This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

Spectroscopic analysis is one of the most widely used analytical tools in scientific research and industry. Although laboratory benchtop spectrometer systems offer superlative resolution and spectral range, their miniaturization is crucial for applications where portability is paramount or where in situ measurements must be made. Advancement in this field over the past three decades is now yielding microspectrometers with performance and footprint near those viable for lab-on-a-chip systems, smartphones, and other consumer technologies. We summarize the technologies that have emerged toward achieving these aims-including miniaturized dispersive optics, narrowband filter systems, Fourier transform interferometers, and reconstructive microspectrometers-and discuss the challenges associated with improving spectral resolution while device dimensions shrink ever further.

/pdf/miniaturization-of-optical-spectrometers-1zlsgu9gno.pdf

Miniaturization of optical spectrometers

Spectrometers with ever-smaller footprints are sought after for a wide range of applications in which minimized size and weight are paramount, including emerging in situ characterization techniques. We report on an ultracompact microspectrometer design based on a single compositionally engineered nanowire. This platform is independent of the complex optical components or cavities that tend to constrain further miniaturization of current systems. We show that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire. Our devices are capable of accurate, visible-range monochromatic and broadband light reconstruction, as well as spectral imaging from centimeter-scale focal planes down to lensless, single-cell-scale in situ mapping.

/pdf/single-nanowire-spectrometers-rcf63il1ot.pdf

Single-nanowire spectrometers

Miniaturized spectrometers have significant potential for portable applications such as consumer electronics, health care, and manufacturing. These applications demand low cost and high spectral resolution, and are best enabled by single-shot free-space-coupled spectrometers that also have sufficient spatial resolution. Here, we demonstrate an on-chip spectrometer that can satisfy all of these requirements. Our device uses arrays of photodetectors, each of which has a unique responsivity with rich spectral features. These responsivities are created by complex optical interference in photonic-crystal slabs positioned immediately on top of the photodetector pixels. The spectrometer is completely complementary metal–oxide–semiconductor (CMOS) compatible and can be mass produced at low cost. Miniaturized components have significant potential for portable applications. Here, the authors demonstrate a spectrometer and hyperspectral imager based on photonic-crystal slabs which can enable single-shot imaging on a compact chip.

/pdf/single-shot-on-chip-spectral-sensors-based-on-photonic-32a95939mn.pdf

Single-shot on-chip spectral sensors based on photonic crystal slabs

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency, especially for non-mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning in the full 2π range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling binary switching of metasurfaces between arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for reconfigurable meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 μm wavelength. The reconfigurable metalens features a record large switching contrast ratio of 29.5 dB. We further validate aberration-free and multi-depth imaging using the metalens, which represents a key experimental demonstration of a non-mechanical tunable metalens with diffraction-limited performance. Here, the authors report an active all-dielectric metasurface platform based on phase change materials, combining phase tuning in the full 2π range, and demonstrate aberration-free and multi-depth imaging with a non-mechanical tunable metalens.

/pdf/reconfigurable-all-dielectric-metalens-with-diffraction-1vcj7ex2ey.pdf

Reconfigurable all-dielectric metalens with diffraction-limited performance

On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an 'elastic-D1' regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.

/pdf/high-performance-and-scalable-on-chip-digital-fourier-3a5fbap96c.pdf

High-performance and scalable on-chip digital Fourier transform spectroscopy

Optical spectrum analysis is the cornerstone of spectroscopic sensing, optical network performance monitoring, and hyperspectral imaging While conventional high-performance spectrometers used to perform such analysis are often large benchtop instruments, on-chip spectrometers have recently emerged as a promising alternative with apparent Size, Weight, and Power (SWaP) advantages Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio (SNR) Here we demonstrate a transformative on-chip digital Fourier transform (dFT) spectrometer that can acquire high-resolution spectra via time- domain modulation of a reconfigurable Mach-Zehnder interferometer The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost SNR and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels We further implemented machine learning regularization techniques to spectrum reconstruction and achieved significant noise suppression and spectral resolution enhancement beyond the classical Rayleigh criterion

Digital Fourier transform spectroscopy: a high-performance, scalable technology for on-chip spectrum analysis

On-chip spectrometers have recently emerged as a promising alternative to conventional benchtop instruments with apparent Size, Weight, and Power (SWaP) advantages for applications including spectroscopic sensing, optical network performance monitoring, RF spectrum analysis, optical coherence tomography, and hyperspectral imaging. Existing onchip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio (SNR). Here we demonstrate a transformative on-chip digital Fourier transform (dFT) spectrometer that can acquire high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost SNR and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explored and implemented machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D1’ regularized regression method that we developed, we achieved significant noise suppression for both broad (> 600 GHz) and narrow ( 1,000) spectral channel counts.

/pdf/chip-scale-high-performance-digital-fourier-transform-dft-1u09e2sx6x.pdf

Chip-scale high-performance digital Fourier Transform (dFT) spectrometers

We describe an on-chip digital Fourier transform (dFT) spectrometer and a machine learning algorithm for spectrum reconstruction. The device resolution scales exponentially with footprint, and we demonstrate 100% resolution enhancement compared to the Rayleigh criterion.

Chip-scale Digital Fourier Transform Spectroscopy

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High-performance and scalable on-chip digital Fourier transform spectroscopy

Digital Fourier transform spectroscopy: a high-performance, scalable technology for on-chip spectrum analysis

Chip-scale high-performance digital Fourier Transform (dFT) spectrometers

Chip-scale Digital Fourier Transform Spectroscopy