Volume 9, Number 3, 2012
Special Issue on Molecular Imaging Technologies and Applications (pp.225-236)
A two-stage source reconstruction algorithm for bioluminescence tomography (BLT) is developed using hybrid finite element method (FEM). The proposed algorithm takes full advantages of linear and quadratic FEMs, which can be used to localize and quantify bioluminescent source accurately. In the first stage, a large permissible region is roughly determined and then iteratively evolved to reduce matrix dimension using efficient linear FEM. In the final stage, high-convergence quadratic FEM is applied to improve reconstruction result. Both numerical simulation and physical experiment are performed to evaluate the proposed algorithm. The relevant results demonstrate that quantitative reconstruction can be well achieved in terms of computation efficiency, source position, power density, and total power when compared with previous studies.
Challenges remain in fluorescence reflectance imaging (FRI) in in vivo experiments, since the target fluorescence signal is often contaminated by the high level of background signal originated from autofluorescence and leakage of excitation light. In this paper, we propose an image subtraction algorithm based on two images acquired using two excitation filters with different spectral regions. One in vivo experiment with a mouse locally injected with fluorescein isothiocyanate (FITC) was conducted to calculate the subtraction coefficient used in our studies and to validate the subtraction result when the exact position of the target fluorescence signal was known. Another in vivo experiment employing a nude mouse implanted with green fluorescent protein (GFP) --- expressing colon tumor was conducted to demonstrate the performance of the employed method to extract target fluorescence signal when the exact position of the target fluorescence signal was unknown. The subtraction results show that this image subtraction algorithm can effectively extract the target fluorescence signal and quantitative analysis results demonstrate that the target-to-background ratio (TBR) can be significantly improved by 33.5 times after background signal subtraction.
This paper presents a new method for modelling and simulation of the dynamic behaviour of the wheel-rail contact. The proposed dynamic wheel-rail contact model comprises wheel-rail contact geometry, normal contact problem, tangential contact problem and wheelset dynamic behaviour on the track. This two-degree of freedom model takes into account the lateral displacement of the wheelset and the yaw angle. Single wheel tread rail contact is considered for all simulations and Kalker's linear theory and heuristic non-linear creep models are employed. The second order differential equations are reduced to first order and the forward velocity of the wheelset is increased until the wheelset critical velocity is reached. This approach does not require solving mathematical equations in order to estimate the critical velocity of the dynamic wheel-rail contact model. The mathematical model is implemented in Matlab using numerical differentiation method. The simulated results compare well with the estimated results based on classical theory related to the dynamic behaviour of rail-wheel contact so the model is validated.
As unmanned aerial vehicles are expected to do more and more advanced tasks, improved range and persistence is required. This paper presents a method of using shallow layer cumulus convection to extend the range and duration of small unmanned aerial vehicles. A simulation model of an X-models XCalubur electric motor-glider is used in combination with a refined 4D parametric thermal model to simulate soaring flight. The parametric thermal model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation.
The use of radio frequency identification (RFID) tags may cause privacy violation of users carrying an RFID tag. Due to the unique identification number of the RFID tag, the possible privacy threats are information leakage of a tag, traceability of the consumer, denial of service attack, replay attack and impersonation of a tag, etc. There are a number of challenges in providing privacy and security in the RFID tag due to the limited computation, storage and communication ability of low-cost RFID tags. Many research works have already been conducted using hash functions and pseudorandom numbers. As the same random number can recur many times, the adversary can use the response derived from the same random number for replay attack and it can cause a break in location privacy. This paper proposes an RFID authentication protocol using a static identifier, a monotonically increasing timestamp, a tag side random number and a hash function to protect the RFID system from adversary attacks. The proposed protocol also indicates that it requires less storage and computation than previous existing RFID authentication protocols but offers a larger range of security protection. A simulation is also conducted to verify some of the privacy and security properties of the proposed protocol.
A form of iterative learning control (ILC) is used to update the set-point for the local controller. It is referred to as set-point-related (SPR) indirect ILC. SPR indirect ILC has shown excellent performance: as a supervision module for the local controller, ILC can improve the tracking performance of the closed-loop system along the batch direction. In this study, an ILC-based P-type controller is proposed for multi-input multi-output (MIMO) linear batch processes, where a P-type controller is used to design the control signal directly and an ILC module is used to update the set-point for the P-type controller. Under the proposed ILC-based P-type controller, the closed-loop system can be transformed to a 2-dimensional (2D) Roesser's system. Based on the 2D system framework, a sufficient condition for asymptotic stability of the closed-loop system is derived in this paper. In terms of the average tracking error (ATE), the closed-loop control performance under the proposed algorithm can be improved from batch to batch, even though there are repetitive disturbances. A numerical example is used to validate the proposed results.
Sliding mode control is an important method used in nonlinear control systems. In robust control systems, the sliding mode control is often adopted due to its inherent advantages of easy realization, fast response and good transient performance as well as its insensitivity to parameter uncertainties and disturbances. In this paper, we derive new results based on the sliding mode control for the anti-synchronization of identical Qi three-dimensional (3D) four-wing chaotic systems (2008) and identical Liu 3D four-wing chaotic systems (2009). The stability results for the anti-synchronization schemes derived in this paper using sliding mode control (SMC) are established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the SMC method is very effective and convenient to achieve global chaos anti-synchronization of the identical Qi four-wing chaotic systems and identical Liu four-wing chaotic systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper.
This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms.
In this paper, we consider the global error bound for the generalized complementarity problem (GCP) with analytic functions. Based on the new technique, we establish computable global error bound under milder conditions, which refines the previously known results.
The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot's yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.
The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.
There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.
Much research has been done on the dependability evaluation of computer systems. However, much of this is gone no further than study of the fault coverage of such systems, with little focus on the relationship between fault coverage and overall system dependability. In this paper, a Markovian dependability model for triple-modular-redundancy (TMR) system is presented. Having fully considered the effects of fault coverage, working time, and constant failure rate of single module on the dependability of the target TMR system, the model is built based on the stepwise degradation strategy. Through the model, the relationship between the fault coverage and the dependability of the system is determined. What is more, the dependability of the system can be dynamically and precisely predicted at any given time with the fault coverage set. This will be of much benefit for the dependability evaluation and improvement, and be helpful for the system design and maintenance.
Purcell's swimmer was proposed by E. M. Purcell to explain bacterial swimming motions. It has been proved experimentally that a swimmer of this kind is possible under inertial-less and high viscous environment. But we could not investigate all the aspects of this mechanism through experiments due to practical difficulties. The computational fluid dynamics (CFD) provides complementary methods to experimental fluid dynamics. In particular, these methods offer the means of testing theoretical advances for conditions unavailable experimentally. Using such methodology, we have investigated the fluid dynamics of force production associated with the Purcell's swimmer. By employing dynamic mesh and user-defined functions, we have computed the transient flow around the swimmer for various stroke angles. Our simulations capture the bidirectional swimming property successfully and are in agreement with existing theoretical and experimental results. To our knowledge, this is the first CFD study which shows the fact that swimming direction depends on stroke angle. We also prove that for small flapping frequencies, swimming direction can also be altered by changing frequency-showing breakdown of Stokes law with inertia.
This paper deals with the simultaneous estimation of states and unknown inputs for a class of Lipschitz nonlinear systems using only the measured outputs. The system is assumed to have bounded uncertainties that appear on both the state and output matrices. The observer design problem is formulated as a set of linear constraints which can be easily solved using linear matrix inequalities (LMI) technique. An application based on manipulator arm actuated by a direct current (DC) motor is presented to evaluate the performance of the proposed observer. The observer is applied to estimate both state and faults.