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Physics has been an important part of the science curriculum in high schools. Without the appropriate high school physics background, it is difficult for a student subsequently to pursue an electronics engineering program at the university level since a good understanding of many concepts in physics is required to comprehend the material covered in electronics-related courses. Unfortunately, a phenomenon observed in today's high schools is a clear diminution of student interest in physics, leading to dwindling physics class sizes compared to a decade ago. The lack of interest in and passion for physics at the high school level ultimately leads to smaller intakes at the university level for electronics engineering courses and/or lower qualification cutoffs for students entering the programs. This results in a significant reduction in good students graduating from engineering programs. This paper will first share survey results that reveal possible reasons behind the decreasing number of students entering engineering programs. Two concerted efforts launched from the National University of Singapore (NUS) to arrest the declining interest in high schools will then be presented. (Contains 3 tables and 17 figures.)

The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks. The linear threshold transfer function has been regarded as an adequate transfer function for recurrent neural networks. Networks with this transfer function form a class of hybrid analog and digital networks which are especially useful for perceptual computations. Networks in discrete time can directly provide algorithms for efficient implementation in digital hardware. The main contribution of this paper is to establish foundations of permitted and forbidden sets. Necessary and sufficient conditions for the linear threshold discrete-time recurrent neural networks are obtained for complete convergence, existence of permitted and forbidden sets, as well as conditionally multiattractivity, respectively. Simulation studies explore some possible interesting practical applications. Index Terms--Complete convergence, discrete-time recurrent neural networks, forbidden set, linear threshold, multiattractivity, permitted set.

This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme. Index Terms--Fault detection, nonlinear model, neural networks (NNs).

Recent developments in nuclear reprogramming and intracytoplasmic sperm injection reflect an increasing need for more advanced and automatic micromanipulation technologies. In this paper, we present an automatic cell injection system, which is capable of visually monitoring the injecting process and controlling the microactuators. Traditionally, cell injection was manually operated, and it was laborious, time consuming, of low accuracy, and prone to contamination due to the handling requirements. An automatic and efficient strategy is required to eliminate these drawbacks. In this paper, a system is developed where the injection process is monitored and controlled automatically via integration of a vision system to an injector manipulation system. The cell is located, and the pipette is positioned and driven by the algorithm to achieve effective penetration. The precision achieved is physically proven to be within a good tolerance range. Index Terms--Cell injection, clarity, entropy, focalization, microrobotic system, visual tracking.

The generalized Hebbian algorithm (GHA) is one of the most widely used principal component analysis (PCA) neural network (NN) learning algorithms. Learning rates of GHA play important roles in convergence of the algorithm for applications. Traditionally, the learning rates of GHA are required to converge to zero so that its convergence can be analyzed by studying the corresponding deterministic continuous-time (DCT) equations. However, the requirement for learning rates to approach zero is not a practical one in applications due to computational roundoff limitations and tracking requirements. In this paper, nonzero-approaching adaptive learning rates are proposed to overcome this problem. These proposed adaptive learning rates converge to some positive constants, which not only speed up the algorithm evolution considerably, but also guarantee global convergence of the GHA algorithm. The convergence is studied in detail by analyzing the corresponding deterministic discrete-time (DDT) equations. Extensive simulations are carried out to illustrate the theory. Index Terms--Deterministic discrete-time (DDT) equation, generalized Hebbian algorithm (GHA), global convergence, neural networks (NNs), principal component analysis (PCA).

Adaptively determining an appropriate number of principal directions for principal component analysis (PCA) neural networks is an important problem to address when one uses PCA neural networks for online feature extraction. In this letter, inspired from biological neural networks, a single-layer neural network model with lateral connections is proposed which uses an improved generalized Hebbian algorithm (GHA) to address this problem. In the proposed model, the number of principal directions can be adaptively determined to approximate the intrinsic dimensionality of the given data set so that the dimensionality of the data set can be reduced to approach the intrinsic dimensionality to any required precision through the network. Index Terms--Biologically plausible model, generalized Hebbian algorithm (GHA) learning algorithm, intrinsic dimensionality, principal component analysis (PCA).

