AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2024
Du, B.; Du, H.; Liu, H.; Niyato, D.; Xin, P.; Yu, J.; Qi, M.; Tang, Y.
YOLO-Based Semantic Communication with Generative AI-Aided Resource Allocation for Digital Twins Construction Journal Article
In: IEEE Internet of Things Journal, vol. 11, no. 5, pp. 7664–7678, 2024, ISSN: 23274662 (ISSN).
Abstract | Links | BibTeX | Tags: Cost reduction, Data transfer, Digital Twins, Edge detection, Image edge detection, Network layers, Object Detection, Object detectors, Objects detection, Physical world, Resource allocation, Resource management, Resources allocation, Semantic communication, Semantics, Semantics Information, Virtual Reality, Virtual worlds, Wireless communications
@article{du_yolo-based_2024,
title = {YOLO-Based Semantic Communication with Generative AI-Aided Resource Allocation for Digital Twins Construction},
author = {B. Du and H. Du and H. Liu and D. Niyato and P. Xin and J. Yu and M. Qi and Y. Tang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173060990&doi=10.1109%2fJIOT.2023.3317629&partnerID=40&md5=60507e2f6ce2b1c345248867a9c527a1},
doi = {10.1109/JIOT.2023.3317629},
issn = {23274662 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Internet of Things Journal},
volume = {11},
number = {5},
pages = {7664–7678},
abstract = {Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this article, we propose a semantic communication framework based on you only look once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the acrlong AI-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new efficient layer aggregation network-horNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services. © 2014 IEEE.},
keywords = {Cost reduction, Data transfer, Digital Twins, Edge detection, Image edge detection, Network layers, Object Detection, Object detectors, Objects detection, Physical world, Resource allocation, Resource management, Resources allocation, Semantic communication, Semantics, Semantics Information, Virtual Reality, Virtual worlds, Wireless communications},
pubstate = {published},
tppubtype = {article}
}
Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this article, we propose a semantic communication framework based on you only look once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the acrlong AI-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new efficient layer aggregation network-horNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services. © 2014 IEEE.
2014
Aprovitola, Andrea; Gallo, Luigi
Edge and Junction Detection Improvement Using the Canny Algorithm with a Fourth Order Accurate Derivative Filter Proceedings Article
In: Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference On, pp. 104–111, IEEE, Marrakech, Morocco, 2014, ISBN: 978-1-4799-7978-3.
Abstract | Links | BibTeX | Tags: Canny algorithm, Derivative of Gaussian, Edge detection
@inproceedings{aprovitolaEdgeJunctionDetection2014,
title = {Edge and Junction Detection Improvement Using the Canny Algorithm with a Fourth Order Accurate Derivative Filter},
author = { Andrea Aprovitola and Luigi Gallo},
doi = {10.1109/SITIS.2014.28},
isbn = {978-1-4799-7978-3},
year = {2014},
date = {2014-11-01},
urldate = {2016-12-06},
booktitle = {Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference On},
pages = {104--111},
publisher = {IEEE},
address = {Marrakech, Morocco},
abstract = {The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.},
keywords = {Canny algorithm, Derivative of Gaussian, Edge detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.
Aprovitola, Andrea; Gallo, Luigi
Edge and junction detection improvement using the Canny algorithm with a fourth order accurate derivative filter Proceedings Article
In: Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, pp. 104–111, IEEE, Marrakech, Morocco, 2014, ISBN: 978-1-4799-7978-3.
Abstract | Links | BibTeX | Tags: Canny algorithm, Derivative of Gaussian, Edge detection
@inproceedings{aprovitola_edge_2014,
title = {Edge and junction detection improvement using the Canny algorithm with a fourth order accurate derivative filter},
author = {Andrea Aprovitola and Luigi Gallo},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7081534},
doi = {10.1109/SITIS.2014.28},
isbn = {978-1-4799-7978-3},
year = {2014},
date = {2014-11-01},
urldate = {2016-12-06},
booktitle = {Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on},
pages = {104–111},
publisher = {IEEE},
address = {Marrakech, Morocco},
abstract = {The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.},
keywords = {Canny algorithm, Derivative of Gaussian, Edge detection},
pubstate = {published},
tppubtype = {inproceedings}
}
The Canny algorithm has been extensively adopted to perform edge detection in images. The Derivative of Gaussian (DoG) proposed by Canny has been shown to be the optimal edge detector to compute the image gradient due to its robustness to noise. However, the DoG has some important drawbacks in relation to images with thin edges of a few pixels width and junctions. The excessive blurring provided by the DoG affects the detection of the double and triple junctions that sometimes appear broken while the corners appear rounded. Such a loss in detail is due to the second order approximation of the finite difference (FD) operator adopted to discretize the DoG detector. In this work an improvement of the Canny algorithm is proposed for images having thin edges, computing the edge detector as a convolution of a fourth order accurate FD with the smoothed Gaussian image. The modified wave number analysis of the FD formulation is adopted to motivate the improvement in the edge resolution gained by the fourth order FD discretization. Quantitative comparisons performed with a second order FD discretization of the edge detector adopting both synthetic and benchmark images highlight an improvement in edge localization and in junction detection.
2012
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra Based Edge Detector for Color Images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchiniCliffordAlgebraBased2012,
title = {Clifford Algebra Based Edge Detector for Color Images},
author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84--91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra based edge detector for color images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchini_clifford_2012,
title = {Clifford Algebra based edge detector for color images},
author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84–91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.