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The first line of defence against wireless attacks in Radio Frequency Identi cation (RFID) systems is authentication of tags and readers. RFID tags are very constrained in terms of power, memory and size of circuit. Therefore, RFID tags are not capable of performing sophisticated cryptographic operations. In this dissertation, we have designed light-weight authentication schemes to securely identify the RFID tags to readers and vice versa. The authentication schemes require simple binary operations and can be readily implemented in resource-constrained Radio Frequency Identi cation (RFID) tags. We provide a formal proof of security based on the di culty of solving the Syndrome Decoding (SD) problem. Authentication veri es the unique identity of an RFID tag making it possible to track a tag across multiple readers. We further protect the identity of RFID tags by a light-weight privacy protecting identifi cation scheme based on the di culty of the Learning Parity with Noise (LPN) complexity assumption. To protect RFID tags authentication against the relay attacks, we have designed a resistance scheme in the analog realm that does not have the practicality issues of existing solutions. Our scheme is based on the chaos-suppression theory and it is robust to inconsistencies, such as noise and parameters mismatch. Furthermore, our solutions are based on asymmetric-key algorithms that better facilitate the distribution of cryptographic keys in large systems. We have provided a secure broadcast encryption protocol to effi ciently distribute cryptographic keys throughout the system with minimal communication overheads. The security of the proposed protocol is formally proven in the adaptive adversary model, which simulates the attacker in the real world.

Numerous examples arise in fields ranging from mechanics to biology where disappearance of Chaos can be detrimental. Preventing such transient nature of chaos has been proven to be quite challenging. The utility of Reinforcement Learning (RL), which is a specific class of machine learning techniques, in discovering effective control mechanisms in this regard is shown. The autonomous control algorithm is able to prevent the disappearance of chaos in the Lorenz system exhibiting meta-stable chaos, without requiring any a-priori knowledge about the underlying dynamics. The autonomous decisions taken by the RL algorithm are analyzed to understand how the system’s dynamics are impacted. Learning from this analysis, a simple control-law capable of restoring chaotic behavior is formulated. The reverse-engineering approach adopted in this work underlines the immense potential of the techniques used here to discover effective control strategies in complex dynamical systems. The autonomous nature of the learning algorithm makes it applicable to a diverse variety of non-linear systems, and highlights the potential of RLenabled control for regulating other transient-chaos like catastrophic events. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Many living organisms have the ability to execute complex behaviors and cognitive processes that are reliable. In many cases, such tasks are generated in the absence of an ongoing external input that could drive the activity on their underlying neural populations. For instance, writing the word "time" requires a precise sequence of muscle contraction in the hand and wrist. There has to be some patterns of activity in the areas of the brain responsible for this behaviour that are endogenously generated every time an individual performs this action. Whereas the question of how such neural code is transformed in the target motor sequence is a question of its own, their origin is perhaps even more puzzling. Most models of cortical and sub-cortical circuits suggest that many of their neural populations are chaotic. This means that very small amounts of noise, such as an additional action potential in a neuron of a network, can lead to completely different patterns of activity. Reservoir computing is one of the first frameworks that provided an efficient solution for biologically relevant neural networks to learn complex temporal tasks in the presence of chaos. We showed that although reservoirs (i.e. recurrent neural networks) are robust to noise, they are extremely sensitive to some forms of structural perturbations, such as removing one neuron out of thousands. We proposed an alternative to these models, where the source of autonomous activity is no longer originating from the reservoir, but from a set of oscillating networks projecting to the reservoir. In our simulations, we show that this solution produce rich patterns of activity and lead to networks that are both resistant to noise and structural perturbations. The model can learn a wide variety of temporal tasks such as interval timing, motor control, speech production and spatial navigation.

Chaos theory is a branch of modern mathematics concerning the non-linear dynamic systems that are highly sensitive to their initial states. It has extensive real-world applications, such as weather forecasting and stock market prediction. The Lorenz system, defined by three ordinary differential equations (ODEs), is one of the simplest and most popular chaotic models. Historically research has focused on understanding the Lorenz system's mathematical characteristics and dynamical evolution including the inherent chaotic features it possesses. In this thesis, we take a data-driven approach and propose the task of predicting future states of the chaotic system from limited observations. We explore two directions, answering two distinct fundamental questions of the system based on how informed we are about the underlying model. When we know the data is generated by the Lorenz System with unknown parameters, our task becomes parameter estimation (a white-box problem), or the ``inverse'' problem. When we know nothing about the underlying model (a black-box problem), our task becomes sequence prediction. We propose two algorithms for the white-box problem: Markov-Chain-Monte-Carlo (MCMC) and a Multi-Layer-Perceptron (MLP). Specially, we propose to use the Metropolis-Hastings (MH) algorithm with an additional random walk to avoid the sampler being trapped into local energy wells. The MH algorithm achieves moderate success in predicting the $\rho$ value from the data, but fails at the other two parameters. Our simple MLP model is able to attain high accuracy in terms of the $l_2$ distance between the prediction and ground truth for $\rho$ as well, but also fails to converge satisfactorily for the remaining parameters. We use a Recurrent Neural Network (RNN) to tackle the black-box problem. We implement and experiment with several RNN architectures including Elman RNN, LSTM, and GRU and demonstrate the relative strengths and weaknesses of each of these methods. Our results demonstrate the promising role of machine learning and modern statistical data science methods in the study of chaotic dynamic systems. The code for all of our experiments can be found on \url{https://github.com/Yajing-Zhao/}

