Front Cover; Advances in Imaging and Electron Physics, Volume 109; Copyright Page; Contents; Contributors; Preface; Forthcoming Contributors; Chapter 1. Development and Applications of a New Deep Level Transient Spectroscopy Method and New Averaging Techniques; I. Introduction; II. Review of the Deep-Level Transient Spectroscopy Method; III. Averaging and Recording of Digital DLTS Transient Signals; IV. Feedback Circuits and Experimental Setup for CC-DLTS and CR-DLTS; V. Constant-Resistance DLTS in Enhancement Mode MOSFETs; VI. Constant-Resistance DLTS in Depletion Mode MOSFETs

VII. Constant-Resistance DLTS in Junction Field-Effect TransistorsVIII. Conclusions and Areas for Future Research; References; Appendix A: Magnitude Errors; Appendix B: Time Constant Errors; Appendix C: Noise Sources and Signal-to-Noise Ratio (SNR) in the DLTS Transients; Appendix D: Electrical Circuit of the Pseudo-Logarithmic Generator; Appendix E: Electrical Circuits of the Feedback Circuit; Appendix F: Listing of a Template for a DLTS Measurement Program; Appendix G: Listing of a Template for a DLTS Analysis Program; Appendix H: Radiation-Induced Defects in Silicon; List of Acronyms

List of SymbolsChapter 2. Complex Dyadic Multiresolution Analyses; I. Introduction; II. The Spline Example; III. Multiresolution and Wavelet; IV. Daubechies' Wavelets; V. Symmetric Daubechies Wavelets; VI. The Phase of SDW Scaling Function; VII. The Mallat Algorithm with Complex Filters; VIII. Restoration from the Phase; IX. lmage Enhancement; X. Complex Shrinkage; XI. Conclusion; References; Reading List; Chapter 3. Lattice Vector Quantization for Wavelet-Based Image Coding; I. Introduction; II. Quantization of Wavelet Coefficients; III. Lattice Quantization Fundamentals; IV. Lattices

V. Quantization Algorithms for Selected LatticesVI. Counting the Lattice Points; VII. Scaling Algorithm; VIII. Selecting a Lattice for Quantization; IX. Entropy Coding of Lattice Vectors; X. Experimental Results; XI. Conclusions; Appendix A: Cartan Matrices of Some Root Systems; References; Chapter 4. Fuzzy Cellular Neural Networks and Their Applications to Image Processing; I. Introduction; II. Fuzzy Cellular Neural Networks; III. Theory of Fuzzy Cellular Neural Networks; IV. FCNN as Computational Arrays; V. Embed Linguistic Statements into FCNN; VI. Learning Algorithms of FCNN

VII. Generic Algorithm for FCNNVIII. Applications of Discrete-Time FCNN; IX. Conclusions and Future Work; References; Index