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This book focuses on neuro-engineering and neural computing, a
multi-disciplinary field of research attracting considerable
attention from engineers, neuroscientists, microbiologists and
material scientists. It explores a range of topics concerning the
design and development of innovative neural and brain interfacing
technologies, as well as novel information acquisition and
processing algorithms to make sense of the acquired data. The book
also highlights emerging trends and advances regarding the
applications of neuro-engineering in real-world scenarios, such as
neural prostheses, diagnosis of neural degenerative diseases, deep
brain stimulation, biosensors, real neural network-inspired
artificial neural networks (ANNs) and the predictive modeling of
information flows in neuronal networks. The book is broadly divided
into three main sections including: current trends in technological
developments, neural computation techniques to make sense of the
neural behavioral data, and application of these
technologies/techniques in the medical domain in the treatment of
neural disorders.
This book focuses on neuro-engineering and neural computing, a
multi-disciplinary field of research attracting considerable
attention from engineers, neuroscientists, microbiologists and
material scientists. It explores a range of topics concerning the
design and development of innovative neural and brain interfacing
technologies, as well as novel information acquisition and
processing algorithms to make sense of the acquired data. The book
also highlights emerging trends and advances regarding the
applications of neuro-engineering in real-world scenarios, such as
neural prostheses, diagnosis of neural degenerative diseases, deep
brain stimulation, biosensors, real neural network-inspired
artificial neural networks (ANNs) and the predictive modeling of
information flows in neuronal networks. The book is broadly divided
into three main sections including: current trends in technological
developments, neural computation techniques to make sense of the
neural behavioral data, and application of these
technologies/techniques in the medical domain in the treatment of
neural disorders.
Multiwavelets are wavelets with multiplicity r, that is r scaling
functions and r wavelets, which define multiresolution analysis
similar to scalar wavelets. They are advantageous over scalar
wavelets since they simultaneously posse symmetry and
orthogonality. In this work, a new method for constructing
multiwavelets with any approximation order is presented. The method
involves the derivation of a matrix equation for the desired
approximation order. The condition for approximation order is
similar to the conditions in the scalar case. Generalized left
eigenvectors give the combinations of scaling functions required to
reconstruct the desired spline or super function. The method is
demonstrated by constructing a specific class of symmetric and
non-symmetric multiwavelets with different approximation orders,
which include Geranimo-Hardin-Massopust (GHM), Daubechies and
Alperts like multi-wavelets, as parameterized solutions. All
multi-wavelets constructed in this work, posses the good properties
of orthogonality, approximation order and short support.
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