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Radio-over-fiber technology provides a simpler pathway for the distribution of wireless signals in broadband wireless networks via optical means. The technology has evolved over the last 30 years, with many challenges already overcome and with many more to address. This paper provides an overview of the technology evolutionary path of RoF with a review of its past, evaluation of its present, and a discussion of its challenges into the future.

Approximately half the world's population is online with the rest is yearning to be connected. Two out of 3 people on Earth communicate with a mobile phone, while two-fifths use social media to keep contacts up-to-date. [1]. Many things in our lives, phones, cameras, sensors in our homes and offices, and cars are being connected to the Internet. Our world is becoming hyper-connected.

Nanophotonic technologies have attracted a lot of attention to co-develop optical and electronic devices on silicon (Si) that further miniaturize modern optical communication systems. Optical properties of these miniaturized devices are highly dependent on waveguide geometry and can be tailored for various applications with minor changes in cross-sectional areas. This paper investigates the performance of widely discussed planar, rib, and photonic crystal Si waveguides by manipulating the dimensions of Si core designed for single mode operation across the 1.2 to 1.6 μm telecommunication bands. Each of the waveguide is studied and compared for a set of properties, including effective refractive index, group index, group-velocity dispersion, and mode loss, with and without narrow bends, which can be instrumental in selecting the correct type and geometry of the waveguide required for any specific application.

This paper proposes a new class of resonant tunneling diodes (RTDs) that are planar and realizable with a single graphene nanoribbon. Unlike conventional RTDs, which incorporate vertical quantum well regions, the proposed devices incorporate two confined planar quantum dots within the single graphene nanoribbon, giving rise to a pronounced negative differential resistance (NDR) effect. The proposed devices, termed here as planar double-quantum-dot RTDs, and their transport properties are investigated using quantum simulations based on nonequilibrium Green’s function formalism and the extended Huckel method. The proposed devices exhibit a unique current–voltage waveform consisting of a single pronounced current peak with an extremely high, in the order of $10^{4}$ , peak-to-valley ratio. The position of the current peak can be tuned between discrete voltage levels, allowing digitized tunability, which is exploited to realize multi-peak NDR devices.

The effectiveness of managing cyclone-induced floods is highly dependent on how fast reasonably accurate predictions can be made, which is a particularly difficult task given the multitude of highly variable physical factors. Even with supercomputers, collecting and processing vast amounts of data from numerous asynchronous sources makes it challenging to achieve high prediction efficiency. This paper presents a model that combines prior knowledge, including rainfall data statistics and topographical features, with any new precipitation data to generate a probabilistic prediction using Bayesian learning, where the advantages of data-oriented and heuristic modeling are combined. The terrain is partitioned into geographic primitives (GPs) based on manual inspection of flood propagation vector fields in order to simplify the stochastic system identification. High calculation efficiency is achieved through statistically summarizing simultaneous events spread across geography into primitives, allowing a distributed updating algorithm leading to parallel computing. Markov chain processes identified for each of these GPs, based on both simulation and measured rainfall data, are then used in real-time predictions of water flow probabilities. The model takes a comprehensive approach, which enables flood prediction even before the landfall of a cyclone through modularizing the algorithm into three prediction steps: cyclone path, rainfall probability density distribution, and temporal dynamics of flood density distribution. Results of comparative studies based on real data of two cyclones (Yasi and Tasha) that made landfall in Queensland, Australia, in 2010/11 show that the model is capable of predicting up to 3 h ahead of the official forecast, with a 33% improvement of accuracy compared to the models presently being used.

