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6th International Symposium on System and Software Reliability, ISSSR 2020

作者： Zhu, Chunhua Tian, Jiake Shi, Zhen Yang, Jing Key Laboratory of Grain Information Processing Control College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China

ISBN: (纸本)9780738104973

This paper proposes a non-destructive, fast, low-cost, non-contact method for detecting stored grain foreign body based on received signal strength (RSS) information, RTI- Grain. The feasibility of stored grain foreign body detection was verified by using RSS data. On this basis, the RTI-Grain detection method was designed. Firstly, the RSSI data is subjected to data normalization, noise elimination and averaging preprocessing, and then ellipse weight model was used to compute the attenuation coefficients in each pixel, finally the Tikhonov regularization was adopted to reconstruct the image of the abnormal area. The experimental verification shows that the foreign body of empty hole, metal and water in granary can be detected, and the detection accuracy is 98.1% at maximum. © 2020 IEEE.

关键词： Component Image reconstruction Radio tomography imaging(RTI) Received signal strength information (RSSI) Stored grain

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A Parallel Beamforming Algorithm for Sonar Signal processing

A Parallel Beamforming Algorithm for Sonar Signal Processing

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Dang, Lanxue Liu, Chongyang Yang, Kang Hou, Yan-E Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781510664616

Sonar is a general term for various underwater acoustic devices, which are used to perform underwater target detection, positioning, identification, tracking and underwater communication, navigation, measurement and other tasks. All sonar devices including active sonar and passive sonar both require a signal processor to execute underwater acoustic signal processing. Beamforming is the main and core component of the sonar signal processor, and its processing speed affects the real-time performance of sonar. Thus, this paper first implements a parallel sonar beamforming algorithm to improve the processing speed of the beamforming algorithm. The proposed algorithm was carried out several experiments, and the experimental results demonstrate that the parallelism of multi-core CPU can bring better speedup ratio and efficiency than the traditional serial CPU algorithm. © 2023 SPIE.

关键词： Beamforming

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Intelligent Service Cluster Construction Method Based on Remote Sensing Application 6

Intelligent Service Cluster Construction Method Based on Rem...

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6th IEEE information Technology and Mechatronics engineering Conference, ITOEC 2022

作者： Zhou, Liming Yang, Wencheng Du, Ying Liu, Cheng Li, Heng Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665431859

The production of remote sensing products needs to go through various links of evaluation and deploy a variety of software and hardware resources. It is necessary to study how to improve the efficiency of service cluster deployment, simplify the evaluation process, and optimize its structure. This paper designs an intelligent service cluster construction method based on remote sensing applications, which realizes the accurate and efficient deployment of various complex network structures and equipment on the visual interface. The method is convenient and simple to operate. It can set various types of resource information according to the needs of users, can choose a more reasonable server cluster deployment plan, improve the efficiency of data storage and processing of remote sensing products, better serve users, and greatly promote The development of related industries. © 2022 IEEE.

关键词： Remote sensing

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Research on compressed sensing of KL domain with limited feedback in MIMO-OFDM system 8

Research on compressed sensing of KL domain with limited fee...

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8th IEEE International Conference on Communication Software and Networks, ICCSN 2016

作者： Hongliang, Fu Chao, Li Henan University of Technology Key Laboratory of Grain Information Processing and Controlling Zhengzhou China Henan University of Technology College of Information Science and Engineering Zhengzhou China

ISBN: (纸本)9781509017805

In order to solve the problem of feedback link overhead in MIMO-OFDM system, and improve the transmission efficiency, a limited feedback method based on KL domain is proposed in this paper. In this method, the state information which is obtained from the channel estimation performed in KL domain sparse at the receiving end. Thereby a small amount of linear measurements are obtained which represents the total channel state information, Then these measurements are fed back to the transmitting end. In the transmitter, the channel state information is reconstructed through orthogonal matching pursuit algorithm. The simulation results show that the proposed limited feedback based on KL (Karhuner-Loeve) domain method can achieve full channel state feedback effect and decreases the overhead in the feedback link. © 2016 IEEE.

