Real-time matting is a challenging research in deep learning. Conventional CNN (Convolutional Neural Networks) approaches are easy to misjudge the foreground and background semantic and have blurry matting edges, which result from CNN’s limited concentration on global context due to receptive field. We propose a real-time matting approach called RMViT (Real-time matting with Vision Transformer) with Transformer structure, attention and content-aware guidance to solve issues above. The semantic accuracy improves a lot due to the establishment of global context and long-range pixel information. The experiments show our approach exceeds a 30% reduction in error metrics compared with existing real-time matting approaches.

Crowdsourcing becomes a popular data-collection method to relieve the burden of high cost and latency for data-gathering. Since the involved users in crowdsourcing are volunteers, need incentives to encourage them to provide data. However, the current incentive mechanisms mostly pay attention to the data quantity, while ignoring the data quality. In this paper, we design a Data-quality awaRe IncentiVe mEchanism (DRIVE) for collaborative tasks based on the Stackelberg game to motivate users with high quality, the highlight of which is the dynamic reward allocation scheme based on the proposed data quality evaluation method. In order to guarantee the data quality evaluation response in real-time, we introduce the mobile edge computing framework. Finally, one case study is given and its real-data experiments demonstrate the superior performance of DRIVE.

With the recent advances in IoT, there is a growing interest in multi-surface computing, where a mobile app can cooperatively utilize multiple devices' surfaces. We propose a novel framework that seamlessly augments mobile apps with multi-surface computing capabilities. It enables various apps to employ multiple surfaces with acceptable performance.

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

Beyond 5G is the next generation mobile communication system expected to be used from around 2030. Services in the 2030s will be composed of multiple systems provided by not only the conventional networking industry but also a wide range of industries. However, the current mobile communication system architecture is designed with a focus on networking performance and not oriented to accommodate and optimize potential systems including service management and applications, though total resource optimizations and service level performance enhancement among the systems are required. In this paper, a new concept of the Beyond 5G cross-industry service platform (B5G-XISP) is presented on which multiple systems from different industries are appropriately organized and optimized for service providers. Then, an architecture of the B5G-XISP is proposed based on requirements revealed from issues of current mobile communication systems. The proposed architecture is compared with other architectures along with use cases of an assumed future supply chain business.

We have developed an all-optical fiber link antenna measurement system for a millimeter wave 5th generation mobile communication frequency band around 28 GHz. Our developed system consists of an optical fiber link an electrical signal transmission system, an antenna-coupled-electrode electric-field (EO) sensor system for 28GHz-band as an electrical signal receiving system, and a 6-axis vertically articulated robot with an arm length of 1m. Our developed optical fiber link electrical signal transmission system can transmit the electrical signal of more than 40GHz with more than -30dBm output level. Our developed EO sensor can receive the electrical signal from 27GHz to 30GHz. In addition, we have estimated a far field antenna factor of the EO sensor system for the 28GHz-band using an amplitude center modified antenna factor estimation equation. The estimated far field antenna factor of the sensor system is 83.2dB/m at 28GHz.

This paper proposes a dual-polarized filtering antenna with extracted-pole unit (EPU) using LTCC substrate. The EPU realizes the high skirt characteristic of the bandpass filter with transmission zeros (TZs) located near the passband without cross coupling. The filtering antenna with EPU is designed and fabricated in 28GHz band for 5G Band-n257 (26.5-29.5GHz). The measured S11 is less than -10.6dB in Band-n257, and the isolation between two ports for dual polarization is greater than 20.0dB. The measured peak antenna gain is 4.0dBi at 28.8GHz and the gain is larger than 2.5dBi in Band-n257. The frequency characteristics of the measured antenna gain shows the high skirt characteristic out of band, which are in good agreement with electromagnetic (EM)-simulated results.

Technology for sixth-generation (6G) mobile communication system is now being widely studied. A sub-Terahertz band is expected to play a great role in 6G to enable extremely high data-rate transmission. This paper has two goals. (1) Introduction of 6G concept and propagation characteristics of sub-Terahertz-band radio waves. (2) Performance evaluation of intelligent reflecting surfaces (IRSs) based on beamforming in a sub-Terahertz band for smart radio environments (SREs). We briefly review research on SREs with reconfigurable intelligent surfaces (RISs), and describe requirements and key features of 6G with a sub-Terahertz band. After that, we explain propagation characteristics of sub-Terahertz band radio waves. Important feature is that the number of multipath components is small in a sub-Terahertz band in indoor office environments. This leads to an IRS control method based on beamforming because the number of radio waves out of the optimum beam is very small and power that is not used for transmission from the IRS to user equipment (UE) is little in the environments. We use beams generated by a Butler matrix or a DFT matrix. In simulations, we compare the received power at a UE with that of the upper bound value. Simulation results show that the proposed method reveals good performance in the sense that the received power is not so lower than the upper bound value.

