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Dissociative experiences, traditionally studied in relation to trauma and PTSD, may be important phenomena across many different psychological conditions, including as a contributory causal factor for psychotic experiences. In this study, the aim was to explore, using network approaches, how dissociative experiences taking the form of a Felt Sense of Anomaly (FSA) relate to both common mental health conditions and psychotic experiences. 6941 individuals from the general population completed online assessments of FSA-dissociation, post-traumatic stress symptoms (PTSS), anxiety, depression, insomnia, worry, distress tolerance, hallucinations, grandiosity, paranoia, and cognitive disorganization. An undirected partial correlation network analysis was used to explore the network structure, then Bayesian inference with Directed Acyclic Graphs (DAGs) was used to identify potential directions of relationships between dissociation and mental health symptoms. Dissociation was found to be highly connected in both network models. Both networks found direct relationships between dissociation and hallucinations, grandiosity, paranoia, cognitive disorganization, anxiety, depression, and PTSS. In the DAGs analysis, the direction of influence between dissociation and hallucinations, PTSS, anxiety and depression was unclear, however it was found to be probable that dissociation influences paranoia (97.66% of sampled DAGs found the direction dissociation to paranoia, versus 2.34% finding the reverse direction), cognitive disorganization (99.74% vs. 0.26%), and grandiosity (93.49% vs. 6.51%). Further, dissociation was found to be a probable influence of insomnia and distress tolerance via indirect pathways. In summary, dissociation is connected to many mental health disorders, and may influence a number of presentations, particularly psychotic experiences. The importance of dissociation in mental health may therefore currently be under-recognised., Highlights • Dissociation is highly connected to other mental health presentations. • Dissociation has a strong relationship with hallucinations. • The direction of effect between dissociation and anxiety, depression and trauma symptoms is unclear. • Dissociation likely influences paranoia, grandiosity and cognitive disorganization.

Robust variants of Wald, Rao and likelihood ratio (LR) tests for the detection of a signal subspace in a signal interference subspace corrupted by contaminated Gaussian noise are proposed in this paper. They are derived using the $\alpha -$ divergence, and the trade-off between the robustness and the power (the probability of detection) of the tests is adjustable using a single hyperparameter $\alpha $ . It is shown that when $\alpha \rightarrow 1$ , these tests are equivalent to their well known classical counterparts. For example the robust LR test coincides with the LR test or the matched subspace detector (MSD). Asymptotic results are provided to support the proposed tests and robustness to outliers is obtained using values of $\alpha . Numerical experiments illustrating the performance of these tests on simulated, real functional magnetic resonance imaging (fMRI), hyperspectral and synthetic aperture radar (SAR) data are also presented.

Power systems have to be reinforced to accommodate a high penetration of renewable energy sources. Recent research shows that the stability margin of the grid degrades with the penetration of grid-tied photovoltaic generators into the grid. In the model presented in the study, the critical level of photovoltaic penetration is associated with a Hopf bifurcation in the field dynamics of the synchronous generators which severely limits the operating domain of the grid. It is shown that the judicious introduction of synchronous condensers in the modelled grid delays this dynamic instability. A small-signal analysis is performed for a system where the reactive power and inertial response is supplemented using a synchronous condenser. The study shows that the modified network tolerates much higher participation of photovoltaic generation in the grid. It is shown that the modified network retains the classical power system control approach and also does not demand a change in the way the photovoltaic generation is injected, thereby making it a natural implementation. The synchronous condenser augments the operational domain of the system significantly, nearly eliminating the need for renewable energy curtailment. A case-study approach is used to present results on improvements in damping ratio, feasibility domain and transient stability.

In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations.In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of factorizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covariance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection.Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain.The proposed method can offer better activation maps in terms of accuracy and spatial smoothness.The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data.

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.

