Frequency diverse array(FDA)radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar.The representative feature of FDA i...Frequency diverse array(FDA)radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar.The representative feature of FDA is range-angle-time-dependent transmit beampattern and its underlying properties are continuously revealed in the research.The formulation and exploitation of the transmit diversity with a frequency increment is the fundamental principle,which brings extra degrees-of-freedom(DOFs)in the transmit dimension.As the FDA radar carries additional information in range,it provides more flexibility in signal processing and also brings in new technical issues.This article overviews the state-of-the-art in FDA radar area and its applications,mainly based on the progress in our group.There are two main catalogs in FDA radar area,namely coherent FDA and FDA-MIMO(multiple-input multiple-output)radars.Potential applications including target parameter estimation,ambiguous clutter suppression,and deceptive jammer suppression are discussed.展开更多
In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,...In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,but recently it has turned to biological applications with astounding results.The essence of signal processing is to describe the natural world by mathematical models and then,based on these models,develop efficient computational tools for solving engineering problems.Here,we underline,with examples,the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers.Based on this approach,a new field has emerged,called cancer systems biology.Despite its short history,cancer systems biology has already produced several success stories tackling previously impracticable problems.Perhaps most importantly,it has been accepted as an integral part of the major endeavors of cancer research,such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas(TCGA) project.Finally,we show that signal processing and cancer research,two fields that are seemingly distant from each other,have merged into a field that is indeed more than the sum of its parts.展开更多
Aiming at the prediction of the size of human cerebral hemorrhage point, a signal processing method based on Resonance Sparse Decomposition (RSSD) algorithm is proposed to decompose and analyze the microwave echo sign...Aiming at the prediction of the size of human cerebral hemorrhage point, a signal processing method based on Resonance Sparse Decomposition (RSSD) algorithm is proposed to decompose and analyze the microwave echo signal. According to the organizational structure of the human brain, a complete human brain model was established, and bleeding points of different sizes were placed at the same position, and 5 antennas were placed around the model (front, back, left, right, and top). RSSD is performed on the obtained echo signal, and Hilbert envelope analysis is performed on the low resonance component obtained by the decomposition, and then the size of the bleeding point is judged. Using CST and MATLAB to conduct simulation analysis and experiments, it is verified that the proposed method can successfully determine the size of the bleeding point, and the effectiveness and feasibility of the method are proved.展开更多
The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-bas...The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.展开更多
内容导读随着5G网络的广泛部署与应用,全球通信产业正迈入以智能化、泛在化、绿色化为核心特征的6G发展新阶段。6G不仅聚焦于极致的通信性能提升,还承载着构建智慧社会、推动产业数字化转型的使命。在这一背景下,物联网(Internet of Thi...内容导读随着5G网络的广泛部署与应用,全球通信产业正迈入以智能化、泛在化、绿色化为核心特征的6G发展新阶段。6G不仅聚焦于极致的通信性能提升,还承载着构建智慧社会、推动产业数字化转型的使命。在这一背景下,物联网(Internet of Things,IoT)作为“万物互联”的核心支撑技术,正朝着更大规模、更高智能、更强感知的方向演进,二者的深度融合成为引领未来通信技术发展的关键动力。展开更多
In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long distances...In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long distances.Hence,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area networks.Even in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things paradigm.In this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying networks.We present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC systems.To reach this analysis,we get channel realizations from random network layouts for indoor and outdoor scenarios.We adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC standards.The concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.展开更多
Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important nodes.In this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important n...Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important nodes.In this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed hypergraph.The algorithm introduces the Jaccard similarity of directed hypergraphs.By comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between nodes.An improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation mechanism.In addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed hypergraphs.Finally,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and monotonicity.Simulations of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.展开更多
