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This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder’s output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-to-interference-plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We

The design of sampling set (DoS) for bandlimited graph signals (GS) has been extensively studied in recent years, but few of them

Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation

Extensive use of text labels and symbols available in the digital media for interpretation and communication of information has gained a lot of attention in the era of digital media. Access of the images with scene text in it through different display devices tend to deform the scene text region while resizing for better viewing experience. We propose an image retargeting operator, which is aware of the scene text present in the image. We perform the normal seam carving depending on the content of the image for the non-text region.

Super-Resolution (SR) is a technique that has been exhaustively exploited and incorporates strategic aspects to image processing. As quantum computers gradually evolve and provide unconditional proof of computational advantage at solving intractable problems over their classical counterparts, quantum computing emerges with the compelling prospect to offer exponential speedup to process computationally expensive operations, such as the ones verified in SR imaging.

In nonacoustic speech recognition based on electromyography, i.e. on electrical muscle activity captured by noninvasive surface electrodes, differences between recording sessions are known to cause deteriorating system accuracy. Efficient adaptation of an existing system to an unseen recording session is therefore imperative for practical usage scenarios. We report on a meta-learning approach to pretrain a deep neural network frontend for a myoelectric speech recognizer in a way that it can be easily adapted to a new session.

Hybrid beamforming has attracted considerable attention in recent years as an efficient and promising technique for the practical implementation of millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) wireless systems. In this paper, we investigate hybrid analog/digital beamforming designs based on a single RF chain architecture (SRCA) for mmWave massive-MIMO. We first revisit the SRCA and then explore its shortcomings.

Voice activity detection (VAD) is an integral part of speech processing for real world problems, and a lot of work has been done to improve VAD performance. Of late, deep neural networks have been used to detect the presence of speech and this has offered tremendous gains. Unfortunately, these efforts have been either restricted to feed-forward neural networks that do not adequately capture frequency and temporal correlations, or the recurrent architectures have not been adequately tested in noisy environments.

We present a novel sampling theorem, and prototypical applications, for Fourier-sparse lattice signals, i.e., data indexed by a finite semi-lattice. A semilattice is a partially ordered set endowed with a meet (or join) operation that returns the greatest lower bound (smallest upper bound) of two elements. Semilattices can be viewed as a special class of directed graphs with a strictly triangular adjacency matrix , which thus cannot be diagonalized.