Adaptive optical processors

Anjan Ghosh

doi:10.1364/OL.14.000604

Optics Letters
Vol. 14,
Issue 12,
pp. 604-606
(1989)
•https://doi.org/10.1364/OL.14.000604

Adaptive optical processors

Anjan Ghosh

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
Anjan Ghosh, "Adaptive optical processors," Opt. Lett. 14, 604-606 (1989)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Abstract

There are two different approaches for improving the accuracy of analog optical associative processors: postprocessing with a bimodal system and preprocessing with a preconditioner. These two approaches can be combined to develop an adaptive optical multiprocessor that can adjust the computational steps depending on the data and produce solutions of linear algebra problems with a specified accuracy in a given amount of time.

Full Article | PDF Article

More Like This

Performance analysis of matrix preconditioning algorithms on parallel optical processors

Anjan Ghosh and Palacharla Paparao
J. Opt. Soc. Am. A 5(1) 39-48 (1988)

Matrix preconditioning: a robust operation for optical linear algebra processors

Anjan Ghosh and Palacharla Paparao
Appl. Opt. 26(14) 2734-2737 (1987)

Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems

John D. Downie and Joseph W. Goodman
Appl. Opt. 28(20) 4298-4304 (1989)

Previous Article Next Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (2)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (1)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

1. SSPPC algorithm
Step 1:	Initialize D₁ = A
Step 2:	Start the outer loop for i = 1, 2, …
(a) Choose ω_i = k_i/λ_max(D_i) such that 0 < k_i < 2
(b) Formulate G_i = I − ω_iD_i
Step 3:	Evaluate the sum $S_{i} = Σ_{j = 0}^{p i - 1} {G_{i}}^{j}$ through an inner loop
Step 4:	Calculate D_i+1 = ω_iS_iD_i
Step 5:	Loop through steps 2, 3, and 4 until i = m, where m is the number of steps required to arrive at the desired condition number
2. SSPPC-based algorithm for matrix inverse calculation
Step 1:	Initialize B₁ = I, D₁ = A
Steps 2 and 3 are the same as in algorithm 1
Step 4:	Calculate B_i+1 = ω_iS_iD_i and D_i+1 = B_i+1A
Step 5:	Loop through steps 2, 3, and 4 while ‖I − D_i+1‖ > ∊, where ∊ is a small positive number that determines the accuracy of the inverse
3. SSPPC-based algorithm for condition-number estimation
Step 1:	Initialize B₁ = I, D₁ = A and calculate ‖A‖ = C_est₀(A), the initial estimate of the condition number
Steps 2 and 3 are same as in algorithm 1
Step 4:	Calculate B_i+1 = ω_iS_iB_i and D_i+1 = B_i+1A
Step 5:	Calculate C_{est_i}(A) = ‖B_i+1‖ ‖A‖
Step 6:	Loop through steps 2, 3, 4, and 5 while [C_{est_i}(A) – C_{est_i−1}(A)]/C_{est_i}(A) > ∊, where ∊ is a small positive number

Abstract

Cited By

Figures (2)

Tables (1)

Equations (1)

Optics Letters