The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform
Phaneendra K. Yalavarthy - One of the best experts on this subject based on the ideXlab platform.
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Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography
Journal of biomedical optics, 2018Co-Authors: Navchetan Awasthi, Sandeep Kumar Kalva, Manojit Pramanik, Phaneendra K. YalavarthyAbstract:As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of Vector Polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods. It is shown using numerical and experimental phantom cases that these extrapolation methods that are proposed in this work can provide significant acceleration (as high as 4.7 times) along with added advantage of improving reconstructed image quality.
Constantine Frangakis - One of the best experts on this subject based on the ideXlab platform.
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Squared Polynomial extrapolation methods with cycling: an application to the positron emission tomography problem
Numerical Algorithms, 2007Co-Authors: Ch. Roland, Ravi Varadhan, Constantine FrangakisAbstract:Roland and Varadhan (Appl. Numer. Math., 55:215–226, 2005) presented a new idea called “squaring” to improve the convergence of Lemarechal’s scheme for solving nonlinear fixed-point problems. Varadhan and Roland (Squared extrapolation methods: A new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm, Department of Biostatistics Working Paper. Johns Hopkins University, http://www.bepress.com/jhubiostat/paper63, 2004) noted that Lemarechal’s scheme can be viewed as a member of the class of Polynomial extrapolation methods with cycling that uses two fixed-point iterations per cycle. Here we combine these two ideas, cycled extrapolation and squaring, and construct a new class of methods, called squared Polynomial methods (SQUAREM), for accelerating the convergence of fixed-point iterations. Our main goal is to evaluate whether the squaring device is effective in improving the rate of convergence of cycled extrapolation methods that use more than two fixed-point iterations per cycle. We study the behavior of the new schemes on an image reconstruction problem for positron emission tomography (PET) using simulated data. Our numerical experiments show the effectiveness of first- and higher-order squared Polynomial extrapolation methods in accelerating image reconstruction, and also their relative superiority compared to the classical, “unsquared” Vector Polynomial methods.
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Squared Polynomial extrapolation methods with cycling: an application to the positron emission tomography problem
Numerical Algorithms, 2007Co-Authors: Ch. Roland, Ravi Varadhan, Constantine FrangakisAbstract:Roland and Varadhan (Appl. Numer. Math., 55:215–226, 2005 ) presented a new idea called “squaring” to improve the convergence of Lemaréchal’s scheme for solving nonlinear fixed-point problems. Varadhan and Roland (Squared extrapolation methods: A new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm, Department of Biostatistics Working Paper. Johns Hopkins University, http://www.bepress.com/jhubiostat/paper63 , 2004 ) noted that Lemaréchal’s scheme can be viewed as a member of the class of Polynomial extrapolation methods with cycling that uses two fixed-point iterations per cycle. Here we combine these two ideas, cycled extrapolation and squaring, and construct a new class of methods, called squared Polynomial methods (SQUAREM), for accelerating the convergence of fixed-point iterations. Our main goal is to evaluate whether the squaring device is effective in improving the rate of convergence of cycled extrapolation methods that use more than two fixed-point iterations per cycle. We study the behavior of the new schemes on an image reconstruction problem for positron emission tomography (PET) using simulated data. Our numerical experiments show the effectiveness of first- and higher-order squared Polynomial extrapolation methods in accelerating image reconstruction, and also their relative superiority compared to the classical, “unsquared” Vector Polynomial methods.
Vijay R. Mankar - One of the best experts on this subject based on the ideXlab platform.
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Curve Cryptographic Processor for Embedded Systems
2015Co-Authors: Sunil Devidas Bobade, Vijay R. MankarAbstract:Elliptic curve cryptography has established itself as a perfect cryptographic tool in embedded environment because of its compact key sizes and security strength at par with that of any other standard public key algorithms. Several FPGA implementations of ECC processor suited for embedded system have been consistently proposed, with a prime focus area being space and time complexities. In this paper, we have modified double point multiplication algorithm and replaced traditional Karatsuba multiplier in ECC processor with a novel modular multiplier. Designed Modular multiplier follows systolic approach of processing the words. Instead of processing Vector Polynomial bit by bit or in parallel, proposed multiplier recursively processes data as 16-bit words. This multiplier when employed in ECC processor reduces drastically the total area utilization. The complete modular multiplier and ECC processor module is synthesized and simulated using Xilinx 14.4 software. Experimental findings show a remarkable improvement in area efficiency, when comparing with other such architectures.
