To date, a number of researchers are seeking for and/or designing novel molecules which function as arithmetic molecular engines. Biomolecules such as deoxyribonucleic acid (DNA) and proteins are examples of promising candidate molecules. In the present article, we showed our view that DNA-based molecules could be used as a novel class of platforms for discrete mathematical operations or tools for natural computation. Here, we report on a novel molecular logic circuit combining exclusive disjunction (XOR) gate and conjunction (AND) gate implemented on a single DNA molecule performing arithmetic operations with simple binary numbers through polymerase chain reactions (PCR); which was inspired by previously developed protein-based computing model allowing simple polynomial algebra over fields through algebraic representation of cyclic inter-conversions in the catalytic modes of a plant enzyme as a cyclic additive group. In addition, we showed that DNA can be used as the platform for image coding and processing leading to DNA-coded animation by using novel PCR-based protocols. Lastly, we discussed the significance of recent attempts in the stream of natural computing and synthetic biological research, by handling DNA and related biomolecules as the media for discrete mathematical operations.

In ecological systems, living organisms are surrounded by a number of chemicals, among which certain portion may be toxic to organisms. Therefore, from the environment-centric point of view, importance of accurate eco-toxicological analyses is increasing day-by-day. Eco-toxicity responses in animals and other organisms against chemicals can be scored by several parameters such as median lethal concentration (LC₅₀) and median lethal dose (LD₅₀), for examples. In the present study, we attempted to perform simulations of eco-toxicological nature of given chemicals based on limited data size (showing apparently incomplete curves of toxicity response) through model experiments performed with green paramecia (Paramecium bursaria) exposed to toxic metal ions, by using practically re-arranged logistic equation and Hill-type equations with an aid by graphical elucidation of Gauss-Newton algorithm determining the constants and/or coefficients.

In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal solutions is an important issue. This paper focuses on the Pareto optimal solution visualization method using the growing hierarchical self-organizing maps (GHSOM) which is one of promising visualization methods. This method has a superior Pareto optimal solution representation capability, compared to the visualization method using the self-organizing maps. However, this method has some shortcomings. This paper proposes a new Pareto optimal solution visualization method using an improved GHSOM based on the batch learning. In the proposed method, the batch learning algorithm is introduced to the GHSOM to obtain a consistent visualization maps for a Pareto optimal solution set. Then, the symmetric transformation of maps is introduced in the growing process in the batch learning GHSOM algorithm to improve readability of the maps. Furthermore, the learning parameter optimization is introduced. The effectiveness of the proposed method is confirmed through numerical experiments with comparing the proposed method to the conventional methods on the Pareto optimal solution representation capability and the readability of the visualization maps.

Many biological and artifact networks often represent modular structures in which the network can be decomposed into several subnetworks. Here, we propose a simple model for the modular network evolution based on the nonlinear denoising in node activities. This model suggests that modular networks can evolve under certain conditions — if the stipulated goals for the networks or the input and target output pairs involve modular features, or if the signal transfer in a node is carried out in a nonlinear manner with respect to the saturation at the upper and lower bounds. Our model highlights the positive role played by noise in modular network evolution.

Rough-set-based interrelationship mining enables to extract characteristics by comparing the values of the same object between different attributes. To apply this interrelationship mining to incomplete decision tables with null values, in this study, we discuss the treatment of null values in interrelationships between attributes. We introduce three types of null values for interrelated condition attributes and formulate a similarity relation by such attributes with these null values.

We have developed a graphical user interface (GUI)-based state estimation filter simulator (called StefAny) that makes it easy to understand and compare the behaviors of filters such as Kalman filters (KFs) and particle filters (PFs). The key feature of StefAny is to show, when a system designer applies a PF, a detailed graph representing the relationship among the distribution and weights of all particles on any arbitrary timeline through simulation. Moreover, the timeline can be specified on another graph showing an estimated time series for each filter. These features enable system designers to easily check the compatibility between a filter and a target distribution, which determines the state estimation accuracy. In this paper, we present the functions of StefAny and demonstrate in detail how StefAny facilitates understanding of the properties of filters via a compatibility check comparison experiment for PFs, point estimation methods, and distributions.

