Cold spells are extreme weather events characterized by the invasion of cold air from high latitudes into the middle and low latitudes,resulting in significant cooling.Cold spells have various adverse health effects,i...Cold spells are extreme weather events characterized by the invasion of cold air from high latitudes into the middle and low latitudes,resulting in significant cooling.Cold spells have various adverse health effects,including epidermal damage,respiratory tract spasms,respiratory immune abnormalities,acute cardiopulmonary diseases,and exacerbation of urinary and endocrine disorders.In response to the frequent cold spells in recent years.展开更多
Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since ...Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF data.In such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying drones.In reality,signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight version.Without losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about 10-fold.For performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.展开更多
Dear Editor, Renal cell carcinoma (RCC) is among the most common human cancers in the United States, with approximately 63,990 new patients and 14,400 deaths annually [1]. However, RCC is not among the top 10 malignan...Dear Editor, Renal cell carcinoma (RCC) is among the most common human cancers in the United States, with approximately 63,990 new patients and 14,400 deaths annually [1]. However, RCC is not among the top 10 malignancies in China in terms of incidence and mortality [2]. The clini-cal and molecular features of RCC differ among distinct pathological types, mainly clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC), and chromophobe renal cell carcinoma (ChRCC). The most common subtype of RCC is ccRCC worldwide. Accord-ing to The Cancer Genome Atlas (TCGA), the somatic mutation landscape of RCC has been revealed by whole- exome sequencing (WES) or whole-genome sequencing (WGS). In our previous WES study, we validated most of the significantly mutated genes reported by the TCGA and identified several novel somatically altered genes [3]. The TCGA study showed that only somatic mutations in BRCA1-associated protein 1 (BAP1) were associated with patients’ poor survival outcomes among all significantly mutated genes [4]. In our previous WES study, BAP1 was somatically mutated in 2 of 15 ccRCC samples [3]. Never-theless, all of these RCC patients lacked follow-up infor-mation. Hence, further analysis is needed to determine whether there are any somatically mutated genes associ-ated with the prognosis of Chinese patients with RCC. However, WES or WGS is time-consuming and costly. Furthermore, compared with targeted sequencing, WES was more likely to generate false positives and false nega-tives due to insufficient base coverage [5].展开更多
文摘Cold spells are extreme weather events characterized by the invasion of cold air from high latitudes into the middle and low latitudes,resulting in significant cooling.Cold spells have various adverse health effects,including epidermal damage,respiratory tract spasms,respiratory immune abnormalities,acute cardiopulmonary diseases,and exacerbation of urinary and endocrine disorders.In response to the frequent cold spells in recent years.
基金supported in part by the Guangzhou Basic and Applied Basic Research Foundation(2023A04J1740)in part by the Shaanxi Provincial Key Research and Development Program(2023-ZDLGY-33,2022ZDLGY05-03,2022ZDLGY05-04)in part by the Fundamental Research Funds for the Central Universities(XJS220116).
文摘Radio frequency(RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence,which has become indispensable for drone surveillance systems.However,since drones operate in unlicensed frequency bands,a large number of co-frequency devices exist in these bands,which brings a great challenge to traditional signal identification methods.Deep learning techniques provide a new approach to complete endto-end signal identification by directly learning the distribution of RF data.In such scenarios,due to the complexity and high dynamics of the electromagnetic environments,a massive amount of data that can reflect the various propagation conditions of drone signals is necessary for a robust neural network(NN)for identifying drones.In reality,signal acquisition and labeling that meet the above requirements are too costly to implement.Therefore,we propose a virtual electromagnetic environment modeling based data augmentation(DA)method to improve the diversity of drone signal data.The DA method focuses on simulating the spectrograms of drone signals transmitted in real-world environments and randomly generates extra training data in each training epoch.Furthermore,considering the limited processing capability of RF receivers,we modify the original YOLOv5s model to a more lightweight version.Without losing the identification performance,more hardware-friendly designs are applied and the number of parameters decreases about 10-fold.For performance evaluation,we utilized a universal software radio peripheral(USRP)X310 platform to collect RF signals of four drones in an anechoic chamber and a practical wireless scenario.Experiment results reveal that the NN trained with augmented data performs as well as that trained with practical data in the complex electromagnetic environment.
基金The study was funded by the National Natural Science Foundation of China(Grant No.81272829)
文摘Dear Editor, Renal cell carcinoma (RCC) is among the most common human cancers in the United States, with approximately 63,990 new patients and 14,400 deaths annually [1]. However, RCC is not among the top 10 malignancies in China in terms of incidence and mortality [2]. The clini-cal and molecular features of RCC differ among distinct pathological types, mainly clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC), and chromophobe renal cell carcinoma (ChRCC). The most common subtype of RCC is ccRCC worldwide. Accord-ing to The Cancer Genome Atlas (TCGA), the somatic mutation landscape of RCC has been revealed by whole- exome sequencing (WES) or whole-genome sequencing (WGS). In our previous WES study, we validated most of the significantly mutated genes reported by the TCGA and identified several novel somatically altered genes [3]. The TCGA study showed that only somatic mutations in BRCA1-associated protein 1 (BAP1) were associated with patients’ poor survival outcomes among all significantly mutated genes [4]. In our previous WES study, BAP1 was somatically mutated in 2 of 15 ccRCC samples [3]. Never-theless, all of these RCC patients lacked follow-up infor-mation. Hence, further analysis is needed to determine whether there are any somatically mutated genes associ-ated with the prognosis of Chinese patients with RCC. However, WES or WGS is time-consuming and costly. Furthermore, compared with targeted sequencing, WES was more likely to generate false positives and false nega-tives due to insufficient base coverage [5].
