Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enorm...Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.展开更多
Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5...Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5 G) technologies. NOMA utilizes power domain in order to superimpose signals of multiple users in a single transmitted signal. This creates a lot of interference at the receive side. Although the use of successive interference cancellation(SIC) technique reduces the interference, but to further improve the receiver performance, in this paper, we have proposed a joint Walsh-Hadamard transform(WHT) and NOMA approach for achieving better performance gains than the conventional NOMA. WHT is a well-known code used in communication systems and is used as an orthogonal variable spreading factor(OVSF) in communication systems. Application of WHT to NOMA results in low bit error rate(BER) and high throughput performance for both low and high channel gain users. Further, it also reduces peak to average power ratio(PAPR) of the user signal. The results are discussed in terms of comparison between the conventionalNOMA and the proposed technique, which shows that it offers high performance gains in terms of low BER at different SNR levels, reduced PAPR, high user throughput performance and better spectral efficiency.展开更多
Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish betwee...Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.展开更多
Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet ...Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet transform is efficient as an edge detector, a l ot of false edges will be extracted from a SAR image, especially in areas of hig h reflectivity, with wavelet transform and a fixed threshold because SAR images are always contaminated by speckle. Therefore, this paper develops an algorithm by using a variable threshold with constant false alarm rates to help suppressin g the speckle in SAR images. Finally, two simulating tests are given and the res ults show that the effect of the multiplicative speckle is controlled and there are few false edges in the gotten edge images. Moreover, owing to the threshold from the wavelet transform coefficients, the algorithm can assure the real time of SAR image aided navigation.展开更多
基金supported by the National Natural Science Foundation of China(61072120)
文摘Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.
基金supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003)
文摘Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5 G) technologies. NOMA utilizes power domain in order to superimpose signals of multiple users in a single transmitted signal. This creates a lot of interference at the receive side. Although the use of successive interference cancellation(SIC) technique reduces the interference, but to further improve the receiver performance, in this paper, we have proposed a joint Walsh-Hadamard transform(WHT) and NOMA approach for achieving better performance gains than the conventional NOMA. WHT is a well-known code used in communication systems and is used as an orthogonal variable spreading factor(OVSF) in communication systems. Application of WHT to NOMA results in low bit error rate(BER) and high throughput performance for both low and high channel gain users. Further, it also reduces peak to average power ratio(PAPR) of the user signal. The results are discussed in terms of comparison between the conventionalNOMA and the proposed technique, which shows that it offers high performance gains in terms of low BER at different SNR levels, reduced PAPR, high user throughput performance and better spectral efficiency.
基金The authors would like to thank the support of the Taif University Researchers Supporting Project TURSP 2020/34,Taif University,Taif Saudi Arabia for supporting this work.
文摘Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.
文摘Edge detecting is a f undamental issue in image analysis and image processing. In this paper, based on wavelet transform, an edge detector of SAR image is studied to serve the image aided navigation. Though wavelet transform is efficient as an edge detector, a l ot of false edges will be extracted from a SAR image, especially in areas of hig h reflectivity, with wavelet transform and a fixed threshold because SAR images are always contaminated by speckle. Therefore, this paper develops an algorithm by using a variable threshold with constant false alarm rates to help suppressin g the speckle in SAR images. Finally, two simulating tests are given and the res ults show that the effect of the multiplicative speckle is controlled and there are few false edges in the gotten edge images. Moreover, owing to the threshold from the wavelet transform coefficients, the algorithm can assure the real time of SAR image aided navigation.
文摘卷积码的盲识别是级联码、Turbo码等高性能编码盲识别的基础,这要求卷积码盲识别方法具有较高的抗噪能力.使用接收解调的软判决信息是提高抗噪能力的关键.本文首先通过理论分析,从概率分布的角度解释现有软判决方法抗噪能力不足的原因,即汉明重量较小的候选解向量会严重削弱现有方法的识别正确概率.然后,提出一种基于最小二乘代价函数的解决方案,理论证明它能够有效减轻汉明重量对识别性能的影响.最后,通过仿真实验,对理论分析的结论进行验证.理论和实验表明,所提的新方法能将卷积码盲识别的抗噪能力提升约1d B.
