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A feedforward optical neural network for image recognition is demonstrated through free-space optics in which the nonlinear activation function is provided by a resonant semiconductor saturable absorption mirror (RSAM). Although free-space optics with liquid crystal based spatial light modulator has the potential to allow parallel processing for improved performance, using a fiber-optic electrooptic modulator for parallel to serial conversion greatly simplifies the experimental setup. We show that this nonlinear activation function introduced by RSAM can provide a notable improvement of 8.1% to classification accuracy in comparison to a purely linear network when tested with the MNIST data set for image classification. The impact of noise is also investigated in system implementation. 

A physics-aware simulation platform is proposed for optical neural networks (ONNs), incorporating phase-only modulation and passive free-space propagation. The platform enables end-to-end training under experimentally realistic constraints, with both the phase mask and propagation distance treated as learnable parameters. To facilitate classification, structured 2D output patterns are introduced, where each label corresponds to a fixed spatial light spot. When evaluated with the MNIST dataset, the system achieves 94.6% accuracy using a single phase modulation layer, demonstrating the effectiveness of spatial encoding in physically plausible ONNs. 

We present a novel measurement technique for P-OFDR with a digitally created linear chirp in the transmitter and vector complex optical field detection to measure the Stokes parameters along the fiber. 

Performance of distributed Raman amplification (DRA) system with dual order forward (FW) pumping is analyzed with the consideration of both pump relative intensity noise (RIN) to signal phase noise transfer and signal nonlinear interference. The efficiencies of pump RIN to signal phase noise transfer are theoretically analyzed and experimentally verified by measuring signal phase noise introduced by small index intensity modulations applied on the pump lasers. The results indicate that the efficiency of 2nd order pump RIN to signal phase noise transfer can be more than 2 orders of magnitude higher than that from the 1st order pump. Although dual order FW Raman pumping corresponds to a slight increase of amplified spontaneous emission (ASE) compared to using only a 1st order pump, its major advantage comes from the reduction of nonlinear interference noise in a dense wavelength-division multiplexing (DWDM) system. Because pump RIN to signal phase noise transfer has lowpass characteristics, systems at high baud rates, such as 100 Gbaud, are less susceptible to the impact of pump laser RIN. 

A high resolution FMCW Lidar system based on a phase-diverse self-homodyne coherent receiver is demonstrated. Using the same linearly chirped waveform for both the transmitted lidar signal and the local oscillator, the self-homodyne coherent receiver performs frequency de-chirping in the photodiodes which significantly simplifies the task of signal processing, and the required receiver bandwidth can be much lower than the signal chirping bandwidth. While only amplitude modulation is required in the lidar transmitter, phase-diverse coherent receiver allows simultaneous detection of target range and velocity through the spectrum of the de-chirped complex waveform. Multi-target detection is also demonstrated experimentally. 

We demonstrate 20-channel coherent transmission using a high-power single-section QW FP-laser diode over 78.3 km single mode fiber. The system capability can reach > 4Tb/s with a single laser in the transmitter using polarizations multiplexing. 

The Optical Network Emulation (ONE) engine is a software tool that offers students the opportunity to learn how to control and operate open optical (wavelength division multiplexing) transport networks, such as those based on the Open ROADM MSA standards. This paper describes multiple modelling techniques that are implemented in the ONE engine to represent the signal power spectral density at any link/fiber section of the emulated transport network. These techniques make use of polynomial fitting and deconvolution computation methods. 

Electrostriction in an optical fiber is introduced by interaction between the forward propagated optical signal and the acoustic standing waves in the radial direction resonating between the center of the core and the cladding circumference of the fiber. The response of electrostriction is dependent on fiber parameters, especially the mode field radius. We demonstrated a novel technique that can be used to characterize fiber properties by means of measuring their electrostriction response under intensity modulation. As the spectral envelope of electrostriction-induced propagation loss is anti-symmetrical, the signal to noise ratio can be significantly increased by subtracting the measured spectrum from its complex conjugate. We show that if the transversal field distribution of the fiber propagation mode is Gaussian, the envelope of the electrostriction-induced loss spectrum closely follows a Maxwellian distribution whose shape can be specified by a single parameter determined by the mode field radius. 

We present a simple technique for in-field determination of fiber types through the measurement of electrostriction effect based on self-phase modulation