Architecture of Atwood Machine Props with Sensor-based Passive Infrared

Abstrak –This study aims to: (1) Design a prototype of Bird Pest Repellent on Rice Plants Using PIR and ESP8266 Sensors in Matekko Bulukumba Village. (2) Knowing the implementation of the prototype of Bird Pest Repellent on Rice Plants Using PIR and ESP8266 Sensors in Matekko Bulukumba Village. This study uses a prototype research method where the stages are as include collection of needs, make prototype, prototype evaluation, encoding system, System testing, System evaluation & Using the system. Based on the results of this study, it can be concluded that: (1) The prototype of Bird Pest Repellent on Rice Plants was designed using 3 components, namely 3 PIR (Passive Infra Red) Sensors, 1 Esp8266 and 3 Servo Motors. This prototype of Bird Pest Repellent on Rice Plants uses a plywood board as a place to place the components of the tool which has a width of 47 centimeters and a length of 50 centimeters. The PIR (Passive Infra Red) sensor will be placed at the end of each square-shaped plywood board. ESP8266 will be placed in the middle of the Servo Motor and the Servo Motor will be placed at the end of the upper plywood board. (2) The implementation of the bird repellent prototype on rice plants is able to detect the presence (bird pests) by means of a servo motor moves the rope and provides notifications to the blynk application.

Deep learning has shown to be capable of learning features from complex datasets such as in the case of biometric authentication. Biometric authentication relies on unique biological qualities to verify a user's identity against a group of potential adversaries for security purposes. In this paper, a deep learning dependent biometric authentication system based on chest motion data captured by a passive infrared (PIR) sensor, CM-PIR, is proposed. PIR sensors are cheap, commercial-off-the-shelf (COTS) components that are dependent on motion across its field of view (FoV) for accurate detection of human subjects. Thus, CM-PIR utilizes deep learning for the accurate detection of stationary human subjects, as well as for biometric authentication. CM-PIR collects chest motion data from sixteen human subjects in nine different home locations. Coefficients of Fourier transform (FFT), discrete wavelet transform (DWT), and the absolute value of an acceleration filter calculated from the raw voltage PIR values were selected as the optimal features for input to the deep learning models for biometric authentication. Preprocessing of these features included a threshold voltage range, normalization, and finally principal component analysis (PCA). CM-PIR is 94% accurate in stationary human presence detection and 75% accurate for biometric authentication using a RNN with a 90 second window size. This work provides a promising solution for stationary human detection and biometric authentication using a PIR sensor in a real-world setting.

Passive infrared (PIR) sensors are the most popular deployed sensors in building lighting control for individual presence detection. However, PIR sensors are motion detectors in nature, responding only to incident radiation variation, which lead to false negative detections, inaccurate occupancy estimation, and uncomfortable lighting swings, short lifetime of the equipment, and waste of energy. In this study, a shutter driven by a Lavet motor PIR (LAMPIR) sensor is developed for presence detection for both stationary and moving occupants. Building off our previous work on chopped PIR (C-PIR) and rotationally-chopped PIR (Ro-PIR) sensors, Lavet motor, a single-phase electro-mechanical vibrator, is introduced, which has many advantages over traditional servo motors and stepper motors in terms of power consumption, size, weight and noise level. Driven by pulsed signal from a microcontroller unit (MCU), the electro-mechanical vibrator drives a semi-transparent long-wave infrared (LWIR) optical shutter to shutter the field of view (FOV) of a PIR sensor periodically. Output voltage generated by a LAMPIR senor for occupied and unoccupied scenarios can be monitored and analyzed to identify presence accurately. Parametric studies are conducted to find the optimal setting of driving signal frequency, shutter width and shuttering period. The LAMPIR sensor reaches an accuracy of 100% for detecting stationary occupants up to a range of 4.5 m and moving occupants up to a range of 10 m, which improves the detection range of both C-PIR and Ro-PIR sensors (4.0 m for stationary and 8.0 m for moving occupancy detection). LAMPIR has a FOV of 90° in horizontal and 100° in vertical, which is reasonable for most applications. For a 17-hour-long presence detection test, LAMPIR can reach an accuracy of 93.52% to classify unoccupied, stationary and moving occupant scenarios. More importantly, the average power consumption of LAMPIR is 0.19 W, which is 82% less than that of the C-PIR sensor and 89% less than that of the Ro-PIR sensor.

