scholarly journals Wearable Health Monitoring: A Real-Time Wearable UV-Radiation Monitor based on a High-Performance p-CuZnS/n-TiO2 Photodetector (Adv. Mater. 43/2018)

2018 ◽  
Vol 30 (43) ◽  
pp. 1870321 ◽  
Author(s):  
Xiaojie Xu ◽  
Jiaxin Chen ◽  
Sa Cai ◽  
Zhenghao Long ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Uv Radiation ◽  
Health Monitoring ◽  
High Performance ◽  
Radiation Monitor

A Real-Time Wearable UV-Radiation Monitor based on a High-Performance p-CuZnS/n-TiO2 Photodetector

2018 ◽  
Vol 30 (43) ◽  
pp. 1803165 ◽  
Author(s):  
Xiaojie Xu ◽  
Jiaxin Chen ◽  
Sa Cai ◽  
Zhenghao Long ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Uv Radiation ◽  
High Performance ◽  
Radiation Monitor

High-performance flexible all-solid-state aqueous rechargeable Zn–MnO2 microbatteries integrated with wearable pressure sensors

2018 ◽  
Vol 6 (30) ◽  
pp. 14594-14601 ◽  
Author(s):  
Bing He ◽  
Qichong Zhang ◽  
Lianhui Li ◽  
Juan Sun ◽  
Ping Man ◽  
...  
Keyword(s):  
Solid State ◽  
Real Time ◽  
Health Monitoring ◽  
High Performance ◽  
Pressure Sensors ◽  
Integrated System

A self-powering, multifunctional, miniaturized integrated system was designed to achieve real-time health monitoring both statically and dynamically.

The Design of Portable Wireless Remote ECG Monitor System

2013 ◽  
Vol 333-335 ◽  
pp. 2375-2379
Author(s):  
Yan Peng ◽  
Zhi Gang Qin
Keyword(s):  
Energy Saving ◽  
Real Time ◽  
Health Monitoring ◽  
Remote Monitoring ◽  
High Performance ◽  
Ecg Signal ◽  
Monitor System ◽  
The Core

The paper designed a portable remote wireless ECG monitor system for daily health monitoring, which used a high-performance ARM11 microprocessor S3C6410 as the core. It can achieve a real-time display and remote monitoring of the ECG signal, with the advantages of portability, energy-saving, powerful function, security and stability. The detector is mainly for family, hospitals and community.

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

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.

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