Linear combination-based energy detection algorithm in low signal-to-noise ratio for cognitive radios

2011 ◽  
pp. n/a-n/a ◽  
Author(s):  
Shibing Zhang ◽  
Zhihua Bao
Keyword(s):  
Linear Combination ◽  
Signal To Noise Ratio ◽  
Cognitive Radios ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Signal To Noise ◽  
Noise Ratio

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

2022 ◽  
Author(s):  
William Ferris ◽  
Larry Albert DeWerd ◽  
Wesley S Culberson
Keyword(s):  
Management System ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Position Error ◽  
Signal To Noise ◽  
Motion Management ◽  
Future Studies ◽  
Planar Images ◽  
Noise Ratio

Abstract Objective: Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach: A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results: The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance: This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Identification of Surrogate Control Variables for a Robust Active Flow Controller of an Experimental High Speed Stator Cascade

2013 ◽  
Author(s):  
S. J. Steinberg ◽  
R. King ◽  
C. Tiedemann ◽  
D. Peitsch
Keyword(s):  
Linear Combination ◽  
High Speed ◽  
Closed Loop ◽  
Signal To Noise Ratio ◽  
Control Variable ◽  
Signal To Noise ◽  
Control Variables ◽  
Noise Ratio ◽  
Flow Controller ◽  
Active Flow

Active flow control is a powerful option to ensure secure operation and enhancement of the performance of axial compressors. To achieve these goals for aerodynamically highly loaded compressor blade profiles even under disturbed conditions, the magnitude of the actuation needs to be adjusted by a closed-loop controller. To this end, sensors must be placed at some meaningful positions at the surface of the blades giving information about the flow situation inside the passages. The sensor information can then lead to surrogate control variables to close the loop. Often, good sensor positions are unknown initially and therefore chosen naively or experience-driven. To obtain more informative surrogate control variables, a different approach is chosen here. Starting with a highly instrumented blade inside a linear stator cascade, featuring 16 pressure gauges in an area which is suspected to lead to high information content with respect to detrimental flow separations at the sidewalls, a Principal Component Analysis is done. The principal components provide valuable information about where and how intensively the flow is influenced by the actuation. This is validated by comparison with the results of oil flow visualizations and wake measurements. The goal is to find a linear combination of as few sensors as possible to provide a meaningful input for the closed-loop controller. As experiments are conducted up to Ma = 0.8, the signal-to-noise ratio becomes a critical issue. For this reason, specifically weighted data are introduced here. A linear combination of sensor data is obtained, describing the main effects of the actuation with an almost linear mapping. For the given set of sensors, that linear combination achieves a maximum signal-to-noise ratio, which makes it well suited as a control variable. The practical usefulness of the control variable within a robust ℋ∞-flow controller is verified in experiments in a high speed stator cascade.

scholarly journals A Direct Current Measurement Method Based on Terbium Gallium Garnet Crystal and a Double Correlation Detection Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2997
Author(s):  
Shen ◽  
Chen ◽  
Yu ◽  
Ge ◽  
Han ◽  
...  
Keyword(s):  
Direct Current ◽  
Measurement Method ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Current Measurement ◽  
Garnet Crystal ◽  
Signal To Noise ◽  
Control Measurement ◽  
Noise Ratio ◽  
Direct Current Measurement

When applying an optical current transformer (OCT) to direct current measurement, output signals exhibit a low signal-to-noise ratio and signal-to-noise band overlap. Sinusoidal wave modulation is used to solve this problem. A double correlation detection algorithm is used to extract the direct current (DC) signal, remove white noise and improve the signal-to-noise ratio. Our sensing unit uses a terbium gallium garnet crystal in order to increase the output signal-to-noise ratio and measurement sensitivity. Measurement errors of single correlation and double correlation detection algorithms are compared, and experimental results showed that this measurement method can control measurement error to about 0.3%, thus verifying its feasibility.

Low-level signals radiomonitoring using periodic spectrum samples analysis

2021 ◽  
Author(s):  
A.A. Potapov
Keyword(s):  
Radio Frequency ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Spectrum Analyzer ◽  
Signal To Noise ◽  
Recording Time ◽  
Low Level ◽  
Periodic Spectrum ◽  
Noise Ratio

The article presents practical implementation of energy detection method based upon non-parametric statistics computed using periodic spectrum samples provided by measuring equipment. The method enables efficient detection and monitoring for signals with low or negative signal-to-noise ratio. The method's sensitivity is limited by measuring equipment inherent noise fluctuations and can be a priory experimentally established for certain experimental hardware settings and desirable spectrum samples lengths. Sensitivity thresholds (in terms of signal-to-noise ratio) for reliable (with probability > 0,98) signal detection for a typical spectrum analyzer used in experiment varied from –11 dB to + 0,6 dB for spectrum samples lengths ranged between 30 000 and 470 spectrums respectively. The suggested energy detection method can be used for unstable and intermittent signals detection, which are active (or above sensitivity threshold) only for a fraction of spectrum sample recording time. The method is independent of signal's modulation (if any is used), amplitude variability profile and signal's probability distribution features. Experimentally determined sensitivity threshold levels for real radio frequency signals coincided within 1,9 dB tolerances with corresponding levels estimated from spectrum analyzer inherent noise fluctuations for all implemented spectrum samples lengths. The data recording time for abovementioned spectrum samples lengths ranged between 207 and 3,2 seconds respectively and was entirely hardware-dependent parameter. Experiment proved equal efficiency and reliability of the suggested method for reliable detection for both white noise signal (generated by analog generator) and broadcasted LTE signal (generated by cellular base stations), which were affected by multi-path propagation effects and average signal level instability due to subscribers time-varying activity. The experiment showed the proposed energy detection method besides detection of low-level radio frequency signals (down to –11 dB SNR) provides highly reliable assessment of the detected signal's signal-to-noise ratio with 0,6 dB tolerance and 0,95 probability. The energy detection method demonstrated zero level of false detections when there was no signal at the spectrum analyzer input (the input port of the instrument was terminated by a matched load), which is essential for method applicability in tasks of highly reliable detection of low-level signals from various types of sources. Taking into account specifications of available hardware, required sensitivity level and limits for data recording time it is possible to choose optimal length of spectrum sample for the energy detection method, which would be the most reasonable for any task in question. The energy detection method based upon non-parametric statistics computed using periodic spectrum samples can be effectively used in detection and radiomonitoring of low-level signals, in radio frequency electromagnetic compatibility research tasks and radio propagation path properties analysis in high loss environment.

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