With the widespread use of Internet of Things (IoT) devices in recent years, we utilize a variety of hardware devices in our daily life. On the other hand, hardware security issues are emerging. Power analysis is one of the methods to detect anomalous behaviors, but it is hard to apply it to IoT devices where an operating system and various software programs are running. In this paper, we propose an anomalous behavior detection method for an IoT device by extracting application-specific power behaviors. First, we measure power consumption of an IoT device, and obtain the power waveform. Next, we extract an application-specific power waveform by eliminating a steady factor from the obtained power waveform. Finally, we extract feature values from the application-specific power waveform and detect an anomalous behavior by utilizing the local outlier factor (LOF) method. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.

Tourist satisfaction plays a very important role in the development of local community tourism. For the development of tourist destinations in local communities, it is important to measure, maintain, and improve tourist destination royalties over the medium to long term. It has been proven that improving tourist satisfaction is a major factor in improving tourist destination royalties. Therefore, to improve tourist satisfaction in local communities, we identified multiple clusters of sightseeing spots and determined that the satisfaction of tourists can be increased based on these clusters of sightseeing spots. Our discovery flow can be summarized as follows. First, we extracted tourism keywords from guidebooks on sightseeing spots. We then constructed a complex network of tourists and sightseeing spots based on the data collected from experiments conducted in Kyoto. Next, we added the corresponding tourism keywords to each sightseeing spot. Finally, by analyzing network motifs, we successfully discovered multiple clusters of sightseeing spots that could be used to improve tourist satisfaction.

Recently, broadband wireless communication has been significantly enhanced; thus, frequency spectrum scarcity has become an extremely serious problem. Spatial frequency reuse based on spectrum databases has attracted significant attention. The spectrum database collects wireless environment information, such as the radio signal strength indicator (RSSI), estimates the propagation coefficient for the propagation loss and shadow effect, and finds a vacant area where the secondary system uses the frequency spectrum without harmful interference to the primary system. Wireless sensor networks are required to collect the RSSI from a radio environmental monitor. However, a large number of RSSI values should be gathered because numerous sensors are spread over the wireless environment. In this study, a data compression technique based on spatial features, such as buildings and houses, is proposed. Using computer simulation and experimental evaluation, we confirm that the proposed compression method successfully reduces the size of the RSSI and restores the original RSSI in the recovery process.

Observations of gravity waves based on High Frequency Surface Wave Radar can make contributions to a better understanding of the energy transfer process between the ocean and the ionosphere. In this paper, through processing the observed data of the ionospheric clutter from HFSWR during the period of the Typhoon Rumbia with short-time Fourier transform method, HFSWR was proven to have the capability of gravity wave detection.

Sketch face recognition refers to matching photos with sketches, which has effectively been used in various applications ranging from law enforcement agencies to digital entertainment. However, due to the large modality gap between photos and sketches, sketch face recognition remains a challenging task at present. To reduce the domain gap between the sketches and photos, this paper proposes a cascaded transformation generation network for cross-modality image generation and sketch face recognition simultaneously. The proposed cascaded transformation generation network is composed of a generation module, a cascaded feature transformation module, and a classifier module. The generation module aims to generate a high quality cross-modality image, the cascaded feature transformation module extracts high-level semantic features for generation and recognition simultaneously, the classifier module is used to complete sketch face recognition. The proposed transformation generation network is trained in an end-to-end manner, it strengthens the recognition accuracy by the generated images. The recognition performance is verified on the UoM-SGFSv2, e-PRIP, and CUFSF datasets; experimental results show that the proposed method is better than other state-of-the-art methods.

This paper presents recent progress on high frequency and wide bandwidth GaN high power amplifiers (PAs) that are usable for high-data-rate wireless communications and modern radar systems. The key devices and design techniques for PA are described in this paper. The results of the state-of-the art GaN PAs for microwave to millimeter-wave applications and design methodology for ultra-wideband GaN PAs are shown. In order to realize high output power density, InAlGaN/GaN HEMTs were employed. An output power density of 14.8 W/mm in S-band was achieved which is 1.5 times higher than that of the conventional AlGaN/GaN HEMTs. This technique was applied to the millimeter-wave GaN PAs, and a measured power density at 96 GHz was 3 W/mm. The modified Angelov model was employed for a millimeter-wave design. W-band GaN MMIC achieved the maximum Pout of 1.15 W under CW operation. The PA with Lange coupler achieved 2.6 W at 94 GHz. The authors also developed a wideband PA. A power combiner with an impedance transformation function based on the transmission line transformer (TLT) technique was adopted for the wideband PA design. The fabricated PA exhibited an average Pout of 233 W, an average PAE of 42 %, in the frequency range of 0.5 GHz to 2.1 GHz.

In this paper, we demonstrate that a formal approach is effective for improving reliability of cooperative robot designs, where the control logics are expressed in concurrent FSMs (Finite State Machines), especially in accordance with the standard FSM4RTC (FSM for Robotic Technology Components), by a case study of cooperative transport robots. In the case study, FSMs are modeled in the formal specification language CSP (Communicating Sequential Processes) and checked by the model-checking tool FDR, where we show techniques for modeling and verification of cooperative robots implemented with the help of the RTM (Robotic Technology Middleware).

