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Quantitative investment decisions based on machine learning and investor attention analysis
Technological and Economic Development of Economy, 2023 According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning.
Jie Gao +3 more
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Ensemble patch transformation: a flexible framework for decomposition and filtering of signal
EURASIP Journal on Advances in Signal Processing, 2020 This paper considers the problem of signal decomposition and filtering by extending its scope to various signals that cannot be effectively dealt with existing methods.
Donghoh Kim, Guebin Choi, Hee-Seok Oh
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Atomic decompositions of audio signals [PDF]
Proceedings of 1997 Workshop on Applications of Signal Processing to Audio and Acoustics, 2002 Signal modeling techniques ranging from basis expansions to parametric approaches have been applied to audio signal processing. Motivated by the fundamental limitations of basis expansions for representing arbitrary signal features and providing means for signal modifications, we consider decompositions in terms of functions that are both signal ...
Goodwin, Michael, Vetterli, Martin
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Journal of Imaging, 2021 Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent
Fatema-Tuz-Zohra Khanam +4 more
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Signal Periodic Decomposition With Conjugate Subspaces [PDF]
IEEE Transactions on Signal Processing, 2016 11 pages, 9 ...
Shi-Wen Deng, Ji-Qing Han 0001
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IEEE Open Access Journal of Power and Energy, 2022 Day-ahead energy forecasting systems struggle to provide accurate demand predictions due to pandemic mitigation measures. Decomposition-Residuals Deep Neural Networks (DR-DNN) are hybrid point-forecasting models that can provide more accurate electricity
Konstantinos Theodorakos +3 more
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Multi-band network fusion for Alzheimer’s disease identification with functional MRI
Frontiers in Psychiatry, 2022 IntroductionThe analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage.
Lingyun Guo +4 more
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Tensor Decomposition for EEG Signals Retrieval [PDF]
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss.
Zehong Cao +4 more
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Representation of signals by local symmetry decomposition [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 In this paper we propose a segmentation of finite support sequences based on the even/odd decomposition of a signal. The objective is to ind a more compact representation of information. To this aim, the paper starts to generalize the even/odd decomposition by concentrating the energy on either the even or the odd part by optimally placing the centre ...
GNUTTI, ALESSANDRO +2 more
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Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD
Shock and Vibration, 2016 An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper.
Peiming Shi, Cuijiao Su, Dongying Han
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