Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Common-mode voltage eliminated three-level inverter using a three-level flying-capacitor inverter and cascaded H-bridge

This paper proposes a new 3 level common mode voltage eliminated inverter using an inverter structure formed by cascading a H-Bridge with a three-level flying capacitor inverter. The three phase space vector polygon formed by this configuration and the polygon formed by the common-mode eliminated states have been discussed. The entire system is simulated in Simulink and the results are experimentally verified. This system has an advantage that if one of devices in the H-Bridge fails, the system can still be operated as a normal 3 level inverter mode at full power. This inverter has many advantages like use of single DC-supply, making it possible for a back to back grid-tied converter application, improved reliability etc.

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

Joint estimation of synchronization impairments in MIMO-OFDM system

A joint Maximum Likelihood (ML) estimation algorithm for the synchronization impairments such as Carrier Frequency Offset (CFO), Sampling Frequency Offset (SFO) and Symbol Timing Error (STE) in single user MIMO-OFDM system is investigated in this work. A received signal model that takes into account the nonlinear effects of CFO, SFO, STE and Channel Impulse Response (CIR) is formulated. Based on the signal model, a joint ML estimation algorithm is proposed. Cramer-Rao Lower Bound (CRLB) for the continuous parameters CFO and SFO is derived for the cases of with and without channel response effects and is used to compare the effect of coupling between different estimated parameters. The performance of the estimation method is studied through numerical simulations.

Data acquisition and processing at ocean bottom for a Tsunami warning system

The design and development of a Bottom Pressure Recorder for a Tsunami Early Warning System is described here. The special requirements that it should satisfy for the specific application of deployment at ocean bed and pressure monitoring of the water column above are dealt with. A high-resolution data digitization and low circuit power consumption are typical ones. The implementation details of the data sensing and acquisition part to meet these are also brought out. The data processing part typically encompasses a Tsunami detection algorithm that should detect an event of significance in the background of a variety of periodic and aperiodic noise signals. Such an algorithm and its simulation are presented. Further, the results of sea trials carried out on the system off the Chennai coast are presented. The high quality and fidelity of the data prove that the system design is robust despite its low cost and with suitable augmentations, is ready for a full-fledged deployment at ocean bed. (C) 2013 Elsevier Ltd. All rights reserved.

A Three-Level Common-Mode Voltage Eliminated Inverter With Single DC Supply Using Flying Capacitor Inverter and Cascaded H-Bridge

A three-level common-mode voltage eliminated inverter with single dc supply using flying capacitor inverter and cascaded H-bridge has been proposed in this paper. The three phase space vector polygon formed by this configuration and the polygon formed by the common-mode eliminated states have been discussed. The entire system is simulated in Simulink and the results are experimentally verified. This system has an advantage that if one of devices in the H-bridge fails, the system can still be operated as a normal three-level inverter at full power. This inverter has many other advantages like use of single dc supply, making it possible for a back-to-back grid-tied converter application, improved reliability, etc.

BILATERAL EDGE DETECTORS

We propose to employ bilateral filters to solve the problem of edge detection. The proposed methodology presents an efficient and noise robust method for detecting edges. Classical bilateral filters smooth images without distorting edges. In this paper, we modify the bilateral filter to perform edge detection, which is the opposite of bilateral smoothing. The Gaussian domain kernel of the bilateral filter is replaced with an edge detection mask, and Gaussian range kernel is replaced with an inverted Gaussian kernel. The modified range kernel serves to emphasize dissimilar regions. The resulting approach effectively adapts the detection mask according as the pixel intensity differences. The results of the proposed algorithm are compared with those of standard edge detection masks. Comparisons of the bilateral edge detector with Canny edge detection algorithm, both after non-maximal suppression, are also provided. The results of our technique are observed to be better and noise-robust than those offered by methods employing masks alone, and are also comparable to the results from Canny edge detector, outperforming it in certain cases.

Low complexity joint estimation of synchronization impairments in sparse channel for MIMO-OFDM system

Low complexity joint estimation of synchronization impairments and channel in a single-user MIMO-OFDM system is presented in this paper. Based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset, sampling frequency offset, and symbol timing error, and channel, a Maximum Likelihood (ML) algorithm for the joint estimation is proposed. To reduce the complexity of ML grid search, the number of received signal samples used for estimation need to be reduced. The conventional channel estimation techniques using Least-Squares (LS) or Maximum a posteriori (MAP) methods fail for the reduced sample under-determined system, which results in poor performance of the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS) based channel estimation method in a sparse fading scenario, where the received samples used for estimation are less than that required for an LS or MAP based estimation. The performance of the estimation method is studied through numerical simulations, and it is observed that CS based joint estimator performs better than LS and MAP based joint estimator. (C) 2013 Elsevier GmbH. All rights reserved.

