Abstract:In modern electronic warfare， the competition between electronic interference and anti-interference is becoming more and more fierce， which has become a hotspot in the radar countermeasure field to develop the identification algorithms for radar active jamming. This paper analyzes the radar active jamming recognition algorithm in details， and summarizes the general process of jamming identification methods in the world. Firstly， the types of common radar jamming are divided， and the jamming mechanism and the signal model of current common radar active jamming signal are introduced in details. Then from the feature-extraction means and the design of the classifiers， the flow of the jamming identification algorithm are analyzed comprehensively. Finally， the future development directions of the radar active jamming identification algorithms are prospected.

Abstract:Transient image is a fast image sequence in which a scene responds to light pulses. By capturing the time dimension information， the transient image realizes the use of the scene information contained in the time domain， and the non-line-of-sight imaging is the most typical application of transient images in the field of scene analysis. It is a technology for imaging objects or scenes outside the line of sight， and has emerged at home and abroad in recent years. According to different imaging mechanisms， this paper classifies different imaging methods of transient images， and compares a variety of non-line-of-sight imaging algorithms based on transient images according to different algorithm principles or implementation effects. Finally， the challenges of non-line-of-sight imaging technology based on transient images are summarized， and the future development direction is prospected.

Abstract:Remote sensing image change detection has resulted in great breakthroughs in the field of land cover observations. However， the noise of remote sensing image will impact the performance of the change detection methods. To improve the accuracy of change detection， a change detection method based on the Siamese multi-scale attention network （SMA-Net） has been proposed. Firstly， we combine atrous convolutional layers with different dilated rates and spatial attention module to get the multi-scale feature extraction module. Then， the feature maps on the same layer are subtracted to get the difference feature maps and the channel attention mechanism is used to enhance the feature extraction effect. Finally， the change detection result is output by fully connection layers. The proposed method is compared with other change detection methods on the original remote sensing image data with or without noise data. The experimental result shows that the change detection method which uses the spectral information of a single pixel as input， like support vector machine method， is susceptible to the image noise， and the convolutional neural network （CNN） based method is much less susceptible to the image noise. The proposed SMA-Net outperforms other methods on the accuracy and is less susceptible to the image noise.

Abstract:In this paper， an active contour model combined with the hybrid signed pressure function is proposed to segment the images with intensity inhomogeneity or noise. Firstly， according to the position of the current active contour， we define a hybrid signed pressure function by using the global and local information of the image， which is a linear combination of a global pressure and a local one with adaptive weights. Then， an evolution equation of the active contour is constructed based on the hybrid signed pressure function. Finally， an alternating iteration algorithm is devolved to solve the model. Different synthetical， medical and real images are used to test the model. The experimental results show that the proposed model is robust to the initial contour and can effectively segment the images with intensity inhomogeneity or noises. Compared with other active contour models， the proposed model has the best performance.

Abstract:After comparing three channels of RGB（Red-green-blue） and three channels of HSV（Hue-saturation-value） in the same scene between clear and fog pictures， a haze removal algorithm based on power exponent stretching is proposed. Firstly， the image is transformed from RGB to HSV space. Then the saturation component and the brightness component are exponentially stretched with power of 1—3，and then they are both adjusted to their suitable range. After stretching transformation of saturation and brightness， the image is transformed from HSV to RGB space to generate enhanced defogging images. Taking the mean value of saturation， brightness index， information entropy and contrast as defog evaluation indexes， the optimal stretching power index combination is determined. The optimal power index combination is used to complete the defogging process. At the same time， it is decided whether to find the optimal power index again according to the change of image average saturation or the length of time interval. Finally， the fog removal algorithm is implemented by multi-process programming with the Python software. When the image resolution is 400 pixel×300 pixel， it takes 5.077—6.160 s to optimize the power index parameters on the raspberry PI. For one frame defogging， the first frame takes longer time of 0.308 s. The other frames take 0.077—0.168 s to removal haze for a single frame.

Abstract:Based on the single shot multibox detector （SSD） single-stage face detection model， this paper proposes a multi-size occlusion face detection method based on a hierarchical attention enhancement network to solve the problem of poor accuracy of face detection under complex partial occlusion. Firstly， on the multi-layer original feature map of SSD basic network， the attention enhancement mechanism is introduced to improve the response value of the visible region of the face. Then， different anchor sizes are designed for different enhancement feature layers to improve the hierarchical recognition effect of multi-scale occluded face. In training， the attention loss function， the classification loss function and the regression loss function are fused into a multi-task loss function to jointly optimize the network parameters. Experiments on the WIDER FACE dataset and the MAFA occlusion face dataset show that the detection accuracy and timeliness of the method are better than those of the current mainstream occlusion face detection methods.