Cognitive radio technology has shown tremendous potential through its spectrum access mechanism for reducing spectrum paucity. With the vacant spectrum, two nodes require consensus for common channel in an apposite time. However, in a non-centralized distributed communication system, the task of attaining one common channel for two nodes is not tractable. In this letter, we attend to a well-known technique for rendezvous in such a cognitive radio network and analytically characterize the performance. The statistical distribution of rendezvous time to obtain common channel is analyzed and a closed form expression is derived. Using the distribution, we further analyze the mean and maximum rendezvous times and moreover propose an improvement in the considered technique. The theoretical analyses are verified using Monte–Carlo simulations and our results show a very precise match between the two.

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.

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.

The classic, two-component, crust-superfluid model of a neutron star can be formulated as a noise-driven, linear dynamical system, in which the angular velocities of the crust and superfluid are tracked using a Kalman filter applied to electromagnetic pulse timing data and gravitational wave data, when available. Here it is shown how to combine the marginal likelihood of the Kalman filter and nested sampling to estimate full posterior distributions of the six model parameters, extending previous analyses based on a maximum-likelihood approach. The method is tested across an astrophysically plausible parameter domain using Monte Carlo simulations. It recovers the injected parameters to $\lesssim 10$ per cent for time series containing $\sim 10^3$ samples, typical of long-term pulsar timing campaigns. It runs efficiently in $\mathcal O(1)$ CPU-hr for data sets of the above size. In a present-day observational scenario, when electromagnetic data are available only, the method accurately estimates three parameters: the relaxation time, the ensemble-averaged spin-down of the system, and the amplitude of the stochastic torques applied to the crust. In a future observational scenario, where gravitational wave data are also available, the method also estimates the ratio between the moments of inertia of the crust and the superfluid, the amplitude of the stochastic torque applied to the superfluid, and the crust-superfluid lag. These empirical results are consistent with a formal identifiability analysis of the linear dynamical system., 15 pages, 7 figures, accepted to MNRAS

Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Recently, an approach using a Bernoulli track-before-detect (TBD) filter has been proposed for an airborne scanning radar in the maritime domain [1]. This work focused on a non-coherent fast scanning mode where the dwell-time (time-on-target) was very short. The current paper explores the benefits of using Doppler information in TBD when the radar is operating in a slower scanning mode. A new TBD filter, which exploits the Doppler information in the form of a point measurement is developed and analysed, with significant improvement demonstrated over the traditional fast scanning operation.

BackgroundSelf-harm thoughts and behaviours (SHTBs) are a serious public health concern in young people. Emerging research suggests that pain may be an important correlate of SHTBs in young people. However, it remains unclear whether this association is driven by the shared association with other correlates of SHTBs. This study used network analysis to delineate the relationship between SHTBs, pain and other correlates of SHTBs in a population-based sample of young people.MethodsWe performed secondary analyses, using data from 7977 young people aged 5–16 years who participated in the British Child and Adolescent Mental Health Survey in 2004. We used χ2 tests and network analysis to examine the complex interplay between SHTBs, pain and other correlates of SHTBs, including psychiatric disorders, childhood trauma, stressful life events, parental distress, family dysfunction, peer problems and inhibitory control deficits.ResultsPain was associated with a doubled risk of SHTBs, and likewise, SHTBs were associated with a doubled risk of pain. Furthermore, network analysis showed that although pain was significantly associated with all measured correlates of SHTBs, except family dysfunction, pain was most strongly associated with SHTBs, after accounting for these measured correlates.ConclusionsTo the best of our knowledge, this is the first study to utilise network analysis to provide novel insights into the complex relationship between SHTBs, pain and other known correlates of SHTBs in young people. Results suggest that pain is an independent correlate of SHTBs. Future research should aim to identify underlying mechanisms.

In this paper, we present a framework for integrating two different types of sensors for hand-gesture recognition using deep-learning. The two sensors utilize completely different approaches for detecting the signal, namely; an ultra-wideband (UWB) impulse radar sensor and a thermal sensor. For robust gesture classification two parallel paths are utilized, each employs a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network on both the radar signal and the thermal signal. The classification results from the two paths are then fused to improve the overall detection probability. The two sensors compliment the capability of each other; while the UWB radar is accurate for radial movement and less accurate for lateral movement, the thermal sensor is vice-versa. Thus, we find that combining both sensors produces near perfect classification accuracy of 99 % for 14 different hand-gestures.

Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.

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.

Pulsar timing experiments typically generate a phase-connected timing solution from a sequence of times-of-arrival (TOAs) by absolute pulse numbering, i.e. by fitting an integer number of pulses between TOAs in order to minimize the residuals with respect to a parametrized phase model. In this observing mode, rotational glitches are discovered, when the residuals of the no-glitch phase model diverge after some epoch, and glitch parameters are refined by Bayesian follow-up. Here an alternative, complementary approach is presented which tracks the pulse frequency $f$ and its time derivative $df/dt$ with a hidden Markov model (HMM), whose dynamics include stochastic spin wandering (timing noise) and impulsive jumps in $f$ and $df/dt$ (glitches). The HMM tracks spin wandering explicitly, as a specific realization of a discrete-time Markov chain. It discovers glitches by comparing the Bayes factor for glitch and no-glitch models. It ingests standard TOAs for convenience and, being fully automated, allows performance bounds to be calculated quickly via Monte Carlo simulations. Practical, user-oriented plots are presented of the false alarm probability and detection threshold (e.g. minimum resolvable glitch size) versus observational scheduling parameters (e.g. TOA uncertainty, mean delay between TOAs) and glitch parameters (e.g. transient and permanent jump sizes, exponential recovery time-scale). The HMM is also applied to $\sim 1$ yr of real data bracketing the 2016 December 12 glitch in PSR J0835-4510 as a proof of principle. It detects the known glitch and confirms that no other glitch exists in the same data with size $> 10^{-7} f$., Accepted for publication in The Astrophysical Journal

The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns of a known matrix. To develop the test, an ad hoc approach, similar to the classical adaptive matched filter (AMF) is used where instead of the maximum likelihood (ML) estimator of the covariance, the minimum α−divergence based estimator is substituted in the likelihood ratio. This test just depends on the single parameter α and as a special case can be turned to the AMF. For a range of α, the proposed test has the benefits of being robust to outliers and the existence of other targets in the training data. Numerical examples illustrating that the proposed detector can achieve better detection rates in such a scenario while providing almost the same performance in a target free scenario are presented.

The problem of detecting a subspace signal in the presence of subspace interference and contaminated Gaussian noise with unknown variance is investigated. The target signal is assumed to lie in a subspace spanned by the columns of a known matrix. To develop the test, the same steps used in the generalized likelihood ratio test (GLRT) are used where instead of the maximum likelihood (ML) estimator of the parameters, the minimum α−divergence based estimator is substituted in the test to increase the robustness of the test against contaminations in noise. This test depends on the single parameter α and as the special case corresponds to the well known GLRT. Numerical examples illustrating that the proposed test can achieve better detection rates in such scenarios are presented. Moreover, the test is applied to real fMRI dataset to detect the active area of the brain for some task-related inputs.

OBJECTIVE: To characterize and evaluate risk factors for comorbidities and death of cats with pelvic fractures. STUDY DESIGN: Retrospective case study. ANIMALS: Cats (n = 280). METHODS: Medical records were reviewed for cats in which pelvic fractures had been diagnosed (January 2003 to November 2016). Retrieved data included signalment, mechanism of injury, clinical findings, diagnostic imaging investigations, type and number of concurrent injuries based on anatomical location, type of therapy, and survival. Pelvic fractures were classified according to location and severity. Descriptive statistics were performed, and logistic regression models were constructed to examine associations between risk factors and outcome. RESULTS: Cases consisted of 280 cats with no (9%), unilateral (43%), and bilateral (48%) involvement of the weight-bearing axis. Sacral fractures were found in 12% of cats. Surgical treatment and mortality rates increased progressively with the severity of the pelvic fractures (P

