Tools for signal compression / / Nicolas Moreau

• Record Nr.: UNINA9910819986703321
• Autore: Moreau Nicolas <1945->
• Pubbl/distr/stampa: London, : ISTE; Hoboken, N.J., : John Wiley & Sons, c2011
• ISBN: 9781118616611; 1118616618; 9781118616628; 1118616626; 9781299315228; 1299315224; 9781118616550; 1118616553
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (216 p.)
• Collana: ISTE
• Disciplina: 621.389/3
• Soggetti: Sound - Recording and reproducing; Data compression (Telecommunication); Speech processing systems
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Tools for Signal Compression; Title Page; Copyright Page; Table of Contents; Introduction; PART 1. TOOLS FOR SIGNAL COMPRESSION; Chapter 1. Scalar Quantization; 1.1. Introduction; 1.2. Optimumscalar quantization; 1.2.1. Necessary conditions for optimization; 1.2.2. Quantization error power; 1.2.3. Further information; 1.2.3.1. Lloyd-Max algorithm; 1.2.3.2. Non-linear transformation; 1.2.3.3. Scale factor; 1.3. Predictive scalar quantization; 1.3.1. Principle; 1.3.2. Reminders on the theory of linear prediction; 1.3.2.1. Introduction: least squares minimization; 1.3.2.2. Theoretical approach1.3.2.3. Comparing the two approaches; 1.3.2.4. Whitening filter; 1.3.2.5. Levinson algorithm; 1.3.3. Prediction gain; 1.3.3.1. Definition; 1.3.4. Asymptotic value of the prediction gain; 1.3.5. Closed-loop predictive scalar quantization; Chapter 2. Vector ; Quantization; 2.1. Introduction; 2.2. Rationale; 2.3. Optimum codebook generation; 2.4. Optimum quantizer performance; 2.5. Using the quantizer; 2.5.1. Tree-structured vector quantization; 2.5.2. Cartesian product vector quantization; 2.5.3. Gain-shape vector quantization; 2.5.4. Multistage vector quantization; 2.5.5. Vector quantization by transform2.5.6. Algebraic vector quantization; 2.6. Gain-shape vector quantization; 2.6.1. Nearest neighbor rule; 2.6.2. Lloyd-Max algorithm; Chapter 3. Sub-band Transform Coding; 3.1. Introduction; 3.2. Equivalence of filter banks and transforms; 3.3. Bit allocation; 3.3.1. Defining the problem; 3.3.2. Optimum bit allocation; 3.3.3. Practical algorithm; 3.3.4. Further information; 3.4. Optimum transform; 3.5. Performance; 3.5.1. Transform gain; 3.5.2. Simulation results; Chapter 4. Entropy Coding; 4.1. Introduction; 4.2. Noiseless coding of discrete, memoryless sources4.2.1. Entropy of a source; 4.2.2. Coding a source; 4.2.2.1. Definitions; 4.2.2.2. Uniquely decodable instantaneous code; 4.2.2.3. Kraft inequality; 4.2.2.4. Optimal code; 4.2.3. Theorem of noiseless coding of a memoryless discrete source; 4.2.3.1. Proposition 1; 4.2.3.2. Proposition 2; 4.2.3.3. Proposition 3; 4.2.3.4. Theorem; 4.2.4. Constructing a code; 4.2.4.1. Shannon code; 4.2.4.2. Huffman algorithm; 4.2.4.3. Example 1; 4.2.5. Generalization; 4.2.5.1. Theorem; 4.2.5.2. Example 2; 4.2.6. Arithmetic coding; 4.3. Noiseless coding of a discrete source with memory4.3.1. New definitions; 4.3.2. Theorem of noiseless coding of a discrete source with memory; 4.3.3. Example of a Markov source; 4.3.3.1. General details; 4.3.3.2. Example of transmitting documents by fax; 4.4. Scalar quantizer with entropy constraint; 4.4.1. Introduction; 4.4.2. Lloyd-Max quantizer; 4.4.3. Quantizer with entropy constraint; 4.4.3.1. Expression for the entropy; 4.4.3.2. Jensen inequality; 4.4.3.3. Optimum quantizer; 4.4.3.4. Gaussian source; 4.5. Capacity of a discrete memoryless channel; 4.5.1. Introduction; 4.5.2. Mutual information
• Sommario/riassunto: This book presents tools and algorithms required to compress/uncompress signals such as speech and music. These algorithms are largely used in mobile phones, DVD players, HDTV sets, etc. In a first rather theoretical part, this book presents the standard tools used in compression systems: scalar and vector quantization, predictive quantization, transform quantization, entropy coding. In particular we show the consistency between these different tools. The second part explains how these tools are used in the latest speech and audio coders. The third part gives Matlab programs simulating t

The Science of Sugar Confectionery / / William P. Edwards

• Record Nr.: UNINA9911026141403321
• Autore: Edwards William P.
• Pubbl/distr/stampa: London, England : , : Royal Society of Chemistry, , [2019]; ©2019
• ISBN: 9781839168529; 1839168528; 9781788015707; 1788015703
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (222 p.)
• Disciplina: 641.853
• Soggetti: Candy industry; Candy; Chewing gum
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Sommario/riassunto: Since the first edition of The Science of Sugar Confectionery (2000), the confectionery industry has responded to ever-changing consumer habits. This new edition has been thoroughly revised to reflect industry's response to market driven nutrition and dietary concerns, as well as changes in legislation, labelling, and technology. Building on the strengths of the first edition, the author's personal knowledge and experience of the sugar confectionery industry is used to provide a thorough and accessible account of the field.Written so the reader needsno more than a rudimentary level of chemistry, this book covers the basic definitions,commonly used and new ingredients in the industry. It thendiscusses the various types of sugar confectionery including"sugar glasses" (boiled sweets), "grained sugar products" (fondants), toffees and fudges, "hydrocolloids" (gums, pastilles and jellies) and concludes with a new chapter on future outlooks. Featuring expanded coverage of special dietary needs, covering topics such as vegetarianism and veganism, religious requirements and supplemented ; products, this new edition reflects current and evolving needs in the sugar confectionery field.