Analysis of Spectrum Occupancy Using Machine Learning Algorithms

In this paper, we analyze the spectrum occupancy using different machine learning techniques. Both supervised techniques (naive Bayesian classifier (NBC), decision trees (DT), support vector machine (SVM), linear regression (LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy is performed. The classified occupancy status is further utilized to evaluate the probability of secondary user outage for the future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with fire fly algorithm (FFA), which is shown to outperform all other algorithms.Comment: 21 pages, 6 figure

Point singularities and suprathreshold stochastic resonance in optimal coding

Motivated by recent studies of population coding in theoretical neuroscience, we examine the optimality of a recently described form of stochastic resonance known as suprathreshold stochastic resonance, which occurs in populations of noisy threshold devices such as models of sensory neurons. Using the mutual information measure, it is shown numerically that for a random input signal, the optimal threshold distribution contains singularities. For large enough noise, this distribution consists of a single point and hence the optimal encoding is realized by the suprathreshold stochastic resonance effect. Furthermore, it is shown that a bifurcational pattern appears in the optimal threshold settings as the noise intensity increases. Fisher information is used to examine the behavior of the optimal threshold distribution as the population size approaches infinity.Comment: 11 pages, 3 figures, RevTe

A phenomenological model of myelinated nerve with a dynamic threshold

To evaluate coding strategies for cochlear implants a model of the human cochlear nerve is required. Nerve models based on voltage-clamp experiments, such as the Frankenhaeuser-Huxley model of myelinated nerve, can have over forty parameters and are not amenable for fitting to physiological data from a different animal or type of nerve. Phenomenological nerve models, such as leaky integrate-and-fire (LIF) models, have fewer parameters but have not been validated with a wide range of stimuli. In the absence of substantial cochlear nerve data, we have used data from a toad sciatic nerve for validation (50 Hz to 2 kHz with levels up to 20 dB above threshold). We show that the standard LIF model with fixed refractory properties and a single set of parameters cannot adequately predict the toad rate-level functions. Given the deficiency of this standard model, we have abstracted the dynamics of the sodium inactivation variable in the Frankenhaeuser-Huxley model to develop a phenomenological LIF model with a dynamic threshold. This nine-parameter model predicts the physiological rate-level functions much more accurately than the standard LIF model. Because of the low number of parameters, we expect to be able to optimize the model parameters so that the model is more appropriate for cochlear implant simulations

Stimulus-dependent refractoriness in the Frankenhaeuser-Huxley model

Phenomenological neural models, such as the leaky integrate-and-fire model, normally have a fixed refractory time-course that is independent of the stimulus. The recovery of threshold following an action potential is typically based on physiological experiments that use a two-pulse paradigm in which the first pulse is suprathreshold and causes excitation and the second pulse is used to determine the threshold at various intervals following the first. In such experiments, the nerve is completely unstimulated between the two pulses. This contrasts the receptor stimuli in normal physiological systems and the electrical stimuli used by cochlear implants and other neural prostheses. A numerical study of the Frankenhaeuser-Huxley conductance-based model of nerve fibre was therefore undertaken to investigate the effect of stimulation on refractoriness. We found that the application of a depolarizing stimulus during the later part of what is classically regarded as the absolute refractory period could effectively prolong the absolute refractory period, while leaving the refractory time-constants and other refractory parameters largely unaffected. Indeed, long depolarizing pulses, which would have been suprathreshold if presented to a resting nerve fibre, appeared to block excitation indefinitely. Stimulation during what is classically regarded as the absolute refractory period can therefore greatly affect the temporal response of a nerve. We conclude that the classical definition of absolute refractory period should be refined to include only the initial period following an action potential when an ongoing stimulus would not affect threshold; this period was found to be about half as long as the classical absolute refractory period. We further conclude that the stimulus-dependent nature of the relative refractory period must be considered when developing a phenomenological nerve model for complex stimuli

Patient acceptance and perceived utility of pre-consultation prevention summaries and reminders in general practice: pilot study

