Semi-Flocking Algorithm for Motion Control of Mobile Sensors in Large-Scale Surveillance Systems

2015 ◽  
Vol 45 (1) ◽  
pp. 129-137 ◽  
Author(s):  
Samaneh Hosseini Semnani ◽  
Otman A. Basir
Keyword(s):  
Motion Control ◽  
Large Scale ◽  
Mobile Sensors ◽  
Surveillance Systems

scholarly journals sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4678
Author(s):  
Chao Chen ◽  
Weiyu Guo ◽  
Chenfei Ma ◽  
Yongkui Yang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Control ◽  
Large Scale ◽  
Human Motion ◽  
Kernel Size ◽  
Continuous Motion ◽  
Convolutional Network ◽  
Continuous Estimation ◽  
Discrete Motion ◽  
Precision Rate

Since continuous motion control can provide a more natural, fast and accurate man–machine interface than that of discrete motion control, it has been widely used in human–robot cooperation (HRC). Among various biological signals, the surface electromyogram (sEMG)—the signal of actions potential superimposed on the surface of the skin containing the temporal and spatial information—is one of the best signals with which to extract human motion intentions. However, most of the current sEMG control methods can only perform discrete motion estimation, and thus fail to meet the requirements of continuous motion estimation. In this paper, we propose a novel method that applies a temporal convolutional network (TCN) to sEMG-based continuous estimation. After analyzing the relationship between the convolutional kernel’s size and the lengths of atomic segments (defined in this paper), we propose a large-scale temporal convolutional network (LS-TCN) to overcome the TCN’s problem: that it is difficult to fully extract the sEMG’s temporal features. When applying our proposed LS-TCN with a convolutional kernel size of 1 × 31 to continuously estimate the angles of the 10 main joints of fingers (based on the public dataset Ninapro), it can achieve a precision rate of 71.6%. Compared with TCN (kernel size of 1 × 3), LS-TCN (kernel size of 1 × 31) improves the precision rate by 6.6%.

scholarly journals Algorithm for Counting Cars in Large-scale Video Surveillance Systems

2020 ◽  
pp. 100-108
Author(s):  
Arsenii Shirokov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Video Surveillance ◽  
Large Scale ◽  
Surveillance Systems ◽  
Motion Model ◽  
People Tracking ◽  
Detection Frequency ◽  
Computational Resources ◽  
Sparse Set ◽  
The Given ◽  
Distributed Tracking

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

Distributed Scheduling Protocols for Energy Efficient Large-Scale Wireless Sensor Networks

2011 ◽  
pp. 233-248
Author(s):  
Cheng Fu ◽  
Bang Wang
Keyword(s):  
Large Scale ◽  
Distributed Scheduling ◽  
Wireless Sensor ◽  
Surveillance Systems ◽  
Sensor Scheduling ◽  
Application Development ◽  
Coverage Area ◽  
Power Conservation ◽  
Coverage Ratio ◽  
Scheduling Protocols

A major design challenge in wireless sensor network application development is to provide appropriate middleware service protocols to control the energy consumption according to specific application scenarios. In common application scenarios such as in monitoring or surveillance systems, it is usually necessary to extend the system monitoring area as large as possible to cover the maximal area. The two issues of power conservation and maximizing the coverage area have to be considered together with both the sensors’ communication connectivity and their power management strategy. In this chapter,the authors proposed novel enhanced sensor scheduling protocols to address the application scenario of typical surveillance systems. Their protocols take into consideration of both power conservation and coverage ratio to search for the balance between the different requirements. They proposed both centralized and de-centralized sensor scheduling versions, and compared the performance of different algorithms using several metrics. The results provide evidence of the advantages of our proposed protocols comparing with existing sensor scheduling protocols.

Feasibility Study of Surveying the Adverse Drug Reaction Surveillance Systems in a Large Community of Hospitals

1992 ◽  
Vol 26 (3) ◽  
pp. 384-391 ◽  
Author(s):  
Abraham G. Hartzema ◽  
Miquel S. Porta ◽  
Hugh H. Tilson ◽  
Carlos R. Herrera ◽  
Jeffrey T. Moss ◽  
...  
Keyword(s):  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Surveillance System ◽  
Large Scale ◽  
Surveillance Systems ◽  
Harris County ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Uniform Definition ◽  
Definition Of

OBJECTIVE: To determine the feasibility of accurately assessing the types of hospital adverse drug reaction (ADR) surveillance systems. DESIGN: Cross-sectional survey by mailed, self-administered questionnaire followed by selected verification interviews. SETTING: Harris County, Texas. PARTICIPANTS: All hospitals in the county with different pharmacy directors. MAIN OUTCOME MEASURE: Self description of surveillance system and number of ADRs reported. RESULTS: Forty-nine of 61 hospitals (80 percent) responded to a questionnaire. Forty-seven (96 percent) of the responding hospitals collected information on ADRs with 11 (22 percent) describing their surveillance system as active. Those individuals most often cited as responsible for ADR surveillance included pharmacists, quality assurance personnel, and nurses. Data were verified by personal interviews for 10 hospitals. The number of ADRs reported during the interviews was significantly lower than that reported in the questionnaires. Overall, the reporting of fatal and severe ADRs were more reliable than the reporting of moderate ADRs. These differences were the result of inadequate documentation and the lack of a uniform definition of ADRs. CONCLUSIONS: These data suggest that a large-scale ongoing survey of surveillance systems and reported adverse event rates has limitations and the reliability of data derived from a questionnaire should be verified. To improve the accuracy of surveys used to monitor hospital ADR surveillance systems, it is essential to develop reliable definitions for classifying ADRs and surveillance methods, as well as accurate measures of ADR documentation procedures.

scholarly journals The U.S. COVID-19 Trends and Impact Survey, 2020-2021: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing and vaccination

2021 ◽  
Author(s):  
Joshua A Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichida La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The U.S

The U.S. COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, Internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey -- over 20 million responses in its first year of operation -- allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

scholarly journals The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

2021 ◽  
Vol 118 (51) ◽  
pp. e2111454118 ◽  
Author(s):  
Joshua A. Salomon ◽  
Alex Reinhart ◽  
Alyssa Bilinski ◽  
Eu Jing Chua ◽  
Wichada La Motte-Kerr ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Large Scale ◽  
Survey Methods ◽  
Health Priorities ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
Essential Information ◽  
Public Health Reporting ◽  
The Us

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

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