Our visual perception of the world — seeing form and colour or navigating the environment — depends on the interaction of light and matter in the environment. Light also has a more fundamental role in regulating rhythms in physiology and behaviour, as well as in the acute secretion of hormones such as melatonin and changes in alertness, where light exposure at short-time, medium-time and long-time scales has different effects on these visual and non-visual functions. Yet patterns of light exposure in the real world are inherently messy: we move in and out of buildings and are therefore exposed to mixtures of artificial and natural light, and the physical makeup of our environment can also drastically alter the spectral composition and spatial distribution of the emitted light. In spatial vision, the examination of natural image statistics has proven to be an important driver in research. Here, we expand this concept to the spectral domain and develop the concept of the ‘spectral diet’ of humans.
Our visual perception of the world – seeing form and colour or navigating the environment – depends on the interaction of light and matter in the environment. Light also has a more fundamental role in regulating rhythms in physiology and behaviour, as well as in the acute secretion of hormones like melatonin and changes in alertness, where light exposure at short, medium and long-time scales has different effects on these visual and non-visual functions. Yet patterns of light exposure in the real world are inherently messy: we move in and out of buildings and are therefore exposed to mixtures of artificial and natural light, and the physical makeup of our environment can also drastically alter the spectral composition and spatial distribution of the illuminant light. In spatial vision, the examination of natural image statistics has proven to be an important driver in research. Here, we expand this concept to the spectral domain and develop the concept of the “spectral diet” of humans.
Given the large impact that the spectrum and intensity of light can have on people's health and well-being, it is of fundamental importance to understand the properties of light received under normal living conditions. Historically, as research into the biological responses of light has traditionally focused on laboratory studies with controlled lighting conditions, little is known about people's light exposure outside of experimental environments. Spectrace is the first wearable compressive spectrometer designed for continuous spectral light tracking in everyday environments. This paper presents the sensor and its evaluation based on wearability considerations and three performance criteria: 1) its accuracy (in terms of spectral sensing capability), 2) its reliability (notably as far as directional response is concerned), and 3) its adaptability to the large dynamics of ambient conditions. Results show the potential use of the newly developed sensor for chronobiological studies and beyond.
Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverage a data-driven approach to learn optimal encoder-array sensitivities for a novel filter-array spectrometer. By taking advantage of the regularities mined from diverse online repositories, we are able to exploit low-dimensional patterns for improved spectral reconstruction from as few as p=2 channels. Furthermore, the performance of SpecRA is largely independent of signal complexity. Our results illustrate the superiority of our method over conventional approaches and provide a framework towards "fourth paradigm" spectral sensing. We hope that this work can help reduce the size, weight and cost constraints of future spectrometers for specific spectral monitoring tasks in applied contexts such as in remote sensing, healthcare, and quality control.
Information is the resolution of uncertainty and manifests itself as patterns. Although complex, most observable phenomena are not random and instead are associated with deterministic, chaotic systems. The underlying patterns and symmetries expressed from these phenomena determine their information content and compressibility. While some patterns, such as the existence of Fourier modes, are easy to extract, advances in machine learning have enabled more comprehensive methods in feature extraction, most notably in their ability to elicit non-linear relationships. Herein we review methods concerned with the encoding and reconstruction of natural signals and how they might inform the discovery of useful transform bases. Additionally, we illustrate the efficacy of data-driven bases over generic ones in encoding information whilst discussing these developments in the context of “fourth paradigm” metrology. Toward this end, we propose that existing metrological standards and norms may need to be redefined within the context of a data-rich world.
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