Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer

Lin, Yun; Tu, Ya; Dou, Zheng; Chen, Lei; Mao, Shiwen

doi:10.1109/tccn.2020.3024610

Cited by 238 publications

(53 citation statements)

References 34 publications

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“…During the recent years, deep learning based methods have made remarkable progress in many fileds [24], such as Internet of Things [25,26], Signal processing [27,28], UAV [29], wireless communications [30], and especially in the field of agriculture [31][32][33][34][35]. These include fruit classification [36][37][38], yield estimation and counting [39,40].…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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“…The portrait system emerged from big data in the field of e-commerce. Similar to the field of communications, accurate signal identification is equivalent to label signal [28]. In the era of big data, user information is flooded in the Internet.…”

Section: The Application Expansion Of Eme Visualization Ismentioning

confidence: 99%

Electromagnetic Environment Portrait Based on Big Data Mining

Guo

Lin

2021

Wireless Communications and Mobile Computing

Self Cite

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With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high-dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

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“…(2) During the analysis, we can calculate the distribution feature in the k-th (1kd) dimension of each cluster: DF ik '=<μ ik ', σ ik ', m ik '> (1in), when o 2 is assumed to be divided into each cluster. In addition, the variation for the distribution feature in the k-th dimension can also be calculated as Equation (3).…”

Section: Figure 1 Comparing a One-dimensional Object With Two Setsmentioning

confidence: 99%

“…As a basic data mining strategy, clustering analysis is significant for discovering the characteristics of data aggregation, which is an unsupervised process [1][2][3]. When the data distribution is unknown, the clustering method is effective at obtaining the inherent distribution of data [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Clustering Analysis Method With High Reliability Based on Wilcoxon-Mann-Whitney Testing

et al. 2021

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As a core step in clustering analysis, distance measurement results can influence clustering accuracy. Existing measurement methods are mostly based on cluster feature information. However, these cluster features may be insufficient and result in losing data information for clusters containing a number of objects. To improve measurement accuracy, we make full use of the distribution characteristics of objects in clusters, i.e., we use descriptive statistics and the Wilcoxon-Mann-Whitney rank sum test in nonparametric statistics to measure distances during clustering. Furthermore, we propose a two-stage clustering algorithm to improve clustering analysis performance. In terms of avoiding preliminarily assuming the number of clusters, with the proposed distance measurement method, the clustering algorithm can discover clusters with arbitrary shapes and improve clustering accuracy. Experiments with multiple datasets compared with other clustering algorithms illustrate the accuracy and efficiency of the proposed clustering algorithm.

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Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer

Cited by 238 publications

References 34 publications

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Electromagnetic Environment Portrait Based on Big Data Mining

A Clustering Analysis Method With High Reliability Based on Wilcoxon-Mann-Whitney Testing

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