An iterative detection/decoding algorithm of correlated sources for the LDPC-based relay systems is presented. The signal from the source-destination(S-D) link is formulated as a highly correlated counterpart from the...An iterative detection/decoding algorithm of correlated sources for the LDPC-based relay systems is presented. The signal from the source-destination(S-D) link is formulated as a highly correlated counterpart from the relay-destination(R-D) link. A special XOR vector is defined using the correlated hard decision information blocks from two decoders and the extrinsic information exchanged between the two decoders is derived by the log-likelihood ratio(LLR) associated with the XOR vector. Such the decoding scheme is different from the traditional turbo-like detection/decoding algorithm, where the extrinsic information is computed by the side information and the soft decoder outputs. Simulations show that the presented algorithm has a slightly better performance than the traditional turbo-like algorithm(Taking the(255,175) EG-LDPC code as an example, it achieves about 0.1 dB performance gains aroundBLER=10^(-4)). Furthermore, the presented algorithm requires fewer computing operations per iteration and has faster convergence rate. For example, the average iteration of the presented algorithm is 33 at SNR=1.8 dB, which is about twice faster than that of the turbo-like algorithm, when decoding the(961,721) QC-LDPC code. Therefore, the presented decoding algorithm of correlated sources provides an alternative decoding solution for the LDPC-based relay systems.展开更多
A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decod...A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.展开更多
By introducing the bit-level multi-stream coded Layered Space-Time （LST） transmitter along with a novel iterative MultiStage Decoding （MSD） at the receiver, the paper shows how to achieve the near-capacity perform...By introducing the bit-level multi-stream coded Layered Space-Time （LST） transmitter along with a novel iterative MultiStage Decoding （MSD） at the receiver, the paper shows how to achieve the near-capacity performance of the Multiple-Input Multiple-Output （MIMO） systems with square Quadrature Amplitude Modulation （QAM）. In the proposed iterative MSD scheme, the detection at each stage is equivalent to multiuser detection of synchronous Code Division Multiple Access （CDMA） multiuser systems with the aid of the binary representation of the transmitted symbols. Therefore, the optimal Soft-Input Soft-Output （SISO） multiuser detection and low-complexity SISO multiuser detection can be utilized herein. And the proposed scheme with low-complexity SISO multiuser detection has polynomial complexity in the number of transmit antennas M, the number of receive antennas N, and the number of bits per constellation point Me. Simulation results demonstrate that the proposed scheme has similar Bit Error Rate （BER） performance to that of the known Iterative Tree Search （ITS） detection.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.展开更多
Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decode...Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.展开更多
Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receive...The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out(SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference(IBI),and so the receiving bit error rate(BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check(LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.展开更多
With the development of manufacture technology, the multi-level cell(MLC)technique dramatically increases the storage density of NAND flash memory. As the result,cell-to-cell interference(CCI) becomes more serious and...With the development of manufacture technology, the multi-level cell(MLC)technique dramatically increases the storage density of NAND flash memory. As the result,cell-to-cell interference(CCI) becomes more serious and hence causes an increase in the raw bit error rate of data stored in the cells.Recently, low-density parity-check(LDPC)codes have appeared to be a promising solution to combat the interference of MLC NAND flash memory. However, the decoding complexity of the sum-product algorithm(SPA) is extremely high. In this paper, to improve the accuracy of the log likelihood ratio(LLR) information of each bit in each NAND flash memory cell, we adopt a non-uniform detection(N-UD) which uses the average maximum mutual information to determine the value of the soft-decision reference voltages.Furthermore, with an aim to reduce the decoding complexity and improve the decoding performance, we propose a modified soft reliabilitybased iterative majority-logic decoding(MSRBI-MLGD) algorithm, which uses a non-uniform quantizer based on power function to decode LDPC codes. Simulation results show that our design can offer a desirable trade-off between the performance and complexity for high-column-weight LDPC-coded MLC NAND flash memory.展开更多
Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and ...Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.展开更多
