Denoising electron holograms using the wavelet hidden Markov model for phase retrieval—Applications to the phase-shifting method

Tamaoka, Takehiro; Midoh, Yoshihiro; Yamamoto, Kazuo; Aritomi, Shodai; Tanigaki, Toshiaki; Nakamura, Masao; Nakamae, Koji; Kawasaki, Masashi; Murakami, Yasukazu

doi:10.1063/5.0027599

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Indeed, the sensitivity during phase analysis depends on the image quality of holograms composed of the interference fringes of electron waves ( 22 ). To solve this problem, we used several methods, including hologram acquisition with negligible mechanical and electrical disturbances using a sophisticated 1.2-MV atomic-resolution holography microscope ( 23 ) with an aberration corrector and reduction of the statistical noise using a wavelet hidden Markov model (WHMM) ( 24 , 25 ). As described below in detail, the holography observations enabled counting of just a few charges responsible for the electric charging in a single supported NP.…”

mentioning

confidence: 99%

Direct identification of the charge state in a single platinum nanoparticle on titanium oxide

Aso

Hōjō

Takahashi

et al. 2022

Science

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A goal in the characterization of supported metal catalysts is to achieve particle-by-particle analysis of the charge state strongly correlated with the catalytic activity. Here, we demonstrate the direct identification of the charge state of individual platinum nanoparticles (NPs) supported on titanium dioxide using ultrahigh sensitivity and precision electron holography. Sophisticated phase-shift analysis for the part of the NPs protruding into the vacuum visualized slight potential changes around individual platinum NPs. The analysis revealed the number (only one to six electrons) and sense (positive or negative) of the charge per platinum NP. The underlying mechanism of platinum charging is explained by the work function differences between platinum and titanium dioxide (depending on the orientation relationship and lattice distortion) and by first-principles calculations in terms of the charge transfer processes.

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confidence: 99%

Direct identification of the charge state in a single platinum nanoparticle on titanium oxide

Aso

Hōjō

Takahashi

et al. 2022

Science

Self Cite

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“…Starting from the initial paths that can be initially identified cumulatively, the library of all paths is subjected to probabilistic calculations and the state correlations between the sets are described after the final results are derived. Furthermore, since the Hidden Markov Model does not have an associated memory condition, only the previous state phase with the highest similarity to that state can be projected [15]. It is also shown that the probability of a particular predicted path occurring in the malicious attack path prediction method is most highly correlated with the last solution in the set of posterior variants of the sensor node.…”

Section: Designing Malicious Attack Path Prediction Methodsmentioning

confidence: 99%

Hidden Markov-based path prediction method for malicious attacks in wireless sensor networks

Ren

2022

International Conference on Network Communication and Information Security (ICNCIS 2022)

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In recent years, with the development of the Internet of things industry, wireless sensor networks, as one of the most important technologies in its sensing layer, have been further studied and applied. However, the security threats it faces are becoming more and more diverse, and the security needs are becoming more and more urgent. However, wireless sensor network nodes are mutually exclusive, which leads to low accuracy of attack path prediction when malicious attacks occur. In order to ensure the security of wireless sensor networks, a hidden Markov based malicious attack path prediction method for wireless sensor networks is designed. The method of authentication coding is introduced to realize data integrity authentication. Extract the malicious software gene sequence, define the constraint conditions of the state transition probability matrix, build the attack means deduction model based on Hidden Markov, and realize the malicious attack path prediction. Experimental results: the average accuracy of the proposed method in four malicious attack scenarios is 73.583%, 55.674%, 32.923% and 85.014%, respectively. The results also verify that the proposed method improves the accuracy of malicious attack path prediction in wireless sensor networks based on Hidden Markov model, and has good feasibility. It provides an important basis for wireless sensor network administrators to implement defense strategies in time.

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“…The user behavior prediction of the proposed method is mainly based on a set of hidden Markov models [22], [23]. First, relevant machine learning methods are used offline to train the prediction model parameters of various behaviors from historical data; Then, according to the partial behaviors of current online users, a finite step Markov process prediction is performed to obtain a relatively complete sequence of user behaviors; Finally, this behavior sequence is used to guide the push of university archives business data.…”

Section: User Behavior Predictionmentioning

confidence: 99%

Design of University Archives Business Data Push System Based on Big Data Mining Technology

Wang,

2023

IJACSA

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Aiming at the problems of low accuracy, recall, coverage and push efficiency of university archives business data, a university archives business data push system based on big data mining technology is designed. Firstly, the overall architecture and topological structure of the university archives business data push system are designed, and then the functional modules of the system are designed. Using big data mining technology to mine user behavior, modeling according to user behavior sequence, and designing a model to predict user behavior sequence based on hidden Markov model theory. Finally, the user behavior sequence is analyzed, and the factors such as user collaboration, similarity of user behavior sequence and data timeliness are comprehensively considered to push university archives business data for users. The experimental results show that the proposed method has high data push accuracy, recall, coverage and push efficiency, and can effectively push the required business data for users.

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Denoising electron holograms using the wavelet hidden Markov model for phase retrieval—Applications to the phase-shifting method

Cited by 12 publications

References 18 publications

Direct identification of the charge state in a single platinum nanoparticle on titanium oxide

Direct identification of the charge state in a single platinum nanoparticle on titanium oxide

Hidden Markov-based path prediction method for malicious attacks in wireless sensor networks

Design of University Archives Business Data Push System Based on Big Data Mining Technology

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