pwc-archive/papers-with-abstracts huggingface dataset:
The Generalized Fourier Transform: A Unified Framework for the Fourier, Laplace, Mellin and $Z$ Transforms
Authors: Pushpendra Singh, Anubha Gupta, Shiv Dutt Joshi
Date: 2021-02-12 00:00:00

This paper introduces Generalized Fourier transform (GFT) that is an extension or the generalization of the Fourier transform (FT). The Unilateral Laplace transform (LT) is observed to be the special case of GFT. GFT, as proposed in this work, contributes significantly to the scholarly literature. There are many salient contribution of this work. Firstly, GFT is applicable to a much larger class of signals, some of which cannot be analyzed with FT and LT. For example, we have shown the applicability of GFT on the polynomially decaying functions and super exponentials. Secondly, we demonstrate the efficacy of GFT in solving the initial value problems (IVPs). Thirdly, the generalization presented for FT is extended for other integral transforms with examples shown for wavelet transform and cosine transform. Likewise, generalized Gamma function is also presented. One interesting application of GFT is the computation of generalized moments, for the otherwise non-finite moments, of any random variable such as the Cauchy random variable. Fourthly, we introduce Fourier scale transform (FST) that utilizes GFT with the topological isomorphism of an exponential map. Lastly, we propose Generalized Discrete-Time Fourier transform (GDTFT). The DTFT and unilateral $z$-transform are shown to be the special cases of the proposed GDTFT. The properties of GFT and GDTFT have also been discussed.
Identification of Edge Disconnections in Networks Based on Graph Filter Outputs
Authors: Shlomit Shaked, Tirza Routtenberg
Date: 2021-02-12 00:00:00

Graphs are fundamental mathematical structures used in various fields to model statistical and physical relationships between data, signals, and processes. In some applications, such as data processing in graphs that represent physical networks, the initial network topology is known. However, disconnections of edges in the network change the topology and may affect the signals and processes over the network. In this paper, we consider the problem of edge disconnection identification in networks by using concepts from graph signal processing (GSP). We assume that the graph signals measured over the vertices of the network can be represented as white noise that has been filtered on the graph topology by a smooth graph filter. We develop the likelihood ratio test (LRT) to detect a specific set of edge disconnections. Then, we provide the maximum likelihood (ML) decision rule for identifying general scenarios of edge disconnections in the network. It is shown that the sufficient statistics of the LRT and ML decision rule are the graph frequency energy levels in the graph spectral domain. However, the ML decision rule leads to a high-complexity exhaustive search over the edges in the network and is practically infeasible. Thus, we propose a low-complexity greedy method that identifies a single disconnected edge at each iteration. Moreover, by using the smoothness of the considered graph filter, we suggest a local implementation of the decision rule, which is based solely on the measurements at neighboring vertices. Simulation results demonstrate that the proposed methods outperform existing detection and identification methods on a synthetic dataset and for line outage identification in power systems from the IEEE 118-bus test case.
Mind the beat: detecting audio onsets from EEG recordings of music listening
Authors: Ashvala Vinay, Alexander Lerch, Grace Leslie
Date: 2021-02-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We propose a deep learning approach to predicting audio event onsets in electroencephalogram (EEG) recorded from users as they listen to music. We use a publicly available dataset containing ten contemporary songs and concurrently recorded EEG. We generate a sequence of onset labels for the songs in our dataset and trained neural networks (a fully connected network (FCN) and a recurrent neural network (RNN)) to parse one second windows of input EEG to predict one second windows of onsets in the audio. We compare our RNN network to both the standard spectral-flux based novelty function and the FCN. We find that our RNN was able to produce results that reflected its ability to generalize better than the other methods. Since there are no pre-existing works on this topic, the numbers presented in this paper may serve as useful benchmarks for future approaches to this research problem.
Point Cloud Resampling Through Hypergraph Signal Processing
Authors: Qinwen Deng, Songyang Zhang, Zhi Ding
Date: 2021-02-12 00:00:00
Tasks: Object

Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.
Alternating projections gridless covariance-based estimation for DOA
Authors: Yongsung Park, Peter Gerstoft
Date: 2021-02-12 00:00:00

