There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

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Principal component analysis

This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.

https://dl.acm.org/doi/pdf/10.1145/2785956.2787487

SpotFi: Decimeter Level Localization Using WiFi

Concrete mathematics, a foundation for computer science, by Ronald L. Graham, Donald E. Knuth and Oren Patashnik. Pp 625. £24·95. 1989. ISBN 0-201-14236-8 (Addison-Wesley)

http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2011/Schuller11-RRE.pdf

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

This correspondence is concerned with source localization and classification for scenarios where both the far-field and near-field narrowband sources may coexist. We propose an efficient MUSIC-based solution that requires neither a multidimensional search nor high-order statistics (HOS). We also derive the stochastic Cramer-Rao bound (CRB) for the problem under consideration. The performance of the proposed method is compared with an existing method and with the CRB.

Efficient Application of MUSIC Algorithm Under the Coexistence of Far-Field and Near-Field Sources

The speckle noise complicates the human and automatic interpretation of synthetic aperture radar (SAR) images. Thus, the reduction of speckle is critical in various SAR image processing tasks. In this paper, we introduce a new spatially adaptive wavelet-based Bayesian method for despeckling the SAR images. The wavelet coefficients of the logarithmically transformed reflectance and speckle noise are modeled using the zero-location Cauchy and zero-mean Gaussian distributions, respectively. These prior distributions are then exploited to develop a Bayesian minimum mean absolute error estimator as well as a maximum a posteriori estimator. A new context-based technique with a reduced complexity is proposed for incorporating the spatial dependency of the wavelet coefficients with the Bayesian estimation processes. Experiments are carried out using typical noise-free images corrupted with simulated speckle noise as well as real SAR images, and the results show that the proposed method performs favorably in comparison to some of the existing methods in terms of the peak signal-to-noise ratio, speckle statistics and structural similarity index, and in its ability to suppress the speckle in the homogeneous regions

Spatially Adaptive Wavelet-Based Method Using the Cauchy Prior for Denoising the SAR Images

An efficient 8 times 8 sparse orthogonal transform matrix is proposed for image compression by appropriately introducing some zeros in the 8 times 8 signed discrete cosine transform (SDCT) matrix. An algorithm for its fast computation is also developed. It is shown that the proposed transform provides a 25% reduction in the number of arithmetic operations with a performance in image compression that is much superior to that of the SDCT and comparable to that of the approximated discrete cosine transform.

Low-complexity 8×8 transform for image compression

In this paper, a detailed study on the four three-moduli sets reported in the literature is carried out from the point of view of the hardware complexity and speed of their residue-to-binary (R/B) converters. First, a new formulation of the Chinese remainder theorem is proposed that reduces the size of the modulo operation. Then, the proposed formulation is applied to derive, in a simple and unified manner, R/B conversion algorithms for three of the sets. Further, using this formulation, a new algorithm along with two corresponding R/B converters for the fourth set is proposed; one of these converters is area efficient while the other is speed efficient. Next, the best R/B converter(s) for each of the sets is chosen based on the hardware complexity and/or speed. These converters are implemented for 8, 16, 32, and 64-bit dynamic ranges, using CMOS VLSI technology. Based on a post-layout performance evaluation for the VLSI implementations of the chosen converters, it is shown that in order to represent 8-, 16-, 32-, and 64-bit binary numbers, the moduli set {2/sup n/,2/sup n/+1,2/sup n/-1} provides the fastest R/B converter and requires the smallest area.

A study of the residue-to-binary converters for the three-moduli sets

The paper proposes a joint probability density function to model the video wavelet coefficients of any two neighboring frames and then applies this statistical model for denoising. The parameter of the density function that measures the correlation between the wavelet coefficients of the two frames is used as an index for the motion. The joint density function is employed for spatial filtering of the noisy wavelet coefficients by developing a bivariate maximum a posteriori estimator. A recursive time averaging of the spatially filtered wavelet coefficients is adopted for further noise reduction. Simulation results on test video sequences show an improved performance both in terms of the peak signal-to-noise ratio and the perceptual quality compared to that of the other denoising algorithms

M.O. Ahmad

Papers

Efficient Application of MUSIC Algorithm Under the Coexistence of Far-Field and Near-Field Sources

Spatially Adaptive Wavelet-Based Method Using the Cauchy Prior for Denoising the SAR Images

Low-complexity 8×8 transform for image compression

A study of the residue-to-binary converters for the three-moduli sets

Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients