Modeling of the pharyngeal muscle in Caenorhabditis elegans based on FitzHugh-Nagumo equations

The pharyngeal pumping motion to send food to the bowel is a rhythmic movement in Caenorhabditis elegans. This paper proposes a simulation-based approach to investigate the mechanisms of rhythm phenomena in the pharyngeal pumping motion. To conduct the simulations, first, we developed a pharyngeal muscle model including 29 cell models which simulate the activity of each cell as a membrane potential based on FitzHugh-Nagumo equations. Then, to compare the response of the model with that of C. elegans, we calculated the electropharyngeogram (EPG), which represents the electrophysiological responses of the pharyngeal cells, using the simulated membrane potentials. The results confirmed that our model could generate the EPG similar to that measured from C. elegans. We proposed a computer simulation of the pumping motion to investigate the mechanisms of rhythm phenomena in living organisms.     

Robust backpropagation training algorithm for multilayered neuraltracking controller

A robust backpropagation training algorithm with a dead zone scheme is used for the online tuning of the neural-network (NN) tracking control system. This assures the convergence of the multilayer NN in the presence of disturbance. It is proved in this paper that the selection of a smaller range of the dead zone leads to a smaller estimate error of the NN, and hence a smaller tracking error of the NN tracking controller. The proposed algorithm is applied to a three-layered network with adjustable weights and a complete convergence proof is provided. The results can also be extended to the network with more hidden layers.     

Multiple sliding mode observers and unknown input estimations for Lipschitz nonlinear systems

Multiple sliding mode observers for state and unknown input estimations of a class of MIMO nonlinear systems are systematically developed in this paper. A new nonlinear transformation is formulated to divide the original system into two interconnected subsystems. The unknown inputs are assumed to be bounded and not necessarily Lipschitz, and do not require any matching condition. Under structural assumptions for the unknown input distribution matrix, the sliding mode terms of the nonlinear observer are designed to track their respective unknown inputs. Also, the unknown inputs can be reconstructed from the multiple sliding mode structurally. The conditions for asymptotic stability of estimation error dynamics are derived. Finally, simulation results are given to demonstrate the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.     

Stability of uncertain quasi-periodic hybrid dynamic systems

This paper derives sufficient conditions for the stability of uncertain quasi-periodic hybrid dynamic systems. These conditions do not require the Lyapunov function to be non-increasing along each trajectory of the continuous variable dynamic system (CVDS), in other words, they do not require every CVDS to be stable, and they also do not require the Lyapunov function to be non-increasing along the whole sequence of the switchings. Furthermore, they do not require the knowledge of the continuous trajectory. With the proposed conditions, the stability bounds for structured perturbations characterized by non-negative matrices that still ensure robust stability are derived.     

An underwater acoustic MAC protocol using reverse opportunistic packet appending

Underwater communication primarily utilizes propagation of acoustic waves in water. Its unique characteristics, including slow propagation speed and low data rates, pose many challenges to Media Access Control (MAC) protocol design. In most existing handshaking-based underwater MAC protocols, only an initiating sender can transmit data packets to its intended receiver after a channel reservation through a Request-to-Send (RTS)/Clear-to-Send (CTS) handshake. This conventional single-node transmission approach is particularly inefficient in underwater environments, as it does not account for long propagation delays. To improve channel utilization in high latency environments, we propose a novel approach that exploits the idle waiting time during a 2-way handshake to set up concurrent transmissions from multiple nodes. The sender can coordinate multiple first-hop neighbors (appenders) to use the current handshake opportunity to transmit (append) their data packets with partially overlapping transmission times. After the sender finishes transmitting its packets to its own receiver, it starts to receive incoming appended packets that arrive in a collision-free packet train. This not only reduces the amount of time spent on control signaling, but it also greatly improves packet exchange efficiency. Based on this idea, we propose an asynchronous, single-channel handshaking-based MAC protocol based on reverse opportunistic packet appending (ROPA). From extensive simulations (single- and multi-hop networks) and comparisons with several existing MAC protocols, including MACA-U, MACA-UPT, BiC-MAC, Slotted-FAMA, DACAP, unslotted Aloha, we show that ROPA significantly increases channel utilization and offers performance gains in throughput and delay while attaining a stable saturation throughput.     

Hybrid Genetic Programming with Local Search Operators for Dynamic Force Identification

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving.     

Robust neural network tracking controller using simultaneous perturbation stochastic approximation

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.     

Spatio-temporal polygonal clustering with space and time as first-class citizens

Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.     

Zero locations of an entire family of polytope polynomials

We extend the result of Soh to study the zeros of an entire family of polytope polynomials. We show that, under some conditions, zeros of the family of polynomials can be inferred from the zero locations of the vertex polynomials.