Abstract: In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished though the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.
Abstract: Retrieval of the surface reflectance is important in the
remotely sensed data analysis to obtain the atmospheric reflectance or
atmospheric correction. The relationship between visible and mid
infrared reflectance over land was investigated and developed in this
study. The surface reflectances of the two visible bands were
measured using a handheld spectroradiometer collected around
Penang Island. In this study, we use the assumption that the 2.1 μm
band is not affected by aerosol and it is transparent to most aerosol
types (except dust). Therefore the satellite observed signal is the
same as the surface signal in 2.1 μm band. The correlation between
the surface reflectance measured by the spectroradiometer in the blue
and red region and the 2.1 μm observed by the satellite has been
established. We investigate five dates of Landsat TM scenes in this
study. The finding obtained by this study indicates that the surface
reflectance can be retrieved from the 2.1 μm band.
Abstract: This article discusses the problem of estimating the
orientation of inclined ground on which a human subject stands based
on information provided by the vestibular system consisting of the
otolith and semicircular canals. It is assumed that body segments are
not necessarily aligned and thus forming an open kinematic chain.
The semicircular canals analogues to a technical gyrometer provide a
measure of the angular velocity whereas the otolith analogues to a
technical accelerometer provide a measure of the translational
acceleration. Two solutions are proposed and discussed. The first is
based on a stand-alone Kalman filter that optimally fuses the two
measurements based on their dynamic characteristics and their noise
properties. In this case, no body dynamic model is needed. In the
second solution, a central extended disturbance observer that
incorporates a body dynamic model (internal model) is employed.
The merits of both solutions are discussed and demonstrated by
experimental and simulation results.
Abstract: This paper proposes a dual tree complex wavelet transform (DT-CWT) based directional interpolation scheme for noisy images. The problems of denoising and interpolation are modelled as to estimate the noiseless and missing samples under the same framework of optimal estimation. Initially, DT-CWT is used to decompose an input low-resolution noisy image into low and high frequency subbands. The high-frequency subband images are interpolated by linear minimum mean square estimation (LMMSE) based interpolation, which preserves the edges of the interpolated images. For each noisy LR image sample, we compute multiple estimates of it along different directions and then fuse those directional estimates for a more accurate denoised LR image. The estimation parameters calculated in the denoising processing can be readily used to interpolate the missing samples. The inverse DT-CWT is applied on the denoised input and interpolated high frequency subband images to obtain the high resolution image. Compared with the conventional schemes that perform denoising and interpolation in tandem, the proposed DT-CWT based noisy image interpolation method can reduce many noise-caused interpolation artifacts and preserve well the image edge structures. The visual and quantitative results show that the proposed technique outperforms many of the existing denoising and interpolation methods.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.