pwc-archive/papers-with-abstracts huggingface dataset:
Cortical representations of Auditory Perception using Graph Independent Component on EEG
Authors: Pranav Sankhe, Ritik Madan
Date: 2021-10-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Recent studies indicate that the neurons involved in a cognitive task aren't locally limited but span out to multiple human brain regions. We obtain network components and their locations for the task of listening to music. The recorded EEG data is modeled as a graph, and it is assumed that the overall activity is a contribution of several independent subnetworks. To identify these intrinsic cognitive subnetworks corresponding to music perception, we propose to decompose the whole brain graph-network into multiple subnetworks. We perform this decomposition to a group of brain networks by performing Graph-Independent Component Analysis. Graph-ICA is a variant of ICA that decomposes the measured graph into independent source graphs. Having obtained independent subnetworks, we calculate the electrode positions by computing the local maxima of these subnetwork matrices. We observe that the computed electrodes' location corresponds to the temporal lobes and the Broca's area, which are indeed involved in the task of auditory processing and perception. The computed electrodes also span the brain's frontal lobe, which is involved in attention and generating a stimulus-evoked response. The weight of the subnetwork that corresponds to the aforementioned brain regions increases with the increase in the music recording's tempo. The results suggest that whole-brain networks can be decomposed into independent subnetworks and analyze cognitive responses to music stimulus.
Efficient Coding Approach Towards Non-Linear Spectro-Temporal Receptive Fields
Authors: Pranav Sankhe, Prasanna Chaporkar
Date: 2021-10-21 00:00:00

Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing. We apply the principle of efficient coding to the LN model of Spectro-Temporal Receptive Fields(STRFs) of the neurons in primary auditory cortex. The Efficient Coding Principle has been previously used to understand early visual receptive fields and linear STRFs in auditory processing. Efficient coding is realized by jointly optimizing the mutual information between stimuli and neural responses subjected to the metabolic cost of firing spikes. We compare the predictions of the efficient coding principle with the physiological observations, which match qualitatively under realistic conditions of noise in stimuli and the spike generation process.
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Authors: Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizen, Michel Dojat
Date: 2021-10-25 00:00:00
Tasks: Anomaly Detection, Medical Image Analysis, Unsupervised Anomaly Detection

Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets. Good candidate approaches are patch-based unsupervised pipelines which have both the advantage to increase the number of input data and to capture local and fine anomaly patterns distributed in the image, while potential inconveniences are the loss of global structural information. We illustrate this trade-off on Parkinson's disease (PD) anomaly detection comparing the performance of two anomaly detection models based on a spatial auto-encoder (AE) and an adaptation of a patch-fed siamese auto-encoder (SAE). On average, the SAE model performs better, showing that patches may indeed be advantageous.
A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations
Authors: Manu Krishnan, Serkan Gugercin, Pablo A. Tarazaga
Date: 2021-10-25 00:00:00

Dynamic mode decomposition (DMD) has emerged as a popular data-driven modeling approach to identifying spatio-temporal coherent structures in dynamical systems, owing to its strong relation with the Koopman operator. For dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function but should generally approximate the input-output behavior of the underlying dynamics. A novel methodology for modeling those classes of dynamical systems is proposed in the present work, using wavelets in conjunction with the input-output dynamic mode decomposition (ioDMD). Our non-intrusive approach constructs numerical models directly from trajectories of the full model's inputs and outputs, without requiring the full-model operators. These trajectories are generated by running a simulation of the full model or observing the original dynamical systems' response to inputs in an experimental framework. Hence, the present methodology is applicable for dynamical systems whose internal state vector measurements are not available. Instead, data from only a few output locations are only accessible, as often the case in practice. The present methodology's applicability is explained by modeling the input-output response of a finite element beam model. The WDMD provides a linear state-space representation of the dynamical system using the response measurements and the corresponding input forcing functions. The developed state-space model is then used to simulate the beam's response towards different types of forcing functions. The method is further validated on an experimental data set using modal analysis on a simple free-free beam, demonstrating the efficacy of the proposed methodology as an appropriate candidate for modeling practical dynamical systems despite having no access to internal state measurements and treating the full model as a black-box.
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
Authors: Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu
Date: 2021-10-25 00:00:00
Tasks: compressed sensing, Image Reconstruction, SSIM

Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image reconstruction from the undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously, especially in the non-Cartesian acquisition setting. This paper aims to contribute to this field by tackling some major concerns in existing approaches.Regarding the learning of the sampling pattern, we perform ablation studies using parameter-free reconstructions like the density compensated (DCp) adjoint operator of the nonuniform fast Fourier transform (NUFFT) to ensure that the learned k-space trajectories actually sample the center of k-space densely. Additionally we optimize these trajectories by embedding a projected gradient descent algorithm over the hardware MR constraints. Later, we introduce a novel hybrid learning approach that operates across multiple resolutions to jointly optimize the reconstruction network and the k-space trajectory and present improved image reconstruction quality at 20-fold acceleration factor on T1 and T2-weighted images on the fastMRI dataset with SSIM scores of nearly 0.92-0.95 in our retrospective studies.
Rate Splitting with Wireless Edge Caching: A System-Level-based Co-design
Authors: Eleni Demarchou, Constantinos Psomas, Ioannis Krikidis
Date: 2021-10-24 00:00:00

Rate splitting (RS) and wireless edge caching are essential means for meeting the quality of service requirements of future wireless networks. In this work, we focus on the cross-layer co-design of wireless edge caching schemes with sophisticated physical layer techniques, which facilitate non-orthogonal multiple access and interference mitigation. A flexible caching-aided RS (CRS) technique is proposed that operates in various modes that specify the cache placement at the receivers. We consider two caching policies: the intelligent coded caching (CC), as well as the well-known most popular content (MPC) policy. Both caching policies are integrated within the design parameters of RS in order to serve multiple cache-enabled receivers. The proposed technique is investigated from a system level perspective by taking into account spatial randomness. We consider a single cell network consisting of center and edge receivers and provide a comprehensive analytical framework for the evaluation of the proposed technique in terms of achieved rates. Specifically, we derive the rate achieved at each receiver under minimum rate constraints while incorporating the cache placement characteristics. Numerical results are presented which highlight the flexibility of the proposed technique and show how caching can be exploited in order to further boost the performance of RS.
A CMOS SoC for Wireless Ultrasonic Power/Data Transfer and SHM Measurements on Structures
Authors: Xinyao Tang, Soumyajit Mandal, Tayfun Ozdemir
Date: 2021-10-24 00:00:00
Tasks: Management, Structural Health Monitoring

This paper describes a highly-integrated CMOS system-on-chip (SoC) for active structural health monitoring (SHM). The chip integrates ultrasonic power and bidirectional half-duplex data transfer, a power management unit (PMU), and an ultrasound transceiver to enable wireless ultrasonically-coupled sensor SHM networks on structures. The PMU includes an active bias-flip rectifier with off-delay compensation, high-efficiency dual-path DC-DC converter with inductor time-sharing, and five switched-capacitor DC-DC converters to generate multi-level spectrally band-limited pulses for guided-wave SHM. The chip was fabricated in a standard 180 nm process and has a die area of $2\times 2$ mm$^{2}$. Test results show power conversion efficiency (PCE) $>85\%$ for the active rectifier, $>70$\% for the inductive DC-DC converter, and $>60$\% for the switched-capacitor DC-DC converters. Output pulses have a peak-to-sidelobe ratio (PSL) $>30$~dB and worst-case out-of-band emissions $<-30$~dB, respectively. The SoC was integrated with a low-power microcontroller and passive components to realize miniaturized (15~mm $\times$ 30~mm) wireless SHM nodes. A set of nodes was deployed on an SHM test-bed (carbon fiber reinforced polymer sheet) representing an airframe panel. Tests on this wireless network confirm both long-range ultrasound power/data transfer and the ability to detect structural damage.
Task-Based Graph Signal Compression
Authors: Pei Li, Nir Shlezinger, Haiyang Zhang, Baoyun Wang, Yonina C. Eldar
Date: 2021-10-24 00:00:00
Tasks: Quantization

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and transmitted. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with quantization, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Furthermore, we show how the proposed approach can be realized using graph filters combining elements corresponding the neighbouring nodes of the graph, thus facilitating distributed implementation at reduced complexity. Our numerical evaluations on both synthetic and real world data shows that the joint sampling and quantization method yields a compact finite bit representation of high-dimensional graph signals, which allows reconstruction of the original signal with accuracy within a small gap of that achievable with infinite resolution quantizers.
Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems
Authors: Xianhua Yu, Dong Li, Yongjun Xu, Ying-Chang Liang
Date: 2021-10-24 00:00:00
Tasks: Quantization

