Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification.

A photonics-enabled spiking timing-dependent convolutional neural network (CNN) is proposed by manipulating photonics multidimensional parameters in terms of wavelength, temporal and spatial, which breaks the traditional CNN architecture mapping from a spatially parallel to a time-dependent series structure. The proposed CNN with the application of real-time image recognition comprises a photonics convolution processor to accelerate the computing and an involved electronic full connection to execute the classification task. A timing-dependent series of matrix-matrix operations is conducted in the photonics convolution processor that can be achieved based on multidimensional multiplexing by the accumulation of carriers from an active mode-locked laser, dispersion latency induced by a dispersion compensation fiber, and wavelength spatial separation via a waveshaper. Incorporated with the electronic full connection, a photonics-enabled CNN is proven to perform a real-time recognition task on the MNIST database of handwritten digits with a prediction accuracy of 90.04%. Photonics enables conventional neural networks to accelerate machine learning and neuromorphic computing and has the potential to be widely used in information processing and computing, such as goods classification, vowel recognition, and speech identification.

Accuracy enhanced microwave frequency measurement based on the machine learning technique.

We propose and experimentally demonstrate a microwave frequency measurement system based on the photonic technique. An amplitude comparison function is constructed to perform frequency-to-power mapping based on a non-sliced broadband optical source. The results are fed into a machine learning module which can be utilized to minimize the differential mode noise of the system caused by the polarization fluctuation. The system is reconfigurable with adjustable measurement bandwidth by adjusting the dispersion group delay of the signals at orthogonal polarizations by a polarization division multiplexed emulator (PDME). In addition, the mapping relationship is reconstructed by stacking method. The results are fed into four machine learning models: support vector regressor (SVR), KNeighbors regressor (KNN), polynomial regressor (PR) and random forest regressor (RFR). The output of the four models then combined by adding them together using linear regression method. By fitting the relationship between frequency and microwave power ratio with machine learning method, the accuracy of microwave frequency measurement system is further improved. The results show that for a measurement system with a bandwidth of 2 GHz and 4 GHz, the maximum error and the average measurement errors are all reduced. The results are promising for applications of modern radar and electronic warfare systems.

Tunable notch microwave photonic filter based on interferometry of a single low-incoherence source.

We present a method to realize a single-notch microwave photonic filter (MPF) based on interferometry of a single, low-coherence broadband optical source (BOS). Normally, a notch MPF based on low-coherence interferometry requires independent control of two optical sources located in different wavebands. In this work, we use a single BOS to accurately perform destructive interference between a bandpass and an all-pass MPF. A frequency and bandwidth freely tunable single-notch MPF can thus be realized. The proposed method is theoretically analyzed and experimentally demonstrated. A notch depth of more than 30 dB and a continuously tunable frequency range from 2 to 18 GHz was demonstrated in the proof-of-concept experiment.