Currently, the proportion of independent travel is increasing in Japan. Therefore, earlier studies supporting itinerary planning have been presented. However, these studies have only insufficiently considered rural tourism. For example, tourist often use public transportation during trips in rural areas, although it is often difficult for a tourist to plan an itinerary for public transportation. Even if an itinerary can be planned, it will entail long waiting times at the station or bus stop. Nevertheless, earlier studies have only insufficiently considered these elements in itinerary planning. On the other hand, navigation is necessary in addition to itinerary creation. Particularly, recent navigation often considers dynamic information. During trips using public transportation, schedule changes are important dynamic information. For example, tourist arrive at bus stop earlier than planned. In such case, the waiting time will be longer than the waiting time included in the itinerary. In contrast, if a person is running behind schedule, a risk arises of missing bus. Nevertheless, earlier studies have only insufficiently considered these schedule changes. In this paper, we construct a tourism application that considers the waiting time to improve the tourism experience in rural areas. We define waiting time using static waiting time and dynamic waiting time. Static waiting time is waiting time that is included in the itinerary. Dynamic waiting time is the waiting time that is created by schedule changes during a trip. With this application, static waiting times is considered in the planning function. The dynamic waiting time is considered in the navigation function. To underscore the effectiveness of this application, experiments of the planning function and experiments of the navigation function is conducted in Tsuruoka City, Yamagata Prefecture. Based on the results, we confirmed that a tourist can readily plan a satisfactory itinerary using the planning function. Additionally, we confirmed that Navigation function can use waiting times effectively by suggesting additional tourist spots.

Aiming at the problem of the deterioration of the contact performance caused by the wear debris generated during the fretting wear of the electrical connector, low-frequency fretting wear experiments were carried out on the contacts of electrical connectors, the accumulation and distribution of the wear debris were detected by the electrical capacitance tomography technology; the influence of fretting cycles, vibration direction, vibration frequency and vibration amplitude on the accumulation and distribution of wear debris were analyzed; the correlation between characteristic value of wear debris and contact resistance value was studied, and a performance degradation model based on the accumulation and distribution of wear debris was built. The results show that fretting wear and performance degradation are the most serious in axial vibration; the characteristic value of wear debris and contact resistance are positively correlated with the fretting cycles, vibration frequency and vibration amplitude; there is a strong correlation between the sum of characteristic value of wear debris and the contact resistance value; the prediction error of ABC-SVR model of fretting wear performance degradation of electrical connectors constructed by the characteristic value of wear debris is less than 6%. Therefore, the characteristic value of wear debris in contact subareas can quantitatively describe the degree of fretting wear and the process of performance degradation.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

A new method for hiding information in digital images is proposed. Our method differs from existing techniques in that the information is hidden in a mixture of colors carefully tuned on a specific device according to the device's signal-to-luminance (gamma) characteristics. Because these reproduction characteristics differ in general from device to device and even from model to model, the hidden information appears when the cover image is viewed on a different device, and hence the hiding property is device-dependent. To realize this, we modulated a cover image using two identically-looking checkerboard patterns and switched them locally depending on the hidden information. Reproducing these two patterns equally on a different device is difficult. A possible application of our method would be secure printing where an image is allowed to be viewed only on a screen but a warning message appears when it is printed.

Quadrature voltage-controlled oscillators (VCOs) with current-weight-average and voltage-weight-average phase-adjusting architectures are studied. The phase adjusting equalizes the oscillation frequency to the LC-resonant frequency. The merits of the equalization are explained by using Leeson's phase noise equation and the impulse sensitivity function (ISF). Quadrature VCOs with the phase-adjusting architectures are fabricated using 180-nm TSMC CMOS and show low-phase-noise performances compared to a conventional differential VCO. The ISF analysis and small-signal analysis also show that the drawbacks of the current-weight-average phase-adjusting and voltage-weight-average phase-adjusting architectures are current-source noise effect and large additional capacitance, respectively. A voltage-average-adjusting circuit with a source follower at its input alleviates the capacitance increase.

Digital Microfluidic Biochips (DMFBs) can execute biochemical experiments very efficiently, and thus they are drawing attention recently. In biochemical experiments on a DMFB, “sample preparation” is an important task to generate a sample droplet with the desired concentration value. We merge/split droplets in a DMFB to perform sample preparation. When we split a droplet into two droplets, the split cannot be done evenly in some cases. By some unbalanced splits, the generated concentration value may have unacceptable errors. This paper shows that we can decrease the impact of errors caused by unbalanced splits if we duplicate some mixing nodes in a given dilution graph for most cases. We then propose an efficient method to transform a dilution graph in order to decrease the impact of errors caused by unbalanced splits. We also present a preliminary experimental result to show the potential of our method.