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.
Joint Dereverberation and Separation with Iterative Source Steering
Authors: Taishi Nakashima, Robin Scheibler, Masahito Togami, Nobutaka Ono
Date: 2021-02-12 00:00:00
Tasks: blind source separation

We propose a new algorithm for joint dereverberation and blind source separation (DR-BSS). Our work builds upon the IRLMA-T framework that applies a unified filter combining dereverberation and separation. One drawback of this framework is that it requires several matrix inversions, an operation inherently costly and with potential stability issues. We leverage the recently introduced iterative source steering (ISS) updates to propose two algorithms mitigating this issue. Albeit derived from first principles, the first algorithm turns out to be a natural combination of weighted prediction error (WPE) dereverberation and ISS-based BSS, applied alternatingly. In this case, we manage to reduce the number of matrix inversion to only one per iteration and source. The second algorithm updates the ILRMA-T matrix using only sequential ISS updates requiring no matrix inversion at all. Its implementation is straightforward and memory efficient. Numerical experiments demonstrate that both methods achieve the same final performance as ILRMA-T in terms of several relevant objective metrics. In the important case of two sources, the number of iterations required is also similar.
Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation
Authors: Prajnesh Kumar, Mingwei Jin, Ting Bu, Santosh Kumar, Yu-Ping Huang
Date: 2021-02-11 00:00:00
Tasks: Financial Analysis, Weather Forecasting

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system's performance and versatility, three benchmark tests are performed. The first is the 10th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE's) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73x10-3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.
Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar via Manifold Optimization
Authors: Huanyu Zhang, Ziping Zhao
Date: 2021-02-10 00:00:00

The problem of joint design of transmit waveforms and receive filters is desirable in many application scenarios of multiple-input multiple-output (MIMO) radar systems. In this paper, the joint design problem is investigated under the signal-to-interference-plus-noise ratio (SINR) performance metric, in which case the problem is formulated to maximize the SINR at the receiver side subject to some practical transmit waveform constraints. A numerical algorithm is proposed for problem resolution based on the manifold optimization method, which has been shown to be powerful and flexible to address nonconvex constrained optimization problems in many engineering applications. The proposed algorithm is able to efficiently solve the SINR maximization problem with different waveform constraints under a unified framework. Numerical experiments show that the proposed algorithm outperforms the existing benchmarks in terms of computation efficiency and achieves comparable SINR performance.
Range Estimation of a Moving Target Using Ultrasound Differential Zadoff-Chu Codes
Authors: Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Mohanad Ahmed, Tareq Y. Al-Naffouri
Date: 2021-02-10 00:00:00

High accuracy range estimation is an essential tool required in many modern applications and technologies. However, continuous range estimation of a moving target is a challenging task, especially under Doppler effects. This paper presents a novel signal design, which we name differential Zadoff-Chu (DZC). Under Doppler effects, DZC sequences improve the performance of the maximum likelihood (ML)-based range estimation compared to its performance when using regular ZC sequences. Moreover, a reduced-complexity ranging algorithm is proposed utilizing DZC sequences and is shown to outperform the regular ZC ML-based range estimation. The proposed system is evaluated in a typical indoor environment, using low-cost ultrasound hardware. Under a low signal to noise ratio (-10 dB SNR), more than 90% of the range estimates are in less than 1.6 mm error, with a movement range from $0.2$ m to 2.2 m and a maximum velocity of 0.5 m/s. For the same movement range, the system provides range estimates with a root mean square error (RMSE) less than 0.76 mm in a high SNR scenario (10 dB), and an MSE less than 0.85 mm in a low SNR scenario (-10 dB). For a larger movement range from 1.8 m to 4.2 m with a maximum velocity of 1.91 m/s, the proposed system provides range estimates with RMSE less than 7.70 mm at 10 dB SNR.
Medical Image Quality Metrics for Foveated Model Observers
Authors: Miguel A. Lago, Craig K. Abbey, Miguel P. Eckstein
Date: 2021-02-09 00:00:00
Tasks: model