An iterative detection and decoding algorithm with outer code decision feedback is proposed for the dual polarized( DP) land mobile satellite( LMS) MIMO systems using concatenated codes. A feedback structure is added ...An iterative detection and decoding algorithm with outer code decision feedback is proposed for the dual polarized( DP) land mobile satellite( LMS) MIMO systems using concatenated codes. A feedback structure is added after the outer decoder in the proposed algorithm. The feedback information is exploited to control the detecting list in the MIMO detector and reduce the number of symbols which have to be processed at each iteration. As a result,the computational complexity is reduced. Meanwhile,the successfully decoded outer code words are used to calculate the more reliable initial information for the inner decoder and the system performance can be improved by this step. The simulation results show that the proposed algorithm can reduce the computational complexity compared to the traditional iterative detection and decoding algorithm and achieve better performance.展开更多
In this paper, we propose a new iterative detection and decoding scheme based on parallel interference cancel (PIC) for coded MIMO-OFDM systems. The performance of proposed receiver is improved through the joint PIC M...In this paper, we propose a new iterative detection and decoding scheme based on parallel interference cancel (PIC) for coded MIMO-OFDM systems. The performance of proposed receiver is improved through the joint PIC MIMO detection and iterative detection and decoding. Its performance is evaluated based on com-puter simulation. The simulation results indicate that the performance of the proposed receiver is greatly im-proved compared to coded MIMO-OFDM systems based on VBLAST detection scheme.展开更多
Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection...Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection that can approach near-optimal performance. However, some extra computational complexity is contained in K-Best SD. In this paper, we propose an improved K-Best SD to reduce the complexity of conventional K-Best SD by assigning K for each level dynamically following some rules. Simulation proves that the performance degradation of the improved K-Best SD is very little and the complexity is significantly reduced.展开更多
Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significa...Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.展开更多
In this paper,a Joint Early Detection and Early Stopping (JEDES) approach for effectively reducing the complexity of turbo decoding with negligible performance loss is proposed. It combines the effectiveness of both e...In this paper,a Joint Early Detection and Early Stopping (JEDES) approach for effectively reducing the complexity of turbo decoding with negligible performance loss is proposed. It combines the effectiveness of both early detection and early stopping techniques. Our simulation results demon-strated that the proposed JEDES scheme based on cyclic redundancy check and trellis splicing can achieve a complexity saving of 15% to 20% at practical bit error rates over the idealized GENIE stopping scheme,which is widely accepted as the theoretically best possible early stopping scheme.展开更多
In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a s...In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.展开更多
The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photograp...The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
基金supported by NSF of China (No.61362010,61661005)NSF of Guangxi (No.2015GXNSFAA139290,2014GXNSFBA118276,2012GXNSFAA053217)
文摘An iterative detection/decoding algorithm of correlated sources for the LDPC-based relay systems is presented. The signal from the source-destination(S-D) link is formulated as a highly correlated counterpart from the relay-destination(R-D) link. A special XOR vector is defined using the correlated hard decision information blocks from two decoders and the extrinsic information exchanged between the two decoders is derived by the log-likelihood ratio(LLR) associated with the XOR vector. Such the decoding scheme is different from the traditional turbo-like detection/decoding algorithm, where the extrinsic information is computed by the side information and the soft decoder outputs. Simulations show that the presented algorithm has a slightly better performance than the traditional turbo-like algorithm(Taking the(255,175) EG-LDPC code as an example, it achieves about 0.1 dB performance gains aroundBLER=10^(-4)). Furthermore, the presented algorithm requires fewer computing operations per iteration and has faster convergence rate. For example, the average iteration of the presented algorithm is 33 at SNR=1.8 dB, which is about twice faster than that of the turbo-like algorithm, when decoding the(961,721) QC-LDPC code. Therefore, the presented decoding algorithm of correlated sources provides an alternative decoding solution for the LDPC-based relay systems.
