In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These j...In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.展开更多
The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although d...The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although deep learning has been increasingly applied to this task,most existing approaches have primarily relied on supervised learning.Since supervised learning requires large amounts of labeled data,recent studies have explored self-supervised and semi-supervised methods to overcome this limitation.However,these approaches have largely assumed a closedset setting,where the classes present in the unlabeled data are considered identical to those in the labeled data.In contrast,this study explores an open-set semi-supervised learning setting,where the unlabeled data may contain additional,unknown classes.To address this challenge,a distance-based prototype network is employed to classify respiratory sounds in an open-set setting.In the first stage,the prototype network is trained using labeled and unlabeled data to derive prototype representations of known classes.In the second stage,distances between unlabeled data and known class prototypes are computed,and samples exceeding an adaptive threshold are identified as unknown.A new prototype is then calculated for this unknown class.In the final stage,semi-supervised learning is employed to classify labeled and unlabeled data into known and unknown classes.Compared to conventional closed-set semisupervised learning approaches,the proposed method achieved an average classification accuracy improvement of 2%–5%.Additionally,in cases of data scarcity,utilizing unlabeled data further improved classification performance by 6%–8%.The findings of this study are expected to significantly enhance respiratory sound classification performance in practical clinical settings.展开更多
Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was use...Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.展开更多
There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are di...There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.展开更多
In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the cl...In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.展开更多
Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class spl...Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.展开更多
The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen cl...The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
Background： Cochlear implants （Cls） can improve speech recognition for children with severe congenital hearing loss, and open-set word recognition is an important efficacy measure. This study examined Mandarin open...Background： Cochlear implants （Cls） can improve speech recognition for children with severe congenital hearing loss, and open-set word recognition is an important efficacy measure. This study examined Mandarin open-set word recognition development among Chinese children with Cls and normal hearing （NH）. Methods： This study included 457 children with CIs and 131 children with NH, who completed the Mandarin lexical neighborhood test. The results for children at 1-8 years alter receiving their Cls were compared to those from the children with NH using linear regression analysis and analysis of variance. Results： Recognition of disyllabic easy words, disyllabic hard words, monosyllabic easy words, and monosyllabic hard words increased with time after CI implantation. Scores for cases with implantation before 3 years old were significantly better than those for implantation after 3 years old. There were significant differences in open-set word recognition between the CI and NH groups. For implantation before 2 years, there was no significant difference in recognition at the ages of 6-7 years, compared to 3-year-old children with NH, or at the age of 10 years, compared to 6-year-old children with NH. For implantation before 3 years, there was no significant difference in recognition at the ages of 8 9 years, compared to 3-year-old children with NH, or at the age of 10 years, compared to 6-year-old children with NH. For implantation after 3 years, there was a significant difference in recognition at the age of 13 years, compared to 3-year-old children with NH. Conclusions： Mandarin open-set word recognition increased with time after CI implantation, and the age at implantation had a significant effect on long-term speech recognition. Chinese children with Cls had delayed but similar development of recognition, compared to norrnal children. Early CI implantation can shorten the gap between children with Cls and normal children.展开更多
A novel face verification algorithm using competitive negative samples is proposed.In the algorithm,the tested face matches not only with the claimed client face but also with competitive negative samples,and all the ...A novel face verification algorithm using competitive negative samples is proposed.In the algorithm,the tested face matches not only with the claimed client face but also with competitive negative samples,and all the matching scores are combined to make a final decision.Based on the algorithm,three schemes,including closestnegative-sample scheme,all-negative-sample scheme,and closest-few-negative-sample scheme,are designed.They are tested and compared with the traditional similaritybased verification approach on several databases with different features and classifiers.Experiments demonstrate that the three schemes reduce the verification error rate by 25.15%,30.24%,and 30.97%,on average,respectively.展开更多
文摘In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
基金supported by Innovative Human Resource Development for Local Intellectualization Programthrough the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156360).
