The investigation of natural-rubber for improving self-powered heat detector based on thermoelectric generators

Fire hazard has destroyed humanity creations. Fire detectors have been developed by using different techniques. Thermoelectric generator (TEG) is a part of energy harvesting which is able to convert heat into electricity because of temperature difference between hot and cold side of thermoelectric device (TE). Different materials are used for thermoelectric generators which depend on the characteristics of the heat source, heat sink and the design of the thermoelectric generator. Many thermoelectric generator materials are currently undergoing research. This paper presented an investigation of seeking an alternative way of detecting fire hazard by developing architecture prototype of a fire detection technique using natural rubber. The thermoelectric prototype used self-powered device which improved the temperature difference gap and stabilized the cold side of TE alongside natural rubber as the cooling material. The technique is relatively simple system realization based on three viable components, i.e. a heat sensor, a low-power RF-transmitter and a RF-receiver. The heat sensor is designed and fabricated by thermoelectric and heat sink with natural rubber (NR) coating. The NR coating is heat absorption reduction. Therefore, the temperature difference is wildly resulting in the higher TE output voltage. The voltage is also supplied to the low-power RF transmitter module. In case of fire hazard, the temperature increases from 26 to 100 °C , the prototype can operate successfully. This technique will solve potentially the power supply issue in fluctuated situations. The rubber coating from rubber trees in Thailand would be a value chain added for bio-economy, supporting a sustainable development goal of the country

Robustness and plasticity in Drosophila heat avoidance

Simple innate behavior is often described as hard-wired and largely inflexible. Here, we show that the avoidance of hot temperature, a simple innate behavior, contains unexpected plasticity in Drosophila. First, we demonstrate that hot receptor neurons of the antenna and their molecular heat sensor, Gr28B.d, are essential for flies to produce escape turns away from heat. High-resolution fly tracking combined with a 3D simulation of the thermal environment shows that, in steep thermal gradients, the direction of escape turns is determined by minute temperature differences between the antennae (0.1°–1 °C). In parallel, live calcium imaging confirms that such small stimuli reliably activate both peripheral thermosensory neurons and central circuits. Next, based on our measurements, we evolve a fly/vehicle model with two symmetrical sensors and motors (a “Braitenberg vehicle”) which closely approximates basic fly thermotaxis. Critical differences between real flies and the hard-wired vehicle reveal that fly heat avoidance involves decision-making, relies on rapid learning, and is robust to new conditions, features generally associated with more complex behavior.

Secure and smart system for monitoring patients with critical cases

<span>Recently, heat-related diseases like COVID19, Chickenpox, Typhoid, and others are increasing significantly; therefore, the need for portable devices to measures the heat of the human body accurately, quickly, easily with low cost has become very necessary to preserve the life of patients. For this reason, a smart system has been developed to monitor the patient's heat, in addition to temperature and humidity of the critical environment such as surgical operating rooms, patients’ isolation rooms and pharmacies, because it can help propagate infectious agents like viruses and bacteria. The proposed system divided into four parts: transmitted part (arduino, heat sensor, and hygrometer sensor), alarm part consists of lights and alarm bell, emergency part (doctors and nurses), and the medical application has been used as the last part. The application can be used only by authorized persons and through the accounts which are granted to them, in order to protect the data from sabotage and maintain the privacy and confidentiality of it.</span>

Sensor Fusion and Convolutional Neural Networks for Indoor Occupancy Prediction Using Multiple Low-Cost Low-Resolution Heat Sensor Data

Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy consumption by more than 50%. However, it is difficult to use high-resolution sensors and cameras for occupancy prediction due to privacy concerns. In this paper, we propose a novel solution for predicting occupancy using multiple low-cost and low-resolution heat sensors. We suggest two different methods for fusing and processing the data captured from multiple heat sensors and we use a Convolutional Neural Network for predicting occupancy. We conduct experiments to assess both the performance of the proposed solutions and analyze the impact of sensor field view overlaps on the prediction results. In summary, our experimental results show that the implemented solutions show high occupancy prediction accuracy and real-time processing capabilities.

Heat sensor for ventilation systems

Based on the experimental results, the authors propose a heat sensor design intended for maintaining the temperature in a greenhouse or a premise, the principle of operation of such device being based on the fiber changing the length as affected by the temperature. There is described a method for manufacturing a thermal drive fiber from a nylon thread and a method of heat treatment to improve its deformation properties. The dependence of the longitudinal force of the thermal drive fiber on the temperature has been obtained.

Reflective Bistable Chiral Splay Nematic Liquid Crystal for Low-Power Heat Sensor

The memory effect of the bistable liquid crystal mode is able to maintain the display information for a long time. The splay state and π twist states are used as the memory states of the bistable chiral splay nematic (BCSN) mode. The transition time from the π twist state to the splay state is sensitive to the temperature. In this paper, for the heat sensor application, the reflective structure of the BCSN mode has been studied by the Jones matrix method. In experiments, the measured contrast ratio can be over 200 with a minimal reflective structure including a single polarizer and a reflector.