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

Vehicles on the road are expected to connect continuously to the Internet at sufficiently high speeds, e.g., several Mbps or higher, to support multimedia applications. However, even when passing through a well-facilitated city area, Internet access can be unreliable and even disconnected if the travel speed is high. We therefore propose a network path selection technique to meet network throughput requirements. The proposed technique is based on the attractor selection model and enables vehicles to switch the path from a route connecting directly to a cellular network to a relay type through neighboring vehicles for Internet access. We also develop a mechanism that prevents frequent path switching when the performance of all available paths does not meet the requirements. We conduct field evaluations by platooning two vehicles in a real-world driving environment and confirm that the proposed technique maintains the required throughput of up to 7Mbps on average. We also evaluated our proposed technique by extensive computer simulations of up to 6 vehicles in a platoon. The results show that increasing platoon length yields a greater improvement in throughput, and the mechanism we developed decreases the rate of path switching by up to 25%.

It is now becoming important for mobile cellular networks to accommodate all kinds of Internet of Things (IoT) communications. However, the contention-based random access and radio resource allocation used in traditional cellular networks, which are optimized mainly for human communications, cannot efficiently handle large-scale IoT communications. For this reason, standardization activities have emerged to serve IoT devices such as Cellular-IoT (C-IoT). However, few studies have been directed at evaluating the performance of C-IoT communications with periodic data transmissions, despite this being a common characteristic of many IoT communications. In this paper, we give the performance analysis results of mobile cellular networks supporting periodic C-IoT communications, focusing on the performance differences between LTE and Narrowband-IoT (NB-IoT) networks. To achieve this, we first construct an analysis model for end-to-end performance of both the control plane and data plane, including random access procedures, radio resource allocation, establishing bearers in the Evolved Packet Core network, and user-data transmissions. In addition, we include the impact of the immediate release of the radio resources proposed in 3GPP. Numerical evaluations show that NB-IoT can support more IoT devices than LTE, up to 8.7 times more, but imposes a significant delay in data transmissions. We also confirm that the immediate release of radio resources increases the network capacity by up to 17.7 times.

Wireless sensor networks provide long-term monitoring of the environment, but sensors are powered by small batteries. Using a mobile charger (MC) to replenish energy of sensors is one promising solution to prolong their usage time. Many approaches have been developed to find the MC's moving path, and they assume that sensors have a fixed sensing rate (SR) and prefer to fully charge sensors. In practice, sensors can adaptively adjust their SRs to meet application demands or save energy. Besides, due to the fully charging policy, some sensors with low energy may take long to wait for the MC's service. Thus, the paper formulates a path and charge (P&C) problem, which asks how to dispatch the MC to visit sensors with adaptive SRs and decide their charging time, such that both survivability and throughput of sensors can be maximized. Then, we propose an efficient P&C scheduling (EPCS) algorithm, which builds the shortest path to visit each sensor. To make the MC fast move to charge the sensors near death, some sensors with enough energy are excluded from the path. Moreover, EPCS adopts a floating charging mechanism based on the ratio of workable sensors and their energy depletion. Simulation results verify that EPCS can significantly improve the survivability and throughput of sensors.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

Privacy violations via spy cameras are becoming increasingly serious. With the recent advent of various smart home IoT devices, such as smart TVs and robot vacuum cleaners, spycam attacks that steal users' information are being carried out in more unpredictable ways. In this paper, we introduce a new spycam attack on a mobile WebVR environment. It is performed by a web attacker who maliciously accesses the back-facing cameras of victims' mobile devices while they are browsing the attacker's WebVR site. This has the power to allow the attacker to capture victims' surroundings even at the desired field of view through sophisticated content placement in VR scenes, resulting in serious privacy breaches for mobile VR users. In this letter, we introduce a new threat facing mobile VR and show that it practically works with major browsers in a stealthy manner.