In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability to significantly enhance energy- and spectrum-efficiency. To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives. In particular, the quantized phase shift (QPS) is assumed to be available at the IRS, which, however, does not always hold and should be fed back to the IRS in practice. Unfortunately, the feedback channel is generally bandwidth-limited, which cannot support a huge amount of feedback overhead of the QPS particularly for a large number of reflecting elements and/or the quantization level of each reflecting element. In order to break this bottleneck, in this letter, we propose a convolutional autoencoder-based scheme, in which the QPS is compressed on the receiver side and reconstructed on the IRS side. In order to solve the problems of mismatched distribution and vanishing gradient, we remove the batch normalization (BN) layers and introduce a denosing module. By doing so, it is possible to achieve a high compression ratio with a reliable reconstruction accuracy in the bandwidth-limited feedback channel, and it is also possible to accommodate existing works assuming available QPS at the IRS. Simulation results confirm the high reconstruction accuracy of the feedback/compressed QPS through a feedback channel, and show that the proposed scheme can significantly outperform the existing compression algorithms.
High-Sensitivity Electric Potential Sensors for Non-Contact Monitoring of Physiological Signals
Authors: Xinyao Tang, Wangbo Chen, Soumyajit Mandal, Kevin Bi, Tayfun Ozdemir
Date: 2021-10-23 00:00:00
Tasks: Contact Detection, EEG, Electroencephalogram (EEG), Sensitivity

The paper describes highly-sensitive passive electric potential sensors (EPS) for non-contact detection of multiple biophysical signals, including electrocardiogram (ECG), respiration cycle (RC), and electroencephalogram (EEG). The proposed EPS uses an optimized transimpedance amplifier (TIA), a single guarded sensing electrode, and an adaptive cancellation loop (ACL) to maximize sensitivity (DC transimpedance $=150$~G$\Omega$) in the presence of power line interference (PLI) and motion artifacts. Tests were performed on healthy adult volunteers in noisy and unshielded indoor environments. Useful sensing ranges for ECG, RC, and EEG measurements, as validated against reference contact sensors, were observed to be approximately 50~cm, 100~cm, and 5~cm, respectively. ECG and RC signals were also successfully measured through wooden tables for subjects in sleep-like postures. The EPS were integrated with a wireless microcontroller to realize wireless sensor nodes capable of streaming acquired data to a remote base station in real-time.
Joint Beamforming and Interference Cancellation in MmWave Wideband Full-Duplex Systems
Authors: Elyes Balti, Brian L. Evans
Date: 2021-10-23 00:00:00
Tasks: Autonomous Driving

Full-duplex (FD) systems have the capability to transmit and receive at the same time in the same frequency band. FD systems can reduce congestion and latency and improve coverage and spectral efficiency. As a relay, they can increase range and decrease outages. Full-duplex (FD) wireless systems have been emerging as a practical solution to provide high bandwidth, low latency, and big data processing in millimeter wave and Terahertz systems to support cellular networks, autonomous driving, platooning, advanced driving assistance and other systems. However, FD systems suffer from loopback self-interference that can swamp the analog-to-digital converters (ADCs) resulting in very low spectral efficiency. In this context, we consider a cellular system wherein uplink and downlink users independently communicate with FD base station. The proposed contributions are (1) three hybrid beamforming algorithms to cancel self-interference and increase the received power, and (2) evaluation of outage probability, spectral efficiency, and energy efficiency of the proposed algorithms. We consider full-digital beamforming and upper bound as benchmarks. Finally, we show the resiliency of Algorithm 2 against self-interference in comparison with Algorithms 1 and 3, as well as conventional approaches such as beam steering, angle search and singular value decomposition.
TELET: A Monotonic Algorithm to Design Large Dimensional Equiangular Tight Frames for Applications in Compressed Sensing
Authors: R. Jyothi, P. Babu
Date: 2021-10-23 00:00:00
Tasks: compressed sensing, Image Reconstruction