基金supported in part by the National Key R&D Program of China(Grant No.2023YFB3307604)the Shanxi Province Basic Research Program Youth Science Research Project(Grant Nos.202303021212054 and 202303021212046)+3 种基金the Key Projects Supported by Hebei Natural Science Foundation(Grant No.E2024203125)the National Science Foundation of China(Grant No.52105391)the Hebei Provincial Science and Technology Major Project(Grant No.23280101Z)the National Key Laboratory of Metal Forming Technology and Heavy Equipment Open Fund(Grant No.S2308100.W17).
文摘A novel dual-branch decoding fusion convolutional neural network model(DDFNet)specifically designed for real-time salient object detection(SOD)on steel surfaces is proposed.DDFNet is based on a standard encoder–decoder architecture.DDFNet integrates three key innovations:first,we introduce a novel,lightweight multi-scale progressive aggregation residual network that effectively suppresses background interference and refines defect details,enabling efficient salient feature extraction.Then,we propose an innovative dual-branch decoding fusion structure,comprising the refined defect representation branch and the enhanced defect representation branch,which enhance accuracy in defect region identification and feature representation.Additionally,to further improve the detection of small and complex defects,we incorporate a multi-scale attention fusion module.Experimental results on the public ESDIs-SOD dataset show that DDFNet,with only 3.69 million parameters,achieves detection performance comparable to current state-of-the-art models,demonstrating its potential for real-time industrial applications.Furthermore,our DDFNet-L variant consistently outperforms leading methods in detection performance.The code is available at https://github.com/13140W/DDFNet.
基金the National Natural Science Foundation of China (No. 60472098 and No. 60502046).
文摘By introducing the bit-level multi-stream coded Layered Space-Time （LST） transmitter along with a novel iterative MultiStage Decoding （MSD） at the receiver, the paper shows how to achieve the near-capacity performance of the Multiple-Input Multiple-Output （MIMO） systems with square Quadrature Amplitude Modulation （QAM）. In the proposed iterative MSD scheme, the detection at each stage is equivalent to multiuser detection of synchronous Code Division Multiple Access （CDMA） multiuser systems with the aid of the binary representation of the transmitted symbols. Therefore, the optimal Soft-Input Soft-Output （SISO） multiuser detection and low-complexity SISO multiuser detection can be utilized herein. And the proposed scheme with low-complexity SISO multiuser detection has polynomial complexity in the number of transmit antennas M, the number of receive antennas N, and the number of bits per constellation point Me. Simulation results demonstrate that the proposed scheme has similar Bit Error Rate （BER） performance to that of the known Iterative Tree Search （ITS） detection.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
基金support for this work was supported by Key Lab of Intelligent and Green Flexographic Printing under Grant ZBKT202301.
文摘Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A multi-constraint loss function composed of one-to-one, one-to-many, and contrastive denoising losses is designed to address the problem of insufficient constraint force in predicting results with traditional methods. This loss function enhances the accuracy of model classification predictions and improves the proximity of regression position predictions to ground truth objects. The proposed method model is evaluated on the popular dataset UCF101-24 and JHMDB-21. Experimental results demonstrate that the proposed method achieves an accuracy of 81.52% on the Frame-mAP metric, surpassing current existing methods.
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金supported by the National Natural Science Foundation of China under Grants 61320106003 and 61401095the Civil Aerospace Technologies Research Project under Grant D010109The Fundamental Research Funds for the Central Universities under Grant YZZ17009
文摘The uplink of mobile satellite communication(MSC) system with hundreds of spot beams is essentially a multiple-input multiple-output(MIMO) channel. Dual-turbo iterative detection and decoding as a kind of MIMO receiver, which exchanges soft extrinsic information between a soft-in soft-out(SISO) detector and an SISO decoder in an iterative fashion, is an efficient method to reduce the uplink inter-beam-interference(IBI),and so the receiving bit error rate(BER).We propose to replace the linear SISO detector of traditional dual-turbo iterative detection and decoding with the AMP detector for the low-density parity-check(LDPC) coded multibeam MSC uplink. This improvement can reduce the computational complexity and achieve much lower BER.