Abstract:The existing eye location algorithms have some disadvantages of single task and performance degrade in complex environment such as illumination， glasses and occlusion， so a multi- efficient， light-YOLO and lightweight neural network， MEL-YOLO， is designed for obtaining eye multi-attributes and landmarks. Based on the YOLOV3 network， combining with the enhanced DS-sandglass block， a denormalized coding and encoding method is used in the regression branch of key points to promote the network positioning depth， and the complete intersection-over-union （CIoU） and the mean square error （MSE） are introduced into the loss function， so promoting the overall performance of the network. On the near-infrared dataset， the MEL-YOLO network achieves the position accuracy of 100%， and achieves the attribute recognition rate and the landmark accuracy rate of 98.7% and 96.5%， while reaches 92% and 91% on the UBIRS dataset. The experimental results demonstrate that the MEL-YOLO network can accurately obtain eye multi-attributes and key point information. Also， it is proved that MEL-YOLO is small and robust， and has the firm generalization ability， thus applying to low-performance edge computing devices.

Abstract:Traditional visual tracking methods based on the Siamese network extract pairs of frames from a large number of videos and train them on the offline independently at the stagey of training. They lack the update of the model features and neglect the background information， so the tracking accuracy is a little bit low in the complex environments such as background clutter. In response to the above problems， this paper proposes a dual-path Siamese network visual tracking method with the attention mechanism. The method mainly includes the feature extractor part and the feature fusion part. In the feature extractor part， the residual network is improved and a dual-path network model is designed. By combining the reusability of the residual networks to features of the former layer and the extraction of new features from the dense networks， these two networks are spliced for the feature extraction. At the same time， this paper uses the dilated convolution to replace the traditional convolution， which improves the resolution on the condition of maintaining a certain receptive field. This dual-path feature extraction method can implicitly update the model features， so that obtain the more accurate image feature information. Moreover， the attention mechanism is introduced to the feature fusion part， which can distribute the different weights to the different parts of the feature maps. In the channel domain， the method screens the valuable target image information and enhances the interdependence between the channels. In the spatial domain， it also pays more attention to the local important information and learns more rich contextual connections， which effectively improves the accuracy of object tracking. To confirm the effectiveness of the method， some experiments are conducted on the OTB100 and VOT2016 datasets. We use precision， success rate and expect average overlap-rate as the evaluation criterion， and their values are 0.868， 0.641 and 0.350 respectively on the two datasets， which increase by 5.1%， 2.0% and 0.9% compared with those of the benchmark model. Experimental results show that the proposed method makes full use of the advantages of different networks， and while ensuring the accuracy of the model， it can adapt to the deformation of the target well， reduce the interference between the similar objects， and achieve more stable tracking effect.

Abstract:Aiming at the problems of low accuracy and slow speed when only relying on traditional object detection and tracking algorithms in complex scenes， a pedestrian detection and tracking algorithm based on GhostNet and attention mechanism is proposed. First， the backbone network of YOLOv3 is replaced with GhostNet to retain the multi-scale prediction part， the Ghost module is used to reduce the parameters and calculations of the deep network model， and the attention mechanism is integrated into the Ghost module to give higher weight to important features. Then， the direct evaluation index GIoU of object detection is introduced to guide the regression task. Finally， the Deep-Sort algorithm is used for tracking. Experiments on public data sets show that： The mean Average precision （mAP） of the improved model reaches 92.53%， and the frame rate is 2.5 times that of the YOLOv3 model； The tracking accuracy of the proposed algorithm is better than that before the improvement and that of other algorithms； The algorithm can track multi-object pedestrians in complex scenes accurately and effectively， and has strong robustness.

Abstract:With the rise of deep learning， person re-identification has gradually become a hot topic in the computer vision field. It performs cross-camera retrieval through a given query image， and finds the images that match the query identity. However， due to the factors such as background and illumination under different cameras， there are a large number of hard negative samples in the collected pedestrian datasets， and the performance of the model trained using these samples is bad and lacks robustness. Therefore， in order to improve the ability of the model to discriminate such negative samples， a novel method of synthesizing images with hard negative samples information through confusion factors is designed. For each input batch images， the similarity measurement is used to find the hard negative sample corresponding to each image， the new generated images with the clues of negative samples are synthesized through the confusion factor， and the model is prompted to mine the negative samples information in a supervised manner thus improving the model robustness. A large number of comparative experiments show that the proposed method achieves high performance on the mainstream datasets. The ablation study proves the effectiveness of the proposed method.

