Recognition Success Rate Research Articles

夹持-拉断式苹果采摘机器人设计与实验

For the standardized apple orchards in China, which are mainly dwarfed and densely planted, firstly, according to the spatial distribution characteristics of fruits within the tree canopy, a clamping-pull-off apple picking robot was developed by analyzing the parameters of apple cultivation and picking methods, in order to replace the manual harvesting operation. Then, the D-H method was used to establish the kinematic Equations of the apple-picking robot, the forward and inverse kinematic calculations were carried out, and the Monte Carlo method was used to analyze the workspace of the robot. Through the robot picking task planning and the simulation of the trajectory of the robotic arm, the scheme of the robot's picking strategy was given, and MATLAB software was applied to simulate the motion trajectory as well as to verify the feasibility of the trajectory planning scheme and the picking strategy. Finally, an apple-picking test bed was set up, the corresponding picking control system program was designed, and 45 apples were selected for picking tests. The results showed that during the robot's picking process, the average time for picking each fruit was 7.59 seconds, the fruit recognition success rate was 86.67%, and the picking damage rate was 5.13%.

Two-Stage and Two-Discriminator generative adversarial network for the inpainting of irregularly incomplete iris images

Due to the influence of the light source environment during image acquisition or the subject not fully opening their eyes, there are phenomena such as light spot interference, eyelash or eyelid occlusion in the iris area. This will cause the loss of effective iris information, ultimately affecting the success rate of recognition. To address the aforementioned issues, this paper introduces a Two-stage and Two-discriminator Generative Adversarial Network (TT-GAN) to restore irregularly incomplete iris images. The specific process is as follows: Firstly, TT-GAN employs gated convolution in place of ordinary convolution to more effectively extract image information; Secondly, TT-GAN incorporates a PC-Attention attention mechanism within the generator network to enhance the network's restoration capabilities; lastly, to ensure consistency in global and local semantic information of the repaired images, dual SN-PatchGAN discriminators are utilized. The experimental results based on three different spectral iris datasets, IITD, CASIA-Iris-Interval, and UBIRIS.V2, show that the repaired iris images significantly improve objective evaluation indicators compared to the incomplete iris images. Among them, TAR increased by 93.42 %, 68.41 %, and 59.72 %, respectively; EER increased by 27.17 %, 30.18 %, and 23.78 %, respectively; PSNR increased by 17.8712 dB, 18.818 dB, and 24.6053 dB respectively; SSIM increased by 0.0325, 0.0529, and 0.1337 respectively.

A signal processing method for continuous mud pressure wave signals in logging-while-drilling systems

For signal transmission of mud pressure waves in logging-while-drilling systems, as more measurement parameters are adopted, the conventional signal transmission rate of approximately 1 bps cannot meet the requirements of parameter uploading. Transmission rates greater than 10 bps are widely used by current Chinese and international enterprises in their continuous wave transmission systems. Due to the increasing transmission rates, a conventional single-channel pressure sensor cannot effectively identify the key features of an original signal with a low signal-to-noise ratio at a high transmission rate. This issue results in a low success rate for pressure wave recognition and decoding. This paper addresses a method for the collection of pressure wave signals using multi-channel pressure sensors through the analysis of the signal transmission changes of mud pressure waves with well depths and echo interference caused by drilling rod reflection. In this research, numerical simulation and experimental verification were used to calculate the cross-correlations of the multi-channel signals to effectively remove interference noises such as reflective waves and improve the signal-to-noise ratio of the original signal. The results showed that this method could effectively improve the SNR after the wave filtering of the original pressure wave. The characteristics of the continuous mud pressure wave signals were analysed using a circulation test. A corresponding band-pass filter was designed to remove pump noises to restore the sinusoidal pressure wave signal required by the original transmission rules.

LPI Radar Signal Recognition Based on Feature Enhancement with Deep Metric Learning

Low probability of intercept (LPI) radar signals are widely used in electronic countermeasures due to their low power and large bandwidth. However, they are susceptible to interference from noise, posing challenges for accurate identification. To address this issue, we propose an LPI radar signal recognition method based on feature enhancement with deep metric learning. Specifically, time-domain LPI signals are first transformed into time–frequency images via the Choi–Williams distribution. Then, we propose a feature enhancement network with attention-based dynamic feature extraction blocks to fully extract the fine-grained features in time–frequency images. Meanwhile, we introduce deep metric learning to reduce noise interference and enhance the time–frequency features. Finally, we construct an end-to-end classification network to achieve the signal recognition task. Experimental results demonstrate that our method obtains significantly higher recognition accuracy under a low signal-to-noise ratio compared with other baseline methods. When the signal-to-noise ratio is −10 dB, the successful recognition rate for twelve typical LPI signals reaches 94.38%.

