In the scene of wideband radar,due to the spread of target scattering points,the attitude and angle of view of the target constantly change in the process of moving.It is difficult to predict,and the actual echo of mu...In the scene of wideband radar,due to the spread of target scattering points,the attitude and angle of view of the target constantly change in the process of moving.It is difficult to predict,and the actual echo of multiple scattered points is not fully matched with the transmitted signal.Therefore,it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection.In addition,the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions.Therefore,this paper proposes a wideband target detection method based on dualchannel correlation processing of range-extended targets.This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself.The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal.The accu-mulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection.Finally,electromagnetic simulation experiments and measured data verify that the proposed method has the advan-tages of high signal to noise ratio(SNR)gain and high detection probability under low SNR conditions.展开更多
The rapid serial visual presentation(RSVP)paradigm has garnered considerable attention in brain–computer interface(BCI)systems.Studies have focused on using cross-subject electroencephalogram data to train cross-subj...The rapid serial visual presentation(RSVP)paradigm has garnered considerable attention in brain–computer interface(BCI)systems.Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models.In this study,we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022.We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection.The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks.We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.展开更多
The systemic fungal organism, Blastomyces dermatitidis causes blastomycosis in animals and hu-mans. This study was designed to evaluate antibody detection in 55 serial serum specimens from 9 dogs with blastomycosis us...The systemic fungal organism, Blastomyces dermatitidis causes blastomycosis in animals and hu-mans. This study was designed to evaluate antibody detection in 55 serial serum specimens from 9 dogs with blastomycosis using B. dermatitidis yeast lysate antigens produced from two human isolates (B5896;B5931) and two dog isolates (ERC-2;T-58) with the indirect enzyme linked im-munosorbent assay (ELISA;peroxidase system) to determine an optimal lysate antigen(s) for use in the ELISA to detect antibody in the dog serum specimens. The mean absorbance values when the lysate antigens were compared with respect to their ability to detect antibody in the day 0 sera from the 9 dogs were 1.024 (ERC-2), 1.351 (B5896), 1.700 (B5931) and 2.084 (T-58) respectively. All of the reagents exhibited a high level of sensitivity and in all instances the amount of antibody declined as the time interval post-treatment increased, but the T-58 lysate prepared from the dog isolate from Tennessee was the optimal reagent. We continue to evaluate antigens for B. derma-titidis antibody detection in different immunodiagnostic assays.展开更多
A facile and low-cost method to prepare periodic Au@metal-organic framework （MOF） （MIL-100（Fe）） nanoparticle arrays was developed. The arrays were fabricated in situ using monolayer colloidal crystals as templat...A facile and low-cost method to prepare periodic Au@metal-organic framework （MOF） （MIL-100（Fe）） nanoparticle arrays was developed. The arrays were fabricated in situ using monolayer colloidal crystals as templates, followed by Au deposition on substrates, and annealing. MIL-100（Fe） coatings were applied on the nanospheres using a simple solvent thermal process. The prepared periodic Au@MIL-100（Fe） nanoparticle （NP） arrays were characterized by two peaks in the visible spectra. The first peak represented the surface plasmon resonance （SPR） of the Au nanospheres, and the other peak, or the diffraction peak originated from the periodic structure in the NP array. After modification with 3-aminophenylboronic acid hemisulfate （PBA）, the Au@MIL-100（Fe） NP arrays exhibited sensitive responses to different glucose concentrations with good selectivity. These responses could be due to the strong interaction between PBA and glucose molecules. The diffraction peak was sensitive at low glucose concentrations （less than 12 mM）, whereas the SPR peak rapidly responded at high concentrations. The peaks thus demonstrated satisfactory complementary sensitivity for glucose detection in different concentration regions. These results can be used to develop a dual-channel biosensor. We also created a standard diagram, which can be used to efficiently monitor blood glucose levels. The proposed strategy can be extended to develop different dual-channel sensors using Au@MIL-100（Fe） NP arrays agents. functionalized with different recognition展开更多
针对基于动态主元分析的故障检测方法存在的主元个数较多以及计算效率低等问题,本文提出基于混合动态主元分析(Hybrid Dynamic Principal Component Analysis,HDP-CA)的复杂过程故障检测方法。该方法采用分步策略消除数据之间的自相关...针对基于动态主元分析的故障检测方法存在的主元个数较多以及计算效率低等问题,本文提出基于混合动态主元分析(Hybrid Dynamic Principal Component Analysis,HDP-CA)的复杂过程故障检测方法。该方法采用分步策略消除数据之间的自相关和互相关性,提高了故障检测的精度和效率。对TE过程典型故障和热连轧过程中断带故障检测结果表明:HDPCA方法提取的主元个数少于DPCA方法提取的主元个数。并且,基于HDPCA的T2和SPE统计量的检测性能和检测精度都由于基于DPCA的统计量。因此,本文提出的方法可以准确有效地检测出故障。展开更多
文摘In the scene of wideband radar,due to the spread of target scattering points,the attitude and angle of view of the target constantly change in the process of moving.It is difficult to predict,and the actual echo of multiple scattered points is not fully matched with the transmitted signal.Therefore,it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection.In addition,the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions.Therefore,this paper proposes a wideband target detection method based on dualchannel correlation processing of range-extended targets.This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself.The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal.The accu-mulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection.Finally,electromagnetic simulation experiments and measured data verify that the proposed method has the advan-tages of high signal to noise ratio(SNR)gain and high detection probability under low SNR conditions.