A mode-pairing quantum key distribution based on heralded pair-coherent source with passive decoy-states is proposed,named HPCS-PDS-MP-QKD protocol,where the light sources at Alice and Bob sides are changed to heralde...A mode-pairing quantum key distribution based on heralded pair-coherent source with passive decoy-states is proposed,named HPCS-PDS-MP-QKD protocol,where the light sources at Alice and Bob sides are changed to heralded pair-coherent sources,and devices designed to implement passive decoy states are included at the transmitter sides to generate the decoy state pulses in the decoy-state window passively.With the defined efficient events and the designed pairing strategy,the key bits and bases can be obtained by data post-processing.Numerical simulation results verify the feasibility of the proposed protocol.The results show that the proposed protocol can exceed PLOB when the pairing interval setting is greater than 10^(3),and the transmission distance exceeds 200 km.When the key transmission distance reaches 300 km and the maximum pairing interval is equivalent to 1,its performance is improved by nearly 1.8 times compared to the original MP-QKD protocol with a weak coherent source(WCS-MP-QKD),and by 6.8 times higher than that of WCS-MPQKD with passive decoy states(WCS-PDS-MP-QKD).Meanwhile,the key transmission distance can reach 480 km,and surpasses the WCS-PDS-MP-QKD protocol by nearly 40 km.When the total pulse length is greater than 10^(11),the key generation rate is almost equal to that of infinite pulses.It is a promising QKD protocol that breaks the PLOB bound without requiring phase tracking and locking,has a longer transmission distance and a higher key generation rate,and eliminates the potential of side channel attack.展开更多
We propose a method to measure the flatness of an object with a petal-like pattern generated by the interference of the measured orbital angular momentum(OAM)beam and the reference OAM beam which carries the opposite ...We propose a method to measure the flatness of an object with a petal-like pattern generated by the interference of the measured orbital angular momentum(OAM)beam and the reference OAM beam which carries the opposite OAM state.By calculating the difference between the petal rotation angle without/with the object,the thickness information of the object,and then the flatness information,can be evaluated.Furthermore,the direction of the object’s flatness can be determined by the petal’s clockwise/counterclockwise rotation.We theoretically analyze the relationship between the object’s thickness and petal rotation angle,and verify the proposed method by experiment.The experimental results show that the proposed method is a high precision flatness measurement and can obtain the convex/concave property of the flatness.For the 1.02 mm glass sample,the mean deviation of the flatness is 1.357×10^(-8) and the variance is 0.242×10^(-16).For the 0.50 mm glass sample,the mean deviation of the flatness is 1.931×10^(-8) and the variance is 2.405×10^(-16).Two different topological charges are adopted for the 2.00 mm glass sample,and their flatness deviations are 0.239×10^(-8)(ℓ=1)and 0.246×10^(-8)(ℓ=2),where their variances are 0.799×10^(-18)(ℓ=1)and 0.775×10^(-18)(ℓ=2),respectively.It is shown that the flatness measured by the proposed method is the same for the same sample when different topological charges are used.All results indicate that the proposed method may provide a high flatness measurement,and will be a promising way to measure the flatness.展开更多
Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy ...Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed.In this paper,we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network,named variational quantum domain adaptation(VQDA).The data are first uploaded by a‘quantum coding module',then the feature information is extracted by several‘quantum convolution layers'and‘quantum pooling layers',which is named‘Feature Extractor'.Subsequently,the labels and the domains of the samples are obtained by the‘quantum fully connected layer'.With a gradient reversal module,the trained‘Feature Extractor'can extract the features that cannot be distinguished from the source and target domains.The simulations on the local computer and IBM Quantum Experience(IBM Q)platform by Qiskit show the effectiveness of the proposed method.The results show that VQDA(with 8 quantum bits)has 91.46%average classification accuracy for DA task between MNIST→USPS(USPS→MNIST),achieves 91.16%average classification accuracy for gray-scale and color images(with 10 quantum bits),and has 69.25%average classification accuracy on the DA task for color images(also with 10 quantum bits).VQDA achieves a 9.14%improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks.Simultaneously,the parameters scale is reduced to 43%by using VQDA when both quantum and classical DA methods have similar classification accuracies.展开更多
To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a de...To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.展开更多
Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high va...Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.展开更多
The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect a...The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect accuracy for certain classes.However,many studies treat deep learning models as black-box entities,lacking interpretability of their predictions.To address these limitations while ensuring good performance,we introduced an Improved SqueezeNet(I-SqueezeNet)by incorporating unique residual connections to improve the prediction performance,and we utilize Local Interpretable Model-Agnostic Explanations(LIME)to understand the interpretability.We evaluated the simplified SqueezeNet and I-SqueezeNet,with both channel and vertical concatenation,and compared their performances with those of some exiting methods such as Dieleman’s CNN,VGG13,DenseNet121,ResNet50,ResNext50,ResNext101,DSCNN and customized CNN in classifying galaxy objects using a dataset from the publicly available Galaxy Zoo Data Challenge Project.Our experiments showed that I-SqueezeNet with vertical concatenation achieved the highest average accuracy of 94.08%compared to other methods.Beyond achieving high accuracy,the application of LIME for model interpretation sheds light on the machine learning features and reasoning processes behind the model’s predictions.This information provides valuable insight into the galaxy morphology decision-making process,paving the way for further functional enhancements.展开更多
