scholarly journals Feedback and Noise‐Signal Detection at Three Performance Levels

1964 ◽  
Vol 36 (5) ◽  
pp. 1042-1043
Author(s):  
Richard A. Campbell
Keyword(s):  
Signal Detection ◽  
Noise Signal ◽  
Performance Levels

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

scholarly journals Research on the Detection of Vehicle Vibration and Noise Signal Based On Negative Entropy Detection Algorithm

2021 ◽  
Vol 2074 (1) ◽  
pp. 012055
Author(s):  
Naibin Zhai ◽  
Haijun Zhao ◽  
Xintao Cui
Keyword(s):  
Signal Detection ◽  
Detection Method ◽  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Noise Signal ◽  
Noise Model ◽  
Vehicle Noise ◽  
Negative Entropy ◽  
And Control ◽  
Coding Bits

Abstract As an important part of vehicle noise signal detection and processing, negative entropy detection algorithm can accurately reduce the number of speech coding bits, ameliorate the recognition accuracy, and establish the noise model in the process of noise reduction. Based on this, this paper first analyses the source and control of vehicle vibration and noise, then studies the principle of negative entropy detection algorithm of vehicle vibration and noise signal, and finally gives the vehicle vibration and noise signal detection method based on negative entropy detection algorithm.

scholarly journals Benchmarking Aided Decision Making in a Signal Detection Task

2017 ◽  
Vol 59 (6) ◽  
pp. 881-900 ◽  
Author(s):  
Megan L. Bartlett ◽  
Jason S. McCarley
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Statistical Models ◽  
Decision Aids ◽  
Detection Task ◽  
Collaborative Decision Making ◽  
Performance Levels ◽  
Binary Signal ◽  
Signal Detection Task ◽  
Collaborative Decision

Objective: A series of experiments examined human operators’ strategies for interacting with highly (93%) reliable automated decision aids in a binary signal detection task. Background: Operators often interact with automated decision aids in a suboptimal way, achieving performance levels lower than predicted by a statistically ideal model of information integration. To better understand operators’ inefficient use of decision aids, we compared participants’ automation-aided performance levels with the predictions of seven statistical models of collaborative decision making. Method: Participants performed a binary signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid that provided either graded (Experiments 1 and 3) or binary (Experiment 2) cues. We compared automation-aided performance with the predictions of seven statistical models of collaborative decision making, including a statistically optimal model and Robinson and Sorkin’s contingent criterion model. Results and Conclusion: Automation-aided sensitivity hewed closest to the predictions of the two least efficient collaborative models, well short of statistically ideal levels. Performance was similar whether the aid provided graded or binary judgments. Model comparisons identified potential strategies by which participants integrated their judgments with the aid’s. Application: Results lend insight into participants’ automation-aided decision strategies and provide benchmarks for predicting automation-aided performance levels.

Study on the Vibration of a Paper-Transferring Mechanism

2010 ◽  
Vol 174 ◽  
pp. 311-314
Author(s):  
Ji Fei Cai ◽  
Yuan Huang ◽  
Xin Zhu Wang
Keyword(s):  
Signal Detection ◽  
Dynamic Analysis ◽  
Signal Analysis ◽  
Structural Modification ◽  
Natural Frequencies ◽  
Vibration Signal ◽  
Noise Signal ◽  
Maximum Acceleration ◽  
The Stability

To improve the stability of a Paper-Transferring Mechanism (PTM), a profound understanding must be made on its vibration characteristics, on which based, a modified design was made. Through kinetic and dynamic analysis of the PTM, the natural frequencies were obtained based on the vibration signal detection and noise signal analysis. To reduce the vibration response of the PTM, first is to reduce its maximum acceleration and reduce the quality of moving parts and improve its quality distribution, then structural modification to the wallboards and pull beams of the platform should be made to render its inherent frequencies from its working frequencies.

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