Sparsity and self-adaptivity in anamorphic stretch transform

Mahjoubfar, Ata; Chan, Jacky C. K.; Asghari, Mohammad Hossein; Jalali, Bahram

doi:10.1109/ciss.2015.7086872

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5. By using proper curvature for the group delay dispersion profile, the envelope time-bandwidth product can be compressed or expanded subject to the sparsity of the signal [44], [45].…”

Section: Signal-to-noise Ratio Enhancementmentioning

confidence: 99%

Tailoring Wideband Signals With a Photonic Hardware Accelerator

Jalali

Mahjoubfar

2015

Proc. IEEE

Self Cite

View full text Add to dashboard Cite

| Digital hardware accelerators are increasingly employed to speed up computation and reduce power dissipation, enabling real-time operation. Inspired by this paradigm, we propose the concept of the photonic hardware acceleratorVan analog optical processing engine that precedes optical-to-electrical conversion and alleviates the burden on subsequent electronics. We propose one specific class of photonic hardware accelerators designed to assist in acquisition, feature extraction, and storage of wideband waveforms.This fundamental unit reshapes, in real time, the spectrotemporal evolution of a wideband streaming signal based on signal's sparsity. Functioning as an information gearbox, the accelerator transforms the signal according to the nonuniform entropy of its spectrum. Nonlinear group delay dispersion modes are introduced as primitive building blocks for such transformations. Representing spectrotemporal basis functions, these modes and their corresponding time-stretch wavelets have distinct and useful properties that depend on their symmetry. We focus on polynomial basis functions, but also discuss their limitations and alternatives such as spline functions that offer more efficient representation of group delay spectra with localized features. They are used to synthesize complex warped spectrotemporal operations that are reconfigurable and can be implemented in both analog optical and digital (computational) domains. We show how these dispersion-based computational primitives reshape the wideband signal to enable nonuniform sampling, compression, and pattern recognition in real time. Additional applications including coding, signal classification, and enhancement of signal-to-noise ratio during ultrafast analog-to-digital conversion are discussed. Fig. 7. Various methods for realizing optical dispersion. (a) Dispersive optical fiber with internal Raman amplification. (b) chirped fiber Bragg grating (CFBG) used in conjunction with a circulator. (c) CMD. The CMD device offers field reconfigurability of phase and group delay dispersion through tuning of the launch angle of light into the multimode waveguide.

show abstract

“…5. By using proper curvature for the group delay dispersion profile, the envelope time-bandwidth product can be compressed or expanded subject to the sparsity of the signal [44], [45].…”

Section: Signal-to-noise Ratio Enhancementmentioning

confidence: 99%

Tailoring Wideband Signals With a Photonic Hardware Accelerator

Jalali

Mahjoubfar

2015

Proc. IEEE

Self Cite

View full text Add to dashboard Cite

show abstract

“…An optical signal's intensity envelope can be tailored to match the bandwidth and record length of the analog to digital converter using engineered group delay dispersion achieved in a nonlinearly chirped Bragg grating or using chromo-modal dispersion [7][8][9]. One can slow down an ultra-fast burst of data, and at the same time, achieve data compression by exploiting sparsity in the original data.…”

Section: Time-bandwidth Engineering Sparse Coding and Optical Data An...mentioning

confidence: 99%

“…By designing the shape of the group delay according to the spectrum of the input signal, more samples are allocated to the information rich portions of the spectrum and fewer to the information sparse regions where they would be redundant. Although the group delay operation is static, the operation is self-adaptive, here meaning that no instantaneous feature detection or adaptivity is required [9]. The prior information needed is the sparsity of the signal's spectral features, i.e.…”

Section: Time-bandwidth Engineering Sparse Coding and Optical Data An...mentioning

confidence: 99%

Silicon photonics coprocessors for energy efficient computing

Jalali¹,

Mahjoubfar²,

Solli³

et al. 2015

2015 2nd International Conference on Opto-Electronics and Applied Optics (IEM OPTRONIX)

Self Cite

View full text Add to dashboard Cite

We introduce three types of analog optical accelerators for enhancing the performance of electronic computing. These include (1) physical computing using complex optical dynamics in silicon for acceleration of scientific computing, (2) spectrotemporal processing and sparse coding using dispersion basis functions and (3) photonic computing primitives for performing nonlinear mathematical operations.

show abstract