Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Dense Crowd Detection Algorithm Based on YOLOv5

Due to the particularity with a large number of labels in dense crowd dataset, the dense crowd detection difficulty is mainly reflected in the missed detection and false detection of small objects. In order to better detect small objects, this paper proposes a more efficient feature fusion network based on YOLOv5. The experiment uses the crowd human public dataset, which improves the average accuracy of detection while ensuring the inference speed. Experimental results show that mAP@0.5 of the improved YOLOv5 network reaches 92.07%, which is 6.21% higher than the original YOLOv5s. The detection frame rate is 49FPS, which has good real-time detection ability. Therefore, the improved algorithm proposed in this work can be a better alternative in dense crowd detection task.
A Path Utilization-Based Congestion-Aware Deadlock-Free Routing for Network-on-Chip

Routing algorithm determines how to select a path for each packet transmission in network-on-chip (NoC). In this work, we propose a path utilization-based congestion-aware deadlock-free routing to enhance the communication efficiency for typical traffic patterns or real applications. Firstly, a path utilization model is proposed to indicate the congested paths in NoC under typical traffic patterns or real applications. Secondly, a greedy congestion-aware deadlock-free adaptive routing algorithm is proposed, which would bypass congested regions adaptively with no deadlock. Simulation results under a set of traffic patterns demonstrate the optimization effect of the proposed routing.
Towards O(1) Seeding of K-Means

K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of square of Euclidean distance of the points in the clusters from the respective means of the clusters. The simplicity and scalability of K-means makes it very appealing. However, K-means suffers from local minima problem, and comes with no guarantee to converge to the optimal cost. K-means++ tries to address the problem by seeding the means using a distance based sampling scheme. However, seeding the means in K-means++ needs $O\left(K\right)$ passes through the entire dataset. This could be very costly in large amount of dataset. Here we propose a method of seeding initial means based on factorizations of higher order moments for bounded data. Our method takes O(1) passes through the entire dataset to extract the initial set of means, and its final cost can be proven to be within $O(\sqrt{K})$ of the optimal cost. We demonstrate the performance of our algorithm in comparison with the existing algorithms on various benchmark datasets.
Improved YOLOv5 lightweight grassland smoke detection algorithm

To address the problems of low detection performance and large memory consumption of traditional smoke and fire detection methods in complex scenes such as grasslands. Based on the YOLOv5 model, a YOLOv5-GDE optimization model is proposed. The C3 module in YOLOv5 is replaced by GhostC3 with a smaller number of parameters, and some standard convolution blocks are replaced by depth-separable convolutions to make the model more lightweight. Finally, to solve the problem of unstable target regression frame, the EIoU loss function is introduced, which effectively improves the convergence speed and detection accuracy of the model. Experimental results on the homemade grassland smoke dataset show that the optimized model reduces the number of parameters and computational effort by 65.4% and 65.8%, respectively, compared with the original model, and the model size is only 36.8% of the original model, which is more suitable for smoke target detection in grassland scenes and more suitable for deployment in embedded devices with limited computational power, under the premise of ensuring detection accuracy.
Optimization Design of Ammunition Scheduling Scheme for Carrier-based Aircraft based on Improved DPSO Algorithm

Abstract: In order to minimize the execution time of ammunition scheduling scheme, an ammunition scheduling model for carrier-based aircraft is established in this paper, and a improved discrete particle swarm optimization (IDPSO) algorithm is designed for multiple links, multiple ammunition points and multiple demand points in the ammunition scheduling process. The IDPSO algorithm overcomes the disadvantage that the basic particle swarm is hard to deal with the discrete problem, by discretizing the particle speed and coordinates. The simulation results show that the IDPSO algorithm has faster convergence speed and global optimization ability. At the same time, the applicability of the scheduling model and the effectiveness of the IDPSO algorithm are verified. CCS CONCEPTS • Computing methodologies • Modeling and simulation • Model development and analysis
Measurement of buried depth and inclination rate of concrete poles based on binocular vision

Aiming at the low efficiency of the traditional detection methods of buried depth and inclination rate of concrete poles, a method of measuring concrete poles based on binocular vision was proposed. Firstly, an improved DeeplabV3+ semantic segmentation algorithm is proposed. Based on the original model structure, the backbone feature extraction network is modified, the feature fusion method is optimized, and the improved CBAM attention mechanism is added to reduce the model complexity and improved the accuracy of concrete pole area segmentation. Secondly, the sub-pixel edge extraction algorithm based on local area effect is used to determine the edge of the concrete pole in the image segmentation area, and the least squares method is used to fit the edge to determine the precise feature points. Finally, the coordinate transformation of binocular vision is used to calculate the depth and inclination of the concrete pole. Experiments show that the method has a buried depth measurement error of less than 10 cm, an error rate of less than 5%, and an inclination measurement error of less than 0.3°, which provides an automated concrete pole buried depth and inclination rate measurement solution
The Method and Implementation of Temperature Drift Self-compensation for 4T CIS Based on FPGA

