Zobrazeno 1 - 10
of 3 015
pro vyhledávání: '"deep neural network (DNN)"'
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2024, Iss 1, Pp 1-19 (2024)
Abstract This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain l
Externí odkaz:
https://doaj.org/article/4cb69dcbfae44923b88de1070dc8e3a1
Publikováno v:
Engineering, Technology & Applied Science Research, Vol 14, Iss 4 (2024)
Identifying the similarity between fine-grained images requires sophisticated techniques. This study presents a deep learning approach to the image similarity problem as an unsupervised learning task. The proposed autoencoder, built on a Deep Neural
Externí odkaz:
https://doaj.org/article/578e000bd101459b8af83277aee57a01
Autor:
Ha-Eun Yang, Nam-Wook Kim, Hong-Gu Lee, Min-Jee Kim, Wan-Gyu Sang, Changju Yang, Changyeun Mo
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for
Externí odkaz:
https://doaj.org/article/b49f6aa0a03f45dcbce0fe21133d6e28
Publikováno v:
Heliyon, Vol 10, Iss 14, Pp e34593- (2024)
This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in th
Externí odkaz:
https://doaj.org/article/ba8cf2f97d754e038341284ed24d261d
Autor:
Sayed Alireza Sajjadi, Sayed Alireza Sadrossadat, Ali Moftakharzadeh, Morteza Nabavi, Mohamad Sawan
Publikováno v:
IEEE Access, Vol 12, Pp 113944-113959 (2024)
In this paper, we propose a method based on deep neural networks for the statistical design of flip-flops, taking into account nonlinear performance constraints. Flip-flop design and manufacturing are influenced by random variations in the technologi
Externí odkaz:
https://doaj.org/article/2bbeab0115fa470ea14ec7bf132bd1d9
Publikováno v:
IEEE Access, Vol 12, Pp 88279-88302 (2024)
Automatic Speech Recognition (ASR) systems have improved and eased how humans interact with devices. ASR system converts an acoustic waveform into the relevant text form. Modern ASR inculcates deep neural networks (DNNs) to provide faster and better
Externí odkaz:
https://doaj.org/article/2f25cf94438d4cf6a8b90bb7e200f6c2
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 11, Pp 266-279 (2024)
By 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, foreca
Externí odkaz:
https://doaj.org/article/ae983992fe504bb0a2dae424227d356a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10600-10611 (2024)
This study compares the performance of five selected machine learning models regarding precipitation climatology during the warm season in 2022 and 2023 over the continental U.S. Input features included retrieved products from the microwave integrate
Externí odkaz:
https://doaj.org/article/8bbcbd22ff00412283ccff79cf472d6c
Publikováno v:
IEEE Access, Vol 12, Pp 80165-80175 (2024)
This paper presents an efficient deep neural network (DNN) accelerator designed for radio frequency (RF) signal modulation recognition. A novel DNN design optimized for mobile applications is demonstrated by combining MobileNetV3-based DNN with a ter
Externí odkaz:
https://doaj.org/article/122c67c3942d418f8d5cfb427575c3f3
Autor:
Junxiang Xu, Divya Jayakumar Nair
Publikováno v:
IEEE Access, Vol 12, Pp 60908-60927 (2024)
In the network structure analysis, we explore an underestimated key metric, the Relative Size of Largest Connected Component (RSLCC) and demonstrate its importance in post-disaster network connectivity assessment. RSLCC was first investigated in the
Externí odkaz:
https://doaj.org/article/e62b4d8ca7df4847814a393e980cf4d5