As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most q...As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.展开更多
Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane...Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985 ℃ and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.展开更多
Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality a...Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).展开更多
Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain les...Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.展开更多
Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgica...Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.展开更多
Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes.This work focuses on the pyramidal slip responses of rolled AZ31 magn...Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes.This work focuses on the pyramidal slip responses of rolled AZ31 magnesium alloy under two loading conditions,compressive and tensile loading along the normal direction.Under the condition where the compressive loading direction is closely parallel to the c-axis of the unit cell,tensile twinning and basal slips are prohibited, dislocations then active and tend to accumulate at grain boundaries and form dislocation walls.Meanwhile,these dislocations exhibit zigzag morphologies,which result from the cross-slip from {10■1} first-order pyramidal plane to {11■2} second-order pyramidal plane,then back to {10■1} first-order pyramidal plane.Under the condition where tensile twins are prevalent,{10■1} first-order and {11■2} second-order pyramidal dislocations are favorable to be activated.Both types of dislocations behave climb-like dissociations onto the basal plane,forming zigzag dislocations.展开更多
Clubroot and herbicide resistance,high oleic acid(OA)content,and early maturity are targets of rapeseed(Brassica napus L.)breeding.The objective of this study was to develop new male-fertility restorer lines by pyrami...Clubroot and herbicide resistance,high oleic acid(OA)content,and early maturity are targets of rapeseed(Brassica napus L.)breeding.The objective of this study was to develop new male-fertility restorer lines by pyramiding favorable genes to improve these traits simultaneously.Seven elite alleles for the four traits were introduced into the restorer line 621R by speed breeding with marker-assisted and phenotypic selection.Six introgression lines(ILs)were developed with four-to seven-gene combinations and crossed with two elite parents to develop hybrids.All ILs and their corresponding hybrids displayed high resistance to both clubroot pathotype 4 and sulfonylurea herbicides.Three ILs and their hybrids showed large increases in OA contents and four showed earlier maturity.These new ILs may be useful in rapeseed hybrid breeding for the target traits.展开更多
Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal windo...Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.展开更多
Deployable mechanism with preferable deployable performance,strong expansibility,and lightweight has attracted much attention because of their potential in aerospace.A basic deployable pyramid unit with good deployabi...Deployable mechanism with preferable deployable performance,strong expansibility,and lightweight has attracted much attention because of their potential in aerospace.A basic deployable pyramid unit with good deployability and expandability is proposed to construct a sizeable deployable mechanism.Firstly,the basic unit folding principle and expansion method is proposed.The configuration synthesis method of adding constraint chains of spatial closed-loop mechanism is used to synthesize the basic unit.Then,the degree of freedom of the basic unit is analyzed using the screw theory and the link dismantling method.Next,the three-dimensional models of the pyramid unit,expansion unit,and array unit are established,and the folding motion simulation analysis is carried out.Based on the number of components,weight reduction rate,and deployable rate,the performance characteristics of the three types of mechanisms are described in detail.Finally,prototypes of the pyramid unit,combination unit,and expansion unit are developed to verify further the correctness of the configuration synthesis based on the pyramid.The proposed deployable mechanism provides aference for the design and application of antennas with a large aperture,high deployable rate,and lightweight.It has a good application prospect in the aerospace field.展开更多
Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis.Bulk single crystal Mg only presents very limited plasticity in...Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis.Bulk single crystal Mg only presents very limited plasticity in c-axis compression,and this behavior was attributed to out-of-plane dissociation of pyramidal dislocations onto the basal plane and resulted in an immobile dislocation configuration.In contrast,other simulations and experiments reported in-plane dissociation of pyramidal dislocations on their slip planes.Thus,the core structure and mode of dissociation of pyramidal dislocations are still not well understood.To better understand the dissociation behavior of pyramidal dislocations in Mg at room temperature,in this work,atomistic simulations were conducted to investigate four types of pyramidal dislocations at 300 K:edge and screw Py-Ⅰ on{1011},edge and screw Py-Ⅱ on{1122}by using a modified embedded atom method (MEAM) potential for Mg and anisotropic elasticity dislocation model.The results show that when energy minimization was performed before relaxation,in-plane dissociation of edge dislocations on respective pyramidal plane could be obtained at room temperature for all four types of dislocation.Without energy minimization,the edge dislocations dissociated out-of-plane onto the basal plane.Calculations of potential energy and hydrostatic stress of individual atoms at the edge dislocation core show that the extraordinarily high energy and atomic stresses in the as-constructed dislocation structures caused the out-of-plane dissociation onto the basal plane.The core structures of all four types of pyramidal dislocation after in-plane dissociation were analyzed by computing the distribution of the Burgers vector.展开更多
This experimental study aimed to investigate the impact of water depth, inlet water temperature,and fins on the productivity of a pyramid solar still in producing distilled water. The experiment was conducted in three...This experimental study aimed to investigate the impact of water depth, inlet water temperature,and fins on the productivity of a pyramid solar still in producing distilled water. The experiment was conducted in three parts, where the first part explored the variation in water depth from 1 cm to 5 cm, the second part evaluated the effect of increasing inlet water temperature from 30℃ to 50℃, and the third part added fins at the bottom of the still at a specific inlet water depth. Results showed that basin depth had a significant impact on the still's production, with a maximum variation of 40.6% observed when the water level was changed from 1 cm to 5 cm. The daily freshwater production from the pyramid solar still ranged from 3.41 kg/m~2 for a water depth of 1 cm to 2.02 kg/m~2 for a depth of 5 cm. Adding fins at the bottom of the pyramid solar still led to a 7.5% increase in productivity, while adjusting the inlet water temperature from 30℃ to 40℃ and 50℃ resulted in a 15.3% and 21.2% increase, respectively. These findings highlighted the essential factors that can influence the productivity of pyramid solar stills and can be valuable in designing and operating efficient water desalination and purification technologies.展开更多
SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in remo...SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.展开更多
We have demonstrated the existence of a pyramid power and have revealed its characteristics by strictly scientific experiments using biosensors. We also revealed the existence of a Bio-Entanglement, an entangled relat...We have demonstrated the existence of a pyramid power and have revealed its characteristics by strictly scientific experiments using biosensors. We also revealed the existence of a Bio-Entanglement, an entangled relationship between biosensors. A parallel study of biosensors (edible cucumber slices) had also been conducted, and we found that the circadian rhythm of gas concentrations emitted from biosensors changes seasonally. The pyramid power and Bio-Entanglement did not change the number of cycles in the periodic approximation curve representing circadian rhythm. Therefore, in this paper we analyzed the influence of the pyramid power and Bio-Entanglement, i.e., their influence on the phase, amplitude, and correlation coefficient of the periodic approximation curve representing the circadian rhythm of emitted gas concentrations. The main results are as follows. 1) The pyramid power shifted the phase of the periodic approximation curve representing the circadian rhythm by 43 minutes. 2) The amplitude of the periodic approximation curve changed with the pyramid power and the Bio-Entanglement. The effect on the lower and upper sections of the biosensors stacked in two layers was different, with a tendency to increase the amplitude of the lower layer and decrease the amplitude of the upper layer. 3) The pyramid power and the Bio-Entanglement affected the correlation coefficient between gas concentration and the periodic approximation curve representing the circadian rhythm of gas concentration. The effect on the lower and upper layers of the biosensors was different, with a tendency for the lower layer correlation coefficient to be larger and the upper layer correlation coefficient to be smaller. Previously we demonstrated that the pyramid power and the Bio-Entanglement affect the ratio of gas concentration, i.e., psi index Ψ. In this paper we demonstrate for the first time that the pyramid power and the Bio-Entanglement affect time, i.e., phase difference.展开更多
