We propose a load balancing algorithm for a multi-RAT (radio access technology) network including a non-terrestrial network (NTN) and a terrestrial network (TN). Fifth generation (5G) and beyond-5G networks consider NTNs to provide connectivity and data delivery to large numbers of user equipments (UEs). However, previous load balancing algorithms do not consider the coexistence of NTNs and TNs and ignore the different resource allocation units in a multi-RAT network. Hence, we define a radio resource utilization ratio (RRUR) as a common load metric to measure the cell load of each RAT and employ an adaptive threshold to determine overloaded cells. The proposed algorithm consists of two steps to overcome the uneven load distribution across 5G cells: intra-RAT load balancing and inter-RAT load balancing. Based on the RRUR of a cell, the algorithm first performs intra-RAT load balancing by offloading the appropriate edge UEs of an overloaded cell to underutilized neighboring cells. If the RRUR of the cell is still higher than a predefined threshold, then inter-RAT load balancing is performed by offloading the delay-tolerant data flows of UEs to a satellite link. Furthermore, the algorithm estimates the impact of moving loads to the target cell load to avoid unnecessary load balancing actions. Simulation results show that the proposed algorithm not only distributes the load across terrestrial cells more evenly but also increases network throughput and the number of quality of service satisfied UEs more than previous load balancing algorithms.INDEX TERMS 5G, cellular network, satellite, NTN, radio access network, multi-RAT, QoS, load balancing, data flows, load measurement.

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An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and trained using processed, real, measured data. To that end, a magnetic pickup sensor extracts the rotational speed of the prime mover of a four-stroke V12 marine diesel engine. The measured signal is then converted into a crank angle degree (CAD) signal that shows the behavior of the combustion strokes of firing cylinders at a particular engine load. The CAD signals are considered an input feature to the designed network for classification of engine loads. For verification, we considered five classes of engine load, and the trained network classifies these classes with an accuracy of 99.4%.

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In this paper, we propose an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. SDPPG is widely used in healthcare applications because of its easy and comfortable measurement. However, an SDPPG signal is vulnerable to movement, which degrades the signal. Degradation of SDPPG signal shapes can result in incorrect diagnosis. The proposed algorithm detects movement noise in a measurement signal using wavelet transform, and removes movement noise by selecting the best signal from among multiple signals measured at different locations. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.