Forest digital twin: A new tool for forest management practices based on Spatio-Temporal Data, 3D simulation Engine, and intelligent interactive environment

Existing forest digitization studies focus on one-way forest management practice visualization simulation, lacking decision-making feedback and virtual-real interaction synchronization. This paper presents the vision of the forest digital twin paradigm. We construct a forest digital twin to explore a new digital carrier of forest resources using remote sensing data, forest inventory data, the Cesium Digital Earth Engine, forest planning theory and parametric 3D modeling technology. The two-way interaction and thinning experiments showed that the forest digital twin could provide a novel pattern for in-depth analysis of forest spatial structure, individual tree dynamic growth and human-digital twin interaction effects. The successful recognition rate in matching the forest structure seen on real forest structure images with the forest digital scene was 91.3%, indicating that the forest digital twin can characterize the real forest structure significantly. The prediction accuracy of the multi-grade growth model integrating the Bayesian method for DBH, H was more than 90.4%. In addition, ASS-FDT interaction is superior to the assessors (ASS) and forest digital twin (FDT) for stand spatial structure overall optimization. The multi-dimensional stand spatial structure index (F-index) increased by 22.82%. The constructed forest digital twin model shows superior performance in optimizing the stand growth model and enhancing the overall stand spatial structure under the decision-making feedback and real-time interaction strategies. The automatic operation pattern provides a user-friendly forest management practice solution.

A Performance Analysis of a Litchi Picking Robot System for Actively Removing Obstructions, Using an Artificial Intelligence Algorithm

Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal.

Non-invasive human thermal comfort assessment based on multiple angle/distance facial key-region temperatures recognition

Accurate monitoring of human thermal comfort is crucial in optimizing HVAC system control scheme and enhancing building energy efficiency. The method based on facial temperature infrared recognition is a non-invasive and real-time thermal comfort predictive strategy, which has been proven great application potential. In response to the limitations of previous fixed-position facial infrared detection in practical applications, this study aims to explore a less restrictive non-intrusive method for assessing human thermal comfort. The temperature features of multiple regions on human face are identified by an infrared camera, and six temperature-sensitive regions are selected through skin thermal sensitivity analysis. Thirty subjects participate in a series of field experiments. To reduce the measurement limitations caused by human postures, the correlation between these six facial regions, indoor air temperature, thermal sensation votes (TSV), and thermal comfort votes (TCV) are analyzed in details to determine two key facial regions for the non-intrusive thermal comfort assessment. By applying the YOLOv5 algorithm, the real-time extraction of facial region temperatures from multiple angles and distances is achieved. On this basis, a data-driven thermal comfort predictive strategy based on facial temperature and optimized SVM model is designed. Results indicate that the method achieves a general accuracy of 85.68% in predicting thermal comfort based on its successful recognition rate of 88.7% for facial key regions.

Clinical application of parathyroid autofluorescence imaging in endoscopic thyroid surgery

Objective:To investigate the application value of near-infrared autofluorescence imaging in identifying and protecting parathyroid glands in endoscopic thyroid surgery. Methods:From May 2022 to February 2023, 158 patients who underwent endoscopic thyroid surgery in the Department of Thyroid and Breast Vascular Surgery of Guilin People's Hospital were selected. The endoscopic fluorescence camera system was used to monitor the parathyroid glands under autofluorescence during endoscopic thyroid surgery. A total of 214 pieces were collected, among which the first 15 cases that could not be preserved in situ during the operation needed to be autotransplanted or the tissue clamped parts that could not be clearly identified as parathyroid glands were sent to fast-frozen pathology to determine whether they were parathyroid glands. Results:Among the first 15 patients who could not be preserved in situ during the operation or whose anatomy could not be clearly defined, 23 parathyroid glands were detected by autofluorescence imaging, 21 parathyroid glands were confirmed by pathology, and 2 were adipose tissue, with an accuracy rate of 91.30%; 158 patients underwent surgery Blood calcium decreased 2 hours after operation compared with preoperative blood calcium（P<0.05）, decreased blood calcium 5 days after operation compared with preoperative blood calcium（P<0.01）, and increased slightly 5 days after the operation compared to blood calcium 2 hours after the operation, but the difference was not statistically significant（P>0.05）; while comparing parathyroid hormone（PTH）, PTH at 2 hours after operation decreased significantly compared with PTH before operation（P<0.01）, and PTH at 5 days after operation compared with PTH before operation PTH also decreased（P<0.01）, but increased compared with PTH 2 hours after operation（P=0.001）. Conclusion:In laparoscopic thyroid surgery, the application of near-infrared autofluorescence imaging technology can help surgeons quickly identify and protect parathyroid glands, and reduce the incidence of permanent hypoparathyroidism. Combining autofluorescence imaging, visual anatomy recognition under magnification of laparoscope, and intraoperative frozen pathological examination "trinity" method can improve the success rate of parathyroid gland recognition.

