V. V. Berdnik and V. A. Loiko, “Retrieval of particle characteristics with high-order neural networks: application to scanning flow cytometry,” Proc. SPIE 6734 (2007).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Using global optimization for a microparticle identification problem with noise data,” J. Global Optimization 32, 325-347(2005).

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Application of the neural network method for determining the characteristics of homogeneous spherical particles,” Opt. Spectrosc. 96, 285-291 (2004).

[CrossRef]

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method,” Opt. Lett. 29, 1019-1021 (2004).

[CrossRef]

K. A. Semyanov, P. A. Tarasov, A. E. Zharinov, A. V. Chernyshev, A. G. Hoekstra, and V. P. Maltsev, “Single-particle sizing from light scattering by spectral decomposition,” Appl. Opt. 43, 5110-5115 (2004).

[CrossRef]

Z. Wang, Z. Ulanowski, and P. H. Kaye, “On solving the inverse scattering problem with RBF neural networks: noise-free case,” Neural Comput. Applic. 8, 177-186 (1999).

Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, “Application of neural networks to the inverse light-scattering problem for spheres,” Appl. Opt. 37, 4027-4033 (1998).

[CrossRef]

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Application of global optimization to particle identification using light scattering,” Inverse Prob. 14, 1053-1067(1998).

A. O. Nascimento, R. Guardani, and M. Giulietti, “Use of neural networks in the analysis of particle size distributions by laser diffraction,” Powd. Technol. 90, 89-94 (1997).

P. G. Hull and Quinby-Hunt, “A neural-network to extract size parameter from light-scattering data,” Proc. SPIE 2963, 448-453 (1997).

F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectutes,” Neural Comput. 7, 219-296(1995).

[CrossRef]

C. M. Bishop, “Training with noise is equivalent to Tikhonov regularization,” Neural Comput. 7, 108-116 (1995).

[CrossRef]

X.-P. Zhang, “Space--scale adaptive noise reduction in images based on thresholding neural network,” Mathematical Programming B 45, 503-528 (1989).

D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming B 45, 503-528 (1989).

A. N. Tichonov and V. Y. Arsenin, *Solutions of Ill-Posed Problems* (Wiley, 1977).

V. A. Babenko, L. A. Astafyeva, and V. N. Kuzmin, *Electromagnetic Scattering in Disperse Media* (Springer, , 2003).

V. A. Babenko, L. A. Astafyeva, and V. N. Kuzmin, *Electromagnetic Scattering in Disperse Media* (Springer, , 2003).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Using global optimization for a microparticle identification problem with noise data,” J. Global Optimization 32, 325-347(2005).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Application of global optimization to particle identification using light scattering,” Inverse Prob. 14, 1053-1067(1998).

V. V. Berdnik and V. A. Loiko, “Retrieval of particle characteristics with high-order neural networks: application to scanning flow cytometry,” Proc. SPIE 6734 (2007).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Application of the neural network method for determining the characteristics of homogeneous spherical particles,” Opt. Spectrosc. 96, 285-291 (2004).

[CrossRef]

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method,” Opt. Lett. 29, 1019-1021 (2004).

[CrossRef]

C. M. Bishop, “Training with noise is equivalent to Tikhonov regularization,” Neural Comput. 7, 108-116 (1995).

[CrossRef]

C. F. Bohren and D. R. Huffman, *Absorption and Scattering of Light by Small Particles* (Wiley-Interscience, 1983).

M. A. van Dilla, P. N. Dean, O. D. Laerum, and M. R. Melamed, *Flowcytometry: Instrumentation and Data Analysis* (Academic, 1985).

D. Dejrmendjian, *Electromagnetic Scattering on Spherical Polydispersions* (Elsevier, 1969).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectutes,” Neural Comput. 7, 219-296(1995).

[CrossRef]

A. O. Nascimento, R. Guardani, and M. Giulietti, “Use of neural networks in the analysis of particle size distributions by laser diffraction,” Powd. Technol. 90, 89-94 (1997).

A. O. Nascimento, R. Guardani, and M. Giulietti, “Use of neural networks in the analysis of particle size distributions by laser diffraction,” Powd. Technol. 90, 89-94 (1997).

S. Haykin, *Neural Networks--A Comprehensive Foundation* (Prentice-Hall, 1999).

C. F. Bohren and D. R. Huffman, *Absorption and Scattering of Light by Small Particles* (Wiley-Interscience, 1983).

P. G. Hull and Quinby-Hunt, “A neural-network to extract size parameter from light-scattering data,” Proc. SPIE 2963, 448-453 (1997).

F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectutes,” Neural Comput. 7, 219-296(1995).

[CrossRef]

Z. Wang, Z. Ulanowski, and P. H. Kaye, “On solving the inverse scattering problem with RBF neural networks: noise-free case,” Neural Comput. Applic. 8, 177-186 (1999).

Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, “Application of neural networks to the inverse light-scattering problem for spheres,” Appl. Opt. 37, 4027-4033 (1998).

