We have built an over-the-sea channel model and demonstrated D-band transmission of quadrature phase shift keying(QPSK)signals at 9 Gbaud over a 30.2 km ultra-long-distance wireless link,including a partly over-the-se...We have built an over-the-sea channel model and demonstrated D-band transmission of quadrature phase shift keying(QPSK)signals at 9 Gbaud over a 30.2 km ultra-long-distance wireless link,including a partly over-the-sea transmission channel at 128 GHz utilizing the photonics-aided technology.To address nonlinear issues,we propose a quadratic convolutional neural network(QuadConvNet)in the wireless receiver to mitigate the nonlinear degradation.This approach demonstrates enhanced nonlinearity and superior learning capabilities for feature extraction,as it optimally utilizes the intrinsic high-order advantages of quadratic neurons for cognition and computation performance.It achieves a bit error rate(BER)for 7 Gbaud QPSK signals below the 7%hard-decision forward error correction(HD-FEC)threshold of 3.8×10^(-3)and the 25%soft-decision forward error correction(SD-FEC)threshold of 4.2×10^(-2)at 9 Gbaud.展开更多
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w...The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFB2905600)the National Natural Science Foundation of China(Nos.62127802,62331004,62305067,U24B20142,U24B20168+1 种基金62427815)the Jiangsu Province Key Project(No.BE2023001-4)。
文摘We have built an over-the-sea channel model and demonstrated D-band transmission of quadrature phase shift keying(QPSK)signals at 9 Gbaud over a 30.2 km ultra-long-distance wireless link,including a partly over-the-sea transmission channel at 128 GHz utilizing the photonics-aided technology.To address nonlinear issues,we propose a quadratic convolutional neural network(QuadConvNet)in the wireless receiver to mitigate the nonlinear degradation.This approach demonstrates enhanced nonlinearity and superior learning capabilities for feature extraction,as it optimally utilizes the intrinsic high-order advantages of quadratic neurons for cognition and computation performance.It achieves a bit error rate(BER)for 7 Gbaud QPSK signals below the 7%hard-decision forward error correction(HD-FEC)threshold of 3.8×10^(-3)and the 25%soft-decision forward error correction(SD-FEC)threshold of 4.2×10^(-2)at 9 Gbaud.
文摘The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.
