Compression of phase-only holograms with JPEG standard and deep learning

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed "JPEG + deep learning" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression

Arisan Baca Tulis: Pemberantasan Buta Aksara Melalui Metode Arisan Yang Unik Dan Menyenangkan

Surabaya as the capital of East Java was haven\u27t free from illiteracy yet, the condition was mostly of them were women aged 30 years and over who haven\u27t completed the compulsory education for nine years. In addition, women are also considered capable of transmitting knowledge and their knowledge to the family, especially the children as closest network. With the fun activity, we expect the new atmosphere of togetherness and gathering with the same degree of age are able to create its own preoccupations and improving the confidence to learn to read and write. This program, is modified such that mothers are often going ahead and daring to write or read the sentences given by instructor, will get a roll of paper that bearing their names. Thus, the more quantity Mothers forward and dare to write and read, the more the roll of their names, and a chance to win the raffle of “Arisan” at the end of training will be even greater. With different and unique method, able to attract mothers to be more diligent in reading and writing, so that illiteracy slowly be decrease, the mothers began to realize how important education started early, and spirit and motivation for learning has begun to form, evidenced by an increase in the ability of mothers to read and write, 77% of mothers who previously could not read, or only able to spell, and be able to read haltingly, and 68% of mothers who can not write, or can only write a few letters, as well as stuttering to write, has increased its ability to 86% have been able to read fluently, and 77% have been able to write correctly

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation

Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage attention mechanisms and RNNs, recent efforts mainly follow two directions for improvement: multi-interest learning and graph convolutional aggregation. Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-level correlations between historical items. Unfortunately, neither of them realizes that these two types of solutions can mutually complement each other, by aggregating multi-level user preference to achieve more precise multi-interest extraction for a better recommendation. To this end, in this paper, we propose a unified multi-grained neural model(named MGNM) via a combination of multi-interest learning and graph convolutional aggregation. Concretely, MGNM first learns the graph structure and information aggregation paths of the historical items for a user. It then performs graph convolution to derive item representations in an iterative fashion, in which the complex preferences at different levels can be well captured. Afterwards, a novel sequential capsule network is proposed to inject the sequential patterns into the multi-interest extraction process, leading to a more precise interest learning in a multi-grained manner.Comment: 10 pages, 7 figure

Visualization 1: Speckleless holographic display by complex modulation based on double-phase method

Optical results of multi-plane holographic display based on proposed method Originally published in Optics Express on 26 December 2016 (oe-24-26-30368

Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression