Background-oriented schlieren technique in experimental research regarding the effect of explosion pressure

In the following paper, experimental results regarding the effect of explosion pressure are obtained from open field experiments with detonation of explosive charges. In addition, sensors that can be used for security applications for the detection of toxic and explosive compounds, as well as mobile systems for the detection of shock waves due to explosions were used to acquire more detailed results. Sensors are the main components in products and systems used to detect chemicals and volatile organic compounds (VOCs) targeting applications in several fields, such as: industrial production and the automotive industry (detection of polluting gases from cars, medical applications, indoor air quality control. The sensory characteristics of a robot depend very much on its degree of autonomy, the applications for which it was designed and the type of work environment. The sensors can be divided into two categories: internal status sensors (sensors that provide information about the internal status of the mobile robot); external status sensors (sensors that provide information about the environment in which the robot operates). Another classification of these could be: distance sensors, position sensors, environmental sensors - sensors that provide information about various properties and characteristics of the environment (example: temperature, pressure, color, brightness), inertial sensors.

An IoT measurement solution for continuous indoor environmental quality monitoring for buildings renovation

The measurement of Indoor Environmental Quality (IEQ) requires the acquisition of multiple quantities regarding thermal comfort and indoor air quality. The IEQ monitoring is essential to investigate the building’s performance, especially when renovation is needed to improve energy efficiency and occupants’ well-being. Thus, IEQ data should be acquired for long periods inside occupied buildings, but traditional measurement solutions could not be adequate. This paper presents the development and application of a non-intrusive and scalable IoT sensing solution for continuous IEQ measurement in occupied buildings during the renovation process. The solution is composed of an IR scanner for mean radiant temperature measurement and a desk node with environmental sensors (air temperature, relative humidity, CO2, PMs). The integration with a BIM-based renovation approach was developed to automatically retrieve building’s data required for sensor configuration and KPIs calculation. The system was installed in a nursery located in Poland to support the renovation process. IEQ performance measured before the intervention revealed issues related to radiant temperature and air quality. Using measured data, interventions were realized to improve the envelope insulation and the occupant’s behaviour. Results from post-renovation measurements showed the IEQ improvement achieved, demonstrating the impact of the sensing solution.

A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors

Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.

Smart post-harvest technology to maintain quality and safety in fresh produce supply chains

In this chapter the main challenges for the postharvest management of fresh produce are summarised. Key areas where the use of new smart technologies can improve crop management are explored, starting with how environmental sensors can be integrated into internet of things (IoT) systems with potential for use in the fresh produce supply chain. The next section summarises how the implementation of low oxygen storage environments is being refined through the use of dynamic controlled atmosphere systems incorporating sensor technologies. Modified atmosphere packaging and the developing field of active and intelligent packaging for fresh produce is then discussed. The chapter ends with future options for how smart technologies may develop in this sector.

Construction of a Simple Domeless Net Radiometer for Demonstrating Energy Balance Concepts in a Laboratory Activity

Even though energy balance concepts are fundamental to solutions of problems in a number of disciplines in the agricultural and life sciences, they are seldom demonstrated in a laboratory activity. Here, we introduce a simple domeless net radiometer to demonstrate how the surface temperature of an object aboveground is regulated by the properties of the surfaces and environmental conditions. The device is based on the early designs of all-wave net radiometers and is composed of a foam disc with its opposing surfaces coated with either white or black paint. Temperatures of the disc’s surfaces are monitored using thermocouple temperature sensors. Using a combination of solar irradiance, albedo of the ground surface, air temperature, and wind speed measurements, the temperatures of the disc’s surfaces can be calculated by means of an energy balance model. We found good agreement between calculated and measured temperatures. In addition to demonstrate important physical concepts under natural outdoor conditions, we believe that the proposed laboratory activity will benefit students by allowing them to gain some experience and practical skills in working with environmental sensors, programming data acquisition systems, and analyzing data. Stimulating students’ creativity as well as developing their analytical and problem-solving skills is another goal of the proposed activity.

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. A calibrated building energy model was created for the building using optimisation tools. Thermal comfort was collected using a portable device. A machine learning (ML) model was trained using collected feedback, environmental data from IoT devices and synthetic datasets (virtual sensors) extracted from a physics-based model. A calibrated energy model was used in co-simulation with the predictive method to estimate comfort levels for the building. The results show the ability of the method to improve the prediction of occupant feedback when compared to traditional thermal comfort approaches of about 25%, the importance of information extracted from the physics-based model and the possibility of leveraging scenario evaluation capabilities of the dynamic simulation model for control purposes.

Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles, Lidars are accurate in determining objects’ positions but significantly less accurate as Radars on measuring their velocities. However, Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR, in this paper, an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, the real vehicle test under various driving environment scenarios is carried out. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of autonomous cars.

Electronic rain meter for mobile sensor node using law of Torricelli

Rain in a city can cause material damage and risk for the population, hence the importance of implementing prevention and mitigation measures. These measures must be taken based on the analysis of the data collected by networks of environmental sensors. The rainfall-meter is one of the instruments used to measure rain, these are designed to operate at a fixed point. Coverage of the entire area of a city requires the installation of several of these elements. This paper shows the development of an electronic rain gauge that can operate in motion applying the principles of fluid dynamics. Two stages are proposed for its elaboration. The first step is the design, construction and testing of the sensor and transducer for the rain gauge. In the second step, the rain gauge communication is implemented. For this, the internet of things technology is incorporated, and the network is designed to provide mobility. The main result is a prototype mobile electronic rain gauge with a measurement error of 8.5%. Besides, mathematical model for the sensor, algorithm for the transducer, and communications architecture are obtained. It can be concluded that, rainfall can be monitoring in a city with few sensitive units in motion.

A Platform to Study the Effects of Home Environment on Health and Wellbeing of Older Adults

While older adults’ living environment is rarely well-tuned to their specific needs, technological advances provide new opportunities to understand, and ultimately optimize, the relationship between the home environment and health outcomes. We aimed to establish proof-of-concept and feasibility of a platform enabling real-time, high-frequency, and simultaneous monitoring of environment, biological variables, and outcomes related to health and wellbeing in older adults. We recruited 7 participants (6 females, 1 male, aged 78-90, MoCA scores 14–28), installed environmental sensors measuring temperature, humidity, and CO2 inside their homes, provided them with wearables that measure sleep, activity, body temperature, and heart rhythms, and asked them to use a tablet to complete four sets of questionnaires and cognitive tests per day for three consecutive weeks. Environmental sensors collected data with no disruption or complaint from participants. Average compliance with the wearables was 81% (ring) and 60% (watch). All participants preferred the ring due to ease-of-use. Compliance was better in those with higher MoCA scores. Three participants were able to use the tablet successfully and completed 90% of prescribed questionnaires and cognitive tests. Cognitive and/or motor issues prevented the other participants from using the tablet. Exit interviews revealed that participants would prefer to complete a maximum of two sets of daily questionnaires and cognitive tests (five minutes each) in longer-term studies. These results suggest that it is feasible to study the impact of the environment on biological rhythms, cognition, and other outcomes in older adults and provide recommendations for ensuring long-term compliance with the protocol.