关键词： Channel state information

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Food Material Consumption Prediction of Catering Enterprises Based on Time Series Model 5

Food Material Consumption Prediction of Catering Enterprises...

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5th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Wang, Nan Liu, Zan Anyang Normal University School of Computer and Information Engineering Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Henan455000 China Key Laboratory of Oracle Information Processing in Henan Province Anyang Henan455000 China

Food material management plays an important role in catering enterprises. Accurately predicting the consumption of each kind of food materials in the future can reduce the cost and improve the operation efficiency of catering enterprises. Based on ARMA and ARIMA models, we took the actual operation data of a big hot pot restaurant as the research object, and tried to forecast the food materials consumption of the restaurant in a relatively short future time. The experimental results show that although there is still room for improvement, the time series model can be used to help the material management of each catering enterprise. © 2021 IEEE.

关键词： Forecasting

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Influence of Elevation and Orbit Interpolation on the Accuracy of R-D Location Model 6

Influence of Elevation and Orbit Interpolation on the Accura...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Fan, Minghu Zuo, Xianyu Tian, Junfeng Key Laboratory of Big Data Analysis and Processing of Henan Province College of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781728129129

R-D model is the most commonly location model in geometric processing of SAR images. How to improve its location accuracy has been one of the focuses in application. Based on the in-depth analysis of the solving process of R-D equation, two kinds of influencing factors are selected to study. They are elevation and satellite orbit interpolation method. Then, aiming at the pixel location of high-resolution spaceborne SAR images, three kinds of DEM and two orbit interpolation algorithms were studied. The experimental results show that these two kinds of factors mainly affect the location accuracy of range direction, both the horizontal and vertical resolution of elevation have great influence on location accuracy, the quartic polynomial orbit interpolation can meet requirements of high accuracy location, and further improvement of the location accuracy need other auxiliary means. © 2019 IEEE.

关键词： Synthetic aperture radar

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Parallel Geometric Correction for Single Spaceborne SAR Image 6

Parallel Geometric Correction for Single Spaceborne SAR Imag...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

ISBN: (纸本)9781728129129

The fast processing of SAR images is of great significance for SAR applications, and parallel computing is the most common solution. The paper studied the parallel computing algorithm for the geometric correction of single spaceborne SAR image, which is based on a classical serial geometric correction algorithm. After fully analyzing the parallel features of the algorithm, a CPU-oriented and a GPU-oriented global parallel correction algorithm were designed. Then, two spaceborne SAR images were used to test the speedup performance of the two algorithms. The experimental results show that the two parallel algorithms can greatly improve the speed of the geometric correction of spaceborne SAR image, and the performance of CPU-oriented parallel algorithm is mainly related to the number of CPU/cores, while the performance of GPU-oriented parallel algorithm is mainly related to the data granularity, and the latter has very high speedup ratio and is an efficient method for geometric correction of spaceborne SAR image. © 2019 IEEE.

关键词： Synthetic aperture radar

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Unmanned Aerial Vehicle Classification and Detection Based on Deep Transfer Learning

Unmanned Aerial Vehicle Classification and Detection Based o...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Wei Meng Meng Tia School of computer and information engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Engineering Laboratory of Spatial Information Processing Kaifeng China

