IoT Aided Smart Light Sensing Automation using Passive Infrared Sensors

2020 ◽  
Author(s):  
Archit Kapoor ◽  
Divyansh Oze ◽  
Achyut Shankar
Keyword(s):  
Infrared Sensors ◽  
Light Sensing ◽  
Passive Infrared

Test bed for measurement of angular parameters of passive infrared sensors

2007 ◽  
Vol 49 (3) ◽  
pp. 198-201 ◽  
Author(s):  
M. Kastek ◽  
H. Madura ◽  
M. Morawski ◽  
T. Piatkowski ◽  
E. Powiada ◽  
...  
Keyword(s):  
Test Bed ◽  
Infrared Sensors ◽  
Passive Infrared

Passive Pedestrian Detection at Unsignalized Crossings

1998 ◽  
Vol 1636 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Dana M. Beckwith ◽  
Katharine M. Hunter-Zaworski
Keyword(s):  
Quality Function Deployment ◽  
Doppler Radar ◽  
Pedestrian Detection ◽  
Future Research ◽  
Operating Characteristics ◽  
Infrared Sensors ◽  
Decision Matrix ◽  
Microwave Radar ◽  
Pedestrian Crossing ◽  
Passive Infrared

The city of Portland, Oregon, is researching ways to provide safe unsignalized crossings for pedestrians. A concept that shows promise is known as passive pedestrian detection. Passive pedestrian detection is the detection of pedestrians in a stationary or moving state at the curbside of or in a pedestrian crossing by means other than those requiring physical response by the pedestrian. Research was conducted to find sensor technologies that can be used to passively detect pedestrians. Five technologies were found to be options for this type of detection: passive infrared, ultrasonic, microwave radar, video imaging, and piezometric. Of these five technologies, passive infrared, ultrasonic, and microwave radar were selected for testing. An unbiased selection of sensors was made by using a decision matrix in the form of the quality function deployment method, which also provides a record of sensor information for future research. Preliminary testing was conducted on the sensors to ensure that the detection of pedestrians was possible and to determine sensor operating characteristics. Sensors then went through secondary tests to ensure proper operation at an unsignalized crossing. The secondary test site was retrofitted with reflective pedestrian crossing signs, yellow beacons, Doppler radar, and passive infrared sensors chosen from the preliminary tests. Initial secondary tests have shown promising results for the Doppler radar and especially for the passive infrared sensors. Future applications of passive pedestrian detection in Portland will involve installation of sensors at signalized pedestrian crossings.

scholarly journals Architecture of Atwood Machine Props with Sensor-based Passive Infrared

2018 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Dandan Luhur Saraswati ◽  
Delia Achadina Putri
Keyword(s):  
Data Analysis ◽  
Machine Tool ◽  
Infrared Sensors ◽  
Physics Laboratory ◽  
Earth Gravity ◽  
Analysis Experiment ◽  
Acceleration Of Gravity ◽  
Passive Infrared

The aim of this research is to develop Atwood machine propswith sensor-based PIR (Passive Infrared) to determine the acceleration value of earth gravity. This type of research is an experiment conducted in the physics laboratory of Universitas Indraprasta PGRI. Atwood machine was developed by using PIR (Passive Infrared) sensors to detect the movement of the objects used. Based on the trialof the result of the data analysis experiment tool, the acceleration of gravity was obtained (9.87 ± 0.08) m/s2. Whereas the value of acceleration of gravity in the literature is 9,80665 m/s2. The obtained results shows that the acceleration of gravity obtained in this research is not much different from the acceleration value of gravity  found in the literature so that Atwood machine tool that is developed by using PIR sensor is recommended to be one alternative tool in determining the acceleration of earth gravity.

scholarly journals Recovering Human Motion Patterns from Passive Infrared Sensors: A Geometric-Algebra Based Generation-Template-Matching Approach

2019 ◽  
Vol 8 (12) ◽  
pp. 554
Author(s):  
Shengjun Xiao ◽  
Linwang Yuan ◽  
Wen Luo ◽  
Dongshuang Li ◽  
Chunye Zhou ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Template Matching ◽  
Human Motion ◽  
Object Identification ◽  
Infrared Sensors ◽  
Motion Patterns ◽  
Human Motion Detection ◽  
Searching Strategy ◽  
Activation Data ◽  
Passive Infrared

The low-cost, indoor-feasibility, and non-intrusive characteristic of passive infrared sensors (PIR sensors) makes it widely used in human motion detection, but the limitation of its object identification ability makes it difficult to further analyze in the field of Geographic Information System (GIS). We present a template matching approach based on geometric algebra (GA) that can recover the semantics of different human motion patterns through the binary activation data of PIR sensor networks. A 5-neighborhood model was first designed to represent the azimuth of the sensor network and establish the motion template generation method based on GA coding. Full sets of 36 human motion templates were generated and then classified into eight categories. According to human behavior characteristics, we combined the sub-sequences of activation data to generate all possible semantic sequences by using a matrix-free searching strategy with a spatiotemporal constraint window. The sub-sequences were used to perform the matching operation with the generation-templates. Experiments were conducted using Mitsubishi Electric Research Laboratories (MERL) motion datasets. The results suggest that the sequences of human motion patterns could be efficiently extracted in different observation periods. The extracted sequences of human motion patterns agreed well with the event logs under various circumstances. The verification based on the environment and architectural space shows that the accuracy of the result of our method was up to 96.75%.

Vehicle speed estimation using two roadside passive infrared sensors

2020 ◽  
Vol 34 (22n24) ◽  
pp. 2040151
Author(s):  
Van Quang Vu ◽  
Van Linh Ngo ◽  
Toan Thang Vu ◽  
Van Phuc Doan
Keyword(s):  
Cross Correlation ◽  
Time Lag ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Infrared Sensors ◽  
Cross Correlation Analysis ◽  
Optical Axes ◽  
Speed Range ◽  
Moving Direction ◽  
Passive Infrared

This paper presents a method for estimating vehicle speed using two roadside passive infrared (PIR) sensors whose optical axes are parallel to each other and perpendicular to the moving direction of vehicles. The vehicle speed was calculated based on the time lag between the two signals received by the PIR sensors, which was evaluated by using cross-correlation analysis. The experiments show that the method has an error of less than 5 km/h over a speed range of 20–60 km/h.

scholarly journals Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3773 ◽  
Author(s):  
Sara Casaccia ◽  
Eleonora Braccili ◽  
Lorenzo Scalise ◽  
Gian Marco Revel
Keyword(s):  
Low Cost ◽  
Quality Parameter ◽  
Motion Sensor ◽  
Infrared Sensors ◽  
Sleep Monitoring ◽  
Non Invasive ◽  
Sleep Parameters ◽  
Set Up ◽  
Reference Sensor ◽  
Passive Infrared

A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.

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