To minimize the damage caused by landslides resulting from torrential rain, residents must quickly evacuate to a place of refuge. To make the decision to evacuate, residents must be able to collect and share disaster information. Firstly, this paper introduces the Grass-roots Information Distribution System and a fixed type monitoring system which our research group has been developing. The fixed type monitoring system is deployed at the location of apparent danger, whereas the Grass-roots Information Distribution System distributes disaster information acquired from the fixed type monitoring system through a mobile ad hoc network (MANET) to residents. The MANET is configured using mobile terminals of residents. Next, in this paper, an information dissemination scheme utilizing a MANET and cellular networks to communicate among mobile terminals is proposed and simulated in the area where our research group has been deploying the distribution system. The MANET topology and information distribution obtained from the simulation results for further field experiments are then discussed.

Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.

The recent widespread use of high-performance terminals has resulted in a rapid increase in mobile data traffic. Therefore, public wireless local area networks (WLANs) are being used often to supplement the cellular networks. Capacity improvement through the dense deployment of access points (APs) is being considered. However, the effective throughput degrades significantly when many users connect to a single AP. In this paper, users are classified into guaranteed bit rate (GBR) users and best effort (BE) users, and we propose a network model to provide those services. In the proposed model, physical APs and the bandwidths are assigned to each service class dynamically using a virtual AP configuration and a virtualized backhaul network, for reducing the call-blocking probability of GBR users and improving the satisfaction degree of BE users. Finally, we evaluate the performance of the proposed model through simulation experiments and discuss its feasibility.

In this paper, we propose a periodic reactance time function for 2-element electronically steerable passive array radiator (ESPAR) antennas applicable to the receivers of both single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems with 2 outputs. Based on the proposed function, we evaluate the power patterns of the antenna for various distances between two antenna elements. Moreover, for the distances, we discuss the correlation properties and the strength of the two outputs to find the appropriate distance for the receiver. From the discussions, we can conclude that distances from 0.1 to 0.35 times the wavelength are effective in terms of receive diversity.

Chipless RFID tags that use the higher-mode resonances of a transmission line resonator are presented in this paper. We have proposed multimode stepped impedance resonators (SIRs) for this application and reported the fundamental characteristics of an experimental system composed of multimode SIRs with open-circuited ends and a near-field electromagnetic detector using capacitive coupling (electric field) probes for the detector. To improve the frequency response and widen the detection range, we introduced multimode SIRs with short-circuited ends and inductive coupling (magnetic field) probes and measured their properties. To reduce the size of the tag and reader, we examined the frequency responses and found that the optimal configuration consisted of C-shaped tags and detector probes with a spatially orthogonal arrangement. The experimental tag system showed good frequency responses, detection range, and frequency detection accuracy. In particular, the spacing between the tag resonator and the transmission line of the probe, which corresponds to the detection distance, was 5mm or more, and was at least 10 times greater than that of previously reported RFID tag systems using near-field electromagnetic coupling.

Recent works on enhancing the comfort and convenience of vehicles have revealed the advantage of various wireless sensor nodes for information collection. For the highly required reliability in vehicles, Reliable Wireless Communication based on Substitute Forwarding (RWCSF) has been proposed. It ensures that 99.999% of communications can finish within 20ms, which is the quality of communication equivalent to wired links. However, the performance was evaluated in very limited situations, where two or more communications never occurred simultaneously and no noise was present. In this paper, we enhance RWCSF to achieve the target communication quality even if two or more communications occur simultaneously. In addition, we evaluate the enhanced method in various types of vehicles that create internal noise.

This paper proposes a low-profile unidirectional supergain antenna applicable to wireless communication devices such as mobile terminals, the Internet of Things and so on. The antennas used for such systems are required to be not only electrically low-profile but also unsusceptible to surrounding objects such as human body and/or electrical equipment. The proposed antenna achieves both requirements due to its supergain property using planar elements and a closely placed planar reflector. The primary antenna is an asymmetric dipole type, and consists of a monopole element mounted on an edge of a rectangular conducting plane. Both elements are placed on a dielectric substrate backed by the planar reflector. It is numerically and experimentally shown that the supergain property is achieved by optimizing the geometrical parameters of the antenna. It is also shown that the impedance characteristics can be successfully adjusted by changing the lengths of the ground plane element and the monopole element. Thus, no additional impedance matching circuit is necessary. Furthermore, it is shown that surrounding objects have insignificant impact on the antenna performance.

