High-performance light trajectory tracking and image sensing devices based on a γ-In2Se3/GaAs heterostructure

2020 ◽  
Vol 8 (39) ◽  
pp. 13762-13769
Author(s):  
Jing-Wei Kang ◽  
Chao Zhang ◽  
Kai-Jun Cao ◽  
Yu Lu ◽  
Chun-Yan Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Trajectory Tracking ◽  
High Performance ◽  
Linear Array ◽  
Image Sensing ◽  
Optoelectronic Applications

A high-performance γ-In2Se3/GaAs heterostructure-based photodetector linear array shows potential in optoelectronic applications such as real-time light trajectory tracking and image sensing.

Multilayered PtSe2/pyramid-Si heterostructure array with light confinement effect for high-performance photodetection, image sensing and light trajectory tracking applications

2021 ◽  
Author(s):  
Mengru Ma ◽  
Huahan Chen ◽  
Kunnan Zhou ◽  
Chao Xie ◽  
Yi Liang ◽  
...  
Keyword(s):  
Real Time ◽  
Trajectory Tracking ◽  
High Performance ◽  
Confinement Effect ◽  
Light Confinement ◽  
Image Sensing ◽  
Photodetector Array ◽  
Potential Applications

A multilayered PtSe2/pyramid-Si heterostructure-based photodetector array consisting of 8×8 device units shows excellent optoelectrical performance with potential applications in NIR image sensing and real-time light trajectory tracking.

Real-Time High Jump Wearable Device with ESP8266 for High-Performance and Low-Injury

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Muhammad Faris Roslan ◽  
◽  
Afandi Ahmad ◽  
Abbes Amira ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Wearable Device

Dielectric and electrical properties of La@NiO SNPs for high-performance optoelectronic applications

2021 ◽  
Author(s):  
Mohd. Shkir ◽  
Aslam Khan ◽  
Kamlesh V. Chandekar ◽  
M.A. Sayed ◽  
Ahmed Mohamed El-Toni ◽  
...  
Keyword(s):  
Electrical Properties ◽  
High Performance ◽  
Optoelectronic Applications

scholarly journals Real-time Array Shape Estimation Method of Horizontal Suspended Linear Array Based on Non-acoustic Auxiliary Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zonglun Che ◽  
Jun Wang ◽  
Jing Zhu ◽  
Bingbing Zhang ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Linear Array ◽  
Estimation Method ◽  
Shape Estimation ◽  
Array Shape Estimation

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Progress Towards Arrays of Microcalorimeter X-Ray Detectors

2001 ◽  
Vol 7 (S2) ◽  
pp. 1050-1051 ◽  
Author(s):  
S.W. Nam ◽  
D.A. Wollman ◽  
Dale E. Newbury ◽  
G.C. Hilton ◽  
K.D. Irwin ◽  
...  
Keyword(s):  
Real Time ◽  
Energy Resolution ◽  
Count Rate ◽  
Small Area ◽  
High Performance ◽  
X Ray ◽  
Pixel Array ◽  
Primary Advantage ◽  
Low X ◽  
Single Pixel

The high performance of single-pixel microcalorimeter EDS (μ,cal EDS) has been shown to be very useful for a variety of microanalysis cases. The primary advantage of jxcal EDS over conventional EDS is the factor of 25 improvement in energy resolution (∽3 eV in real-time). This level of energy resolution is particularly important for applications such as nanoscale contaminant analysis where it is necessary to resolve peak overlaps at low x-ray energies. Because μcal EDS offers practical solutions to many microanalysis problems, several companies are proceeding with commercialization of single-pixel μal EDS technology. Two drawbacks limiting the application of uxal EDS are its low count rate (∽500 s−1) and small area (∽0.04 mm for a bare single pixel, ∽5 mm2 with a polycapillary optic). We are developing a 32x32 pixel array with a total area of 40 mm2 and with a total count rate between 105 s−1 and 106 s−1.

scholarly journals Stochastic-Depth Ambient Occlusion

2021 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
Jop Vermeer ◽  
Leonardo Scandolo ◽  
Elmar Eisemann
Keyword(s):  
Real Time ◽  
High Performance ◽  
Depth Map ◽  
Visual Quality ◽  
Ambient Light ◽  
Missing Information ◽  
Time Performance ◽  
Ambient Occlusion ◽  
Depth Buffer ◽  
Screen Space

Ambient occlusion (AO) is a popular rendering technique that enhances depth perception and realism by darkening locations that are less exposed to ambient light (e.g., corners and creases). In real-time applications, screen-space variants, relying on the depth buffer, are used due to their high performance and good visual quality. However, these only take visible surfaces into account, resulting in inconsistencies, especially during motion. Stochastic-Depth Ambient Occlusion is a novel AO algorithm that accounts for occluded geometry by relying on a stochastic depth map, capturing multiple scene layers per pixel at random. Hereby, we efficiently gather missing information in order to improve upon the accuracy and spatial stability of conventional screen-space approximations, while maintaining real-time performance. Our approach integrates well into existing rendering pipelines and improves the robustness of many different AO techniques, including multi-view solutions.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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