Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Dopple...Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.展开更多
Multipath and continuous wave (CW) interference may cause severe performance degradation of global navigation satellite system (GNSS) receivers. This paper analyzes the code tracking performance of early-minus-late po...Multipath and continuous wave (CW) interference may cause severe performance degradation of global navigation satellite system (GNSS) receivers. This paper analyzes the code tracking performance of early-minus-late power (EMLP) discriminator of GNSS receivers in the presence of multipath and CW interference. An analytical expression of the code tracking error is suggested for EMLP discriminator, and it can be used to assess the effect of multipath and CW interference. The derived expression shows that the combined effects include three components: multipath component;CW interference component and the combined component of multipath and CW interference. The effect of these components depends on some factors which can be classified into two categories: the receiving environment and the receiver parameters. Numerical results show how these factors affect the tracking performances. It is shown that the proper receiver parameters can suppress the combined effects of multipath and CW interference.展开更多
The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator...The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator based on multi-exposure LiGA like process and sacrificial layer process. The new CMG discriminator has the following characters except low cost： 1） it has only discrimination teeth sections; 2） the thickness of each gear layer exceeds one hundred micrometers; 3） it is axially driven by a separate dectronic magnetic micromotor directly; 4） its CMG is made of metal and is batch fabricated in the assembled state; 5） it is prevented from rotating in the opposite direction by pawl/ratchet wheel mechanism; 6） it has simpler structure. This device has better strength and reliability in abnormal environment compared to the existing surface micro machining （SMM） discriminator.展开更多
In this paper we propose the derivation of the expressions for the non-coherent Delay Locked Loop (DLL) Discriminator Curve (DC) in the absence and presence of Multipath (MP). Also derived, are the expressions of MP t...In this paper we propose the derivation of the expressions for the non-coherent Delay Locked Loop (DLL) Discriminator Curve (DC) in the absence and presence of Multipath (MP). Also derived, are the expressions of MP tracking errors in non-coherent configuration. The proposed models are valid for all Binary Offset Carrier (BOC) modulated signals in Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) and Future Galileo. The non-coherent configuration is used whenever the phase of the received signal cannot be estimated and thus cannot be demodulated. Therefore, the signal must be treated in a transposed band by the non-coherent DLL. The computer implementations show that the proposed models coincide with the numerical ones.展开更多
Due to variable time for charge collection,energy resolution of nuclear detectors declines,especially compound semiconductor detectors like cadmium zinc telluride(CdZnTe) detector.To solve this problem,an analog rise-...Due to variable time for charge collection,energy resolution of nuclear detectors declines,especially compound semiconductor detectors like cadmium zinc telluride(CdZnTe) detector.To solve this problem,an analog rise-time discriminator based on charge comparison principle is designed.The reference charge signal after attenuation is compared with the deconvoluted and delayed current signal.It is found that the amplitude of delayed current signal is higher than that of the reference charge signal when rise time of the input signal is shorter than the discrimination time,thus generating gating signal and triggering DMCA(digital multi-channel analyzer) to receive the total integral charge signal.When rise time of the input signal is longer than discrimination time,DMCA remains inactivated and the corresponding total integral charge signal is abandoned.Test results show that combination of the designed rise-time discriminator and DMCA can reduce hole tailing of CdZnTe detector significantly.Energy resolution of the system is 0.98%@662 keV,and it is still excellent under high counting rates.展开更多
Recently,the technology of digital image forgery based on a generative adversarial network(GAN)has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye ...Recently,the technology of digital image forgery based on a generative adversarial network(GAN)has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person’s face or a specific part with the original image.Thus,much attention has been paid to digital image forgery as a social issue.Further,document forgery through GANs can completely change the meaning and context in a document,and it is difficult to identify whether the document is forged or not,which is dangerous.Nonetheless,few studies have been conducted on document forgery and new forgery-related attacks have emerged daily.Therefore,in this study,we propose a novel convolutional neural network(CNN)forensic discriminator that can detect forged text or numeric images by GANs using CNNs,which have been widely used in image classification for many years.To strengthen the detection performance of the proposed CNN forensic discriminator,CNN was trained after image preprocessing,including salt and pepper as well asGaussian noises.Moreover,we performed CNN optimization to make existing CNN more suitable for forged text or numeric image detection,which have mainly focused on the discrimination of forged faces to date.The test evaluation results using Hangul texts and numbers showed that the accuracy of forgery discrimination of the proposed method was significantly improved by 20%in Hangul texts and 5%in numbers compared with that of existing state-of-the-art methods,which proved the proposed model performance superiority and verified that it could be a useful tool in reducing crime potential.展开更多
Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.展开更多
The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spa...The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.展开更多
