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1. |
Record Nr. |
UNINA9910139416203321 |
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Titolo |
Epigenetic targets in drug discovery |
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Pubbl/distr/stampa |
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[Place of publication not identified], : Wiley VCH, 2009 |
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ISBN |
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Collana |
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Methods and principles in medicinal chemistry Epigenetic targets in drug discovery |
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Disciplina |
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Soggetti |
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Pharmacogenomics |
Epigenesis, Genetic |
Drug Discovery - methods |
Pharmacogenetics - methods |
Health & Biological Sciences |
Pharmacy, Therapeutics, & Pharmacology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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2. |
Record Nr. |
UNINA9910143551903321 |
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Autore |
Shenoi B. A |
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Titolo |
Introduction to Digital Signal Processing and Filter Design [[electronic resource]] |
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Pubbl/distr/stampa |
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ISBN |
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1-280-23885-2 |
9786610238859 |
0-470-24257-4 |
0-471-65637-2 |
0-471-65638-0 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (441 p.) |
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Disciplina |
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Soggetti |
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Design and construction |
Digital techniques |
Discrete-time systems |
Electric filters, Digital |
Electric filters, Digital - Design and construction |
Microorganisms |
Signal processing - Digital techniques |
Signal processing |
Discrete-time systems - Design and construction |
Electrical & Computer Engineering |
Engineering & Applied Sciences |
Telecommunications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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INTRODUCTION TO DIGITAL SIGNAL PROCESSING AND FILTER DESIGN; CONTENTS; Preface; 1 Introduction; 1.1 Introduction; 1.2 Applications of DSP; 1.3 Discrete-Time Signals; 1.3.1 Modeling and Properties of Discrete-Time Signals; 1.3.2 Unit Pulse Function; 1.3.3 Constant Sequence; 1.3.4 Unit Step Function; 1.3.5 Real Exponential Function; 1.3.6 Complex Exponential Function; 1.3.7 Properties of cos(w(0)n); 1.4 |
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History of Filter Design; 1.5 Analog and Digital Signal Processing; 1.5.1 Operation of a Mobile Phone Network; 1.6 Summary; Problems; References; 2 Time-Domain Analysis and z Transform |
2.1 A Linear, Time-Invariant System2.1.1 Models of the Discrete-Time System; 2.1.2 Recursive Algorithm; 2.1.3 Convolution Sum; 2.2 z Transform Theory; 2.2.1 Definition; 2.2.2 Zero Input and Zero State Response; 2.2.3 Linearity of the System; 2.2.4 Time-Invariant System; 2.3 Using z Transform to Solve Difference Equations; 2.3.1 More Applications of z Transform; 2.3.2 Natural Response and Forced Response; 2.4 Solving Difference Equations Using the Classical Method; 2.4.1 Transient Response and Steady-State Response; 2.5 z Transform Method Revisited; 2.6 Convolution Revisited |
2.7 A Model from Other Models2.7.1 Review of Model Generation; 2.8 Stability; 2.8.1 Jury-Marden Test; 2.9 Solution Using MATLAB Functions; 2.10 Summary; Problems; References; 3 Frequency-Domain Analysis; 3.1 Introduction; 3.2 Theory of Sampling; 3.2.1 Sampling of Bandpass Signals; 3.3 DTFT and IDTFT; 3.3.1 Time-Domain Analysis of Noncausal Inputs; 3.3.2 Time-Shifting Property; 3.3.3 Frequency-Shifting Property; 3.3.4 Time Reversal Property; 3.4 DTFT of Unit Step Sequence; 3.4.1 Differentiation Property; 3.4.2 Multiplication Property; 3.4.3 Conjugation Property; 3.4.4 Symmetry Property |
3.5 Use of MATLAB to Compute DTFT3.6 DTFS and DFT; 3.6.1 Introduction; 3.6.2 Discrete-Time Fourier Series; 3.6.3 Discrete Fourier Transform; 3.6.4 Reconstruction of DTFT from DFT; 3.6.5 Properties of DTFS and DFT; 3.7 Fast Fourier Transform; 3.8 Use of MATLAB to Compute DFT and IDFT; 3.9 Summary; Problems; References; 4 Infinite Impulse Response Filters; 4.1 Introduction; 4.2 Magnitude Approximation of Analog Filters; 4.2.1 Maximally Flat and Butterworth Approximation; 4.2.2 Design Theory of Butterworth Lowpass Filters; 4.2.3 Chebyshev I Approximation |