With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power i...With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process...Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.展开更多
In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on it...In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.展开更多
This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)decoding.It is known that the absolute inco...This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)decoding.It is known that the absolute incorrect log-likelihood ratio(LLR)values from the SC decoding can be very large.This phenomenon dramatically deteriorates the error correcting performance of the outer LDPC codes.In this paper,the LLR values of polar codes are regulated by a log processing before being sent to the LDPC decoder.Simulation results show that the log processing is an efficient approach with a low optimization complexity compared with the existing procedures to improve the performance of the serial concatenation systems.展开更多
With appropriate geometry configuration, helicopter- borne rotating synthetic aperture radar （ROSAR） can break through the limitations of monostatic synthetic aperture radar （SAR） on forward-looking imaging. With ...With appropriate geometry configuration, helicopter- borne rotating synthetic aperture radar （ROSAR） can break through the limitations of monostatic synthetic aperture radar （SAR） on forward-looking imaging. With this capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with the frequency modulated continuous wave （FMCW） technology. A novel geometric configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. Firstly, by per- forming the equivalent phase center principle, the separated trans- mitting and receiving antenna system is equalized to the case of system configuration with antenna for both transmitting and receiving signals. Based on this, the accurate two-dimensional spectrum is obtained and the Doppler frequency shift effect in- duced by the continuous motion of the platform during the long pulse duration is compensated. Next, the impacts of the velocity approximation error on the imaging algorithm are analyzed in de- tail, and the system parameters selection and resolution analysis are presented. The well-focused SAR image is then obtained by using the improved Omega-K algorithm incorporating the accurate compensation method for the velocity approximation error. FJnally, correctness of the analysis and effectiveness of the proposed al- gorithm are demonstrated through simulation results.展开更多
The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with.The proposed approach is applied in two steps：first,the ambiguous slant-range veloc...The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with.The proposed approach is applied in two steps：first,the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method,and second,the unambiguous slant-range velocity is found using a track-based criterion.The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large.Besides,the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal,therefore,the location performance would not be sacrificed in order to suppress clutter and/or interference.The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)(Nos.61931016,62071344 and 61911530246)the Key Laboratory Equipment Advanced Research Fund(No.6142206200210).
文摘Frequency diverse array(FDA)radar has been studied for more than 15 years and has attracted a lot of attention due to its potential advantages over the well-known phased array radar.The representative feature of FDA is range-angle-time-dependent transmit beampattern and its underlying properties are continuously revealed in the research.The formulation and exploitation of the transmit diversity with a frequency increment is the fundamental principle,which brings extra degrees-of-freedom(DOFs)in the transmit dimension.As the FDA radar carries additional information in range,it provides more flexibility in signal processing and also brings in new technical issues.This article overviews the state-of-the-art in FDA radar area and its applications,mainly based on the progress in our group.There are two main catalogs in FDA radar area,namely coherent FDA and FDA-MIMO(multiple-input multiple-output)radars.Potential applications including target parameter estimation,ambiguous clutter suppression,and deceptive jammer suppression are discussed.
文摘In this editorial we introduce the research paradigms of signal processing in the era of systems biology.Signal processing is a field of science traditionally focused on modeling electronic and communications systems,but recently it has turned to biological applications with astounding results.The essence of signal processing is to describe the natural world by mathematical models and then,based on these models,develop efficient computational tools for solving engineering problems.Here,we underline,with examples,the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers.Based on this approach,a new field has emerged,called cancer systems biology.Despite its short history,cancer systems biology has already produced several success stories tackling previously impracticable problems.Perhaps most importantly,it has been accepted as an integral part of the major endeavors of cancer research,such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas(TCGA) project.Finally,we show that signal processing and cancer research,two fields that are seemingly distant from each other,have merged into a field that is indeed more than the sum of its parts.