This paper proposes a lossless coding method for HDR color images stored in a floating point format called Radiance RGBE. In this method, three mantissa and a common exponent parts, each of which is represented in 8-bit depth, are encoded using the block-adaptive prediction technique with some modifications considering the data structure.

This paper presents an efficient method for solving PnP, PnPf, and PnPfr problems, which are the problems of determining camera parameters from 2D-3D point correspondences. The proposed method is derived based on a simple usage of linear algebra, similarly to the classical DLT methods. Therefore, the new method is easier to understand, easier to implement, and several times faster than the state-of-the-art methods using Gröbner basis. Contrary to the existing Gröbner basis methods, the proposed method consists of three algorithms depending on the number of the points and the 3D point configuration. Experimental results show that the proposed method is as accurate as the state-of-the-art methods even in near-planar scenes while achieving up to three times faster.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

Recently the data-driven learning of dynamic systems has become a promising approach because no physical knowledge is needed. Pure machine learning approaches such as Gaussian process regression (GPR) learns a dynamic model from data, with all physical knowledge about the system discarded. This goes from one extreme, namely methods based on optimizing parametric physical models derived from physical laws, to the other. GPR has high flexibility and is able to model any dynamics as long as they are locally smooth, but can not generalize well to unexplored areas with little or no training data. The analytic physical model derived under assumptions is an abstract approximation of the true system, but has global generalization ability. Hence the optimal learning strategy is to combine GPR with the analytic physical model. This paper proposes a method to learn dynamic systems using GPR with analytic ordinary differential equations (ODEs) as prior information. The one-time-step integration of analytic ODEs is used as the mean function of the Gaussian process prior. The total parameters to be trained include physical parameters of analytic ODEs and parameters of GPR. A novel method is proposed to simultaneously learn all parameters, which is realized by the fully Bayesian GPR and more promising to learn an optimal model. The standard Gaussian process regression, the ODE method and the existing method in the literature are chosen as baselines to verify the benefit of the proposed method. The predictive performance is evaluated by both one-time-step prediction and long-term prediction. By simulation of the cart-pole system, it is demonstrated that the proposed method has better predictive performances.

This review provides a current overview of the fabrication processes for superconducting digital circuits at CRAVITY (clean room for analog and digital superconductivity) at the National Institute of Advanced Industrial Science and Technology (AIST), Japan. CRAVITY routinely fabricates superconducting digital circuits using three types of fabrication processes and supplies several thousand chips to its collaborators each year. Researchers at CRAVITY have focused on improving the controllability and uniformity of device parameters and the reliability, which means reducing defects. These three aspects are important for the correct operation of large-scale digital circuits. The current technologies used at CRAVITY permit ±10% controllability over the critical current density (Jc) of Josephson junctions (JJs) with respect to the design values, while the critical current (Ic) uniformity is within 1σ=2% for JJs with areas exceeding 1.0 µm2 and the defect density is on the order of one defect for every 100,000 JJs.

This paper investigates an adversarial model in the scenario of private information retrieval (PIR) from n coded storage servers, called Byzantine adversary. The Byzantine adversary is defined as the one altering b server responses and erasing u server responses to a user's query. In this paper, two types of Byzantine adversaries are considered; 1) the classic omniscient type that has the full knowledge on n servers as considered in existing literature, and 2) the reasonable limited-knowledge type that has information on only b+u servers, i.e., servers under the adversary's control. For these two types, this paper reveals that the resistance of a PIR scheme, i.e., the condition of b and u to correctly obtain the desired message, can be expressed in terms of a code parameter called the coset distance of linear codes employed in the scheme. For the omniscient type, the derived condition expressed by the coset distance is tighter and more precise than the estimation of the resistance by the minimum Hamming weight of the codes considered in existing researches. Furthermore, this paper also clarifies that if the adversary is limited-knowledge, the resistance of a PIR scheme could exceed that for the case of the omniscient type. Namely, PIR schemes can increase their resistance to Byzantine adversaries by allowing the limitation on adversary's knowledge.

We investigate an interactive evolutionary computation (IEC) using multiple users' gaze information when users partially participate in each design evaluation. Many previous IEC systems have a problem that user evaluation loads are too large. Hence, we proposed to employ user gaze information for evaluating designs generated by IEC systems in order to solve this problem. In this proposed system, users just view the presented designs, not assess, then the system automatically creates users' favorite designs. With the user's gaze information, the proposed system generates coordination that can satisfy many users. In our previous study, we verified the effectiveness of the proposed system from a real system operation viewpoint. However, we did not consider the fluctuation of the users during a solution candidate evaluation. In the actual operation of the proposed system, users may change during the process due to the user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed in real environments. The first situation changes the number of users evaluating the designs for each generation. The second situation employs various users from the predefined population to evaluate the designs for each generation. From the experimental results in the first situation, we confirm that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users. Also, from the results in the second situation, we verify that the proposed system can also generate coordination which both users who participate in the coordination evaluation can more satisfy.

