Abstract:With the development of satellite communication technology, the traditional spectrum monitoring mode is difficult to meet the monitoring needs. In order to meet the urgent needs of modern satellite signal receivers for larger bandwidth, higher resolution and stronger real-time processing, an automatic broadband spectrum monitoring system was developed. The system realizes the large-bandwidth and high-resolution display of the satellite signal spectrum through RF direct sampling technology and channelization processing. Experimental results show that the system can monitor the L-band signal in real time and generate the panoramic spectrum of the L-band signal with a spectral resolution of less than 1 kHz. Through the in-depth analysis of spectrum data, the system can automatically identify and store signal parameters, and its portability and flexible design make the system flexible to deploy a variety of application scenarios, which perfectly fits the development direction of modern satellite signal receiver technology.

Abstract:In sparse code division multiple access (SCMA) systems, because multiple user data are superimposed on the same subcarrier and the channel conditions of different users are different, accurate channel estimation cannot be performed directly. To solve this problem, a channel estimation method for SCMA systems based on orthogonal superposition is proposed. This method uses the sparsity of SCMA codebook to design a new pilot structure with multi-user timeslot multiplexing, which can ensure the orthogonal subcarrier resources and reduce the timeslot overhead of pilot resources after the same timeslot is superimposed on the pilot data of different users. On this basis, a pilot structure of local orthogonal superposition is proposed, and the channel fading coefficients of adjacent subcarriers are estimated by using the correlation between subcarriers to further reduce the time slot overhead of pilot resources. The simulation results show that the channel estimation MSE performance of orthogonal superposition method is improved by 3 dB and the local orthogonal superposition performance is further improved by 1.7 dB compared with the traditional method under the same total time cost.

Abstract:A copper wire laying monitoring system based on STM32 was designed to address the industrial production issues caused by the deviation and tilting of copper wires during the laying process of flat copper wires wrapped on I-shaped wheels in industry. The system matched the color of copper wire through the corrosion center algorithm, tracked the copper wire in real-time, determined the position of the copper wire and its width on the LCD screen, and detected the offset or tilt of the copper wire, providing data for subsequent copper wire correction. The system used MT9V034 camera as the image acquisition module, LCD display screen as the image display module, STM32H743 and serial port screen as the control module and human-machine interaction module, respectively. The experimental results show that the system can effectively detect the offset and tilt of copper wires while tracking them and the measurement error for line width is within 5 px.

Abstract:In response to the shortcomings of existing motion pose recognition algorithms in terms of accuracy and efficiency in human pose detection, this paper proposes an efficient detection algorithm based on an improved YOLOv8pose. This algorithm optimizes the occlusion scenario of key points by introducing the RL-SEAM module, enhances the utilization of contextual information through the C2f-Context mechanism, enhances the model′s ability to recognize complex poses, and uses the Pose_SA lightweight detection head to improve the model′s effectiveness and efficiency in recognizing motion poses. The experimental results show that the improved YOLOv8pose algorithm has achieved significant improvement in human motion pose recognition tasks. Its number of parameters and model size have been reduced by 14.24 and 10.94 percentage points respectively compared to the original YOLOv8n benchmark model. At the same time, accuracy, recall, and mean average precision have been improved by 7.60、7.60 and 10.54 percentage points respectively compared to the original model. Therefore, the YOLOv8-LSP model proposed in this article helps to solve the challenges faced in human motion pose recognition tasks, such as key point occlusion and complex and variable postures.

Abstract:The integration of the integrated communications/navigation/identification (ICNI) system for unmanned aerial vehicles necessitates that the master control module of the ICNI performs functions such as system control and data forwarding via multiple bus communications. This requirement imposes higher demands on the processing performance and interface resources of the core processor. Consequently, there is a need for a more lightweight design for the health control system within the module. This paper presents a fully domestic CAN bus-based health management system designed using a CPU+MCU distributed architecture within the main control module. The FT-2000/4 serves as the core processor, while the MCU acts as the coprocessor to implement the CAN bus interface unit. The two components facilitate application layer data exchange over the CAN bus through SPI full-duplex communication, enabling the CPU to query the status and issue commands to each module/unit in the system, thereby achieving effective health management. The system is capable of real-time monitoring of the health status of each module. Notably, the circuit design utilizes only the SPI and GPIO interfaces of the core processor, without occupying additional interface resources or programmable logic (FPGA) resources. This approach simplifies the hardware design and alleviates the challenges associated with circuit layout and structural design. Furthermore, the cost of the health management circuitry is at least 60% lower than that of other typical designs in the industry, addressing the need for cost-effective solutions.

