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With the rapid development of urban traffic, a large number of vehicles in cities not only bring convenience to people, but also bring a series of traffic problems, including traffic congestion and high traffic accident rates. Driving speed and waiting time of vehicles are two important factors of traffic problems. To simulate the real urban road traffic flow, a one-dimensional traffic flow grid model was proposed, which considered the nearest and next neighbour car at the same time, and connected the front and rear neighbour cars to optimize the traffic flow. The experiment results showed that our traffic flow grid model can simulate the real urban road traffic flow. In addition, we tried to optimize the urban traffic network model and improved the traffic speed of vehicles and reduced the waiting time.

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance. [ABSTRACT FROM AUTHOR]

Fruit detection is essential for harvesting robot platforms. However, complicated environmental attributes such as illumination variation and occlusion have made fruit detection a challenging task. In this study, a Transformer-based mask region-based convolution neural network (R-CNN) model for tomato detection and segmentation is proposed to address these difficulties. Swin Transformer is used as the backbone network for better feature extraction. Multi-scale training techniques are shown to yield significant performance gains. Apart from accurately detecting and segmenting tomatoes, the method effectively identifies tomato cultivars (normal-size and cherry tomatoes) and tomato maturity stages (fully-ripened, half-ripened, and green). Compared with existing work, the method has the best detection and segmentation performance for these tomatoes, with mean average precision (mAP) results of 89.4% and 89.2%, respectively. [ABSTRACT FROM AUTHOR]

Embedding similarity-based methods obtained good results in unsupervised anomaly detection (AD). This kind of method usually used feature vectors from a model pre-trained by ImageNet to calculate scores by measuring the similarity between test samples and normal samples. Ultimately, anomalous regions are localized based on the scores obtained. However, this strategy may lead to a lack of sufficient adaptability of the extracted features to the detection of anomalous patterns for industrial anomaly detection tasks. To alleviate this problem, we design a novel anomaly detection framework, MFFA, which includes a pseudo sample generation (PSG) block, a local-global feature fusion perception (LGFFP) block and an anomaly map compensation (AMC) block. The PSG block can make the pre-trained model more suitable for real-world anomaly detection tasks by combining the CutPaste augmentation. The LGFFP block aggregates shallow and deep features on different patches and inputs them to CaiT (Class-attention in image Transformers) to guide self-attention, effectively interacting local and global information between different patches, and the AMC block can compensate each other for the two anomaly maps generated by the nearest neighbor search and multivariate Gaussian fitting, improving the accuracy of anomaly detection and localization. In experiments, MVTec AD dataset is used to verify the generalization ability of the proposed method in various real-world applications. It achieves over 99.1% AUROCs in detection and 98.4% AUROCs in localization, respectively. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks have made significant progress in the field of intelligent analysis of remote-sensing images. However, the semantic segmentation task in high-resolution remote-sensing (HRRS) images always faces the problem of large-scale variation and complex background samples, which causes difficulties in distinguishing confusable ground objects. In this letter, we propose a novel multipath feature refinement network (MFRNet) to alleviate the above problems. We design the feature refinement module (FRM) to fuse features at various scales, which helps to capture different levels of spatial information. It also alleviates the boundary ambiguity problem by enhancing the learning of features with boundary information. The multiscale feature attention module (MFAM) combines atrous convolution and non-local block to obtain larger receptive fields and long-range contextual information, while the feature fusion module (FFM) balances semantic and spatial information, further improving the embedding of locally discriminative features. Experimental results on ISPRS Potsdam and LoveDA datasets indicate that the proposed MFRNet outperforms other semantic segmentation methods and excels in the accuracy and consistency of object boundary segmentation. [ABSTRACT FROM AUTHOR]