To date, numerous books have been published on so-called “pyramid power” but there have been few academic papers on this subject other than our own. Since 2007, to demonstrate the pyramid power, we have undertaken s...To date, numerous books have been published on so-called “pyramid power” but there have been few academic papers on this subject other than our own. Since 2007, to demonstrate the pyramid power, we have undertaken strictly scientific experiments using a pyramidal structure (PS) that we have carefully constructed. In previous reports, we used the edible cucumber, Cucumis sativus as an effective and practical biosensor. Through measurement and analysis of volatile components (gas concentrations) emitted from the biosensor, we were able to demonstrate the existence of the pyramid power and revealed some of its characteristics. In a paper published in 2022, we showed that gas concentration release from this biosensor displayed a circadian rhythm and that this rhythm changed with the season. Based on the result that the biosensor had a periodic diurnal oscillation called a circadian rhythm, we questioned whether or not pyramid power and Bio-Entanglement also had periodic diurnal oscillations. In this paper, we investigated that possibility. Our results have shown that pyramid power and Bio-Entanglement do not exhibit significant periodic diurnal oscillations. Thus we have revealed for the first time that the field associated with pyramid power is a type of static field that always exerts a constant influence. We expect that our research results will be widely accepted in the future and will become the foundation for a new research field in science, with a wide range of applications.展开更多
Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearabl...Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments.Consequently,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts.This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability.The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body parts.The experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor activity.Moreover,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score.Furthermore,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62172268 and 62302289)the Shanghai Science and Technology Project(Grant Nos.21JC1402800 and 23YF1416200)。
文摘As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.
基金the National Key Research and Development Program(2021YFA0716400)the National Natural Science Foundation of China(62225405,62350002,61991443)+1 种基金the Key R&D Project of Jiangsu Province,China(BE2020004)the Collaborative Innovation Centre of Solid-State Lighting and Energy-Saving Electronics.
文摘Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985 ℃ and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.
基金supported and founded by the Guizhou Provincial Science and Technology Project under the Grant No.QKH-Basic-ZK[2021]YB311the Youth Science and Technology Talent Growth Project of Guizhou Provincial Education Department under Grant No.QJH-KY-ZK[2021]132+2 种基金the Guizhou Provincial Science and Technology Project under the Grant No.QKH-Basic-ZK[2021]YB319the National Natural Science Foundation of China(NSFC)under Grant 61902085the Key Laboratory Program of Blockchain and Fintech of Department of Education of Guizhou Province(2023-014).
文摘Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1).
文摘Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.
文摘Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.
基金supported by the Bejing Municipal Natural Science Foundation (No.2214072)the Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities) (FRF-IDRY-20-034)the Office of China Postdoctoral Council under Award No.YJ20200248。
文摘Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes.This work focuses on the pyramidal slip responses of rolled AZ31 magnesium alloy under two loading conditions,compressive and tensile loading along the normal direction.Under the condition where the compressive loading direction is closely parallel to the c-axis of the unit cell,tensile twinning and basal slips are prohibited, dislocations then active and tend to accumulate at grain boundaries and form dislocation walls.Meanwhile,these dislocations exhibit zigzag morphologies,which result from the cross-slip from {10■1} first-order pyramidal plane to {11■2} second-order pyramidal plane,then back to {10■1} first-order pyramidal plane.Under the condition where tensile twins are prevalent,{10■1} first-order and {11■2} second-order pyramidal dislocations are favorable to be activated.Both types of dislocations behave climb-like dissociations onto the basal plane,forming zigzag dislocations.
基金supported by the China Agriculture Research System of MOF and MARA(CARS-12)the Open Fund of the National Key Laboratory of Crop Genetic Improvement(ZK201909)。
文摘Clubroot and herbicide resistance,high oleic acid(OA)content,and early maturity are targets of rapeseed(Brassica napus L.)breeding.The objective of this study was to develop new male-fertility restorer lines by pyramiding favorable genes to improve these traits simultaneously.Seven elite alleles for the four traits were introduced into the restorer line 621R by speed breeding with marker-assisted and phenotypic selection.Six introgression lines(ILs)were developed with four-to seven-gene combinations and crossed with two elite parents to develop hybrids.All ILs and their corresponding hybrids displayed high resistance to both clubroot pathotype 4 and sulfonylurea herbicides.Three ILs and their hybrids showed large increases in OA contents and four showed earlier maturity.These new ILs may be useful in rapeseed hybrid breeding for the target traits.