Highly coherent light, although beneficial in specific applications, suffers from the formation of speckles, resulting in poor imaging, lighting, and projection/display quality. Moreover, the long coherence length limits the resolution in interference based sensing. This has led to the emergence of edge-emitting semiconductor low coherence light sources (e.g., broadband lasers, superluminescent diodes, etc.), which have been used in display applications, optical coherence tomography, and random bit generation. However, edge emission prevents the ease of fabricating two-dimensional arrays. Conversely, vertical-cavity surface-emitting lasers (VCSELs) have recently been widely used in consumer electronics due to the unique advantages of surface emission. Nevertheless, they still suffer from issues caused by high coherence. The aim of this dissertation is to design low-coherence surface-emitting lasers to push simultaneous illumination and optical wireless communication (OWC) toward reliable implementation with higher speeds. To that end, we demonstrate, for the first time, the use of chaotic cavities to lower the coherence of VCSELs without increasing their emission area, which would lower their speed. Not only did the chaotic cavity result in doubling the number of modes (lowering the coherence) compared to conventional VCSELs, but it also resulted in an increase in the optical power of up to 60%. We also show that chaotic-cavity broad-area VCSELs can achieve significantly broader modulation bandwidths (up to 5 GHz) and higher data rates (up to 12.6 Gb/s) compared to other low-coherence light sources, while achieving a lower speckle contrast. We further report a novel technique of lowering the speckle contrast 2 by carefully designing the AC signal used for communication. We show that the apparent spatial coherence is dramatically decreased by inserting a short chirp signal between symbols. Using this method with a chaotic-cavity VCSEL, the number of apparent modes can be up to 450 modes, compared to 88 modes measured from a conventional broad-area VCSEL. The simplicity of implementing the reported design, which requires no additional fabrication steps, makes it a promising solution for applications that would benefit from the lower speckle density of the emitted light as well as those that rely on lower temporal coherence.

E-government is basically described as using all available electronic media to provide an online public services companies, agencies, citizens or persons in certain country or region. This provision can be provided by the government institutions, agencies, or organisation, in addition to public and private sectors subject to government policies and legislation. Political instability, armed conflict, corruption and chaotic situations are considered to be an obstacle confronting public services delivery and governance in some developing countries around the world. Therefore, Libya is selected a case study of this research. Post the 2011 ousting of the Gadhafi regime in Libya, the country has been experiencing a severe and deep-rooted environment of conflict and chaos, which has destabilised and in some cases dismantled government institutions throughout the country. Within this environment, the original aim of this study was to explore the possibility of implementing e-government services that can provide public services to citizens and, if so, how and what services could be utilised. An exploratory qualitative pilot study was conducted to investigate the feasibility of e-government implementation in Libya utilising the knowledge of government officials. The study found that, the Libyan government had recently and successfully implemented an online e-passport service. An extensive literature review carried out in relation to e-government implementation to help understanding lesions learned and factors behind such success then to utilise the knowledge for further services implementations. Critical success factors of e-government implementation were addressed but available ones are related to stable countries under normal situations. This research is aiming to investigate its implementation in chaotic environment where not much of research is available. During the chaotic environment and instability, different factors may emerge to drive the implementation and the usage of e-services such environment. From government perspectives, it is noticed that cases of corruption, lack of citizens’ safety and poor infrastructure were found to be drivers behind the success of existing government institutions and departments of implement e-passport system. Social collaboration and trust in government institutions’ commitment were emerged from the citizens’ perspectives as factors encouraged the citizens to use the e-passport system. Quantitative data analysed using structural equation modelling techniques using SmartPLS (3.2.7) together with the SPSS 23 were used to analyse the collected data. The outcome were used to propose a framework that can improve the implementation of public e-services while the country at unrest. Another contribution of this studies is the proposal of social collaboration model towards better e-services in such environment.

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