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Prashanth Barla, Hemalatha Shivarama, Ganesan Deepa and Ujjwal Ujjwal
Hybrid magnetic tunnel junction/complementary metal oxide semiconductor (MTJ/CMOS) circuits based on in-memory-computation (IMC) architecture is considered as the next-generation candidate for the digital integrated circuits. However, the energy consumpt...
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Roman Golman, Robert Giterman and Adam Teman
Embedded memories occupy an increasingly dominant part of the area and power budgets of modern systems-on-chips (SoCs). Multi-ported embedded memories, commonly used by media SoCs and graphical processing units, occupy even more area and consume higher p...
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Konstantinos Dolaptsis, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selçuk Arslan, Yücel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Gündogdu, Muhammad Qaswar, Danyal Bustan and Abdul Mounem Mouazen
Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize?s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approac...
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Zeqin Tian, Dengfeng Chen and Liang Zhao
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large ...
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Mykhailo Lohachov, Ryoji Korei, Kazuo Oki, Koshi Yoshida, Issaku Azechi, Salem Ibrahim Salem and Nobuyuki Utsumi
This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study?s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli gro...
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Dibo Dong, Shangwei Wang, Qiaoying Guo, Yiting Ding, Xing Li and Zicheng You
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network?long short-term memory joint fr...
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Yin Tang, Lizhuo Zhang, Dan Huang, Sha Yang and Yingchun Kuang
In view of the current problems of complex models and insufficient data processing in ultra-short-term prediction of photovoltaic power generation, this paper proposes a photovoltaic power ultra-short-term prediction model named HPO-KNN-SRU, based on a S...
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Youssef Moawad, Wim Vanderbauwhede and René Steijl
As quantum computing technology continues to develop, the need for research into novel quantum algorithms is growing. However, such algorithms cannot yet be reliably tested on actual quantum hardware, which is still limited in several ways, including qub...
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Miguel Ruiz Cabello, Antonio J. Martín Valverde, Borja Plaza, Malte Frövel, David Poyatos, Amelia R. Bretones, Alberto G. Bravo and Salvador G. García
Efficiently modeling thin features using the finite-difference time-domain (FDTD) method involves a considerable reduction in the spatial mesh size. However, in real-world scenarios, such reductions can lead to unaffordable memory and CPU requirements. I...
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Zhiqian Zhang, Lei Liu, Lin Quan, Guohong Shen, Rui Zhang, Yuqi Jiang, Yuxiong Xue and Xianghua Zeng
Accurately predicting proton flux in the space radiation environment is crucial for satellite in-orbit management and space science research. This paper proposes a proton flux prediction method based on a hybrid neural network. This method is a predictiv...
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