User authentication for accurate biometric systems is becoming necessary in modern real-world applications. Authentication systems based on biometric identifiers such as faces and fingerprints are being applied in a variety of fields in preference over existing password input methods. Face imaging is the most widely used biometric identifier because the registration and authentication process is noncontact and concise. However, it is comparatively easy to acquire face images using SNS, etc., and there is a problem of forgery via photos and videos. To solve this problem, much research on face spoofing detection has been conducted. In this paper, we propose a method for face spoofing detection based on convolution neural networks using the color and texture information of face images. The color-texture information combined with luminance and color difference channels is analyzed using a local binary pattern descriptor. Color-texture information is analyzed using the Cb, S, and V bands in the color spaces. The CASIA-FASD dataset was used to verify the proposed scheme. The proposed scheme showed better performance than state-of-the-art methods developed in previous studies. Considering the AI FPGA board, the performance of existing methods was evaluated and compared with the method proposed herein. Based on these results, it was confirmed that the proposed method can be effectively implemented in edge environments.

The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensor’s data. The simulation results are validated through computer software simulation.

This paper proposes a run-time monitor and bandwidth assignment scheme for delay-sensitive traffic in ATM (asynchronous transfer mode) network. The traffic monitor function is thought to be indispensable to support the QoS (quality of services) of the accepted calls and carry out the network resource management efficiently at the same time. The proposed scheme monitors user data cells by its counter which is driven by system clocks and makes a report on the monitor results to the upper plane such as control plane or network management plane. This feature makes it possible to do the run-time bandwidth assignment and can moderately reduce the size of cell buffers which is inevitable to observe input cell rates in existing traffic controls. Furthermore, any processing delays resulting from usage parameter controls can also be eliminated. The proposed scheme is conformed to efficiently manage the calls and reliably improve the overall system performances without a drop in QoS by computer simulations.

The authors propose a new method for the modeling and call admission control (CAC) of variable bit rate video source, which come to the front of ATM networks as hot issues nowadays. First, the modeling of video source is accomplished using the three-state Markov chains including the effects of scene change at which the bit rate of video source is abruptly increased. Also, using two AR models, they improve the defects which an AR model has in modeling a video source. In addition, they represent the analytical model of a video source so that a network manager can acquire the information which is very important in managing the entire networks. CAC is accomplished using the previously defined analytical model. A routing manager calculates the cell loss probability of a chosen VP where a new call is connected so that the routing manager decides whether this new call is accepted or not. This calculation is accomplished through the GB/D/1-S queuing system. Using BIA (bandwidth increasing algorithm), they check whether the calls rejected by the routing manager could be accepted if possible. Finally, the applicable procedures to suitable allocate bandwidth to each VP on a link are presented in detail.>