scholarly journals Enhancement of Radar Detection Accuracy Using H-Beam Wave Polarization in Random Media

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2804
Author(s):  
Hosam El-Ocla
Keyword(s):  
Random Media ◽  
Incident Angle ◽  
Object Identification ◽  
Detection Accuracy ◽  
Finite Width ◽  
Wave Polarization ◽  
Beam Wave ◽  
H Beam ◽  
Incident Waves ◽  
The Impact

This work addresses the range in which the accuracy of object identification is enhanced regardless of radar parameters. We compute the radar cross-section (RCS) of conducting objects in free space and random media. We use beam wave incidence and postulate its coherency with a finite width around an object located in the far field. Accordingly, we examine the impact of radar parameters on the RCS, where these parameters include the incident angle, target size and complexity, medium fluctuation intensity and the beam size of the incident waves. H-wave polarization is assumed for the waves’ incidence.

scholarly journals Effects of Medium Characteristics on Laser RCS of Airplane with E-Wave Polarization

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hosam El-Ocla
Keyword(s):  
Plane Wave ◽  
Cross Sections ◽  
Random Media ◽  
Incident Beam ◽  
Finite Width ◽  
Wave Polarization ◽  
Inflection Points ◽  
The Mean ◽  
Wave Incidence ◽  
Fluctuations Intensity

Plane wave incidence should be postulated to have an authentic target detection. Practically, the plane wave is incapable usually of keeping its power in the far field especially when propagating through an inhomogeneous medium. Consequently, we assume an incident beam wave with a finite width around the target. In this work, we calculate numerically a laser radar cross section (LRCS) of conducting targets having smooth cross sections with inflection points such as airplane in random media. Effects of fluctuations intensity of random media on the LRCS performance are studied in this paper. E-wave polarization (E-wave incidence) is considered while the mean target size is approximately twice the wavelength.

scholarly journals Towards Transabdominal Functional Photoacoustic Imaging of the Placenta: Improvement in Imaging Depth Through Optimization of Light Delivery

2021 ◽  
Author(s):  
Kristie Huda ◽  
Kenneth F. Swan ◽  
Cecilia T. Gambala ◽  
Gabriella C. Pridjian ◽  
Carolyn L. Bayer
Keyword(s):  
Delivery System ◽  
Light Propagation ◽  
Ex Vivo ◽  
Photoacoustic Imaging ◽  
Incidence Angle ◽  
Incident Angle ◽  
Placental Tissue ◽  
Light Delivery ◽  
Imaging Depth ◽  
The Impact

AbstractFunctional photoacoustic imaging of the placenta could provide an innovative tool to diagnose preeclampsia, monitor fetal growth restriction, and determine the developmental impacts of gestational diabetes. However, transabdominal photoacoustic imaging is limited in imaging depth due to the tissue’s scattering and absorption of light. The aim of this paper was to investigate the impact of geometry and wavelength on transabdominal light delivery. Our methods included the development of a multilayer model of the abdominal tissue and simulation of the light propagation using Monte Carlo methods. A bifurcated light source with varying incident angle of light, distance between light beams, and beam area was simulated to analyze the effect of light delivery geometry on the fluence distribution at depth. The impact of wavelength and the effects of variable thicknesses of adipose tissue and muscle were also studied. Our results showed that the beam area plays a major role in improving the delivery of light to deep tissue, in comparison to light incidence angle or distance between the bifurcated fibers. Longer wavelengths, with incident fluence at the maximum permissible exposure limit, also increases fluence within deeper tissue. We validated our simulations using a commercially available light delivery system and ex vivo human placental tissue. Additionally, we compared our optimized light delivery to a commercially available light delivery system, and conclude that our optimized geometry could improve imaging depth more than 1.6×, bringing the imaging depth to within the needed range for transabdominal imaging of the human placenta.

scholarly journals FA-YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuangjiang Du ◽  
Baofu Zhang ◽  
Pin Zhang ◽  
Peng Xiang ◽  
Hong Xue
Keyword(s):  
Object Detection ◽  
Background Information ◽  
Detection Accuracy ◽  
Infrared Images ◽  
Improved Method ◽  
Negative Sample ◽  
Remote Sensing Images ◽  
Complex Background ◽  
Small Targets ◽  
The Impact

Infrared target detection is a popular applied field in object detection as well as a challenge. This paper proposes the focus and attention mechanism-based YOLO (FA-YOLO), which is an improved method to detect the infrared occluded vehicles in the complex background of remote sensing images. Firstly, we use GAN to create infrared images from the visible datasets to make sufficient datasets for training as well as using transfer learning. Then, to mitigate the impact of the useless and complex background information, we propose the negative sample focusing mechanism to focus on the confusing negative sample training to depress the false positives and increase the detection precision. Finally, to enhance the features of the infrared small targets, we add the dilated convolutional block attention module (dilated CBAM) to the CSPdarknet53 in the YOLOv4 backbone. To verify the superiority of our model, we carefully select 318 infrared occluded vehicle images from the VIVID-infrared dataset for testing. The detection accuracy-mAP improves from 79.24% to 92.95%, and the F1 score improves from 77.92% to 88.13%, which demonstrates a significant improvement in infrared small occluded vehicle detection.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

The impact of incident angle on multiple-bit upset in SRAMs

2003 ◽  
Vol 209 ◽  
pp. 367-370 ◽  
Author(s):  
Q.X. Zhang ◽  
M.D. Hou ◽  
J. Liu ◽  
Z.G. Wang ◽  
Y.F. Jin ◽  
...  
Keyword(s):  
Incident Angle ◽  
The Impact

Impact Dynamics Analysis of Shed Tunnel Structure Hit by Collapse Rock-Fall

2011 ◽  
Vol 99-100 ◽  
pp. 1023-1026 ◽  
Author(s):  
Lu Yang ◽  
Shi Min Li ◽  
Dai Heng Chen ◽  
Zhi Min Wu
Keyword(s):  
Contact Force ◽  
Impact Energy ◽  
Incident Angle ◽  
Great Influence ◽  
Rock Fall ◽  
Tunnel Structure ◽  
Protective Structure ◽  
Additional Energy ◽  
Finite Element Software ◽  
The Impact

This paper bases on the prototype of the actual shed tunnel structure, study on contact force, displacement, damage, energy of shed tunnel under impact of rock-fall. By ABAQUS finite element software to simulate the process of roll-fall impact knowable: Rock-fall at different speeds and incident angle shocks on shed tunnel has great influence to concrete protective structure of contact force and displacement; Concrete protective structure damage the worst hit area of occurred with roll-fall contact area, the second is inclined leg column top and in connection with the pillars of the beam damage is also very serious, In practical projects first should pay attention to strengthen the intensity of the pillars with beam joints and prevent damage; From the angle of energy we can see that shed tunnel is mainly through the concrete protective structure to absorb and consumption impact energy, soil cushion absorption and consumption impact energy is very limited, to alleviate the impact of concrete protective layer rolling damage, and suggestions in shed tunnel bearing place additional energy shock absorber to increases protection structure system soft degrees under the condition of minimize the shed tunnel weight, achieve the purpose of decrease shock energy.

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