Modulation code optimization for holographic data storage.

The performance of page-oriented holographic data storage systems, especially capacity, transmission speed, and symbol error rate, critically depends on the modulation code design. Under the constraint of maintaining approximately 20% code weight, this paper proposes an optimized 5:22 two-dimensional equal-weight sparse modulation code featuring elongated code block geometry. We theoretically evaluated the code rates and decoding efficiencies of various encoding formats with equivalent code weights, demonstrating that this design achieves optimal performance among existing binary encodings.

Anti-noise performance analysis in amplitude-modulated collinear holographic data storage using deep learning.

In an amplitude-modulated collinear holographic data storage system, optical system aberration and experimental noise due to the recording medium often result in a high bit error rate (BER) and low signal-to-noise ratio (SNR) in directly read detector data. This study proposes an anti-noise performance analysis using deep learning. End-to-end convolutional neural networks were employed to analyze noise resistance in encoded data pages captured by the detector.

Phase retrieval based on deep learning with bandpass filtering in holographic data storage.

A phase retrieval method based on deep learning with bandpass filtering in holographic data storage is proposed. The relationship between the known encoded data pages and their near-field diffraction intensity patterns is established by an end-to-end convolutional neural network, which is used to predict the unknown phase data page. We found the training efficiency of phase retrieval by deep learning is mainly determined by the edge details of the adjacent phase codes, which are the high-frequency components of the phase code.

Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method.

Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method is proposed, which serves to both reduce media consumption and to shorten the iterative number of phase code retrieval. Generally, high-fidelity phase retrieval requires twice Nyquist frequency in phase-modulated holographic data storage. To increase storage density, we only recorded and captured the signal with Nyquist size and used the frequency expanded method to realize high-fidelity phase retrieval.

Lensless phase retrieval based on deep learning used in holographic data storage.

This paper proposes a lensless phase retrieval method based on deep learning (DL) used in holographic data storage. By training an end-to-end convolutional neural network between the phase-encoded data pages and the corresponding near-field diffraction intensity images, the new unknown phase data page can be predicted directly from the intensity image by the network model without any iterations. The DL-based phase retrieval method has a higher storage density, lower bit-error-rate (BER), and higher data transfer rate compared to traditional iterative methods.

[A prospective randomized controlled trial of Sapylin combined with chemotherapy for advanced colon cancer following radical operation].

Objective: To evaluate the effect of Sapylin combined with intraperitoneal and systemic chemotherapy for advanced colon cancer following radical operation on local recurrence, hepatic metastasis, and overall survival rate.

Methods: From Jan. 2004 to Dec.