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Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing Algorithm
Real-Time Recursive Hyperspectral Sample and Band Processing, 2017Co-Authors: Chein-i ChangAbstract:The simplex growing algorithm (SGA) developed by Chang et al. (A growing method for simplex-based endmember extraction algorithms. IEEE Transactions on Geoscience and Remote Sensing 44(10): 2804–2819, 2006b) has been used for finding endmembers and was studied in Chap. 10 of Chang (Real time progressive hyperspectral image processing: endmember finding and anomaly detection, Springer, New York, 2016). It can be considered a sequential version of the well-known N-finder endmember finding algorithm (N-FINDR) developed by Winter (Proceedings of 13th international conference on applied geologic remote sensing, Vancouver, BC, Canada, pp. 337–344, 1999a; Image spectrometry V, Proceedings of SPIE 3753, pp. 266–277, 1999b) to find endmembers one after another by growing simplexes one vertex at a time. However, one of the major hurdles for N-FINDR and SGA is the calculation of a simplex volume (SV), as discussed in Chap. 2, which poses a great challenge in designing any algorithms using a SV to find endmembers. This chapter develops an Orthogonal Projection (OP)-based approach to SGA, called OPSGA, which essentially resolves this computational issue. The idea is based on a geometric SV (GSV) from structures of simplexes. If we consider a j-vertex simplex Sj specified by j previously found endmembers as a base and the next endmember mj+1 to be found as a new vertex to be added to Sj to form a new (j + 1)-vertex simplex, Sj+1, then calculating the GSV of this new (j + 1)-vertex simplex, Sj+1, is equivalent to multiplying the GSV of Sj, which is considered a base with the OP on the base Sj from mj+1, which is considered its height. As a result, finding mj+1 to yield a Sj+1 with the maximal GSV is equivalent to finding mj+1 with the maximal OP on Sj. On this interpretation, OPSGA converts the issue of calculating a determinant-based SV (DSV) to finding OP without actually computing matrix determinants. Accordingly, OPSGA can be considered a technique for calculating a GSV by OP (GSV-OP). To further reduce the computational complexity, OPSGA is also extended to a recursive Kalman filtering-like recursive hyperspectral sample processing of OPSGA (RHSP-OPSGA), which has several advantages and benefits in terms of computational savings and hardware implementation over N-FINDR and SGA.
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recursive Orthogonal Projection based simplex growing algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Chein-i ChangAbstract:The simplex growing algorithm (SGA) has been widely used for finding endmembers. It can be considered as a sequential version of the well-known endmember finding algorithm, N-finder algorithm (N-FINDR), which finds endmembers one at a time by growing simplexes. However, one of the major hurdles for N-FINDR and SGA is the calculation of simplex volume (SV) which poses a great challenge in designing any algorithm using SV as a criterion for finding endmembers. This paper develops an Orthogonal Projection (OP)-based SGA (OP-SGA) which essentially resolves this computational issue. It converts the issue of calculating SV to calculating the OP on previously found simplexes without computing matrix determinants. Most importantly, a recursive Kalman filter-like OP-SGA, to be called recursive OP-SGA (ROP-SGA), can be also derived to ease computation. By virtue of ROP-SGA, several advantages and benefits in computational savings and hardware implementation can be gained for which N-FINDR and SGA do not have.
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linear spectral unmixing using least squares error Orthogonal Projection and simplex volume for hyperspectral images
Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2015Co-Authors: Chein-i ChangAbstract:Linear spectral unmixing (LSU) and Simplex Volume (SV) are closely related. The link between these two has been recognized recently by the fact that simplex can be realized by two physical abundance constraints, Abundance Sum-to-one Constraint (ASC) and Abundance Non-negativity Constraint (ANC). In other words, all data sample vectors are embraced by a simplex with vertices which are actually the set of signatures used to unmix data sample vectors where the data sample vectors outside the simplex are considered as unwanted sample vectors such as noisy samples, bad sample vectors. On the other hand, LSU is solved by Least Squares Error (LSE) which uses the principle of Orthogonality to derive the solution. Therefore, LSU is also equivalent to being solved by Orthogonal Projection (OP). This paper explores applications of LSU using these criteria, simplex, LSE and OP in data unmixing.