Research has been carried out aimed at:(1) to produce a home security system using a PIR (passive infrared) sensor with voice warnings and telegram notifications based on the MCU node microcontroller. (2) to determine the performance of the resulting home security system. The design uses a node mcu microcontroller as the main controller. The design uses a motion sensor (PIR) to detect motion that occurs inside the house. The design uses an internet modem to send telegram notifications to homeowners. The speed of communication between the microcontroller and Telegram depends on the internet network connection. Sending messages via telegram to the mcu node, it is also assumed that electricity and internet are in a stable condition. A home security system using a PIR (passive infrared) sensor with sound warnings and telegram notifications based on the MCU node microcontroller can be generated. Testing of each component (power supply, PIR sensor, MCU node microcontroller, MP3 player module, limit switch sensor and ESP8266) indicates that each part is functioning and working properly. The performance of the resulting home security system is very good. After the system is activated, any movement detected in the living room and/or terrace of the house as well as windows and/or doors that are detected are open, the system will respond by emitting a sound through the speaker in the form of a warning or informing that a house break-in has occurred while sending a chat telegram to the user.

Food is necessary to support daily life and in pets like cats. Pet feeders are usually carried out routinely by pet owners by taking the time to stay at home and feed the pets. With activities requiring animal owners to be outside the home for long periods, it is necessary to design a prototype that can automatically assist the process of providing food, especially dry food, to pets. So pet feeders can be carried out even though the owner is not at home and does not have spare time. The system was built using the C language related to the Code Vision tools that support the hex file compiler into the microcontroller. The research method used is the experimental method, namely conducting trials (trial and error) directly on the research object and the comparative testing method for testing the built system. The auto pet feeder prototype uses the ATMega8535 IC by utilizing a PIR (Passive InfraRed) sensor to detect movement around the animal bowl. The PIR sensor detects well as expected, and it has an effective detection range of up to 5 meters. Not only detects humans, but the PIR sensor can also detect other living things. The PIR sensor will send a signal to the microcontroller to open the valve from the food measuring station, which is driven by a DC motor based on the detected motion to drop food into the animal's bowl. PhotoDioda controls the amount of food falling into the animal's bowl. The test results of this tool show that at a distance of 0–6 meters, the PIR sensor can provide feedback or detect animal movements well.

Smart trash cans use a proximity sensor (ultrasonic) to detect the height of the trash, the PIR (Passive Infra Red) sensor to detect human presence and Arduino as the data processing center. This trash can also has an LED that functions as an indicator and the buzzer functions as an alarm when the trash can is full. If the garbage reaches half of the trash, the LED will turn yellow, if the garbage reaches a height of 4cm from the trash lid, then the LED will turn red and so the trash can will be locked even though the PIR sensor detects humans. Buzzer will live to attract the attention of cleaning officers to dispose of trash. To dispose of garbage, the clerk needs to shift the open and close trash button so that the lid will open. The author hopes that this smart trash can can increase public awareness to dispose of garbage in its place. The results of study show that PIR sensors work by well in detecting the presence of humans and ultrasonic sensors in detecting the height of garbage are not accurate, so that if the trash if the trash can is empty, sometimes the garbage is minus, whereasif the garbage is < 3.5 cm, the sensor fails detect it.

