A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Motion Analysis for Short and Long Jump

Motion analysis is very essential in sport activities to enhance the performance of an athlete and to ensure the correctness of regimes. Expensive methods of motion analysis involving the use of sophisticated technology has led to limited application of motion analysis in sports. Towards this, in this paper we have integrated a low-cost method for motion analysis using three axis accelerometer, three axis magnetometer and microcontroller which are very accurate and easy to use. Seventeen male subjects performed two experiments, standing short jumps and long jumps over a wide range of take-off angles. During take-off and landing the acceleration and angles at different joints of the body are recorded using accelerometers and magnetometers, and the data is captured using Lab VIEW software. Optimum take-off angle in these jumps are calculated using the recorded data, to identify the optimum projection angle that maximizes the distance achieved in a jump. The results obtained for optimum take off angle in short jump and long jump is in agreement with those obtained using other methodologies and theoretical calculations assuming jump to be a projectile motion. The impact force (acceleration) is also analysed and is found to progressively decrease from foot to neck.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

Coexistence of fast photodarkening and slow photobleaching in Ge19As21Se60 thin films

We experimentally demonstrate the coexistence of two opposite photo-effects, viz. fast photodarkening (PD) and slow photobleaching (PB) in Ge19As21Se60 thin films, when illuminated with a laser of wavelength 671 nm. PD appears to begin instantaneously upon light illumination and saturates in tens of seconds. By comparison, PB is a slower process that starts only after PD has saturated. Both PD and PB follow stretched exponetial dependence on time. Modeling of overall change as a linear sum of two contributions suggests that the changes in As and Ge parts of glass network respond to light effectively indepndent of each other. (C) 2012 Optical Society of America

Practical fully three-dimensional reconstruction algorithms for diffuse optical tomography

We have developed an efficient fully three-dimensional (3D) reconstruction algorithm for diffuse optical tomography (DOT). The 3D DOT, a severely ill-posed problem, is tackled through a pseudodynamic (PD) approach wherein an ordinary differential equation representing the evolution of the solution on pseudotime is integrated that bypasses an explicit inversion of the associated, ill-conditioned system matrix. One of the most computationally expensive parts of the iterative DOT algorithm, the reevaluation of the Jacobian in each of the iterations, is avoided by using the adjoint-Broyden update formula to provide low rank updates to the Jacobian. In addition, wherever feasible, we have also made the algorithm efficient by integrating along the quadratic path provided by the perturbation equation containing the Hessian. These algorithms are then proven by reconstruction, using simulated and experimental data and verifying the PD results with those from the popular Gauss-Newton scheme. The major findings of this work are as follows: (i) the PD reconstructions are comparatively artifact free, providing superior absorption coefficient maps in terms of quantitative accuracy and contrast recovery; (ii) the scaling of computation time with the dimension of the measurement set is much less steep with the Jacobian update formula in place than without it; and (iii) an increase in the data dimension, even though it renders the reconstruction problem less ill conditioned and thus provides relatively artifact-free reconstructions, does not necessarily provide better contrast property recovery. For the latter, one should also take care to uniformly distribute the measurement points, avoiding regions close to the source so that the relative strength of the derivatives for measurements away from the source does not become insignificant. (c) 2012 Optical Society of America

Low complexity adaptation for SISO channel shortening equalizers

Since the days of Digital Subscriber Links (DSL), time domain equalizers (TEQ's) have been used to combat time dispersive channels in Multicarrier Systems. In this paper, we propose computationally inexpensive techniques to recompute TEQ weights in the presence of changes in the channel, especially over fast fading channels. The techniques use no extra information except the perturbation to the channel itself, and provide excellent approximations to the new TEQ weights. Adaptation methods for two existing Channel shortening algorithms are proposed and their performance over randomly varying, randomly perturbed channels is studied. The proposed adaptation techniques are shown to perform admirably well for small changes in channels for OFDM systems. (C) 2012 Elsevier GmbH. All rights reserved.

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Applying Genetic Algorithms to Optimize Power in Tiled SNUCA Chip Multicore Architectures

We propose a novel technique for reducing the power consumed by the on-chip cache in SNUCA chip multicore platform. This is achieved by what we call a "remap table", which maps accesses to the cache banks that are as close as possible to the cores, on which the processes are scheduled. With this technique, instead of using all the available cache, we use a portion of the cache and allocate lesser cache to the application. We formulate the problem as an energy-delay (ED) minimization problem and solve it offline using a scalable genetic algorithm approach. Our experiments show up to 40% of savings in the memory sub-system power consumption and 47% savings in energy-delay product (ED).

