As one of the fields of Artificial Intelligence (AI), Optical Character Recognition (OCR) systems have wide application in both industrial production and daily life. Conventional OCR systems are commonly designed and implement data computation on the basis of microprocessors; the performance of the processor relates to the effect of the computation. However, due to the “Memory-wall” problem and Von Neumann bottlenecks, the drawbacks of traditional processor-based computing for OCR systems are gradually becoming apparent. In this paper, an approach based on the Memory-Centric Computing and “Memory-Tree” algorithm has been proposed to perform hardware optimization of traditional OCR systems. The proposed algorithm was first designed in software implementation using C/C++ and OpenCV to verify the feasibility of the idea and then the RTL conversion of the algorithm was done using the Xilinx Vitis High Level Synthesis (HLS) tool to implement the hardware. This work chose Xilinx Alveo U50 FPGA Accelerator to complete the hardware design, which can be connected to the x86 CPU in the PC by PCIe to form heterogeneous computing. The results of the hardware implementation show that the system this work designed can recognize characters of English capital letters and numbers within 34.24 us. The power of FPGA is 18.59 W, which saves 77.87% of energy consumption compared to the 84 W of the processor in PC.
Heart failure is a common, complex clinical syndrome with high morbidity and mortality. Hemodynamic parameter evaluation is useful for early detection, clinical outcome monitoring, timely treatment, and the overall prognosis of heart failure patients. Therefore, continuous monitoring of hemodynamic parameters helps in the evaluation of patients with suspected heart failure. The hemodynamic parameters change with respect to the contraction and expansion of the heart. Hence, in this research, two circuit-less 30 mm spherical receiver coils were implanted in both the left and right sides of the heart and an external transceiver coil was placed above the chest. The changes in the reflection coefficient of the transceiver coil at the resonant frequency changed depending on the distance between the implanted coils, allowing the contraction and expansion of the heart to be determined. This work was carried out at 13.56 MHz, considering the safety limits imposed by the FCC. The proposed reflection coefficient monitoring technique may distinguish healthy patients from heart failure and heart attack patients. The reflection coefficients at a maximum distance of 50 mm for simulation and measurement are −10.3 dB and −10.6 dB, respectively, at the resonant frequency.
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