Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems enable extraordinarily high data rates and necessitate precise channel state information (CSI) for effective beamforming. To explore the structural sparsity feature, we propose an advanced low-rank matrix factorization technique for channel estimation. Our algorithm treats channel estimation as a problem of low-rank matrix completion by exploiting the simultaneous sparsity and low-rank nature of the channel. First, we decompose the channel matrix into two low-rank factor matrices, refining them through projected gradient descent to satisfy the constraints of low-rankness and sparsity simultaneously. For improved results, we introduce a deep unfolding network, named Deep Unfolding Low-Rank Factorization Net for Channel Estimation (DULRF-CE), which integrates the low-rank matrix factorization model into the neural network design. This network includes a module capable of nonlinear sparse transformations and a denoising module, giving it the ability to learn a more accurate low-rank decomposition and a sparser channel representation. Our experimental results show that the DULRF-CE algorithm provides a significant advance, offering up to 4.3 dB improvement in average normalized mean square error performance over existing matrix-completion-based channel estimation techniques in various measurement scenarios.

We present a novel framework to efficiently acquire anisotropic reflectance in a pixel-independent fashion, using a deep gated mixture-of-experts. While existing work employs a unified network to handle all possible input, our network automatically learns to condition on the input for enhanced reconstruction. We train a gating module that takes photometric measurements as input and selects one out of a number of specialized decoders for reflectance reconstruction, essentially trading generality for quality. A common pre-trained latent-transform module is also appended to each decoder, to offset the burden of the increased number of decoders. In addition, the illumination conditions during acquisition can be jointly optimized. The effectiveness of our framework is validated on a wide variety of challenging near-planar samples with a lightstage. Compared with the state-of-the-art technique, our quality is improved with the same number of input images, and our input image number can be reduced to about 1/3 for equal-quality results. We further generalize the framework to enhance a state-of-the-art technique on non-planar reflectance scanning.

We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple LEDs are then aggregated to produce a final depth map. For appearance, learned light patterns are cast through a transparent mask to efficiently probe angularly-varying reflectance. Per-point BRDF parameters are differentiably optimized with respect to corresponding measurements, and stored in texture maps as the final reflectance. We establish a differentiable pipeline for the joint capture to automatically optimize both the mask and light patterns towards optimal acquisition quality. The effectiveness of our light is demonstrated with a wide variety of physical objects. Our results compare favorably with state-of-the-art techniques.

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.

Instagram is an popular photo-sharing smart phone application of social network and is widely used among tourists to record their journey information such as location, time and content. Consequently, huge volume of spatio-temporal data are generated in the form of trajectories. Discovering useful patterns from these trajectories can reveal valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called Journey Group (JG), which is a group trajectory pattern reflecting a large number of users who walk through a common trajectory and depart from the trajectory for several times allowed. In this paper, we focus on data generated by Instagram to discover the JG Trajectory Patterns i.e., JG T-Patterns, from travel trajectories. Previous researches on T-Patterns mining concentrate on GPS-based data, which is different from Instagram data, a kind of UGC-based (User Generated Content based) data. GPS-based data is dense because it is often generated automatically by moving devices in a certain pace, while UGC-based data is sparse because data is generated randomly by the uploading of users. Aiming at this, a novel JG T-Patterns mining strategy from UGC-based data is also proposed. Finally, a demo for discovering hot travel routes based on JG T-Patterns from Instagram trajectories, named Pop Tour, is implemented and experimental results show that Pop Tour is an effective system.

The basic idea behind parallel database systems is to perform operations in parallel to reduce the response time and improve the system throughput. Data placement is a key factor on the performance of parallel database systems. This paper proposes two data partition strategies to decluster XML documents with very large size, path schema based path instance balancing (PSPIB) strategy, in which all path instances with the same path schema in a data tree are declustered evenly over all sites, and node schema based node round-robin (NSNRR) strategy, in which all node objects with the same node schema in a data tree are declustered over all sites in a round-robin way. Accordingly, two query processing algorithms are proposed based on the two partition methods, parallel path merge (PPM) algorithm and parallel pipelining path join (PPPJ) algorithm. The performance analysis and evaluation on the two data placement strategies and corresponding query processing algorithms are given in this paper.

With the rapid development of the Internet and the World Wide Web (WWW), very large amount of information is available and ready for downloading, most of which are free of charge. At the same time, hard disks with large capacity are available at affordable prices. Most of us nowadays often dump a large number of various types of documents into our computers without much thinking. On the other hand, file systems have not changed too much during the past decades. Most of them organize files in directories that form a tree structure, and a file is identified by its name and pathname in the directory tree. Remembering name of files created sometime ago and digging them out from a disk with dozen gigabytes of data in hundred thousands of files becomes never an easy task. Tools available for helping such a search are still far from satisfactory.Xbase (XML-based document BASE) is a prototype system aiming at addressing the above problem. By XML-based, we meant that XML is used to define the metadata. The current version of XBase stores text-based files, including semi-structured data such as XML, HTML, plain text documents (e.g., tex files, computer programs) and those files that can be converted into text (e.g., postscript files, PDF files). In XBase, file name is optional. Users can just load a file into XBase without giving a name and the directory where it should be stored. XBase will automatically associate it with attributes such as the time when the file was saved, its source, its size and type, and etc., To retrieve those files, XBase provides three access methods, explorative browsing, querying using query languages, and keyword based search.