BackgroundAdolescence can be a challenging time, characterized by self-consciousness, heightened regard for peer acceptance, and fear of rejection. Interpersonal concerns are amplified by unpredictable social interactions, both online and offline. This developmental and social context is potentially conducive to the emergence of paranoia. However, research on paranoia during adolescence is scarce.MethodOur aim was to examine the prevalence, structure, and probabilistic causal mechanisms of adolescent paranoia. A representative school cohort of 801 adolescents (11–15 y) completed measures of paranoia and a range of affective, cognitive, and social factors. A Bayesian approach with Directed Acyclic Graphs (DAGs) was used to assess the causal interactions with paranoia.ResultsParanoid thoughts were very common, followed a continuous distribution, and were hierarchically structured. There was an overall paranoia factor, with sub-factors of social fears, physical threat fears, and conspiracy concerns. With all other variables controlled, DAG analysis identified paranoia had dependent relationships with negative affect, peer difficulties, bullying, and cognitive-affective responses to social media. The causal directions could not be fully determined, but it was more likely that negative affect contributed to paranoia and paranoia impacted peer relationships. Problematic social media use did not causally influence paranoia.ConclusionsThere is a continuum of paranoia in adolescence and occasional suspicions are common at this age. Anxiety and depression are closely connected with paranoia and may causally contribute to its development. Paranoia may negatively impact adolescent peer relationships. The clinical significance of paranoia in adolescents accessing mental health services must now be established.

Spectrum occupancy prediction allows cognitive radio secondary users to exploit temporal spectrum opportunities one step-ahead. Temporal correlations in spectrum sensing measurements can be utilized to predict primary user activity patterns. Where applicable, cooperative spectrum prediction has the potential to improve prediction accuracy compared to single user (local) spectrum prediction. This letter presents the concept and methods for soft fusion-based cooperative spectrum occupancy prediction. The proposed methods were simulated and the results show significant improvement in prediction error over local, and hard fusion-based spectrum prediction.

Demand side management (DSM) aims to efficiently manage power flow by engaging energy customers, through offering incentives via price signals to alter their consumption patterns or directly controlling their loads. However, the integration of renewable energy generators and batteries in residential premises requires new approaches for DSM as they offer more flexibility. Moreover, as there are often a large number of residential energy customers within a distribution network, it is quite challenging to accommodate all of them in the DSM. In this paper, we propose an Aggregated Method (AM) that allows the treatment of distribution grid as a composition of several microgrids, which helps to aggregate underlying energy customers’ power and energy constraints and operating preferences. In addition, we provide methods for distributing the aggregated energy demand decisions among the participating energy customers. In contrast to the alternative centralized method, our approach requires less computational time to obtain decisions and hence scales well with increasing network size. Moreover, our results indicate that when using our method, energy customers receive more benefits through satisfying their energy requirements and operating conditions. Our overall analyses showed that the proposed framework can be easily adopted by the electricity market operators to create scalable DSM programs.

This paper investigates the asymptotic and nonasymptotic behavior of the quantized primal-dual (PD) algorithm in network utility maximization (NUM) problems, in which a group of agents maximizes the sum of their individual concave objective functions under linear constraints. In the asymptotic scenario, we use the information-theoretic notion of differential entropy power to establish universal bounds on the maximum exponential convergence rates of joint PD, primal and dual variables under optimum-achieving quantization schemes. These results provide tradeoffs between the speed of exponential convergence, the agents' objective functions, the communication bit rates, and the number of agents and constraints. In the nonasymptotic scenario, we obtain lower bounds on the mean square distance of joint PD, primal and dual variables from the optimal solution at any time instant. These bounds hold regardless of the quantization scheme used.

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.

