Query: SIGNAL PROCESSING LETTERS

title: Microcomb Based Photonic RF Signal Processing
abstract: Microcombs are powerful tools as sources of multiple wavelength channels for photonic radio frequency (RF) signal processing. They offer a compact device footprint, large numbers of wavelengths, and wide Nyquist bands. Here, we review recent progress on microcomb based photonic RF signal processors, including true time delays, reconfigurable filters, Hilbert transformers, differentiators, and channelizers. The strong potential of optical micro combs for RF photonics applications in terms of functions and integrability is also discussed.

title: Velocity Based Signal Features for the Assessment of Parkinsonian Handwriting
abstract: This letter investigates different velocity based signal processing techniques to the aim of Parkinson's disease classification through handwriting. It is showed that combining new velocity based features with classic features improves state of the art performance on the PaHaW dataset.

title: One Bit MUSIC
abstract: In this letter, we consider the problem of direction of arrival (DOA) estimation with one bit quantized array measurements. With analysis, it is shown that, under mild conditions the one bit covariance matrix can be approximated by the sum of a scaled unquantized covariance matrix and a scaled identity matrix. Although the scaling parameters are unknown because of the extreme quantization, they do not affect the subspace based DOA estimators. Specifically, the signal and noise subspaces can be straightforwardly determined through the eigendecomposition of the one bit covariance matrix, without pre processing such as unquantized covariance matrix reconstruction. With so obtained subspaces, the most classical multiple signal classification (MUSIC) technique can be applied to determine the signal DOAs. The resulting method is thus termed as one bit MUSIC. Thanks to the simplicity of this method, it can be very easily implemented in practical applications, whereas the DOA estimation performance is comparable to the case with unquantized covariance matrix reconstruction, as demonstrated by various simulations.

title: PhaseNet: A Deep Convolutional Neural Network for Two Dimensional Phase Unwrapping
abstract: Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap count (integer jump of 2 \pi$ at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel wise classification layer. The relationship between the absolute phase and the wrap count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality guided phase unwrapping algorithm and also with the well known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.

title: Griffin Lim Like Phase Recovery via Alternating Direction Method of Multipliers
abstract: Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing. When only an amplitude spectrogram is available and no explicit information is given for the phases, the Griffin Lim algorithm (GLA) is one of the most utilized methods for phase recovery. However, GLA often requires many iterations and results in low perceptual quality in some cases. In this letter, we propose two novel algorithms based on GLA and the alternating direction method of multipliers (ADMM) for better recovery with fewer iteration. Some interpretation of the existing methods and their relation to the proposed method are also provided. Evaluations are performed with both objective measure and subjective test.

title: A New Analysis for Support Recovery With Block Orthogonal Matching Pursuit
abstract: Compressed sensing is a signal processing technique, which can accurately recover sparse signals from linear measurements with far fewer number of measurements than those required by the classical Shannon Nyquist theorem. Block sparse signals, i.e. the sparse signals whose nonzero coefficients occur in few blocks, arise from many fields. Block orthogonal matching pursuit (BOMP) is a popular greedy algorithm for recovering block sparse signals due to its high efficiency and effectiveness. By fully using the block sparsity of block sparse signals, BOMP can achieve very good recovery performance. This letter proposes a sufficient condition to ensure that BOMP can exactly recover the support of block $K$ sparse signals under the noisy case. This condition is better than existing ones.

title: Nonlocal Metasurfaces for Optical Signal Processing.
abstract: Optical analog signal processing has been gaining significant attention as a way to overcome the speed and energy limitations of digital techniques. Metasurfaces offer a promising avenue towards this goal due to their efficient manipulation of optical signals over deeply subwavelength volumes. To date, metasurfaces have been proposed to transform signals in the spatial domain, e.g. for beam steering, focusing, or holography, for which angular dependent responses, or nonlocality, are unwanted features that must be avoided or mitigated. Here, we show that the metasurface nonlocality can be engineered to enable signal manipulation in the momentum domain over an ultrathin platform. We explore nonlocal metasurfaces performing basic mathematical operations, paving the way towards fast and power efficient ultrathin devices for edge detection and optical image processing.

title: Sampling of Signals Bandlimited to a Disc in the Linear Canonical Transform Domain for IEEE Signal Processing Letters
abstract: We derive sampling formula for signals that are bandlimited to a disc of radius <inline formula> <tex math notation="LaTeX"$R$/tex math>/inline formula> in the linear canonical transform (LCT) domain. By bandlimitedness in a disc <inline formula><tex math notation="LaTeX"$D$/tex math>/inline formula> in the LCT domain, we mean that the LCT <inline formula><tex math notation="LaTeX"$F(\omega)/tex math>/inline formula> of a signal <inline formula><tex math notation="LaTeX"$f(t)/tex math>/inline formula> vanishes outside the disc <inline formula><tex math notation="LaTeX"$D./tex math>/inline formula> We first express the signal in polar coordinates and then obtain the sampling formula. The samples of the angle <inline formula><tex math notation="LaTeX" \theta$/tex math>/inline formula> are taken at <inline formula><tex math notation="LaTeX"$2N+1$/tex math> /inline formula> uniformly distributed points on the unit circle while the samples of the radial distance <inline formula><tex math notation="LaTeX"$r$/tex math>/inline formula> are taken at the zeros of the Bessel function. As a special case, we obtain sampling formula for signals that are bandlimited to a disc in the fractional Fourier transform domain.

title: A Deep Learning Loss Function Based on the Perceptual Evaluation of the Speech Quality
abstract: This letter proposes a perceptual metric for speech quality evaluation, which is suitable, as a loss function, for training deep learning methods. This metric, derived from the perceptual evaluation of the speech quality algorithm, is computed in a per frame basis and from the power spectra of the reference and processed speech signal. Thus, two disturbance terms, which account for distortion once auditory masking and threshold effects are factored in, amend the mean square error (MSE) loss function by introducing perceptual criteria based on human psychoacoustics. The proposed loss function is evaluated for noisy speech enhancement with deep neural networks. Experimental results show that our metric achieves significant gains in speech quality (evaluated using an objective metric and a listening test) when compared to using MSE or other perceptual based loss functions from the literature.

title: 18 km low crosstalk OAM WDM transmission with 224 individual channels enabled by a ring core fiber with large high order mode group separation.
abstract: The space domain is regarded as the only known physical dimension of lightwaves left to be exploited for optical communications. Very recently, much research effort has been devoted to using orbital angular momentum (OAM) spatial modes to increase the transmission capacity in fiber optic communications. However, long distance low crosstalk high order OAM multiplexing transmission in fiber is quite challenging. Here we design and fabricate a graded index ring core fiber to effectively suppress radially high order modes and greatly separate high order OAM mode groups. By exploiting high order OAM mode group multiplexing, together with wavelength division multiplexing (WDM) i.e. 12.5 Gbaud 8 array quadrature amplitude modulation (8 QAM) signals over OAM+4 and OAM+5 modes on 112 WDM channels (224 individual channels) we experimentally demonstrate 8.4 Tbit/s data transmission in an 18 km OAM fiber with low crosstalk. Multiple input multiple output digital signal processing is not required in the experiment because of the large high order mode group separation of the OAM fiber. The demonstrations may open a door to find more fiber optic communication and interconnect applications exploiting high order OAM modes.