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Section: Publications & Resources

Publication: IEEE Open Journal of Signal Processing

In this paper, we consider robust channel estimation for a millimeter wave (mmWave) massive MIMO system with uniform planar arrays (UPA). For many gridless angle estimation methods of mmWave channels, the channel gains needs to be time-invariant during training. We propose a gridless method that is applicable to time-invariant and time-varying channels, and the proposed method is robust to channel variations. 

Section: Publications & Resources

Publication: IEEE Open Journal of Signal Processing

With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. 

Section: Publications & Resources

Publication: IEEE Open Journal of Signal Processing

The Discrete Wavelet Transform (DWT) has gained attention in the area of Multi-Carrier Modulation (MCM) because it can overcome some well known limitations of Discrete Fourier Transform (DFT) based MCM systems. Its improved spectral containment removes the need for a cyclic prefix, be it that appropriate equalization then has to be added as the cyclic convolution property no longer holds. Most DWT based MCM systems in the literature use Time-domain EQualizers (TEQs) to mitigate the channel distortion. 

Section: Publications & Resources

Publication: IEEE Open Journal of Signal Processing

An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation.

Section: Publications & Resources

Publication: IEEE Open Journal of Signal Processing

The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in image input. In this article, we aim at explaining this vulnerability through the framework of sparsity. We show the connection between adversarial attacks and sparse representations, with a focus on explaining the universality and transferability of adversarial examples in neural networks.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Adversarial attack approaches to speaker identification either need high computational cost or are not very effective, to our knowledge. To address this issue, in this letter, we propose a novel generation-network-based approach, called symmetric saliency-based encoder-decoder (SSED), to generate adversarial voice examples to speaker identification.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Image registration is a basic task in computer vision, for its wide potential applications in image stitching, stereo vision, motion estimation, and etc. Most current methods achieve image registration by estimating a global homography matrix between candidate images with point-feature-based matching or direct prediction. However, as real-world 3D scenes have point-variant photograph distances (depth), a unified homography matrix is not sufficient to depict the specific pixel-wise relations between two images.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

The correlation filter(CF)-based tracker is a classic and effective model in the field of visual tracking. For a long time, most CF-based trackers solved filters using only ridge regression equations with l2 -norm, which can make the trained model noisy and not sparse. As a result, we propose a model of adaptive sparse spatially-regularized correlation filters (AS2RCF). Aiming to suppress the noise mixed in the model, we improve it by introducing an l1 -norm spatial regularization term. 

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Iterative hard thresholding (IHT) and hard thresholding pursuit (HTP) are two kinds of classical hard thresholding-based algorithms widely used in compressed sensing. Restricted isometry constant (RIC) of sensing matrix which ensures the convergence of iterative algorithms plays a key role in guaranteeing successful recovery. In the analysis of sufficient condition to ensure recovery performance, the RIC δ3s is generally used in previous literature, while δ2s is rarely addressed. In this letter, we first show that the theoretical optimal step-length is 1 while using sufficient condition in terms of δ2s .

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

3D face reconstruction from a single image still suffers from low accuracy and inability to recover textures in invisible regions. In this paper, we propose a method for generating a 3D portrait with complete texture. The coarse face-and-head model and texture parameters are obtained using 3D Morphable Model fitting. We design an image-geometric inverse renderer that acquires normal, albedo, and light to jointly reconstruct the facial details.