Compiler-assisted energy optimization for clustered VLIW processors

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Although clustering helps by improving the clock speed, reducing the energy consumption of the logic, and making the design simpler, it introduces extra overheads by way of inter-cluster communication. This communication happens over long global wires having high load capacitance which leads to delay in execution and significantly high energy consumption. Inter-cluster communication also introduces many short idle cycles, thereby significantly increasing the overall leakage energy consumption in the functional units. The trend towards miniaturization of devices (and associated reduction in threshold voltage) makes energy consumption in interconnects and functional units even worse, and limits the usability of clustered architectures in smaller technologies. However, technological advancements now permit the design of interconnects and functional units with varying performance and power modes. In this paper, we propose scheduling algorithms that aggregate the scheduling slack of instructions and communication slack of data values to exploit the low-power modes of functional units and interconnects. Finally, we present a synergistic combination of these algorithms that simultaneously saves energy in functional units and interconnects to improves the usability of clustered architectures by achieving better overall energy-performance trade-offs. Even with conservative estimates of the contribution of the functional units and interconnects to the overall processor energy consumption, the proposed combined scheme obtains on average 8% and 10% improvement in overall energy-delay product with 3.5% and 2% performance degradation for a 2-clustered and a 4-clustered machine, respectively. We present a detailed experimental evaluation of the proposed schemes. Our test bed uses the Trimaran compiler infrastructure. (C) 2012 Elsevier Inc. All rights reserved.

AIWIN - A fast and jigless inspection technique for machined parts

Precision inspection of manufactured components having multiple complex surfaces and variable tolerance definition is an involved, complex and time-consuming function. In routine practice, a jig is used to present the part in a known reference frame to carry out the inspection process. Jigs involve both time and cost in their development, manufacture and use. This paper describes 'as is where is inspection' (AIWIN), a new automated inspection technique that accelerates the inspection process by carrying out a fast registration procedure and establishing a quick correspondence between the part to inspect and its CAD geometry. The main challenge in doing away with a jig is that the inspection reference frame could be far removed from the CAD frame. Traditional techniques based on iterative closest point (ICP) or Newton methods require either a large number of iterations for convergence or fail in such a situation. A two-step coarse registration process is proposed to provide a good initial guess for a modified ICP algorithm developed earlier (Ravishankar et al., Int J Adv Manuf Technol 46(1-4):227-236, 2010). The first step uses a calibrated sphere for local hard registration and fixing the translation error. This transformation locates the centre for the sphere in the CAD frame. In the second step, the inverse transformation (involving pure rotation about multiple axes) required to align the inspection points measured on the manufactured part with the CAD point dataset of the model is determined and enforced. This completes the coarse registration enabling fast convergence of the modified ICP algorithm. The new technique has been implemented on complex freeform machined components and the inspection results clearly show that the process is precise and reliable with rapid convergence. © 2011 Springer-Verlag London Limited.

Computational algorithms inspired by biological processes and evolution

In recent times computational algorithms inspired by biological processes and evolution are gaining much popularity for solving science and engineering problems. These algorithms are broadly classified into evolutionary computation and swarm intelligence algorithms, which are derived based on the analogy of natural evolution and biological activities. These include genetic algorithms, genetic programming, differential evolution, particle swarm optimization, ant colony optimization, artificial neural networks, etc. The algorithms being random-search techniques, use some heuristics to guide the search towards optimal solution and speed-up the convergence to obtain the global optimal solutions. The bio-inspired methods have several attractive features and advantages compared to conventional optimization solvers. They also facilitate the advantage of simulation and optimization environment simultaneously to solve hard-to-define (in simple expressions), real-world problems. These biologically inspired methods have provided novel ways of problem-solving for practical problems in traffic routing, networking, games, industry, robotics, economics, mechanical, chemical, electrical, civil, water resources and others fields. This article discusses the key features and development of bio-inspired computational algorithms, and their scope for application in science and engineering fields.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

High field carrier transport in graphene: Insights from fast current transient

In this work, we observe gate tunable negative differential conductance (NDC) and current saturation in single layer and bilayer graphene transistor at high source-drain field, which arise due to the interplay among (1) self-heating, (2) hot carrier injection, and (3) drain induced minority carrier injection. The magnitude of the NDC is found to be reduced for a bilayer, in agreement with its weaker carrier-optical phonon coupling and less efficient hot carrier injection. The contributions of different mechanisms to the observed results are decoupled through fast transient measurements with nanosecond resolution. The findings provide insights into high field transport in graphene. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4754103]

Fast image reconstruction for fluorescence microscopy

Real-time image reconstruction is essential for improving the temporal resolution of fluorescence microscopy. A number of unavoidable processes such as, optical aberration, noise and scattering degrade image quality, thereby making image reconstruction an ill-posed problem. Maximum likelihood is an attractive technique for data reconstruction especially when the problem is ill-posed. Iterative nature of the maximum likelihood technique eludes real-time imaging. Here we propose and demonstrate a compute unified device architecture (CUDA) based fast computing engine for real-time 3D fluorescence imaging. A maximum performance boost of 210x is reported. Easy availability of powerful computing engines is a boon and may accelerate to realize real-time 3D fluorescence imaging. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.4754604]