices. Since the PLL behaves as a nonlinear device (at least during acquisition), computer simulation can be used to great advantage in gaining insight into the behavior of the PLL and the devices derived from the PLL. The purpose of this Synthesis Lecture is to provide basic theoretical analyses of the PLL and devices derived from the PLL and simulation models suitable for supplementing undergraduate and graduate courses in communications. The Synthesis Lecture is also suitable for self study by practicing engineers. A significant component of this book is a set of basic MATLAB-based simulations that illustrate the operating characteristics of PLL-based devices and enable the reader to investigate the impact of varying system parameters. Rather than providing a comprehensive treatment of the underlying theory of phase-locked loops, theoretical analyses are provided in sufficient detail in order to explain how simulations are developed. The references point to sources currently available that treat this subject in considerable technical depth and are suitable for additional study. Download MATLAB codes (.zip) Table of Contents: Introduction / Basic PLL Theory / Structures Developed From The Basic PLL / Simulation Models / MATLAB Simulations / Noise Performance Analysis
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
Beyond Debit & Credit: The Untold Story of Indian Bankers Do you know what it meant when bankers said “Chinese cuisine”, “Punjabi food”, or “Gujarati thali”? An RBI governor let down his hair at his farewell dinner to swing to “Lungi Dance” from the Shah Rukh Khan-starrer Chennai Express. The chairman of a financial institution relied on “signals” from the idol of a goddess kept in his cabin for clearing loan proposals. A public sector bank chairman liked to munch hot chilies with his lunch. A kitchen help was deputed to mop up the sweat from his bald head! Roller Coaster is a string of such stories and revelations from the country’s foremost banking journalist's affair with the industry—even though banks were not ideal partners for such liaisons. He has seen the industry and dramatis personae grow over two and a half decades, first as a rookie reporter, then as an editor and a columnist, and, finally, as an author. The book brings to light the lives of India’s commercial and central bankers. But it does not discuss their successes, failures, or the ever-evolving dynamics of monetary and fiscal policies. It's about their persona, warts and all—how they are as leaders, how they evolved, and how they changed the culture and ethos of the Indian banking sector. Dive in for inside information about some of the biggest names associated with Indian banking—Uday Kotak, Sandeep Bakhshi, Amitabh Chaudhry, V. Vaidyanathan, as well as C. Rangarajan, Bimal Jalan, Y. V. Reddy, D. Subbarao, Raghuram Rajan, Urjit Patel, Shaktikanta Das, and many more.
For the past 25 years, Tamal Bandyopadhyay has been a keen student of Indian banking. A lifelong reporter and journalist, he is an award-winning national business columnist and a bestselling author. He is widely recognised for ‘Banker’s Trust’, a weekly column whose unerring ability to anticipate and dissect major policy decisions in India’s banking and finance has earned him a large print and digital audience around the world. The column won Tamal the Ramnath Goenka Award for Excellence in Journalism (commentary and interpretative writing) for 2017. Banker’s Trust now appears in Business Standard, where he is a Consulting Editor. Previously, Tamal has had stints with three other national business dailies in India, and was a founding member of Mint newspaper and Livemint.com. He is also a Senior Adviser to Jana Small Finance Bank Ltd. Between 2014 and 2018, as an adviser on strategy for Bandhan Bank Ltd, he had a ringside view of the first-ever transformation of a microfinance institution in India into a universal bank. Author of five other books, Tamal is widely recognised as a contributor to the Oxford Handbook of the Indian Economy and Making of New India: Transformation Under Modi Government. In 2019, LinkedIn named him as one of the ‘most influential voices in India’.
This is the story of Bandhan, the only bank that emerged in eastern India after Independence. Founded by the son of a sweet vendor, with a mere Rs 2 lakh, the sum total of his life savings. On 17 June, 2015, Chandra Shekhar Ghosh stepped out of the Reserve Bank of India building in Mumbai with the much-coveted banking licence, beating some of the country's top corporate houses. This moment compensated for all the frustrations that had come along the way. A year later, Bandhan Bank was launched with 6.7 million small borrowers. So, how did Ghosh build India's biggest MFI from scratch and then, along with his team, transform it into a universal bank? Bandhan: The Making of a Bank chronicles that journey. This is also Ghosh's personal story-of a boy growing up in small-town Agartala struggling with poverty, but relentless in his ambition to make it big. He battles competition, hostile moneylenders, a tough economic climate and the perpetual lack of resources. Nobody in India perhaps knows better than him the psyche of a small borrower and the alchemy of doing business with the poor, profitably. This is one of India's biggest entrepreneurial stories.
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