1,720,964 research outputs found
System and method for transcoding spectral data with task-based optimization via symmetric non-negative matrix factorization
No full textA computer-implemented method for reconstructing/recovering high-resolution visible light spectral data at a target resolution d, that comprises obtaining a configuration of a low- resolution multi-channel imaging sensor of resolution p, the configuration being enabled to produce measurements results of any one of a plurality of n determined visible light spectra, the low-resolution multi-channel imaging sensor comprising a number p of optical sensors arranged at locations in the spectrometer corresponding each to an attributed measurable wavelength of the visible light, the configuration comprising a definition of the attributed measurable wavelength for each location. The method comprises obtaining a library L comprising a previously measured set of the plurality of n determined visible light spectra at a high-resolution resolution d, the resolution d being greater than the resolution p, constructing a vector of p channel peak wavelengths by determining the first p channels with the highest evenness to maximize encoded information. The method comprises obtaining a set of reference spectra from L by finding the first k cluster centers such that f(H,k) = min(||A- HH'||2) to be used to fit regularization parameters for unknown spectra via Tikhonov regularization and adaptive dictionary learning.AVP-R-TTOLIPIDAlternative title(s) : (de) System und verfahren zur transcodierung von spektraldaten mit aufgabenbasierter optimierung mittels symmetrischer nichtnegativer matrixfaktorisierung (fr) Système et procédé de transcodage de données spectrales avec optimisation à base de tâches via une factorisation de matrice symétrique non négativ
Theory and application of a data-driven approach to compressive spectrometry in the assessment of neurophotic stimulation
No full textRecent advances in biology and neuroscience have elucidated pathways in which visible light initiates a signaling process, via the eye, responsible for activating direct and indirect biological responses. While the full impact of unnatural light patterns on human biology remains poorly understood, controlled laboratory studies, and a handful of field studies, have strongly indicated that exposure to light (and darkness) outside of the natural light-dark cycle is a factor in increased disease prevalence and general malaise related to anomalous wake and sleep behavior enabled by modern lighting. Consequently, there exists a mismatch between our evolved needs â based on the natural day-night cycle â and the needs driven by modern lifestyle enabled by technology â which typically include an over exposure to artificial light at night and restricted access to natural light during the day. In the same way that our nutritional diet has evolved with modern convenience, so has our spectral diet. From a neurobiological perspective, we can think of discrete instances of spectral composition and intensity of light received over the day as micronutrients, that when concatenated over a 24h period, compose a diet. Relating spectral diet information to neurobiological outcomes (i.e., "neurophotic" effects), is a central aim of multidisciplinary research teams. Unfortunately, collecting spectral information in the field is challenging due to constraints associated with existing encoders. The central aim of this thesis is to circumvent these constraints by approaching spectral sensing from the angle of compressed sensing and information theory by developing SpecRA: an adaptive reconstruction algorithm. In order to demonstrate the efficacy of SpecRA in practice, we develop a novel compact spectrometer \textit{Spectrace}, in tandem and test its performance against existing state-of-the-art devices. The approach taken herein allows us to maximize the amount of information recovered from cheaper, low-fidelity, filter-array sensors by pushing beyond the limits of conventional signal reconstruction though the exploitation of regularities in the natural world. As a first application of this research, we investigate the diversity of so-called spectral diets in a limited subset of commuters with the aim of elucidating structures and properties of modern spectral diets in populations with shared behaviors, in order to better anticipate their prevalence and potential impact on health and well-being. Indeed, by applying this methodology to other domains, we could also develop more efficient sensing methods of other types of chaotic signals thereby mitigating constraints and enabling greater opportunity for data collection across a diversity of physical phenomena.LIPI
Measurement in the Age of Information
No full textInformation 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.LIPI
Spectral Measurement and Classification in the Era of Big Data
No full textThe measurement and classification of light is essential across many scientific disciplines. Devices used to measure light range from the highly precise scanning spectroradiometers to the more practical compact multichannel filter-array type imaging sensors and the ubiquitous RGB pixel. While there have been numerous successful efforts to reconstruct spectrum from RGB, RGB-to-spectrum reconstruction has historically been limited to natural scenes and other edge cases under strict constraints. However, information theory and recent advances in deep learning have shed new light on the vast amount of redundancy contained within data collected in the natural world, including light. In this paper, we will investigate how analytic methods can help map high dimensional spectra data to a low-dimensional feature space with minimal inductive bias. Through a better understanding of the intrinsic dimension of the data, we can use the features expressed in this representation to exploit regularities and make tasks like data compression, measurement and classification more efficient. The aim of this analysis is to help inform how and when low-dimensional representation of spectra is useful in practice for designing compact sensors as well as for lossy data compression and robust classification.LIPI
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Towards a wearable sensor for spectrally-resolved personal light monitoring
No full textGiven 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.LIPI
Towards ‘Fourth Paradigm’ Spectral Sensing
Get PDFReconstruction 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.LIPI
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