A recently proposed model observer mimics the foveated nature of the human visual system by processing the entire image with varying spatial detail, executing eye movements and scrolling through slices. The model can predict how human search performance changes with signal type and modality (2D vs. 3D), yet its implementation is computationally expensive and time-consuming. Here, we evaluate various image quality metrics using extensions of the classic index of detectability expressions and assess foveated model observers for location-known exactly tasks. We evaluated foveated extensions of a Channelized Hotelling and Non-prewhitening model with an eye filter. The proposed methods involve calculating a model index of detectability (d') for each retinal eccentricity and combining these with a weighting function into a single detectability metric. We assessed different versions of the weighting function that varied in the required measurements of the human observers' search (no measurements, eye movement patterns, and size of the image and median search times). We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D vs. 3D search performance for a small microcalcification-like signal and a larger mass-like. The metric with weighting function based on median search times was the second best at predicting human results. The findings provide a set of model observer tools to evaluate image quality in the early stages of imaging system evaluation or design without implementing the more computationally complex foveated search model.
Sparse Antenna Array Design for MIMO Radar Using Softmax Selection
Authors: Konstantinos Diamantaras, Zhaoyi Xu, Athina Petropulu
Date: 2021-02-09 00:00:00

MIMO transmit arrays allow for flexible design of the transmit beampattern. However, the large number of elements required to achieve certain performance using uniform linear arrays (ULA) maybe be too costly. This motivated the need for thinned arrays by appropriately selecting a small number of elements so that the full array beampattern is preserved. In this paper, we propose Learn-to-Select (L2S), a novel machine learning model for selecting antennas from a dense ULA employing a combination of multiple Softmax layers constrained by an orthogonalization criterion. The proposed approach can be efficiently scaled for larger problems as it avoids the combinatorial explosion of the selection problem. It also offers a flexible array design framework as the selection problem can be easily formulated for any metric.
Does Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?
Authors: Di Che, Junho Cho, Xi Chen
Date: 2021-02-09 00:00:00

Probabilistic constellation shaping (PCS) has been widely applied to amplified coherent optical transmissions owing to its shaping gain over the uniform signaling and fine-grained rate adaptation to the underlying fiber channel condition. These merits stimulate the study of applying PCS to short-reach applications dominated by intensity modulation (IM) direct detection (DD) systems. As commercial IM-DD systems typically do not employ optical amplification to save the cost and power consumption, they are no longer subject to an average power constraint (APC) but a peak power constraint (PPC), which poses unique challenges to take full advantages of PCS. This paper provides a comprehensive investigation of PCS in IM-DD systems without optical amplifiers. In particular, we reveal that if the transmitter enhances the peak-to-average power ratio of the signal, a PPC system can be partially or even fully converted to an APC system in which the classical PCS offers its merits. The findings are verified through an IM-DD experiment using 4- and 8-ary pulse amplitude modulations.
Sparse Factorization-based Detection of Off-the-Grid Moving targets using FMCW radars
Authors: Gilles Monnoyer de Galland, Thomas Feuillen, Luc Vandendorpe, Laurent Jacques
Date: 2021-02-09 00:00:00

In this paper, we investigate the application of continuous sparse signal reconstruction algorithms for the estimation of the ranges and speeds of multiple moving targets using an FMCW radar. Conventionally, to be reconstructed, continuous sparse signals are approximated by a discrete representation. This discretization of the signal's parameter domain leads to mismatches with the actual signal. While increasing the grid density mitigates these errors, it dramatically increases the algorithmic complexity of the reconstruction. To overcome this issue, we propose a fast greedy algorithm for off-the-grid detection of multiple moving targets. This algorithm extends existing continuous greedy algorithms to the framework of factorized sparse representations of the signals. This factorized representation is obtained from simplifications of the radar signal model which, up to a model mismatch, strongly reduces the dimensionality of the problem. Monte-Carlo simulations of a K-band radar system validate the ability of our method to produce more accurate estimations with less computation time than the on-the-grid methods and than methods based on non-factorized representations.
A Function Based on Chebyshev Polynomials as an Alternative to the Sinc Function in FIR Filter Design
Authors: Paul W. Oxby
Date: 2020-11-20 00:00:00

The sinc function is often used as the basis for the design of discrete linear-phase FIR filters. However the Fourier transform of the truncated sinc function exhibits ripple in the pass band due to the Gibbs phenomenon. This paper introduces an alternative function based on Chebyshev polynomials whose Fourier transform decreases monotonically in the pass band. Furthermore this function features an intrinsic window function with an adjustable parameter influencing the Fourier transform in the transition and stop bands.
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
Authors: Rintaro Ikeshita, Tomohiro Nakatani
Date: 2021-02-09 00:00:00
Tasks: blind source separation

