Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system. This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376, by the U.S. Office of Naval Research under Grant No. N00014-08-1-380, and by NASA under Cooperative Agreement No. NNX07AK49A.     

Distributed decision propagation in mobile-agent proximity networks

This paper develops a distributed algorithm for decision/awareness propagation in mobile-agent networks. A time-dependent proximity network topology is adopted to represent a mobile-agent scenario. The agent-interaction policy formulated here is inspired from the recently developed language-measure theory. Analytical results related to convergence of statistical moments of agent states are derived and then validated by numerical simulation. The results show that a single (user-defined) parameter in the agent interaction policy can be identified to control the trade-off between Propagation Radius (i.e. how far a decision spreads from its source) and Localisation Gradient (i.e. the extent to which the spatial variations may affect localisation of the source) as well as the temporal convergence properties.

     

Enhancement in hydrogen storage in carbon nanotubes under modified conditions

We investigate the hydrogen adsorbing characteristics of single-walled carbon nanotubes (CNTs) through fundamental molecular dynamics simulations that characterize the role of ambient pressure and temperature, the presence of surface charges on the CNTs, inclusion of metal ion interconnects, and nanocapillary effects. While the literature suggests that hydrogen spillover due to the presence of metallic contaminants enhances storage on and inside the nanotubes, we find this to be significant for alkali and not transition metals. Charging the CNT surfaces does not significantly enhance hydrogen storage. We find that the bulk of the hydrogen storage occurs inside CNTs due to their nanocapillarity effect. Storage is much more dependent on external thermodynamic conditions such as the temperature and the pressure than on these facets of the CNT structure. The dependence of storage on the external thermodynamic conditions is analyzed and the optimal range of operating conditions is identified.     

Designing Energy Efficient Strategies Using Markov Decision Process for Crowd-Sensing Applications

Mobile Networks and Applications - In mobile crowd-sensing, smartphone users take part in sensing and then share the data to the server (cloud) and get an incentive. These data can be utilized for...     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

Multi-objective optimization of the heat transmission and fluid forces around a rounded cornered square cylinder

The unsteady fluid stream and warmth transmission nearby a square cylinder with sharp and rounded cornered edges are numerically examined, and then the roundness of the corner is predicted and optimized for the minimum fluid forces and maximum heat transmission rate. The roundness of the cylinder corner is changing 0.5D (circle) to 0.71D (square); D is the depth of the cylinder. The fluid flow and the heat transmission features around the sharp and curved cornered square cylinder are evaluated with the streamline, isotherm patterns, pressure coefficient, drag and lift coefficients, local Nusselt number (Nu_local) and average Nusselt number (Nu_avg) at different Re and for several roundness values. These characteristics are predicted by the gene expression programming, and then the multi-objective genetic algorithm is utilized for the optimization. A number of combinations of values of corners have been found in the form of Pareto-optimal solution to compromise the minimum fluid forces with maximum heat transfer rate.

     

Ultrafast excited state deactivation of doped porous anodic alumina membranes

Free-standing, bi-directionally permeable and ultra-thin anodic aluminum oxide (AAO) membranes establish attractive templates (host) for the synthesis of nano-dots and rods of various materials (guest). This is due to their chemical and structural integrity and high periodicity on length scales of 5-150 nm which are often used to host photoactive nano-materials for various device applications including dye-sensitized solar cells. In the present study, AAO membranes are synthesized by using electrochemical methods and a detailed structural characterization using FEG-SEM, XRD and TGA confirms the porosity and purity of the material. Defect-mediated photoluminescence quenching of the porous AAO membrane in the presence of an electron accepting guest organic molecule (benzoquinone) is studied by means of steady-state and picosecond/femtosecond-resolved luminescence measurements. Using time-resolved luminescence transients, we have also revealed light harvesting of complexes of porous alumina impregnated with inorganic quantum dots (Maple Red) or gold nanowires. Both the F?rster resonance energy transfer and the nano-surface energy transfer techniques are employed to examine the observed quenching behavior as a function of the characteristic donor-acceptor distances. The experimental results will find their relevance in light harvesting devices based on AAOs combined with other materials involving a decisive energy/charge transfer dynamics.     

Selective Filters and Sinusoidal Oscillators Using CFA Transimpedance Pole

Using a single current feedback amplifier (CFA) device, two new variable frequency sinusoidal RC oscillators are presented. The transadmittance pole of the device (AD-844) has been utilized in the design for generating sine wave signals covering a range of 1 MHz≤f ₀≤31 MHz. Under open-loop conditions, both circuits exhibit resonance characteristics at moderate Q-values (1≤Q≤9). These responses have been experimentally verified with hardware circuit implementation and PSPICE macromodel simulation.     

A methodology to control direct-fired furnaces

A suboptimal control strategy to control heat treatment processes in a direct-fired batch type furnace is formulated. It minimizes deviations from a prescribed temporal load temperature profile and the energy input in terms of the fuel consumption. The control method requires a model for the heat transfer and combustion, which is described. Application of the method shows that the fuel input and heat transfer to the load can be controlled to maintain a specified instantaneous temperature. This is accomplished through a feedback loop that is constructed by comparing the desired and measured temperatures. A parametric study is conducted to demonstrate the model.     

Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames

Occlusion edges correspond to range discontinuity in a scene from the point of view of the observer. Detection of occlusion edges is an important prerequisite for many machine vision and mobile robotic tasks. Although they can be extracted from range data, extracting them from images and videos would be extremely beneficial. We trained a deep convolutional neural network (CNN) to identify occlusion edges in images and videos with just RGB, RGB-D and RGB-D-UV inputs, where D stands for depth and UV stands for horizontal and vertical components of the optical flow field respectively. The use of CNN avoids hand-crafting of features for automatically isolating occlusion edges and distinguishing them from appearance edges. Other than quantitative occlusion edge detection results, qualitative results are provided to evaluate input data requirements and to demonstrate the trade-off between high resolution analysis and frame-level computation time that is critical for real-time robotics applications.