To enable passive infrared (PIR) sensors to detect stationary occupants, we recently invented a synchronized low energy electronically chopped PIR (SLEEPIR) sensor consisting of PIR sensor, a polymer dispersed liquid crystal (PDLC) infrared shutter and a driving circuit. By electrically chopping the long-wave infrared (8- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$12~\mu \text{m}$ </tex-math></inline-formula> ) radiation received by the PIR sensor, the SLEEPIR sensor can detection both stationary and moving occupants. In this paper, we developed a theoretic model to better understand the output of the SLEEPIR) sensor, and thus optimize its fabrication process and detection performance. The model indicated that its performance is mainly depending on the effective modulation and response time of the PDLC shutter in addition to the time constants of the PIR sensor. We empirically found that the effective modulation and response time are largely dependent on the frequency and amplitude of the driving signal, as well as the mass ratio, cell gap and cooling rate of the PDLC shutter. In the end we obtained optimized parameters that can produce the maximal SLEEPIR output, and experimentally validated our obtained optimal parameters and fabrication conditions.

Traffic lights at crossroads help regulate the flow of vehicles so they can run smoothly and avoid congestion. One of the causes of traffic congestion is a traffic control light system that is not optimal because it still uses a fixed timer system, so the lights' duration is less efficient in its application. When the green light is on, the streets are deserted. But there is a traffic jam on another road. Therefore the concept of traffic light timing needs to be developed to get efficient timing. This study aims to create a traffic light control system that uses a PIR (Passive Infrared) motion sensor and an ultrasonic sensor based on the Arduino Mega2560 microcontroller based on the congestion level. The method used in this traffic control prototype is an experimental method of reading vehicle objects mounted with PIR sensors and ultrasonic sensors. This system can detect the movement of vehicles approaching a traffic light, measure the distance between the car and the traffic light, and set the timing of the light signal according to the level of congestion detected. This system has been tested using vehicle movement simulations brought closer to the PIR and ultrasonic sensors at different distances. The test results show the system can work properly and produce the light signal according to the detected traffic conditions. For its application to traffic lights in the future, it is feasible to help the traffic unit.

Obstructive sleep apnea (OSA) is a common but serious sleep disorder in humans which affects oxygenation due to upper airway collapse, and leads to multiple health sequelae. Common symptoms of OSA include repeated arousal during sleep and gasping for air, which we have previously shown can be identified on video recordings as coarse human movement. In this work, we aimed to use a low cost passive off-body device that would continuously allow monitoring for OSA severity in a privacy-preserving manner. A passive infrared (PIR) sensor, was used to monitor coarse human movement during overnight sleep in a clinical sleep-lab. Simultaneously, polysomnograms were recorded and OSA labels were assigned by an expert. A cohort of 32 elderly male participants, 14 with severe OSA and 18 controls were studied. A Raspberry Pi was used to record the PIR sensor data (a series of time-stamps at which movement was registered). Movement time-stamp data were processed to obtain novel activity metrics based on time series statistics and entropy-based statistics. A logistic regression model was trained and tested with leave one out cross validation to classify participants into OSA vs control. A total of 11 features plus periodic leg movement were used. An out of sample F1-score $(\mathcal{F}_{1})=0.89$ and an accuracy $(Acc) =91\%$ was obtained using five features when a combination of entropy, distribution statistics, and PLM information were used, and $\mathcal{F}_{1}=0.83, Acc=84\%$ without PLM from 10 features. This suggests that data from the PIR sensor alone could be used for OSA evaluation on an ongoing basis in this population. The proposed method therefore provides a novel, simple, and scalable method for monitoring OSA.

People with dementia often have low physical activity and some sleep problems. This study focused on daily life activities and sleeping conditions, and examined the use of these parameters for detecting dementia. Five passive infrared (IR) sensors were installed in each of 14 subject's houses. Each patient lived alone. The subjects' in-house movements were recorded by the passive IR sensor for approximately 3 months (average, 78 days). Based on these records, the following parameters of life activities were assessed: (1) the number of outings, (2) total sleep time, (3) number of sleep interruptions, and (4) sleep rhythm. Subjects with impaired cognition (Mini Mental State Examination [MMSE] < 24) had a significantly lesser number of outings (p = 0.001) and a tendency toward a shorter sleep time (p = 0.054) in comparison with control subjects (MMSE >or= 24). These results suggest that the monitoring of life activities by using passive infrared sensors could be an efficient method for detecting dementia.