In this paper, a temperature drift self-compensation method for 4T CMOS image sensor based on FPGA is designed to solve the problems of pixel drift rise and image noise caused by temperature change. This method can automatically extract the compensation coefficient of temperature drift, compensate the difference and adjust the dynamic range, so as to suppress the noise effect of temperature on image data. The experimental results show that the temperature drift self-compensation method has a good compensation effect. The checkerboard simulation reduces the mean value of the image by 14.63%, the peak signal-to-noise ratio is relatively stable, and the average gradient is increased by about 21%, which effectively weakens the influence of temperature on image brightness and detail clarity. It can be proved that the proposed method provides a reliable guarantee for the application of 4T CMOS image sensor in the space field under high temperature environment.
Research on Multi-level Classification Models for Imbalanced Network Intrusion Dataset

This paper explores the use of machine learning related methods to solve intrusion detection and bandwidth prediction for computer networks. It put forward multi-level classification models which cleverly solved the problem of categories of samples imbalanced dataset classification, and proposed a LSTM model for network intrusions bandwidth consumption prediction. The related research work has provided a reference for further network intrusion detection and bandwidth prediction.
DEPA: Determining Exploit Primitives Automatically for Interactive Programs

Automated Exploit Generation (AEG) is a well-known difficult task, especially for heap vulnerabilities. The premise of this task is the determination of exploit primitives, and prior research efforts for exploit primitive determination are usually based on vulnerability identification. However, it is not always easy to discovery bugs using fuzzing or symbolic technologies for some programs. In this paper, we present a solution DEPA to determine exploit primitives based on primitive-crucial-behavior model for heap vulnerabilities. The core of DEPA contains two novel techniques, 1) primitive-crucial-behavior identification through pointer dependence analysis, and 2) exploit primitive determination method which includes triggering both vulnerabilities and exploit primitives. We evaluate DEPA on real-world CTF (capture the flag) programs with heap vulnerabilities and DEPA can discovery arbitrary write and arbitrary jump exploit primitives for some programs except for program multi-heap. Results showed that primitive-crucial-behavior identification and determining exploit primitives are accurate and effective by using our approach. In addition, DEPA is superior to the state-of-the-art tools in determining exploit primitives for the heap internal overflow vulnerability.
Speech Emotion Recognition Based on Multi-feature Fusion and DCNN

In order to get more effective information and improve the recognition accuracy, this paper proposes a speech emotion recognition model based on multi-feature fusion and deep convolutional neural network. First, the speech emotion data is preprocessed to obtain the two-dimensional three-channel fusion feature parameters, which are used as the input layer of the AlexNet DCNN model. Second, the model is improved. Batch normalization is added after each convolutional layer, and use genetic algorithm and simulated annealing algorithm to optimize the model. Final, we use SoftMax classifier to classify emotion. In this paper, the cross-validation method is used to evaluate the model, and it is verified on the EMO-DB and IEMOCAP datasets. The experimental results verify that the method is superior to the existing speech emotion recognition technology.
Developing an algorithm for Sprouted Potato Recognition Based On Mobilenet-Yolov4

Aiming at the problems of poor environmental anti-interference, poor mobility of equipment, slow detection rate, false detection and missed detection in the traditional image recognition method of sprouted potato, this paper proposes a sprouted potato based on a lightweight image deep learning model (MobileNet-YOLOv4). The identification method can improve the accuracy and speed of sprouting potato identification, and at the same time improve the mobility of identification equipment. First, the collected sprouting potato picture sample data is enhanced by CutMix and Mosaic data to improve the generalization of the sample data, and the LabelImg tool is used for image labeling and data set production, and then based on the YOLOv4 model, use the more lightweight MobileNeV3 network structure replaces the main network structure (Backbone) in the original YOLOv4 model, thereby reducing the overall parameter amount of the model, improving the detection speed and the generalization of the model used in the device. Second, optimize the regression box loss function of the model, using the EIoU loss function with higher positioning accuracy is used to improve the accuracy of sprouting potato identification. Finally, the experimental results show that the improved EIoU+MobileNetv3-YOLOv4 model reduces the number of parameters by about 78% compared to the original YOLOv4 model, and the detection speed is improved 28%, the identification speed of sprouted potatoes is faster, the false detection and missed detection rate is lower, and the identification accuracy rate reaches 97.18%, thus providing better technical support for potato automation, high quality and rapid storage.
Applying Embedding Methods to Process Mining

