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About 10,000 papers for “sensor data analysis”

Sensor data analysis

Ayan Kumar Panja, Amartya Mukherjee, Nilanjan Dey · Elsevier eBooks · 2022

Cited by 0

Analysis of sensor data.

Carla Agurto (10156643), Stephen Heisig (10156646), Avner Abrami (10156649) et al. · Figshare · 2021

<p><b>A—</b>Analytic Pipeline. <b>B—</b>Three-dimensional data extracted by each sensor at each step (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0244842#sec007" target="_blank">Methods</a>) were converted to a 1D signal by concatenating the 3 axes (x, y and z). <b>C</b>–For each sensor for either left or right steps, we extracted the coefficients of the first three principal components. Projection of the temporal profile on the coefficients of the principle components produced the reduced dimensionality values used in the main analysis. <b>D—</b>Reconstruction of the acceleration trajectories (main panel) from movements in the RDS space (inset).</p> <p>(DOCX)</p>

Cited by 0Open access

Irradiance sensor array data analysis

Raja Rajan, Rajendran · DR-NTU (Nanyang Technological University) · 2016

Singapore’s tropical climate, proximity to the equator and large supply of sunshine has opened up the possibility of harnessing solar energy as an alternative source of clean energy for the country. However, the island country also experiences frequent intermittent cloud movements throughout most days. This prevents solar PV installations from being able to harness maximum solar energy throughout the day, thereby limiting grid penetration capabilities of solar energy throughout the country. The studies done in this paper extended several works done by researchers in other areas of the globe to quantify the local solar irradiance variability using various metrics such as Coefficient of Variability, Variability Index and solar irradiance ramps along the dimensions of time and space. The results of studies have shown that for any given day, the variability of the solar irradiance at various hours of the day can vary as much as the irradiance itself. Also, when multiple readings of irradiance data is obtained over a larger surface area, the overall variability for the area drops by a significant percentage, and the solar irradiance ramp distributions even out towards lower magnitudes with longer durations. From these studies, it can be noted that following the trends of the solar irradiance variability, PV systems have a high possibility of producing less-varied, more stable power output if the power generated was aggregated from multiple sources over a larger land area.

Cited by 0

Multi-Sensor Degradation Data Analysis

Diao-Yan Hua, Al-Khalifa, Khalifa N., A.M.S. Hamouda et al. · SHILAP Revista de lepidopterología · 2013

Multiple sensors are commonly used for degradation monitoring of critical components. The degradation indicators derived from these sensors are significantly different in terms of scale and interpretation. Since these indicators reflect the same degradation path but have different scales and noise, the fusion of the data corresponding to these indicators is important for the condition monitoring and accurate reliability estimation of the components. In this paper, we develop a unified degradation path that considers statistics-based degradation and physics-based degradation models. We also estimate the reliability of such components. Simulation results show that the unified degradation model approximates the actual degradation path effectively. Additionally, more accurate estimates of reliability are obtained when compared with individual degradation paths.

Cited by 4Open access

Analysis of Space Sensor Data.

Andrew J. Mazzella, Delorey Jr., E. Dennis · 1994

Abstract : This Final Report summarizes the major tasks accomplished under referenced contract. RDP Incorporated, Waltham, MA functioned as prime contractor for these efforts, with the Institute for Space Research, Boston College, Chestnut Hill, MA as the major subcontractor.

Cited by 0

Sensor Data Analysis in Smart Buildings

Mane Penton, Manuel A · CUNY Academic Works (City University of New York) · 2020

Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the CO2, temperature and humidity values of the room and occupancy by applying ML methods such as p-analysis, naive, logistic regression models, feature selection and others. The result, together with the implementation of an automatic infrastructure for light and HVAC systems, can be used to save money and resources.

Cited by 0Open access

WaveLab - Hartmann Sensor Data Analysis Software

Allan Wirth · Adaptive Optics · 1995

WaveLab is a Hartmann wavefront sensor data analysis system that permits virtually any Hartmann test data to be quickly converted to wavefronts and all derivative types of information such as point spread functions, OTFs, and encircled energies. It uses a graphical interface that makes standard data reduction straightforward and also provides a command line interface for customized data handling. It is user customizable in two ways: WaveLab is an extension to the tcl scripting language and all of the high level reduction routines are in the form of tcl scripts. The user may add their own scripts to handle data in specialized ways. Secondly, the user may add c language functions to the WaveLab / tcl system. This allows the inclusion of more esoteric or complex routines than maybe easily coded at the tcl script level. All the data files used or generated by WaveLab are in an extension of the FITS format.

Cited by 0

Consistency Analysis of Sensor Data Distribution

Femminella, Mauro, Reali, Gianluca · arXiv (Cornell University) · 2013

In this paper we analyze the probability of consistency of sensor data distribution systems (SDDS), and determine suitable evaluation models. This problem is typically difficult, since a reliable model taking into account all parameters and processes which affect the system consistency is unavoidably very complex. The simplest candidate approach consists of modeling the state sojourn time, or holding time, as memoryless, and resorting to the well known solutions of Markovian processes. Nevertheless, it may happen that this approach does not fit with some working conditions. In particular, the correct modeling of the SDDS dynamics requires the introduction of a number of parameters, such as the packet transfer time or the packet loss probability, the value of which may determine the suitability of unsuitability of the Markovian model. Candidate alternative solutions include the Erlang phase-type approximation of nearly constant state holding time and a more refined model to account for overlapping events in semi-Markov processes.

Cited by 0Open access

Data Analysis from the Bluetooth Sensor

Alžbeta Kanáliková, Emília Bubeníková, Peter Peniak · 2024

The article deals with project teaching using the PBL (Project Based Learning) methodology. This methodology aims to work in a project-based manner; that is, through a specific creative assignment, students will implement a specific solution in successive steps. In our case, it is a synthesis of the solutions of several projects (final theses) implemented through the PBL methodology. The goals of the projects followed each other over time. Gradually, a sensor network was first created, and then the data from sensors supporting Bluetooth was cleaned and analyzed. Based on data analysis, localization and identification of mobile devices were carried out. A web application filters and analyzes the data into categories according to distance and then displays the results through graphs and tables. The use of such an application is possible, for example, in the localization of mobile devices (telephones, iPads, hands-free, and other devices). The goal of localization can be used, for example, in determining the overcrowding of designated zones (e.g., shopping centers, squares), but also in epidemiological measures (e.g., like the COVID-19 pandemic), where faster transmission of the disease is possible in a crowded space.

Cited by 0

Improving sensor data analysis through diverse data source integration

Jennifer Casper, Ronald Albuquerque, Jeremy Hyland et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009

Daily sensor data volumes are increasing from gigabytes to multiple terabytes. The manpower and resources needed to analyze the increasing amount of data are not growing at the same rate. Current volumes of diverse data, both live streaming and historical, are not fully analyzed. Analysts are left mostly to analyzing the individual data sources manually. This is both time consuming and mentally exhausting. Expanding data collections only exacerbate this problem. Improved data management techniques and analysis methods are required to process the increasing volumes of historical and live streaming data sources simultaneously. Improved techniques are needed to reduce an analysts decision response time and to enable more intelligent and immediate situation awareness. This paper describes the Sensor Data and Analysis Framework (SDAF) system built to provide analysts with the ability to pose integrated queries on diverse live and historical data sources, and plug in needed algorithms for upstream processing and filtering. The SDAF system was inspired by input and feedback from field analysts and experts. This paper presents SDAF's capabilities, implementation, and reasoning behind implementation decisions. Finally, lessons learned from preliminary tests and deployments are captured for future work.

Cited by 1