A method for reconstructing realistic 3D human faces from computer tomography images and color photographs is proposed in this paper. This can be linked easily with the underlying bone and soft tissue models. An iteration algorithm has been developed for automatically estimating the virtual camera parameters to match the projected 3D CT image with 2D color photographs using known point correspondence. An approach has been proposed to select landmarks using a mouse with minimum error. Six landmarks from each image have been selected for front face matching and five for each side face matching.

In leased line services used by ISPs (Internet Service Providers) the bandwidth is fixed, but the traffic changes dynamically. Therefore, there is a necessity for ISPs to accommodate extra capacity to meet peak usage demands; many resources are not used in off-peak hours. To address this, we propose an auction method for the dynamic allocation of bandwidth to ISPs sharing backbone networks. By this method, backbone networks can be used effectively as each ISP is able to secure bandwidth according to its own policy. The Internet users can also be expected to receive good services, as it enables them to obtain information about all ISPs, such as the access fee and QoS (quality of service) provided, and to select congenial ISPs from among all ISPs according to this information. In this study, we compare a dynamic bandwidth allocation service with a leased line service (fixed allocation of bandwidth to ISPs) by using the users' utility to estimate the effectiveness of the proposed method.

It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.