pwc-archive/papers-with-abstracts huggingface dataset:
Optimal model-based beamforming and independent steering for spherical loudspeaker arrays
Authors: Boaz Rafaely, Dima Khaykin
Date: 2023-10-06 00:00:00

Spherical loudspeaker arrays have been recently studied for directional sound radiation, where the compact arrangement of the loudspeaker units around a sphere facilitated the control of sound radiation in three-dimensional space. Directivity of sound radiation, or beamforming, was achieved by driving each loudspeaker unit independently, where the design of beamforming weights was typically achieved by numerical optimization with reference to a given desired beam pattern. This is in contrast to the methods already developed for microphone arrays in general and spherical microphone arrays in particular, where beamformer weights are designed to satisfy a wider range of objectives, related to directivity, robustness, and side-lobe level, for example. This paper presents the development of a physical-model-based, optimal beamforming framework for spherical loudspeaker arrays, similar to the framework already developed for spherical microphone arrays, facilitating efficient beamforming in the spherical harmonics domain, with independent steering. In particular, it is shown that from a beamforming perspective, the spherical loudspeaker array is similar to the spherical microphone array with microphones arranged around a rigid sphere. Experimental investigation validates the theoretical framework of beamformer design.
Zones of quiet in a broadband diffuse sound field
Authors: Boaz Rafaely
Date: 2023-10-06 00:00:00

The zones of quiet in pure-tone diffuse sound fields have been studied extensively in the past, both theoretically and experimentally, with the well known result of the 10\,dB attenuation extending to about a tenth of a wavelength. Recent results on the spatial-temporal correlation of broadband diffuse sound fields are used in this study to develop a theoretical framework for predicting the extension of the zones of quiet in broadband diffuse sound fields. This can be used to study the acoustic limitations imposed on local active sound control systems such as an active headrest when controlling broadband noise. Spatial-temporal correlation is first revised, after which derivations of the diffuse field zones of quiet in the near-field and the far-field of the secondary source are presented. The theoretical analysis is supported by simulation examples comparing the zones of quiet for diffuse fields excited by tonal and broadband signals. It is shown that as a first approximation the zone of quiet of a low-pass filtered noise is comparable to that of a pure-tone with a frequency equal to the center frequency of the broadband noise bandwidth.
Spatial sampling and beamforming for spherical microphone arrays
Authors: Boaz Rafaely
Date: 2023-10-06 00:00:00

Spherical microphone arrays have been recently studied for spatial sound recording, speech communication, and sound field analysis for room acoustics and noise control. Complementary theoretical studies presented progress in spatial sampling and beamforming methods. This paper reviews recent results in spatial sampling that facilitate a wide range of spherical array configurations, from a single rigid sphere to free positioning of microphones. The paper then presents an overview of beamforming methods recently presented for spherical arrays, from the widely used delay-and-sum and Dolph-Chebyshev, to the more advanced optimal methods, typically performed in the spherical harmonics domain.
A privacy-preserving method using secret key for convolutional neural network-based speech classification
Authors: Shoko Niwa, Sayaka Shiota, Hitoshi Kiya
Date: 2023-10-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, image-classification, Image Classification, Privacy Preserving, Speaker Verification, speech-recognition, Speech Recognition

In this paper, we propose a privacy-preserving method with a secret key for convolutional neural network (CNN)-based speech classification tasks. Recently, many methods related to privacy preservation have been developed in image classification research fields. In contrast, in speech classification research fields, little research has considered these risks. To promote research on privacy preservation for speech classification, we provide an encryption method with a secret key in CNN-based speech classification systems. The encryption method is based on a random matrix with an invertible inverse. The encrypted speech data with a correct key can be accepted by a model with an encrypted kernel generated using an inverse matrix of a random matrix. Whereas the encrypted speech data is strongly distorted, the classification tasks can be correctly performed when a correct key is provided. Additionally, in this paper, we evaluate the difficulty of reconstructing the original information from the encrypted spectrograms and waveforms. In our experiments, the proposed encryption methods are performed in automatic speech recognition~(ASR) and automatic speaker verification~(ASV) tasks. The results show that the encrypted data can be used completely the same as the original data when a correct secret key is provided in the transformer-based ASR and x-vector-based ASV with self-supervised front-end systems. The robustness of the encrypted data against reconstruction attacks is also illustrated.
A Plug-and-Play Image Registration Network
Authors: Junhao Hu, Weijie Gan, Zhixin Sun, Hongyu An, Ulugbek S. Kamilov
Date: 2023-10-06 00:00:00
Tasks: Image Registration