Novel occupancy detection method based on convolutional neural network model using PIR sensor and smart meter data

Motion detection is a fundamental process that essentially detects a change in the position of an object with respect to its surrounding environment. It is highly demanding in various applications such as health care, defense and surveillance. The existing solutions for motion detection in indoor application are complex and expensive as it requires high end cameras and sensors. Hence, the objective of this paper is to provide an alternative cost effective solution. In the proposed motion detection system, Passive Infra Red (PIR) sensors are used to detect the presence of motion. The control unit of the PIR sensor transmits the trigger interrupt to the control unit of the camera via ZigBee communication. The rotational element present in the camera control unit rotates the camera in line of sight with the PIR sensor. The detected motion is then captured by the camera sensor and stored in Raspberry Pi, a microcontroller, which is then wirelessly transmitted to the receiver for further processing. The proposed motion detection system has been developed and validated in real time. It is observed that the performance of the developed system in terms of delay and QoS are acceptable for single event detection. However the proposed system can be extended for detection of multiple events.

In homes and offices, it is very common for occupants to forget to switch OFF the lighting and fans when leaving the premises. This can be attributed to human forgetfulness and the epileptic power supply which causes interruption that results in users forgetting the state of their appliances (whether they are ON or OFF). Consequently, these appliances would continue to work whenever power is restored when the occupants might have vacated the premise. This action is not a small contributor to energy wastage in a country like Nigeria where there is an inadequate energy supply to go round the populace. In this work, a simple but robust automatic home and office power control system is developed to auto-detect the presence of an occupant in the room through the passive infrared (PIR) sensor and control the electrical appliances (lighting and fan source) in the room. Certain conditions must be met for the operation of lighting and the fan source. The lighting comes up when the PIR sensor senses the presence of an occupant and the room is in darkness, while the fan would work when there is an occupant and the temperature in the room is above 35°C. These conditions are programmed to suit the need of the occupant but cannot be changed by the user. The device automatically switches off within five minutes after the last occupant leaves the room. Keywords— Microcontroller, Passive infrared (PIR) sensor, Solid State Relay (SSR), and Switching

Footfall refers to the number of people present in, or who have passed through, a given location during a specific period. Accurate footfall data is a vital metric that, when analyzed, can yield valuable insights such as conversion rates, traffic heatmaps, planning strategies, and occupancy indices. Traditional counting methods employ manpower or automated systems such as turnstiles, camera-based detection, radio-frequency fingerprinting, and various transducers (ultrasonic, force, infrared). However, these approaches present challenges including high deployment cost, maintenance requirements, privacy concerns, and inconsistent accuracy.This project proposes a cost-effective IoT-based solution utilizing Passive Infrared (PIR) sensors for footfall counting. The system is designed to deliver comparable functionality to existing commercial solutions while eliminating privacy risks and significantly reducing cost and maintenance overhead. The integration of IoT capabilities further enables real-time monitoring and data analysis, making the solution well-suited for applications in smart infrastructure, retail, and institutional environments.

This study looks at the application of delayed action Passive Infrared (PIR) sensors in the control of water use for urinal flushing. In this we briefly review the literature on urinal controls before reviewing four different approaches to PIR urinal controls. Existing literature discusses some of the pros and cons of different types of urinal control. However, the literature does not consider the differences that occur within individual approaches, based on the way controls operate. This study was initiated at the University of Surrey during 2019 following a water saving audit, in an attempt to bring down one of their largest users of water. This paper therefore aims to identify the most effective way to reduce water consumption of urinal systems, through retro-fitting PIR control systems within the variety of settings across university campuses. This paper also reviews the different reductions achieved over periods of differing use, during term-time, holidays, COVID-19 lockdowns, and the ‘new normal’. It found that grouped delayed action flushing was the most effective form of urinal control for reducing water use. It, achieved a 59–64% adjusted reduction during non-COVID-19 periods, and a 35% reduction against the control group during lockdowns saving 98,000 L/day on average across the study.