Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-div...Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-division multiplexing-based visible light communication(OFDM-VLC)system is presented.In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC,a quantized mathematical model of NHS OFDM-VLC is established.Based on the proposed quantized model,a closed-form bit error rate(BER)expression is derived.The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC.In addition,channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.展开更多
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati...The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.展开更多
Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnos...Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.展开更多
This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 bac...This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.展开更多
In this paper, a new speech recognition method was proposed, which integrated a VQ distortion measure and a discrete HMM. The VQ HMM uses a VQ distortion measure at each state instead of a discrete output probabili...In this paper, a new speech recognition method was proposed, which integrated a VQ distortion measure and a discrete HMM. The VQ HMM uses a VQ distortion measure at each state instead of a discrete output probability used by a discrete HMM. The VQ HMM is described, and its speech recognition performance is compared with the conventional HMMs through the experiments on speaker independent Chinese spoken digit recognition. The comparisons confirm that the new method over performed traditional HMMs.展开更多
基金supported by the National Natural Science Foundation of China(No.62201508)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ21F010001 and LQ23F010004)the State Key Laboratory of Millimeter Waves of Southeast University,China(No.K202212).
文摘Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-division multiplexing-based visible light communication(OFDM-VLC)system is presented.In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC,a quantized mathematical model of NHS OFDM-VLC is established.Based on the proposed quantized model,a closed-form bit error rate(BER)expression is derived.The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC.In addition,channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.
文摘The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.
基金funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant numbers AP14969403 and AP23485820.
文摘Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 62376215)the Key Research and Development Project of Shaanxi Province(No.2025CY-YBXM-044)+3 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)the Ningbo Top Medical and Health Research Program(No.2023030716).
文摘This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.
文摘In this paper, a new speech recognition method was proposed, which integrated a VQ distortion measure and a discrete HMM. The VQ HMM uses a VQ distortion measure at each state instead of a discrete output probability used by a discrete HMM. The VQ HMM is described, and its speech recognition performance is compared with the conventional HMMs through the experiments on speaker independent Chinese spoken digit recognition. The comparisons confirm that the new method over performed traditional HMMs.