Finger vein recognition is considered to be a very promising biometric identification technology due to its excellent recognition performance. However, in the real world, the finger vein recognition system inevitably suffers from the single-sample problem: that is, only one sample is registered per class. In this case, the performance of many classical finger vein recognition algorithms will decline or fail because they cannot learn enough intra-class variations. To solve this problem, in this paper, we propose a SIFT-flow-based virtual sample generation (SVSG) method. Specifically, first, on the generic set with multiple registered samples per class, the displacement matrix of each class is obtained using the scale-invariant feature transform flow (SIFT-flow) algorithm. Then, the key displacements of each displacement matrix are extracted to form a variation matrix. After removing noise displacements and redundant displacements, the final global variation matrix is obtained. On the single sample set, multiple virtual samples are generated for the single sample according to the global variation matrix. Experimental results on the public database show that this method can effectively improve the performance of single-sample finger vein recognition. [ABSTRACT FROM AUTHOR]

English education is one of the most active research directions in the field of natural language processing. With the gradual implementation of deep learning in various fields, more and more industries have begun to use deep learning to carry out more efficient work. In the field of education, it is also urgent to adopt a more intelligent set of algorithms to relieve the pressure of teachers to correct test papers, and also to increase the fairness of non-subjective evaluations in the process of scoring. Teachers conduct teaching evaluation when the concept of teaching evaluation is not clear; there are defects in learning evaluation goals; there are many problems in the relationship between ability evaluation and knowledge evaluation; in the process of English teaching evaluation, the phenomenon of using summative evaluation instead of procedural evaluation is very serious. Therefore, this subject uses deep learning to study the problem of text line positioning and recognition. At the same time, this subject also builds a text scoring network based on RNN and STLM as a quantitative evaluation index for text line detection and recognition algorithms. We will examine students later. Whether problem-based learning theory can be used to promote deep learning among students to determine whether students' systematic use of PBL in teaching can promote the use of deep learning in college English courses. Finally, comparing the effects of deep learning and shallow learning, it is concluded that the evaluation of deep English teaching can provide students with more learning opportunities, access to more learning-related materials, and questions are more transparent and are free, which is easy. It is speculated that the purpose of this problem is to facilitate the use of deep learning methods to find meaning types. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient network to tackle three critical problems in high spatial resolution (HSR) remote sensing image segmentation: (i) feature misalignment, (i i) insufficient contextual information extraction and (i i i) various class imbalance issues. In detail, we propose a novel Feature Alignment Block (FAB) to suppress misalignment issues with the guide of an anchor map. Further, to extract sufficient information, we design a Contextual Augmentation Block (CAB) to augment features of different semantic levels. Finally, we present an Annealing Online Hard Example Mining (AOHEM) strategy to handle the various class imbalance issues with a view to dynamically adjust the focus of the network. We apply the above proposed designs to FPN to form our Attention-based Alignment Network (AANet). Experimental results demonstrate that the proposed method achieves promising results on the challenging iSAID and Vaihingen datasets with a better trade-off between accuracy and complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of modern information technology, students' education and computer technology begin to blend, and the modern teaching mode is quite different from the traditional education mode known in the past. In view of the current college English teaching in the information age, this study puts forward the way of integrating computer information technology with college English teaching, improves MLP algorithm, puts forward a new artificial intelligence algorithm, improves its calculation efficiency, and uses the optimized GA-MLP-NN (Genetic Neural Network Algorithm for the Multilayer Perceptron) algorithm in college students' oral correction program. Firstly, GA-MLP-NN algorithm is used to optimize college English teaching so that more complex structures can be learned and dealt with. Incremental hidden layer unit neural network is added, which makes the operation more accurate based on S-type recursive function. Then, the oral English system is established, using the GA-MLP-NN neural network model. Finally, we evaluate the parameters of the model, design a comparative experiment and a questionnaire survey to verify the rationality and feasibility of the guess, which proves that this method can deal with more complex programs, and make students learn English more handy and close to students' needs by using computer technology. [ABSTRACT FROM AUTHOR]