Abstract:In view of the high incidence of intraoperative hypothermia and complex influencing factors in patients undergoing anesthesia， a prediction model of intraoperative hypothermia based on feature selection and XGBoost optimization is proposed to better assist doctors in the clinical diagnosis of patients. Firstly， the random forest （RF） is used to deal with the high-dimensional data sets， and features are selected by the RF out-of-bag estimation. Then， XGBoost hyperparameters are optimized using the genetic algorithm based on elite retention strategy， i.e.， EGA. Finally， the prediction is trained according to the optimal parameters and thus can be used to predict intraoperative hypothermia. This model combines the advantages of three algorithms to improve model generalization ability and prediction accuracy. The experimental result shows that the proposed model performs better other seven machine learning classification prediction models such as logistic regression， support vector machine， and so on in prediction accuracy， precision， recall and AUC， and overcomes the three representative hyperparameter tuning methods.

Abstract:Acute pancreatitis （AP） is one of the most common digestive disease， while the analysis based on medical images of AP still depends on simple manual features with low efficiency and accuracy， which is not commensurate with AP’s harmfulness. Due to the anatomical variation of pancreas and complications of AP， AP has complex imaging manifestations and large appearance pattern variation of lesions that exist among patients and lesion kinds. It is challenging for diagnosis of acute pancreatitis based on CT images. To address these issues， we propose a dual-attention network for acute pancreatitis diagnosis. Specifically， the dual-attention network utilizes the global feature to generate local attention feature for each local feature on different stages， and final classification is facilitated by the fusion of multi-scale attention features focusing on lesions of different scales. Meanwhile， channel-domain attention is used to produce attention features based on the dependencies between each channel to improve the model’s feature representation ability. We evaluate the proposed method on the collected real acute pancreatitis dataset. Results show that the proposed network achieve superior performance in acute pancreatitis diagnosis compared with several competing methods， with the sensitivity improved by 3.4%. And the improvement of area under the curve （AUC） the proposed network brings to ResNet is 2.7% higher than other attention model such as SENet.

Abstract:The radiation treatment plan system needs to calculate the dose distribution accurately based on CT images， but sometimes clinical MR images can only be obtained. Image synthesis effectively creates new modality images from another modality， which enhances image information. This paper presents a new method of synthesizing high precision and definition of CT images from MR images. To synthesize clearly pseudo CT images， an improved cycle-consistent generative adversarial network （CycleGAN） with densely connected convolutional network （DenseNet） is proposed. Avoiding the disappearance of input information and the vanishing of gradient information， the improved network can synthesize more credible CT images. Compared with the original method， the proposed method is reduced by 5.9% on mean absolute error， increased by 1.1% on structural similarity and increased by 4.4% on peak signal to ratio， which is trained and tested on the dataset of 18 patients. And compared with the deep convolutional neural network and the atlas-based method， the improved CycleGAN is reduced by 0.065% and 0.55% on relative error， respectively. The proposed method can synthesize more vivid CT images owing to the advantages of deep learning model， which better meets the requirements of dose calculation in radiation treatment planning system.

Abstract:The technology of brain-computer interface （BCI） based on motor imagery （MI） has developed rapidly in the past few decades and been widely used in various fields. To compare the brain electrical activity difference between motor execution （ME） and MI， a method based on the time-frequency domain analysis of electroencephalogram （EEG） is proposed. The visually induced upper limb ME and MI control experiments are conducted and the EEG signals of ten healthy subjects are collected and preprocessed. Then the signals are decomposed and converted into eigenvalues of each band through the time-frequency analysis method. Finally， the power values of each band of ME and MI are analyzed and the power differences between each band of ME and MI are computed. The results show that the alpha wave is dominant wave in the process of MI while the delta wave is dominant wave in the process of ME. Compared with MI， the alpha wave during ME shows a downward trend and the delta wave shows an upward trend. The results of this study show that there is significant difference in EEG between ME and MI， which is important for improving the real-time and universal performance of MI based BCI systems.

Abstract:Different from traditional image text recognition tasks， the Burmese optical character recognition （OCR） requires computers to recognize complex characters nested and combined by multiple characters in a receptive field， which brings great challenges to Burmese OCR tasks. To solve this problem， a Burmese OCR method based on knowledge distillation is proposed. This paper constructs a model of teacher network and student network using the framework of convolutional neural networks （CNN）+ recurrent neural networks （RNN） to train in an integrated learning way. In the training process， the teacher integrated sub-network is coupled with the student network to realize the alignment of the local character image features corresponding to a single receptive field in the student network and the overall character image features in the teacher network， so as to enhance the acquisition of local features in long sequence character images. The experimental results show that the performance of our model is better than the baseline by 2.9% and 2.7% respectively without and with background noise images as training data sets.

