The aim of this study is to compare forces generated by three different orthodontic closed coil springs supplied by three companies, optical fiber Bragg gratins are used to evaluate the force of closing of springs. ...
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We characterize the impact of the Cell Broadband Engine architecture, on commonly used radar DSP algorithms. We use the capabilities of the CBE to accelerate several key computational kernels including Matrix Multipli...
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This paper presents a smart antenna system using a combination of nonconventional least square (NCLS) optimization and direct data domain least square (D3LS) for direction-of-arrival (DOA) estimation and beamforming, ...
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Reservoir Computing (RC) is a computational model in which a trained readout layer interprets the dynamics of a component called a reservoir that is excited by external input stimuli. The reservoir is often constructe...
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In this paper we describe an extension to the MATLAB Phased Array Toolkit that adds a configurable clutter object to model clutter signals returned along a specified signal path. The clutter model is based on the Simk...
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In this paper we describe an extension to the MATLAB Phased Array Toolkit that adds a configurable clutter object to model clutter signals returned along a specified signal path. The clutter model is based on the Simkins Unified Clutter Model[1]. The current implementation supports sea clutter in any of five sea states with configurable polarization, grazing angle, and beam width. We describe the implementation and give examples of modeled clutter returns.
A smart antenna was proposed to improve wireless communication systems. The smart antenna may comprises direction-of-arrival (DOA) estimation and/or adaptive antenna. The DOA estimation technique is used to estimate d...
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A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling f...
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A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nonstationary environments. eGNN constructs interpretable multi-sized local models using fuzzy neural information fusion. An incremental learning algorithm builds the neural network topology from the information contained in data streams. Here we emphasize fuzzy intervals and objects with trapezoidal membership functions. Triangular fuzzy numbers, intervals, and numeric data are particular instances of trapezoids. An example concerning weather time series forecasting illustrates the neural network performance. The goal is to extract, from monthly temperature data, information of interest to attain accurate one-step forecasts and better rapport with reality. Simulation results suggest that eGNN learns from fuzzy data successfully and is competitive with state-of-the-art approaches.
In this paper we describe a novel clutter cancellation platform based on a two stage approach that combines a feedback guided predictive front-end hybrid clutter canceller with high performance back-end filtering and ...
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In this paper we describe a novel clutter cancellation platform based on a two stage approach that combines a feedback guided predictive front-end hybrid clutter canceller with high performance back-end filtering and target detection. The front-end architecture is based on an FPGA implementation of a Kalman filter that predicts target locations in real time and removes the target signals from the incoming data prior to hybrid cancellation. The back-end is user configurable and exploits high performance GPU and multi-core parallel hardware to simultaneously compute multiple clutter suppression and target detection algorithms coupled to an intelligent selection strategy for selecting the most accurate result. These target locations are fed back to the FPGA Kalman filter periodically to update the target predictions.
Flow quantification with high-frequency power Doppler ultrasound can be performed using the wall-filter selection curve (WFSC) method [M. Elfarnawany et al., Ultrasound Med. Biol. 38, 1429-1439 (2012)]. The WFSC metho...
Flow quantification with high-frequency power Doppler ultrasound can be performed using the wall-filter selection curve (WFSC) method [M. Elfarnawany et al., Ultrasound Med. Biol. 38, 1429-1439 (2012)]. The WFSC method plots color pixel density (CPD) as a function of wall filter cut-off velocity as a means of objectively selecting an operating point cut-off velocity. In this study, an in vivo video microscopy (IVVM) system was used to measure the size of small (140-400 μm diameter) mouse testicular vessels immediately after the vessels were imaged with 30 MHz power Doppler. The mouse remained on the same platform throughout ultrasound and IVVM imaging. Measurements in four image planes from three mice demonstrated that, similar to previously reported flow-phantom data, in vivo WFSCs exhibit distinct, sloped "characteristic intervals" at cut-off velocities where the CPD approaches the gold-standard IVVM estimate of vascular volume fraction. A wide range of operating point cut-off velocities (4.5 to 12 mm/s) was obtained, which indicates that use of a predetermined cut-off can produce substantial errors in cross-sectional studies that employ power Doppler to quantify vascularity. The WFSC method is a promising strategy for adapting the cut-off velocity to intersubject and longitudinal variations in blood flow during microvascular imaging experiments.
We characterize the impact of the Cell Broadband Engine architecture, on commonly used radar DSP algorithms. We use the capabilities of the CBE to accelerate several key computational kernels including Matrix Multipli...
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We characterize the impact of the Cell Broadband Engine architecture, on commonly used radar DSP algorithms. We use the capabilities of the CBE to accelerate several key computational kernels including Matrix Multiplication, Matrix Inversion, and the Finite Impulse Response (FIR) filter. These algorithms are implemented and benchmarked as library routines within the X-Midas Toolkit. We observe speedups as large as 1200x for complex matrix multiplication, but speedups of 40x to 60x are more typical. We find that system I/O overhead within the X-Midas toolkit severely limited the performance of the applications.
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