Adaptive epileptic seizure prediction system

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.     

Scheduling strategies for efficient ETL execution

Extract-transform-load (ETL) workflows model the population of enterprise data warehouses with information gathered from a large variety of heterogeneous data sources. ETL workflows are complex design structures that run under strict performance requirements and their optimization is crucial for satisfying business objectives. In this paper, we deal with the problem of scheduling the execution of ETL activities (a.k.a. transformations, tasks, operations), with the goal of minimizing ETL execution time and allocated memory. We investigate the effects of four scheduling policies on different flow structures and configurations and experimentally show that the use of different scheduling policies may improve ETL performance in terms of memory consumption and execution time. First, we examine a simple, fair scheduling policy. Then, we study the pros and cons of two other policies: the first opts for emptying the largest input queue of the flow and the second for activating the operation (a.k.a. activity) with the maximum tuple consumption rate. Finally, we examine a fourth policy that combines the advantages of the latter two in synergy with flow parallelization.     

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.     

Quadratic stabilizability of switched linear systems with polytopic uncertainties

In this paper, we consider quadratic stabilizability via state feedback for both continuous-time and discrete-time switched linear systems that are composed of polytopic uncertain subsystems. By state feedback, we mean that the switchings among subsystems are dependent on system states. For continuous-time switched linear systems, we show that if there exists a common positive definite matrix for stability of all convex combinations of the extreme points which belong to different subsystem matrices, then the switched system is quadratically stabilizable via state feedback. For discrete-time switched linear systems, we derive a quadratic stabilizability condition expressed as matrix inequalities with respect to a family of non-negative scalars and a common positive definite matrix. For both continuous-time and discrete-time switched systems, we propose the switching rules by using the obtained common positive definite matrix.     

Decentralised event-triggered cooperative control with limited communication

This note studies event-triggered control of Multi-Agent Systems (MAS) with first-order integrator dynamics. It extends previous work on event-triggered consensus by considering limited communication capabilities through strict peer-to-peer non-continuous information exchange. The approach provides both a decentralised control law and a decentralised communication policy. Communication events require no global information and are based only on local state errors; agents do not require a global sampling period or synchronous broadcasting as in sampled-data approaches. The proposed decentralised event-triggered control technique guarantees that the inter-event times for each agent are strictly positive. Finally, the ideas in this note are used to consider the practical scenario where agents are able to exchange only quantised measurements of their states.     

A discussion of objective function representation methods in global optimization

Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization (GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov’s superposition and its application in GO. Finally, we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.     

Growth and Deposition of Hygroscopic Particulate Matter in the Human Lungs

Transport and fate of inhaled particulate matter in the human lungs is calculated for realistic physicochemical conditions by a new dosimetry model. The model solves a variant of the general dynamic equation for the size evolution of respirable particles within the human tracheobronchial airways, starting at the tracheal entrance. We focus on ambient anthropogenic aerosols, which are of concern in inhalation toxicology because of their potential irritant and toxic effects on humans. The aerosols considered are polydisperse with respect to size and heterodisperse with respect to thermodynamic state and chemical composition, having initially bimodal lognormal size distribution that evolves with time as a result of condensation-evaporation and deposition processes. The architecture of the human lung is described by Weibel's symmetric bronchial tree. Simulations reveal that, due to the rapid growth of submicron-sized particles, increased number and mass fractions of the particle population can be found in the intermediate size range 0.1 < φ < 1     

Shear crack growth in brittle materials modeled by constrained Cosserat elasticity

The propagation of in-plane shear cracks is investigated in brittle microstructured materials modeled by the constrained Cosserat elasticity. This theory introduces characteristic material lengths in order to describe the scale effects that emerge from the underlying microstructure and has proved to be very effective for modeling complex microstructured materials. An exact solution is obtained based on integral transforms and the Wiener-Hopf technique. Numerical results are presented illustrating the dependence of the stress intensity factor and the energy release rate upon the loading profile, the propagation velocity, and the characteristic material lengths of Cosserat elasticity. It is shown that depending on the Cosserat microstructural lengths the limiting crack propagation velocity can be significantly lower than the classical Rayleigh limit. Moreover, strengthening effects are observed when the characteristic material lengths become comparable to the geometrical lengths of the problem, a behavior that has been experimentally verified in fracture of ceramics.