Today’s electronic devices are fabricated using highly toxic materials and processes which limits their applications in environmental sensing applications and mandates complex encapsulation methods in biological and medical applications. This paper proposes a fully resorbable high density bio-compatible and environmentally friendly solution processable memristive crossbar arrays using silk fibroin protein which demonstrated bipolar resistive switching ratio of 104 and possesses programmable device lifetime characteristics before the device gracefully bio-degrades, minimizing impact to environment or to the implanted host. Lactate dehydrogenase assays revealed no cytotoxicity on direct exposure to the fabricated device and support their environmentally friendly and biocompatible claims. Moreover, the correlation between the oxidation state of the cations and their tendency in forming conductive filaments with respect to different active electrode materials has been investigated. The experimental results and the numerical model based on electro-thermal effect shows a tight correspondence in predicting the memristive switching process with various combinations of electrodes which provides insight into the morphological changes of conductive filaments in the silk fibroin films.

In this paper, we propose a scalable approach toward all-printed high-performance metal oxide thin-film transistors (TFTs), using a high-resolution electrohydrodynamic (EHD) printing process. Direct EHD micropatterning of metal oxide TFTs is based on diverse precursor solutions to form semiconducting materials (In2O3, In-Ga-ZnO (IGZO)), conductive metal oxide (Sn-doped In2O3 (ITO)), as well as aluminum oxide (Al2O3) gate dielectric at low temperatures. The fully printed TFT devices exhibit excellent electron transport characteristics (average electron mobilities of up to 117 cm2 V-1 s-1), negligible hysteresis, excellent uniformity, and stable operation at low-operating voltage. Furthermore, integrated logic gates such as NOT and NAND have been printed and demonstrated. All-printed logic with individual gating and symmetric input/output behavior, which is crucial for large-scale integration, is also demonstrated. The devices and fabrication process described in this paper enable high-performance and high-reliability transparent electronics.

We consider an inverse reinforcement learning problem involving “us” versus an “enemy” radar equipped with a Bayesian tracker. By observing the emissions of the enemy radar, how can we identify if the radar is cognitive (constrained utility maximizer)? Given the observed sequence of actions taken by the enemy's radar, we consider three problems: (i) Are the enemy radar's actions (waveform choice, beam scheduling) consistent with constrained utility maximization? If so how can we estimate the cognitive radar's utility function that is consistent with its actions. We formulate, and solve the problem in terms of the spectra (eigenvalues) of the state, and observation noise covariance matrices, and the algebraic Riccati equation. (ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar's actions in noise or the radar observes our probe signal in noise? We propose a statistical detector with a tight Type-II error bound. (iii) How can we optimally probe (interrogate) the enemy's radar by choosing our state to minimize the Type-II error of detecting if the radar is deploying an economic rational strategy, subject to a constraint on the Type-I detection error? We present a stochastic optimization algorithm to optimize our probe signal. The main analysis framework used in this paper is that of revealed preferences from microeconomics.

In this paper, we address the problem of optimal beam-forming for an antenna array generating beams at multiple frequencies simultaneously. The key challenge here is to perform automatic current allocation among antenna elements, which is a hard combinatorial problem. We formulate it mathematically and propose a fast algorithm that solves it using available optimisation tools. We also derive conditions under which the unconstrained beam-forming problem can be solved exactly. The conditions lead to several uncertainty principles for the antenna design. The developed algorithm is tested in numerical simulations and its performance is demonstrated.

Australian household PV adoption rates are the highest in the world and this is causing a rise in technical problems and the cost of the distribution system. This paper offers a predictive model of household PV purchases in Australia and this could be used in policy to better manage PV uptake patterns. The analysis used 1.6 million domestic PV installation decisions over 11 years from 2006 to 2017 and is statistically significant. Autoregressive integrated moving average (ARIMA) modelling was used to reduce non-stationarity in the data and Granger Causal modelling showed the most effective policy levers are price, subsidy, business confidence and PV feed-in tariffs. This analysis develops a model of Australian PV adoption and increases understanding of consumer roles in the future electricity system. This is compared to other similar models in the literature. The key contribution is that the scale of the model creates a temporal prediction that is not in other literature. The second contribution is that the model may apply to other household energy decisions. This was measured by comparing Australian PV adoption to solar hot water adoption.

