Tracking-error model-based predictive control for mobile robots in real time

In this paper, a model-predictive trajectory-tracking control applied to a mobile robot is presented. Linearized tracking-error dynamics is used to predict future system behavior and a control law is derived from a quadratic cost function penalizing the system tracking error and the control effort. Experimental results on a real mobile robot are presented and a comparison of the control obtained with that of a time-varying state-feedback controller is given. The proposed controller includes velocity and acceleration constraints to prevent the mobile robot from slipping and a Smith predictor is used to compensate for the vision-system dead-time. Some ideas for future work are also discussed.     

Mechanical properties of alternating high-low sp3 content thick non-hydrogenated diamond-like amorphous carbon films

Thick non-hydrogenated DLC films (∼ 1μm), consisting of alternating sub-layers of high/low sp³ content, were deposited onto n++ Si substrates using the filtered cathodic vacuum arc method. These films were systematically studied to determine how the changes in composition of the sub-layers would affect the mechanical properties such as intrinsic stress, hardness, friction coefficient, wear rate and surface roughness. Variations of both the ratio of hard to soft layers (from 1:3 to 3:1) and thickness of individual layers (from 12.5nm to 75nm) were studied in detail. The stress of the film was sufficiently lowered (7.8GPa–2.4GPa) by the multilayer approach. The results indicated that although hardness has some correlations with the internal composition of the film, the reduced Young's modulus is largely not affected. Wear and frictional characterizations also showed that the multilayer was a good candidate for many mechanical applications.     

Modeling and simulation of drug delivery from a new type of biodegradable polymer micro-device

In this paper, modeling and simulation of a new type of controlled drug delivery micro-device based on biodegradable polymers is reported. The micro-device consists of micro-chambers arrays for drug storage to achieve linear release. The micro-chambers are fabricated with polyanhydrides (CPP-SA) using the UV-LIGA technology and the controlled release process are the combined results of the design of the micro-chambers and the biodegradable characteristics of the polymer. This type of drug delivery system has some unique advantages in controlled long-term drug delivery, such as larger loading volume than the matrices release systems, easier control for the release rate, etc. It is necessary to optimize the structure for the long-term and zero-order drug release. Based on the Monte Carlo erosion model, the drug release model is founded for the drug delivery system and using the new model, the drug release profiles from the delivery systems with different structures are simulated. The simulated results indicate that the effect of the drug delivery is dependent on the micro-structure of the delivery system and the simulated drug profiles of coaxial rings micro-cavity shape equal to zero-order released model approximatively. The simulated results are very important to the application research of the new biodegradable polymer micro-device.     

An empirical study of software piracy among tertiary institutions in Singapore

We used a survey technique at Singapore's three universities to examine perceptions of software piracy and to attempt to discover its underlying factors. About 500 responses were gathered from students and staff. By means of cluster and factor analysis, we were able to identify three groups that had been influenced by attitudes towards software publishers, general acceptance, convenience, and ethics. A decision tree method linked each pirate profile to demographic and computer-related variables. It showed that, while age was negatively related to software piracy, computer experience or computer usage demonstrated an ambiguous relationship to software piracy. Moreover, older respondents who used university software mainly at their workplace tended to pirate less frequently, while students tended to be pirates more often than university employees. Also Malays were the least frequent pirates in all the Singapore ethnic groups.     

Emergent self-organizing feature map for recognizing road sign images

Road sign recognition system remains a challenging part of designing an Intelligent Driving Support System. While there exist many approaches to classify road signs, none have adopted an unsupervised approach. This paper proposes a way of Self-Organizing feature mapping for recognizing a road sign. The emergent self-organizing map (ESOM) is employed for the feature mapping in this study. It has the capability of visualizing the distance structures as well as the density structure of high-dimensional data sets, in which the ESOM is suitable to detect non-trivial cluster structures. This paper discusses the usage of ESOM for road sign detection and classification. The benchmarking against some other commonly used classifiers was performed. The results demonstrate that the ESOM approach outperforms the others in conducting the same simulations of the road sign recognition. We further demonstrate that the result obtained with ESOM is significantly more superior than traditional SOM which does not take into the boundary effect like ESOM did.     

On the classification performance of TAN and general Bayesian networks

Over a decade ago, Friedman et al. introduced the Tree Augmented Naïve Bayes (TAN) classifier, with experiments indicating that it significantly outperformed Naïve Bayes (NB) in terms of classification accuracy, whereas general Bayesian network (GBN) classifiers performed no better than NB. This paper challenges those claims, using a careful experimental analysis to show that GBN classifiers significantly outperform NB on datasets analyzed, and are comparable to TAN performance. It is found that the poor performance reported by Friedman et al. are not attributable to the GBN per se, but rather to their use of simple empirical frequencies to estimate GBN parameters, whereas basic parameter smoothing (used in their TAN analyses but not their GBN analyses) improves GBN performance significantly. It is concluded that, while GBN classifiers may have some limitations, they deserve greater attention, particularly in domains where insight into classification decisions, as well as good accuracy, is required.     

A novel associative memory approach to speech enhancement in a vehicular environment

Numerous attempts have been undertaken to apply the spectral subtraction method to cancel noise perturbations but these efforts have yet to produce an algorithm that is able to adapt well to the environmental changes in the perturbations. In addition, the variants of the spectral subtraction method so far proposed in the literature would require a non-voice activity detector (NVAD), for a single microphone system, to store the perturbation. This is used as an estimate for the reference signal. Inaccuracy in the perturbation estimates causes the cleaned speech to be corrupted by musical artifacts, which is unacceptable. Post processing of signals corrupted by the musical artifacts is very costly. This paper provides an alternative approach that employs associative memory for speech enhancement. Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories. A set of stereo microphones captures the corrupted speech in a vehicle and is used to point to the closest associative memory location. The Wiener filter approach is used to cancel the noise. The paper discusses novel examples of the associative memories using the cerebellum model for noise modeling. Experimental results show the potential of these novel soft computing architectures in generating and adapting the required Weiner filters to cancel perturbation even at signal to noise ratio (SNR) of less than −13 dB.