A secure tag ownership transfer scheme in a closed loop RFID system

In this paper we propose a novel secure tag ownership transfer scheme for closed loop RFID systems. An important property of our method is that the ownership transfer is guaranteed to be atomic and the scheme is protected against desynchronisation leading to permanent DoS. Further, it is suited to the computational constraints of EPC Class-1 Gen-2 passive RFID tags as they only use the CRC and PRNG functions that passive RFID tags are capable of. We provide a detailed security analysis to show that our scheme satisfies the required security properties of tag anonymity, tag location privacy, forward secrecy, forward untraceability while being resistant to replay, desynchronisation and server impersonation attacks. Performance comparisons show that our scheme is practical and can be implemented on passive low-cost RFID tags.

Multi-layer implantable antenna for closed loop deep brain stimulation system

A multi-layer circular planar inverted-F antenna is designed and simulated at the industrial, scientific, and medical (ISM) band of 915 MHz for closed loop deep brain stimulation implant. The ISM band is considered due to the capabilities of small antenna size, high data rate, and long transmission range. In the proposed four-layer antenna, the top three radiating layers are meandered, and a high permittivity substrate and superstrate materials are used to limit the radius and the height of the antenna to 3.5 mm and 2.2 mm, respectively. The bottom layer works as a ground plate. The Roger RO3210 of εr = 10.2 and δ = 0.003 is used as a dielectric substrate and superstrate. The resonance frequency of the proposed antenna is 915 MHz with a bandwidth of 12 MHz at the return loss of -10 dB in free space. The stacked layered structure reduces the antenna size, and the circular shape makes it easily implantable into the human head. The antenna parameters (e.g. 3D gain pattern), SAR value, and electric field distribution within a six layers spherical head model are evaluated by using the finite element method (FEM). The feasibility of the wireless transmission of power, control and command signal to the implant in the human head is also examined. © 2012 IEEE.

Closed loop deep brain stimulation: an evolving technology

Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.

RFID tag ownership transfer protocol for a closed loop system

In the RFID system a tag is attached to an object which might own by a number of people during its life cycle. As a result, the RFID system requires to transfer ownership of the tag. The ownership transfer has to protect privacy of current and new owner. There are number of ownership transfer protocol proposed to achieve secure ownership transfer. However, most of them are impractical or insecure to implement on current passive RFID tags. We are presenting an ownership transfer protocol using timer based shared secret for closed loop RFID systems. The protocol will ensure security and privacy of involved parties in the idle circumstances. Our comparison shows that the proposed protocol is more secure and practical than existing similar ones.

Scenarios for an RFID tag ownership transfer protocol for a closed loop system

In RFID system a tag is attached to an object which might own by a number of owners during its life time. This requires the RFID system to transfer ownership of the tag to its new owner. The ownership transfer has to protect privacy of current and new owner. Many ownership tag ownership transfer exists in the literature, however, most of them are impractical or insecure to implement on current passive RFID tags. We are proposing a timer based ownership transfer protocol for closed loop RFID systems. The proposal in this paper includes two implement scenario to cover diverse tags type. The protocol will ensure security and privacy of involved parties in the idle circumstances. Our comparison shows that the proposed protocol is more secure and practical than existing similar ones.

Motion/Force transmission analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains

 This thesis presents several results regarding the kinematic performance analysis of axis-symmetric parallel mechanisms with closed-loop sub-chains. Screw theory based methods have been utilised to generate new indices, along with a formal procedure, enabling the systematic and complete singularity and motion/force transmission analysis of parallel mechanisms with these closed-loop sub-chains.

Reduced-order output feedback controllers for discrete-time systems

This article considers the stabilization by output feedback controllers for discrete-time systems. The controller can place all of the closed-loop poles within a specified disk D(-α, 1/β), centred at (-α,0) with radius 1/β, where | - α|  + 1/β < 1. The design method involves the decomposition of the system into two portions. The first portion comprises of all of the poles that are lying outside of the specified disk. A reduced-order model is constructed for this portion. The second portion comprises of all of the remaining poles of the system and is characterized by an H_∞-norm bound. The controller design is then accomplished by using H_∞-control theory. It is shown that, subject to the solvability of an algebraic Riccati equation, output feedback controllers can be systematically derived. The order of the controller is low, and can be as low as the number of the open-loop poles that are lying outside of the specified disk. A step-by-step design algorithm is provided. Numerical examples are given to illustrate the attractiveness of the design method.

Constrained pole placement for linear systems using low-order output feedback controllers

The paper presents a simple approach to the problem of designing low-order output feedback controllers for linear continuous systems. The controller can place all of the closed-loop poles within a circle, C(- , 1/ β) , with centre at - and radius of 1/ β in the left half s-plane. The design method is based on transformation of the original system and then applying the bounded-real-lemma to the transformed system. It is shown that subjected to the solvability of an algebraic Riccati equation (ARE), output feedback controllers can then be systematically derived. Furthermore, the order of the controller is low and equals only the number of the open-loop poles lying outside the circle. A step-by-step design algorithm is given. Numerical examples are given to illustrate the design method.

Cold rolling mill thickness control using the cascade-correlation neural network

The improvements in thickness accuracy of a steel strip produced by a tandem cold-roIling mill are of substantial interest to the steel industry. In this paper, we designed a direct model-reference adaptive control (MRAC)  scheme that exploits the natural level of excitation existing in the closed-loop with a dynamically constructed cascade-correlation neural network (CCNN) as a controller for cold roIling mill thickness control. Simulation results show that the combination of a such a direct MRAC scheme and the dynamically constructed CCNN significantly improves the thickness accuracy in the presence of disturbances and noise in comparison with to the conventional PID controllers.

A loop-shaping approach to intelligent control

In this paper, we propose a loop-shaping approach to in telligent control with dynamically constructed neurocon troller. In the proposed control scheme, the process uncer tainly is reduced in the controller rather than in the process, without explicit identification of the process under control. The inherent noise/distrurbances in the process are utilized to satisfy persistency of excitation condition. The use of a reference model in form of a filter allow the frequency response of the closed-loop to be adapted in line with the changes in frequency response of the filter. The approach is evaluated on the example of control of polymerization reactor with promising results.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Observer-based control for time-varying delay neural networks with nonlinear observation

This paper studies the problem of designing observer-based controllers for a class of delayed neural networks with nonlinear observation. The system under consideration is subject to nonlinear observation and an interval time-varying delay. The nonlinear observation output is any nonlinear Lipschitzian function and the time-varying delay is not required to be differentiable nor its lower bound be zero. By constructing a set of appropriate Lyapunov-Krasovskii functionals and utilizing the Newton-Leibniz formula, some delay-dependent stabilizability conditions which are expressed in terms of Linear Matrix Inequalities (LMIs) are derived. The derived conditions allow simultaneous computation of two bounds that characterize the exponential stability rate of the closed-loop system. The unknown observer gain and the state feedback observer-based controller are directly obtained upon the feasibility of the derived LMIs stabilizability conditions. A simulation example is presented to verify the effectiveness of the proposed result.