Extent: 8p.BACKGROUND: Patients attending general practices receive only about sixty per cent of the preventive services that are indicated for them. This pilot study explores patient acceptability and perceived utility of automatically generated prevention summary and reminder sheets provided to patients immediately before consultations with their general practitioners. METHODS: Adult patients attending a general practitioner in a practice in Adelaide and a general practitioner in a practice in Melbourne, Australia for consultations in January and February 2009 received automatically-generated prevention summary and reminder sheets that highlighted indicated preventive activities that were due to be performed, and that encouraged the patient to discuss these with the general practitioner in the consultation. Patients completed a post-consultation questionnaire and were interviewed about their experience of receiving the sheets. RESULTS: Sixty patients, median age 53 years (interquartile range 40-74) years, and 58% female, were recruited. Seventy eight per cent of patients found the sheets clear and easy to understand, 75% found them very or quite useful, 72% reported they had addressed with their general practitioner all of the preventive activities that were listed on the sheets as being due to be performed. A further 13% indicated that they had addressed most or some of the activities. 78% of patients said that they would like to keep receiving the sheets. Themes emerging from interviews with patients included: patient knowledge was enhanced; patient conceptions of health and the GP consultation were broadened; the consultation was enhanced; patient pro-activity was encouraged; patients were encouraged to plan their health care; the intervention was suitable for a variety of patients. CONCLUSIONS: Most patients reported that they found the prevention summary and reminder sheets acceptable and useful. The actual increase in performance of preventive activities that may result from this new intervention needs to be tested in randomised controlled trials.Oliver R. Frank, Nigel P. Stocks and Paul Aylwar

Phase locking below rate threshold in noisy model neurons

The property of a neuron to phase-lock to an oscillatory stimulus before adapting its spike rate to the stimulus frequency plays an important role for the auditory system. We investigate under which conditions neurons exhibit this phase locking below rate threshold. To this end, we simulate neurons employing the widely used leaky integrate-and-fire (LIF) model. Tuning parameters, we can arrange either an irregular spontaneous or a tonic spiking mode. When the neuron is stimulated in both modes, a significant rise of vector strength prior to a noticeable change of the spike rate can be observed. Combining analytic reasoning with numerical simulations, we trace this observation back to a modulation of interspike intervals, which itself requires spikes to be only loosely coupled. We test the limits of this conception by simulating an LIF model with threshold fatigue, which generates pronounced anticorrelations between subsequent interspike intervals. In addition we evaluate the LIF response for harmonic stimuli of various frequencies and discuss the extension to more complex stimuli. It seems that phase locking below rate threshold occurs generically for all zero mean stimuli. Finally, we discuss our findings in the context of stimulus detection

Enhanced processing in arrays of optimally tuned nonlinear biomimetic sensors : a coupling-mediated Ringelmann effect and its dynamical mitigation

Inspired by recent results on self-tunability in the outer hair cells of the mammalian cochlea, we describe an array of magnetic sensors where each individual sensor can self-tune to an optimal operating regime. The self-tuning gives the array its “biomimetic” features. We show that the overall performance of the array can, as expected, be improved by increasing the number of sensors but, however, coupling between sensors reduces the overall performance even though the individual sensors in the system could see an improvement. We quantify the similarity of this phenomenon to the Ringelmann effect that was formulated 103 years ago to account for productivity losses in human and animal groups. We propose a global feedback scheme that can be used to greatly mitigate the performance degradation that would, normally, stem from the Ringelmann effect

ASPREN surveillance system for influenza-like illness - a comparison with FluTracking and the National Notifiable Diseases Surveillance System

Public health surveillance systems are fundamental to the prevention and control of infectious diseases. Data obtained by sentinel surveillance systems may be used to inform public health decision making, priority setting and subsequent action

Approximating non-Gaussian Bayesian networks using minimum information vine model with applications in financial modelling

Many financial modeling applications require to jointly model multiple uncertain quantities to presentmore accurate, near future probabilistic predictions. Informed decision making would certainly benefitfrom such predictions. Bayesian networks (BNs) and copulas are widely used for modeling numerousuncertain scenarios. Copulas, in particular, have attracted more interest due to their nice property ofapproximating the probability distribution of the data with heavy tail. Heavy tail data is frequentlyobserved in financial applications. The standard multivariate copula suffer from serious limitations whichmade them unsuitable for modeling the financial data. An alternative copula model called the pair-copulaconstruction (PCC) model is more flexible and efficient for modeling the complex dependence of finan-cial data. The only restriction of PCC model is the challenge of selecting the best model structure. Thisissue can be tackled by capturing conditional independence using the Bayesian network PCC (BN-PCC).The flexible structure of this model can be derived from conditional independences statements learnedfrom data. Additionally, the difficulty of computing conditional distributions in graphical models for non-Gaussian distributions can be eased using pair-copulas. In this paper, we extend this approach furtherusing the minimum information vine model which results in a more flexible and efficient approach inunderstanding the complex dependence between multiple variables with heavy tail dependence andasymmetric features which appear widely in the financial applications