[CrossRef]

E. Kissa, D*ispersions: Characterization, Testing, and Measurement*, Vol. 84 of Surfactant Science Series (Marcel Dekker, 1999).

V. A. Babenko, L. A. Astafyeva, and V. N. Kuzmin, *Electromagnetic Scattering in Disperse Media* (Springer, , 2003).

M. A. van Dilla, P. N. Dean, O. D. Laerum, and M. R. Melamed, *Flowcytometry: Instrumentation and Data Analysis* (Academic, 1985).

D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming B 45, 503-528 (1989).

V. V. Berdnik and V. A. Loiko, “Retrieval of particle characteristics with high-order neural networks: application to scanning flow cytometry,” Proc. SPIE 6734 (2007).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Application of the neural network method for determining the characteristics of homogeneous spherical particles,” Opt. Spectrosc. 96, 285-291 (2004).

[CrossRef]

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method,” Opt. Lett. 29, 1019-1021 (2004).

[CrossRef]

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

K. A. Semyanov, P. A. Tarasov, A. E. Zharinov, A. V. Chernyshev, A. G. Hoekstra, and V. P. Maltsev, “Single-particle sizing from light scattering by spectral decomposition,” Appl. Opt. 43, 5110-5115 (2004).

[CrossRef]

V. P. Maltsev, “Scanning flow cytometry for individual particle analysis,” Rev. Sci. Instrum. 71, 243-255 (2000).

[CrossRef]

V. P. Maltsev and K. A. Semyanov, *Characterization of Bio-Particles from Light Scattering* (VSP, 2004).

M. A. van Dilla, P. N. Dean, O. D. Laerum, and M. R. Melamed, *Flowcytometry: Instrumentation and Data Analysis* (Academic, 1985).

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Application of the neural network method for determining the characteristics of homogeneous spherical particles,” Opt. Spectrosc. 96, 285-291 (2004).

[CrossRef]

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method,” Opt. Lett. 29, 1019-1021 (2004).

[CrossRef]

A. O. Nascimento, R. Guardani, and M. Giulietti, “Use of neural networks in the analysis of particle size distributions by laser diffraction,” Powd. Technol. 90, 89-94 (1997).

D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming B 45, 503-528 (1989).

F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectutes,” Neural Comput. 7, 219-296(1995).

[CrossRef]

P. G. Hull and Quinby-Hunt, “A neural-network to extract size parameter from light-scattering data,” Proc. SPIE 2963, 448-453 (1997).

K. A. Semyanov, P. A. Tarasov, A. E. Zharinov, A. V. Chernyshev, A. G. Hoekstra, and V. P. Maltsev, “Single-particle sizing from light scattering by spectral decomposition,” Appl. Opt. 43, 5110-5115 (2004).

[CrossRef]

V. P. Maltsev and K. A. Semyanov, *Characterization of Bio-Particles from Light Scattering* (VSP, 2004).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

A. N. Tichonov and V. Y. Arsenin, *Solutions of Ill-Posed Problems* (Wiley, 1977).

S. Twomey, *Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements* (Elsevier, 1977).

Z. Wang, Z. Ulanowski, and P. H. Kaye, “On solving the inverse scattering problem with RBF neural networks: noise-free case,” Neural Comput. Applic. 8, 177-186 (1999).

Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, “Application of neural networks to the inverse light-scattering problem for spheres,” Appl. Opt. 37, 4027-4033 (1998).

[CrossRef]

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Using global optimization for a microparticle identification problem with noise data,” J. Global Optimization 32, 325-347(2005).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Application of global optimization to particle identification using light scattering,” Inverse Prob. 14, 1053-1067(1998).

M. A. van Dilla, P. N. Dean, O. D. Laerum, and M. R. Melamed, *Flowcytometry: Instrumentation and Data Analysis* (Academic, 1985).

Z. Wang, Z. Ulanowski, and P. H. Kaye, “On solving the inverse scattering problem with RBF neural networks: noise-free case,” Neural Comput. Applic. 8, 177-186 (1999).

Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, “Application of neural networks to the inverse light-scattering problem for spheres,” Appl. Opt. 37, 4027-4033 (1998).

[CrossRef]

R. Xu, *Particle Characterization: Light Scattering Methods* (Kluwer, 2000).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Using global optimization for a microparticle identification problem with noise data,” J. Global Optimization 32, 325-347(2005).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Application of global optimization to particle identification using light scattering,” Inverse Prob. 14, 1053-1067(1998).

X.-P. Zhang, “Space--scale adaptive noise reduction in images based on thresholding neural network,” Mathematical Programming B 45, 503-528 (1989).

M. Bartholdi, G. C. Salzman, R. D. Hielbert, and M. Kerker, “Differential light scattering photometer for rapid analysis of single particles in flow,” Appl. Opt. 19, 1573-1581 (1980).