This study proposes a design and calibration method for a small-footprint, low-frequency, and low-power gate leakage timer using a differential leakage technique for IoT applications. The proposed gate leakage timer is different from conventional ones because it is composed of two leakage sources and exploits differential leakage current for the charging capacitor. This solution alleviates the inherent trade-off between small-footprint and low-frequency in the conventional gate leakage timer. Furthermore, a calibration method to suppress variations of the output frequency is proposed in this paper. To verify the effectiveness of the proposed gate leakage timer, a test chip was fabricated using 55-nm-DDC-CMOS technology. The test chip successfully demonstrates the highest figure of merit (FoM) of the product of the capacitor area (0.072µm2) and output frequency (0.11Hz), which corresponds to an improvement by a factor of 2,121 compared to the conventional one. It also demonstrates the operation with 4.5pW power consumption. The total footprint can be reduced to be 28µm2, which enables low-cost and low-power IoT edges. The scaling scenario shows that the proposed technique is conducive to technology scaling.

In the library, recognizing the activity of the reader can better uncover the reading habit of the reader and make book management more convenient. In this study, we present the design and implementation of a reading activity recognition approach based on passive RFID tags. By collecting and analyzing the phase profiling distribution feature, our approach can trace the reader's trajectory, recognize which book is picked up, and detect the book misplacement. We give a detailed analysis of the factors that can affect phase profiling in theory and combine these factors with relevant activities. The proposed approach recognizes the activities based on the amplitude of the variation of phase profiling, so that the activities can be inferred in real time through the phase monitoring of tags. We then implement our approach with off-the-shelf RFID equipment, and the experiments show that our approach can achieve high accuracy and efficiency in activity recognition in a real-world situation. We conclude our work and further discuss the necessity of a personalized book recommendation system in future libraries.

With the spread of map applications, route generation has become a familiar function. Most of route generation methods search a route from a starting point to a destination point with the shortest time or shortest length, but more enjoyable route generation is recently focused on. Particularly, cyclic-route generation for strolling requires to suggest to a user more than one route passing through several POIs (Point-of-Interests), to satisfy the user's preferences as much as possible. In this paper, we propose a multiple cyclic-route generation method with a route length constraint considering POIs. Firstly, our proposed method finds out a set of reference points based on the route length constraint. Secondly, we search a non-cyclic route from one reference point to the next one and finally generate a cyclic route by connecting these non-cyclic routes. Compared with previous methods, our proposed method generates a cyclic route closer to the route length constraint, reduces the number of the same points passing through by approximately 80%, and increases the number of POIs passed approximately 1.49 times.

Smartphone users often want to customize the positions and functions of physical buttons to accommodate their own usage patterns; however, this is unfeasible for electronic mobile devices based on COTS (Commercial Off-The-Shelf) due to high production costs and hardware design constraints. In this letter, we present the design and implementation of customized virtual buttons that are localized using only common built-in sensors of electronic mobile devices. We develop sophisticated strategies firstly to detect when a user taps one of the virtual buttons, and secondly to locate the position of the tapped virtual button. The virtual-button scheme is implemented and demonstrated in a COTS-based smartphone. The feasibility study shows that, with up to nine virtual buttons on five different sides of the smartphone, the proposed virtual buttons can operate with greater than 90% accuracy.

This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.

In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

Femtocell has been considered as a key promising technology to improve the capacity of a cellular system. However, the femtocells deployed inside a macrocell coverage are potentially suffered from excessive interference. This paper proposes a novel radio resource optimization in closed access femtocell networks based on bat algorithm. Bat algorithm is inspired by the behavior of bats in their echolocation process. While the original bat algorithm is designed to solve the complex optimization problem in continuous search space, the proposed modified bat algorithm extends the search optimization in a discrete search space which is suitable for radio resource allocation problem. The simulation results verify the convergence of the proposed optimization scheme to the global optimal solution and reveal that the proposed scheme based on modified bat algorithm facilitates the improvement of the femtocell network capacity.