Recently,deep learning-based image outpainting has made greatly notable improvements in computer vision field.However,due to the lack of fully extracting image information,the existing methods often generate unnatural...Recently,deep learning-based image outpainting has made greatly notable improvements in computer vision field.However,due to the lack of fully extracting image information,the existing methods often generate unnatural and blurry outpainting results in most cases.To solve this issue,we propose a perceptual image outpainting method,which effectively takes the advantage of low-level feature fusion and multi-patch discriminator.Specifically,we first fuse the texture information in the low-level feature map of encoder,and simultaneously incorporate these aggregated features reusability with semantic(or structural)information of deep feature map such that we could utilizemore sophisticated texture information to generate more authentic outpainting images.Then we also introduce a multi-patch discriminator to enhance the generated texture,which effectively judges the generated image from the different level features and concurrently impels our network to produce more natural and clearer outpainting results.Moreover,we further introduce perceptual loss and style loss to effectively improve the texture and style of outpainting images.Compared with the existing methods,our method could produce finer outpainting results.Experimental results on Places2 and Paris StreetView datasets illustrated the effectiveness of our method for image outpainting.展开更多
Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.Howev...Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.展开更多
Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the t...Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the target class, this paper introduces an oblique projection algorithm to determine the coefficients of a KND so that it is extended to a new version called extended KND (eKND). In eKND construction, the desired output vector of the target class is obliquely projected onto the relevant subspace along the subspace related to other classes. In addition, a simple technique is proposed to calculate the associated oblique projection operator. Experimental results on handwritten digit recognition show that the algorithm performes better than a KND classifier and some other commonly used classifiers.展开更多
A new neutron-gamma discriminator based on the support vector machine(SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintil...A new neutron-gamma discriminator based on the support vector machine(SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination(PSD) property. The SVM algorithm is implemented in field programmable gate array(FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.展开更多
Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period....Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period. And when the initial frequency offset is large, the frequency discriminator can＇ t work normally. To solve these problems, a new FLL discriminating algorithm is introduced. The least-squares discriminator is used in this new algorithm. As the least-squares discriminator has a short process unit period, the correspond- ing frequency discriminating range is large. And the data bit reversal just influence one process unit period, so the least-squares discriminated result will not be affected. Compared with traditional frequency discriminator, the least-squares algorithm can effectively solve the problem of data bit reversal and can endure larger initial frequency offset.展开更多
Many organizations persist in working with others that engage in known,remediable structural discrimination.We name this practice interorganizational structural discrimination(ISD)and argue it is a pivotal contributor...Many organizations persist in working with others that engage in known,remediable structural discrimination.We name this practice interorganizational structural discrimination(ISD)and argue it is a pivotal contributor to inequities in science and medicine.We urge organizations to leverage their relationships and demand progress from collaborators.展开更多
Constant fraction discriminator （CFD） is one of theoretic method which can locate timing point at same fraction of echo pulse in pulsed time-of-flight （TOF） laser rangefinding. In this paper, the theory of CFD met...Constant fraction discriminator （CFD） is one of theoretic method which can locate timing point at same fraction of echo pulse in pulsed time-of-flight （TOF） laser rangefinding. In this paper, the theory of CFD method was analyzed in reality condition. The design, simulation and printed-circuit-board （PCB） performance of CFD circuit were shown. Finally, an over amplified method was introduced, by which the influence of direct-current （DC） bias error could be reduced. The experimental results showed that timing discriminator could set the timing point to a certain point on echo pulse, which did not depend on the amplitude of echo pulse.展开更多
To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from mul...To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function,gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.展开更多
At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an ...At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevatio...Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.展开更多
Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental ...Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental trajectories of discrimination perception,vocational identity,and psychological resilience in this population.It further examined the longitudinal mediating role of vocational identity in the relationship between discrimination perception and psychological resilience.Methods:A total of 526 students from five vocational high schools in Guangdong,China,were assessed via convenience sampling at two time points:baseline(T1,September 2023)and six-month follow-up(T2,March 2024).Measures of discrimination perception,psychological resilience,and vocational identity were administered.Data were analyzed using a cross-lagged panel model to test for bidirectional relationships.Results:Over the six-month period,students showed significant decreases in discrimination perception and vocational identity,but a significant increase in psychological resilience.The cross-lagged model revealed significant bidirectional relationships:discrimination perception and psychological resilience negatively predicted each other over time(β=−0.124,p<0.01;β=−0.200,p<0.001),while psychological resilience and vocational identity positively predicted each other(β=0.084,p<0.05;β=0.076,p<0.05).The mediation analysis revealed a dual-pathway mechanism.T1 discrimination perception exerted both a significant direct negative effect on T2 psychological resilience(β=−0.332,p<0.001)and a significant indirect positive effect via T1 vocational identity(indirect effect=0.020,95%CI[0.001,0.046]).This confirms a partial mediating role,indicating that vocational identity functions as a compensatory mechanism,transforming the experience of discrimination perception into a potential source of psychological resilience.Conclusions:For vocational high school students,perception of discrimination directly undermines psychological resilience,but also indirectly fosters it through the positive development of vocational identity.These findings highlight vocational identity as a pivotal mechanism in the complex relationship between social adversity and mental resilience.展开更多
文摘Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.
