This study proposes a mathematical model of a gesture-based pointing interface system for simulating pointing behaviors in various situations. We assume an interaction between a pointing interface and a user as a human-in-the-loop system and describe it using feedback control theory. The model is formulated as a hybrid of a target value follow-up component and a disturbance compensation one. These are induced from the same feedback loop but with different parameter sets to describe human pointing characteristics well. The two optimal parameter sets were determined individually to represent actual pointing behaviors accurately for step input signals and random walk disturbance sequences, respectively. The calibrated model is used to simulate pointing behaviors for arbitrary input signals expected in practical situations. Through experimental evaluations, we quantitatively analyzed the performance of the proposed hybrid model regarding how accurately it can simulate actual pointing behaviors and also discuss the advantage regarding the basic non-hybrid model. Model refinements for further accuracy are also suggested based on the evaluation results.

In this paper, we analyze the stability of XCP (eXplicit Control Protocol) in a network with heterogeneous XCP flows (i.e., XCP flows with different propagation delays). Specifically, we model a network with heterogeneous XCP flows using fluid-flow approximation. We then derive the conditions that XCP control parameters should satisfy for stable XCP operation. Furthermore, through several numerical examples and simulation results, we quantitatively investigate effect of system parameters and XCP control parameters on stability of the XCP protocol. Our findings include: (1) when XCP flows are heterogeneous, XCP operates more stably than the case when XCP flows are homogeneous, (2) conversely, when variation in propagation delays of XCP flows is large, operation of XCP becomes unstable, and (3) the output link bandwidth of an XCP router is independent of stability of the XCP protocol.

This paper describes an analytical framework for the weighted max-min flow control of elastic flows in packet networks using PID and PII2 controller when flows experience heterogeneous round-trip delays. Our algorithms are scalable in that routers do not need to store any per-flow information of each flow and they use simple first come first serve (FCFS) discipline, stable in that the stability is proven rigorously when there are flows with heterogeneous round-trip delays. We first suggest two closed-loop system models that approximate our flow control algorithms in continuous-time domain where the purpose of the first algorithm is to achieve the target queue length and that of the second is to achieve the target utilization. The slow convergence [1] of many rate-based flow control algorithms, which use queue lengths as input signals, can be resolved by the second algorithm. Based on these models, we find the conditions for controller gains that stabilize closed-loop systems when round-trip delays are equal and extend this result to the case of heterogeneous round-trip delays with the help of Zero exclusion theorem. We simulate our algorithms with optimal gain sets for various configurations including a multiple bottleneck network to verify the usefulness and extensibility of our algorithms.

On the basis of generalized theory of system design the behavior of the different design trajectories in the design phase space was analyzed. An additional acceleration effect of the design process has been discovered by the analysis of various design strategies with different initial points. This effect can be understood well on the basis of the elaborated design methodology by means of the different design trajectory analysis. This effect is displayed for all analyzed circuits and it reduces additionally the total computer time for the system design.

Several gateway-based congestion control mechanisms have been proposed to support an end-to-end congestion control mechanism of TCP (Transmission Control Protocol). One of promising gateway-based congestion control mechanisms is a RED (Random Early Detection) gateway. Although effectiveness of the RED gateway is fully dependent on a choice of control parameters, it has not been fully investigated how to configure its control parameters. In this paper, we analyze the steady state behavior of the RED gateway by explicitly modeling the congestion control mechanism of TCP. We first derive the equilibrium values of the TCP window size and the buffer occupancy of the RED gateway. Also derived are the stability condition and the transient performance index of the network using a control theoretic approach. Numerical examples as well as simulation results are presented to clearly show relations between control parameters and the steady state behavior.

A feedback-based congestion control mechanism is essential to realize an efficient data transfer service in packed-switched networks. TCP (Transmission Control Protocol) is a feedback-based congestion control mechanism, and has been widely used in the current Internet. An improved version of TCP called TCP Vegas has been proposed and studied in the literature. It can achieve better performance than TCP Reno. In previous studies, performance analysis of a window-based flow control mechanism based on TCP Vegas only for a simple network topology has been performed. In this paper, we extend the analysis to a generic network topology where each connection is allowed to have a different propagation delay and to traverse multiple bottleneck links. We first derive equilibrium values of window sizes of TCP connections and the number of packets waiting in a router's buffer. We also derive throughput of each TCP connection in steady state, and investigate the effect of control parameters of TCP Vegas on fairness among TCP connections. We then present several numerical examples, showing how control parameters of TCP Vegas should be configured for achieving both stability and better transient performance.

We applied control theory to nerve-muscle in order to model and systematize the muscle system. The association between nerve stimulation frequencies and electromyogram (EMG) amplitude was studied in rat nerve-muscle under normal and hypokalemic conditions. From these results, we modeled the nerve-muscle and simulated frequency response from the nerve-muscle system which can be expressed as a closed loop transfer function.

The formulation of the process of analog system design has been done on the basis of the control theory application. This approach generalizes the design process and produces different design trajectories inside the same optimization procedure. The problem of the optimal design algorithm construction is defined as the minimal-time problem of the control theory. The main equations for the proposed design methodology were elaborated. These equations include the special control functions that are introduced artificially to generalize the design problem. Optimal dependencies of the control functions give the possibility to reduce the total computer design time. This idea was tested with different optimization algorithms of the design process. Numerical results of some simple electronic circuit design demonstrate the efficiency of the proposed approach. These examples show that the traditional design strategy is not time-optimal and the potential computer time gain of the optimal design strategy increases when the size and complexity of the system increase.

This paper proposes a new formulation which minimizes the differential entropy for an optimal control problem. The conventional criterion of the optimal regulator control is a standard quadratic cost function E[M{x(t)}2} + N{v(t)}2], where x(t) is a state variable, u(t) is an input value, and M and N are positive weights. However, increasing the number of the variables of the system it is complex to find the solution of the optimal regulator control. Therefore, the simplicity of the solution is required. In contrast to the optimal regulator control, we propose the minimum entropy control which minimizes a differential entropy of the weighted sum of x(t) and u(t). This solution is derived on the assumptions that the linear control and x(t)u(t) 0 are satisfied. As the result, the formula of the minimum entropy control is very simple and clear. This result will be useful for the further work with multi variables of simple control formulation.