A Seventeen-level Inverter With a Single DC-link For Motor Drives

In the present paper, a novel topology for generating a 17-level inverter using three-level flying capacitor inverter and cascaded H-bridge modules with floating capacitors. The proposed circuit is analyzed and various aspects of it are presented in the paper. This circuit is experimentally verified and the results are shown. The stability of the capacitor balancing algorithm has been verified during sudden acceleration. This circuit has many pole voltage redundancies. This circuit has an advantage of balancing all the capacitor voltages instantaneously by switching through the redundancies. Another advantage of this topology is its ability to generate all the 17 pole voltages from a single DC link which enables back to back converter operation. Also, the proposed inverter can be operated at all load power factors and modulation indices. Another advantage is, if one of the H-bridges fail, the inverter can still be operated at full load with reduced number of levels.

Nanodiamond-Based Thermal Fluids

Dispersions of nanodiamond (average size similar to 6 nm) within dielectric insulator mineral oil are reported for their enhanced thermal conductivity properties and potential applications in thermal management. Dynamic and kinematic viscosities-very important parameters in thermal management by nanofluids-are investigated. The dependence of the dynamic viscosity is well-described by the theoretical predictions of Einstein's model. The temperature dependence of the dynamic viscosity obeys an Arrhenius-like behavior, where the activation energy and the pre-exponential factor have an exponential dependence on the filler fraction of nanodiamonds. An enhancement in thermal conductivity up to 70% is reported for nanodiamond based thermal fluids. Additional electron microscopy, Raman spectroscopy and X-ray diffraction analysis support the experimental data and their interpretation.

Green colored nano-pigments derived from Y2BaCuO5: NIR reflective coatings

A green colored nano-pigment Y2BaCuO5 with impressive near infra-red (NIR) reflectance (61% at 1100 nm) was synthesized by a nano-emulsion method. The developed nano-crystalline powders were characterized by X-ray diffraction (XRD), Transmission electron microscopy (TEM), UV-vis-NIR diffuse reflectance spectroscopy and CIE-L*a*b* 1976 color scales. The XRD and Rietveld analyses of the designed pigment powders reveal the orthorhombic crystal structure for Y2BaCuO5, where yttrium is coordinated by seven oxygen atoms with the local symmetry of a distorted trigonal prism, barium is coordinated by eleven oxygen atoms, and the coordination polyhedron of copper is a distorted square pyramid CuO5]. The UV-vis spectrum of the nano-pigment exhibits an intense d-d transition associated with CuO5 chromophore between 2.1 and 2.5 eV in the visible domain. Therefore, a green color has been displayed by the developed nano-pigment. The potential utility of the nano-pigments as ``Cool Pigments'' was demonstrated by coating on to a building roofing material like cement slab and PVC coatings. (C) 2014 Elsevier Ltd. All rights reserved.

Role of surface chemistry in modulating drug release kinetics in titania nanotubes

Surface chemistry and the intrinsic porous architectures of porous substrates play a major role in the design of drug delivery systems. An interesting example is the drug elution characteristic from hydrothermally synthesised titania nanotubes with tunable surface chemistry. The variation in release rates of Ibuprofen (IBU) is largely influenced by the nature of the functional groups on titania nanotubes and pH of suspending medium. To elucidate the extent of interaction between the encapsulated IBU and the functional groups on titania nanotubes, the release profiles have been modelled with an empirical Hill equation. The analysis aided in establishing a probable mechanism for the release of IBU from the titania nanotubes. The study of controlled drug release from TiO2 has wider implication in the context of biomedical engineering. (C) 2014 Elsevier B.V. All rights reserved.

Field Emission with Ultra low Turn On Voltage from Metal Decorated Carbon Nanotubes

A simple and scalable method of decorating 3D-carbon nanotube (CNT) forest with metal particles has been developed. The results observed in aluminum (AI) decorated CNTs and copper (Cu) decorated CNTs on silicon (Si) and Inconel are compared with undecorated samples. A significant improvement in the field emission characteristics of the cold cathode was observed with ultralow turn on voltage (E-to similar to 0.1 V/mu m) due to decoration of CNTs with metal nanoparticles. Contact resistance between the CNTs and the substrate has also been reduced to a large extent, allowing us to get stable emission for longer duration without any current degradation, thereby providing a possibility of their use in vacuum microelectronic devices.