Recently, massive multiple-input multiple-output (MIMO) system has attracted tremendous research interests in wireless communications. Among various performance aspects and design problems regarding massive MIMO, precoding at the base station (BS) is one of the most important function components to ensure reliable downlink transmission. This paper provides a survey of linear precoding techniques for downlink transmission under both single-cell (SC) and multicell (MC) scenarios. In an SC scenario, a series of classical precoding techniques are reviewed and compared. In an MC scenario, massive MIMO suffers from pilot contamination problems, where orthogonal pilot sequences have to be reused among cells, leading to estimation errors of channel state information at the transmitter. Precoding techniques with and without the cooperation among BSs are reviewed and the cooperative precoding techniques are further divided into partial and full cooperations. To the best of our knowledge, this is the first work that comprehensively surveys all the existing linear precoding techniques. The comprehensive review reveals current challenges in designing the precoders, where technical merits are usually bounded by practical implementation issues. We show that a viable precoding technique for massive MIMO systems is still unknown to date, and suggest several research potentials that are worthy of future research efforts.

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data.

The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification. Despite the rich literature on such a pipeline, we show in this paper that the pre-transform and hand-crafted features could simply be replaced by end-to-end DNNs. Specifically, we experimentally verify that by only using standard components, a light-weight neural network could outperform the state-of-the-art methods for the ASVspoof2019 challenge. The proposed model is termed Time-domain Synthetic Speech Detection Net (TSSDNet), having ResNet- or Inception-style structures. We further demonstrate that the proposed models also have attractive generalization capability. Trained on ASVspoof2019, they could achieve promising detection performance when tested on disjoint ASVspoof2015, significantly better than the existing cross-dataset results. This paper reveals the great potential of end-to-end DNNs for synthetic speech detection, without hand-crafted features.

Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of multi-scale objects, especially small objects in high resolution satellite (HRS) images, has not been adequately explored. As a result, the detection performance turns out to be poor. To address this problem, we first propose a unified multi-scale convolutional neural network (CNN) for geospatial object detection in HRS images. It consists of a multi-scale object proposal network and a multi-scale object detection network, both of which share a multi-scale base network. The base network can produce feature maps with different receptive fields to be responsible for objects with different scales. Then, we use the multi-scale object proposal network to generate high quality object proposals from the feature maps. Finally, we use these object proposals with the multi-scale object detection network to train a good object detector. Comprehensive evaluations on a publicly available remote sensing object detection dataset and comparisons with several state-of-the-art approaches demonstrate the effectiveness of the presented method. The proposed method achieves the best mean average precision (mAP) value of 89.6%, runs at 10 frames per second (FPS) on a GTX 1080Ti GPU.

The waveform diversity of multiple-input-multiple-output (MIMO) radar systems offers many advantages in comparison to the phased-array counterpart. One of the advantages is that it allows one to design MIMO radar with flexible transmit beampatterns, which has several useful applications. Many researchers have proposed solutions to this problem in the recent decade. However, these designs pay little attention to certain aspects of the performance such as the ripples within the energy focusing section, the attenuation of the sidelobes, the width of the transition band, the angle step-size, and the required number of transmit antennas. In this paper, we first propose several methods that can indirectly or directly control the ripple levels within the energy focusing section and the transition bandwidth. These methods are based on existing problem formulations. More importantly, we reformulate the design as a feasibility problem (FP). Such a formulation enables a more flexible and efficient design that achieves the most preferable beampatterns with the least system cost. Using this formulation, an empirical MIMO radar beampattern formula is obtained. This MIMO radar beampattern formula is similar to Kaiser's formula in conventional finite-impulse-response (FIR) filter design. The performances of the proposed methods and formulations are evaluated via numerical examples.