Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important rese...Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.展开更多
Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic r...Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.展开更多
In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distort...In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.展开更多
Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)les...Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.展开更多
基金Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304)the National Natural Science Foundation of China (Grant No. 61876186)。
文摘Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.
基金Project supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.81701346 and 61603198)Qinglan Team of Universities in Jiangsu Province(Jiangsu Teacher Letter[2020]10 and Jiangsu Teacher Letter[2021]11).
文摘Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.
基金supported by the National Natural Science Foundation of China under Grant No.61671185 and 62071153.
文摘In the field of specific emitter identification(SEI),power amplifiers(PAs)have long been recognized as significant contributors to unintentional modulation characteristics.To enhance signal quality,digital pre-distortion(DPD)techniques are commonly employed in practical applications to mitigate the nonlinear effects of PAs.However,DPD techniques may diminish the distinctive characteristics of individual transmitters,potentially compromising SEI performance.This study investigates the influence of SEI in the presence of DPD applied to PAs.We construct a semi-physical emitter platform using AD9361 and ZYNQ,incorporating memory and non-memory models to emulate an amplification system comprising DPD devices and PAs.Furthermore,we delve into the analysis and evaluation of LMS-based and QRDRLS-based DPD algorithms to ascertain their efficacy in compensating for amplifier nonlinearity.Finally,we conduct a comprehensive set of experiments to demonstrate the adverse impact of DPD techniques on SEI.Our findings demonstrate a direct correlation between the degree of DPD performance and its impact magnitude on SEI,thereby providing a foundational basis for future studies investigating SEI techniques under DPD.
基金supported by the Commissioned Research through SRM University-AP,India,Research Grant–Central Facility under Grant No.SRMAP/URG/CF/2023-24/045.
文摘Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.
