Non-Fullerene-Based Printed Organic Photodiodes with High Responsivity and Megahertz Detection Speed

2018 ◽  
Vol 10 (49) ◽  
pp. 42733-42739 ◽  
Author(s):  
Noah Strobel ◽  
Mervin Seiberlich ◽  
Tobias Rödlmeier ◽  
Uli Lemmer ◽  
Gerardo Hernandez-Sosa
Keyword(s):  
High Responsivity ◽  
Detection Speed

High-Responsivity Solar-Blind Ultraviolet Photodetectors Based on Carbon Nanodots/Graphene Hybrids

2021 ◽  
Vol 68 (3) ◽  
pp. 1101-1106
Author(s):  
Yong Fang ◽  
Zhiwei Zhao ◽  
Mengru Zhu ◽  
Zhengjin Weng ◽  
Chao Fang ◽  
...  
Keyword(s):  
Carbon Nanodots ◽  
High Responsivity ◽  
Solar Blind ◽  
Ultraviolet Photodetectors

High-Responsivity Solar-Blind Photodetector Based on $ \hbox{Mg}_{0.46}\hbox{Zn}_{0.54}\hbox{O}$ Thin Film

2012 ◽  
Vol 33 (7) ◽  
pp. 1033-1035 ◽  
Author(s):  
Qinghong Zheng ◽  
Feng Huang ◽  
Jin Huang ◽  
Qichan Hu ◽  
Dagui Chen ◽  
...  
Keyword(s):  
Thin Film ◽  
High Responsivity ◽  
Solar Blind

scholarly journals Evaluation of CogSport for acute concussion diagnosis in cricket

2021 ◽  
Vol 7 (2) ◽  
pp. e001061
Author(s):  
Kira James ◽  
Anna E Saw ◽  
Richard Saw ◽  
Alex Kountouris ◽  
John William Orchard
Keyword(s):  
Symptom Severity ◽  
Clinical Utility ◽  
Specific Activity ◽  
Median Number ◽  
Diagnostic Utility ◽  
Diagnostic Ability ◽  
Clinical Indicator ◽  
Retrospective Study Design ◽  
Variable Presentation ◽  
Detection Speed

ObjectiveThe diagnosis of sport-related concussion is a challenge for practitioners given the variable presentation and lack of a universal clinical indicator. The aim of this study was to describe the CogSport findings associated with concussion in elite Australian cricket players, and to evaluate the diagnostic ability of CogSport for this cohort.MethodsA retrospective study design was used to evaluate CogSport performance of 45 concussed (male n=27, mean age 24.5±4.5 years; female n=18, 23.5±3.5 years) compared with 45 matched non-concussed (male n=27, mean age 27.3±4.5 years; female n=18, 24.1±4.5 years) elite Australian cricket players who sustained a head impact during cricket specific activity between July 2015 and December 2019.ResultsMedian number of reported symptoms on the day of injury for concussed players was 7 out of 24, with a median symptom severity of 10 out of 120. CogSport performance deteriorated significantly in concussed cricket players’ Detection speed (p<0.001), Identification speed (p<0.001), One Back speed (p=0.001) and One Back accuracy (p=0.022) components. These components, when considered independently and together, had good diagnostic utility.ConclusionThis study demonstrated good clinical utility of CogSport for identifying concussed cricket players, particularly symptoms and Detection, Identification and One Back components. Therefore, CogSport may be considered a useful tool to assist concussion diagnosis in this cohort, and the clinician may place greater weight on the components associated with concussion diagnosis.

Fabrication and characterization of novel high-speed InGaAs/InP uni-traveling-carrier photodetector for high responsivity

2015 ◽  
Vol 24 (10) ◽  
pp. 108506
Author(s):  
Qing-Tao Chen ◽  
Yong-Qing Huang ◽  
Jia-Rui Fei ◽  
Xiao-Feng Duan ◽  
Kai Liu ◽  
...  
Keyword(s):  
High Speed ◽  
High Responsivity ◽  
Fabrication And Characterization

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

Low Dark Current and High Responsivity 1020nm InGaAs/GaAs Nano-Ridge Waveguide Photodetector Monolithically Integrated on a 300-mm Si Wafer

2021 ◽  
pp. 1-1
Author(s):  
Cenk Ibrahim Ozdemir ◽  
Yannick De Koninck ◽  
Didit Yudistira ◽  
Nadezda Kuznetsova ◽  
Marina Baryshnikova ◽  
...  
Keyword(s):  
Dark Current ◽  
Ridge Waveguide ◽  
High Responsivity ◽  
Monolithically Integrated ◽  
Si Wafer

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

scholarly journals Gas Sensors Based on Mechanically Exfoliated MoS2 Nanosheets for Room-Temperature NO2 Detection

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2123 ◽  
Author(s):  
Wenli Li ◽  
Yong Zhang ◽  
Xia Long ◽  
Juexian Cao ◽  
Xin Xin ◽  
...  
Keyword(s):  
Gas Sensors ◽  
High Performance ◽  
Density Functional ◽  
Room Temperature ◽  
Mos2 Nanosheets ◽  
Mechanical Exfoliation ◽  
High Responsivity ◽  
Recovery Behavior ◽  
No2 Gas Sensors ◽  
No2 Detection

The unique properties of MoS2 nanosheets make them a promising candidate for high-performance room temperature gas detection. Herein, few-layer MoS2 nanosheets (FLMN) prepared via mechanical exfoliation are coated on a substrate with interdigital electrodes for room-temperature NO2 detection. Interestingly, compared with other NO2 gas sensors based on MoS2, FLMN gas sensors exhibit high responsivity for room-temperature NO2 detection, and NO2 is easily desorbed from the sensor surface with an ultrafast recovery behavior, with recovery times around 2 s. The high responsivity is related to the fact that the adsorbed NO2 can affect the electron states within the entire material, which is attributed to the very small thickness of the MoS2 nanosheets. First-principles calculations were carried out based on the density functional theory (DFT) to verify that the ultrafast recovery behavior arises from the weak van der Waals binding between NO2 and the MoS2 surface. Our work suggests that FLMN prepared via mechanical exfoliation have a great potential for fabricating high-performance NO2 gas sensors.

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