With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direc...With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.展开更多
The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed ma...The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental platform.Based on such a platform,we first construct a real signal dataset with ten modulation methods.Then,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature classification.We also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated signals.Finally,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other algorithms.It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.展开更多
目的探讨多模式预康复策略对老年衰弱结直肠癌手术患者的功能状态及短期预后的影响。方法回顾性收集2021年1月—2023年6月接受手术治疗的老年衰弱结直肠癌患者的临床资料,所有患者均采用加速康复外科(enhanced recovery after surgery,E...目的探讨多模式预康复策略对老年衰弱结直肠癌手术患者的功能状态及短期预后的影响。方法回顾性收集2021年1月—2023年6月接受手术治疗的老年衰弱结直肠癌患者的临床资料,所有患者均采用加速康复外科(enhanced recovery after surgery,ERAS)路径管理,依据是否实行多模式预康复分为预康复组和对照组。采用1∶2倾向性评分匹配平衡组间基线差异所致的混杂偏倚,比较匹配后两组患者资料,卡钳值设为0.1。主要观察指标为术后4周6分钟步行距离(6-minute walk distance,6MWD);次要观察指标为衰弱指数(frail index,FI)、手术时间、术中出血量、术后并发症、术后住院时间、术后30 d再入院率/再手术率及术后90 d死亡率。结果本研究最终共纳入182例患者(上海交通大学医学院附属第九人民医院62例,临沂市人民医院61例,聊城市人民医院59例),预康复组31例,对照组151例。经1∶2匹配后成功匹配93例患者,预康复组31例,对照组62例。术后4周预康复组6MWD改善值大于对照组[(46.3±33.7)m vs(7.6±30.2)m,P=0.002],预康复组6MWD改善值>20 m的患者数占比多于对照组(55%vs 9.9%,P=0.033)。FI预康复组小于对照组(中位数2 vs 3,P=0.024)。两组间手术时间、并发症发生率、术后30 d再入院率/再手术率及术后90 d死亡率差异无统计学意义(P>0.05)。术后住院时间预康复组长于对照组[(14.0±4.3)d vs(11.3±4.2)d,P=0.007]。结论多模式预康复策略未增加围手术期风险,有助于改善老年衰弱结直肠癌患者术后功能状态,可能提高患者对手术应激的耐受性,从而使其更好地从ERAS中获益。展开更多
The ground ice content in permafrost serves as one of the dominant properties of permafrost for the study of global climate change, ecology, hydrology and engineering construction in cold regions. This paper initially...The ground ice content in permafrost serves as one of the dominant properties of permafrost for the study of global climate change, ecology, hydrology and engineering construction in cold regions. This paper initially attempts to assess the ground ice volume in permafrost layers on the Qinghai-Tibet Plateau by considering landform types, the corresponding lithological composition, and the measured water content in various regions. An approximation demonstrating the existence of many similarities in lithological composition and water content within a unified landform was established during the calculations. Considerable knowledge of the case study area, here called the Source Area of the Yellow(Huanghe) River(SAYR) in the northeastern Qinghai-Tibet Plateau, has been accumulated related to permafrost and fresh water resources during the past 40 years. Considering the permafrost distribution, extent, spatial distribution of landform types, the ground ice volume at the depths of 3.0–10.0 m below the ground surface was estimated based on the data of 101 boreholes from field observations and geological surveys in different types of landforms in the permafrost region of the SAYR. The total ground ice volume in permafrost layers at the depths of 3.0–10.0 m was approximately(51.68 ± 18.81) km^3, and the ground ice volume per unit volume was(0.31 ± 0.11) m^3/m^3. In the horizontal direction, the ground ice content was higher in the landforms of lacustrine-marshland plains and alluvial-lacustrine plains, and the lower ground ice content was distributed in the erosional platforms and alluvial-proluvial plains. In the vertical direction, the volume of ground ice was relatively high in the top layers(especially near the permafrost table) and at the depths of 7.0–8.0 m. This calculation method will be used in the other areas when the necessary information is available, including landform type, borehole data, and measured water content.展开更多
基金supported by the National Basic Research Program of China。
文摘With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
基金supported by the major key project of Peng Cheng Laboratory under grant PCL2023AS31 and PCL2023AS1-2the National Key Research and Development Program of China(No.2019YFA0706604)the Natural Science Foundation(NSF)of China(Nos.61976169,62293483,62371451)。
文摘The underwater wireless optical communication(UWOC)system has gradually become essential to underwater wireless communication technology.Unlike other existing works on UWOC systems,this paper evaluates the proposed machine learningbased signal demodulation methods through the selfbuilt experimental platform.Based on such a platform,we first construct a real signal dataset with ten modulation methods.Then,we propose a deep belief network(DBN)-based demodulator for feature extraction and multi-class feature classification.We also design an adaptive boosting(Ada Boost)demodulator as an alternative scheme without feature filtering for multiple modulated signals.Finally,it is demonstrated by extensive experimental results that the Ada Boost demodulator significantly outperforms the other algorithms.It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power.A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio(SNR)is higher.
基金Under the auspices of the Chinese Academy of Sciences(CAS)Key Research Program(No.KZZD-EW-13)National Natural Science Foundation of China(No.91647103)
文摘The ground ice content in permafrost serves as one of the dominant properties of permafrost for the study of global climate change, ecology, hydrology and engineering construction in cold regions. This paper initially attempts to assess the ground ice volume in permafrost layers on the Qinghai-Tibet Plateau by considering landform types, the corresponding lithological composition, and the measured water content in various regions. An approximation demonstrating the existence of many similarities in lithological composition and water content within a unified landform was established during the calculations. Considerable knowledge of the case study area, here called the Source Area of the Yellow(Huanghe) River(SAYR) in the northeastern Qinghai-Tibet Plateau, has been accumulated related to permafrost and fresh water resources during the past 40 years. Considering the permafrost distribution, extent, spatial distribution of landform types, the ground ice volume at the depths of 3.0–10.0 m below the ground surface was estimated based on the data of 101 boreholes from field observations and geological surveys in different types of landforms in the permafrost region of the SAYR. The total ground ice volume in permafrost layers at the depths of 3.0–10.0 m was approximately(51.68 ± 18.81) km^3, and the ground ice volume per unit volume was(0.31 ± 0.11) m^3/m^3. In the horizontal direction, the ground ice content was higher in the landforms of lacustrine-marshland plains and alluvial-lacustrine plains, and the lower ground ice content was distributed in the erosional platforms and alluvial-proluvial plains. In the vertical direction, the volume of ground ice was relatively high in the top layers(especially near the permafrost table) and at the depths of 7.0–8.0 m. This calculation method will be used in the other areas when the necessary information is available, including landform type, borehole data, and measured water content.
