Search

Recently, the effectiveness of image-based sensors, which are based on image-processing techniques, has been analysed in terms of occupancy lighting control in comparison with a passive infrared (PIR) sensor, which monitors changes in infrared radiation from moving occupants. However, the image-based sensor has two shortcomings: occupants cannot be accurately detected in a dark environment, and the overall energy consumption from the light and sensor is excessive. Therefore, we evaluated a combined approach that employs both the PIR sensor and the image-based sensor simultaneously. Because the combined sensor uses the image-based sensor only in a sufficiently bright environment, the first shortcoming can be overcome. In addition, an experiment, was conducted at a single office, demonstrating that the combined sensor's overall energy consumption decreased by 3.59% or more compared with either of its constituent sensors individually. Thus, the combined sensor resolves the major shortcomings of the image-based sensor's performance. Further discussion and careful analysis are included in this paper.

Detecting human activity through cameras and machine learning methods raises significant privacy concerns, while alternatives like thermal cameras can be expensive. Passive infrared (PIR) sensors present a cost-effective and privacy-preserving solution, commonly used in home settings for motion detection. This study introduces a system for monitoring human activeness using a single PIR sensor, focusing on privacy preservation. The proposed one-dimensional model, based on the Laplace distribution, emphasizes the role of the parameter μ in defining velocity distributions. Through real-world experiments with a Raspberry Pi and PIR sensor, the effectiveness of the model in capturing human activeness is validated. The study investigates how different μ values correlate with activity levels and detect abnormalities. Additionally, the paper addresses the stochastic nature of human behavior, and the impact of μ on predictability and variability, and provides insights into detection thresholds and interval times. The findings highlight the potential for enhancing abnormality detection and suggest a comprehensive understanding of human activeness.

Camera trap data are biased when an animal passes through a camera’s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection. [ABSTRACT FROM AUTHOR]

The evolvement of smart glass technology has gained a lot of interest through its energy-saving potential as one of the heating, ventilating, and airconditioning (HVAC) system. This paper focuses on polymer dispersed liquid crystal (PDLC) film, a smart glazing film that changes its opacity in response to an electrical impulse. The power consumption of the smart film is considerably small. However, improper handling of the smart film such as not turning off the film after usage can lead to energy wastage. Hence, connecting the smart film to an internet of things (IoT) controller would be one of the possible solutions to ensure that the film is maintained properly. The objective of the work here is to develop a smart, low cost and efficient IoT-enabled smart controller for PDLC films with energy-saving features. In pursuance of materializing this concept, this paper delineates the design of a smart controller for the PDLC films. The implementation of the IoT features, NodeMCU, and environmental sensors enabled the smart film to be capable of switching automatically. In addition, voice-command features were also incorporated into the controller. With the successful development of the IoT smart controller, the PDLC films can operate autonomously and wirelessly. [ABSTRACT FROM AUTHOR]

Grégory Ben-Sadoun,1,2 Emeline Michel,3,4 Cédric Annweiler,1,5– 7 Guillaume Sacco3,5,8 1Department of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, France; 2Normandie Université, UNICAEN, INSERM, COMETE, CYCERON, CHU Caen, Caen, 14000, France; 3Université Côte d’Azur, Centre Hospitalier Universitaire de Nice, Clinique Gériatrique du Cerveau et du Mouvement, Nice, France; 4Université Côte d’Azur, LAMHESS, Nice, France; 5Laboratoire de Psychologie des Pays de la Loire, Univ Angers, Université de Nantes, EA 4638 LPPL, SFR CONFLUENCES, Angers, F-49000, France; 6School of Medicine, Health Faculty, University of Angers, Angers, France; 7Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada; 8Université Côte d’Azur, CoBTek, Nice, FranceCorrespondence: Grégory Ben-SadounDepartment of Geriatric Medicine and Memory Clinic, Research Center on Autonomy and Longevity, University Hospital of Angers, Angers, FranceEmail bensadoun.gregory@hotmail.fr; gregory.bensadoun@chu-angers.fr; gregory.ben-sadoun@unicaen.frAbstract: Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness–ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16× 16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26– 28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were “conference papers”, five were “original articles” and one was a found in arXiv.org (an open access repository of scientific research). Since four authors “duplicated” their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85– 90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.Keywords: fall detection, older adults, passive infrared sensor, thermal sensor, thermopile

Despite the rapid development of technologies for the detection of pedestrians in traffic, there are still significant problems with automated vehicle management systems. Often these problems are caused by different external conditions, weather disasters, different light levels, day and night, and more. All these factors can affect the incorrect assessment of the system and the performed detection of the pedestrian object. Pedestrian fatalities have risen recently, even as vehicles are equipped with sophisticated avoidance technology. Vehicle-based sensors can fail to identify pedestrians especially when those pedestrians are small, too far or too close to the vehicle, or partially occluded by nearby objects. Pedestrians have variable physical characteristics and appear in a variety of environments with different background features. The paper analyzes the possibilities and problems that arise in the detection and recognition of pedestrians. Pedestrian safety is one of the important issues of traffic safety in urban areas. The application of various technologies in vehicles provides enormous opportunities to reduce accidents in which pedestrians are most often injured. The development of intelligent pedestrian recognition systems can be divided into two directions. Development of hardware part related to different types of sensors, and development of software part based on artificial neural networks and machine learning techniques. The paper analyzes various technologies used in pedestrian detection in automated vehicles with an emphasis on hardware development. A low-cost system based on two types of sensors, ultrasonic sensor and passive infrared, is proposed. Aspects of the application of such sensors with possibilities of implementation in complex computer vision systems are analyzed. The most common classes of sensors presently used in automated driving applications include visible-light cameras (VLC), light detection, and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

The shortcomings of switches include; power loss due to mechanical contacts, difficulties in their access especially in darkness as well as wear and tear. Likewise, the problems of using a web server and mobile communication include network problems (distortion or attenuation) necessitates the development of infrared radiation home automation controller. Microcontroller-based home automation control was implemented using the passive infrared sensor (PIR) to sense an infrared radiation and output a voltage that serves as an input to the controller. The design was made to automatically switch ON any low power load connected between its output and the grid. The system operational mode is a self-automated mode that makes the controller capable of monitoring and controlling different appliances in the home automatically in response to the signal that comes to the sensor. However, the system was designed to maintain its output in the ON state as long as it senses the infrared radiation and then switch off when it senses no radiation in its vicinity. Flowcode V5 - software was used in the implementation of the system and the ATMEGA 328 microcontroller as system controller. Also, additional automation was implemented at the controller based on the ATMEGA 328 microcontroller for ease, security, and relatively low cost. [ABSTRACT FROM AUTHOR]