Abstract:Aiming at the problem of uncertainty of GNSS measurement noise variance parameter in the traditional combined navigation filtering algorithm, this paper, based on the principle of SINS/GNSS dynamic differential sequence, improves the traditional Sage-Husa adaptive extended Kalman filtering algorithm (AEKF) method of estimating the measurement variance array based on the information of residual sequences, utilizes the high-precision characteristics of short-term positioning of SINS and combines with the smoothing of bounded layers to isolate the abnormal observation information of GNSS, so that the improved adaptive filtering algorithm can maintain a high level of positioning accuracy under different noise environments of GNSS. The fault detection algorithm isolates the abnormal observation information of GNSS, so that the improved adaptive filtering algorithm can maintain high positioning accuracy under different noise environments of GNSS. The experimental results of the actual sports car show that in the low-density anomaly noise environment in GNSS work, the algorithm in this paper improves the average positioning accuracy by 39.9% and 7.9% compared with the EKF algorithm and the traditional Sage-Husa algorithm, and in the high-density anomaly environment, the overall positioning accuracy is improved by 64.5% and 29.1%. Therefore, the algorithm in this paper effectively improves the anti-interference ability of the combined navigation system against different measurement noises.

Abstract:Errors can occur in the 2D projection contact angle measurements of droplets on uneven material surfaces, and currently, only a few international equipment manufacturers offer 3D contact angle measurement solutions. This paper proposes a 3D droplet contact angle measurement method aimed at providing a complete and practical 3D contact angle measurement solution. The system employs an array of circular structured light for 3D detection, suitable for liquids with contact angles less than 90° and good diffuse reflection properties.The main steps include: feature region selection, 3D point cloud acquisition, droplet surface fitting, and contact angle measurement. In the image preprocessing stage, a method based on HSV space combined with morphological operations is proposed. For feature region selection, an area filtering method is introduced. Geometric calculations are used to convert 2D point clouds to 3D point clouds, and a point cloud matching algorithm is proposed to optimize point cloud fusion.Compared to 2D measurement methods, experimental results show that the proposed method significantly improves measurement accuracy and reliability, achieving a maximum error of less than 1°, with most errors being less than 0.5°. Additionally, it allows for further analysis of material physical properties and surface uniformity.

Abstract:Time domain reflection technology, as a technique that can intuitively reflect changes in transmission line impedance, is widely used in the field of impedance detection. However, there are problems such as inaccurate calibration and low impedance testing accuracy in the process of measuring the characteristic impedance of transmission lines. In response to these issues, this article designs a time-domain measurement and calibration system for characteristic impedance. The system uses the shortest interval estimation algorithm to measure the voltage on the transmission line, achieving the measurement of the characteristic impedance of the transmission line. By measuring four coaxial cables with different lengths and characteristic impedances, the measurement error is controlled within ±1%, meets the requirements of impedance measurement. The actual results show that the system can accurately measure the impedance of transmission lines. This method simplifies the operation process, avoids subjective bias caused by manual selection, and provides a new method for the development of transmission line impedance detection technology.

Abstract:Specific emitter identification is playing an increasingly important role in the military and civilian sectors. With the rapid development of deep learning technology, the recognition performance of SEI method has been significantly improved. However, these methods often rely on a large number of radiation source sample data, and perform poorly in practical application scenarios with limited sample size. To solve this problem, this study proposes a novel deep learning network model CRCPA-GCN to achieve SEI in small-sample scenarios. The model integrates CPCA and GCNet attention modules in a multi-layer complex convolutional neural network, and the recognition performance in small-shot scenarios is significantly improved by using the methods of class reconstruction and adversarial training. In this study, a series of experiments were carried out on public datasets and compared with the current mainstream SEI networks. Experimental results show that the proposed CRCPA-GCN network model achieves an accuracy of 95.81% under the learning condition of 20 shot, which is better than other mainstream SEI networks and performs well in robustness.

Abstract:To address the limitations of conventional pulsed remote field eddy current testing (PRFECT) in accurately determining the circumferential position of defects, we propose an innovative array-based PRFECT probe. This method involves increasing the number of receiving coils and modifying the relative positions of the receiving and excitation coils to enhance defect localization capabilities. To address the issue of weak signals detected, a hybrid signal denoising technique combining successive variational mode decomposition (SVMD) and singular value decomposition (SVD) is introduced. Initially, the signal is decomposed into a series of modal functions using SVMD. Subsequently, components for reconstruction are selected based on the Pearson correlation coefficient. The retained components are then denoised using the SVD method, and these denoised components are superimposed to reconstruct the signal. Both simulations and experimental results demonstrate that the proposed novel probe effectively locates defect positions, The proposed algorithm can improve the Denoising Signal-to-Noise Ratio of the measured key signal to 9.30, better than traditional algorithm.