文摘Aiming at the prediction of the size of human cerebral hemorrhage point, a signal processing method based on Resonance Sparse Decomposition (RSSD) algorithm is proposed to decompose and analyze the microwave echo signal. According to the organizational structure of the human brain, a complete human brain model was established, and bleeding points of different sizes were placed at the same position, and 5 antennas were placed around the model (front, back, left, right, and top). RSSD is performed on the obtained echo signal, and Hilbert envelope analysis is performed on the low resonance component obtained by the decomposition, and then the size of the bleeding point is judged. Using CST and MATLAB to conduct simulation analysis and experiments, it is verified that the proposed method can successfully determine the size of the bleeding point, and the effectiveness and feasibility of the method are proved.
文摘The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.
文摘内容导读随着5G网络的广泛部署与应用,全球通信产业正迈入以智能化、泛在化、绿色化为核心特征的6G发展新阶段。6G不仅聚焦于极致的通信性能提升,还承载着构建智慧社会、推动产业数字化转型的使命。在这一背景下,物联网(Internet of Things,IoT)作为“万物互联”的核心支撑技术,正朝着更大规模、更高智能、更强感知的方向演进,二者的深度融合成为引领未来通信技术发展的关键动力。
基金supported by the Spanish Government and EU,under project PID2019-109842RB-I00/AEI/10.13039/501100011033。
文摘In Power Line Communications(PLC),there are regulatory masks that restrict the transmit power spectral density for electromagnetic compatibility reasons,which creates coverage issues despite the not too long distances.Hence,PLC networks often employ repeaters/relays,especially in smart grid neighborhood area networks.Even in broadband indoor PLC systems that offer a notable data rate,relaying may pave the way to new applications like being the backbone for wireless technologies in a cost-effective manner to support the Internet-of-things paradigm.In this paper,we study Multiple-Input Multiple-Output(MIMO)PLC systems that incorporate inband full-duplex functionality in relaying networks.We present several MIMO configurations that allow end-to-end half-duplex or full-duplex operations and analyze the achievable performance with state-of-the-art PLC systems.To reach this analysis,we get channel realizations from random network layouts for indoor and outdoor scenarios.We adopt realistic MIMO channel and noise models and consider transmission techniques according to PLC standards.The concepts discussed in this work can be useful in the design of future PLC relay-aided networks for different applications that look for a coverage extension and/or throughput:smart grids with enhanced communications in outdoor scenarios,and“last meter”systems for high-speed connections everywhere in indoor ones.
基金Project supported by the National Natural Science Foundation of China(Grant No.62166010)the Guangxi Natural Science Foundation(Grant No.2023GXNSFAA026087).
文摘Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important nodes.In this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed hypergraph.The algorithm introduces the Jaccard similarity of directed hypergraphs.By comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between nodes.An improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation mechanism.In addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed hypergraphs.Finally,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and monotonicity.Simulations of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.
基金Project supported by the National Natural Science Foundation of China(Grant No.62375140)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX241191 and SJCX250315)the Open Research Fund of the National Laboratory of Solid State Microstructures(Grant No.M36055)。
文摘A mode-pairing quantum key distribution based on heralded pair-coherent source with passive decoy-states is proposed,named HPCS-PDS-MP-QKD protocol,where the light sources at Alice and Bob sides are changed to heralded pair-coherent sources,and devices designed to implement passive decoy states are included at the transmitter sides to generate the decoy state pulses in the decoy-state window passively.With the defined efficient events and the designed pairing strategy,the key bits and bases can be obtained by data post-processing.Numerical simulation results verify the feasibility of the proposed protocol.The results show that the proposed protocol can exceed PLOB when the pairing interval setting is greater than 10^(3),and the transmission distance exceeds 200 km.When the key transmission distance reaches 300 km and the maximum pairing interval is equivalent to 1,its performance is improved by nearly 1.8 times compared to the original MP-QKD protocol with a weak coherent source(WCS-MP-QKD),and by 6.8 times higher than that of WCS-MPQKD with passive decoy states(WCS-PDS-MP-QKD).Meanwhile,the key transmission distance can reach 480 km,and surpasses the WCS-PDS-MP-QKD protocol by nearly 40 km.When the total pulse length is greater than 10^(11),the key generation rate is almost equal to that of infinite pulses.It is a promising QKD protocol that breaks the PLOB bound without requiring phase tracking and locking,has a longer transmission distance and a higher key generation rate,and eliminates the potential of side channel attack.