Software System for Automatic Grading of Paper Tests

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.

Low-cost livestock sorting information management system based on deep learning

Modern pig farming leaves much to be desired in terms of efficiency, as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight. This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases. Furthermore, confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs. This research executed an experiment to construct a deep-learning sorting mechanism, leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months. This research integrated a Kalman filter-based algorithm to augment the precision of the dynamic sorting operation. This research experiment unveiled a pioneering machine vision sorting system powered by deep learning, adept at handling live imagery for multifaceted recognition objectives. The Individual recognition model based on Residual Neural Network (ResNet) monitors livestock weight for sustained data forecasting, whereas the Wasserstein Generative Adversarial Nets (WGAN) image enhancement algorithm bolsters recognition in distinct settings, fortifying the model's resilience. Notably, system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety. Experimental outcomes validate the superiority of this proposed system over traditional counterparts. It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage. This findings reflect an 89% success rate in livestock ID recognition, a 32% surge in obscured image recognition, a 95% leap in livestock categorization accuracy, and a remarkable 98% success rate in discerning images of unwell pigs. In essence, this research augments identification efficiency, curtails operational expenses, and provides enhanced tools for disease monitoring.

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

AbstractIn the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.

Realtime human face tracking and recognition system on uncontrolled environment

&lt;span lang="EN-US"&gt;Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer.&lt;/span&gt;

A fast LiDAR place recognition and localization method by fusing local and global search

Place recognition is an important branch of Simultaneous Localization and Mapping (SLAM), which finds revisited places, thereby reducing error accumulations. Most mainstream methods only concentrate on the similarity of two LiDAR scans and cannot obtain the pose information. In this paper, we design a descriptor named Occupied Place Description (OPD) and propose an efficient map-aided place recognition and localization method. Our method can effectively detect loop events, obtain the vehicle’s poses, and overcome the overlap dependence of pairwise point cloud registration. It is composed of four modules. First, the map data generation is designed to store the map database as binary files. Then, a trajectory initialization consisting of global search, local search, and trajectory score computation is proposed to determine the vehicle’s initial poses. Next, an online localization method is developed by combining descriptor similarity and Kalman Filtering, which only takes less than 5ms per localization. Finally, loop events are detected based on pose information. Exhaustive evaluations on KITTI and MulRan datasets demonstrate the superiority of our method. The average absolute trajectory error (ATE) is 0.5 m and the average relative rotation error is 2.7 degrees. F1 score measures the success rate of place recognition and our average maximum F1 score achieves nearly 0.987. The source code will be available in https://github.com/ShiPC-AI/Occupied-Place-Description.

Design and implementation of a virtual teacher teaching system algorithm based on facial expression recognition in the era of big data

Abstract With the development of big data, virtual course teaching has developed rapidly. In virtual teaching, the lecturer’s position is pivotal, and the virtual teacher’s lecture mainly conveys information through intuitive facial expressions, so the effectiveness of facial synthesis is crucial. However, traditional facial expression synthesis methods suffer from local distortion and lack of subtle expressions due to driving complex topological models and delays due to large computational effort. To this end, this paper proposes a facial expression extraction method based on feature point texture mapping. First, a video face expression feature point tracking algorithm is designed. Firstly, we design a video face expression feature point tracking algorithm. We use a monocular camera to capture facial expression images, use a texture mapping algorithm to detect faces and extract face feature points, and add a time threshold processing mechanism to the relevant filtering algorithm to detect face frame tracking to realize face feature point tracking in video. Secondly, the parameters are controlled by face expression animation, including head pose and face expression parameters. The head pose is solved by the Laplace algorithm, and the head rotation matrix and translation vector are output to establish the coordinate system. Finally, the stability and real-time performance of the feature point texture mapping algorithm are verified by expression capture test and recognition effect. The results of this paper show that: in the expression capture test, the average data transmission time is about 1ms, and the total expression transmission time of each video image frame is 32ms, which is 1.23ms and 9.8ms shorter than the traditional algorithm, respectively; in the expression recognition effect test, the successful recognition rate is increased by 10.2% on average compared with the traditional algorithm; and in the application to the classroom teaching effect test, 86.6% of the students are satisfied with the virtual teacher. 86.6% of the students highly agreed with the virtual teacher teaching, and 83.3% of the students thought that the model had a positive effect on the learners’ learning, which proved the superiority, real-time, stability and practicality of the algorithm designed in this paper compared with the traditional algorithm. The research work in this paper also provides solutions to the problems of virtual course teaching.

Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy

BackgroundWith advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy.MethodsCervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states.ResultsThe amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT.ConclusionsThe KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.

Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting

For high-speed robotic cut-and-catch harvesting, efficient trellis grape recognition and picking point positioning are crucial factors. In this study, a new method for the rapid positioning of picking points based on synchronous inference for multi-grapes was proposed. Firstly, a three-dimensional region of interest for a finite number of grapes was constructed according to the “eye to hand” configuration. Then, a feature-enhanced recognition deep learning model called YOLO v4-SE combined with multi-channel inputs of RGB and depth images was put forward to identify occluded or overlapping grapes and synchronously infer picking points upwards of the prediction boxes of the multi-grapes imaged completely in the three-dimensional region of interest (ROI). Finally, the accuracy of each dimension of the picking points was corrected, and the global continuous picking sequence was planned in the three-dimensional ROI. The recognition experiment in the field showed that YOLO v4-SE has good detection performance in various samples with different interference. The positioning experiment, using a different number of grape bunches from the field, demonstrated that the average recognition success rate is 97% and the average positioning success rate is 93.5%; the average recognition time is 0.0864 s; and the average positioning time is 0.0842 s. The average positioning errors of the x, y, and z directions are 2.598, 2.012, and 1.378 mm, respectively. The average positioning error of the Euclidean distance between the true picking point and the predicted picking point is 7.69 mm. In field synchronous harvesting experiments with different fruiting densities, the average recognition success rate is 97%; the average positioning success rate is 93.606%; and the average picking success rate is 92.78%. The average picking speed is 6.18 s×bunch−1, which meets the harvesting requirements for high-speed cut-and-catch harvesting robots. This method is promising for overcoming time-consuming harvesting caused by the problematic positioning of the grape stem.

Fine-Grained and Real-Time Gesture Recognition by Using IMU Sensors

Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

Proton Radiation Effects of CMOS Image Sensors on Different Star Map Recognition Algorithms for Star Sensors

Star sensors are widely used by satellites for their precise pointing accuracy. However, protons in space will cause cumulative effects and single-event transients in the imaging systems of star sensors. These effects will affect the success rate of star map recognition of star sensors. In this paper, proton irradiation experiments and field tests were carried out in turn, and three typical star recognition algorithms were used to recognize the star maps. The results showed that cumulative effects led to a decrease in the number of identifiable stars, which greatly affected the recognition success rate of the grid algorithm. Hot pixels caused by displacement damage effects increased the star centroid positioning error, leading to a decrease in the recognition success rate of the triangle algorithm and pyramid algorithm. Single-event transients produced by protons hitting the image sensor are similar to the grayscale value and shape of a star, and were recognized as “false stars”, which had a significant impact on the success rate of the three recognition algorithms. In general, the pyramid algorithm was more effective than the other two algorithms in identifying the affected star map, and the recognition success rate of the grid algorithm was significantly reduced.

Design of An Intelligent Demodulation Method for X-ray Spectrum Communication

X-ray communication based on X-ray spectrum modulation information is a novel communication method, which can effectively improve communication ability and anti-interference ability. In this paper, four characteristic X-rays are chosen to modulate information. The influence of channel attenuation, random noise in space, and miss-shooting rate caused by adjacent targets are discussed. Based on error-correcting output codes and a 10-fold cross-validation method, a neural network intelligent classifier is designed to realize intelligent information demodulation. Simulation results show that if the miss-shooting rate is lower than 0.4 and the photon retention rate after channel attenuation is higher than 0.0008, then the classifier can achieve a recognition success rate of more than 99%. This study provides reliable index requirements for the performance of the four-target material source equipment to be designed next. This paper also provides a theoretical basis for the subsequent further realization of space communication capabilities.

Fast Color Image Matching Based on Occurrence Probability Analysis of Pixel Pair Hue Values

We propose a fast and reliable method for detecting objects using color information. The probability of the occurrence of a hue pattern in a template image is calculated for each two-pixel pair, and only those pixel pairs with extremely low probability are carefully selected for matching. Since such pixels have high distinctiveness, it can be used for highly reliable matching without being affected by ambient disturbances, and since only a small number of pixels are used, it is also fast. In real image experiments, we achieved a recognition success rate of 93% and a processing time of 47 msec by using only 0.5% (143 pixels) of the template image. When 0.1% (29 pixels) of the template image was used, the recognition success rate was 7 msec, confirming that practical matching with a good balance between reliability and speed was achieved.