The performance of process mining algorithms on a particular event log highly depends on the number of different processes present in the logs. Prior event log clustering can help find out which processes certain events in the logs belong to. Since log clustering is not always a simple task, a preliminary transition from logs to log embeddings can be an important step in solving process mining problems. In this paper, we apply different embedding methods to a dataset of event logs. By transitioning to log embeddings and applying clustering methods we improve the efficiency of process mining. The experiment results suggest that embeddings capturing events order perform better than others.
Ultra-Broadband RCS Reduction Using "Bow-Shaped" Polarization Conversion Metasurface

A novel ultra-wideband (UWB) metasurface has been proposed to realize radar cross-section (RCS) reduction based on double layer chessboard structured polarization conversion metasurface (PCM). The “bow-shaped” unit cell consists of three split rings and one strap, which can produce five resonance points to improve the bandwidth of convert linear polarized waves into their orthogonal polarized ones. The structure has an ultra-wideband linear polarization conversion efficiency with at least a 90% polarization conversion ratio (PCR) from 7.3 to 26.1 GHz. Using the proposed cell and its mirror cell to form a chessboard structured metasurface, and comparing it with a metal surface of the same size, the obtained simulation results show that the proposed metasurface realizes a 10dB monostatic RCS reduction from 7.3-25.8 GHz.
MLRaft: Improvement of Raft Based on Multi-log Synchronization Model

Raft is currently the most popular distributed consensus protocol. Due to the single leader mechanism of Raft, the performance of the distributed system is degraded to that of a single-machine system. Aiming at this shortcoming, this paper proposes a MLRaft protocol based on the multi-log synchronization model. MLRaft divides a single log file in Raft into n log files. For each log file, a leader can be elected to maintain the consistency of the log file. Multiple leaders jointly undertake the requests of the distributed system, which improves the performance of the distributed system and makes the entire cluster more balanced. At the same time, this paper also proposes the priority election method and the dynamic transfer mechanism of leader to ensure the uniform distribution of multiple leaders in distributed nodes. Finally, this paper built a 3-node KV storage system, and tested three aspects of throughput, latency, and load balance. The experimental results show that MLRaft has better performance than Raft.
Fruit and vegetable disease identification based on updating the activation function for the ConvNeXt model

Early identification of lesion types is important for fruit and vegetable treatment. Currently, fruit and vegetable disease classification is designed based on convolutional neural networks, and traditional convolutional neural networks perform poorly in fine-grained inter-class similarity tasks. In this paper, the ConvNeXt model, which has performed well in recent years, is chosen as the backbone network. The model is trained in its entirety with binary classification pre-learning followed by multi-classification recognition. The GELU activation function in ConvNeXt may suffer from neuron "necrosis", and the efficient activation function ERelu, which is more suitable for small inter-class variation, is chosen to replace it. The model was validated to be effective in the multi-classification task of fruit and vegetable diseases, and the overall accuracy of the model reached 89.20%, an improvement of 0.61% over the original model. In addition, the trained model is built on the service platform of WeChat applets in this paper to facilitate practical applications.
A Dynamic SLAM Algorithm Based on Lidar-Vision Fusion

SLAM (simultaneous localization and mapping) is based on the assumption of a static environment, and the external sensors equipped with SLAM have their own advantages and disadvantages. Therefore, poor lighting conditions, lack of geometric features and dynamic objects in the scene will interfere with the positioning accuracy of the SLAM algorithm based on a single sensor. To solve this problem, this paper proposes a dynamic SLAM algorithm based on Lidar-vision fusion. The algorithm provides depth information for image features through the fusion of monocular image sequences and Lidar scans. After rough positioning with fusion data, dynamic objects in the scenes are eliminated to further optimize the positioning accuracy. The comparative experimental results evaluated on 11 sequences in the KITTI odometry datasets demonstrate that the localization accuracy of the proposed algorithm is better than the vision-based ORB-SLAM2 and DynaSLAM algorithms.
Contrast-Teacher-Student(CTS): A novel Semi-supervised Learning Approach for Sleep Staging

Semi-supervised learning (SSL) can work well with limited labeled data and enormous unlabeled data. It is suitable for areas such as medical treatment or biological research, whose data is sufficient but the label is high-cost. Unfortunately, few researchers are doing relevant studies. In this paper, we propose Contrast-Teacher- Student(CTS) model, a novel semi-supervised learning approach for physiological signal processing which is important in the diagnosis of some diseases. Our method achieves good performance with only a small amount of labeled data, and our method outperforms the current mainstream semi-supervised learning methods on the public dataset.
A robust ground segmentation method for vehicle lidar