基金supported by the National Natural Science Foundation of China(Grant No.62375140)the Open Research Fund of National Laboratory of Solid State Microstructures(Grant No.M36055).
文摘We propose a method to measure the flatness of an object with a petal-like pattern generated by the interference of the measured orbital angular momentum(OAM)beam and the reference OAM beam which carries the opposite OAM state.By calculating the difference between the petal rotation angle without/with the object,the thickness information of the object,and then the flatness information,can be evaluated.Furthermore,the direction of the object’s flatness can be determined by the petal’s clockwise/counterclockwise rotation.We theoretically analyze the relationship between the object’s thickness and petal rotation angle,and verify the proposed method by experiment.The experimental results show that the proposed method is a high precision flatness measurement and can obtain the convex/concave property of the flatness.For the 1.02 mm glass sample,the mean deviation of the flatness is 1.357×10^(-8) and the variance is 0.242×10^(-16).For the 0.50 mm glass sample,the mean deviation of the flatness is 1.931×10^(-8) and the variance is 2.405×10^(-16).Two different topological charges are adopted for the 2.00 mm glass sample,and their flatness deviations are 0.239×10^(-8)(ℓ=1)and 0.246×10^(-8)(ℓ=2),where their variances are 0.799×10^(-18)(ℓ=1)and 0.775×10^(-18)(ℓ=2),respectively.It is shown that the flatness measured by the proposed method is the same for the same sample when different topological charges are used.All results indicate that the proposed method may provide a high flatness measurement,and will be a promising way to measure the flatness.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62375140 and 61871234)。
文摘Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed.In this paper,we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network,named variational quantum domain adaptation(VQDA).The data are first uploaded by a‘quantum coding module',then the feature information is extracted by several‘quantum convolution layers'and‘quantum pooling layers',which is named‘Feature Extractor'.Subsequently,the labels and the domains of the samples are obtained by the‘quantum fully connected layer'.With a gradient reversal module,the trained‘Feature Extractor'can extract the features that cannot be distinguished from the source and target domains.The simulations on the local computer and IBM Quantum Experience(IBM Q)platform by Qiskit show the effectiveness of the proposed method.The results show that VQDA(with 8 quantum bits)has 91.46%average classification accuracy for DA task between MNIST→USPS(USPS→MNIST),achieves 91.16%average classification accuracy for gray-scale and color images(with 10 quantum bits),and has 69.25%average classification accuracy on the DA task for color images(also with 10 quantum bits).VQDA achieves a 9.14%improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks.Simultaneously,the parameters scale is reduced to 43%by using VQDA when both quantum and classical DA methods have similar classification accuracies.
基金supported by the National Natural Science Foundation of China(62301598).
文摘To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
基金Hefei Municipal Natural Science Foundation,Grant/Award Number:2022009Suqian Guiding Program Project,Grant/Award Number:Z202309Suqian Traditional Chinese Medicine Science and Technology Plan,Grant/Award Number:MS202301。
文摘Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease.However,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process.In this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and quantification.The FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel classification.Additionally,the IEN incorporates specifically designed modules to extract relevant clinical indices.The authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable results.Through extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of 0.908.The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.
文摘The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect accuracy for certain classes.However,many studies treat deep learning models as black-box entities,lacking interpretability of their predictions.To address these limitations while ensuring good performance,we introduced an Improved SqueezeNet(I-SqueezeNet)by incorporating unique residual connections to improve the prediction performance,and we utilize Local Interpretable Model-Agnostic Explanations(LIME)to understand the interpretability.We evaluated the simplified SqueezeNet and I-SqueezeNet,with both channel and vertical concatenation,and compared their performances with those of some exiting methods such as Dieleman’s CNN,VGG13,DenseNet121,ResNet50,ResNext50,ResNext101,DSCNN and customized CNN in classifying galaxy objects using a dataset from the publicly available Galaxy Zoo Data Challenge Project.Our experiments showed that I-SqueezeNet with vertical concatenation achieved the highest average accuracy of 94.08%compared to other methods.Beyond achieving high accuracy,the application of LIME for model interpretation sheds light on the machine learning features and reasoning processes behind the model’s predictions.This information provides valuable insight into the galaxy morphology decision-making process,paving the way for further functional enhancements.