Three-dimensional (3D) navigation using a computer-assisted technique is being increasingly performed in minimally invasive surgical procedures because it can provide stereoscopic information regarding the operating field to the surgeon. In this paper, the development of a real-time arthroscopic system utilizing an endoscopic camera and optical fiber to navigate a normal vector for a reconstructed knee joint surface is described. A specific navigation approach suitable for use in a rendered surface was presented in extenso. A small-sized endoscopic tube was utilized arthroscopically on a cadaveric knee joint to show the potential application of the developed system. Experimental results of underwater navigation on a synthetic knee joint showed that our system allows for a higher accuracy than a freehand technique. The mean angle of navigation for the proposed technique is 9.5^◦ (range, 5^◦ to 17^◦; SD, 2.86^◦) versus 14.8^◦ (range, 6^◦ to 26^◦; SD, 7.53^◦) and 12.6^◦ (range, 4^◦ to 17^◦; SD, 3.98^◦) for two sites using a freehand technique.

Particle filter is one promising method to estimate the internal states in dynamical systems, and can be used for various applications such as visual tracking and mobile-robot localization. The major drawback of particle filter is its large computational amount, which causes long computational-time and large power-consumption. In order to solve this problem, this paper proposes an Field-Programmable Gate Array (FPGA) platform for particle filter. The platform is designed using the OpenCL-based design tool that allows users to develop using a high-level programming language based on C and to change designs easily for various applications. The implementation results demonstrate the proposed FPGA implementation is 106 times faster than the CPU one, and the power-delay product of the FPGA implementation is 1.1% of the CPU one. Moreover, implementations for three different systems are shown to demonstrate flexibility of the proposed platform.

Person name clustering disambiguation is the process that partitions name mentions according to corresponding target person entities in reality. The existed methods can not realize effective identification of important features to disambiguate person names. This paper presents a method of Chinese person name disambiguation based on two-stage clustering. This method adopts a stage-by-stage processing model to identify and utilize different types of important features. Firstly, we extract three kinds of core evidences namely direct social relation, indirect social relation and common description prefix, recognize document-pairs referring to the same person entity, and realize initial clustering of person names with high precision. Then, we take the result of initial clustering as new initial input, utilize the statistical properties of multi-documents to recognize and evaluate important features, and build a double-vector representation of clusters (cluster feature vector and important feature vector). Based on the processes above, the final clustering of person names is generated, and the recall of clustering is improved effectively. The experiments have been conducted on the dataset of CLP2010 Chinese person names disambiguation, and experimental results show that this method has good performance in person name clustering disambiguation.

Heartbeat can reflect the dynamics of the heart control system, and it is also a commonly used index in health monitoring, exercise load calculation and psycho-physiological arousal quantification. This paper fuses three heartbeat measures, i.e. the running mean, the range of local Hurst exponents and the relative fluctuation, to construct a system that can automatically quantify the heartbeat activity both from its static aspect and from its dynamic aspect in a real-time manner. Experiments show that the system can reveal the heartbeat arousal difference between physically relaxed status and exercise-loaded status. When the affective heartbeat data in literature are quantified by this system, the results also show the capability of the system to illustrate psycho-physiological arousal.

Parameter learning of Intuitionistic Fuzzy Rule-Based Systems (IFRBSs) is discussed and applied to medical diagnosis with intent of establishing a sound tradeoff between interpretability and accuracy. This study aims to improve the accuracy of IFRBSs without sacrificing its interpretability. This paper proposes an Objective Programming Method with an Interpretability-Accuracy tradeoff (OPMIA) to learn the parameters of IFRBSs by tuning the types of membership and non-membership functions and by adjusting adaptive factors and rule weights. The proposed method has been validated in the context of a medical diagnosis problem and a well-known publicly available auto-mpg data set. Furthermore, the proposed method is compared to Objective Programming Method not considering the interpretability (OPMNI) and Objective Programming Method based on Similarity Measure (OPMSM). The OPMIA helps achieve a sound a tradeoff between accuracy and interpretability and demonstrates its advantages over the other two methods.

RANdom SAmple Consensus (RANSAC) has been applied to many 3D image processing problems such as homography matrix estimation problems and shape detection from 3D point clouds, and is one of the most popular robust estimator methods. However, RANSAC has a problem related to the trade-off between computational cost and stability of search because RANSAC is based on random sampling. Genetic Algorithm SAmple Consensus (GASAC) based on a population-based multi-point search was proposed in order to improve RANSAC. GASAC can improve the performance of search. However, it is sometimes difficult to maintain the genetic diversity in the search if the large size of outliers is included in a data set. Furthermore, a computational time of GASAC sometimes is slower than that of RANSAC because of calculation of the genetic operators. This paper proposes Evolution Strategy SAmple Consensus (ESSAC) as a new robust estimator. ESSAC is based on Evolution Strategy in order to maintain the genetic diversity. In ESSAC, we apply two heuristic searches to ESSAC. One is a search range control, the other is adaptive/self-adaptive mutation. By applying these heuristic searches, the trade-off between computational speed and search stability can be improved. Finally, this paper shows several experimental results in order to evaluate the effectiveness of the proposed method.

Traffic congestion is a serious problem for people living in urban areas, causing social problems such as time loss, economical loss, and environmental pollution. Therefore, we propose a multi-agent-based traffic light control framework for intelligent transport systems. Achieving consistent traffic flow necessitates the real-time adaptive coordination of traffic lights; however, many conventional approaches are of the centralized control type and do not have this feature. Our multi-agent-based control framework combines both indirect and direct coordination. Reaction to dynamic traffic flow is attained by indirect coordination, whereas green-wave formation, which is a systematic traffic flow control strategy involving several traffic lights, is attained by direct coordination. We present the detailed mechanism of our framework and verify its effectiveness using simulation to carry out a comparative evaluation.

Computation tree logic (CTL) is known to be one of the most useful temporal logics for verifying concurrent systems by model checking technologies. However, CTL is not sufficient for handling inconsistency-tolerant and probabilistic accounts of concurrent systems. In this paper, a paraconsistent (or inconsistency-tolerant) probabilistic computation tree logic (PpCTL) is derived from an existing probabilistic computation tree logic (pCTL) by adding a paraconsistent negation connective. A theorem for embedding PpCTL into pCTL is proven, thereby indicating that we can reuse existing pCTL-based model checking algorithms. A relative decidability theorem for PpCTL, wherein the decidability of pCTL implies that of PpCTL, is proven using this embedding theorem. Some illustrative examples involving the use of PpCTL are also presented.

A teleoperation robot system is connected through a network. However, stochastic delay in such a network can affect its performance, or even make the system unstable. To solve this problem, this paper proposes a teleoperation robot system control method based on fuzzy sliding mode. In the proposed method, a delay generator generates variable delay conforming to a shift gamma distribution designed to simulate actual network delay. In addition, a proposed fuzzy sliding mode controller based on switching gain adjustment is used to rectify the chattering phenomenon in the sliding mode controller of the teleoperation robot system. In the controller, the master hand uses impedance control and realizes feedback from the slave hand. Controller simulation comparison results show that the proposed fuzzy sliding mode controller effectively eliminates the sliding mode control chattering phenomenon as the slave hand stabilizes the tracking velocity of the master hand. Consequently, the system exhibits improved dynamic performance.

This manuscript describes a robot interaction for the driving assistance system of an Ultra-Compact Electric Vehicle (UCEV). Fun-to-drive and safety are important for improving the commercial value of UCEV. To improve fun-to-drive and safety, the improvement of the driving skills is important. However, the driving assistance system of an ordinary vehicle only considers the objective driving evaluation. Therefore, we propose an interactive driving assistance system that considers the relation between the subjective as well as the objective driving evaluation. Furthermore, we install a communicating robot within a UCEV to interact with human beings in real time. As a first step, we propose a driving evaluation system by applying a simplified fuzzy inference, and an interaction timing estimation method by applying a spiking neural network. Through an off-line simulation experiment, we verify the effectiveness of our proposal that is able to generate a robot utterances content as well as estimate reasonable timing.

The x-means determines the suitable number of clusters automatically by executing k-means recursively. The Bayesian Information Criterion is applied to evaluate a cluster partition in the x-means. A novel type of x-means clustering is proposed by introducing cluster validity measures that are used to evaluate the cluster partition and determine the number of clusters instead of the information criterion. The proposed x-means uses cluster validity measures in the evaluation step, and an estimation of the particular probabilistic model is therefore not required. The performances of a conventional x-means and the proposed method are compared for crisp and fuzzy partitions using eight datasets. The comparison shows that the proposed method obtains better results than the conventional method, and that the cluster validity measures for a fuzzy partition are effective in the proposed method.