The impact of Victoria's real time prescription monitoring system (SafeScript) on a cohort of people who inject drugs

2020 ◽  
Vol 213 (3) ◽  
pp. 141
Author(s):  
Dagnachew M Fetene ◽  
Peter Higgs ◽  
Suzanne Nielsen ◽  
Filip Djordjevic ◽  
Paul Dietze
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
People Who Inject Drugs ◽  
Prescription Monitoring ◽  
The Impact

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Remote distributed monitoring system of switched reluctance motor

2019 ◽  
Vol 52 (3-4) ◽  
pp. 276-290
Author(s):  
Yan Cai ◽  
Wenlong Xie ◽  
Haihua Zhang
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Real Time ◽  
Monitoring System ◽  
Digital Signal ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Motor ◽  
The Impact ◽  
Dynamic Performance Analysis

The reliable operation, dynamic performance analysis and control strategy research of a switched reluctance motor (SRM) require an online monitoring system to display and record its operating status. However, due to the large amount of data, the nonlinear electromagnetic characteristics and the harsh working environment of SRM, it is very difficult to monitor a SRM’s operation status in real time. In order to solve these problems, a new structure of the SRM monitoring system, which uses Digital Signal Processor (DSP) and the hardwired Transmission Control Protocol/Internet Protocol embedded Ethernet controller W5500, is presented in this paper. The W5500 and DSP’s direct memory access modules are employed for data capture and transfer to reduce the digital signal processing workload. The digital signal processing program is implemented by the hybrid programming method, which shortens the filtering time. Consequently, the DSP has sufficient resources to acquire multiple signals with a high sampling frequency and can adopt a more complex filtering algorithm, which enhances the accuracy and real-time performance of the system. Moreover, the amplitude–frequency characteristics of signals are analyzed. Then, the detection circuits and finite impulse response filters are designed to achieve the targeted acquisition and filtering. Besides, the impact of harsh environment on the system is reduced by adjusting the data transmission modes according to different working conditions. As a result, the scope of application of the system has been extended. The proposed system has a novel structure and strong practicability, which exhibits great guiding significance for the development of a SRM.

scholarly journals Artificial Intelligence Enabled Traffic Monitoring System

2020 ◽  
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Real Time Traffic ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

FBGs Real-Time Impact Damage Monitoring System of GFRP Beam Based on CC-LSL Algorithm

2018 ◽  
Vol 18 (05) ◽  
pp. 1850075 ◽  
Author(s):  
E. Vorathin ◽  
Z. M. Hafizi ◽  
S. A. Che Ghani ◽  
J. P. Siregar ◽  
K. S. Lim
Keyword(s):  
Composite Materials ◽  
Real Time ◽  
Monitoring System ◽  
Cross Correlation ◽  
Impact Damage ◽  
Dynamic Strain ◽  
Internal Damage ◽  
Measuring Device ◽  
Glass Fiber Reinforced ◽  
The Impact

Glass-fiber reinforced polymer (GFRP) composite materials have an undisputed dominance over conventional metallic materials. However, susceptibility to barely visible or invisible internal damage due to impact has increased the demand for these composite materials in robust real-time structural health monitoring (SHM) system since they are capable of localizing the source of impact. Thus, in this paper, an in situ FBG sensor was embedded in a GFRP beam, providing an online real-time monitoring system and with the knowledge of cross-correlation linear source location (CC-LSL) algorithm, the impact location was capable of being determined in a split second. The consistency of cross-correlation function in providing repeatable results for all trials estimated a consistent time difference for all the impact points. The CC-LSL algorithm also revealed that the highest percentage of error was only 4.21% away from the actual hit. In the meantime, FBGs also showed good results as a dynamic strain measuring device in capturing frequency response at certain orientations compared to the AE sensor.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

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