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Meningitis caused by infectious pathogens is associated with vessel damage and infarct formation, however the physiological cause is often unknown. Cryptococcus neoformans is a human fungal pathogen and causative agent of cryptococcal meningitis, where vascular events are observed in up to 30% of patients, predominantly in severe infection. Therefore, we aimed to investigate how infection may lead to vessel damage and associated pathogen dissemination using a zebrafish model that permitted noninvasive in vivo imaging. We find that cryptococcal cells become trapped within the vasculature (dependent on their size) and proliferate there resulting in vasodilation. Localised cryptococcal growth, originating from a small number of cryptococcal cells in the vasculature was associated with sites of dissemination and simultaneously with loss of blood vessel integrity. Using a cell-cell junction tension reporter we identified dissemination from intact blood vessels and where vessel rupture occurred. Finally, we manipulated blood vessel tension via cell junctions and found increased tension resulted in increased dissemination. Our data suggest that global vascular vasodilation occurs following infection, resulting in increased vessel tension which subsequently increases dissemination events, representing a positive feedback loop. Thus, we identify a mechanism for blood vessel damage during cryptococcal infection that may represent a cause of vascular damage and cortical infarction during cryptococcal meningitis.

A systematic, semi-automated search for pulsar glitches in the first UTMOST public data release is presented. The search is carried out using a hidden Markov model which incorporates both glitches and timing noise into the model of the assumed phase evolution of the pulsar. Glitches are detected through Bayesian model selection between models with and without glitches present with minimal human intervention. Nine glitches are detected among seven objects, all of which have been previously reported. No new glitches were detected. Injection studies are used to place 90 per cent frequentist upper limits on the size of undetected glitches in each of the 282 objects searched. The mean upper limit obtained is $\Delta f^{90{{\%}}}/f = 1.9 \times 10^{-8}$, with a range of $4.1 \times 10^{-11} \le \Delta f^{90{{\%}}}/f \le 2.7 \times 10^{-7}$, assuming step events with no post-glitch recoveries. It is demonstrated that including glitch recovery has a mild effect, in most cases increasing the upper limit by a factor of ≲5 conservatively assuming complete recovery on a time-scale of $100\ \mathrm{d}$.

In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system's MTTR gains range from 11% to over 95% for various parametric values of the system model.

Large-scale penetration of grid-following inverters into the electricity network presents various technical challenges to grid reliability. It is well-known that the ability of a grid to maintain a stable frequency is inhibited by adding traditional grid-tied photovoltaic (PV) generators. In this work, a detailed model of a simplified grid is presented, and it is shown that the proportion of PV generation and instability are positively correlated. The main instability phenomenon is captured by a Hopf Bifurcation in the field dynamics of the synchronous generator. Such a Hopf bifurcation severely constricts the feasible operating domain of the grid and may hinder normal operation. Modifying traditional grid-tied PV controllers and its impact on grid stability is assessed through small-signal, bifurcation and transient numerical analysis. Traditional PV controllers that are modified to virtual synchronous machine (VSM) type controllers show improvement in system damping. Unlike traditional grid-tied inverters, VSM inverters participate in critical modes of the synchronous generator (SG) and augment the operational domain of the SG+VSM system significantly, more importantly, almost eliminating the need for renewable energy curtailment. A case-study approach is used to present some key results on improvements in damping ratio, feasibility domain and transient stability. Finally, a feasibility domain curve is introduced and discussed in an aim to generalize the overall stability of any such system.

Using deep-learning techniques for analyzing radar signatures has opened new possibilities in the field of smart-sensing, especially in the applications of hand-gesture recognition. In this paper, we present a framework, using deep-learning techniques, to classify hand-gesture signatures generated from an ultra-wideband (UWB) impulse radar. We extract the signals of 14 different hand-gestures and represent each signature as a 3-dimensional tensor consisting of range-Doppler frame sequence. These signatures are passed to a convolutional neural network (CNN) to extract the unique features of each gesture, and are then fed to a classifier. We compare 4 different classification architectures to predict the gesture class, namely; (i) fully connected neural network (FCNN), (ii) k -Nearest Neighbours ( k -NN), (iii) support vector machine (SVM), (iv) long short term memory (LSTM) network. The shape of the range-Doppler-frame tensor and the parameters of the classifiers are optimized in order to maximize the classification accuracy. The classification results of the proposed architectures show a high level of accuracy above 96 % and a very low confusion probability even between similar gestures.

The number of small sophisticated wireless sensors which share the electromagnetic spectrum is expected to grow rapidly over the next decade and interference between these sensors is anticipated to become a major challenge. In this paper we study the interference mechanisms in one such sensor, automotive radars, where our results are directly applicable to a range of other sensor situations. In particular, we study the impact of radar waveform design and the associated receiver processing on the statistics of radar–radar interference and its effects on sensing performance. We propose a novel interference mitigation approach based on pseudo-random cyclic orthogonal sequences (PRCOS), which enable sensors to rapidly learn the interference environment and avoid using frequency overlapping waveforms, which in turn results in a significant interference mitigation with analytically tractable statistical characterization. The performance of our new approach is benchmarked against the popular random stepped frequency waveform sequences (RSFWS), where both simulation and analytic results show considerable interference reduction. Furthermore, we perform experimental measurements on commercially available automotive radars to verify the proposed model and framework.

A variety of resource allocation problems on networked systems, for example, those in cyber-physical systems or Internet-of-things applications, require distributed solution methods. Modern distributed algorithms usually require bandwidth-limited digital communication between the system and its users, who are often modeled as independent decision makers with individual preferences. This paper presents a quantitative information flow and knowledge gain analysis of decentralized iterative algorithms with bounded trajectories in the context of convex network utility maximization problems and strategic games with a unique Nash equilibrium solution. First, a novel generic framework is introduced to quantify knowledge gain in network resource allocation problems using entropy by taking into account priors in the solution space. Second, a general result is presented on the interplay between quantization of information and distributed algorithm performance both for linear and sublinear convergence. Third, information flow in distributed algorithms is studied and a lower bound is derived on the total amount of information exchanged for convergence under uniform quantization. The well-known primal-dual decomposition algorithm is used as an example to illustrate the results. Finally, convergence guarantees for distributed algorithms with estimation are investigated. This paper establishes specific links between information concepts and iterative algorithms in addition to building a foundation for integrating learning schemes into distributed optimization.

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.

Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I : Eratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

This paper investigates the dynamics of Ebola virus transmission in West Africa during 2014. The reproduction numbers for the total period of epidemic and for different consequent time intervals are estimated based on a simple linear model. It contains one major parameter - the average infectious period that defines the dynamics of epidemics. Numerical implementations are carried out on data collected from three countries Guinea, Sierra Leone and Liberia as well as the total data collected worldwide. Predictions are provided by considering different scenarios involving the average times of infectiousness for the next few months and the end of the current epidemic is estimated according to each scenario.