Detection of cotton bolls in the field environments is one of crucial techniques for many precision agriculture applications, including yield estimation, disease and pest recognition and automatic harvesting. Because of the complex conditions, such as different growth periods and occlusion among leaves and bolls, detection in the field environments is a task with considerable challenges. Despite this, the development of deep learning technologies have shown great potential to effectively solve this task. In this work, we propose an Improved YOLOv5 network to detect unopened cotton bolls in the field accurately and with lower cost, which combines DenseNet, attention mechanism and Bi-FPN. Besides, we modify the architecture of the network to get larger feature maps from shallower network layers to enhance the ability of detecting bolls due to the size of cotton boll is generally small. We collect image data of cotton in Aodu Farm in Xinjiang Province, China and establish a dataset containing 616 high-resolution images. The experiment results show that the proposed method is superior to the original YOLOv5 model and other methods such as YOLOv3,SSD and FasterRCNN considering the detection accuracy, computational cost, model size and speed at the same time. The detection of cotton boll can be further applied for different purposes such as yield prediction and identification of diseases and pests in earlier stage which can effectively help farmers take effective approaches in time and reduce the crop losses and therefore increase production. [ABSTRACT FROM AUTHOR]

Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction error on sub-backbones of one training image are used and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition. [ABSTRACT FROM AUTHOR]

Devices such as smartphones and tablets have spurred interest in off-the-person electrocardiogram (ECG) biometric recognition. While the advantage of using multi-feature information for establishing identities has been widely recognized, computational sparse representation models for multi-feature biometric recognition have only recently received more attention. We propose a unified sparse representation framework which collaboratively exploits joint and specific patterns for ECG biometric recognition. In particular, unlike joint sparse representation, which only considers the consistency among sparsity patterns of multiple features, we combine the consistent and pairwise constraints, which not only learn latent discriminant representations for all features but capture the interactions between them. In addition, our framework is universal and easily adapts to other multi-feature sparse representation models by just tuning the regularization parameters. The optimization problem is solved by an efficient alternating direction method of multipliers (ADMM). Extensive experiments on two publicly available off-the-person datasets demonstrate that our method can achieve competitive or even superior performance compared to state-of-the-art ECG biometric recognition methods. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) biometric recognition has recently received considerable attention and is considered to be a promising biometric trait. Although some promising results on PPG biometric recognition have been reported, challenges in noise sensitivity and poor robustness remain. To address these issues, a PPG biometric recognition framework is presented in this article, that is, a PPG biometric recognition model based on a sparse softmax vector and k-nearest neighbor. First, raw PPG data are rerepresented by sliding window scanning. Second, three-layer features are extracted, and the features of each layer are represented by a sparse softmax vector. In the first layer, the features are extracted by PPG data as a whole. In the second layer, all the PPG data are divided into four subregions, then four subfeatures are generated by extracting features from the four subregions, and finally, the four subfeatures are averaged as the second layer features. In the third layer, all the PPG data are divided into 16 subregions, then 16 subfeatures are generated by extracting features from the 16 subregions, and finally, the 16 subfeatures are averaged as the third layer features. Finally, the features with first, second, and third layers are combined into three-layer features. Extensive experiments were conducted on three PPG datasets, and it was found that the proposed method can achieve a recognition rate of 99.95%, 97.21%, and 99.92% on the respective sets. The results demonstrate that the proposed method can outperform current state-of-the-art methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroom-teaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods powerfully boost the recognition accuracy of finger veins, but real-time recognition cannot be guaranteed, especially in large-scale applications. Moreover, previous studies focused on either the matching task to enhance the accuracy or the indexing task to improve the efficiency. This paper proposes a finger vein code indexing method and combines it with a finger vein pattern matching method into an integration framework for improving both accuracy and efficiency. With the extracted vein patterns, the direction of each vein segment is detected and represented by the elliptical direction map as a feature for indexing, which will be encoded into a binary code by the angle K-means. The similarity between vein direction codes is measured by the grouped hamming distance in indexing, and further weighted by the overlap degree of the corresponding vein patterns to return the candidates for the probe. In addition, based on the above distance measurement, only vein segments with the same direction code are considered in following probe-to-candidate matching. Experimental results indicate that our indexing method outperforms the state-of-the-art methods and has competitive potential in performing the matching task. The results also indicate that the integration framework highly improves the identification efficiency with a slight improvement on the accuracy. [ABSTRACT FROM AUTHOR]