Abstract:Aiming at the problems of weak anti-interference ability, short transmission distance and complex cable in the traditional analog signal transmission mode of strain balance, an integrated digital balance based on micro high-precision data collector was developed and applied. By embedding the micro high-precision data collector into the strain balance and integrating the microcontroller unit (MCU) with floating-point operation ability in the micro high-precision integrated data collector, the real-time calculation and output of the actual force/torque of the balance were realized by combining the static calibration formula of the balance, and the transformation of the measurement signal of the balance from analog mode to digital mode was realized, thus improving the anti-interference ability of the test system. The anti-interference test in the wind tunnel showed that the signal transmission of the integrated balance was almost zero interference in the complex electromagnetic environment of the conventional hypersonic wind tunnel; the wind tunnel test of high lifting body model showed that the integrated balance test has good repeatability (high precision), and the data rules were consistent with the measured results of conventional balance. The maximum deviations of axial force coefficient, normal force coefficient and pitching moment coefficient were 2.1%, 1.8% and 3.6%, respectively.

Abstract:Acoustic emission detection methods are widely used in the defect detection of equipment, for most of the equipment generated acoustic emission signal amplitude is small, large noise and features are difficult to extract the problem, this paper proposes a signal processing method: the CCSO algorithm based on the Pearson correlation coefficient-envelope entropy minimum principle optimizes the processing method of parameters in VMD. In this method, the cross algorithm is integrated on top of the classical flock optimization algorithm, and the key parameters in the VMD, namely the modal number K and the penalty factor α, are accurately optimized by the improved CCSO algorithm. By using the CCSO-VMD method based on the new fitness function, the analog signal was analyzed, and the signal-to-noise ratio reached 25.814 1 dB. This result proves that the CCSO-VMD algorithm based on the new objective function can significantly reduce the noise level while retaining the valid information in the signal to the greatest extent. In addition, this paper proposes a comprehensive spectral difference index, and the CSDI value can effectively distinguish the acoustic emission signals in different states.

Abstract:Major depression is characterized by low mood and slow thinking, and emotion is the attitude experience of object and the corresponding behavioral response of human brain neurons. Many articles have designed classification network of depression and emotion classification, but the network function is single, can only complete a single classification task, and does not combine mental illness with human emotions, language expression, blinking and other behaviors well. The article explores the correlation of index characteristics between emotion classification and depression diagnosis, then designs a network to verify the feasibility of diagnosing depression through emotion across categories and datasets. The differential entropy is extracted as the input feature of the network, and the convolutional neural network is used to study the emotion classification of SEED-IV and the MODMA emotion proportion. Analyze the microstate parameters of the two datasets, samples with the same microstate type are analyzed and the correlation between the two microstate is explored. The difference of α and γ rhythm classification results and microstate correlation coefficients can be used to classify emotions and diagnose depression. After verifying that parameters in both α and γ rhythms show the correlation between emotion and depression, the design experiment proves that abnormal brain characteristics of patients with depression can be captured by adding microstate features, and the diagnosis of depression can be completed by adding correlated microstate parameters to CNN used for emotion recognition.

Abstract:In order to improve the accuracy of pressure classification method. realize the deep mining of multi-modal information interaction and multi-dimensional three-dimensional fusion features, a multi-modal pressure identification method based on model classification is proposed. A new psychological stress index model is constructed based on the amplitude characteristics of speech signals and the amplitude characteristics of each frequency band of EEG signals, and a psychological stress classification method for the model is proposed to solve the problems of limited subjective assessment accuracy and unclear stress classification basis. The labels of MAHNOB-HCI data set are reconstructed based on the model classification, and the multi-dimensional stereo fusion features containing EEG time-frequency-space information and speech time-frequency information are constructed to solve the problem of missing pressure information caused by the single feature research method. Compared with the single modal method, the recognition accuracy of the proposed method is increased by 10.72% and 3.36%, respectively.Compared with the conventional dual-modal method, the recognition accuracy is increased by 7.51%. To sum up, the proposed method can more accurately reveal the relationship between the full-band information of heterogeneous data and psychological stress, and effectively improve the recognition performance.