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VLSI architecture for an area efficient Elliptic Curve Cryptographic processor for embedded systems
2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015Co-Authors: Sunil Devidas Bobade, Vijay R. MankarAbstract:Elliptic curve cryptography has established itself as a perfect cryptographic tool in embedded environment because of its compact key sizes and security strength at par with that of any other standard public key algorithms. Several FPGA implementations of ECC processor suited for embedded system have been consistently proposed, with a prime focus area being space and time complexities. In this paper, we have modified double point multiplication algorithm and replaced traditional Karatsuba multiplier in ECC processor with a novel modular multiplier. Designed Modular multiplier follows systolic approach of processing the words. Instead of processing Vector Polynomial bit by bit or in parallel, proposed multiplier recursively processes data as 16-bit words. This multiplier when employed in ECC processor reduces drastically the total area utilization. The complete modular multiplier and ECC processor module is synthesized and simulated using Xilinx 14.4 software. Experimental findings show a remarkable improvement in area efficiency, when comparing with other such architectures.
Navchetan Awasthi - One of the best experts on this subject based on the ideXlab platform.
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Vector extrapolation methods for accelerating iterative reconstruction methods in limited-data photoacoustic tomography
Journal of biomedical optics, 2018Co-Authors: Navchetan Awasthi, Sandeep Kumar Kalva, Manojit Pramanik, Phaneendra K. YalavarthyAbstract:As limited data photoacoustic tomographic image reconstruction problem is known to be ill-posed, the iterative reconstruction methods were proven to be effective in terms of providing good quality initial pressure distribution. Often, these iterative methods require a large number of iterations to converge to a solution, in turn making the image reconstruction procedure computationally inefficient. In this work, two variants of Vector Polynomial extrapolation techniques were deployed to accelerate two standard iterative photoacoustic image reconstruction algorithms, including regularized steepest descent and total variation regularization methods. It is shown using numerical and experimental phantom cases that these extrapolation methods that are proposed in this work can provide significant acceleration (as high as 4.7 times) along with added advantage of improving reconstructed image quality.
Ch. Roland - One of the best experts on this subject based on the ideXlab platform.
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Squared Polynomial extrapolation methods with cycling: an application to the positron emission tomography problem
Numerical Algorithms, 2007Co-Authors: Ch. Roland, Ravi Varadhan, Constantine FrangakisAbstract:Roland and Varadhan (Appl. Numer. Math., 55:215–226, 2005) presented a new idea called “squaring” to improve the convergence of Lemarechal’s scheme for solving nonlinear fixed-point problems. Varadhan and Roland (Squared extrapolation methods: A new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm, Department of Biostatistics Working Paper. Johns Hopkins University, http://www.bepress.com/jhubiostat/paper63, 2004) noted that Lemarechal’s scheme can be viewed as a member of the class of Polynomial extrapolation methods with cycling that uses two fixed-point iterations per cycle. Here we combine these two ideas, cycled extrapolation and squaring, and construct a new class of methods, called squared Polynomial methods (SQUAREM), for accelerating the convergence of fixed-point iterations. Our main goal is to evaluate whether the squaring device is effective in improving the rate of convergence of cycled extrapolation methods that use more than two fixed-point iterations per cycle. We study the behavior of the new schemes on an image reconstruction problem for positron emission tomography (PET) using simulated data. Our numerical experiments show the effectiveness of first- and higher-order squared Polynomial extrapolation methods in accelerating image reconstruction, and also their relative superiority compared to the classical, “unsquared” Vector Polynomial methods.
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Squared Polynomial extrapolation methods with cycling: an application to the positron emission tomography problem
Numerical Algorithms, 2007Co-Authors: Ch. Roland, Ravi Varadhan, Constantine FrangakisAbstract:Roland and Varadhan (Appl. Numer. Math., 55:215–226, 2005 ) presented a new idea called “squaring” to improve the convergence of Lemaréchal’s scheme for solving nonlinear fixed-point problems. Varadhan and Roland (Squared extrapolation methods: A new class of simple and efficient numerical schemes for accelerating the convergence of the EM algorithm, Department of Biostatistics Working Paper. Johns Hopkins University, http://www.bepress.com/jhubiostat/paper63 , 2004 ) noted that Lemaréchal’s scheme can be viewed as a member of the class of Polynomial extrapolation methods with cycling that uses two fixed-point iterations per cycle. Here we combine these two ideas, cycled extrapolation and squaring, and construct a new class of methods, called squared Polynomial methods (SQUAREM), for accelerating the convergence of fixed-point iterations. Our main goal is to evaluate whether the squaring device is effective in improving the rate of convergence of cycled extrapolation methods that use more than two fixed-point iterations per cycle. We study the behavior of the new schemes on an image reconstruction problem for positron emission tomography (PET) using simulated data. Our numerical experiments show the effectiveness of first- and higher-order squared Polynomial extrapolation methods in accelerating image reconstruction, and also their relative superiority compared to the classical, “unsquared” Vector Polynomial methods.
