A Real-time clustering system for spatio-temporal signals from network of neurons

Over past few years, the studies of cultured neuronal networks have opened up avenues for understanding the ion channels, receptor molecules, and synaptic plasticity that may form the basis of learning and memory. The hippocampal neurons from rats are dissociated and cultured on a surface containing a grid of 64 electrodes. The signals from these 64 electrodes are acquired using a fast data acquisition system MED64 (Alpha MED Sciences, Japan) at a sampling rate of 20 K samples with a precision of 16-bits per sample. A few minutes of acquired data runs in to a few hundreds of Mega Bytes. The data processing for the neural analysis is highly compute-intensive because the volume of data is huge. The major processing requirements are noise removal, pattern recovery, pattern matching, clustering and so on. In order to interface a neuronal colony to a physical world, these computations need to be performed in real-time. A single processor such as a desk top computer may not be adequate to meet this computational requirements. Parallel computing is a method used to satisfy the real-time computational requirements of a neuronal system that interacts with an external world while increasing the flexibility and scalability of the application. In this work, we developed a parallel neuronal system using a multi-node Digital Signal processing system. With 8 processors, the system is able to compute and map incoming signals segmented over a period of 200 ms in to an action in a trained cluster system in real time.

WCET Estimation for Executables in the Presence of Data Caches

This paper describes techniques to estimate the worst case execution time of executable code on architectures with data caches. The underlying mechanism is Abstract Interpretation, which is used for the dual purposes of tracking address computations and cache behavior. A simultaneous numeric and pointer analysis using an abstraction for discrete sets of values computes safe approximations of access addresses which are then used to predict cache behavior using Must Analysis. A heuristic is also proposed which generates likely worst case estimates. It can be used in soft real time systems and also for reasoning about the tightness of the safe estimate. The analysis methods can handle programs with non-affine access patterns, for which conventional Presburger Arithmetic formulations or Cache Miss Equations do not apply. The precision of the estimates is user-controlled and can be traded off against analysis time. Executables are analyzed directly, which, apart from enhancing precision, renders the method language independent.

Real-time Computer Simulation of Three Dimensional Elastostatics using the Finite Point Method

Real-time simulation of deformable solids is essential for some applications such as biological organ simulations for surgical simulators. In this work, deformable solids are approximated to be linear elastic, and an easy and straight forward numerical technique, the Finite Point Method (FPM), is used to model three dimensional linear elastostatics. Graphics Processing Unit (GPU) is used to accelerate computations. Results show that the Finite Point Method, together with GPU, can compute three dimensional linear elastostatic responses of solids at rates suitable for real-time graphics, for solids represented by reasonable number of points.

Spectral Finite Element Modeling of Complex Structures for Applications in Real-time Structural Health Monitoring

In this paper we discuss the recent progresses in spectral finite element modeling of complex structures and its application in real-time structural health monitoring system based on sensor-actuator network and near real-time computation of Damage Force Indicator (DFI) vector. A waveguide network formalism is developed by mapping the original variational problem into the variational problem involving product spaces of 1D waveguides. Numerical convergence is studied using a h()-refinement scheme, where is the wavelength of interest. Computational issues towards successful implementation of this method with SHM system are discussed.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Ship-Mounted Real-Time Surface Observational System on board Indian Vessels for Validation and Refinement of Model Forcing Fields

A network of ship-mounted real-time Automatic Weather Stations integrated with Indian geosynchronous satellites Indian National Satellites (INSATs)] 3A and 3C, named Indian National Centre for Ocean Information Services Real-Time Automatic Weather Stations (I-RAWS), is established. The purpose of I-RAWS is to measure the surface meteorological-ocean parameters and transmit the data in real time in order to validate and refine the forcing parameters (obtained from different meteorological agencies) of the Indian Ocean Forecasting System (INDOFOS). Preliminary validation and intercomparison of analyzed products obtained from the National Centre for Medium Range Weather Forecasting and the European Centre for Medium-Range Weather Forecasts using the data collected from I-RAWS were carried out. This I-RAWS was mounted on board oceanographic research vessel Sagar Nidhi during a cruise across three oceanic regimes, namely, the tropical Indian Ocean, the extratropical Indian Ocean, and the Southern Ocean. The results obtained from such a validation and intercomparison, and its implications with special reference to the usage of atmospheric model data for forcing ocean model, are discussed in detail. It is noticed that the performance of analysis products from both atmospheric models is similar and good; however, European Centre for Medium-Range Weather Forecasts air temperature over the extratropical Indian Ocean and wind speed in the Southern Ocean are marginally better.

Fragment-based real-time object tracking: a sparse representation approach

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l(1) framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l(1) regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach.

Real time monitoring of petroleum leakage detection using etched fiber Bragg grating

Detection of petroleum leakages in pipelines and storage tanks is a very important as it may lead to significant pollution of the environment, accidental hazards, and also it is a very important fuel resource. Petroleum leakage detection sensor based on fiber optics was fabricated by etching the fiber Bragg grating (FBG) to a region where the total internal reflection is affected. The experiment shows that the reflected Bragg's wavelength and intensity goes to zero when etched FBG is in air and recovers Bragg's wavelength and intensity when it is comes in contact with petroleum or any external fluid. This acts as high sensitive, fast response fluid optical switch in liquid level sensing, petroleum leakage detection etc. In this paper we present our results on using this technique in petroleum leakage detection.

