Ranjeet S. Bhamber

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

Optical frequency conversion, pulse compression and signal copying using triangular pulses

We propose techniques of optical frequency conversion, pulse compression and signal copying based on a combination of cross-phase modulation using triangular pump pulses and subsequent propagation in a dispersive medium.

All-optical TDM to WDM signal conversion and partial regeneration using XPM with triangular pulses

We propose a new all-optical, all-fibre scheme for conversion of time-division multiplexed to wavelength-division multiplexed signals using cross-phase modulation with triangular pulses. Partial signal regeneration using this technique is also demonstrated.

Respiratory function monitoring using a real-time three-dimensional fiber-optic shaping sensing scheme based upon fiber Bragg gratings

Abstract. An array of in-line curvature sensors on a garment is used to monitor the thoracic and abdominal movements of a human during respiration. The results are used to obtain volumetric changes of the human torso in agreement with a spirometer used simultaneously at the mouth. The array of 40 in-line fiber Bragg gratings is used to produce 20 curvature sensors at different locations, each sensor consisting of two fiber Bragg gratings. The 20 curvature sensors and adjoining fiber are encapsulated into a low-temperature-cured synthetic silicone. The sensors are wavelength interrogated by a commercially available system from Moog Insensys, and the wavelength changes are calibrated to recover curvature. A three-dimensional algorithm is used to generate shape changes during respiration that allow the measurement of absolute volume changes at various sections of the torso. It is shown that the sensing scheme yields a volumetric error of 6%. Comparing the volume data obtained from the spirometer with the volume estimated with the synchronous data from the shape-sensing array yielded a correlation value 0.86 with a Pearson’s correlation coefficient p<0.01.

Design of Raman-based NOLM for optical 2R regeneration of RZ-DPSK transmission

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

Feasibility of soliton-like DPSK transmission at 40 Gb/s with in-line semiconductor optical amplifiers

We analyze a soliton-like phase-shift keying 40-Gb/s transmission system using cascaded in-line semiconductor optical amplifiers. Numerical optimization of the proposed soliton-like regime is presented

Arbitrary real-time three-dimensional corporal object sensing and reconstruction scheme

A real-time three-dimensional (3D) object sensing and reconstruction scheme is presented that can be applied on any arbitrary corporeal shape. Operation is demonstrated on several calibrated objects. The system uses curvature sensors based upon in-line fiber Bragg gratings encapsulated in a low-temperature curing synthetic silicone. New methods to quantitatively evaluate the performance of a 3D object-sensing scheme are developed and appraised. It is shown that the sensing scheme yields a volumetric error of 1% to 9%, depending on the object.

Adaptive Electrical Signal Post-processing with Varying Representations in Optical Communication Systems

Improving bit error rates in optical communication systems is a difficult and important problem. Error detection and correction must take place at high speed, and be extremely accurate. Also, different communication channels have different characteristics, and those characteristics may change over time. We show the feasibility of using simple artificial neural networks to address these problems, and examine the effect of using different representations of signal waveforms on the accuracy of error correction. The results we have obtained lead us to the conclusion that a machine learning system based on these principles can improve on the performance of existing error correction hardware at the speed required, whilst being able to adapt to suit the characteristics of different communication channels.

Lumped dispersion mapping and performance margins in existing SMF-DCF terrestrial links

We demonstrate that the transmission of 40 Gbits/s return-to-zero differential phase-shift keying (RZ-DPSK) signals is robust to lumped dispersion mapping on a typical installed terrestrial single-mode fiber/dispersion compensating fiber (SMF-DCF) link and will withstand, in this case, propagation through over 800 km of SMF with zero in-line group-velocity dispersion compensation while maintaining similar performance to configurations with periodic mapping. We establish that upgrading similar point-to-point links, which have lumped dispersion maps, are compatible with 40 Gbits/s RZ-DPSK and that economic benefits can be realized when implementing lumped dispersion mapping in new 40 Gbits/s RZ-DPSK terrestrial links, while incurring a relatively low performance penalty. (c) 2008 Optical Society of America.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.