[CrossRef]

D. H. Tycko, M. H. Metz, E. A. Epstein, and A. Grinbaum, “Flow-cytometric light scattering measurement of red blood cell volume and hemoglobin concentration,” Appl. Opt. 24, 1355-1365 (1985).

[CrossRef]

C. Lee Giles and T. Maxwell, “Learning, invariance, and generalization in high-order neural networks,” Appl. Opt. 26, 4972-4978 (1987).

[CrossRef]

Z. Ulanowski, Z. Wang, P. H. Kaye, and I. K. Ludlow, “Application of neural networks to the inverse light-scattering problem for spheres,” Appl. Opt. 37, 4027-4033 (1998).

[CrossRef]

S. Min and A. Gomez, “High-resolutionsize measurement if single spherical particles with a fast Fourier transform of the angular scattering intensity,” Appl. Opt. 35, 4919-4926(1996).

[CrossRef]

K. A. Semyanov, P. A. Tarasov, A. E. Zharinov, A. V. Chernyshev, A. G. Hoekstra, and V. P. Maltsev, “Single-particle sizing from light scattering by spectral decomposition,” Appl. Opt. 43, 5110-5115 (2004).

[CrossRef]

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Application of global optimization to particle identification using light scattering,” Inverse Prob. 14, 1053-1067(1998).

S. Zakovic, Z. J. Ulanowski, and M. C. Bartholomew-Biggs, “Using global optimization for a microparticle identification problem with noise data,” J. Global Optimization 32, 325-347(2005).

V. V. Berdnik, K. Gilev, A. Shvalov, V. P. Maltsev, and V. A. Loiko, “Characterization of spherical particles using high-order neural networks and scanning flow cytometry,” J. Quant. Spectrosc. Radiative Transf. 102, 62-72(2006).

X.-P. Zhang, “Space--scale adaptive noise reduction in images based on thresholding neural network,” Mathematical Programming B 45, 503-528 (1989).

D. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming B 45, 503-528 (1989).

F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectutes,” Neural Comput. 7, 219-296(1995).

[CrossRef]

C. M. Bishop, “Training with noise is equivalent to Tikhonov regularization,” Neural Comput. 7, 108-116 (1995).

[CrossRef]

Z. Wang, Z. Ulanowski, and P. H. Kaye, “On solving the inverse scattering problem with RBF neural networks: noise-free case,” Neural Comput. Applic. 8, 177-186 (1999).

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method,” Opt. Lett. 29, 1019-1021 (2004).

[CrossRef]

A. Ishimaru, R. J. Marks, L. Tsang, C. M. Lam, D. C. Park, and S. Kitamura, “Particle-size distribution determination using optical sensing and neural networks,” Opt. Lett. 15, 1221-1223 (1990).

[CrossRef]

V. V. Berdnik, R. D. Mukhamedjarov, and V. A. Loiko, “Application of the neural network method for determining the characteristics of homogeneous spherical particles,” Opt. Spectrosc. 96, 285-291 (2004).

[CrossRef]

A. O. Nascimento, R. Guardani, and M. Giulietti, “Use of neural networks in the analysis of particle size distributions by laser diffraction,” Powd. Technol. 90, 89-94 (1997).

P. G. Hull and Quinby-Hunt, “A neural-network to extract size parameter from light-scattering data,” Proc. SPIE 2963, 448-453 (1997).

V. V. Berdnik and V. A. Loiko, “Retrieval of particle characteristics with high-order neural networks: application to scanning flow cytometry,” Proc. SPIE 6734 (2007).

V. P. Maltsev, “Scanning flow cytometry for individual particle analysis,” Rev. Sci. Instrum. 71, 243-255 (2000).

[CrossRef]

C. F. Bohren and D. R. Huffman, *Absorption and Scattering of Light by Small Particles* (Wiley-Interscience, 1983).

D. Dejrmendjian, *Electromagnetic Scattering on Spherical Polydispersions* (Elsevier, 1969).

V. A. Babenko, L. A. Astafyeva, and V. N. Kuzmin, *Electromagnetic Scattering in Disperse Media* (Springer, , 2003).

V. P. Maltsev and K. A. Semyanov, *Characterization of Bio-Particles from Light Scattering* (VSP, 2004).

www.nvidia.com

S. Haykin, *Neural Networks--A Comprehensive Foundation* (Prentice-Hall, 1999).

R. Xu, *Particle Characterization: Light Scattering Methods* (Kluwer, 2000).

S. Twomey, *Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements* (Elsevier, 1977).

A. N. Tichonov and V. Y. Arsenin, *Solutions of Ill-Posed Problems* (Wiley, 1977).

E. Kissa, D*ispersions: Characterization, Testing, and Measurement*, Vol. 84 of Surfactant Science Series (Marcel Dekker, 1999).

M. A. van Dilla, P. N. Dean, O. D. Laerum, and M. R. Melamed, *Flowcytometry: Instrumentation and Data Analysis* (Academic, 1985).