Currently, major security incidents caused by the “unlicensed flying” of Unmanned Aerial Vehicle (UAV) have emerged one after another, which poses a grave threat to the security issues of public facilities and sensitive areas. Whether it can timely detect and prevent “unlicensed flying” of UAV has become a social concern. In response to this demand, the transfer learning method is adopted in this paper to conduct twoclassification and detection on UAV images. Image recognition technology based on transfer learning is an effective method to improve recognition accuracy by applying deep learning models to small samples. Different from the large number of training samples required by deep learning, transfer learning transfers the weights of the pre-trained deep neural network, and uses only small sample data to obtain good results in UAV image recognition. First of all, this paper proposes to construct a UAV data set according to different types of UAV shape structures, to perfect the classification and detection effect and the generalization ability of the model. Then, based on the transfer learning method, experimental comparison is made between three classic deep convolutional neural network classification models (Inception V3, ResNet 101 and VGG16) and two classic deep convolutional neural network detection models (Faster RCNN and SSD). Finally, an experimental evaluation is conducted on the collected UAV test data set. Compared with the traditional recognition model, the image classification model based on transfer learning employed in this paper has achieved important improvements in accuracy, recall and precision. Especially in the InceptionV3 model of transfer training, the recall reaches 96.98%. In addition, the image detection model based on transfer learning has achieved good detection results in accuracy, recall and F1-score.

关键词： Training Deep learning Image recognition Shape Transfer learning Buildings Unmanned aerial vehicles

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A Lightweight Honeynet Design In the Internet of Things

A Lightweight Honeynet Design In the Internet of Things

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiaoyu Du Guanying Zhou Yujing Wang Song Tao College of Computer and Information Engineering Henan University Kaifeng Henan Engineering Laboratory of Spatial Information Processing Henan University Kaifeng

With the wide use of Internet of Things(IoT) devices, security problems follow. To defend and analyze the security threats of IoT, honeypot and honeynet play an increasingly important role. Existing Honeynet management methods are not suitable for the management of simple IoT due to the high cost and complicated construction environment. This paper proposes a lightweight IoT Honeynet based on the MQTT. It manages the honeypot devices in the IoT network through a custom protocol. At the same time, the number of honeypots can be dynamically adjusted according to the current attacked state of the IoT network to save its resources, and the logs of the honeypots can also be collected by the honeynet as an important basis for analyzing hacker attacks. Finally, the experimental result proved the effectiveness of the proposed framework.

关键词： Privacy Protocols Costs Computer hacking Dynamic scheduling Software Hardware

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Star extraction by star sensors for daytime images affected by atmospheric turbulence

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Chinese Journal of Aeronautics 2025年

作者： Wanxiang GOU Yinhu ZHAN Chonghui LI Shuai TONG Yong ZHENG Yuan YANG Hanxu LI College of Geospatial Information Information Engineering University Zhengzhou 450000 China Key Laboratory of Smart Earth Beijing 100029 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou 450000 China Collaborative Innovation Center of BDS Navigation Application Technology Henan Province Zhengzhou 450000 China

Daytime star images captured by dedicated near-space star sensors are characterized by short exposures, high noise, and low Signal-to-Noise Ratios (SNRs). Such imaging is also affected by atmospheric turbulence, causing optical phenomena, such as scintillation, distortion, and jitter. This causes difficulty in recording high-precision star images during the daytime. This study proposes an adaptive star point extraction method based on dynamically predicting stars’ positions. First, it predicts the approximate position of stars based on the star catalog, sensor attitude, observation time, and other information, improving the extraction accuracy. Second, it employs a regional SNR sorting method that adaptively selects star images with higher SNRs, suppressing the scintillation effect and enhancing the SNR of star images. Third, depending on the star’s motion trajectory characteristics on the image plane, it utilizes the centroid smoothing method for extraction, thus overcoming the impact of star drift. Field experiments demonstrate that the proposed method can effectively overcome star scintillation, drift, and irregular imaging caused by atmospheric turbulence, achieving a 100% success rate. Moreover, the extraction accuracy improves by more than 80% compared to traditional adaptive methods, attaining a value of 0.05 pixels (0.5’’), thereby meeting the requirements of daytime astronomical attitude determination and positioning.

关键词： Astronomical navigation Star sensor Daytime star image Atmospheric turbulence Star extraction Centroid accuracy

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