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Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR) Technique in EIT: A Simulation Study

inherent ill-posedness of the system, direct analytical methods fail to get the unique solution of this problem and hence a minimization algorithm [ 39 , 40 , 41 ] is found as the best way to obtain its approximate solution. In the minimization algorithm, an objective function, formed by the difference between the experimental or measured data (V m ) and the model predicted data (V c ), is minimized by the Gauss-Newton method [ 39 , 40 , 41 , 42 , 43 ] to find the approximate solution. Conductivity reconstruction in EIT is a nonlinear, highly ill-posed [ 39 , 40

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Studies in Rheoencephalography (REG)

ongoing and continuous within the scientific community. Internet-based data search systems today trace REG literature back for decades. These articles illustrate the wide spectrum of REG research; however, a literature review is beyond the scope of this article. A search in Medline/Pubmed using the keyword ‘rheoencephalography’ produced 297 hits including three reviews and three free full tests; a Google search resulted in 21, 200 hits (December 27, 2009). REG is included in a recent international book-length publication [ 3 ] and is mentioned as a potential method for

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Detection and elimination of signal errors due to unintentional movements in biomedical magnetic induction tomography spectroscopy (MITS)

Introduction Magnetic induction tomography spectroscopy (MITS), the combination of magnetic induction tomography (MIT) [ 1 , 2 , 3 ] and magnetic induction spectroscopy (MIS) [ 4 , 5 ], is a contactless, non-invasive near-field imaging modality aiming at the reconstruction of the passive electromagnetic properties (PEP) of different materials. MITS requires time-harmonic excitation (primary) magnetic fields to be coupled from a transmitting coil array to the material under investigation. As a direct consequence, eddy currents will be induced in the

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Estimating electrical properties and the thickness of skin with electrical impedance spectroscopy: Mathematical analysis and measurements

and its properties in vivo due to the diverse functions of the skin and the factors affecting the skin condition. To date, a range of different techniques – confocal Raman spectrometer [ 1 ], optical coherence tomography [ 2 ], reflectance confocal microscopy [ 3 ], ultrasound imaging [ 4 ], biopsy [ 5 ] and transepidermal water loss in combination with stratum corneum stripping [ 6 , 7 ] – have been employed to determine the thickness of skin with findings that often vary from one method to another. The situation is exacerbated when attempts are made to measure

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