# Current Research in Future Information and Communication Engineering 2022
## 2 Future Information and Communication Engineering 2022
Four papers focused on deep-learning-based IOT systems. The first paper, authored by [PERSON] et al. [12], proposed and evaluated an intelligent monitoring framework based on the IoT by applying a recurrent neural network (RNN) for the predictive maintenance of a biobanking system in real time. The second paper, authored by [PERSON] et al. [13], analyzed the effect of compressed sensing (CS) rates (from 100% to 10%) and video resolutions (1280 \(\times\) 720, 640 \(\times\) 480, 480 \(\times\) 360) in the IoT sensing device on the pose score of the PoseNet model in the artificial intelligence of things (AIoT) edge server. In this article, when the CS rate is 80% and the video resolution is 1280 \(\times\) 720, the most effective data traffic mitigation and pose score were achieved. The third paper, authored by [PERSON] et al. [14], introduced a fall detection system that combines a simple threshold method (STM) and LSTM, creating the STM-LSTM-based fall detection system. In terms of validating the data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in the LSTM and normalization method were found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128 hidden layer nodes, and a regularization rate of 0.015. In the fourth paper, authored by [PERSON] et al. [15], automatic pet monitoring applications include real-time monitoring and monitoring systems that accurately identify pets using the latest methods for classifying pet activities. An LSTM-based method to detect and classify dog activity based on sensor data (i.e., accelerometers and gyroscopes) was proposed. The activities of dogs using wearable sensor data and using cyclic RNN techniques were investigated. Three papers focused on machine learning. The first one, authored by [PERSON] et al. [16], presented a new multiple learning to prediction algorithm model that used three different combinations of machine learning methods to improve the accuracy of the \(\alpha\)-\(\beta\) filter algorithm. The deep belief network (DBN), the deep extreme learning machine (DELM), and the support vector machine (SVM) as three different learning algorithms were used. The second paper, authored by [PERSON] et al. [17], attempted to develop AI suitable for solving a problem when the number of scatterers was two or three, respectively, based on convolutional neural network (CNN) models and artificial neural network (ANN) models to solve the problem of inverse scattering in some cases. To verify the performance and overfitting of the developed learning model, new limited view data that were not used for learning were created and used. The third paper, authored by [PERSON] et al. [18], proposed and evaluated an intelligent direction of arrival (DOA) estimation error calibration method based on transfer learning which learns error knowledge from a small number of actual signal samples and improves the DOA estimation accuracy in a real application. In this article, a deep-CNN-based intelligent DOA estimation model to learn the mapping between the input signals and their azimuths was constructed. One paper focused on optical communication. A dispersion map that can simultaneously compensate for a distorted wavelength division multiplexed signal while increasing the configurational flexibility was proposed by [PERSON] et al. [19]. It was notable that the flexibility can be increased by randomly arranging residual dispersion per span (RDPS) in the former half, and compensation improvement can be achieved by inversing the order of the RDPS arrangement of the former half in the latter half. One paper focused on image processing. To improve the blurring phenomenon that occurred in the edges of the image as a result of additive white Gaussian noise (AWGN) removal, a modified local steering kernel algorithm based on image matching was proposed and evaluated by [PERSON] et al. [20]. One paper focused on semiconductor displays. The article of [PERSON] et al.