The use of the electronic spreadsheet as a modeling and simulation tool in the engineering fields is increasingly popular with an increasing number of tertiary institutions, including spreadsheet as a teaching resource in the core curriculum of these fields. Concurrently, the dramatic improvement in Internet speed allows certain course material to be delivered online, facilitating electronic-learning (E-learning). E-learning frees students from the restriction of time, although they are still largely constrained by space to the nearest wired computer. However, the boundary of computing has expanded with the advance in wireless communication; now numerous handheld mobile devices (e.g., personal digital assistants (PDAs) and cellular phones) are available that will allow computing to be accomplished on the move. In turn, this wireless trend has contributed to move E-learning toward mobile learning (M-learning). Based on the previously mentioned trends, this paper presents the development, implementation, and evaluation of a mobile spreadsheet-based Proportional, Integral, Derivative (PID) control simulation system that aims to enhance the learning of the student in the field of control engineering. The system has been used in an industrial control system course at the National University of Singapore (NUS), Singapore. The mobile spreadsheet system developed is able to work with most handheld mobile devices, including the cellular phone. The methods and difficulties (e.g., limited storage space and small screen display of a cellular phone) involved in the development of the system will be duly discussed. Feedback from students that is used to assess the usefulness of the system will be released to share the benefits of such an M-learning system. Index Terms--File conversion, image processing, mobile education, modeling, Proportional, Integral, Derivative (PID) controller, simulation.

Recurrent neural networks of the Lotka--Volterra model have been proven to possess characteristics which are desirable in some neural computations. A clear understanding of the dynamical properties of a recurrent neural network is necessary for efficient applications of the network. This paper studies the global convergence of general Lotka--Volterra recurrent neural networks with variable delays. The contributions of this paper are: 1) sufficient conditions are established for lower positive boundedness of the networks; 2) global exponential stability conditions are obtained for the networks. These conditions are totally independent of the variable delays which are therefore allowed to be uncertain; 3) novel Lyapunov functionals are constructed to establish delays dependent conditions for global asymptotic stability, and 4) simulation results and examples are provided to supplement and illustrate the theoretical contributions presented. Index Terms--Delay, global convergence, Lotka--Volterra recurrent neural networks.

The convergence of Oja's principal component analysis (PCA) learning algorithms is a difficult topic for direct study and analysis. Traditionally, the convergence of these algorithms is indirectly analyzed via certain deterministic continuous time (DCT) systems. Such a method will require the learning rate to converge to zero, which is not a reasonable requirement to impose in many practical applications. Recently, deterministic discrete time (DDT) systems have been proposed instead to indirectly interpret the dynamics of the learning algorithms. Unlike DCT systems, DDT systems allow learning rates to be constant (which can be a nonzero). This paper will provide some important results relating to the convergence of a DDT system of Oja's PCA learning algorithm. It has the following contributions: 1) A number of invariant sets are obtained, based on which we can show that any trajectory starting from a point in the invariant set will remain in the set forever. Thus, the nondivergence of the trajectories is guaranteed. 2) The convergence of the DDT system is analyzed rigorously. It is proven, in the paper, that almost all trajectories of the system starting from points in an invariant set will converge exponentially to the unit eigenvector associated with the largest eigenvalue of the correlation matrix. In addition, exponential convergence rate are obtained, providing useful guidelines for the selection of fast convergence learning rate. 3) Since the trajectories may diverge, the careful choice of initial vectors is an important issue. This paper suggests to use the domain of unit hyper sphere as initial vectors to guarantee convergence. 4) Simulation results will be furnished to illustrate the theoretical results achieved. Index Terms--Eigenvalue, eigenvector, neural network, Oja's learning algorithm, principal component analysis (PCA).

This paper studies the multistability of a class of discrete-time recurrent neural networks with unsaturating piecewise linear activation functions. It addresses the nondivergence, global attractivity, and complete stability of the networks. Using the local inhibition, conditions for nondivergence are derived, which not only guarantee nondivergence, but also allow for the existence of multiequilibrium points. Under these nondivergence conditions, global attractive compact sets are obtained. Complete stability is studied via constructing novel energy functions and using the well-known Cauchy Convergence Principle. Examples and simulation results are used to illustrate the theory. Index Terms--Complete stability, discrete-time, global attractivity, multistability, nondivergence, recurrent neural networks, unsaturating piecewise linear activation functions.

This paper presents the development of an Internet-based system to allow monitoring of important process variables from a distributed control system (DCS). The system is formulated as an add-on to an undergraduate experiment on the development of a desktop DCS. This paper describes the hardware and software design considerations which enable the users to access the process variables on the DCS, remotely and effectively, using only a commonly available web browser. Index Terms--Distributed control, internet, remote monitoring, virtual experiment.

In this paper, the application of a dual-relay feedback approach toward modeling of frictional effects in servomechanisms is addressed. The friction model consists of Coulomb and viscous friction components, both of which can be automatically extracted from suitably designed relay experiments. At the same time, the dynamical model of the servomechanical system can be obtained from the experiments. Thus, a proportional-integral-derivative feedback motion controller and a feedforward friction compensator can be automatically tuned in this manner. The friction model obtained is also directly applicable to initialization of an adaptive control scheme proposed. Results from simulation and experiments are presented to illustrate the practical appeal of the proposed method. Index Terms--Adaptive control, friction compensation, friction modeling, relay feedback.