Zobrazeno 1 - 10
of 6 815
pro vyhledávání: '"neural nets"'
Publikováno v:
International Journal of Intelligent Computing and Cybernetics, 2024, Vol. 17, Issue 4, pp. 759-782.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJICC-07-2024-0310
Autor:
Zeinab Nazemi Absardi, Reza Javidan
Publikováno v:
IET Communications, Vol 18, Iss 18, Pp 1151-1165 (2024)
Abstract Deploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged
Externí odkaz:
https://doaj.org/article/0ed620a0702143acb9bcd5232c06df3f
Publikováno v:
IET Radar, Sonar & Navigation, Vol 18, Iss 10, Pp 1652-1669 (2024)
Abstract Aiming at the problems of slow speed and poor accuracy of traditional millimeter wave sparse imaging, a sparse imaging algorithm based on graph convolution model is proposed from the perspective of sparse signal recovery. The graph signal mo
Externí odkaz:
https://doaj.org/article/01054fef95274a068bab56b2083a7e58
Publikováno v:
IET Radar, Sonar & Navigation, Vol 18, Iss 10, Pp 1814-1828 (2024)
Abstract The classification and recognition of underwater targets by an active sonar system remain challenging and complex. Traditional methods have limited classification performance in time and spatially varying ocean channels. An active sonar targ
Externí odkaz:
https://doaj.org/article/475250576f7542d3af3fcb5835ca9f34
Autor:
Shyam Venkatasubramanian, Sandeep Gogineni, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
Publikováno v:
IET Radar, Sonar & Navigation, Vol 18, Iss 10, Pp 1638-1651 (2024)
Abstract Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data‐driven approach to improve accuracy in radar
Externí odkaz:
https://doaj.org/article/db45abe081794452a6c11582bfff0937
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 9, Iss 5, Pp 1331-1345 (2024)
Abstract Although deep convolution neural network (DCNN) has achieved great success in computer vision field, such models are considered to lack interpretability in decision‐making. One of fundamental issues is that its decision mechanism is consid
Externí odkaz:
https://doaj.org/article/ea0ff044de9147e6954baba801f74f9a
Publikováno v:
IET Computer Vision, Vol 18, Iss 7, Pp 1043-1056 (2024)
Abstract Image captioning aims to automatically generate a natural language description of a given image, and most state‐of‐the‐art models have adopted an encoder–decoder transformer framework. Such transformer structures, however, show two m
Externí odkaz:
https://doaj.org/article/1327fe6db62647578eabf73ed147dd8c
Publikováno v:
IET Computer Vision, Vol 18, Iss 7, Pp 922-934 (2024)
Abstract Due to the richness of natural image semantics, natural image colourisation is a challenging problem. Existing methods often suffer from semantic confusion due to insufficient semantic understanding, resulting in unreasonable colour assignme
Externí odkaz:
https://doaj.org/article/495382d2d02d43ef9c3b056b204eccc3
Publikováno v:
IET Computer Vision, Vol 18, Iss 7, Pp 992-1003 (2024)
Abstract Human action recognition based on graph convolutional networks (GCNs) is one of the hotspots in computer vision. However, previous methods generally rely on handcrafted graph, which limits the effectiveness of the model in characterising the
Externí odkaz:
https://doaj.org/article/617303b98b5448678cac64dc5045540e
Publikováno v:
IET Computer Vision, Vol 18, Iss 7, Pp 875-887 (2024)
Abstract Deep learning‐based face recognition models have demonstrated remarkable performance in benchmark tests, and knowledge distillation technology has been frequently accustomed to obtain high‐precision real‐time face recognition models sp
Externí odkaz:
https://doaj.org/article/7a484f31a9964c82afb7e0031fff1e89