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WHISPERS - An Orthogonal Projection approach to simplex growing algorithm
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015Co-Authors: Chein-i ChangAbstract:Simplex growing algorithm (SGA) is an endmember finding algorithm which grows simplexes one vertex at a time by finding vertexes which yield maximal simplex volumes via determinant calculation. This paper presents a new version of SGA, called Orthogonal Projection based simplex growing algorithm (OPSGA) which takes advantage of a simplex's geometric structure to calculate simplex volume by multiplying its height with its base where the height is the magnitude of the newly generated endmember and the base is the volume of the simplex formed by previous endmembers. With such a simple structure OPSGA can be very easily implemented with significant saving of computing time. Most importantly, OPSGA bridges gaps between fully constrained SGA and most commonly used Orthogonal Projection-based unconstrained technique, Automatic target generation process (ATGP) to find endmembers.
Petros Okubagzi - One of the best experts on this subject based on the ideXlab platform.
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accuracy of predicted Orthogonal Projection angles for valve deployment during transcatheter aortic valve replacement
Journal of Cardiovascular Computed Tomography, 2018Co-Authors: Arie Steinvil, Gaby Weissman, Guy Weigold, Christian Shults, Rebecca Torguson, Andrew W Ertel, Toby Rogers, Edward Koifman, Kyle Buchanan, Petros OkubagziAbstract:Abstract Background Multi-detector computed tomography (MDCT) predicted Orthogonal Projection angles have been introduced to guide valve deployment during transcatheter aortic valve replacement (TAVR). Our aim was to investigate the accuracy of MDCT prediction methods versus actual angiographic deployment angles. Methods Retrospective analysis of 2 currently used MDCT methods: manual multiplanar reformations (MR) and the semiautomatic optimal angle graph (OAG). Paired analysis was used to compare the 2-dimensional distributions and means. Results We included 101 patients with a mean (±SD) age of 81 ± 9 years. The MR and OAG methods were used in 46 and 55 patients, respectively. A ≥5% change from the predicted MDCT range in left anterior oblique/right anterior oblique (LAO/RAO) and the cranial/caudal (CRA/CAU) angle occurred in 42% and 58% of patients, respectively. The mean predicted versus actual deployment angles were significantly different (CRA/CAU: -2.6 ± 11.5 vs. -7.6 ± 10.7, p Conclusions Currently used MDCT methods for TAVR implantation angles are significantly modified before actual valve deployment. Thus, further refinement of these prediction methods is required.
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tct 736 accuracy of predicted Orthogonal Projection angles for valve deployment during transcatheter aortic valve implantation
Journal of the American College of Cardiology, 2016Co-Authors: Arie Steinvil, Gaby Weissman, Guy Weigold, Andrew Ertel, Smita Negi, Sang Yeub Lee, Christian Shults, Rebecca Torguson, Petros Okubagzi, Augusto D PichardAbstract:Computerized tomography (CT) predicted Orthogonal Projection angles for valve deployment have been introduced to guide transcatheter aortic valve replacement (TAVR). Often, these angles are used initially as general guidance and modified before valve deployment. The relation between the predicted
Arie Steinvil - One of the best experts on this subject based on the ideXlab platform.
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accuracy of predicted Orthogonal Projection angles for valve deployment during transcatheter aortic valve replacement
Journal of Cardiovascular Computed Tomography, 2018Co-Authors: Arie Steinvil, Gaby Weissman, Guy Weigold, Christian Shults, Rebecca Torguson, Andrew W Ertel, Toby Rogers, Edward Koifman, Kyle Buchanan, Petros OkubagziAbstract:Abstract Background Multi-detector computed tomography (MDCT) predicted Orthogonal Projection angles have been introduced to guide valve deployment during transcatheter aortic valve replacement (TAVR). Our aim was to investigate the accuracy of MDCT prediction methods versus actual angiographic deployment angles. Methods Retrospective analysis of 2 currently used MDCT methods: manual multiplanar reformations (MR) and the semiautomatic optimal angle graph (OAG). Paired analysis was used to compare the 2-dimensional distributions and means. Results We included 101 patients with a mean (±SD) age of 81 ± 9 years. The MR and OAG methods were used in 46 and 55 patients, respectively. A ≥5% change from the predicted MDCT range in left anterior oblique/right anterior oblique (LAO/RAO) and the cranial/caudal (CRA/CAU) angle occurred in 42% and 58% of patients, respectively. The mean predicted versus actual deployment angles were significantly different (CRA/CAU: -2.6 ± 11.5 vs. -7.6 ± 10.7, p Conclusions Currently used MDCT methods for TAVR implantation angles are significantly modified before actual valve deployment. Thus, further refinement of these prediction methods is required.