基金supported by the research team of Xi’an Traffic Engineering Institute and the Young and middle-aged fund project of Xi’an Traffic Engineering Institute (2022KY-02).
文摘Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.
基金Supported by National Natural Science Foundation of China(Grant No.52075467)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20220649)+1 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.23KJB460010)Jiangsu Provincial Key R&D Project(Grant No.BE2022062).
文摘Deployable mechanism with preferable deployable performance,strong expansibility,and lightweight has attracted much attention because of their potential in aerospace.A basic deployable pyramid unit with good deployability and expandability is proposed to construct a sizeable deployable mechanism.Firstly,the basic unit folding principle and expansion method is proposed.The configuration synthesis method of adding constraint chains of spatial closed-loop mechanism is used to synthesize the basic unit.Then,the degree of freedom of the basic unit is analyzed using the screw theory and the link dismantling method.Next,the three-dimensional models of the pyramid unit,expansion unit,and array unit are established,and the folding motion simulation analysis is carried out.Based on the number of components,weight reduction rate,and deployable rate,the performance characteristics of the three types of mechanisms are described in detail.Finally,prototypes of the pyramid unit,combination unit,and expansion unit are developed to verify further the correctness of the configuration synthesis based on the pyramid.The proposed deployable mechanism provides aference for the design and application of antennas with a large aperture,high deployable rate,and lightweight.It has a good application prospect in the aerospace field.
基金the support from U.S.National Science Foundation (NSF) (CMMI-2016263,2032483)supported by National Science Foundation grant number ACI-1548562,on Bridges Pylon at Pittsburgh Supercomputing Center through TG-MAT200001the support provided by National Natural Science Foundation of China (51971168 and 52022076)。
文摘Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis.Bulk single crystal Mg only presents very limited plasticity in c-axis compression,and this behavior was attributed to out-of-plane dissociation of pyramidal dislocations onto the basal plane and resulted in an immobile dislocation configuration.In contrast,other simulations and experiments reported in-plane dissociation of pyramidal dislocations on their slip planes.Thus,the core structure and mode of dissociation of pyramidal dislocations are still not well understood.To better understand the dissociation behavior of pyramidal dislocations in Mg at room temperature,in this work,atomistic simulations were conducted to investigate four types of pyramidal dislocations at 300 K:edge and screw Py-Ⅰ on{1011},edge and screw Py-Ⅱ on{1122}by using a modified embedded atom method (MEAM) potential for Mg and anisotropic elasticity dislocation model.The results show that when energy minimization was performed before relaxation,in-plane dissociation of edge dislocations on respective pyramidal plane could be obtained at room temperature for all four types of dislocation.Without energy minimization,the edge dislocations dissociated out-of-plane onto the basal plane.Calculations of potential energy and hydrostatic stress of individual atoms at the edge dislocation core show that the extraordinarily high energy and atomic stresses in the as-constructed dislocation structures caused the out-of-plane dissociation onto the basal plane.The core structures of all four types of pyramidal dislocation after in-plane dissociation were analyzed by computing the distribution of the Burgers vector.
文摘This experimental study aimed to investigate the impact of water depth, inlet water temperature,and fins on the productivity of a pyramid solar still in producing distilled water. The experiment was conducted in three parts, where the first part explored the variation in water depth from 1 cm to 5 cm, the second part evaluated the effect of increasing inlet water temperature from 30℃ to 50℃, and the third part added fins at the bottom of the still at a specific inlet water depth. Results showed that basin depth had a significant impact on the still's production, with a maximum variation of 40.6% observed when the water level was changed from 1 cm to 5 cm. The daily freshwater production from the pyramid solar still ranged from 3.41 kg/m~2 for a water depth of 1 cm to 2.02 kg/m~2 for a depth of 5 cm. Adding fins at the bottom of the pyramid solar still led to a 7.5% increase in productivity, while adjusting the inlet water temperature from 30℃ to 40℃ and 50℃ resulted in a 15.3% and 21.2% increase, respectively. These findings highlighted the essential factors that can influence the productivity of pyramid solar stills and can be valuable in designing and operating efficient water desalination and purification technologies.