Abstract:In the measurement of environmental parameters of rocket cabin, the traditional wired measurement method will increase the overall weight of the rocket cabin. Therefore, it is of great significance to use wireless sensor network to replace the traditional wired measurement method to reduce the weight of the rocket cabin. Aiming at the above problems, a wireless sensor network based on AD hoc network protocol is proposed, which is based on wireless transceiver chip CC1310 to build a wireless sensor network, selects 915 MHz frequency band as the data transmission channel, and designs an AD hoc network protocol, the wireless sensor nodes in the network only need to keep the same static address as the collector. When the node is powered on, it can automatically join the network, and the collector will assign a unique dynamic address to each node, adds CSMA/CA mechanism to prevent channel data collision, and is successfully applied to this wireless sensor network. The test results show that: when the data transmission rate of the wireless sensor network is 250 Kbps and the signal sampling frequency is 100 SPS, the wireless sensor node can automatically join the network created by the collector after being powered on, and successfully upload the sensor data according to the time interval configured by the collector, and no node data loss within the range of 10 m. It can meet the measurement needs of environmental parameters in the capsule of the launch vehicle.

Abstract:The MTPA control strategy of traditional virtual injection method IPMSM is proposed to solve the problem of slow dynamic response due to the complex coordinate transformation process and the cascade of band-pass filter and low-pass filter in signal processing. Firstly, in the virtual signal injection process, the high frequency signal is directly injected into the id and iq obtained by sampling and filtering, which simplifies the complex root operation process. Secondly, a new MTPA criterion formula is derived according to the improved virtual signal injection method. After demodulation of the high frequency response signal, the optimal current vector angle is obtained through the low-pass filter and integrator in turn. Finally, the effectiveness of the improved virtual injection method was verified by simulation and the 8.4 kW built-in PMSM experimental platform. The experimental results show that the improved virtual injection method can effectively simplify the calculation, and still has strong robustness after eliminating the cascade response. The loading experimental speed overshoot is only 3 r/min (0.375%), and the dynamic response time is improved by about 0.2 s.

Abstract:In order to explore the difficulties in detecting fine cracks and the occurrence of breaks in the aerial view of UAVs, a network called ASE-Net is proposed based on the U-Net architecture. First, an improved VGG-16 is used as the encoder to extract the broken feature information. Second, multi-scale feature fusion block (MSFF) module and channel enhanced strip pooling (CESP) module are introduced at the minimum scale network layer. Finally, the ECA_X attention module is added to the decoding stage. The experimental results indicate that the model presented in this paper achieves a mIoU of 0.820 9, a mPA of 0.930 2, and a mPrecision of 0.865 1 on the self-constructed UAV aerial pavement breakage dataset. These results represent improvements of 15.97%, 12.72%, and 11.02% over the baseline U-Net, respectively. Ultimately, the model in this work has been demonstrated to exhibit better performance and generalization ability than other standard models utilizing the open-source dataset Crack500. The model can realize accurate detection of small cracks, potholes, and repairs on the road surface, effectively solving the fracture problem of crack detection, and enhancing the effect of pavement damage detection in large-size aerial images.

Abstract:In order to reduce the influence of background interference on pavement defect detection and solve the problem that the features that can be extracted from smallsized slender cracks are very limited, this paper is improved based on the YOLOv8 model. Firstly, the C2f-Dysnake module was designed by fusing the C2f in the network with dynamic serpentine convolution, which improved the sensitivity to the shape and boundary of the target and enhanced the feature extraction ability of the slender cracks. Secondly, the reparameterized generalization feature pyramid network RepGFPN and the dynamic upsampler DySample were combined to form a new neck network RDFPN, which increased the attention to the low-level feature map and improved the detection ability of small targets. Finally, the MPCA attention mechanism is added to the backbone network to capture the position relationship at different scales and improve the feature extraction ability of the backbone network. Experimental results show that the improved algorithm improves mAP50 by 2.3% and reaches 98 fps on the RDD2022 dataset, and the detection speed reaches 98 fps, which has obvious advantages over other algorithms and verifies the effectiveness and superiority of the proposed method.

Abstract:Knee osteoarthritis is a common disease in the elderly population, which is highly disabling. Automatic diagnosis of knee osteoarthritis based on deep learning algorithm has important application value. Therefore, an automatic diagnosis algorithm of knee osteoarthritis based on improved Swin Transformer model is proposed. The transfer learning is protected by replacing the global average pooling layer of the neck network with a two-layer fully connected layer plus ReLU activation function. Adding full connection layer and Tanh activation function to the head network to combine more nonlinear features; in the process of data preprocessing and model training, data enhancement is realized by relying on Albumentations library and adding Mixup module respectively. The experimental results show that the proposed algorithm can effectively improve the classification accuracy of X-ray images of knee osteoarthritis, and the diagnostic accuracy reaches 76.0% on the public data set of Kaggle website. At the same time, the generalization experiments on other X-ray image data sets of knee osteoarthritis and medical image data sets in different fields show that it has good generalization ability, which further proves the effectiveness of the proposed algorithm.

