The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.

Determining the size of source effect of a radiation thermometer is not an easy task and manufacturers of these thermometers usually do not indicate the deviation to the measured temperature due to this effect. It is one of the main uncertainty components when measuring with a radiation thermometer and it may lead to erroneous estimation of the actual temperature of the measured target. We present an empiric model to estimate the magnitude of deviation of the measured temperature with a long-wavelength infrared radiation thermometer due to the size of source effect. The deviation is calculated as a function of the field of view of the thermometer and the diameter of the radiating source. For thermometers whose field of view size at 90% power is approximately equal to the diameter of the radiating source, it was found that this effect may lead to deviations of the measured temperature of up to 6% at 200ºC and up to 14% at 500ºC. Calculations of the temperature deviation with the proposed model are performed as a function of temperature and as a function of the first order component of electrical signal.