Generalized approximate message passing (GAMP) is introduced into distributed compressed sensing (DCS) to reconstruct jointly sparse signals under the mixed support-set model. A GAMP algorithm with known support-set is presented and the matching pursuit generalized approximate message passing (MPGAMP) algorithm is modified. Then, a new joint recovery algorithm, referred to as the joint MPGAMP algorithm, is proposed. It sets up the jointly shared support-set of the signal ensemble with the support exploration ability of matching pursuit and recovers the signals' amplitudes on the support-set with the good reconstruction performance of GAMP. Numerical investigation shows that the joint MPGAMP algorithm provides performance improvements in DCS reconstruction compared to joint orthogonal matching pursuit, joint look ahead orthogonal matching pursuit and regular MPGAMP.

It is known that policy gradient algorithm can not guarantee the convergence to a Nash equilibrium in mixed policies when it is applied in matrix games. To overcome this problem, we propose a novel multi-agent reinforcement learning (MARL) algorithm called a policy gradient lagging anchor (PGLA) algorithm. And we prove that the agents' policies can converge to a Nash equilibrium in mixed policies by using the PGLA algorithm in two-player two-action matrix games. By simulation, we confirm the convergence and also show that the PGLA algorithm has a better convergence than the LR-I lagging anchor algorithm.

The methods proposed in this paper enable resynchronization when a synchronization deviation occurs in a sensor node without a beacon or an ack in a wireless sensor network under ultra-limited but stable resources such as the energy generated from tiny solar cell batteries. The method for a single-hop network is straightforward; when a receiver does not receive data, it is simply placed in recovery mode, in which the receiver sets its cycle length TB to (b±γ)T, where b is non-negative integer, 0 < γ < 1, and T is its cycle length in normal mode, and in which the receiver sets its active interval WB to a value that satisfies WB ≥ W + γT, where W is its active interval in normal mode. In contrast, a sender stays in normal mode. Resynchronization methods for linear multi-hop and tree-based multi-hop sensor networks are constructed using the method for a single-hop network. All the methods proposed here are complete because they are always able to resynchronize networks. The results of simulations based on the resynchronization methods are given and those of an experiment using actual sensor nodes with wireless modules are also presented, which show that the methods are feasible.

In recent years we have witnessed an increasing demand to process queries on large datasets in Non-ordered Discrete Data Spaces (NDDS). In particular, one type of query in an NDDS, called box queries, is used in many emerging applications including error corrections in bioinformatics and network intrusion detection in cybersecurity. Effective indexing methods are necessary for efficiently processing queries on large datasets in disk. However, most existing NDDS indexing methods were not designed for box queries. Several recent indexing methods developed for box queries on a large NDDS dataset in disk are based on the popular data-partitioning approach. Unfortunately, a space-partitioning based indexing scheme, which is more effective for box queries in an NDDS, has not been studied before. In this paper, we propose a novel indexing method based on space-partitioning, called the BINDS-tree, for supporting efficient box queries on a large NDDS dataset in disk. A number of effective strategies such as node split based on minimum span and cross optimal balance, redundancy reduction utilizing a singleton dimension inheritance property, and a space-efficient structure for the split history are incorporated in the constructing algorithm for the BINDS-tree. Experimental results demonstrate that the proposed BINDS-tree significantly improves the box query I/O performance, comparing to that of the state-of-the-artdata-partitioning based NDDS indexing method.

This letter proposes a fast superpixel segmentation method based on boundary sampling and interpolation. The basic idea is as follow: instead of labeling local region pixels, we estimate superpixel boundary by interpolating candidate boundary pixel from a down-sampling image segmentation. On the one hand, there exists high spatial redundancy within each local region, which could be discarded. On the other hand, we estimate the labels of candidate boundary pixels via sampling superpixel boundary within corresponding neighbour. Benefiting from the reduction of candidate pixel distance calculation, the proposed method significantly accelerates superpixel segmentation. Experiments on BSD500 benchmark demonstrate that our method needs half the time compared with the state-of-the-arts while almost no accuracy reduction.