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Articles 1 - 5 of 5
Full-Text Articles in Signal Processing
Signal Flow Graph Approach To Efficient Dst I-Iv Algorithms, Sirani M. Perera
Signal Flow Graph Approach To Efficient Dst I-Iv Algorithms, Sirani M. Perera
Sirani Mututhanthrige Perera
In this paper, fast and efficient discrete sine transformation (DST) algorithms are presented based on the factorization of sparse, scaled orthogonal, rotation, rotation-reflection, and butterfly matrices. These algorithms are completely recursive and solely based on DST I-IV. The presented algorithms have low arithmetic cost compared to the known fast DST algorithms. Furthermore, the language of signal flow graph representation of digital structures is used to describe these efficient and recursive DST algorithms having (n1) points signal flow graph for DST-I and n points signal flow graphs for DST II-IV.
Projected Nesterov’S Proximal-Gradient Signal Recovery From Compressive Poisson Measurements, Renliang Gu, Aleksandar Dogandžić
Projected Nesterov’S Proximal-Gradient Signal Recovery From Compressive Poisson Measurements, Renliang Gu, Aleksandar Dogandžić
Aleksandar Dogandžić
We develop a projected Nesterov’s proximal-gradient (PNPG) scheme for reconstructing sparse signals from compressive Poisson-distributed measurements with the mean signal intensity that follows an affine model with known intercept. The objective function to be minimized is a sum of convex data fidelity (negative log-likelihood (NLL)) and regularization terms. We apply sparse signal regularization where the signal belongs to a nonempty closed convex set within the domain of the NLL and signal sparsity is imposed using total-variation (TV) penalty. We present analytical upper bounds on the regularization tuning constant. The proposed PNPG method employs projected Nesterov’s acceleration step, function restart, and …
Assessment Of The Impact Of Clothing And Environmental Conditions On Visible Light 3d Scanning, Pann Ajjimaporn, Jeremy Straub, Scott Kerlin
Assessment Of The Impact Of Clothing And Environmental Conditions On Visible Light 3d Scanning, Pann Ajjimaporn, Jeremy Straub, Scott Kerlin
Jeremy Straub
The quality of models produced by visible light 3D scanners is influenced by multiple factors. To max-imize model accuracy and detail levels, the correct combination of lighting texture, subject posture and software usage must be selected. The work described herein has been performed to measure the effect of different lighting and envi-ronmental conditions on human 3D scanning results.
Interactionless Calendar-Based Training For 802.11 Localization, Mark Chang, Andrew J. Barry, Noah L. Tye
Interactionless Calendar-Based Training For 802.11 Localization, Mark Chang, Andrew J. Barry, Noah L. Tye
Mark L. Chang
This paper presents our work in solving one of the weakest links in 802.11-based indoor-localization: the training of ground-truth received signal strength data. While crowdsourcing this information has been demonstrated to be a viable alternative to the time consuming and accuracy-limited process of manual training, one of the chief drawbacks is the rate at which a system can be trained. We demonstrate an approach that utilizes users' calendar and appointment information to perform interactionless training of an 802.11-based indoor localization system. Our system automatically determines if a user attended a calendar event, resulting in accuracy comparable to our previously published …
Markov Chain Monte Carlo Defect Identification In Nde Images, Aleksandar Dogandžić, Benhong Zhang
Markov Chain Monte Carlo Defect Identification In Nde Images, Aleksandar Dogandžić, Benhong Zhang
Aleksandar Dogandžić
We derive a hierarchical Bayesian method for identifying elliptically‐shaped regions with elevated signal levels in NDE images. We adopt a simple elliptical parametric model for the shape of the defect region and assume that the defect signals within this region are random following a truncated Gaussian distribution. Our truncated‐Gaussian model ensures that the signals within the defect region are higher than the baseline level corresponding to the noise‐only case. We derive a closed‐form expression for the kernel of the posterior probability distribution of the location, shape, and defect‐signal distribution parameters (model parameters). This result is then used to develop Markov …