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Describes the outphasing approach to amplifier linearization that uses power-combining techniques to create a linear output waveform from two nonlinear input sources, and presents recent advances in resolving the limitations caused by the strict matching requirements between the two amplifiers and the microwave power wasted in the power-combing network. The authors discuss the linearity performance of outphasing amplifier systems, correction schemes based on training vectors, and power recycling in outphasing amplifiers. Zhang is an engineer with Qualcomm, while Larson and Asbeck are affiliated with the University of California, San Diego. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com).
Nanocrystals research has been an area of significant interest lately, due to the wide variety of potential applications in semiconductor, optical and biomedical fields. This book consists of a collection of research work on nanocrystals processing and characterization of their structural, optical, electronic, magnetic and mechanical properties. Various methods for nanocrystals synthesis are discussed in the book. Size-dependent properties such as quantum confinement, superparamagnetism have been observed in semiconductor and magnetic nanoparticles. Nanocrystals incorporated into different material systems have proven to possess improved properties. A review of the exciting outcomes nanoparticles study has provided indicates further accomplishments in the near future.
Describes the outphasing approach to amplifier linearization that uses power-combining techniques to create a linear output waveform from two nonlinear input sources, and presents recent advances in resolving the limitations caused by the strict matching requirements between the two amplifiers and the microwave power wasted in the power-combing network. The authors discuss the linearity performance of outphasing amplifier systems, correction schemes based on training vectors, and power recycling in outphasing amplifiers. Zhang is an engineer with Qualcomm, while Larson and Asbeck are affiliated with the University of California, San Diego. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com).
Biometrics such as fingerprint, face, gait, iris, voice and signature, recognizes one's identity using his/her physiological or behavioral characteristics. Among these biometric signs, fingerprint has been researched the longest period of time, and shows the most promising future in real-world applications. However, because of the complex distortions among the different impressions of the same finger, fingerprint recognition is still a challenging problem. Computational Algorithms for Fingerprint Recognition presents an entire range of novel computational algorithms for fingerprint recognition. These include feature extraction, indexing, matching, classification, and performance prediction/validation methods, which have been compared with state-of-art algorithms and found to be effective and efficient on real-world data. All the algorithms have been evaluated on NIST-4 database from National Institute of Standards and Technology (NIST). Specific algorithms addressed include: -Learned template based minutiae extraction algorithm, -Triplets of minutiae based fingerprint indexing algorithm, -Genetic algorithm based fingerprint matching algorithm, -Genetic programming based feature learning algorithm for fingerprint classification, -Comparison of classification and indexing based approaches for identification, -Fundamental fingerprint matching performance prediction analysis and its validation. Computational Algorithms for Fingerprint Recognition is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.
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