Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments

This paper presents a data acquisition system consisting of multiple RGB-D sensors and digital single-lens reflex (DSLR) cameras. A systematic data processing procedure for integrating these two kinds of devices to generate three-dimensional point clouds of indoor environments is also developed and described. In the developed system, DSLR cameras are used to bridge the Kinects and provide a more accurate ray intersection condition, which takes advantage of the higher resolution and image quality of the DSLR cameras. Structure from Motion (SFM) reconstruction is used to link and merge multiple Kinect point clouds and dense point clouds (from DSLR color images) to generate initial integrated point clouds. Then, bundle adjustment is used to resolve the exterior orientation (EO) of all images. Those exterior orientations are used as the initial values to combine these point clouds at each frame into the same coordinate system using Helmert (seven-parameter) transformation. Experimental results demonstrate that the design of the data acquisition system and the data processing procedure can generate dense and fully colored point clouds of indoor environments successfully even in featureless areas. The accuracy of the generated point clouds were evaluated by comparing the widths and heights of identified objects as well as coordinates of pre-set independent check points against in situ measurements. Based on the generated point clouds, complete and accurate three-dimensional models of indoor environments can be constructed effectively.

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Mobile air monitoring data processing strategies and effects on spatial air pollution trends

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-10443-2013 ◽

2013 ◽

Vol 6 (6) ◽

pp. 10443-10480 ◽

Cited By ~ 1

Author(s):

H. L. Brantley ◽

G. S. W. Hagler ◽

S. Kimbrough ◽

R. W. Williams ◽

S. Mukerjee ◽

...

Keyword(s):

Air Quality ◽

Data Processing ◽

Event Detection ◽

Ultrafine Particles ◽

Air Pollutant ◽

Mobile Monitoring ◽

Background Estimation ◽

Spatial Gradients ◽

Processing Strategies ◽

Spatial Trends

Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data processing approaches. The study being reported here utilized a large mobile monitoring dataset collected on a roadway network in central North Carolina to explore common data processing strategies including time-alignment, short-term emissions event detection, background estimation, and averaging techniques. One-second time resolution measurements of ultrafine particles ≤ 100 nm in diameter (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) were collected on twelve unique driving routes that were repeatedly sampled. Analyses demonstrate that the multiple emissions event detection strategies reported produce generally similar results and that utilizing a median (as opposed to a mean) as a summary statistic may be sufficient to avoid bias in near-source spatial trends. Background levels of the pollutants are shown to vary with time, and the estimated contributions of the background to the mean pollutant concentrations were: BC (6%), PM2.5–10 (12%), UFPs (19%), CO (38%), PM10 (45%), NO2 (51%), PM2.5 (56%), and CO2 (86%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

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Regularization by Denoising for simultaneous source separation

Geophysics ◽

10.1190/geo2021-0068.1 ◽

2021 ◽

pp. 1-56

Author(s):

Breno Bahia ◽

Rongzhi Lin ◽

Mauricio Sacchi

Keyword(s):

Inverse Problem ◽

Inverse Problems ◽

Data Processing ◽

Spectrum Analysis ◽

Numerical Experiments ◽

Source Separation ◽

Singular Spectrum Analysis ◽

Source Data ◽

Blended Data ◽

Simultaneous Source

Denoisers can help solve inverse problems via a recently proposed framework known as regularization by denoising (RED). The RED approach defines the regularization term of the inverse problem via explicit denoising engines. Simultaneous source separation techniques, being themselves a combination of inversion and denoising methods, provide a formidable field to explore RED. We investigate the applicability of RED to simultaneous-source data processing and introduce a deblending algorithm named REDeblending (RDB). The formulation permits developing deblending algorithms where the user can select any denoising engine that satisfies RED conditions. Two popular denoisers are tested, but the method is not limited to them: frequency-wavenumber thresholding and singular spectrum analysis. We offer numerical blended data examples to showcase the performance of RDB via numerical experiments.

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RFID-Enabled Location Determination Within Indoor Environments

International Journal of Ambient Computing and Intelligence ◽

10.4018/jaci.2009062205 ◽

2009 ◽

Vol 1 (4) ◽

pp. 63-86 ◽

Cited By ~ 17

Author(s):

Kevin Curran ◽

Stephen Norrby

Keyword(s):

Supply Chain ◽

Radio Frequency ◽

Radio Frequency Identification ◽

Automatic Identification ◽

Indoor Environments ◽

Location Determination ◽

Asset Tracking ◽

Current Location ◽

Toll Collection ◽

Main Components

The ability to track the real-time location and movement of items or people offers a broad range of useful applications in areas such as safety, security and the supply chain. Current location determination technologies, however, have limitations that heavily restrict how and where these applications are implemented, including the cost, accuracy of the location calculation and the inherent properties of the system. The Global Positioning System (GPS), for example, cannot function indoors and is useful only over large-scaled areas such as an entire city. Radio Frequency Identification (RFID) is an automatic identification technology which has seen increasingly prominent use over the last few decades. The technology uses modulated Radio Frequency signals to transfer data between its two main components, the reader and the transponder. Its many applications include supply chain management, asset tracking, security clearance and automatic toll collection. In recent years, advancements in the technology have allowed the location of transponders to be calculated while interfacing with the reader. This article documents an investigation into using an active RFID based solution for tracking.

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Use of RFID in Supply Chain Data Processing

Information Security and Ethics ◽

10.4018/978-1-59904-937-3.ch082 ◽

2008 ◽

pp. 1184-1191

Author(s):

Jan Owens ◽

Suresh Chalasani ◽

Jayavel Sounderpandian

Keyword(s):

Supply Chain ◽

Data Processing ◽

Radio Frequency ◽

Supply Chains ◽

Radio Frequency Identification ◽

Department Of Defense ◽

Frequency Identification ◽

Processing And Storage ◽

And Storage ◽

Existing Data

The use of Radio Frequency Identification (RFID) is becoming prevalent in supply chains, with large corporations such as Wal-Mart, Tesco, and the Department of Defense phasing in RFID requirements on their suppliers. The implementation of RFID can necessitate changes in the existing data models and will add to the demand for processing and storage capacities. This article discusses the implications of the RFID technology on data processing in supply chains.

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