High-Altitude Platform Station (HAPS) provides communication services from an altitude of 20km via a stratospheric platform such as a balloon, solar-powered airship, or other aircraft, and is attracting much attention as a new mobile communication platform for ultra-wide coverage areas and disaster-resilient networks. HAPS can provide mobile communication services directly to the existing smartphones commonly used in terrestrial mobile communication networks such as Fourth Generation Long Term Evolution (4G LTE), and in the near future, Fifth Generation New Radio (5G NR). In order to design efficient HAPS-based cell configurations, we need a radio wave propagation model that takes into consideration factors such as terrain, vegetation, urban areas, suburban areas, and building entry loss. In this paper, we propose a new vegetation loss model for Recommendation ITU-R P.833-9 that can take transmission frequency and seasonal characteristics into consideration. It is based on measurements and analyses of the vegetation loss of deciduous trees in different seasons in Japan. Also, we carried out actual stratospheric measurements in the 700MHz band in Kenya to extend the lower frequency limit. Because the measured results show good agreement with the results predicted by the new vegetation loss model, the model is sufficiently valid in various areas including actual HAPS usage.

In recent years, High-Altitude Platform Station (HAPS) has become the most interesting topic for next generation mobile communication systems, because platforms such as Unmanned Aerial Vehicles (UAVs), balloons, airships can provide ultra-wide coverage, up to 200km in diameter, from altitudes of around 20 km. It also offers resiliency to damage caused by disasters and so ensures the stability and reliability of mobile communications. In order to further integrate HAPS with existing terrestrial mobile communication networks in providing mobile services to users, radio wave propagation models such as terrain, vegetation loss, human shielding loss, building entry loss, urban/suburban areas must be taken into consideration when designing HAPS-based cell configurations. This paper proposes a human body shielding propagation loss model that considers the basic signal attenuation by the human body at high elevation angles. It also analyzes the effect of changes in actual urban/suburban environments due to the arrival of multipath radio waves for HAPS communications in the frequency range of 0.7 to 3.3GHz. Measurements in actual urban/rural environments in Japan and actual stratospheric base station measurements in Kenya are carried out to confirm the validity of the proposed model. Since the measured results agree well with the results predicted by the proposed model, the model is good enough to provide estimates of human loss in various environments.

Mobile phone systems continue to evolve from the 2nd generation, which began in the early 1990s, to the 5th generation, which is now in service. Along with this evolution, the power amplifier (PA) is also evolved. The characteristics required for PA are changing with each generation. In this paper, we will give an overview of the evolution of PAs from the 2nd generation mobile phones such as GSM (global system for mobile communications) to the 5th generation mobile phones that is often called NR (new radio), in particular, the circuit system. Specifically, the following five items will be described. (1) Ramp-up and ramp-down power control circuit corresponding to GSM, (2) Self-bias circuit technology for improving linearity that becomes important after W-CDMA (wideband code division multiple access), (3) Power mode switching methods for improving efficiency at low output power, (4) Power combining methods that have become important since LTE (long term evolution), and (5) Backoff efficiency improvement methods represented by ET (envelop tracking) and Doherty PA.

In this study, we consider fake message attacks in sparse mobile ad hoc networks, in which nodes are chronically isolated. In these networks, messages are delivered to their destination nodes using store-carry-forward routing, where they are relayed by some nodes. Therefore, when a node has messages in its buffer, it can falsify the messages easily. When malicious nodes exist in the network, they alter messages to create fake messages, and then they launch fake message attacks, that is, the fake messages are spread over the network. To analyze the negative effects of a fake message attack, we model the system dynamics without attack countermeasures using a Markov chain, and then formalize some performance metrics (i.e., the delivery probability, mean delivery delay, and mean number of forwarded messages). This analysis is useful for designing countermeasures. Moreover, we consider a hash-based countermeasure against fake message attacks using a hash of the message. Whenever a node that has a message and its hash encounters another node, it probabilistically forwards only one of them to the encountered node. By doing this, the message and the hash value can be delivered to the destination node via different relay nodes. Therefore, even if the destination node receives a fake message, it can verify the legitimacy of the received message. Through simulation experiments, we evaluate the effectiveness of the hash-based countermeasure.

Particle filters have been widely used for state estimation problems in nonlinear and non-Gaussian systems. Their performance depends on the given system and measurement models, which need to be designed by the user for each target system. This paper proposes a novel method to design these models for a particle filter. This is a numerical optimization method, where the particle filter design process is interpreted into the framework of reinforcement learning by assigning the randomnesses included in both models of the particle filter to the policy of reinforcement learning. In this method, estimation by the particle filter is repeatedly performed and the parameters that determine both models are gradually updated according to the estimation results. The advantage is that it can optimize various objective functions, such as the estimation accuracy of the particle filter, the variance of the particles, the likelihood of the parameters, and the regularization term of the parameters. We derive the conditions to guarantee that the optimization calculation converges with probability 1. Furthermore, in order to show that the proposed method can be applied to practical-scale problems, we design the particle filter for mobile robot localization, which is an essential technology for autonomous navigation. By numerical simulations, it is demonstrated that the proposed method further improves the localization accuracy compared to the conventional method.