Automated segmentation of choroidal neovascularization (CNV) in optical coherence tomography (OCT) images plays an important role for the treatment of CNV disease. This paper proposes multi-scale convolutional neural networks with structure prior to segment CNV from OCT data. The proposed framework consists of two stages. In the first stage, the structure prior learning method based on sparse representation-based classification and the local potential function is developed to capture the global spatial structure and local similarity structure prior. The obtained prior can be used to improve the distinctiveness between CNV and background patches. In the second stage, multi-scale CNN model with incorporation of the learned structure prior is constructed for CNV segmentation. In this stage, multi-scale analysis is used to capture effective contextual information, which is robust to varying sizes of CNV. The proposed method was evaluated on 15 spectral domain OCT data with CNV. The experimental results demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

Abstract Biometric human identification refers to the automated recognition of individuals based on their biological or behavioral characteristics. Finger vein recognition and electrocardiogram (ECG) recognition have received a lot of attention for over ten years and these two traits are viewed as ones of promising biometric traits because of their unique advantages. However, these two independent unimodal biometrics, which using a single biometric trait for personal recognition, usually cannot satisfy the requirements of real-world applications. Finger vein and ECG signals have their unique advantages and also have their respective shortcomings. It has been well established that multimodal biometrics systems can overcome the respective defects of unimodal approach. Thus, how to jointly exploit these two modalities and develop an effective and efficient multimodal identification system are still open problems. However, to the best of our knowledge, there has been no study on the multimodal biometric systems using finger vein and ECG signals. Therefore, in this paper we make the first attempt to integrate finger vein with ECG signals for personal identification using various fusion strategies. A new multimodal biometric method based on Discriminant correlation analysis (DCA) for the fusion of finger vein and ECG is proposed. Extensive experiments are conducted on a merged bi-modal dataset named VeinECG coming from FVPolyU finger vein dataset and ECG-ID Dataset. Experimental results show that the proposed multimodal system is substantially superior to two individual unimodal systems in terms of both recognition accuracy and security. [ABSTRACT FROM AUTHOR]

Finger vein recognition has received a lot of attention recently and is viewed as a promising biometric trait. In related methods, vein pattern-based methods explore intrinsic finger vein recognition, but their performance remains unsatisfactory owing to defective vein networks and weak matching. One important reason may be the neglect of deep analysis of the vein anatomy structure. By comprehensively exploring the anatomy structure and imaging characteristic of vein patterns, this paper proposes a novel finger vein recognition framework, including an anatomy structure analysis-based vein extraction algorithm and an integration matching strategy. Specifically, the vein pattern is extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile. In addition, the extracted vein pattern is further thinned and refined to obtain a reliable vein network. In addition to the vein network, the relatively clear vein branches in the image are mined from the vein pattern, referred to as the vein backbone. In matching, the vein backbone is used in vein network calibration to overcome finger displacements. The similarity of two calibrated vein networks is measured by the proposed elastic matching and further recomputed by integrating the overlap degree of corresponding vein backbones. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art. [ABSTRACT FROM AUTHOR]

Finger vein pattern, as a promising hand-based biometric technology, has been well studied in recent years. In this paper, a new superpixel-based finger vein recognition method is presented. In the proposed method, we develop two types of effective superpixels, i.e. stable superpixel and discriminative superpixel to represent finger vein image and these superpixels are expected to play different roles in matching stage. In detail, the stable and discriminative superpixels are firstly learned from the training images for each enrolled class. When verifying a testing image, we just compare the superpixels at the same location as the two types of superpixels in template. Then, the two types of superpixels are combined utilizing a reversible weight-based fusion method in score level. Additionally, to further improve the recognition performance, we explore the superpixel context feature (SPCF). For each superpixel the SPCF is obtained by comparing the current superpixel with its surrounding neighbors. In the final matching stage, we integrate the matching score of two types of superpixels and it of the SPCF using the weighted SUM fusion method. The experimental results on two open finger vein databases, i.e. PolyU and SDUMLA-FV, show that our method not only performs better than the existing superpixel-based method, but also has advantages in comparison with some traditional ones. [ABSTRACT FROM AUTHOR]