Physical random bit generators (RBGs) that generate unpredictable bits at high speed are now of increasing importance in information science and technology. since they are expected to generate unpredictable random bits with high speed. From the viewpoint of applications for security, it is a crucial issue to be able to give a guarantee of the unpredictability of random bits. In this paper, we review research on physical RBGs, particularly focusing our attention on the studies of the unpredictability of random bits generated by laser systems, which are promising entropy sources for physical RBGs. Finally, we report recent research results on noise-robustness, which could be crucial for reliability of physical RBG.

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This paper examines the problem of quality enhancement for video streaming in OpenFlow-based network. OpenFlow can be used in future Internet; it can manage and perform routing for a large number of network flow. We employ OpenFlow to propose a routing mechanism to enhances quality of video streaming. We use expected video distortion as routing metric and apply multipath routing to Multiple Description Coded video. Our simulation results highlight the benefit of exploiting OpenFlow to transmit video.

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This paper proposes a scheme for the distributed power saving mechanism of IEEE 802.11e networks in consideration of the Quality of Service requirement. In the proposed protocol, each station enters into sleep-mode and active-mode periodically according to the appropriate trigger interval. For obtaining the appropriate trigger interval, this paper presents the objective function of power consumption in consideration of the acceptable delay required by each access category. Each station optimizes its trigger interval according to the objective function. Simulation results show the effectiveness of the proposed scheme.

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The phase reduction method is a dimension reduction method for weakly driven limit-cycle oscillators, which has played an important role in the theoretical analysis of synchronization phenomena. Recently, we proposed a generalization of the phase reduction method [W. Kurebayashi et al., Phys. Rev. Lett. 111, 2013]. This generalized phase reduction method can robustly predict the dynamics of strongly driven oscillators, for which the conventional phase reduction method fails. In this generalized method, the external input to the oscillator should be properly decomposed into a slowly varying component and remaining weak fluctuations. In this paper, we propose a simple criterion for timescale decomposition of the external input, which gives accurate prediction of the phase dynamics and enables us to systematically apply the generalized phase reduction method to a general class of limit-cycle oscillators. The validity of the criterion is confirmed by numerical simulations.

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This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its application to the multi-optima problems (M-OPT). Particles converge at multiple optima simultaneously, since SW-TOPO disconnect the transmission of information and separate the topology of the particles. We introduce the switching path length as a basic measure to evaluate the switched topology. Also, applying the proposed PSO to typical benchmark functions of the M-OPT, the algorithm efficiency is investigated.

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We propose a low-complexity indoor localization scheme using a hybirid of particle swarm optimization (PSO) and Newton-Raphson (NR) search. The signal of global positioning system (GPS) can be utilized outdoors only, and other schemes are needed for indoor localization. A triangulation-based location estimation using ultra-wide band (UWB) signals between more than three reference terminals and the target node is widely used for centimeter-order localization. In particular, a time of arrival (TOA)-based least square (LS) estimation is popular because the balanced performance in terms of calculation complexity and the accuracy is obtained. However, when the height of reference terminals and the target node is close, the three-dimensional LS-based estimation tends to fall into a local-minimum solution and it needs an accurate initial value of search to keep the estimation performance, resulting in the calculation complexity increase. Therefore, in this paper, we adopt a particle swarm optimization (PSO) method which effectively searches in wide-area space and propose an LS-based localization scheme using the combination of PSO and NR method achieving lower calculation complexity. The improved performances are shown with comparing to conventional search schemes by computer simulations.

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The cochlea is a highly nonlinear biological sound processor the major components of which are lymph (viscous fluid), a basilar membrane (vibrating membrane in the viscous fluid), outer hair cells (active dumpers for the basilar membrane), inner hair cells (neural transducers), and spiral ganglion cells (parallel spikes density modulators). In this paper, a novel cochlea partition model based on a concept of an asynchronous bifurcation processor is presented. It is shown that the presented model can reproduce typical nonlinear responses of partitions of biological cochleae such as nonlinear DC response, nonlinear band-pass filtering, and adaptation. Also, FPGA experiments validate reproductions of these nonlinear responses.

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The regulatory interactions among genes are summarized by the gene regulatory network. Recently, the gene regulatory network that is described by the differential equations is widely used, and a lot of inference methods using time course data of the gene expression levels have been proposed. One of the successful inference methods of the gene regulatory network is the method using the neural network. In this study, as a method to improve a performance of the gene regulatory network inference using the neural networks, we propose the method to apply a kind of majority rule to the conventional method. Our proposed method infers the regulatory interactions in the gene network based on the results of a lot of trials of the inference using neural networks. In the simulations, we evaluate our proposed method using artificially defined gene regulatory networks. The results show the validity of the proposed method. The results also suggest that the strategy of the proposed method is applicable to various methods using the heuristic solver.

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We propose the notion of a Markov property for finding maximally unbiased networks under the constraint of a prescribed two-point degree correlation. We present a framework for modeling the three-point degree correlation - the degree correlation of a subgraph composed of three nodes - on which the Markov property is introduced. The topological features of the Markovian networks - networks satisfying the Markov property - are fully characterized solely by the two-point degree correlation. We theoretically investigate the topological characteristics of Markovian networks and derive the analytical formulas for their graph theoretical metrics. We present a comparative analysis of autonomous-system- (AS-) and router-level topologies in terms of whether they are Markovian. The results of the analysis show that AS-level topologies are Markovian while the router-level topologies are not. The router-level topologies should largely depend on the physical locations of routers, the dependency of which prevents the router-level topologies from being Markovian.

