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Section: Events

Submission Deadline: March 15, 2020 Call for Proposals Document

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years.

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

In this paper we propose a family of index modulation systems which can operate with low-power consumption and low operational complexity for multi-user communication. This is particularly suitable for non-time sensitive Internet of Things (IoT) applications such as telemetry, smart metering, and soon.

Section: Publications & Resources

Publication: IEEE Journal of Selected Topics in Signal Processing

A novel scheme called dual-polarized spatial media-based modulation (DP-SMBM), which combines judiciously the media-based modulation (MBM), spatial modulation (SM), and dual-polarized (DP) antennas, is proposed in this paper. The DP-SMBM introduces a new DP domain to convey additional information without occupying extra physical space, effectively enhancing the transmission rate and alleviating the finite space issue.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Two-directional two-dimensional canonical correlation analysis ((2D) 2 CCA) directly seeks linear relationship between different image data sets without reshaping images into vectors. However, it fails in finding the nonlinear correlation. 

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Although deep convolutional neural networks (DCNN) show significant improvement for single depth map (SD) super-resolution (SR) over the traditional counterparts, most SDSR DCNNs do not reuse the hierarchical features for depth map SR resulting in blurred high-resolution (HR) depth maps. They always stack convolutional layers to make network deeper and wider.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

A significantly low cost and tractable progressive learning approach is proposed and discussed for efficient spatiotemporal monitoring of a completely unknown, two dimensional correlated signal distribution in localized wireless sensor field. The spatial distribution is compressed into a number of its contour lines and only those sensors that their sensor observations are in a margin of the contour levels are reporting to the information fusion center (FC).

Section: Publications & Resources

Publication: IEEE Transactions on Multimedia

Using deep convolutional neural networks (CNN) to predict the depth from a single image has received considerable attention in recent years due to its impressive performance. However, existing methods process each single image independently without leveraging the multiview information of video sequences in practical scenarios.

Section: Publications & Resources

Publication: IEEE Transactions on Multimedia

The scalable video coding extensions of the High Efficient Video Coding (HEVC) standard (SHVC) have adopted a new quadtree-structured coding unit (CU). The SHVC test model (SHM) needs to test seven intermode sizes and one intramode size at depth levels of “0,” “1,” “2,” and four intermode sizes and two intramode sizes at a depth level of “3” for interframe CUs.