Abstract:Addressing the limitations of the LSTR algorithm in practical applications, particularly its single-scale feature extraction and lack of effective capture of local lane features, this paper introduces the Vit-CoMer backbone network for the first time in lane detection tasks, proposing the LSCoMer lane detection model. Initially, the model employs a MRFP module after the feature extraction network to enrich multi-scale features, thereby enhancing detection accuracy. Additionally, a CTI module is integrated at both the beginning and the end of the Transformer structure to promote effective fusion between CNN′s local features and Transformer′s global features, enhancing the latter’s sensitivity to local details. Experimental results indicate that this method achieves an accuracy of 96.68% on the TuSimple dataset, which is a 0.5% improvement over the original LSTR method and significantly outperforms similar methods like PolyLaneNet. On the CULane dataset, our method improves the F1 score by 3.02% compared to the LSTR method.

Abstract:In addressing the issue of ineffective extraction of rich defect features for small-scale surface defects on steel due to their low contrast and small proportion, this paper proposes a solution for small-target defect detection. Leveraging the relationship between contextual information integration and enhanced feature fusion, we introduce the following approaches: incorporating the sliding window mechanism Swin Transformer, which integrates feature information from different blocks hierarchically and through local windows to enhance the contrast of defect features while reducing convolutional operation density; the model employs Coordinate Attention to obtain more positional information, enhancing the diversity of features related to small-target defects. Additionally, we propose the steel surface small-target defect detection model SFNet based on self-attention feature fusion, integrating features with richer semantic information across different scales using the CSP-FCN feature fusion module. Experimental results demonstrate that SFNet achieves superior detection performance on the NEU-DET and GC10-DET public datasets compared to current classic object detection models. Furthermore, the proposed model achieves an average precision improvement of 3% and 3.7%, respectively, while reducing the parameter count to half of its original size.

Abstract:Equipment that operates in harsh and variable underwater environments is essential for conducting underwater research and development. The current underwater target detection models are too large in parameter count and computation, which limits the deployment of underwater equipment with limited resources. In order to solve the problem of excessive parameter count and computational volume of underwater detection models, a lightweight underwater target detection model RCE-YOLO is proposed.Firstly, the spatial attention weights of RFAConv are utilized to improve the ability of CBS to process the information in the receptive domain and to enhance the ability of C2f to fuse spatial feature information, so as to enhance the model′s ability of detecting small and dense targets. Second, the CCFM is fused with the Dysample module, which is able to utilize the different scale information more effectively and reduce the blurring and distortion produced by the original sampling through the internal point sampling method. Finally, the Efficient multi-scale attention mechanism is fused in the SPPF forward propagation process, which makes the model focus on the key information of the underwater target and reduces the false detection rate and misdetection rate. The experimental results show that the improved lightweight model is validated on the dataset DUO, and the mAP50 and mAP50:90 values reach 83.6% and 64.2%, respectively, which are 1.4% and 1.2% higher compared to the mAP50 and mAP50:90 values of the benchmark model of YOLOv8, and the number of parameters and the amount of computation drop by 32.3% and 0.9 G, respectively. compared to other The target detection model meets the needs of underwater target detection in harsh and variable environments, and lays the foundation for lightweight deployment of underwater equipment.

Abstract:In order to accurately identify the fall posture of the elderly, an improved YOLOv8s fall detection model is proposed. Firstly, the SE attention mechanism module is introduced into the backbone network of the YOLOv8s model, which divides the channel features into multiple subgraph features, and fuses the features of different groups, so that the network can adaptively focus on the key features, suppresses the features that contribute less to the current task, and improves the feature extraction ability; secondly, the CIoU loss function is replaced by EIoU to accelerate the convergence rate and improve the accuracy and stability of the model. Finally, the trained model is verified on URFD+ and other data sets. The experimental results show that the precision rate of the model reaches 99.50%, the recall rate reaches 99.00%, and the mAP50 reaches 99.50%, which is better than the original model. Compared with the YOLOv5s+K-means++ model, the accuracy is increased by 3.22%, the recall rate is increased by 5.32%, and the mAP50 is increased by 2.38%. Compared with the C2D-YOLO model, the accuracy is increased by 10.00%, the recall rate is increased by 11.40%, and the mAP50 is increased by 7.80%. Compared with the YOLOv5s+C3new model, the accuracy is increased by 2.50%, the recall rate is increased by 6.80%, and the mAP50 is increased by 4.1%. The improved model has greater advantages than the original model and the current advanced model.