文摘Multipath and continuous wave (CW) interference may cause severe performance degradation of global navigation satellite system (GNSS) receivers. This paper analyzes the code tracking performance of early-minus-late power (EMLP) discriminator of GNSS receivers in the presence of multipath and CW interference. An analytical expression of the code tracking error is suggested for EMLP discriminator, and it can be used to assess the effect of multipath and CW interference. The derived expression shows that the combined effects include three components: multipath component;CW interference component and the combined component of multipath and CW interference. The effect of these components depends on some factors which can be classified into two categories: the receiving environment and the receiver parameters. Numerical results show how these factors affect the tracking performances. It is shown that the proper receiver parameters can suppress the combined effects of multipath and CW interference.
文摘The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator based on multi-exposure LiGA like process and sacrificial layer process. The new CMG discriminator has the following characters except low cost： 1） it has only discrimination teeth sections; 2） the thickness of each gear layer exceeds one hundred micrometers; 3） it is axially driven by a separate dectronic magnetic micromotor directly; 4） its CMG is made of metal and is batch fabricated in the assembled state; 5） it is prevented from rotating in the opposite direction by pawl/ratchet wheel mechanism; 6） it has simpler structure. This device has better strength and reliability in abnormal environment compared to the existing surface micro machining （SMM） discriminator.
文摘In this paper we propose the derivation of the expressions for the non-coherent Delay Locked Loop (DLL) Discriminator Curve (DC) in the absence and presence of Multipath (MP). Also derived, are the expressions of MP tracking errors in non-coherent configuration. The proposed models are valid for all Binary Offset Carrier (BOC) modulated signals in Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) and Future Galileo. The non-coherent configuration is used whenever the phase of the received signal cannot be estimated and thus cannot be demodulated. Therefore, the signal must be treated in a transposed band by the non-coherent DLL. The computer implementations show that the proposed models coincide with the numerical ones.
基金supported by the Natural Science Foundation of China(NSFC)(No.41474159)National High-tech R&D Program of China(863 Program)(Nos.2012AA061803 and 2014AA093403)Open Foundation of Applied Nuclear Techniques in Geosciences Key Laboratory of Sichuan Province(No.gnzds2014006)
文摘Due to variable time for charge collection,energy resolution of nuclear detectors declines,especially compound semiconductor detectors like cadmium zinc telluride(CdZnTe) detector.To solve this problem,an analog rise-time discriminator based on charge comparison principle is designed.The reference charge signal after attenuation is compared with the deconvoluted and delayed current signal.It is found that the amplitude of delayed current signal is higher than that of the reference charge signal when rise time of the input signal is shorter than the discrimination time,thus generating gating signal and triggering DMCA(digital multi-channel analyzer) to receive the total integral charge signal.When rise time of the input signal is longer than discrimination time,DMCA remains inactivated and the corresponding total integral charge signal is abandoned.Test results show that combination of the designed rise-time discriminator and DMCA can reduce hole tailing of CdZnTe detector significantly.Energy resolution of the system is 0.98%@662 keV,and it is still excellent under high counting rates.
