Electrical and Electronic Engineering - Research Publications
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ItemInternet of Things for Structural Health Monitoring(CRC Press, 2016-05)The Internet revolution led to the interconnection between people at an unprecedented scale and pace. The ability of the sensor networks to send data over the Internet further enhanced the scope and usage of the sensor networks. The Internet uses unique address to identify the devices connected to the network. Structural Health Monitoring (SHM) implies monitoring of the state of the structures through sensor networks in an online mode and are pertinent to aircraft and buildings. SHM can be further divided into two categories: global health monitoring and local health monitoring. Continuous online SHM would be an ideal solution. SHM is performed by using acoustic sensors, ultrasonic sensors, strain gauges, optical fibers, and so on. Video cameras can also be used for SHM. SHM can be achieved in real-time and rich analytics. With the advent of smart sensors—sensors with programmable microprocessors, memory, and processing—has reduced load of central data processing, communication overhead while proving continuous SHM status.
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ItemPATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS(WORLD SCIENTIFIC PUBL CO PTE LTD, 2013-01-01)Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.
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ItemExtremum Seeking Methods for Online Automotive Calibration(Springer, 2014-01-01)The automotive calibration process is becoming increasingly difficult as the degrees of freedom in modern engines rises with the number of actuators. This is coupled with the desire to utilise alternative fuels to gasoline and diesel for the promise of lower CO2 levels in transportation. However, the range of fuel blends also leads to variability in the combustion properties, requiring additional sensing and calibration effort for the engine control unit (ECU). Shifting some of the calibration effort online whereby the engine controller adjusts its operation to account for the current operating conditions may be an effective alternative if the performance of the controller can be guaranteed within some performance characteristics. This tutorial chapter summarises recent developments in extremum seeking control, and investigates the potential of these methods to address some of the complexity in developing fuel-flexible controllers for automotive powertrains.
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ItemNonlinear Sampled-Data Systems(Springer, 2014)Sampled-data systems are control systems in which the feedback law is digitally implemented via a computer. They are prevalent nowadays due to the numerous advantages they offer compared to analog control. Nonlinear sampled-data systems arise in this context when either the plant model or the controller is nonlinear. While their linear counterpart is now a mature area, nonlinear sampled-data systems are much harder to deal with and, hence, much less understood. Their inherent complexity leads to a variety of methods for their modeling, analysis, and design. A summary of these methods is presented in this entry.
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ItemNonlinear Sampled-Data Systems(Springer London, 2015)Sampled-data systems are control systems in which the feedback law is digitally implemented via a computer. They are prevalent nowadays due to the numerous advantages they offer compared to analog control. Nonlinear sampled-data systems arise in this context when either the plant model or the controller is nonlinear. While their linear counterpart is now a mature area, nonlinear sampled-data systems are much harder to deal with and, hence, much less understood. Their inherent complexity leads to a variety of methods for their modeling, analysis, and design. A summary of these methods is presented in this entry.
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ItemOutput Feedback Event-Triggered Control(Springer, 2016)Event-triggered control has been proposed as an alternative implementation to conventional time-triggered approach in order to reduce the amount of transmissions. The idea is to adapt transmissions to the state of the plant such that the loop is closed only when it is needed according to the stability or/and the performance requirements. Most of the existing event-triggered control strategies assume that the full state measurement is available. Unfortunately, this assumption is often not satisfied in practice. There is therefore a strong need for appropriate tools in the context of output feedback control. Most existing works on this topic focus on linear systems. The objective of this chapter is to first summarize our recent results on the case where the plant dynamics is nonlinear. The approach we follow is emulation as we first design a stabilizing output feedback law in the absence of sampling then we consider the network and we synthesize the event-triggering condition. The latter combines techniques from event-triggered and time-triggered control. The results are then proved to be applicable to linear time-invariant (LTI) systems as a particular case. We then use these results as a starting point to elaborate a co-design method, which allows us to jointly construct the feedback law and the triggering condition for LTI systems where the problem is formulated in terms of linear matrix inequalities (LMI). We then exploit the flexibility of the method to maximize the guaranteed minimum amount of time between two transmissions. The results are illustrated on physical and numerical examples.
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ItemPeriodic Event-Triggered Control(CRC Press, 2015-11-24)This chapter discusses periodic event-triggered control (PETC) strategies, their benefits, and two analysis and design frameworks for linear and nonlinear plants. It focuses on approaches to PETC that include a formal analysis framework, which apply for continuous- time plants and incorporate intersample behavior in the analysis. The chapter explores PETC as a class of event-triggered control (ETC) strategies that combines the benefits of periodic time-triggered control and event-triggered control. In ETC, the control task is executed after the occurrence of an event, generated by some well-designed event-triggering condition, rather than the elapse of a certain fixed period of time, as in conventional periodic sampled-data control. The PETC strategy is based on the idea of having an event-triggering condition that is verified only periodically, instead of continuously as in most existing ETC schemes. There is a strong need for systematic methods to construct PETC strategies that appropriately take into account the features of the paradigm.
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ItemImpact of quantized inter-agent communications on game-theoretic and distributed optimization algorithms(Springer, 2018-01-01)Quantized inter-agent communications in game-theoretic and distributed optimization algorithms generate uncertainty that affects the asymptotic and transient behavior of such algorithms. This chapter uses the information-theoretic notion of differential entropy power to establish universal bounds on the maximum exponential convergence rates of primal-dual and gradient-based Nash seeking algorithms under quantized communications. These bounds depend on the inter-agent data rate and the local behavior of the agents’ objective functions, and are independent of the quantizer structure. The presented results provide trade-offs between the speed of exponential convergence, the agents’ objective functions, the communication bit rates, and the number of agents and constraints. For the proposed Nash seeking algorithm, the transient performance is studied and an upper bound on the average time required to settle inside a specified ball around the Nash equilibrium is derived under uniform quantization. Furthermore, an upper bound on the probability that the agents’ actions lie outside this ball is established. This bound decays double exponentially with time.
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ItemNew Era in the Supply Chain Management with Blockchain: A Survey(IGI Global, 2019)The results show that transparency and auditability, security and indelibility, and distribution and sustainability are the key attributes of blockchain-based solutions in 56% of the articles reviewed. These three aspects represent the foundation of blockchain technologies which may contribute positively to improve supply management processes. Moreover, immutability, tracking and tracing, and smart contracts are also included in nearly a third of the cases. Moreover, efficiencies and costs through this technology would reduce the costs in payment of intermediaries, reduce paperwork, and help in the shipment of physical documents. Supply chain plays a critical role in the global trade and urgently needs to reassess its models in searching for greater efficiencies. Moreover, better results in visibility across the chain will increase trust for the customers and all interested parties. Secure transactions, strong security mechanisms that prevent fraud and illegal practices, could be achieved through the blockchain.
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ItemTransparency Measures in an International Context(Springer International Publishing, 2016)