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

Recently, deep learning for image generation with a guide for the generation has been progressing. Many methods have been proposed to generate the animation of facial expression change from a single face image by transferring some facial expression information to the face image. In particular, the method of using facial landmarks as facial expression information can generate a variety of facial expressions. However, most methods do not focus on anime characters but humans. Moreover, we attempted to apply several existing methods to anime characters by training the methods on an anime character face dataset; however, they generated images with noise, even in regions where there was no change. The first order motion model (FOMM) is an image generation method that takes two images as input and transfers one facial expression or pose to the other. By explicitly calculating the difference between the two images based on optical flow, FOMM can generate images with low noise in the unchanged regions. In the following, we focus on the aspect of the face image generation in FOMM. When we think about the employment of facial landmarks as targets, the performance of FOMM is not enough because FOMM cannot use a facial landmark as a facial expression target because the appearances of a face image and a facial landmark are quite different. Therefore, we propose an advanced FOMM method to use facial landmarks as a facial expression target. In the proposed method, we change the input data and data flow to use facial landmarks. Additionally, to generate face images with expressions that follow the target landmarks more closely, we introduce the landmark estimation loss, which is computed by comparing the landmark detected from the generated image with the target landmark. Our experiments on an anime character face image dataset demonstrated that our method is effective for landmark-guided face image generation for anime characters. Furthermore, our method outperformed other methods quantitatively and generated face images with less noise.

Lifelong language learning (LLL) aims at learning new tasks and retaining old tasks in the field of NLP. LAMOL is a recent LLL framework following data-free constraints. Previous works have been researched based on LAMOL with additional computing with more time costs or new parameters. However, they still have a gap between multi-task learning (MTL), which is regarded as the upper bound of LLL. In this paper, we propose Metacognitive Adaptation (Metac-Adapt) almost without adding additional time cost and computational resources to make the model generate better pseudo samples and then replay them. Experimental results demonstrate that Metac-Adapt is on par with MTL or better.

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the l1 norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the l1 norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than l1 for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

This paper proposes a migration model for distributed server allocation. In distributed server allocation, each user is assigned to a server to minimize the communication delay. In the conventional model, a user cannot migrate to another server to avoid instability. We develop a model where each user can migrate to another server while receiving services. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-complete. We introduce a heuristic algorithm. Numerical result shows that the proposed model reduces the average communication delay by 59% compared to the conventional model at most.

Multi-hop V2V communication is a fundamental way to realize data transmission in Vehicular Ad-hoc Networks (VANET). It has excellent potential in intelligent transportation systems and automatic vehicle driving, and positively affects the safety, reliability, and comfort of vehicles. With advantages in speed and trajectory, distribution along the route, size, etc., the urban buses have become prospective relay nodes for urban VANETs. However, it is a considerable challenge to construct stable and reliable (meeting the requirements of bandwidth, delay, and bit error rate) multi-hop routing because of the complexity of the urban road and bus line network in the communication area, as well as many unevenly distributed buses on the road, etc. Given this above, this paper proposes a new hierarchical routing algorithm based on V2V geographic topology segmentation. Urban hierarchical routing is divided into two layers. The first layer of routing is called coarse routing, which is composed of areas; the second layer of routing is called internal routing (bus routing within the area). Q-learning is used to formulate the sequence of buses that transmit information within each area. Details are as follows: Firstly, based on a city map containing road network information, the entire city is divided into small grids by physical streets. Secondly, based on an analysis of the characteristics of the adjacent grid bus lines, the grids with the same routing attributes are integrated into the same area, reducing the algorithm's computational complexity during route discovery. Then, for the calculated area set, a coarse route composed of the selected area is established by filtering out a group of areas satisfying from the source node to the destination node. Finally, the bus sequence between anchor intersections is selected within the chosen area, and a complete multi-hop route from the source node to the destination node is finally constructed. Sufficient simulations show that the proposed routing algorithm has more stable performance in terms of packet transmission rate, average end-to-end delay, routing duration, and other indicators than similar algorithms.