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tct 736 accuracy of predicted Orthogonal Projection angles for valve deployment during transcatheter aortic valve implantation
Journal of the American College of Cardiology, 2016Co-Authors: Arie Steinvil, Gaby Weissman, Guy Weigold, Andrew Ertel, Smita Negi, Sang Yeub Lee, Christian Shults, Rebecca Torguson, Petros Okubagzi, Augusto D PichardAbstract:Computerized tomography (CT) predicted Orthogonal Projection angles for valve deployment have been introduced to guide transcatheter aortic valve replacement (TAVR). Often, these angles are used initially as general guidance and modified before valve deployment. The relation between the predicted
Augusto D Pichard - One of the best experts on this subject based on the ideXlab platform.
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tct 736 accuracy of predicted Orthogonal Projection angles for valve deployment during transcatheter aortic valve implantation
Journal of the American College of Cardiology, 2016Co-Authors: Arie Steinvil, Gaby Weissman, Guy Weigold, Andrew Ertel, Smita Negi, Sang Yeub Lee, Christian Shults, Rebecca Torguson, Petros Okubagzi, Augusto D PichardAbstract:Computerized tomography (CT) predicted Orthogonal Projection angles for valve deployment have been introduced to guide transcatheter aortic valve replacement (TAVR). Often, these angles are used initially as general guidance and modified before valve deployment. The relation between the predicted
Jian Zhang - One of the best experts on this subject based on the ideXlab platform.
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sidelobe suppression with Orthogonal Projection for multicarrier systems
IEEE Transactions on Communications, 2012Co-Authors: Jian Zhang, Xiaojing Huang, Antonio Cantoni, Jay Y GuoAbstract:Sidelobe suppression, or out-of-band emission reduction, in multicarrier systems is conventionally achieved via time-domain windowing which is spectrum inefficient. Although some sidelobe cancellation and signal predistortion techniques have been proposed for spectrum shaping, they are generally not well balanced between complexity and suppression performance. In this paper, an efficient and low-complexity sidelobe suppression with Orthogonal Projection (SSOP) scheme is proposed. The SSOP scheme uses an Orthogonal Projection matrix for sidelobe suppression, and adopts as few as one reserved subcarrier for recovering the distorted signal in the receiver. Unlike most known approaches, the SSOP scheme requires multiplications as few as the number of subcarriers in the band, and enables straightforward selection of parameters. Analytical and simulation results show that more than 50dB sidelobe suppression can be readily achieved with only a slight degradation in receiver performance.
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Sidelobe Suppression with Orthogonal Projection for Multicarrier Systems
IEEE Transactions on Communications, 2012Co-Authors: Jian Zhang, Xiaojing Huang, Antonio Cantoni, Y. Jay GuoAbstract:Sidelobe suppression, or out-of-band emission reduction, in multicarrier systems is conventionally achieved via time-domain windowing which is spectrum inefficient. Although some sidelobe cancellation and signal predistortion techniques have been proposed for spectrum shaping, they are generally not well balanced between complexity and suppression performance. In this paper, an efficient and low-complexity sidelobe suppression with Orthogonal Projection (SSOP) scheme is proposed. The SSOP scheme uses an Orthogonal Projection matrix for sidelobe suppression, and adopts as few as one reserved subcarrier for recovering the distorted signal in the receiver. Unlike most known approaches, the SSOP scheme requires multiplications as few as the number of subcarriers in the band, and enables straightforward selection of parameters. Analytical and simulation results show that more than 50dB sidelobe suppression can be readily achieved with only a slight degradation in receiver performance.11 page(s
