Recent critiques suggest family science is operating on a narrow definition of family that privileges US/western-centric perspectives and White, heteronormative, and nuclear families. Understanding that self-assessment is key to scientific growth, this study systematically assessed the publication patterns of the top six family science journals across 10 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) along two dimensions: the sociodemographics (continent/country, ethnic-racial group, and LGBTQ) and family subsystems studied (couple, parentchild, coparental, sibling, kin, and overall family context). Of the 3932 coded studies, 85% included North American and Western European samples. Within U.S.-based studies, White samples made up more than half of all research. Less than 3% of all coded studies focused on LGBTQ families. Most research focused on the parent-child and couple subsystems and less than 5% focused on kin or siblings. We provide a critical discussion regarding the need for more representation in family science journals, and recommendations for research methods, publication, and reporting requirements.
This article presents results of the recognition process of acoustic fingerprints from a noise source using spectral characteristics of the signal. Principal Components Analysis (PCA) is applied to reduce the dimensionality of extracted features and then a classifier is implemented using the method of the k-nearest neighbors (KNN) to identify the pattern of the audio signal. This classifier is compared with an Artificial Neural Network (ANN) implementation. It is necessary to implement a filtering system to the acquired signals for 60Hz noise reduction generated by imperfections in the acquisition system. The methods described in this paper were used for vessel recognition.
How to citeComplete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Abstract-This paper presents an implementation of Artificial Neural Networks (ANN) for acoustic fingerprints recognition, applied to the identification of marine vessels. In many cases, the vessel recognition process from an audible signal is performed by human operators, which could lead to failures in the identification process. Before entering the ANN classification process, the signal is filtered and its spectral characteristics are extracted. A comparison of the classification process between three types of neural networks is presented.
In spite of the lack of low cost biosignal specific purpose generators, there have been processing strategies to simulate control signals and dynamic states of different pathological diseases. Commercial simulators are not able to reproduce all signal behavior; thereby, we developed a light based emulator able to provide different signal patterns using colorimetry and spectrophotometry. Several parameters are configurable according to customer requirements. Amplitude and frequency are controlled by the usage of a DC motor, a coloured band, a mechanical structure, and, particularly, an oximetry probe. The prototype emulator provides basic signals such as sine, square, triangle, or complex physiological signals, as photoplethysmograms (PPG) and Electrocardiograms (ECG). Other complex patterns include sources of error and chaotic characteristics such as noise or motion artifact, can be simulated. The device may be used to analyze, determine and verify data acquisition platforms, medical instrumentation, training tool interfaces, among others. Preliminary results have contributed to the construction and validation of complex patterns; corroborating practicality, operability and reliability of the proposed product and reducing developing time on representations.
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