基金This research was funded by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MOE)(No.2021R1I1A3055973)the Soonchunhyang University Research Fund。
文摘Recently,the technology of digital image forgery based on a generative adversarial network(GAN)has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person’s face or a specific part with the original image.Thus,much attention has been paid to digital image forgery as a social issue.Further,document forgery through GANs can completely change the meaning and context in a document,and it is difficult to identify whether the document is forged or not,which is dangerous.Nonetheless,few studies have been conducted on document forgery and new forgery-related attacks have emerged daily.Therefore,in this study,we propose a novel convolutional neural network(CNN)forensic discriminator that can detect forged text or numeric images by GANs using CNNs,which have been widely used in image classification for many years.To strengthen the detection performance of the proposed CNN forensic discriminator,CNN was trained after image preprocessing,including salt and pepper as well asGaussian noises.Moreover,we performed CNN optimization to make existing CNN more suitable for forged text or numeric image detection,which have mainly focused on the discrimination of forged faces to date.The test evaluation results using Hangul texts and numbers showed that the accuracy of forgery discrimination of the proposed method was significantly improved by 20%in Hangul texts and 5%in numbers compared with that of existing state-of-the-art methods,which proved the proposed model performance superiority and verified that it could be a useful tool in reducing crime potential.
文摘Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
文摘The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.
基金This work was supported by the Sichuan Science and Technology program(2019JDJQ0002,2019YFG0496,2021016,2020JDTD0020)partially supported by National Science Foundation of China 42075142.
文摘Recently,deep learning-based image outpainting has made greatly notable improvements in computer vision field.However,due to the lack of fully extracting image information,the existing methods often generate unnatural and blurry outpainting results in most cases.To solve this issue,we propose a perceptual image outpainting method,which effectively takes the advantage of low-level feature fusion and multi-patch discriminator.Specifically,we first fuse the texture information in the low-level feature map of encoder,and simultaneously incorporate these aggregated features reusability with semantic(or structural)information of deep feature map such that we could utilizemore sophisticated texture information to generate more authentic outpainting images.Then we also introduce a multi-patch discriminator to enhance the generated texture,which effectively judges the generated image from the different level features and concurrently impels our network to produce more natural and clearer outpainting results.Moreover,we further introduce perceptual loss and style loss to effectively improve the texture and style of outpainting images.Compared with the existing methods,our method could produce finer outpainting results.Experimental results on Places2 and Paris StreetView datasets illustrated the effectiveness of our method for image outpainting.
基金supported by the Key R&D Program of Shaanxi Province,China(Grant Nos.2022GY-274,2023-YBSF-505)the National Natural Science Foundation of China(Grant No.62273273).
文摘Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.
基金Supported by the key project of Chinese Ministry of Education(No.1051150)
文摘Previously, a novel classifier called Kernel-based Nonlinear Discriminator (KND) was proposed to discriminate a pattern class from other classes by minimizing mean effect of the latter. To consider the effect of the target class, this paper introduces an oblique projection algorithm to determine the coefficients of a KND so that it is extended to a new version called extended KND (eKND). In eKND construction, the desired output vector of the target class is obliquely projected onto the relevant subspace along the subspace related to other classes. In addition, a simple technique is proposed to calculate the associated oblique projection operator. Experimental results on handwritten digit recognition show that the algorithm performes better than a KND classifier and some other commonly used classifiers.
基金partially supported by the National Science and Technology Major Project of Ministry of Science and Technology of China (Grant Nos. 2014GB109003 and 2015GB111002)National Natural Science Foundation of China (Grant Nos. 11375195, 11575184, 11375004 and 11775068)
文摘A new neutron-gamma discriminator based on the support vector machine(SVM) method is proposed to improve the performance of the time-of-flight neutron spectrometer. The neutron detector is an EJ-299-33 plastic scintillator with pulse-shape discrimination(PSD) property. The SVM algorithm is implemented in field programmable gate array(FPGA) to carry out the real-time sifting of neutrons in neutron-gamma mixed radiation fields. This study compares the ability of the pulse gradient analysis method and the SVM method. The results show that this SVM discriminator can provide a better discrimination accuracy of 99.1%. The accuracy and performance of the SVM discriminator based on FPGA have been evaluated in the experiments. It can get a figure of merit of 1.30.
文摘Frequency lock loops （FLL） discriminating algorithms for direct-sequence spread-spectrum are discussed. The existing algorithms can＇t solve the problem of data bit reversal during one pre-detection integral period. And when the initial frequency offset is large, the frequency discriminator can＇ t work normally. To solve these problems, a new FLL discriminating algorithm is introduced. The least-squares discriminator is used in this new algorithm. As the least-squares discriminator has a short process unit period, the correspond- ing frequency discriminating range is large. And the data bit reversal just influence one process unit period, so the least-squares discriminated result will not be affected. Compared with traditional frequency discriminator, the least-squares algorithm can effectively solve the problem of data bit reversal and can endure larger initial frequency offset.
文摘Many organizations persist in working with others that engage in known,remediable structural discrimination.We name this practice interorganizational structural discrimination(ISD)and argue it is a pivotal contributor to inequities in science and medicine.We urge organizations to leverage their relationships and demand progress from collaborators.