4.2.4 Properties of Chebyshev Polynomials4.2.5 Design Theory of Chebyshev I Lowpass Filters; 4.2.6 Chebyshev II Approximation; 4.2.7 Design of Chebyshev II Lowpass Filters; 4.2.8 Elliptic Function Approximation; 4.3 Analog Frequency Transformations; 4.3.1 Highpass Filter; 4.3.2 Bandpass Filter; 4.3.3 Bandstop Filter; 4.4 Digital Filters; 4.5 Impulse-Invariant Transformation; 4.6 Bilinear Transformation; 4.7 Digital Spectral Transformation; 4.8 Allpass Filters; 4.9 IIR Filter Design Using MATLAB; 4.10 Yule-Walker Approximation; 4.11 Summary; Problems; References |
5 Finite Impulse Response Filters |
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Sommario/riassunto |
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A practical and accessible guide to understanding digital signal processingIntroduction to Digital Signal Processing and Filter Design was developed and fine-tuned from the author's twenty-five years of experience teaching classes in digital signal processing. Following a step-by-step approach, students and professionals quickly master the fundamental concepts and applications of discrete-time signals and systems as well as the synthesis of these systems to meet specifications in the time and frequency domains. Striking the right balance between mathematical derivations and theory, the |
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3. |
Record Nr. |
UNINA9910137536603321 |
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Autore |
Riikka Mottonen |
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Titolo |
Multisensory and sensorimotor interactions in speech perception / / edited by Kaisa Tiippana, Jean-Luc Schwartz and Riikka Möttönen |
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Pubbl/distr/stampa |
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Frontiers Media SA, 2015 |
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France : , : Frontiers Media SA, , 2015 |
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ISBN |
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Descrizione fisica |
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1 online resource (263 pages) : illustrations; digital file(s) |
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Collana |
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Frontiers Research Topics |
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Soggetti |
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Philology & Linguistics |
Languages & Literatures |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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Speech is multisensory since it is perceived through several senses. Audition is the most important one as speech is mostly heard. The role of vision has long been acknowledged since many articulatory gestures can be seen on the talker's face. Sometimes speech can even be felt by touching the face. The best-known multisensory illusion is the McGurk effect, where incongruent visual articulation changes the auditory percept. The interest in the McGurk effect arises from a major general question in multisensory research: How is information from different senses combined? Despite decades of research, a conclusive explanation for the illusion remains elusive. This is a good demonstration of the challenges in the study of multisensory integration. Speech is special in many ways. It is the main means of human communication, and a manifestation of a unique language system. It is a signal with which all humans have a lot of experience. We are exposed to it from birth, and learn it through development in face-to-face contact with others. It is a signal that we can both perceive and produce. The role of the motor system in speech perception has been debated for a long time. Despite very active current research, it is still unclear to which extent, and in which role, the motor system is involved in speech perception. Recent evidence shows that brain areas |
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involved in speech production are activated during listening to speech and watching a talker's articulatory gestures. Speaking involves coordination of articulatory movements and monitoring their auditory and somatosensory consequences. How do auditory, visual, somatosensory, and motor brain areas interact during speech perception? How do these sensorimotor interactions contribute to speech perception? It is surprising that despite a vast amount of research, the secrets of speech perception have not yet been solved. The multisensory and sensorimotor approaches provide new opportunities in solving them. Contributions to the research topic are encouraged for a wide spectrum of research on speech perception in multisensory and sensorimotor contexts, including novel experimental findings ranging from psychophysics to brain imaging, theories and models, reviews and opinions. |
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