Distant temperature and humidity monitoring: prediction and measurement

Abstract

Sensing environmental measuring parameters has a pivotal role in our everyday lives. Most of our daily life activities depend upon environmental conditions. Accurate information about these parameters also helps in several industrial applications like ventilation rate calculation, energy prediction, stock maintenance in warehouses, and saving from harmful conditions. The emergence of machine learning can make it easy to predict such time series problems. This paper describes the design of a remotely controlled robotic car for measuring and predicting humidity and temperature. A customized app for accessing the robotic car is designed to indicate predicted and realtime measured values of humidity and temperature. A sensor installed builtin helps in the measurement. The recurrent neural network (RNN) model is used to predict humidity and temperature. For this purpose, experiments are carried out in both outdoor and indoor settings. Accuracy of 85% and 90% is achieved in an outdoor environment and indoor settings.

Design of an orthopedic smart splint using nickel-titanium shape memory alloy

“…The results showed that there is a very slight difference between the percentage of temperature variable recorded at each wire when compared with each other which proves that the heat transfer process is not affected by the dimensions of the wire used. Knowing the amount of moisture inside the splint is very important, especially in cases that have undergone surgery or an injury in addition to a fracture, as humidity plays a significant role in the spread of pathogenic bacteria that cause many types of skin infections at the injury site [46]. Figure 9 shows the humidity value inside and outside the cast taken from the humidity sensor [47] connected inside and outside the splint, the results showed a slight difference in the indoor humidity values over time, and these values are known to be changed according to the physiological nature of the human being in terms of sweating and other environmental conditions.…”

Comparison of Influences on Pedestrain Traffic Accidents

“…[9] These decreases do not account for accidents brought on by speeding, aggressive driving, overcompensation, inexperience, delayed response times, inattention, and other driving errors. [10] Around 90% of all crashes are thought to be the result of driver mistake. [11] Other human variables, such as inattention, distraction, or speeding, are frequently determined to have contributed to the collision incidence and/or injury severity, even when the primary cause of a crash is assigned to the vehicle, Impact Factor-6.752 www.irjmets.com highway, or environment.…”

Opportunities, Challenges, and Policy Suggestions for Preparing a Country Autonomous Cars

“…[15] Apart from that, evasive actions should be determined by whether an object in the path of the vehicle is a huge cardboard box or a large concrete block because computer vision has a considerably harder time determining material composition than people do. [16]It is essential for AVs to detect [17] the things in their path when a crash is unavoidable so they can respond appropriately. [18] Responsibility for these occurrences is a significant worry and might significantly impede adoption.…”