基金supported in part by the NSF of China (61471131, 61771149, 61501126)NSF of Guangdong Province 2016A030310337+1 种基金the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2018D02)the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017-ZJ022)
文摘With the development of manufacture technology, the multi-level cell(MLC)technique dramatically increases the storage density of NAND flash memory. As the result,cell-to-cell interference(CCI) becomes more serious and hence causes an increase in the raw bit error rate of data stored in the cells.Recently, low-density parity-check(LDPC)codes have appeared to be a promising solution to combat the interference of MLC NAND flash memory. However, the decoding complexity of the sum-product algorithm(SPA) is extremely high. In this paper, to improve the accuracy of the log likelihood ratio(LLR) information of each bit in each NAND flash memory cell, we adopt a non-uniform detection(N-UD) which uses the average maximum mutual information to determine the value of the soft-decision reference voltages.Furthermore, with an aim to reduce the decoding complexity and improve the decoding performance, we propose a modified soft reliabilitybased iterative majority-logic decoding(MSRBI-MLGD) algorithm, which uses a non-uniform quantizer based on power function to decode LDPC codes. Simulation results show that our design can offer a desirable trade-off between the performance and complexity for high-column-weight LDPC-coded MLC NAND flash memory.
文摘Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis,treatment,and tracking of complex conditions,including neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases.While no definitive methods of diagnosis or treatment exist for either disease,researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers.Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment.However,such techniques require further development aimed at improving transparency,adaptability,and reproducibility.In this review,we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer’s and Parkinson’s diseases.
基金Sponsored by the Postdoctoral Science Foundation of China(Grant No.2011M500640)
文摘An iterative detection and decoding algorithm with outer code decision feedback is proposed for the dual polarized( DP) land mobile satellite( LMS) MIMO systems using concatenated codes. A feedback structure is added after the outer decoder in the proposed algorithm. The feedback information is exploited to control the detecting list in the MIMO detector and reduce the number of symbols which have to be processed at each iteration. As a result,the computational complexity is reduced. Meanwhile,the successfully decoded outer code words are used to calculate the more reliable initial information for the inner decoder and the system performance can be improved by this step. The simulation results show that the proposed algorithm can reduce the computational complexity compared to the traditional iterative detection and decoding algorithm and achieve better performance.
文摘In this paper, we propose a new iterative detection and decoding scheme based on parallel interference cancel (PIC) for coded MIMO-OFDM systems. The performance of proposed receiver is improved through the joint PIC MIMO detection and iterative detection and decoding. Its performance is evaluated based on com-puter simulation. The simulation results indicate that the performance of the proposed receiver is greatly im-proved compared to coded MIMO-OFDM systems based on VBLAST detection scheme.
文摘Multiple Input Multiple Output (MIMO) technology is of great significance in high data rate wireless communication. The K-Best Sphere Decoding (K-Best SD) algorithm was proposed as a powerful method for MIMO detection that can approach near-optimal performance. However, some extra computational complexity is contained in K-Best SD. In this paper, we propose an improved K-Best SD to reduce the complexity of conventional K-Best SD by assigning K for each level dynamically following some rules. Simulation proves that the performance degradation of the improved K-Best SD is very little and the complexity is significantly reduced.
文摘Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo (MCMC) simulation technique for Multiple-Input Multiple-Output (MIMO) systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability (APP) estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.
基金the National Natural Science Foundation of China (NSFC)the Research Grants Council of Hong Kong (RGC) joint research scheme (No.60218001/N_ HKUST617-02 and No.60272070).
文摘In this paper,a Joint Early Detection and Early Stopping (JEDES) approach for effectively reducing the complexity of turbo decoding with negligible performance loss is proposed. It combines the effectiveness of both early detection and early stopping techniques. Our simulation results demon-strated that the proposed JEDES scheme based on cyclic redundancy check and trellis splicing can achieve a complexity saving of 15% to 20% at practical bit error rates over the idealized GENIE stopping scheme,which is widely accepted as the theoretically best possible early stopping scheme.
基金funded by the Undergraduate Higher Education Teaching and Research Project(No.FBJY20230216)Research Projects of Putian University(No.2023043)the Education Department of the Fujian Province Project(No.JAT220300).
文摘In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.
文摘The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