This paper proposes a physical-layer cell identity (PCID) detection method that uses joint estimation of the frequency offset and secondary synchronization signal (SSS) sequence for the 5G new radio (NR) initial access with beamforming transmission at a base station. Computer simulation results show that using the PCID detection method with the proposed joint estimation yields an almost identical PCID detection probability as the primary synchronization signal (PSS) detection probability at an average received signal-to-noise ratio (SNR) of higher than approximately -5dB suggesting that the residual frequency offset is compensated to a sufficiently low level for the SSS sequence estimation. It is also shown that the PCID detection method achieves a high PCID detection probability of greater than 90% and 50% at the carrier frequency of 30 and 50GHz, respectively, at the average received SNR of 0dB for the frequency stability of a user equipment oscillator of 3ppm.

The Tokyo subway is one of the most complex subway networks in the world and it is difficult to compute a visually readable metro map using existing layout methods. In this paper, we present a new method that can generate complex metro maps such as the Tokyo subway network. Our method consists of two phases. The first phase generates rough metro maps. It decomposes the metro networks into smaller subgraphs and partially generates rough metro maps. In the second phase, we use a local search technique to improve the aesthetic quality of the rough metro maps. The experimental results including the Tokyo metro map are shown.

In order to realize the higher bit rates compared for the fifth-generation (5G) mobile communication system, massive MIMO technologies in higher frequency bands with wider bandwidth are being investigated for 5G evolution and 6G. One of practical method to realize massive MIMO in the high frequency bands is hybrid beamforming (BF). With this approach, user selection is an important function because its performance is highly affected by inter-user interference. However, the computational complexity of user selection in multi-user massive MIMO is high because MIMO channel matrix size excessive. Furthermore, satisfying user fairness by proportional fairness (PF) criteria leads to further increase of the complexity because re-calculation of precoding and postcoding matrices is required for each combination of selected users. To realize a fair and low-complexity user selection algorithm for multi-user massive MIMO employing hybrid BF, this paper proposes a two-step user selection algorithm that combines PF based user selection and chordal distance user selection. Computer simulations show that the proposed two-step user selection algorithm with higher user fairness and lower computational complexity can achieve higher system performance than the conventional user selection algorithms.

In March 2020, the 5th generation mobile communication system (5G) was launched in Japan. Frequency bands of 3.7GHz, 4.5GHz and 28GHz were allocated for 5G services, and the 5G use cases fall into three broad categories: Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). The use cases and services that take advantage of the characteristics of each category are expected to be put to practical use, and experiments of practical use are underway. This paper introduces and demonstrates a touchless gate that can identify, authenticate and allow passage through the gate by using these features and 5G beam tracking to estimate location by taking advantage of the low latency of 5G and the straightness of the 28GHz band radio wave and its resistance to spreading. Since position estimation error due to reflected waves and other factors has been a problem, we implement an algorithm that tracks the beam and estimates the user's line of movement, and by using an infrared sensor, we made it possible to identify the gate through which the user passes with high probability. We confirmed that the 5G touchless gate is feasible for gate passage. In addition, we demonstrate that a new service based on high-speed high-capacity communication is possible at gate passage by taking advantage of the wide bandwidth of the 28GHz band. Furthermore, as a use case study of the 5G touchless gate, we conducted a joint experiment with an airline company.

Accurate and automatic quantitative cephalometry analysis is of great importance in orthodontics. The fundamental step for cephalometry analysis is to annotate anatomic-interested landmarks on X-ray images. Computer-aided automatic method remains to be an open topic nowadays. In this paper, we propose an efficient deep learning-based coarse-to-fine approach to realize accurate landmark detection. In the coarse detection step, we train a deep learning-based deformable transformation model by using training samples. We register test images to the reference image (one training image) using the trained model to predict coarse landmarks' locations on test images. Thus, regions of interest (ROIs) which include landmarks can be located. In the fine detection step, we utilize trained deep convolutional neural networks (CNNs), to detect landmarks in ROI patches. For each landmark, there is one corresponding neural network, which directly does regression to the landmark's coordinates. The fine step can be considered as a refinement or fine-tuning step based on the coarse detection step. We validated the proposed method on public dataset from 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge. Compared with the state-of-the-art method, we not only achieved the comparable detection accuracy (the mean radial error is about 1.0-1.6mm), but also largely shortened the computation time (4 seconds per image).

Data parallelism is the dominant method used to train deep learning (DL) models on High-Performance Computing systems such as large-scale GPU clusters. When training a DL model on a large number of nodes, inter-node communication becomes bottle-neck due to its relatively higher latency and lower link bandwidth (than intra-node communication). Although some communication techniques have been proposed to cope with this problem, all of these approaches target to deal with the large message size issue while diminishing the effect of the limitation of the inter-node network. In this study, we investigate the benefit of increasing inter-node link bandwidth by using hybrid switching systems, i.e., Electrical Packet Switching and Optical Circuit Switching. We found that the typical data-transfer of synchronous data-parallelism training is long-lived and rarely changed that can be speed-up with optical switching. Simulation results on the Simgrid simulator show that our approach speed-up the training time of deep learning applications, especially in a large-scale manner.