Dynamic processes leading to natural disasters often translate into random walks in state spaces which are inhomogeneous in transport characteristics. In other words, such random walks will behave differently in different parts of a network which would have different values for transport properties (\(d\!\!f\) and dw if using methods from Chap. 4). Thus, for such networks the application of \(\textit{MFPT}\) calculation methods introduced in Chap. 4 along with many other methods described in literature are not straight forward. This chapter proposes that using the novel concept of dividing the node distribution into patches/clusters known as network primitives (NPs) where all nodes within each primitive share common transport variables, and adopting a ‘hop-wise’ approach to calculate \(\textit{MFPT}\) between any source and target pair as an extension to the methods described under Chap. 4, can be a viable solution for predicting \(\textit{MFPT}\) for random walks in inhomogeneous networks. This methodology’s potential is demonstrated through simulated random walks and with a case study using the dataset of past cyclone tracks over the North Atlantic Ocean. The predictions using the presented method are compared to real data averages and predictions assuming homogeneous transport properties.

This chapter consists of three major subsections. Following a discussion of the ability to model a large class of environmental processes governing natural disasters as random walks, this chapter provides the theoretical background for this book which will be useful throughout the rest of the chapters. The first subsection will summarise the main notion used throughout the book and the adaptation of the theoretical background for natural disaster prediction in an engineering applications perspective. The second subsection will review recent research advancements in \(M\!F\!P\!T\) prediction methods. The final subsection will briefly describe some mathematical tools used in the proceeding chapters.

This book discusses natural disaster prediction methods by analysing the state changes of the dynamics behind natural disasters. It shows that many such state changes behave as random walks in their respective state spaces. Therefore, this book mainly focusses on methods that predict the \(\textit{MFPT}\) for random walks resulting from natural processes leading to natural disasters viewed in an engineering perspective. The main objective of this book is to address network inhomogeneity and directional bias that affect random walks’ \(\textit{MFPT}\) calculations. The motivation behind this discussion is the large class of process behind natural disaster dynamics of which the state transitions translate to random walks where the non-linear interactions with the environment result in biased random walks showing inhomogeneous transport characteristics.

Taking forward the concept of calculating the mean first passage time for natural disasters, this chapter focusses on discussing generic methods for calculations in directionally biased networks. Following a short discussion on the current state of art in \(M\!F\!P\!T\) prediction for biased random walks, a case invariant modification of transport variables is described as a viable solution to the problem of addressing directional bias. The empirical equation that modifies the walk dimension leads the way to significantly better \(M\!F\!P\!T\) estimations when compared to the use of conventional transport variables. A range of simulated random walks on hypothetical networks are used to describe this model. Following this, a comprehensive case study is presented to validate the model with the real dataset of archived cyclone tracks over the North Atlantic Ocean. The comparative studies of prediction results and the generic nature of model development indicate wider applicability of the presented methods.

The prediction of \({\textit{MFPT}}\) in the propagation of cyclone induced flood is comprehensively discussed in this chapter as a special case study to gain an entrance to a more generic study in the proceeding chapters. The inhomogeneity in terrain is addressed using a novel concept of dividing the terrain into geographic primitives (GPs) identified through flood propagation vector fields. A modularised approach including a Bayesian framework is followed allowing easy and flexible assimilation of data and integration of outputs from other models when they become available. Simultaneous events distributed in the environment leading to biased propagation are encapsulated into transition probability matrices (TPM) allowing efficient real time predictions via computation through a Markov chain. Comparative studies using real datasets show the models ability of predicting up to 3 h ahead of official forecasts with a 33 % improvement of accuracy when compared to other models currently used.

Optical delay and corresponding group index of structure composed of slab waveguide coupled with slotted ring resonator are investigated in this paper. Ring resonators have attracted a lot of attention to develop optical delay line which has numerous applications in communication. Properties of these micro ring resonators are highly dependent on waveguide geometry and can be tailored for various applications with minor changes. This paper investigates performance of single and multi stage ring resonators by manipulating geometry and gap between slab and ring waveguide for single mode propagation which may assist to make well informed decisions in the selection process of best-suited ring waveguide for particular application. This investigation is further extended to verify the tolerance of the design in case of fabrication errors with dimension of ±10nm.