文摘SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.
文摘We have demonstrated the existence of a pyramid power and have revealed its characteristics by strictly scientific experiments using biosensors. We also revealed the existence of a Bio-Entanglement, an entangled relationship between biosensors. A parallel study of biosensors (edible cucumber slices) had also been conducted, and we found that the circadian rhythm of gas concentrations emitted from biosensors changes seasonally. The pyramid power and Bio-Entanglement did not change the number of cycles in the periodic approximation curve representing circadian rhythm. Therefore, in this paper we analyzed the influence of the pyramid power and Bio-Entanglement, i.e., their influence on the phase, amplitude, and correlation coefficient of the periodic approximation curve representing the circadian rhythm of emitted gas concentrations. The main results are as follows. 1) The pyramid power shifted the phase of the periodic approximation curve representing the circadian rhythm by 43 minutes. 2) The amplitude of the periodic approximation curve changed with the pyramid power and the Bio-Entanglement. The effect on the lower and upper sections of the biosensors stacked in two layers was different, with a tendency to increase the amplitude of the lower layer and decrease the amplitude of the upper layer. 3) The pyramid power and the Bio-Entanglement affected the correlation coefficient between gas concentration and the periodic approximation curve representing the circadian rhythm of gas concentration. The effect on the lower and upper layers of the biosensors was different, with a tendency for the lower layer correlation coefficient to be larger and the upper layer correlation coefficient to be smaller. Previously we demonstrated that the pyramid power and the Bio-Entanglement affect the ratio of gas concentration, i.e., psi index Ψ. In this paper we demonstrate for the first time that the pyramid power and the Bio-Entanglement affect time, i.e., phase difference.
文摘To date, numerous books have been published on so-called “pyramid power” but there have been few academic papers on this subject other than our own. Since 2007, to demonstrate the pyramid power, we have undertaken strictly scientific experiments using a pyramidal structure (PS) that we have carefully constructed. In previous reports, we used the edible cucumber, Cucumis sativus as an effective and practical biosensor. Through measurement and analysis of volatile components (gas concentrations) emitted from the biosensor, we were able to demonstrate the existence of the pyramid power and revealed some of its characteristics. In a paper published in 2022, we showed that gas concentration release from this biosensor displayed a circadian rhythm and that this rhythm changed with the season. Based on the result that the biosensor had a periodic diurnal oscillation called a circadian rhythm, we questioned whether or not pyramid power and Bio-Entanglement also had periodic diurnal oscillations. In this paper, we investigated that possibility. Our results have shown that pyramid power and Bio-Entanglement do not exhibit significant periodic diurnal oscillations. Thus we have revealed for the first time that the field associated with pyramid power is a type of static field that always exerts a constant influence. We expect that our research results will be widely accepted in the future and will become the foundation for a new research field in science, with a wide range of applications.
基金supported by the Thailand Science Research and Innovation Fundthe University of Phayao(Grant No.FF66-UoE001)King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-66-KNOW-05.
文摘Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical impli-cations.Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments.Consequently,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts.This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability.The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body parts.The experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor activity.Moreover,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score.Furthermore,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.
基金supported by the International Research Center of Big Data for Sustainable Development Goals [grant number CBAS2022GSP01]the National Natural Science Foundation of China [grant numbers 42276203 and 42030406]+1 种基金the Natural Science Foundation of Shandong Province [grant number ZR2021MD001]the Laoshan Laboratory [grant number LSKJ202204302].