An Equiangular tight frame (ETF) - also known as the Welch-bound-equality sequences - consists of a sequence of unit norm vectors whose absolute inner product is identical and minimal. Due to this unique property, these frames are preferred in different applications such as in constructing sensing matrices for compressed sensing systems, robust transmission, and quantum computing. Construction of ETFs involves solving a challenging non-convex minimax optimization problem, and only a few methods were successful in constructing them, albeit only for smaller dimensions. In this paper, we propose an iterative algorithm named TEchnique to devise Large dimensional Equiangular Tight-frames (TELET-frames) based on the majorization minimization (MM) procedure - in which we design and minimize a tight upper bound for the ETF cost function at every iteration. Since TELET is designed using the MM approach, it inherits useful properties of MM such as monotonicity and guaranteed convergence to a stationary point. Subsequently, we use the derived frames to construct optimized sensing matrix for compressed sensing systems. In the numerical simulations, we show that the proposed algorithm can generate complex and real frames (in the order of hundreds) with very low mutual coherence value when compared to the state-of-the-art algorithm, with a slight increase in computational cost. Experiments using synthetic data and real images reveal that the optimized sensing matrix obtained through the frames constructed by TELET performs better, in terms of image reconstruction accuracy, than the sensing matrix constructed using state-of-the-art methods.
New Methods for MLE of Toeplitz Structured Covariance Matrices with Applications to RADAR Problems
Authors: Augusto Aubry, Prabhu Babu, Antonio De Maio, Rikhabchand Jyothi
Date: 2021-10-23 00:00:00

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced and two iterative algorithms are proposed for the optimization of the equivalent problem. Both the strategies are based on the Majorization Minimization (MM) framework and hence enjoy nice properties such as monotonicity and ensured convergence to a stationary point of the equivalent MLE problem. The proposed algorithms are also extended to deal with MLE of other related covariance structures, namely, the banded Toeplitz, Toeplitz-block-Toeplitz, low rank Toeplitz structure plus a scalar matrix (accounting for white noise), and finally Toeplitz matrices satisfying a condition number constraint. Through numerical simulations, it is shown that new methods provide satisfactory performance levels in terms of both mean square estimation error and signal-to-interference-plus-noise ratio.
Adversarial Multi-Player Bandits for Cognitive Radar Networks
Authors: William W. Howard, R. M. Buehrer, Anthony Martone
Date: 2021-10-22 00:00:00

We model a radar network as an adversarial bandit problem, where the environment pre-selects reward sequences for each of several actions available to the network. This excludes environments which vary rewards in response to the learner's actions. Adversarial environments include those with third party emitters which enter and exit the environment according to some criteria which does not depend on the radar network. The network consists of several independent radar nodes, which attempt to attain the highest possible SINR in each of many time steps. We show that in such an environment, simple sub-band selection algorithms are unable to consistently attain high SINR. However, through the use of adversarial multi-player bandit algorithms, a radar network can continue to track targets without a loss in tracking precision.
Multiplication-Avoiding Variant of Power Iteration with Applications
Authors: Hongyi Pan, Diaa Badawi, Runxuan Miao, Erdem Koyuncu, Ahmet Enis Cetin
Date: 2021-10-22 00:00:00
Tasks: Image Reconstruction, Recommendation Systems

Power iteration is a fundamental algorithm in data analysis. It extracts the eigenvector corresponding to the largest eigenvalue of a given matrix. Applications include ranking algorithms, recommendation systems, principal component analysis (PCA), among many others. In this paper, we introduce multiplication-avoiding power iteration (MAPI), which replaces the standard $\ell_2$-inner products that appear at the regular power iteration (RPI) with multiplication-free vector products which are Mercer-type kernel operations related with the $\ell_1$ norm. Precisely, for an $n\times n$ matrix, MAPI requires $n$ multiplications, while RPI needs $n^2$ multiplications per iteration. Therefore, MAPI provides a significant reduction of the number of multiplication operations, which are known to be costly in terms of energy consumption. We provide applications of MAPI to PCA-based image reconstruction as well as to graph-based ranking algorithms. When compared to RPI, MAPI not only typically converges much faster, but also provides superior performance.
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods
Authors: Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio
Date: 2021-10-22 00:00:00

The ideal observer (IO) sets an upper performance limit among all observers and has been advocated for assessing and optimizing imaging systems. For general joint detection and estimation (detection-estimation) tasks, estimation ROC (EROC) analysis has been established for evaluating the performance of observers. However, in general, it is difficult to accurately approximate the IO that maximizes the area under the EROC curve. In this study, a hybrid method that employs machine learning is proposed to accomplish this. Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks. Unlike traditional MCMC methods, the hybrid method is not limited to use of specific utility functions. In addition, a purely supervised learning-based sub-ideal observer is proposed. Computer-simulation studies are conducted to validate the proposed method, which include signal-known-statistically/background-known-exactly and signal-known-statistically/background-known-statistically tasks. The EROC curves produced by the proposed method are compared to those produced by the MCMC approach or analytical computation when feasible. The proposed method provides a new approach for approximating the IO and may advance the application of EROC analysis for optimizing imaging systems.