ABSTRACT
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless communication systems. However, because of the problem of a high peak-to-average power ratio (PAPR), OFDM symbols can be distorted at the MIMO OFDM transmitter. It degrades the signal detection and channel estimation performance at the MIMO OFDM receiver. In this paper, three deep neural network (DNN) models are proposed to solve the problem of non-linear distortions introduced by the power amplifier (PA) of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 × 2 MIMO OFDM and 4 × 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map the signals at the receiver. Proposed model type II uses the DNN model to learn and filter out the channel noises at the receiver. Proposed model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust bit error rate (BER) performances of the proposed receivers are achieved through the software and hardware implementation results. In addition, we have also implemented appropriate hardware architectures for the proposed DNN models using special techniques, such as quantization and pipeline to check the feasibility in practice, which recent studies have not done. Our hardware architectures are successfully designed and implemented on the Virtex 7 vc709 FPGA board.
ABSTRACT
AIM: To determine the proportion of contrast-associated acute kidney injury (CA-AKI) after percutaneous coronary intervention (PCI) and the predictive value of urine neutrophil gelatinase-associated lipocalin (uNGAL) for CA-AKI in elderly patients with chronic coronary artery disease. METHODS: A total of 509 patients who had planned percutaneous coronary intervention (mean age was 63.58 ± 11.63 years and 63.3% of males) were divided into two groups: group 1 (n = 153; elderly patients) with ≥70 years old and group 2 (n = 356) with <70 years old. Urine NGAL was measured by the ELISA method. Clinical and laboratory data were collected on the day before intervention. CA-AKI was defined based on Kidney Disease: Improving Global Outcomes criteria. RESULTS: The ratio of CA-AKI in group 1 was 23.5% which was higher than that of group 2 (8.7%) with a p-value < 0.001. Urine NGAL level in group 1 was significantly higher than that of group 2 [31.3 (19.16-55.13) ng/ml vs. 19.86 (13.21-29.04) ng/ml, p < 0.001]. At a cut-off value of 44.43 ng/ml, uNGAL had a predictive value for CA-AKI in all patients (AUC = 0.977, p < 0.001). Especially at a cut-off value of 44.14 ng/ml, uNGAL had a predictive value for CA-AKI in elderly patients (AUC = 0.979, p < 0.001). CONCLUSIONS: The rate of CA-AKI after PCI in elderly patients was 23.5%. Urine NGAL before PCI had a good predictive value for CA-AKI in elderly patients with chronic coronary artery disease.