Effective utilization of radio spectrum by the secondary user has become a tremendous issue for cognitive radio communication. The agreement of having a common channel for both transmitter and receiver is precondition for effectual transmission in the communication system. In this paper, prime-number theory based multi-radio rendezvous is proposed for asymmetric channel conditions. Statistical distribution analysis of rendezvous is illustrated for different scenarios of channel system. Based on analysis, total probability of rendezvous and time to rendezvous with distribution for single and multi-radio are clarified. The performances are demonstrated through montecarlo based simulation in Matlab. The analytical views and proposed method have shown outperformance over conventional methods for guaranteed rendezvous.

Target detection is a fundamental problem in radar systems. One of the most common techniques used in passive radars for detection is Cross-correlation of data received from the surveillance channel and the reference channel. However, the optimality of this kind of detection gets shaky when the reference channel is noisy. Here in this research, singular vectors based detector has been proposed by using the combination of all the singular vectors present in the received data matrices. Furthermore, a simple closed-form expression is derived for the threshold of the detector and compared with the results obtained from computational simulation using the Monte-Carlo method. Moreover, as the proposed detector is a random matrix theory (RMT) inspired detector, so the performance of this detector which we will now call Summated Singular Vectors based Detector (SSVD) is also compared with other RMT based detectors. Our results show improved detection probability and hence an improved performance as compared to existing known methods.

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&ndash, 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.

This article shows a socio-technical evaluation of the Australian case which has international implications for energy policy and regulation. Australia is one of few places globally that have faced domestic PV (photovoltaic system) adoption of above 50% of feeder connections. This leads to grid issues and is an emerging issue globally. Grid issues include over-voltage, thermal overload, frequency instability and voltage instability. This paper offers a policy process to regulate PV. This research extends earlier econometric modelling of Australian PV adoption data and extends it to focus on PV regulation in low voltage grids. This paper explores five policy options to help regulate PV in low voltage grids: the role of distribution businesses, inverter regulation, PV export limits, cost reflective pricing, and storage. Policy complexity comes from the need to incorporate many stakeholder perspectives, and this research contributes to policy clarity by seeking a consensus.

The region of interest (ROI) of medical images in content-based image retrieval (CBIR) systems often require content authentication and verification. This is because adversarial modification of the stored image could have lethal effect on research, diagnostic outcome and the outcome of some forensic investigations. In this work, both robust watermarking and Fragile Steganography were combined with image search features to design a medical image retrieval system that incorporates ROI integrity verification. Original ROI features were pre-computed and embedded into archival images and utilised during retrieval for image integrity checks. The average global image PSNR was 38.36dB while the ROI PSNR was maintained at an average of 46dB with all watermark search features retrieved at zero bit error rate (BER) provided the attack on the image is not perceptible.

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data. The parameterization avoids the irregularities and unidentifiability of latent variable models. The parameters used are all fully identifiable and causally-interpretable quantities., 30 pages

Artificial neural networks (ANN), deep learning, and neuromorphic systems are exciting new processing architectures being used to implement a wide variety of intelligent and adaptive systems. To date, these architectures have been primarily realized using traditional complementary metal-oxide-semiconductor (CMOS) processes or otherwise conventional semiconductor fabrication processes. Thus, the high cost associated with the design and fabrication of these circuits has limited the broader scientific community from applying new ideas, and arguably, has slowed research progress in this exciting new area. Solution-processed electronics offer an attractive option for providing low-cost rapid prototyping of neuromorphic devices. This article proposes a novel, wholly solution-based process used to produce low-cost transparent synaptic transistors capable of emulating biological synaptic functioning and thus used to construct ANN. We have demonstrated the fabrication process by constructing an ANN that encodes and decodes a 100 × 100 pixel image. Here, the synaptic weights were configured to achieve the desired image processing functions.