Change in refractive index and corresponding change in optical properties like propagation loss, group index and dispersion in active PIN silicon waveguide with and without photonic crystal core are investigated by changing doping concentration. This investigation sets a bound on doping density which is utilized to change effective refractive index for various applications like modulation, switching, dispersion compensation, slowing light and so on. Influences of depleting carriers on junction capacitance and reverse current behaviors are also investigated for same waveguides. This sets a benchmark for the design and selection of appropriate waveguide for various applications in silicon photonics platform.

Changes in refractive index and the corresponding changes in the characteristics of an optical waveguide in enabling propagation of light are the basis for many modern silicon photonic devices. Optical properties of these active nanoscale waveguides are sensitive to the little changes in geometry, external injection/biasing, and doping profiles, and can be crucial in design and manufacturing processes. This paper brings the active silicon waveguide for complete characterization of various distinctive guiding parameters, including perturbation in real and imaginary refractive index, mode loss, group velocity dispersion, and bending loss, which can be instrumental in developing optimal design specifications for various application-centric active silicon waveguides.

This paper reviews some of the research work we have carried out in the transport of broadband OFDM signals using digitized wireless signal transport in a fiber-wireless environment. We compare the performance against analog transport strategy.

Epilepsy is one of the most common serious neurological disorders, affecting about 60 million people worldwide. There are currently about 220,000 Epilepsy patients in Australia, a significant proportion of whom are elderly and live in rural areas. In this chapter, we present the applications, requirements, solutions, and further research problems for a Centralized Real-time E-Healthcare System (CREHS) for Epilepsy patients in Australia, utilizing wearable wireless sensors and broadband technologies. Later, the authors propose, optimize, and validate biosensor devices to detect epileptic seizures. In addition, the chapter also provides a detailed description of the structures, features, and detailed implementation scenarios for the system.

This paper presents a computationally efficient method to forecast floods stochastically. The main purpose of the method is to encapsulate prior knowledge as off-line calculations leading to earliest possible warnings. The computational efficiency is improved through exploiting the stereotypical features of hydrology and its dependence on topography by combining the parallel water-flow processes into parallel calculation through a probability transition matrix. Efficiency is improved further with the use of properties of Markov matrices in the general equation of the model. Extensive simulations on real rainfall data over parts of Queensland, Australia, during January 2012, revealed that this method was capable of improving the calculation efficiency by over 18 times with respect to gradient based calculations.

This paper presents analysis on optimum drive amplitudes and wavelength stability in a novel scheme for dual baseband generation using a single laser and a modulator considering receiver sensitivity and error floors arising due to optical filtering.

We propose a simple and stable seeding source that enables ultra dense WDM-PON based on injection locking devices and for the first time experimentally demonstrate a 12.5 GHz channel spaced DWDM-PON with 1.25 Gb/s transmission.

We demonstrate a wavelength reuse scheme facilitated through sub-carrier downstream transmission that achieves source free optical network units in a WDM-PON. The scheme only requires baseband detection for both downstream and upstream data recovery.

An optical seeding source with a single phase modulator is used for cost-effective upstream transmission in a super-dense WDM-PON (SDWDM-PON) in conjunction with reflective semiconductor optical amplifiers (RSOAs) at the optical network units. The scheme is suited for simple upgrade from a 100 GHz channel spaced DWDM-PON. The scheme is experimentally demonstrated in an SDWDM-PON with 12.5 GHz channel spacing and 1.25 Gbit/s data rate per channel.

We demonstrate a simple scheme for the generation and separation of two independent high bandwidth baseband optical channels using a single laser and modulator for provision of independent services in WDM passive optical networks.

Influences of geometry to tailor dispersion with corresponding propagation loss are presented for circular and bragg core silicon waveguides. Enhanced dispersion with low loss (1–1.5dB/cm) demands careful selection of waveguides according to nature of applications.

Optical wireless networks are being explored as a wireless alternative for provision of multi gigabits/second wireless and this paper presents an overview of recent progress and outstanding challenges. and technologies.