Ground points of lidar is the redundant information in the perception process of autonomous driving. Removal of ground points is beneficial to the improvement of accuracy and real-time performance in the perception process. Aiming at the problems of under-segmentation and over-segmentation in current methods, proposes a segmentation method based on point cloud normal clustering. First, the original point cloud is preprocessed to reduce the time cost of the whole process; then, the preprocessed point cloud is quickly pre-segmented by the ray threshold method, and the ground point cloud containing some non-ground points is obtained; finally, according to the normal vector features of ground point and non-ground points, we filter out the non-ground points in the ground point cloud to obtain the final ground point cloud. The experimental results show that the average precision and recall rate of this method in various road conditions can reach 90%, and the average time consumption is 50 ms, which has good real-time performance and adaptability.
Evaluating the Accuracy of 128-Section Multi-Detector Computed Tomography (MDCT) in Detecting Coronary Artery Stenosis

Catheter-Based Coronary Angiography (CCA) and computed tomography coronary angiography (CTCA) are both used in the diagnosis of coronary artery disease (CAD). The current study aims to evaluate the accuracy of CTCA compared to CCA as a gold standard to find an alternative method to CCA in diagnosing CAD. 65 patients with suspected coronary heart disease underwent CCA and CTCA procedures using 128-section computed tomography (MDCT). The CT images were analyzed by three consultant radiologists with more than 10 years of experience. CCA and CTCA reports were compared, and sensitivity, specificity, positive expected value (PPV) and negative expected value (NPV) were analyzed. These values were found for the 'per artery' and 'per patient' criteria. The results showed that the sensitivity, specificity of PPV, and NPV for accuracy measures of 'per patient' analyzes were 96.8%, 97%, 96.8%, and 97%, respectively. For left main coronary artery (LM), left anterior Descending (LAD), Circumflex artery (CX), and Right Coronary Artery (RCA), the results were 100%, 98.40%, 75%, 100%, 90.6%, 97%, 96.6 %, 91.4% and 61.1%, 100%, 100%, 87% and 73.70%, 97.8%, 93.3%, 90%, respectively with p<. 001. The study concluded that it is possible to use MDCT as an alternative part of CCA, which can be used to reduce CCA pain, procedure time, and at a lower cost.
EEG emotion recognition based on WPD-1DCNN-LSTM

As an important task in the advanced stage of artificial intelligence, the study of emotional EEG has received more and more attention in recent years. In order to improve the accuracy and reliability of EEG emotion recognition and prevent extracting a large number of redundant features with little relevance, this paper proposes a WPD-1DCNN-LSTM EEG emotion recognition algorithm, which first extracts EEG rhythm power and alignment entropy features from EEG signals reconstructed using wavelet packet decomposition (WPD), and then constructs a 1DCNN-LSTM network in the DEAP dataset from the validity -Emotional regions were classified into four categories at the arousal level: anxious and irritable emotion, depressed and sad emotion, pleasant and relaxed emotion, and excited and agitated emotion. The experimental results showed that the method achieved 98.6% classification accuracy on the DEAP dataset, which verified the feasibility and effectiveness of the method proposed in the paper.
An extended frequency scaling algorithm for the dechirp received bistatic SAR with high squint angle

Bistatic synthetic aperture radar (BiSAR) is an extended and complementary observation method to conventional monostatic SAR remote sensing. When high range and azimuth resolution are required in BiSAR, the transmitting platform, i.e., the LEO satellite, should work in the spotlight or sliding spotlight mode, which means a wide bandwidth Chirp signal is transmitted, and the targets are illuminated for a long enough time as well. In order to reduce the sampling frequency in the receiving system and improve the efficiency of the focusing procedure for this BiSAR con figuration, the echoes should be dechirp received. A bistatic extended frequency scaling algorithm (Bi-EFSA) is proposed in this paper to deal with the dechirp received bistatic SAR echoes. The first step of Bi-EFSA is the range walk correction in the 2-D time domain, which reduces the space variance of the range curvatures and removals the serious coupling between range and azimuth caused by the high squint angle. After that, the range compression is achieved by secondary range compression (SRC) and bulk shift. Then, the space variance of the azimuth frequency modulation rate, introduced by the range walk correction step, is corrected by implementing the azimuth nonlinear chirp scaling (ANCS) operation. Following this, the focused results in the range frequency and azimuth time domain are obtained by the azimuth compression. The point targets simulations validate the effectiveness of the proposed algorithm.
Robust Image Matching for Camera Pose Estimation Using Oriented Fast and Rotated Brief

This paper presents a novel image matching method for camera pose estimation based on point cloud segmentation. The Oriented Fast and Rotated Brief (ORB) is employed to extract the key points, which are then extracted based on matched point cloud planes. The point cloud planes are segmented based on the depth image, and then matched by the distance of the centroid between planes. The putative corresponding key points on the planes are generated based on the distance of their 3-D coordinates and the descriptors of the key points are further matched based on the putative corresponding key points. As an additional constraint, the spatial relative position in 3-D spaces solves the problem that the descriptors of each key point in some scenarios are too similar which may lead to a mismatch. According to the experimental results, the superiority of the proposed approach is illustrated by comparing with the existing matching methods.