Color representation method using RGB color binary-weighted computer-generated holograms

Fujiwara, Masato; Tsuji, Naoki; Araki, Hiromitsu; Ikawa, Shohei; Maeda, Yoshinobu; Niwase, Hiroaki; Oikawa, Minoru; Kakue, Takashi; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

doi:10.3788/col201816.080901

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Usually, the SLM is a phase-modulation (PM) SLM for the high diffraction efficiency. Alternatively, the 3D display can also be realized by using a binary SLM; i.e., the modulation is 0, π in phase or 0, 1 in amplitude [2][3][4][5][6]. The display quality of a binary SLM is usually low because of the limited degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%

Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms

et al. 2021

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The image generated by binary computer-generated holograms (CGHs) always suffers from serious speckle noise. Thanks to the fast frame rate of the binary spatial light modulator, the speckle can be significantly suppressed by intensity accumulation, i.e., the sequential display of multiple CGHs of the same scene. If enough randomness is added to the CGHs, the speckle noise can be mostly averaged out. Intuitively, the quality of the reconstructed image should be proportional to the number of intensity accumulation. However, there is no simple method to predict the dependence of the average noise and accumulation number, and we can only know the results after finishing the full computation. In this paper, we propose an empirical formula of the average noise based on the speckle phenomenon in a laser projector. Using this model, we have confirmed that the randomness induced by random phase is equivalent to that induced by random down-sampling for the generation of binary CGHs. In addition, if the computational efficiency is a concern, the CGH calculated with iterations is not recommended for intensity accumulation display. Finally, there is an upper-quality limit of the reconstructed image by intensity accumulation. Thus, a strategy for efficient intensity accumulation is suggested.

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Section: Introductionmentioning

confidence: 99%

Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms

et al. 2021

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AMD‐CNN: Android malware detection via feature graph and convolutional neural networks

Arslan

Taşyürek

2022

Concurrency and Computation

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Summary Android malware has become a serious threat to mobile device users, and effective detection and defence architectures are needed to solve this problem. Recently, machine learning techniques have been widely used to deal with Android malicious apps. These methods are based on a simple feature set and have difficulty detecting up‐to‐date malware. Therefore, more robust and efficient classification methodologies are needed. In this article, AMD‐CNN, an Android malware detection tool, is proposed, and it uses graphical representations to detect malicious apks. In the first step, the features related to the androidmanifest.xml file are extracted and converted into a vector consisting of one or zero. The feature vector is then converted to 2D‐code images and used in training the CNN network. The model needs low‐resource consumption to run on mobile devices and allow real‐time applications to be analyzed. The experiments with 1920 malicious and benign apks show that the malware detection rate (accuracy) was 96.2% and precision, recall, and F‐score values were 97.9%, 98.2%, and 98.1%, respectively. The average time and memory space to analyze each application are 0.035 s and 3.38 MB. AMD‐CNN is an efficient and robust tool and has advantages over previous studies.

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Color representation method using RGB color binary-weighted computer-generated holograms

Cited by 2 publications

References 21 publications

Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms

Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms

AMD‐CNN: Android malware detection via feature graph and convolutional neural networks

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