文摘The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases.However,auscultation is highly subjective,making it challenging to analyze respiratory sounds accurately.Although deep learning has been increasingly applied to this task,most existing approaches have primarily relied on supervised learning.Since supervised learning requires large amounts of labeled data,recent studies have explored self-supervised and semi-supervised methods to overcome this limitation.However,these approaches have largely assumed a closedset setting,where the classes present in the unlabeled data are considered identical to those in the labeled data.In contrast,this study explores an open-set semi-supervised learning setting,where the unlabeled data may contain additional,unknown classes.To address this challenge,a distance-based prototype network is employed to classify respiratory sounds in an open-set setting.In the first stage,the prototype network is trained using labeled and unlabeled data to derive prototype representations of known classes.In the second stage,distances between unlabeled data and known class prototypes are computed,and samples exceeding an adaptive threshold are identified as unknown.A new prototype is then calculated for this unknown class.In the final stage,semi-supervised learning is employed to classify labeled and unlabeled data into known and unknown classes.Compared to conventional closed-set semisupervised learning approaches,the proposed method achieved an average classification accuracy improvement of 2%–5%.Additionally,in cases of data scarcity,utilizing unlabeled data further improved classification performance by 6%–8%.The findings of this study are expected to significantly enhance respiratory sound classification performance in practical clinical settings.
基金supported by the Natural Science Foundation of Beijing Municipality(No.7222320)the Capital Health Research and Development Special Fund(No.2022–2–6081)+1 种基金the Scientific Research Fund of Aerospace Center Hospital(No.YN202301)Aerospace Medical Health Science and Technology Research Projects(No.2021YK09).
文摘Objective This study aimed to develop a few-shot learning model for lung nodule detection in CT images by leveraging visual open-set object detection.Methods The Lung Nodule Analysis 2016(LUNA16)public dataset was used for validation.It was split into training and testing sets in an 8:2 ratio.Classical You Only Look Once(YOLO)models of three sizes(n,m,x)were trained on the training set.Transfer learning experiments were then conducted using the mainstream open-set object detection models derived from Detection Transformer(DETR)with Improved DeNoising AnchOr Boxes(DINO),i.e.,Grounding DINO and Open-Vocabulary DINO(OV-DINO),as well as our proposed few-shot learning model,across a range of different shot sizes.Finally,all trained models were compared on the test set.Results After training on LUNA16,the precision,recall,and mean average precision(mAP)of the different-sized YOLO models showed no significant differences,with peak values of 82.8%,73.1%,and 77.4%,respectively.OV-DINO’s recall was significantly higher than YOLO’s,but it did not show clear advantages in precision or mAP.Using only one-fifth of the training samples and one-tenth of the training epochs,our proposed model outperformed both YOLO and OV-DINO,achieving improvements of 6.6%,9.3%,and 6.9%in precision,recall,and mAP,respectively,with final values of 89.4%,96.2%,and 87.7%.Conclusion The proposed few-shot learning model demonstrates stronger scene transfer capabilities,requiring fewer samples and training epochs,and can effectively improve the accuracy of lung nodule detection.
基金fully supported by National Natural Science Foundation of China(61871422)Natural Science Foundation of Sichuan Province(2023NSFSC1422)Central Universities of South west Minzu University(ZYN2022032)。
文摘There are all kinds of unknown and known signals in the actual electromagnetic environment,which hinders the development of practical cognitive radio applications.However,most existing signal recognition models are difficult to discover unknown signals while recognizing known ones.In this paper,a compact manifold mixup feature-based open-set recognition approach(OR-CMMF)is proposed to address the above problem.First,the proposed approach utilizes the center loss to constrain decision boundaries so that it obtains the compact latent signal feature representations and extends the low-confidence feature space.Second,the latent signal feature representations are used to construct synthetic representations as substitutes for unknown categories of signals.Then,these constructed representations can occupy the extended low-confidence space.Finally,the proposed approach applies the distillation loss to adjust the decision boundaries between the known categories signals and the constructed unknown categories substitutes so that it accurately discovers unknown signals.The OR-CMMF approach outperformed other state-of-the-art open-set recognition methods in comprehensive recognition performance and running time,as demonstrated by simulation experiments on two public datasets RML2016.10a and ORACLE.
基金supported by the National Natural Science Foundation of China(Nos.U20B2067).
文摘In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.
文摘Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.