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR.
Towards Non-contact 3D Ultrasound for Wrist Imaging
Authors: Antony Jerald, A. N. Madhavanunni, Gayathri Malamal, Mahesh Raveendranatha Panicker
Date: 2023-10-06 00:00:00
Tasks: 3D Reconstruction, GPU, Position

Objective: The objective of this work is an attempt towards non-contact freehand 3D ultrasound imaging with minimal complexity added to the existing point of care ultrasound (POCUS) systems. Methods: This study proposes a novel approach of using a mechanical track for non-contact ultrasound (US) scanning. The approach thus restricts the probe motion to a linear plane, to simplify the acquisition and 3D reconstruction process. A pipeline for US 3D volume reconstruction employing an US research platform and a GPU-based edge device is developed. Results: The efficacy of the proposed approach is demonstrated through ex-vivo and in-vivo experiments. Conclusion: The proposed approach with the adjustable field of view capability, non-contact design, and low cost of deployment without significantly altering the existing setup would open doors for up gradation of traditional systems to a wide range of 3D US imaging applications. Significance: Ultrasound (US) imaging is a popular clinical imaging modality for the point-of-care bedside imaging, particularly of the wrist/knee in the pediatric population due to its non-invasive and radiation free nature. However, the limited views of tissue structures obtained with 2D US in such scenarios make the diagnosis challenging. To overcome this, 3D US imaging which uses 2D US images and their orientation/position to reconstruct 3D volumes was developed. The accurate position estimation of the US probe at low cost has always stood as a challenging task in 3D reconstruction. Additionally, US imaging involves contact, which causes difficulty to pediatric subjects while monitoring live fractures or open wounds. Towards overcoming these challenges, a novel framework is attempted in this work.
Hessian-based Similarity Metric for Multimodal Medical Image Registration
Authors: Mohammadreza Eskandari, Houssem-Eddine Gueziri, D. Louis Collins
Date: 2023-10-06 00:00:00
Tasks: Image Registration, Medical Image Registration

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Authors: Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon
Date: 2023-10-05 00:00:00
Tasks: Hyperspectral Unmixing, Tensor Decomposition

Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework. Tensor models such as canonical polyadic decomposition allow for this kind of unmixing but lack a general framework and interpretability of the results. In this article, we propose an interpretable methodological framework for low-rank multifeature hyperspectral unmixing based on tensor decomposition (MultiHU-TD) that incorporates the abundance sum-to-one constraint in the alternating optimization alternating direction method of multipliers (ADMM) algorithm and provide in-depth mathematical, physical, and graphical interpretation and connections with the extended linear mixing model. As additional features, we propose to incorporate mathematical morphology and reframe a previous work on neighborhood patches within MultiHU-TD. Experiments on real HSIs showcase the interpretability of the model and the analysis of the results. Python and MATLAB implementations are made available on GitHub.
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
Authors: Olfa Ben Yahia, Zineb Garroussi, Olivier Bélanger, Brunilde Sansò, Jean-François Frigon, Stéphane Martel, Antoine Lesage-Landry, Gunes Karabulut Kurt
Date: 2023-10-06 00:00:00

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture.
A physics-informed generative model for passive radio-frequency sensing
Authors: Stefano Savazzi, Federica Fieramosca, Sanaz Kianoush, Vittorio Rampa, Michele D'Amico
Date: 2023-10-06 00:00:00

Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby. These wireless devices may be co-located members of a Wireless Local Area Network (WLAN) or even cellular devices connected with a Wide Area Network (WAN). Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. Thus, GNNs can be used to simulate/reconstruct missing samples, or learn physics-informed data distributions. The paper discusses a Variational Auto-Encoder (VAE) technique and its adaptations to incorporate a relevant EM body diffraction method with applications to passive RF sensing and localization/tracking. The proposed EM-informed generative model is verified against classical diffraction-based EM body tools and validated on real RF measurements. Applications are also introduced and discussed.
A Comprehensive Indoor Environment Dataset from Single-family Houses in the US
Authors: Sheik Murad Hassan Anik, Xinghua Gao, Na Meng
Date: 2023-10-05 00:00:00

The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes.
Wireless strain and temperature monitoring in reinforced concrete using Surface Acoustic Wave (SAW) sensors
Authors: Pierre Jeltiri, Firas Al-Mahmoud, Rémi Boissière, Baptiste Paulmier, Tony Makdissy, Omar Elmazria, Pascal Nicolay, Sami Hage-Ali
Date: 2023-10-02 00:00:00

Monitoring the health of civil engineering structures using implanted deformation, temperature and corrosion sensors would further improve maintenance and extend the service life of those structures. However, sensor integration poses a number of problems, due to the presence of cables and on-board electronics. Passive, wireless SAW sensors offer a very promising solution, here. We used commercial SAW devices mounted on steel rebars to carry out an initial feasibility study. Without cables or embedded electronics, we were able to measure the deformation of a concrete beam subjected to bending load. We were also able to measure the temperature continuously over a three-week period.
Magnetic SAW RFID Sensor Based on Love Wave for Detection of Magnetic Field and Temperature
Authors: Prince Mengue, Laurine Meistersheim, Sami Hage-Ali, Cécile Floer, Sébastien Petit-Watelot, Daniel Lacour, Michel Hehn, Omar Elmazria
Date: 2023-10-02 00:00:00

Magnetic field measurement including a temperature compensation is essential for a magnetic field sensor. This study investigates a magnetic surface acoustic wave (MSAW) sensor in a reflective delay line configuration with two acoustic propagation paths with and without magnetic field sensitive layer. The delay in path with sensitive layer leads to magnetic field detection and the one without enable temperature measurement and thus compensation for the first path. The developed sensor is based on a ZnO/LiNbO$_3$ Ycut (X-direction) layered structure as Love wave platform. Love wave as a shear wave being more favorable for magnetic detection. Co-Fe-B is considered as sensitive layer to detect magnetic field changes and is deposited on the top of ZnO, but only on one of the two paths. We combined an original configuration of connected IDTs with a high electromechanical coupling coefficient (K$^2$) mode to improve the signal amplitude. The achieved sensor exhibits a high temperature and magnetic field sensitivity of -63 ppm/$^\circ$C and -781 ppm/mT, respectively. The temperature compensation method for magnetic field measurement is demonstrated using a differential measurement by subtracting the delay times obtained for the two paths with and without the sensitive layer. Finally, The sensor exhibited good repeatability at various temperatures. Moreover, the device developed allows in addition to the multisensor functionality, the radio frequency identification (RFID) which is necessary for the deployment of sensor networks.
Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform
Authors: Weronika Potok, Onno van der Groen, Sahana Sivachelvam, Marc Bächinger, Flavio Fröhlich, Laszlo B. Kish, Nicole Wenderoth
Date: 2023-09-28 00:00:00

Stochastic Resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature which cause "deterministic amplitude resonance" (DAR). Here we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether non-random, high-frequency, transcranial alternating current stimulation applied to visual cortex could induce resonance-like effects and enhance performance of a visual detection task. We demonstrated in 28 participants that applying 80 Hz triangular-waves or sine-waves with tACS reduced visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS.
Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
Authors: Taha Yassine, Luc Le Magoarou, Matthieu Crussière, Stephane Paquelet
Date: 2023-09-28 00:00:00

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition
Authors: Danial Ahangarani, Mohammad Shirazi, Navid Ashraf
Date: 2023-09-26 00:00:00
Tasks: Activity Recognition, Contrastive Learning, Human Activity Recognition, Time Series

The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning's proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task.