The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation in the latent class analysis. However, the EM algorithm comes with no guarantees of reaching the global optimum. We study the geometry of the latent class model in order to understand the behavior of the maximum likelihood estimator. In particular, we characterize the boundary stratification of the binary latent class model with a binary hidden variable. For small models, such as for three binary observed variables, we show that this stratification allows exact computation of the maximum likelihood estimator. In this case we use simulations to study the maximum likelihood estimation attraction basins of the various strata. Our theoretical study is complemented with a careful analysis of the EM fixed point ideal which provides an alternative method of studying the boundary stratification and maximizing the likelihood function. In particular, we compute the minimal primes of this ideal in the case of a binary latent class model with a binary or ternary hidden random variable., Comment: Revised version after incorporating reviewers' comments

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.

Recent interest in Internet of Things has resulted in increased activities in the area of dynamic spectrum access, which fosters cognitive radio networks that provide opportunistic access. This letter proposes optimal opportunistic access through a Markov decision process for a user of interest in a single channel environment competing with other users. Consequently, we analyze the model in terms of interference and wastage and compare it with other Markov models from the existing literature. One should note that the models are compared to give an overall evaluation of performance, as a detailed comparison cannot be made, due to the use of different system models, transitional probabilities, etc.

The problem of inferring the distribution of a random vector given that its norm is large requires modeling a homogeneous limiting density. We suggest an approach based on graphical models which is suitable for high-dimensional vectors. We introduce the notion of one-component regular variation to describe a function that is regularly varying in its first component. We extend the representation and Karamata's theorem to one-component regularly varying functions, probability distributions and densities, and explain why these results are fundamental in multivariate extreme-value theory. We then generalize the Hammersley–Clifford theorem to relate asymptotic conditional independence to a factorization of the limiting density, and use it to model multivariate tails.

In this letter, we present an analytic framework for formulating the statistical distribution of the nearest neighbor distance in hard-core point processes. We apply this framework to Matern hard-core point process (MHC) to derive the cumulative distribution function of the contact distance in three cases. The first case is between a point in an MHC process and its nearest neighbor from the same process. The second case is between a point in an independent Poisson point process and the nearest neighbor from an MHC process. The third case is between a point in the complement of an MHC process and its sibling MHC process. We test the analytic results against Monte Carlo simulations to verify their consistency.

In recent times, the finger flexibility assessment by means of reachable space is considered as an effective tool to describe the range of motion of the hand. Existing approaches numerically compute the reachable space using forward kinematics such as exhaustive scanning or Monte Carlo methods. In this paper, we provide explicit formulas mathematically determining the reachable space boundary. Green’s theorem is used to deduce the corresponding capacity formula for the size of the reachable space as opposed to an implicit numerical solution. Using this new mechanism, we accurately quantify and compare the reachable space of different subjects in order to effectively compare the functionality of the fingers. We evaluate the performance of our proposed method against the kinematic feed-forward (KFF) approach in calculating the reachable space. The execution time to capture the reachable space is significantly less than that for the standard KFF method. The computational cost for quantifying the reachable space capacity is significantly improved due to explicit capacity formulas resulting from the abstract form of boundary descriptions of the reachable space, unique to the proposed approach.

