Lam Fat Yeung

A Mobility Enabled Inpatient Monitoring System Using a ZigBee Medical Sensor Network

This paper presents a ZigBee In-Patient Monitoring system embedded with a new ZigBee mobility management solution. The system enables ZigBee device mobility in a fixed ZigBee network. The usage, the architecture and the mobility framework are discussed in details in the paper. The evaluation shows that the new algorithm offers a good efficiency, resulting in a low management cost. In addition, the system can save lives by providing a panic button and can be used as a location tracking service. A case study focused on the Princes of Wales Hospital in Hong Kong is presented and findings are given. This investigation reveals that the developed mobile solutions offer promising value-added services for many potential ZigBee applications.

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.

Affine invariant object shape matching using genetic algorithm with multi-parent orthogonal recombination and migrant principle

Genetic algorithm has served as a viable means for object shape matching. However, the success rate of correctly matching a pair of contours is generally low and inconsistent. In this paper a novel scheme based on the integration of two techniques: the migrant principle and multi-parent orthogonal recombination, are introduced to alleviate the problem. The proposed algorithm has been evaluated with object contours that are captured under good lighting condition, as well as images that have been contaminated with noise. In both cases experimental results revealed that the proposed method exhibited superior performance over its peers in terms of stability, speed, and success rate.

A Novel Actuator for Underwater Robots

Pneumatic muscles have been used for a number of years as actuators in robotic systems, usually for those that mimic human actions. They are most commonly used in systems designed to aid physically handicapped people. This feasibility study reports mechanical testing and modeling results for the Shadowtrade artificial muscle pneumatic actuator working in both air and underwater. The static and dynamic tension-contraction measurements were recorded through both the force and contraction relationship test and the dynamic response and time relationship test. A model of the properties of the muscle working in water was derived from the measurements of the tests that can be considered for its feasibility for use in undersea robotics.

Exploration for novel uses of air muscles as hydraulic muscles for underwater actuator

Since a dextrous robotic arm has been designed for use on land and in space, further development could be its use underwater for performing delicate tasks, just like a human hand, for operations such as picking up small or irregular size objects. Hitherto there has been no report on studies on modelling and control of rubber artificial muscles in hydraulic mode. The feasibility of developing these actuators using water as the articulating medium for underwater robots was therefore investigated by studying the properties of the hydraulic rubber muscle actuator. The rubber muscles used in the present tests were developed by the Shadowtrade Robot Co. Ltd. for installing in dextrous robotic arms. The objectives of this study were, thus, first, to define the kinematics of the hydraulic muscle. A test set up was designed to obtained contraction, force and response time values based on reference of a model defined for the rubber muscle when used in air. Second, to compare the performance of the rubber muscle in air and underwater using the test set up and thus third, to obtain a modelling of the hydraulic rubber muscle which was derived from that of the air rubber muscle.

Using multi-frequency modulation in a modem for the transmission of near-realtime video in an underwater environment

The system described in this paper uses multicarrier modulation, and has been successfully tested at a data rate up to 10 kbps over 1 km. The system algorithm generates 48 frequencies for transmitting 48 parallel bits of data in each packet. A long transmitted signal sequence is combined with synchronisation, zero gap and information packets. The long multi-frequency signal packets have been implemented to minimise the effect of multipath fading, which is a particular problem in shallow, open water environments. To acquire the starting point of the transmitting sequence, a linear frequency modulation (LFM) signal is used for synchronisation. In order to reduce noise, adaptive threshold packets are used to set up a suitable signal. Experimental results from sea-trials have shown that the system can cope with multipath fading environments.

Robust measurement selection for biochemical pathway experimental design

As a general lack of quantitative measurement data for pathway modelling and parameter identification process, time-series experimental design is particularly important in current systems biology research. This paper mainly investigates state measurement/observer selection problem when parametric uncertainties are considered. Based on the extension of optimal design criteria, two robust experimental design strategies are investigated, one is the regularisation-based design method, and the other is Taguchi-based design approach. By implementing to a simplified IkappaBalpha - NF - kappaB signalling pathway system, two design approaches are comparatively studied. When large parametric uncertainty is present, by assuming that different parametric uncertainties are identical in scale, two methods tend to provide a similar uniform design result.

A parameter estimation method for biological systems modelled by ODE/DDE models using spline approximation and differential evolution algorithm

The inverse problem of identifying unknown parameters of known structure dynamical biological systems, which are modelled by ordinary differential equations or delay differential equations, from experimental data is treated in this paper. A two stage approach is adopted: first, combine spline theory and Nonlinear Programming (NLP), the parameter estimation problem is formulated as an optimization problem with only algebraic constraints; then, a new differential evolution (DE) algorithm is proposed to find a feasible solution. The approach is designed to handle problem of realistic size with noisy observation data. Three cases are studied to evaluate the performance of the proposed algorithm: two are based on benchmark models with priori-determined structure and parameters; the other one is a particular biological system with unknown model structure. In the last case, only a set of observation data available and in this case a nominal model is adopted for the identification. All the test systems were successfully identified by using a reasonable amount of experimental data within an acceptable computation time. Experimental evaluation reveals that the proposed method is capable of fast estimation on the unknown parameters with good precision.

A similarity matrix-based hybrid algorithm for the contact map overlaps problem

This paper proposes a similarity matrix-based hybrid algorithm for the contact map overlap (CMO) problem in protein structure alignment. In this algorithm, Genetic Algorithm (GA) is used as a framework, in which the initial solutions are constructed with similarity matrix heuristic, and Extremal Optimization (EO) is embedded as a mutated operator. In this process, EO quickly approaches near-optimal solutions and GA generates improved global approximations. Five similarity measurements including ratio, inner product, cosine function, Jaccard index and Dice coefficient have been exploited to compute the similarity matrix between two contact maps. The simulations demonstrate that our algorithm is significantly faster and gets better results for most of the test sets.

Extremal Optimization for the Protein Structure Alignment

This paper proposes a combinational optimization algorithm Extremal Optimization (EO) for protein structure alignment based on the Contact map overlap (CMO) model. EO is a meta-heuristic algorithm, as genetic algorithm and simulated annealing, but with a local fitness introduced to guide the improvement of the optimization. By exploiting similarity matrix between two contact maps, the results demonstrate that our algorithm is significantly faster and gets better results for most of the test sets.