We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed. Conventional BSS algorithms for the optimization of a multi-input multi-output convolutional beamformer have suffered from a huge computational cost when $M$ is large. We here propose a computationally efficient method that integrates a weighted prediction error (WPE) dereverberation method and a fast BSS method called independent vector extraction (IVE), which has been developed for less reverberant environments. We show that, given the power spectrum for each source, the optimization problem of the new method can be reduced to that of IVE by exploiting the stationary condition, which makes the optimization easy to handle and computationally efficient. An experiment of speech signal separation shows that, compared to a conventional method that integrates WPE and independent vector analysis, our proposed method achieves much faster convergence while maintaining its separation performance.
A 3D Non-stationary MmWave Channel Model for Vacuum Tube Ultra-High-Speed Train Channels
Authors: YingJie Xu, Kai Yu, Li Li, Xianfu Lei, Li Hao, Cheng-Xiang Wang
Date: 2021-01-17 00:00:00

As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios. In this paper, a three-dimensional non-stationary millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed to investigate the channel characteristics of UHST channels in vacuum tube scenarios, taking into account the waveguide effect and the impact of tube wall roughness on channel. Then, based on the proposed model, some important time-variant channel statistical properties are studied and compared with those in existing HST and tunnel channels. The results obtained show that the multipath effect in vacuum tube scenarios will be more obvious than tunnel scenarios but less than existing HST scenarios, which will provide some insights for future research on vacuum tube UHST wireless communications.
Smart Black Box 2.0: Efficient High-bandwidth Driving Data Collection based on Video Anomalies
Authors: Ryan Feng, Yu Yao, Ella Atkins
Date: 2021-01-03 00:00:00
Tasks: Action Detection, Anomaly Detection, Autonomous Vehicles, Data Compression

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The Smart Black Box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with a FIFO strategy. Results show that SBB data compression can increase the anomalous-to-normal memory ratio by ~25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to a FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.
ICEYE microsatellite SAR constellation status update: Evaluation of first commercial imaging modes
Authors: Vladimir Ignatenko, Pekka Laurila, Andrea Radius, Leszek Lamentowski, Oleg Antropov, Darren Muff
Date: 2021-02-08 00:00:00
Tasks: Earth Observation

The ICEYE constellation features the first operational microsatellite based X-band SAR sensors suitable for all weather day-and-night Earth Observation. In this paper, we report on the status of the ICEYE Constellation and describe the characteristics of the first operational imaging modes.
Self-Sustainable Reconfigurable Intelligent Surface Aided Simultaneous Terahertz Information and Power Transfer (STIPT)
Authors: Yijin Pan, Kezhi Wang, Cunhua Pan, Huiling Zhu, Jiangzhou Wang
Date: 2021-02-08 00:00:00

This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which is assisted by reconfigurable intelligent surface (RIS) for both the information data and power transmission. We aim to maximize the information users' (IUs') data rate while guaranteeing the energy users' (EUs') and RIS's power harvesting requirements. To solve the formulated non-convex problem, the block coordinate descent (BCD) based algorithm is adopted to alternately optimize the transmit precoding of IUs, RIS's reflecting coefficients, and RIS's coordinate. The Penalty Constrained Convex Approximation (PCCA) Algorithm is proposed to solve the intractable optimization problem of the RIS's coordinate, where the solution's feasibility is guaranteed by the introduced penalties. Simulation results confirm that the proposed BCD algorithm can significantly enhance the performance of STIPT by employing RIS.
On Procrustes Analysis in Hyperbolic Space
Authors: Puoya Tabaghi, Ivan Dokmanic
Date: 2021-02-07 00:00:00
Tasks: Translation

Congruent Procrustes analysis aims to find the best matching between two point sets through rotation, reflection and translation. We formulate the Procrustes problem for hyperbolic spaces, review the canonical definition of the center of point sets, and give a closed form solution for the optimal isometry for noise-free measurements. We also analyze the performance of the proposed method under measurement noise.