基金the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province(Grant No.2020ZDZX3018)the Special Fund for Science and Technology of Guangdong Province(Grant No.2020182)the Wuyi University and Hong Kong&Macao joint Research Project(Grant No.2019WGALH16)。
文摘The rapid serial visual presentation(RSVP)paradigm has garnered considerable attention in brain–computer interface(BCI)systems.Studies have focused on using cross-subject electroencephalogram data to train cross-subject RSVP detection models.In this study,we performed a comparative analysis of the top 5 deep learning algorithms used by various teams in the event-related potential competition of the BCI Controlled Robot Contest in World Robot Contest 2022.We evaluated these algorithms on the final data set and compared their performance in cross-subject RSVP detection.The results revealed that deep learning models can achieve excellent results with appropriate training methods when applied to cross-subject detection tasks.We discussed the limitations of existing deep learning algorithms in cross-subject RSVP detection and highlighted potential research directions.
文摘The systemic fungal organism, Blastomyces dermatitidis causes blastomycosis in animals and hu-mans. This study was designed to evaluate antibody detection in 55 serial serum specimens from 9 dogs with blastomycosis using B. dermatitidis yeast lysate antigens produced from two human isolates (B5896;B5931) and two dog isolates (ERC-2;T-58) with the indirect enzyme linked im-munosorbent assay (ELISA;peroxidase system) to determine an optimal lysate antigen(s) for use in the ELISA to detect antibody in the dog serum specimens. The mean absorbance values when the lysate antigens were compared with respect to their ability to detect antibody in the day 0 sera from the 9 dogs were 1.024 (ERC-2), 1.351 (B5896), 1.700 (B5931) and 2.084 (T-58) respectively. All of the reagents exhibited a high level of sensitivity and in all instances the amount of antibody declined as the time interval post-treatment increased, but the T-58 lysate prepared from the dog isolate from Tennessee was the optimal reagent. We continue to evaluate antigens for B. derma-titidis antibody detection in different immunodiagnostic assays.
基金The authors acknowledge the financial support from the National Basic Research Program of China （No. 2012CB932303）, the National Natural Science Foundation of China （Nos. 51371165 and 51571189）, the State Key Program of National Natural Science Foundation of China （No. 51531006）, the Anhui Pro- vincial Natural Science Foundation （No. 1508085JGD07）, the Cross-disciplinary Collaborative Teams Program in CAS, and the CAS/SAFEA International Partnership Program for Creative Research Teams.
文摘A facile and low-cost method to prepare periodic Au@metal-organic framework （MOF） （MIL-100（Fe）） nanoparticle arrays was developed. The arrays were fabricated in situ using monolayer colloidal crystals as templates, followed by Au deposition on substrates, and annealing. MIL-100（Fe） coatings were applied on the nanospheres using a simple solvent thermal process. The prepared periodic Au@MIL-100（Fe） nanoparticle （NP） arrays were characterized by two peaks in the visible spectra. The first peak represented the surface plasmon resonance （SPR） of the Au nanospheres, and the other peak, or the diffraction peak originated from the periodic structure in the NP array. After modification with 3-aminophenylboronic acid hemisulfate （PBA）, the Au@MIL-100（Fe） NP arrays exhibited sensitive responses to different glucose concentrations with good selectivity. These responses could be due to the strong interaction between PBA and glucose molecules. The diffraction peak was sensitive at low glucose concentrations （less than 12 mM）, whereas the SPR peak rapidly responded at high concentrations. The peaks thus demonstrated satisfactory complementary sensitivity for glucose detection in different concentration regions. These results can be used to develop a dual-channel biosensor. We also created a standard diagram, which can be used to efficiently monitor blood glucose levels. The proposed strategy can be extended to develop different dual-channel sensors using Au@MIL-100（Fe） NP arrays agents. functionalized with different recognition
文摘针对基于动态主元分析的故障检测方法存在的主元个数较多以及计算效率低等问题,本文提出基于混合动态主元分析(Hybrid Dynamic Principal Component Analysis,HDP-CA)的复杂过程故障检测方法。该方法采用分步策略消除数据之间的自相关和互相关性,提高了故障检测的精度和效率。对TE过程典型故障和热连轧过程中断带故障检测结果表明:HDPCA方法提取的主元个数少于DPCA方法提取的主元个数。并且,基于HDPCA的T2和SPE统计量的检测性能和检测精度都由于基于DPCA的统计量。因此,本文提出的方法可以准确有效地检测出故障。