文摘With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
基金Project(2017YFC1405600)supported by the National Key R&D Program of ChinaProject(18JK05032)supported by the Scientific Research Project of Education Department of Shaanxi Province,China。
文摘Due to the limited scenes that synthetic aperture radar(SAR)satellites can detect,the full-track utilization rate is not high.Because of the computing and storage limitation of one satellite,it is difficult to process large amounts of data of spaceborne synthetic aperture radars.It is proposed to use a new method of networked satellite data processing for improving the efficiency of data processing.A multi-satellite distributed SAR real-time processing method based on Chirp Scaling(CS)imaging algorithm is studied in this paper,and a distributed data processing system is built with field programmable gate array(FPGA)chips as the kernel.Different from the traditional CS algorithm processing,the system divides data processing into three stages.The computing tasks are reasonably allocated to different data processing units(i.e.,satellites)in each stage.The method effectively saves computing and storage resources of satellites,improves the utilization rate of a single satellite,and shortens the data processing time.Gaofen-3(GF-3)satellite SAR raw data is processed by the system,with the performance of the method verified.
基金supported by the National Natural Science Foundation of China(61671352)the open foundation of Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology)(CRKL160206)Xi’an University of Science and Technology Doctor(after)Start Gold Project(2017QDJ018)
文摘In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.
基金supported in part by National Natural Science Foundation of China through grant 61501002in part by Natural Science Project of Ministry of Education of Anhui through grant KJ2015A102+1 种基金in part by Talents Recruitment Program of Anhui Universityin part by the Key Laboratory Project of the Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China, Anhui University
文摘This paper studies the decoding performance of low-density parity-check(LDPC)codes in a serial concatenation system with polar codes employing the successive cancellation(SC)decoding.It is known that the absolute incorrect log-likelihood ratio(LLR)values from the SC decoding can be very large.This phenomenon dramatically deteriorates the error correcting performance of the outer LDPC codes.In this paper,the LLR values of polar codes are regulated by a log processing before being sent to the LDPC decoder.Simulation results show that the log processing is an efficient approach with a low optimization complexity compared with the existing procedures to improve the performance of the serial concatenation systems.
基金supported by the National Basic Research Program of China(2011CB707001)the Fundamental Research Funds for the Central Universities(106112015CDJXY500001CDJZR165505)
文摘With appropriate geometry configuration, helicopter- borne rotating synthetic aperture radar （ROSAR） can break through the limitations of monostatic synthetic aperture radar （SAR） on forward-looking imaging. With this capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with the frequency modulated continuous wave （FMCW） technology. A novel geometric configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. Firstly, by per- forming the equivalent phase center principle, the separated trans- mitting and receiving antenna system is equalized to the case of system configuration with antenna for both transmitting and receiving signals. Based on this, the accurate two-dimensional spectrum is obtained and the Doppler frequency shift effect in- duced by the continuous motion of the platform during the long pulse duration is compensated. Next, the impacts of the velocity approximation error on the imaging algorithm are analyzed in de- tail, and the system parameters selection and resolution analysis are presented. The well-focused SAR image is then obtained by using the improved Omega-K algorithm incorporating the accurate compensation method for the velocity approximation error. FJnally, correctness of the analysis and effectiveness of the proposed al- gorithm are demonstrated through simulation results.
基金supported by the National Natural Science Foundation of China (60901066)the New Teacher Foundation of Ministry of Education (20090203120006)the Fundamental Research Funds for the Central University (10000902013)
文摘The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with.The proposed approach is applied in two steps：first,the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method,and second,the unambiguous slant-range velocity is found using a track-based criterion.The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large.Besides,the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal,therefore,the location performance would not be sacrificed in order to suppress clutter and/or interference.The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.
