Space-Time Block Coding for Wireless Communications

This paper provides a systematic review of the mutual coupling in multiple-input multiple-output (MIMO) systems, including the effects on performances of MIMO systems and various decoupling techniques. The mutual coupling changes the antenna characteristics in an array, and therefore, degrades the system performance of the MIMO system and causes the spectral regrowth. Although the system performance can be partially improved by calibrating out the mutual coupling in the digital domain, it is more effective to use decoupling techniques (from the antenna point) to overcome the mutual coupling effects. Some popular decoupling techniques for MIMO systems (especially for massive MIMO base station antennas) are also presented.

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A Review of Mutual Coupling in MIMO Systems

Novel miniaturized two-layer electromagnetic band gap (EBG) structures are presented for reducing the electromagnetic coupling between closely spaced ultra wideband (UWB) planar monopoles on a common ground. The proposed EBG structures employ two closely coupled arrays, one comprising linear conducting patches and the other comprising apertures (slits) in the ground plane. The two arrays are printed on either side of a very thin dielectric layer ( $\bf{55}\;\upmu\mathrm{m}$ ) with a rotation between the elements to produce maximum coupling for miniaturizing. A microstrip line excitation is initially used for the efficient analysis and design of the slit–patch EBG structures, which are subsequently employed between two UWB printed monopoles. The proposed EBG structure has a small footprint and produces a significant reduction of the mutual coupling across the wide operating band of the UWB antennas. Simulated results and measurements of fabricated prototypes are presented.

/pdf/miniaturized-double-layer-ebg-structures-for-broadband-2go4w8yijf.pdf

Miniaturized Double-Layer EBG Structures for Broadband Mutual Coupling Reduction Between UWB Monopoles

Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results. As we know, the video data follow a hierarchical structure, i.e., a video is composed of shots, and a shot is composed of several frames. Generally, shots provide the activity-level information for people to understand the video content. While few existing summarization approaches pay attention to the shot segmentation procedure. They generate shots by some trivial strategies, such as fixed length segmentation, which may destroy the underlying hierarchical structure of video data and further reduce the quality of generated summaries. To address this problem, we propose a structure-adaptive video summarization approach that integrates shot segmentation and video summarization into a Hierarchical Structure-Adaptive RNN, denoted as HSA-RNN. We evaluate the proposed approach on four popular datasets, i.e., SumMe, TVsum, CoSum and VTW. The experimental results have demonstrated the effectiveness of HSA-RNN in the video summarization task.

/pdf/hsa-rnn-hierarchical-structure-adaptive-rnn-for-video-r5c303qarp.pdf

HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization

Video summarization via minimum sparse reconstruction

Keyframe selection has been crucial for effective and efficient video content analysis. While most of the existing approaches represent individual frames with global features, we, for the first time, propose a keypoint-based framework to address the keyframe selection problem so that local features can be employed in selecting keyframes. In general, the selected keyframes should both be representative of video content and containing minimum redundancy. Therefore, we introduce two criteria, coverage and redundancy, based on keypoint matching in the selection process. Comprehensive experiments demonstrate that our approach outperforms the state of the art.

https://www.covic.otago.ac.nz/~jdeng/print/TCSVT13-KeypointBasedKFSelection-pre.pdf

Keypoint-Based Keyframe Selection

Video summarization helps users obtain quick comprehension of video content. Recently, some studies have utilized local features to represent each video frame and formulate video summarization as a coverage problem of local features. However, the importance of individual local features has not been exploited. In this paper, we propose a novel Bag-of-Importance (BoI) model for static video summarization by identifying the frames with important local features as keyframes, which is one of the first studies formulating video summarization at local feature level, instead of at global feature level. That is, by representing each frame with local features, a video is characterized with a bag of local features weighted with individual importance scores and the frames with more important local features are more representative, where the representativeness of each frame is the aggregation of the weighted importance of the local features contained in the frame. In addition, we propose to learn a transformation from a raw local feature to a more powerful sparse nonlinear representation for deriving the importance score of each local feature, rather than directly utilize the hand-crafted visual features like most of the existing approaches. Specifically, we first employ locality-constrained linear coding (LCC) to project each local feature into a sparse transformed space. LCC is able to take advantage of the manifold geometric structure of the high dimensional feature space and form the manifold of the low dimensional transformed space with the coordinates of a set of anchor points. Then we calculate the l2 norm of each anchor point as the importance score of each local feature which is projected to the anchor point. Finally, the distribution of the importance scores of all the local features in a video is obtained as the BoI representation of the video. We further differentiate the importance of local features with a spatial weighting template by taking the perceptual difference among spatial regions of a frame into account. As a result, our proposed video summarization approach is able to exploit both the inter-frame and intra-frame properties of feature representations and identify keyframes capturing both the dominant content and discriminative details within a video. Experimental results on three video datasets across various genres demonstrate that the proposed approach clearly outperforms several state-of-the-art methods.

A Bag-of-Importance Model With Locality-Constrained Coding Based Feature Learning for Video Summarization

This letter investigates the effect of antenna mutual coupling involving the unconventional concepts of transmitting and receiving mutual impedances on the multiple-input multiple-output (MIMO) channel capacity. Suitable expressions for the optimum power allocation strategy under the influence of antenna mutual coupling are derived. The undertaken numerical analysis shows a significant difference in the calculated results for the optimized average channel capacity for the cases when antenna mutual coupling is taken into account or neglected. The obtained results indicate that to obtain the correct optimum power allocation strategy antenna mutual coupling effects need to be properly taken into consideration. This claim is supported by computer simulation examples.

Optimizing MIMO Channel Capacities Under the Influence of Antenna Mutual Coupling

This paper reports on channel estimation for a multiple input multiple output system, in which the transmitter is equipped with a linear array antenna while the receiver uses a circular array antenna. A model that includes spatial correlated channels and effects of mutual coupling is presented to investigate the performance of a training-based minimum mean-square error MIMO channel estimation method. It is demonstrated that spatial correlation improves the channel estimation.

MMSE Channel Estimation for MIMO System with Receiver Equipped with a Circular Array Antenna

The coronary sinus or other vessel is segmented by finding a centerline and then using the centerline to locate the boundary of the vessel. For finding the centerline, a refinement process uses multi-scale sparse appearance learning. For locating the boundary, the lumen is segmented as a graph cut problem.

Shiyang Lu

Papers

Keypoint-Based Keyframe Selection

A Bag-of-Importance Model With Locality-Constrained Coding Based Feature Learning for Video Summarization

Optimizing MIMO Channel Capacities Under the Influence of Antenna Mutual Coupling

MMSE Channel Estimation for MIMO System with Receiver Equipped with a Circular Array Antenna

Sparse appearance learning-based segmentation