Comparative Evaluation of Switching and Average Models of a DC-DC Boost Converter for Real-Time Simulation

This paper presents a comparative evaluation of the average and switching models of a dc-dc boost converter from the point of view of real-time simulation. Both the models are used to simulate the converter in real-time on a Field Programmable Gate Array (FPGA) platform. The converter is considered to function over a wide range of operating conditions, and could do transition between continuous conduction mode (CCM) and discontinuous conduction mode (DCM). While the average model is known to be computationally efficient from the perspective of off-line simulation, the same is shown here to consume more logical resources than the switching model for real-time simulation of the dc-dc converter. Further, evaluation of the boundary condition between CCM and DCM is found to be the main reason for the increased consumption of resources by the average model.

Co-registration of speech production datasets from electromagnetic articulography and real-time magnetic resonance imaging

This paper describes a spatio-temporal registration approach for speech articulation data obtained from electromagnetic articulography (EMA) and real-time Magnetic Resonance Imaging (rtMRI). This is motivated by the potential for combining the complementary advantages of both types of data. The registration method is validated on EMA and rtMRI datasets obtained at different times, but using the same stimuli. The aligned corpus offers the advantages of high temporal resolution (from EMA) and a complete mid-sagittal view (from rtMRI). The co-registration also yields optimum placement of EMA sensors as articulatory landmarks on the magnetic resonance images, thus providing richer spatio-temporal information about articulatory dynamics. (C) 2014 Acoustical Society of America

Toward Real-Time Availability of 3D Temperature Maps Created with Temporally Constrained Reconstruction

PurposeTo extend the previously developed temporally constrained reconstruction (TCR) algorithm to allow for real-time availability of three-dimensional (3D) temperature maps capable of monitoring MR-guided high intensity focused ultrasound applications. MethodsA real-time TCR (RT-TCR) algorithm is developed that only uses current and previously acquired undersampled k-space data from a 3D segmented EPI pulse sequence, with the image reconstruction done in a graphics processing unit implementation to overcome computation burden. Simulated and experimental data sets of HIFU heating are used to evaluate the performance of the RT-TCR algorithm. ResultsThe simulation studies demonstrate that the RT-TCR algorithm has subsecond reconstruction time and can accurately measure HIFU-induced temperature rises of 20 degrees C in 15 s for 3D volumes of 16 slices (RMSE = 0.1 degrees C), 24 slices (RMSE = 0.2 degrees C), and 32 slices (RMSE = 0.3 degrees C). Experimental results in ex vivo porcine muscle demonstrate that the RT-TCR approach can reconstruct temperature maps with 192 x 162 x 66 mm 3D volume coverage, 1.5 x 1.5 x 3.0 mm resolution, and 1.2-s scan time with an accuracy of 0.5 degrees C. ConclusionThe RT-TCR algorithm offers an approach to obtaining large coverage 3D temperature maps in real-time for monitoring MR-guided high intensity focused ultrasound treatments. Magn Reson Med 71:1394-1404, 2014. (c) 2013 Wiley Periodicals, Inc.

Real-time magnetic resonance imaging and electromagnetic articulography database for speech production research (TC)

USC-TIMIT is an extensive database of multimodal speech production data, developed to complement existing resources available to the speech research community and with the intention of being continuously refined and augmented. The database currently includes real-time magnetic resonance imaging data from five male and five female speakers of American English. Electromagnetic articulography data have also been presently collected from four of these speakers. The two modalities were recorded in two independent sessions while the subjects produced the same 460 sentence corpus used previously in the MOCHA-TIMIT database. In both cases the audio signal was recorded and synchronized with the articulatory data. The database and companion software are freely available to the research community. (C) 2014 Acoustical Society of America.

Advances in Image Reconstruction Methods for Real-Time Magnetic Resonance Thermometry

Magnetic Resonance Imaging (MRI) has been widely used in cancer treatment planning, which takes the advantage of high-resolution and high-contrast provided by it. The raw data collected in the MRI can also be used to obtain the temperature maps and has been explored for performing MR thermometry. This review article describes the methods that are used in performing MR thermometry, with an emphasis on reconstruction methods that are useful to obtain these temperature maps in real-time for large region of interest. This article also proposes a prior-image constrained reconstruction method for temperature reconstruction in MR thermometry, and a systematic comparison using ex-vivo tissue experiments with state of the art reconstruction method is presented.

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

We have developed a real-time imaging method for two-color wide-field fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique. To enable simultaneous observation of two dyes (primary and secondary), we exploit their spectral properties that allow parallel recording in both the channels. The key advantage of this technique is the use of a single wavelength of light to excite both the primary dye and the secondary dye. The primary and secondary dyes respectively give rise to fluorescence and bleed-through signal, which after normalization were merged to obtain two-color 3D images. To realize real-time imaging, we employed maximum likelihood (ML) and maximum a posteriori (MAP) techniques on a high-performance computing platform (GPU). The results show two-fold improvement in contrast while the signal-to-background ratio (SBR) is improved by a factor of 4. We report a speed boost of 52 and 350 for 2D and 3D images respectively. Using this system, we have studied the real-time protein aggregation in yeast cells and HeLa cells that exhibits dot-like protein distribution. The proposed technique has the ability to temporally resolve rapidly occurring biological events.