The issue of copying values or references has historically been studied for managing memory objects, especially in distributed systems. In this paper, we explore a new topic on copying values v.s. references, for memory page compaction on virtualized systems. Memory page compaction moves target physical pages to a contiguous memory region at the operating system kernel level to create huge pages. Memory virtualization provides an opportunity to perform memory page compaction by copying the references of the physical pages. That is, instead of copying pages' values, we can move guest physical pages by changing the mappings of guest-physical to machine-physical pages. The goal of this paper is a quantitative comparison between value- and reference-based memory page compaction. To do so, we developed a software mechanism that achieves memory page compaction by appropriately updating the references of guest-physical pages. We prototyped the mechanism on Linux 4.19.29 and the experimental results show that the prototype's page compaction is up to 78% faster and achieves up to 17% higher performance on the memory-intensive real-world applications as compared to the default value-copy compaction scheme.

Edge computing, which has been gaining attention in recent years, has many advantages, such as reducing the load on the cloud, not being affected by the communication environment, and providing excellent security. Therefore, many researchers have attempted to implement neural networks, which are representative of machine learning in edge computing. Neural networks can be divided into inference and learning parts; however, there has been little research on implementing the learning component in edge computing in contrast to the inference part. This is because learning requires more memory and computation than inference, easily exceeding the limit of resources available for edge computing. To overcome this problem, this research focuses on the optimizer, which is the heart of learning. In this paper, we introduce our new optimizer, hardware-oriented logarithmic momentum estimation (Holmes), which incorporates new perspectives not found in existing optimizers in terms of characteristics and strengths of hardware. The performance of Holmes was evaluated by comparing it with other optimizers with respect to learning progress and convergence speed. Important aspects of hardware implementation, such as memory and operation requirements are also discussed. The results show that Holmes is a good match for edge computing with relatively low resource requirements and fast learning convergence. Holmes will help create an era in which advanced machine learning can be realized on edge computing.

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmentation. DNN accelerator on FPGA and especially Convolutional Neural Network (CNN) is a hot topic. More research and education should be conducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN accelerator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and executed on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without image recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.

This paper shows structural optimal design of optical waveguide components utilizing an efficient 3D frequency-domain and 2D time-domain beam propagation method (BPM) with an alternating direction implicit (ADI) scheme. Usual optimal design procedure is based on iteration of numerical simulation, and total computational cost of the optimal design mainly depends on the efficiency of numerical analysis method. Since the system matrices are tridiagonal in the ADI-based BPM, efficient analysis and optimal design are available. Shape and topology optimal design shown in this paper is based on optimization of density distribution and sensitivity analysis to the density parameters. Computational methods of the sensitivity are shown in the case of using the 3D semi-vectorial and 2D time-domain BPM based on ADI scheme. The validity of this design approach is shown by design of optical waveguide components: mode converters, and a polarization beam splitter.

Connected cars generate a huge amount of Internet of Things (IoT) sensor information called Controller Area Network (CAN) data. Recently, there is growing interest in collecting CAN data from connected cars in a cloud system to enable life-critical use cases such as safe driving support. Although each CAN data packet is very small, a connected car generates thousands of CAN data packets per second. Therefore, real-time CAN data collection from connected cars in a cloud system is one of the most challenging problems in the current IoT. In this paper, we propose an Edge computing-enhanced network Redundancy Elimination service (EdgeRE) for CAN data collection. In developing EdgeRE, we designed a CAN data compression architecture that combines in-vehicle computers, edge datacenters and a public cloud system. EdgeRE includes the idea of hierarchical data compression and dynamic data buffering at edge datacenters for real-time CAN data collection. Across a wide range of field tests with connected cars and an edge computing testbed, we show that the EdgeRE reduces bandwidth usage by 88% and the number of packets by 99%.

Network Function Virtualization (NFV) enables the embedding of Virtualized Network Function (VNF) into commodity servers. A sequence of VNFs can be chained in a particular order to form a service chain (SC). This paper considers placing multiple SCs in a geo-distributed edge system owned by multiple service providers (SPs). For a pair of SC and SP, minimizing the placement cost while meeting a latency constraint is formulated as an integer programming problem. As SC clients and SPs are self-interested, we study the matching between SCs and SPs that respects individual's interests yet maximizes social welfare. The proposed matching approach excludes any blocking individual and block pair which may jeopardize the stability of the result. Simulation results show that the proposed approach performs well in terms of social welfare but is suboptimal concerning the number of placed SCs.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.