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• W2063860487 abstract "Ultrasonic detection and characterization of targets concealed by scattering noise is remarkably challenging. In this study, a neural network (NN) coupled to split-spectrum processing (SSP) is examined for target echo visibility enhancement using experimental measurements with input signal-to-noise ratio around 0 dB. The SSP-NN target detection system is trainable and consequently is capable of improving the target-to-clutter ratio by an average of 40 dB. The proposed system is exceptionally robust and outperforms the conventional techniques such as minimum, median, average, geometric mean, and polarity threshold detectors. For realtime imaging applications, a field-programmable gate array (FPGA)-based hardware platform is designed for system-onchip (SoC) realization of the SSP-NN target detection system. This platform is a hardware/software co-design system using parallel and pipelined multiplications and additions for highspeed operation and high computational throughput." @default.
• W2063860487 created "2016-06-24" @default.
• W2063860487 creator A5002920793 @default.
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• W2063860487 date "2012-07-01" @default.
• W2063860487 modified "2023-10-03" @default.
• W2063860487 title "System-on-chip design for ultrasonic target detection using split-spectrum processing and neural networks" @default.
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• W2063860487 doi "https://doi.org/10.1109/tuffc.2012.2336" @default.
• W2063860487 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22828831" @default.
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