title: System-on-chip design for ultrasonic target detection using split-spectrum processing  and neural networks.
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.
authors: Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA.
authors: Jafar
authors: J
authors: Saniie
authors: Erdal
authors: E
authors: Oruklu
authors: Sungjoon
authors: S
authors: Yoon
mesh_terms: D000465:Algorithms; D017076:Computer-Aided Design; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007089:Image Enhancement / Q000295:instrumentation*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D016247:Information Storage and Retrieval / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D014463:Ultrasonography / Q000295:instrumentation*