Vibrational risk assessment as a signal detection problem: Escape hatching of red‐eyed treefrog eggs

2007 ◽  
Vol 121 (5) ◽  
pp. 3080-3080
Author(s):  
Karen M. Warkentin ◽  
Michael S. Caldwell ◽  
J. Gregory McDaniel
Keyword(s):  
Risk Assessment ◽  
Signal Detection ◽  
Detection Problem

scholarly journals Space Lower Bounds for the Signal Detection Problem

2020 ◽  
Author(s):  
Faith Ellen ◽  
Rati Gelashvili ◽  
Philipp Woelfel ◽  
Leqi Zhu
Keyword(s):  
Signal Detection ◽  
Lower Bounds ◽  
Detection Problem

scholarly journals Hypothesis Testing for Signal Detection Problem and Large Deviations

2001 ◽  
Vol 162 ◽  
pp. 187-203 ◽  
Author(s):  
Taizo Chiyonobu
Keyword(s):  
Signal Detection ◽  
Continuous Time ◽  
Error Probability ◽  
Diffusion Processes ◽  
Large Deviation ◽  
Observation Time ◽  
Optimal Decision ◽  
Detection Problem ◽  
Stationary Diffusion ◽  
Decision Region

We consider a signal detection problem for the continuous-time stationary diffusion processes. The optimal decision region is given by Neyman-Pearson’s lemma. We establish certain large deviation estimates, and with the help of it we show that the error probability of the second kind of the signal detection tends to zero or one exponentially fast, depending on the fixed exponent of the decay of the error probability of the first kind, as the observation time goes to infinity.

Areas of Interest as a Signal Detection Problem in Behavioral Eye-Tracking Research

2015 ◽  
Vol 29 (2-3) ◽  
pp. 103-115 ◽  
Author(s):  
Jacob L. Orquin ◽  
Nathaniel J. S. Ashby ◽  
Alasdair D. F. Clarke
Keyword(s):  
Eye Tracking ◽  
Signal Detection ◽  
Detection Problem ◽  
Areas Of Interest

Diagnosis of the Joint Backlash of a Mechanism: Wigner-Ville Distribution Combined With Correlation Techniques

1995 ◽  
Author(s):  
M.-C. Pan ◽  
B. Verbeure ◽  
H. Van Brussel ◽  
P. Sas
Keyword(s):  
Signal Detection ◽  
Operating Conditions ◽  
Degree Of Freedom ◽  
Two Dimensional ◽  
Detection Problem ◽  
Time Frequency ◽  
Normal Operating ◽  
Link Mechanism ◽  
Pattern Differentiation ◽  
Correlation Techniques

Abstract The aim of this paper is to develop appropriate techniques to detect and classify the joint backlash of a robot by monitoring its vibration response during normal operating conditions. In this investigation, Wigner-Ville distributions combined with two-dimensional correlation techniques have been employed to diagnose various degrees of single-joint backlash. The method also allows to detect and to single out backlash present in two joints of a multi-link mechanism. In the work reported here, the Wigner-Ville distribution based signal detection and the generalized symmetrical Itakura distance were proposed as tools for pattern differentiation. Initially the proposed methods have been applied to quantify the backlash of a single joint. Consecutively, the detection problem has been generalized to diagnose faults in two joints simultaneously. Due to the extra degree of freedom given by the 2D nature of the WVD’s, certain time-frequency regions were chosen as reference signatures in the case of single-joint backlash, and the signatures, spanning over two impact transients at the reverses of motion, were chosen in the cases of double-joint backlash. The proposed techniques have been successfully implemented on a two-link mechanism.

scholarly journals A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yonghua Wang ◽  
Shunchao Zhang ◽  
Yongwei Zhang ◽  
Pin Wan ◽  
Jiangfan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Signal Detection ◽  
Empirical Mode Decomposition ◽  
Spectrum Sensing ◽  
Clustering Algorithm ◽  
Cooperative Spectrum Sensing ◽  
Information Geometry ◽  
Electromagnetic Environment ◽  
Detection Problem ◽  
Mode Decomposition

In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.

scholarly journals On Sequential Decisions by Human Observers in a Signal Detection Problem

1958 ◽  
Vol 30 (7) ◽  
pp. 673-673
Author(s):  
John A. Swets ◽  
Mary McKey ◽  
David M. Green
Keyword(s):  
Signal Detection ◽  
Detection Problem ◽  
Sequential Decisions
Sign in / Sign up

Export Citation Format

Share Document