基金supported by the General Program of the National Natural Science Foundation of China (Grant No.62575116)the National Natural Science Foundation of China (Grant No.62262005)+1 种基金the High-level Innovative Talents in Guizhou Province (Grant No.GCC[2023]033)the Open Project of the Text Computing and Cognitive Intelligence Ministry of Education Engineering Research Center(Grant No.TCCI250208)。
文摘The node labels collected from real-world applications are often accompanied by the occurrence of in-distribution noise(seen class nodes with wrong labels) and out-of-distribution noise(unseen class nodes with seen class labels), which significantly degrade the superior performance of recently emerged open-set graph neural networks(GNN). Nowadays, only a few researchers have attempted to introduce sample selection strategies developed in non-graph areas to limit the influence of noisy node labels. These studies often neglect the impact of inaccurate graph structure relationships, invalid utilization of noisy nodes and unlabeled nodes self-supervision information for noisy node labels constraint. More importantly, simply enhancing the accuracy of graph structure relationships or the utilization of nodes' self-supervision information still cannot minimize the influence of noisy node labels for open-set GNN. In this paper, we propose a novel RT-OGNN(robust training of open-set GNN) framework to solve the above-mentioned issues. Specifically, an effective graph structure learning module is proposed to weaken the impact of structure noise and extend the receptive field of nodes. Then, the augmented graph is sent to a pair of peer GNNs to accurately distinguish noisy node labels of labeled nodes. Third, the label propagation and multilayer perceptron-based decoder modules are simultaneously introduced to discover more supervision information from remaining nodes apart from clean nodes. Finally, we jointly optimize the above modules and open-set GNN in an end-to-end way via consistency regularization loss and cross-entropy loss, which minimizes the influence of noisy node labels and provides more supervision guidance for open-set GNN optimization.Extensive experiments on three benchmarks and various noise rates validate the superiority of RT-OGNN over state-of-the-art models.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金This study was supported by a grant from the National Natural Science Foundation of China （No. 81670923）.
文摘Background： Cochlear implants （Cls） can improve speech recognition for children with severe congenital hearing loss, and open-set word recognition is an important efficacy measure. This study examined Mandarin open-set word recognition development among Chinese children with Cls and normal hearing （NH）. Methods： This study included 457 children with CIs and 131 children with NH, who completed the Mandarin lexical neighborhood test. The results for children at 1-8 years alter receiving their Cls were compared to those from the children with NH using linear regression analysis and analysis of variance. Results： Recognition of disyllabic easy words, disyllabic hard words, monosyllabic easy words, and monosyllabic hard words increased with time after CI implantation. Scores for cases with implantation before 3 years old were significantly better than those for implantation after 3 years old. There were significant differences in open-set word recognition between the CI and NH groups. For implantation before 2 years, there was no significant difference in recognition at the ages of 6-7 years, compared to 3-year-old children with NH, or at the age of 10 years, compared to 6-year-old children with NH. For implantation before 3 years, there was no significant difference in recognition at the ages of 8 9 years, compared to 3-year-old children with NH, or at the age of 10 years, compared to 6-year-old children with NH. For implantation after 3 years, there was a significant difference in recognition at the age of 13 years, compared to 3-year-old children with NH. Conclusions： Mandarin open-set word recognition increased with time after CI implantation, and the age at implantation had a significant effect on long-term speech recognition. Chinese children with Cls had delayed but similar development of recognition, compared to norrnal children. Early CI implantation can shorten the gap between children with Cls and normal children.
基金supported by the National Natural Science Foundation of China (No.69972024)the National High Technology Research and Development Program of China (No.2001A4114081).
文摘A novel face verification algorithm using competitive negative samples is proposed.In the algorithm,the tested face matches not only with the claimed client face but also with competitive negative samples,and all the matching scores are combined to make a final decision.Based on the algorithm,three schemes,including closestnegative-sample scheme,all-negative-sample scheme,and closest-few-negative-sample scheme,are designed.They are tested and compared with the traditional similaritybased verification approach on several databases with different features and classifiers.Experiments demonstrate that the three schemes reduce the verification error rate by 25.15%,30.24%,and 30.97%,on average,respectively.