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The implementation of a reaction-diffusion (RD) cellular automata (CA) model on an FPGA is presented. The model generates Turing-like patterns, e.g., striped and spotted patterns observed in marking patterns over animal skins, human fingerprints, etc. Moreover, this model has simple dynamics and generates striped or spotted patterns at its equilibrium that is reached within few cycles, which implies that the model is suitable for hardware implementation. To this aim, a digital processor architecture based on the RDCA model is proposed. Finally, the self-organized patterns generated on the FPGA by the implemented processor are presented.

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Because of the huge growth in the number of Internet users, data packets flowing in communication networks have also growth, and as a result, some packets can become congested in communication networks. If packet congestion occurs in a communication network, the packets are trapped in congested nodes, and then the transmission of these packets to their destinations is delayed. Further, the packets could be removed from the communication network in the worst case. To overcome these undesirable problems, an efficient routing strategy based on mutually connected neural networks has been proposed. This neural-based routing strategy shows good performance for regular topological communication networks. However, the performance of the routing strategy declines in irregular topological communication networks. To improve its performance for irregular topological communication networks, we propose in this paper a new neural-based routing strategy with the transmission information. Numerical experiments show that the performance of the proposed strategy is enhanced by the newly added transmission information as compared to the conventional routing strategies. Further, the proposed routing strategy shows better performance for other topological complex communication networks.

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In this study, we investigate a moving behavior of the network which is observed before an amoeba turns into ‘fruiting bodies’, namely it sporulates. More specifically, we observe how Physarum polycephalum behaves after a severe environmental change; exposure to certain strong light. Through systematic, controlled experiments (in constant dark condition, 26°C) we obtained four evidences, suggesting an efficient mechanism of the network's moving by which Physarum polycephalum makes the sporulation more effective, which is considered to be important for its survival. Our finding adds a new knowledge to the biological aspect of network science.

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Timing synchronization is an important integrating component in wireless distributed systems, such as mobile ad-hoc networks, M2M networks, and wireless sensor networks, and therefore, various timing synchronization algorithms have been proposed so far. Recently, Imai and Suzuki developed a new synchronization algorithm based on a time division multiple access (TDMA) protocol. Despite of its efficiency in synchronization for vehicle-to-vehicle communications, their algorithm sometimes suffers from a certain undesired synchronous pattern, i.e., a so called mode-lock state or a deadlock. Although their algorithm takes certain precautions to avoid this mode-lock state, in around more than 10% of instances this state is observed to persist and the desired perfect synchronization is not realized. Then, first we investigate the mechanism of this persisting mode-lock state for their algorithm. With this insight to the mode-lock state, we propose a new mode-lock free (i.e., mode-lock eliminating) distributed algorithm that always leads to a perfect synchronization. From systematic, comparative simulations, we observe that the proposed algorithm always eliminates mode-lock states, and eventually leads to the perfect synchronization.In addition, we observe the algorithm realizes even faster synchronization, compared with the algorithm by Imai and Suzuki, although these observed properties are not mathematically proved in this study.

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Many dynamical processes such as the spread of epidemic diseases take place on temporal networks. In this paper, we study a special but important case of the temporal networks where the network topology changes periodically in time. We derive the time evolution operator based on the Floquet theory by expanding the periodic Laplacian matrix with respect to the coupling strength. In particular, the first and the second order terms of the expansion are explicitly given with respect to the time integral of the product of the time dependent Laplacian matrices. Using this series expansion, response of the system to a perturbation is also presented. In particular, the deviation from the fast switching approximation, which replaces the time dependent Laplacian matrix with its time average, appears in the lowest order term of the expansion.

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Stochastic resonance (SR) is an interesting phenomenon in that noise enhances system response. Despite attractive phenomenon of SR that noise enhances system response, enhancement of the weak signal below device sensitivity, and few researchers have addressed the SR effect in communication systems. This paper discusses the SR effect in communication systems. We focus on the problem in which communication cannot be established when the received signal strength is below receiver sensitivity. The purpose of this study is to evaluate the bit error rate (BER) performance of the SR receiver and reveal the SR effect in communication systems. We propose an analysis method for the SR receiver using a non-dynamical device that exhibits SR effect. The numerical results show that the SR effect can improve the BER compared to a system without SR. The contribution of the paper is two folds: The first contribution of our present study is that the BER of the SR receiver using a non-dynamical device can analytically be derived. The second contribution of our study is that the number of samples per symbol, the received signal amplitude, and the receiver sensitivity are three important parameters. We further derive the maximum performance gain by the SR system. Although our focus is on primary communication systems; however, our findings can be applied to other systems.

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A mathematical model is proposed to describe the nonlinear dynamics of the echolocation behavior of a bat during free flight in an enclosed chamber. In the model, a bat dynamically controls its flight and pulse directions (φ_f and φ_p respectively), to avoid collision with multiple walls of the chamber; namely, the bat varies φ_f and φ_p repulsively from the closest wall, minimizing the difference between φ_f and φ_p. Numerical simulation of the model with specific parameter values demonstrates that the bat can successfully fly in the chamber while emitting pulses along the inner periphery of an eight-shaped flight path, which is consistent with the results of a behavioral experiment conducted by using a Japanese house bat (Pipistrellus abramus).

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For the case that p is any prime number, we have already constructed all CR (complement reverse) sequences in the de Bruijn sequences of length 2^2p+1. In this research, with the help of the Dyck language, we characterize CR sequences in the de Bruijn sequences of length 2^2m+1 where m (≥4) is a non-prime number. In virtue of this characterization, we show that for any odd number n, there exist CR sequences in the de Bruijn sequences of length 2ⁿ, which completely settles the fundamental problem posed by Fredricksen on existence of the CR sequences. Consequently, we establish an algorithm for generating all CR sequences in the de Bruijn sequences of length 2ⁿ for any odd n.

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