Abstract:Extreme learning machine has a series of achievements on batch processing due to high-activity processing， superior performance， less manual parameter settings and so on， which has been successfully applied in multi-label classification. However， data streams emerging in the real-world applications present the characteristics of high-volume， high-speed， multi-label and concept drift， which poses the challenges in accuracy， time and space consumptions for traditional multi-label classification algorithms. Therefore， this paper proposes a multi-label classification data stream ensemble approach based on kernel extreme learning machine （KELM）. Firstly， to adapt to the environment of data streams， the sliding window mechanism is used to partition data chunks， and an ensemble model consisted of k KELM models is built on k data chunks. Meanwhile， considering the label correlation， the Apriori algorithm is used to achieve the association rules of labels， and the confidence of label occurrence is introduced in the prediction using the generated model. Secondly， the MUENLForest model is introduced to detect whether a concept drift occurs in the new arriving data chunk， correspondingly the loss function is specified to update the ensemble model for adapting to concept drifts. Finally，massive experiments on the real multi label data sets demonstrate that the proposed approach outperforms the traditional multi label classification methods in accuracy and can adapt data drifts in multi label data streams quickly.

Abstract:Flame equivalence ratio measurement using flame color modeling method， is an emerging research direction in the combustion diagnosis technology. At present， the modeling methods mainly use the blue/green color features （B/G） in the RGB（Red-green-blue）model as the modeling input， however， the color equivalence ratio modeling by single color ratio fitting has large uncertainty and measurement errors. Therefore， this paper proposes to use the multi-color feature parameters under different-color models as the modeling inputs. Firstly， the digital flame color distribution （DFCD） technology is used to process the methane premixed flame image and obtain the region of interest （RoI） of flame images. Secondly， the flame color feature variables are comprehensively analyzed， and the multi-color features under different color models are designed and extracted， which are 36 color features. Then， the Spearman rank correlation analysis and random forest （RF） algorithm are used to screen out the deeper color features， and 16 dimensional high-quality features are selected. At last， the optimal support vector machine （SVM） parameters are selected using the grid search method （GSM）. Furthermore， the equivalence ratio measurement model of premixed methane flame is trained by SVM using the feature subset constructed. The algorithm is compared with the traditional BP neural network and the extreme learning machine （ELM） algorithm. Experimental results show that the algorithm has better regression prediction effect， and the mean square error （MSE） decreases to 0.023.

Abstract:To improve the intelligent design of the rural power distribution network， this paper proposes to identify the typical power targets that affect the design of the distribution network in the aerial images using deep neural networks. Firstly， we use UAV to obtain high spatial resolution aerial images of the distribution network planning area， and construct a data set containing 11 categories and 32 118 typical power targets. Then， through the practical comparison of Faster-RCNN， YOLO and single shot multibox detector （SSD） methods， SSD is selected to detect and identify typical power targets. Finally， feasible areas of distribution network pole planning are obtained. Experimental results show that compared with Faster-RCNN and YOLO， SSD can effectively detect and identify typical power targets such as the substation， distribution room and box transformer， and the recognition accuracy reaches 68.5%， which meets the practical requirements. The proposed method provides the technical support for the power design， reduces the labor cost and improves the efficiency of distribution network design.

Abstract:Aiming at the different threat levels under phased array radar multi-target tracking， the Bayesian Cramer-Rao lower bound （BCRLB） of the target position estimation is used as the allocation criterion. The paper establishes a multi-target tracking time resource allocation optimization model based on the threat degree. The model based on the threat degree to track the target can be divided into two categories and different types use different time resource allocation methods. Due to the time-consuming operation and optimization algorithm， this paper also proposes a multi-target tracking time resource fitting method based on back propagation（BP） neural network. Computer simulation shows that the model and the method can keep the target tracking in the best state， and the BP neural network reduces time consumption by more than two thousand times.

Abstract:The cooperation mechanism in unmanned aerial vehicle（UAV） ad-hoc network is studied. A cooperative time division multiple access （TDMA） protocol for UAV ad-hoc network based on dynamic relay selection is proposed. The protocol introduces the dual-queue cooperation mechanism when transmitting the relay packets， an independent media access control （MAC） layer relay packet buffer queue is introduced in addition to the network layer packet buffer queue. The protocol can also realize the dynamic selection of the default relay node and the helper relay node， so as to adapt to the heavy traffic load and the rapid change of network topology. The simulation results show that the through the dynamic selection mechanism of the relay node， the proposed cooperative TDMA protocol can obtain higher packet delivery rate and lower end-to-end delay than the traditional TDMA protocol and opportunistic cooperative relay time division multiple access （OCR-TDMA） protocol， when the network traffic load is heavy and the topology changes rapidly.

Abstract:This paper designs a JPEG compressed domain digital watermarking system based on FPGA， realizing the real-time embedding of watermark information in JPEG image. After watermarking information is preprocessed by binary and Arnold transform， watermark is embedded into the quantized DCT coefficients with improved LSB embedding algorithm. Then， to complete the JPEG compressed domain digital watermark embedding， the modified DCT coefficients are processed by entropy coding process， and JPEG encoding file is generated. Finally， the design is implemented and tested by the joint system of FPGA develop board and host computer. The results show that the proposed algorithm has a good performance of invisible effect and robustness， and a high throughput.