Roadmap for the next generation of dynamic function allocation theories and strategies

Dynamic function allocation theories have matured, but not to the extent that they can be implemented in actual environments. The authors identify a roadmap to help to get to that stage, after a brief review of relevant function allocation theories and the gaps in knowledge in adaptive allocation theories. Past research has focused on short‐term implementations of dynamic function allocation, which last for minutes (but never more than a few hours). Stable and enduring implementations of dynamic function allocation, lasting over days or possibly years, require a better and scientific understanding of the interaction among trigger mechanisms, adaptation strategies, and interruption strategies. The possibility of an unintentional consequence of this adaptive allocation, that the automated agent becomes confined to limited levels of automation, has to be ruled out after experimental evidence. © 2010 Wiley Periodicals, Inc.     

Reinterpretable Imager: Towards Variable Post‐Capture Space,Angle and Time Resolution in Photography

We describe a novel multiplexing approach to achieve tradeoffs in space, angle and time resolution in photography. We explore the problem of mapping useful subsets of time‐varying 4D lightfields in a single snapshot. Our design is based on using a dynamic mask in the aperture and a static mask close to the sensor. The key idea is to exploit scene‐specific redundancy along spatial, angular and temporal dimensions and to provide a programmable or variable resolution tradeoff among these dimensions. This allows a user to reinterpret the single captured photo as either a high spatial resolution image, a refocusable image stack or a video for different parts of the scene in post‐processing. A lightfield camera or a video camera forces a‐priori choice in space‐angle‐time resolution. We demonstrate a single prototype which provides flexible post‐capture abilities not possible using either a single‐shot lightfield camera or a multi‐frame video camera. We show several novel results including digital refocusing on objects moving in depth and capturing multiple facial expressions in a single photo.     

Polynomial based scheme (PBS) for establishing Authentic Associations in Wireless Mesh Networks

The varying degree of mobility of Mesh Clients has provided much more flexibility in Wireless Mesh Networks, and establishing an Authentic Association among entities is a non-trivial problem. In this paper, we introduce a Polynomial Based scheme which provides pair-wise connectivity, low communication, marginal storage overhead and high scalability while making on the fly Authentic Association feasible. The proposed scheme is also observed to be resilient against both traffic analysis and node capture attacks.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors

In this paper, we consider the fundamental problem of frequency estimation of multiple sinusoidal signals with stationary errors. We propose genetic algorithm and outlier-insensitive criterion function based technique for the frequency estimation problem. In the simulation studies and real life data analysis, it is observed that the proposed genetic algorithm based robust frequency estimators are able to resolve frequencies of the sinusoidal model with high degree of accuracy. Among the proposed methods, the genetic algorithm based least squares estimator, in the no-outlier scenario, provides efficient estimates, in the sense that their mean square errors attain the corresponding Cramér-Rao lower bounds. In the presence of outliers, the proposed robust methods perform quite well and seem to have a fairly high breakdown point with respect to level of outlier contamination. The proposed methods significantly do not depend on the initial guess values required for other iterative frequency estimation methods.     

Communication-aware heuristics for run-time task mapping on NoC-based MPSoC platforms

Efficient run-time mapping of tasks onto Multiprocessor System-on-Chip (MPSoC) is very challenging especially when new tasks of other applications are also required to be supported at run-time. In this paper, we present a number of communication-aware run-time mapping heuristics for the efficient mapping of multiple applications onto an MPSoC platform in which more than one task can be supported by each processing element (PE). The proposed mapping heuristics examine the available resources prior to recommending the adjacent communicating tasks on to the same PE. In addition, the proposed heuristics give priority to the tasks of an application in close proximity so as to further minimize the communication overhead. Our investigations show that the proposed heuristics are capable of alleviating Network-on-Chip (NoC) congestion bottlenecks when compared to existing alternatives. We map tasks of applications onto an 8 × 8 NoC-based MPSoC to show that our mapping heuristics consistently leads to reduction in the total execution time, energy consumption, average channel load and latency. In particular, we show that energy savings can be up to 44% and average channel load is improved by 10% for some cases.     

Effect of geometry on droplet formation in the squeezing regime in a microfluidic T-junction

In the surface tension-dominated microchannel T-junction, droplets can be formed as a result of the mixing of two dissimilar, immiscible fluids. This article presents results for very low Capillary numbers and different flow rates of the continuous and dispersed phases. Through three-dimensional lattice Boltzmann-based simulations, the mechanism of the formation of “plugs” in the squeezing regime has been examined and the size of the droplets quantified. Results for Re_\textc << 1 Re_{\text{c}} \ll 1 show the dependence of flow rates of the two fluids on the length of the droplets formed, which is compared with existing experimental data. It is shown that the size of plugs formed decreases as the Capillary number increases in the squeezing regime. This article clearly shows that the geometry effect, i.e., the widths of the two channels and the depth of the assembly, plays an important role in the determination of the length of the plugs, a fact that was ignored in earlier experimental correlations.     

Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach

In this paper, the automatic segmentation of a multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. The automatic clustering problem is thereafter modelled as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. A multiobjective clustering technique, named MCMOClust, is used to solve this problem. MCMOClust utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously to automatically evolve the appropriate number of clusters present in MR brain images. A semi-supervised method is used to select a single solution from the final Pareto optimal front of MCMOClust. The present method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the present method over Fuzzy C-means, Expectation Maximization clustering algorithms and a newly developed symmetry based fuzzy genetic clustering technique (Fuzzy-VGAPS), are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCMOClust) is also compared with the available ground truth information.     

Adjacent assembly of self-assembled monolayers for the construction of selective bio-platforms

Selective patterning of bio-substances onto solid platforms is of increasing importance in many areas and widely used for various applications ranging from bio-sensing to cell and tissue engineering. In this study, a new fabrication scheme for the construction of highly selective bio-platforms is presented. The method is based on a direct patterning of poly(ethyleneglycol) (PEG) bio-inert layers on a conducting indium tin oxide (ITO) substrate using electron beam lithography and subsequent assembly of modified amine reactive layers onto the exposed areas. The process is found to create very high “surface contrast” between adhesive and repulsive regions onto the substrate. The platforms are shown to be enable efficient for selective adsorption of a variety of bio-substances including protein arrays, latex beads, and single cells. The high resolution of the technique makes it also applicable for the construction and deposition of bio-structures at the sub-micron scale. The reported technique employs standard lithography and surface chemistry processes, which makes it useful and easy to adopt for a variety of applications and other conductive substrates.     

A dynamic attribute-based data filtering and recovery scheme for web information processing

Web data being transmitted over a network channel on the Internet with excessive amount of data causes data processing problems, which include selectively choosing useful information to be retained for various data applications. In this paper, we present an approach for filtering less-informative attribute data from a source Website. A scheme for filtering attributes, instead of tuples (records), from a Website becomes imperative, since filtering a complete tuple would lead to filtering some informative, as well as less-informative, attribute data in the tuple. Since filtered data at the source Website may be of interest to the user at the destination Website, we design a data recovery approach that maintains the minimal amount of information for data recovery purpose while imposing minimal overhead for data recovery at the source Website. Our data filtering and recovery approach (1) handles a wide range of Web data in different application domains (such as weather, stock exchanges, Internet traffic, etc.), (2) is dynamic in nature, since each filtering scheme adjusts the amount of data to be filtered as needed, and (3) is adaptive, which is appealing in an ever-changing Internet environment.