基金This work was sponsored by the National Natural Science Foundation of China （Grant No. 40801123）.
文摘Constant fraction discriminator （CFD） is one of theoretic method which can locate timing point at same fraction of echo pulse in pulsed time-of-flight （TOF） laser rangefinding. In this paper, the theory of CFD method was analyzed in reality condition. The design, simulation and printed-circuit-board （PCB） performance of CFD circuit were shown. Finally, an over amplified method was introduced, by which the influence of direct-current （DC） bias error could be reduced. The experimental results showed that timing discriminator could set the timing point to a certain point on echo pulse, which did not depend on the amplitude of echo pulse.
基金supported by National High Technology Research and Development Program of China(863)(Grant No.2013AA1548)
文摘To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function,gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.
基金supported by the National Natural Science Foundation of China(NSFC)(No.12075308)。
文摘At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
文摘Background and Objectives:The perception of sound in the vertical plane supports spatial hearing by enabling listeners to detect sources located above and below.Sounds originating from both the front and back elevations along the mid-sagittal plane further contribute to a three-dimensional auditory experience.This study aimed to characterize the variability in vertical sound localization abilities among normal-hearing(NH)individuals using spatialized audio.Materials and Methods:Fifty-one NH participants(aged 18 to 35 years)completed three vertical localization tasks under headphones as part of a single-group,within-subject experimental study.These tasks included two-plane identification:(1)top-down localization,(2)front-back localization,and one discrimination task in the front plane.Hierarchical Cluster Analysis(HCA)was employed to identify distinct patterns in spatial localization profiles specific to the vertical-median plane.Fisher's Discriminant Function Analysis(FDA)was used to validate the accuracy of HCA and estimate classification error.Results:HCA revealed three distinct listener clusters:(1)cluster 1 with good performance across all three tasks,(2)cluster 2 with selective impairment in top-bottom identification,and(3)cluster 3 with selective deficits in front-back identification.FDA validated group membership of the clusters identified by the HCA,with a prediction accuracy of 98%.Conclusions:Individuals with clinically NH exhibited three distinct vertical localization profiles:uniform performers,those impaired in top-bottom identification,and those impaired in front-back identification.These profiles may be linked to the interplay between acoustic and non-acoustic perceptual factors.
基金supported by the Guangdong Provincial Philosophy and Social Science“14th Five-Year Plan”Discipline Co-Construction Project(Grant No.GD22XJY14)the 2022 Guangdong Provincial Higher Education Teaching Reform Project(Grant No.Yue Jiao Gao[2023]4)Guangdong Polytechnic Normal University’s Project for Enhancing the Research Capacity of Doctoral Application Institution(Grant No.22GPNUZDJS48).
文摘Objectives:Psychological resilience is a critical resource for vocational high school students navigating social biases and fostering mental well-being.This six-month longitudinal study investigated the developmental trajectories of discrimination perception,vocational identity,and psychological resilience in this population.It further examined the longitudinal mediating role of vocational identity in the relationship between discrimination perception and psychological resilience.Methods:A total of 526 students from five vocational high schools in Guangdong,China,were assessed via convenience sampling at two time points:baseline(T1,September 2023)and six-month follow-up(T2,March 2024).Measures of discrimination perception,psychological resilience,and vocational identity were administered.Data were analyzed using a cross-lagged panel model to test for bidirectional relationships.Results:Over the six-month period,students showed significant decreases in discrimination perception and vocational identity,but a significant increase in psychological resilience.The cross-lagged model revealed significant bidirectional relationships:discrimination perception and psychological resilience negatively predicted each other over time(β=−0.124,p<0.01;β=−0.200,p<0.001),while psychological resilience and vocational identity positively predicted each other(β=0.084,p<0.05;β=0.076,p<0.05).The mediation analysis revealed a dual-pathway mechanism.T1 discrimination perception exerted both a significant direct negative effect on T2 psychological resilience(β=−0.332,p<0.001)and a significant indirect positive effect via T1 vocational identity(indirect effect=0.020,95%CI[0.001,0.046]).This confirms a partial mediating role,indicating that vocational identity functions as a compensatory mechanism,transforming the experience of discrimination perception into a potential source of psychological resilience.Conclusions:For vocational high school students,perception of discrimination directly undermines psychological resilience,but also indirectly fosters it through the positive development of vocational identity.These findings highlight vocational identity as a pivotal mechanism in the complex relationship between social adversity and mental resilience.
