Mach edges: a key role for 3rd derivative filters in spatial vision

Edges are key points of information in visual scenes. One important class of models supposes that edges correspond to the steepest parts of the luminance profile, implying that they can be found as peaks and troughs in the response of a gradient (first-derivative) filter, or as zero-crossings (ZCs) in the second-derivative. A variety of multi-scale models are based on this idea. We tested this approach by devising a stimulus that has no local peaks of gradient and no ZCs, at any scale. Our stimulus profile is analogous to the classic Mach-band stimulus, but it is the local luminance gradient (not the absolute luminance) that increases as a linear ramp between two plateaux. The luminance profile is a smoothed triangle wave and is obtained by integrating the gradient profile. Subjects used a cursor to mark the position and polarity of perceived edges. For all the ramp-widths tested, observers marked edges at or close to the corner points in the gradient profile, even though these were not gradient maxima. These new Mach edges correspond to peaks and troughs in the third-derivative. They are analogous to Mach bands - light and dark bars are seen where there are no luminance peaks but there are peaks in the second derivative. Here, peaks in the third derivative were seen as light-to-dark edges, troughs as dark-to-light edges. Thus Mach edges are inconsistent with many standard edge detectors, but are nicely predicted by a new model that uses a (nonlinear) third-derivative operator to find edge points.

Interaction of Kelvin waves and nonlocality of energy transfer in superfluids

We argue that the physics of interacting Kelvin Waves (KWs) is highly nontrivial and cannot be understood on the basis of pure dimensional reasoning. A consistent theory of KW turbulence in superfluids should be based upon explicit knowledge of their interactions. To achieve this, we present a detailed calculation and comprehensive analysis of the interaction coefficients for KW turbuelence, thereby, resolving previous mistakes stemming from unaccounted contributions. As a first application of this analysis, we derive a local nonlinear (partial differential) equation. This equation is much simpler for analysis and numerical simulations of KWs than the Biot-Savart equation, and in contrast to the completely integrable local induction approximation (in which the energy exchange between KWs is absent), describes the nonlinear dynamics of KWs. Second, we show that the previously suggested Kozik-Svistunov energy spectrum for KWs, which has often been used in the analysis of experimental and numerical data in superfluid turbulence, is irrelevant, because it is based upon an erroneous assumption of the locality of the energy transfer through scales. Moreover, we demonstrate the weak nonlocality of the inverse cascade spectrum with a constant particle-number flux and find resulting logarithmic corrections to this spectrum.

Design of Raman-based nonlinear loop mirror for all-optical 2R regeneration of differential phase-shift-keying transmission

We present a concept for all-optical regeneration of signals modulated in phase-sensitive modulation formats, which is based on a new design of Raman amplified nonlinear optical loop mirror (RA-NOLM). We demonstrate simultaneous amplitude-shape regeneration and phase-noise reduction in high-speed differential phase-shift-keying transmission systems by use of the RA-NOLM combined with spectral filtering.

Design of Raman-based nonlinear loop mirror for all-optical 2R regeneration of differential phase-shift-keying transmission

We present a concept for all-optical regeneration of signals modulated in phase-sensitive modulation formats, which is based on a new design of Raman amplified nonlinear optical loop mirror (RA-NOLM). We demonstrate simultaneous amplitude-shape regeneration and phase-noise reduction in high-speed differential phase-shift-keying transmission systems by use of the RA-NOLM combined with spectral filtering. © 2006 IEEE.

Bandpass switching based on a nonlinear optical loop mirror and an in-fibre grating reflector

Nonlinear optical loop mirror (NOLM) requires breaking the loop symmetry to enable the counter propagating pulses to acquire a differential π phase shift. This is achieved with either an asymmetric fused fibre coupler at the input or by the inclusion of an asymmetrically located gain or loss element within the loop. By introducing a frequency selective loss element, nonlinear switching may be confined to a narrow band of wavelengths or multiple wavelengths. This configuration may have applications in time-wavelength demultiplexing. We demonstrate this technique of bandpass switching in the soliton regime using a fibre-Bragg grating reflector as the wavelength dependent loss.

Semiconductor optical amplifier-based nonlinear optical-loop mirror with feedback

The behavior of a semiconductor optical amplifier (SOA)-based nonlinear loop mirror with feedback has been investigated as a potential device for all-optical signal processing. In the feedback device, input signal pulses (ones) are injected into the loop, and amplified reflected pulses are fed back into the loop as switching pulses. The feedback device has two stable modes of operation - block mode, where alternating blocks of ones and zeros are observed, and spontaneous clock division mode, where halving of the input repetition rate is achieved. Improved models of the feedback device have been developed to study its performance in different operating conditions. The feedback device could be optimized to give a choice of either of the two stable modes by shifting the arrival time of the switching pulses at the SOA. Theoretically, it was found possible to operate the device at only tens of fJ switching pulse energies if the SOA is biased to produce very high gain in the presence of internal loss. The clock division regime arises from the combination of incomplete SOA gain recovery and memory of the startup sequence that is provided by the feedback. Clock division requires a sufficiently high differential phase shift per unit differential gain, which is related to the SOA linewidth enhancement factor.

Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations

This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.