Fine-grained access control (FGAC) must be supported by relational databases to satisfy the requirements of privacy preserving and Internet-based applications. Though much work on FGAC models has been conducted, there are still a number of ongoing problems. We propose a new FGAC model which supports the specification of open access control policies as well as closed access control policies in relational databases. The negative authorization is supported, which allows the security administrator to specify what data should not be accessed by certain users. Moreover, multiple policies defined to regulate user access together are also supported. The definition and combination algorithm of multiple policies are thus provided. Finally, we implement the proposed FGAC model as a component of the database management system (DBMS) and evaluate its performance. The performance results show that the proposed model is feasible.

Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands. The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals, with a positive peak of potential about 300 ms posterior to the onset of target stimulus. In this virtual environment, the participants can obtain a more immersed experience with the BCI system, such as controlling a virtual hand or walking around in the virtual world. Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail. Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system. After a short time of practice, most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.

Methods for pressure sore monitoring remain both a clinical and research challenge. Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions. In this paper a technique is introduced for the assessment of pressure sores in guinea pigs, using captured color images. Sores were artificially induced, utilizing a system particularly developed for this purpose. Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation. Different segments of the color images were divided and labeled into three classes, based on their severity status. For quantitative analysis, a color based texture model, which is invariant against monotonic changes in illumination, is proposed. The texture model has been developed based on the local binary pattern operator. Tissue segments were classified, using the texture model and its features as inputs to a combination of neural networks. Our method is capable of discriminating tissue segments in different stages of pressure sore generation, and therefore can be a feasible tool for the early assessment of pressure sores.

Two new heuristic models are developed for motion planning of point robots in known environments. The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and the probabilistic roadmap (PRM) method acting as a local obstacle avoidance planner. For the PSO component, new improvements are proposed in initial particle generation, the weighting mechanism, and position- and velocity-updating processes. Moreover, two objective functions which aim to minimize the path length and oscillations, govern the robot’s movements towards its goal. The PSO and PRM components are further intertwined by incorporating the best PSO particles into the randomly generated PRM. The second model combines a genetic algorithm component with the PRM method. In this model, new specific selection, mutation, and crossover operators are designed to evolve the population of discrete particles located in continuous space. Thorough comparisons of the developed models with each other, and against the standard PRM method, show the advantages of the PSO method.

To facilitate the application of support vector machines (SVMs) in embedded systems, we propose and test a parallel and scalable digital architecture based on the sequential minimal optimization (SMO) algorithm for training SVMs. By taking advantage of the mature and popular SMO algorithm, the numerical instability issues that may exist in traditional numerical algorithms are avoided. The error cache updating task, which dominates the computation time of the algorithm, is mapped into multiple processing units working in parallel. Experiment results show that using the proposed architecture, SVM training problems can be solved effectively with inexpensive fixed-point arithmetic and good scalability can be achieved. This architecture overcomes the drawbacks of the previously proposed SVM hardware that lacks the necessary flexibility for embedded applications, and thus is more suitable for embedded use, where scalability is an important concern.

The efficiency of inductive power links driven by Class-E amplifiers may deteriorate due to variation in the coupling coefficient when the relative position of the radio frequency (RF) coils changes. To solve this problem, a new design methodology of power links is presented in this paper. The aim of the new design is to use the feedback signal, which is a phase difference between the driving signal and the output current of the Class-E amplifier, to adjust the duty cycle and angular frequency of the driving signal to maintain the optimum state of the inductive power link, and to adjust the supply voltage to keep the output power constant when the coupling coefficient of the RF coils changes. The parameter adjustments with respect to the coupling coefficient and the feedback signal are derived from the design equation of the inductive power link. To validate the feedback control rules, a prototype of the inductive power link was constructed, and its performance validated with the coupling coefficient set at 0.2 and a duty cycle of 0.5. The experimental results showed that, by adjusting the duty cycle, the angular frequency, and the supply voltage, the power link can be kept in optimal operation with a constant output power when the coupling coefficient changes from 0.2 to 0.1 to 0.25.

Motion estimation is an important issue in H.264 video coding systems because it occupies a large amount of encoding time. In this paper, a novel search algorithm which utilizes an adaptive hexagon and small diamond search (AHSDS) is proposed to enhance search speed. The search pattern is chosen according to the motion strength of the current block. When the block is in active motion, the hexagon search provides an efficient search means; when the block is inactive, the small diamond search is adopted. Simulation results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.

This paper develops a modified optimization procedure for coordination of a power system stabilizer (PSS) and a thyristor controlled series compensator (TCSC) controller to enhance the power system small signal stability. The new approach employs eigenvalue-based and time-domain simulation based objective functions simultaneously to improve the optimization convergence rate. A modified particle swarm optimization (MPSO) algorithm is used as the optimization algorithm. The results of simulations and eigenvalue analysis for a single machine infinite bus (SMIB) system equipped with the proposed PSS and TCSC controllers confirm that the new approach is effective in enhancing the system stability.

Multicell converters are an interesting alternative for medium voltage and high power applications, because of the increased number of output voltage levels and apparent frequency. The two most significant types of multicell converter are the flying capacitor multicell (FCM) converter and its derivative, stacked multicell (SM) converter. Balancing flying capacitor voltages is an important constraint to the proper performance of FCM and SM converters. Thus, observation of the flying capacitor voltages used in active control is valuable, but using voltage sensors for observation increases cost and size of the converter. This paper deals with a new strategy to estimate the flying capacitor voltages of both FCM and SM converters. The proposed strategy is based on a discrete time model of the converter and uses only a load current sensor. The circuit was simulated using PSCAD/EMTDC software and simulation results were presented to validate the effectiveness of the proposed estimation strategy in observing the flying capacitor voltages. Simplicity is the most significant advantage of the proposed strategy, its performance being based on simple equations.