Background A cognitive account identifies six key psychological maintenance factors for persecutory delusions. However, a complex system of causation is likely where these factors interact in their influence on paranoid ideas. We set out to evaluate the causal dynamics of paranoia with theory-driven network approaches. Methods 1809 patients with non-affective psychosis attending UK mental health services completed assessments of paranoia, hallucinations, insomnia, self-esteem, worry, anxious avoidance, analytic reasoning, and psychological well-being. To assess causal patterns, we estimated, first, an undirected partial correlation network and then, second, adopted a Bayesian approach with Directed Acyclic Graphs to discover the directed causal pathways best supported by the data. Results The networks showed that with all other variables controlled, paranoia had direct causal interactions with hallucinations, negative self-beliefs, insomnia, worry, and avoidance. Hallucinations and negative self-beliefs were most directly linked to paranoia, whereas indirect paths had prominent influences on the causal effects for insomnia, worry, and avoidance. The direction of these interactions was uncertain, but negative self-beliefs and insomnia were more likely to influence paranoia than vice versa. Self-report reasoning was likely unrelated to paranoia once other factors were controlled. Causal factors were highly interconnected, with insomnia, negative self-beliefs, avoidance, and worry most directly linked to other variables. Most interactions were likely reciprocal, except for hallucinations which were unlikely to influence other variables and significantly caused by insomnia and avoidance. Discussion The findings are consistent with a complex system of interacting causation in the maintenance of paranoia. The patterns observed support the cognitive model of persecutory delusions, highlighting multiple pathways of causal interaction between paranoia and theoretically important factors. Interventions directly targeting these factors are likely to lead to multiple benefits, alleviating paranoia both directly and indirectly through connections with other causally related symptoms.

Underutilization of radio spectrum is being rehabilitated into resources with the dynamic configuration characteristics of cognitive radio. Upon detection of vacant channel, consensus of transmitter and receiver on one channel is precondition for data transmission which is termed as rendezvous. Blind and asymmetric views of channels necessitate a substantial cost on agreement of transmitter and receiver for attaining a common channel. In this paper, analytical views of statistical distribution with asymmetric channel for rendezvous are put into display. Upon analysis of statistical characteristics, the rendezvous times for different repetition rates and delays are obtained. Based on the analysis, we have perceived finest repetition rate for different point of views. Through Matlab simulations, it is demonstrated that, proper guaranteed consensus of channel within optimal time is ensured with statistical distribution over conventional system.

The low-voltage (LV) distribution network has been largely excluded from detailed analytical consideration and as a consequence it is now the least understood and most unpredictable element of the electricity grid. Many advanced network control and optimisation methods require feeder parameters to be known in advance, and thus accurate low-voltage power network models are an important prerequisite. Smart meters, being the only reliable source of information in such networks, improve the affordability of low voltage automation infrastructure and allow for accurate measurements at almost every node. We propose a fully smart meter data driven method for line impedance calculations by iteratively solving a non-convex problem for each line. The performance of our algorithms is demonstrated for different measurement accuracy scenarios through simulations.

Bayesian network models with latent variables are widely used in statistics and machine learning. In this paper we provide a complete algebraic characterization of Bayesian network models with latent variables when the observed variables are discrete and no assumption is made about the state-space of the latent variables. We show that it is algebraically equivalent to the so-called nested Markov model, meaning that the two are the same up to inequality constraints on the joint probabilities. In particular these two models have the same dimension. The nested Markov model is therefore the best possible description of the latent variable model that avoids consideration of inequalities, which are extremely complicated in general. A consequence of this is that the constraint finding algorithm of Tian and Pearl (UAI 2002, pp519-527) is complete for finding equality constraints. Latent variable models suffer from difficulties of unidentifiable parameters and non-regular asymptotics; in contrast the nested Markov model is fully identifiable, represents a curved exponential family of known dimension, and can easily be fitted using an explicit parameterization., 41 pages

this paper presents a capacity-improved Spread Spectrum (SS) watermarking method for data hiding and security for medical image and Electronic Medical Record (EMR) transmission in Teleradiology. SS watermarking is more secure than other watermarking methods but currently has low data carrying capacity due to the spreading of a single bit in larger cover sample in order to achieve higher robustness and security. In this work, a new method in spatial domain is proposed in order to improve on the current hiding capacity of just one bit per sample to up to 6 bits per sample (8×8 pixel block). This new watermark compression encoding method is suitably adapted for blind SS watermarking for hiding patient data securely in medical images of high pixel depth. The new method was successfully implemented and evaluated using Magnetic Resonance Image (MRI) scans.