scholarly journals Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

2017 ◽  
Author(s):  
Hamed Komari Alaie ◽  
Hassan Farsi
Keyword(s):  
Shallow Water ◽  
Persian Gulf ◽  
Prior Distribution ◽  
Detection Method ◽  
Adaptive Threshold ◽  
Maximum Likelihood Algorithm ◽  
Statistical Classifier ◽  
Passive Sonar ◽  
True Detection ◽  
Target Detecting

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar the targets are detected by sonar equation (with constant threshold) which increase the detection error in shallow water. Purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Towards a Lateral Line Sensor to Supplement Sonar in Shallow Water

2014 ◽  
Author(s):  
Tim Ziemer
Keyword(s):  
Shallow Water ◽  
Lateral Line ◽  
Near Field ◽  
Sensory System ◽  
Digital Signal ◽  
Detection Methods ◽  
Whole Body ◽  
Water Displacement ◽  
Marine Animals ◽  
Passive Sonar

Sonar provides vessels with a sensory system to detect and identify still and moving obstacles. In shallow water both active and passive sonar meet their limits. Acoustical methods exist, aiming at supporting sonar systems by means of digital signal processing, or, coming from the field of biomimetics, imitating echolocation principles of marine animals. This paper introduces a sensor system combining these approaches by the use of a vector sensor array applying Near-field Acoustical Holography (NAH) imitating the Lateral Line organ (LL) of fish; a passive method to supplement active and passive sonar. LL is able to localize obstacles due to their dipole-like water displacement by comparing low-frequency water accelerations distributed along the whole body. In contrast to pressure, accelerations are highly evanescent and do not propagate into the far-field. Thus LL does not suffer under reverberation or scattering. The performance of the proposed NAH-based LL-sensor is tested by a computer simulation of a source in absence and in presence of a disturbing source. The LL-sensor has proven to be more robust than pressure detection methods like beamforming and conventional NAH.

Some shallow water octocorals (Coelenterata: Anthozoa) of the Persian Gulf

Zootaxa ◽  
2009 ◽  
Vol 2058 (1) ◽  
pp. 1-52 ◽  
Author(s):  
K. SAMIMI NAMIN ◽  
L. P. VAN OFWEGEN
Keyword(s):  
New Species ◽  
Shallow Water ◽  
Persian Gulf ◽  
Genus Level ◽  
Three New Species ◽  
The Persian Gulf

A collection of octocorals from the Persian Gulf is examined, and all species are identified to at least genus level. Sinularia erecta Tixier-Durivault, 1945; S. compressa Tixier-Durivault, 1945; Subergorgia suberosa (Pallas, 1766); Junceella juncea (Pallas, 1766); and Acanthogorgia spinosa Hiles, 1899, could be identified further. With some doubts Trimuricea reticulata (Thomson & Simpson, 1909), Menella cf. kanisa Grasshoff, 2000, and Verrucella cf. reticulata (Thomson & Simpson, 1909) are tentatively identified. Subergorgia perezi Stiasny, 1940 is synonymised with Subergorgia suberosa (Pallas, 1766), and Echinogorgia bahrelfarsi Stiasny, 1940 is assigned to Menella. Three new species are described and depicted.

A record of global change: OAE 1a in Dariyan shallow-water platform carbonates, southern Tethys, Persian Gulf, Iran

Facies ◽  
2016 ◽  
Vol 62 (4) ◽  
Author(s):  
Mehrangiz Naderi-Khujin ◽  
Ali Seyrafian ◽  
Hossein Vaziri-Moghaddam ◽  
Vahid Tavakoli
Keyword(s):  
Global Change ◽  
Shallow Water ◽  
Persian Gulf ◽  
Platform Carbonates ◽  
Southern Tethys

scholarly journals Specular Highlight Detection Based on Color Distribution for Endoscopic Images

2021 ◽  
Vol 8 ◽  
Author(s):  
Baoxian Yu ◽  
Wanbing Chen ◽  
Qinghua Zhong ◽  
Han Zhang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Specular Reflection ◽  
Disease Diagnosis ◽  
Adaptive Threshold ◽  
The Other ◽  
Imaging Systems ◽  
Color Distribution ◽  
Endoscopic Images ◽  
Highlight Detection

Endoscopic imaging systems have been widely used in disease diagnosis and minimally invasive surgery. Practically, specular reflection (a.k.a. highlight) always exists in endoscopic images and significantly affects surgeons’ observation and judgment. Motivated by the fact that the values of the red channel in nonhighlight area of endoscopic images are higher than those of the green and blue ones, this paper proposes an adaptive specular highlight detection method for endoscopic images. Specifically, for each pixel, we design a criterion for specular highlight detection based on the ratio of the red channel to both the green and blue channels. With the designed criteria, we take advantage of image segmentation and then develop an adaptive threshold with respect to the differences between the red channel and the other ones of neighboring pixels. To validate the proposed method, we conduct experiments on clinical data and CVC-ClinicSpec open database. The experimental results demonstrate that the proposed method yields an averaged Precision, Accuracy, and F1-score rate of 88.76%, 99.60% and 72.56%, respectively, and outperforms the state-of-the-art approaches based on color distribution reported for endoscopic highlight detection.

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