pwc-archive/papers-with-abstracts huggingface dataset:
BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms
Authors: Marcelo Lerendegui, Kai Riemer, Bingxue Wang, Christopher Dunsby, Meng-Xing Tang
Date: 2022-11-01 00:00:00
Tasks: Diagnostic, Super-Resolution

Ultrasound contrast enhanced imaging has seen widespread uptake in research and clinical diagnostic imaging. This includes applications such as vector flow imaging, functional ultrasound and super-resolution Ultrasound Localization Microscopy (ULM). All of these require testing and validation during development of new algorithms with ground truth data. In this work we present a comprehensive simulation platform BUbble Flow Field (BUFF) that generates contrast enhanced ultrasound images in vascular tree geometries with realistic flow characteristics and validation algorithms for ULM. BUFF allows complex micro-vascular network generation of random and user-defined vascular networks. Blood flow is simulated with a fast Computational Fluid Dynamics (CFD) solver and allows arbitrary input and output positions and custom pressures. The acoustic field simulation is combined with non-linear Microbubble (MB) dynamics and simulates a range of point spread functions based on user-defined MB characteristics. The validation combines both binary and quantitative metrics. BFF's capacity to generate and validate user-defined networks is demonstrated through its implementation in the Ultrasound Localisation and TRacking Algorithms for Super Resolution (ULTRA-SR) Challenge at the International Ultrasonics Symposium (IUS) 2022 of the Institute of Electrical and Electronics Engineers (IEEE). The ability to produce ULM images, and the availability of a ground truth in localisation and tracking enables objective and quantitative evaluation of the large number of localisation and tracking algorithms developed in the field. BUFF can also benefit deep learning based methods by automatically generating datasets for training. BUFF is a fully comprehensive simulation platform for testing and validation of novel ULM techniques and is open source.
MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images
Authors: Hongyang He, Feng Ziliang, Yuanhang Zheng, Shudong Huang, HaoBing Gao
Date: 2022-11-01 00:00:00
Tasks: Decoder

In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique.Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly.Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies.By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications. However, transformer-based architectures lack global semantic information interaction and require a large-scale training dataset, making it challenging to train with small data samples. In order to solve these challenges, we present a hierarchical contextattention transformer network (MHITNet) that combines the multi-scale, transformer, and hierarchical context extraction modules in skip-connections. The multi-scale module captures deeper CT semantic information, enabling transformers to encode feature maps of tokenized picture patches from various CNN stages as input attention sequences more effectively. The hierarchical context attention module augments global data and reweights pixels to capture semantic context.Extensive trials on three datasets show that the proposed MHITNet beats current best practises
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
Authors: Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer
Date: 2022-11-01 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively.
An open-source software package for on-the-fly deskewing and live viewing of volumetric lightsheet microscopy data
Authors: Jacob R. Lamb, Edward N. Ward, Clemens F. Kaminski
Date: 2022-10-31 00:00:00
Tasks: GPU, Navigate

Oblique plane microscopy, OPM, is a form of lightsheet microscopy that permits volumetric imaging of biological samples at high temporal and spatial resolution. However, the imaging geometry of OPM, and related variants of light sheet microscopy, distorts the coordinate frame of the presented image sections with respect to real space coordinate frame in which the sample is moved to navigate to regions of interest. This makes live viewing and practical operation of such microscopes difficult. We present an open-source software package that utilises GPU acceleration and multiprocessing to transform the display of OPM imaging data in real time to produce live views that mimic that produced by standard widefield microscopes. Image stacks can be acquired, processed and plotted at rates of several Hz, making live operation of OPMs, and similar microscopes, more user friendly and intuitive.
Unsupervised Parameter Estimation using Model-based Decoder
Authors: Franz Weißer, Michael Baur, Wolfgang Utschick
Date: 2022-11-03 00:00:00
Tasks: Decoder, model, parameter estimation

In this work, we consider the use of a model-based decoder in combination with an unsupervised learning strategy for direction-of-arrival (DoA) estimation. Relying only on unlabeled training data we show in our analysis that we can outperform existing unsupervised machine learning methods and classical methods. The proposed approach consists of introducing a model-based decoder in an autoencoder architecture which leads to a meaningful representation of the statistical model in the latent space of the autoencoder. Our numerical simulations show that the performance of the presented approach is not affected by correlated signals and performs well for both, uncorrelated and correlated, scenarios. This is a result of the fact, that, in the proposed framework, the signal covariance matrix and the DOAs are estimated simultaneously.
Data Converter Design Space Exploration for IoT Applications: An Overview of Challenges and Future Directions
Authors: Buddhi Prakash Sharma, Anu Gupta, Chandra Shekhar
Date: 2022-11-03 00:00:00

Human lives are improving with the widespread use of cutting-edge digital technology like the Internet of Things (IoT). Recently, the pandemic has shown the demand for more digitally advanced IoT-based devices. International Data Corporation (IDC) forecasts that by 2025, there will be approximately 42 billion of these devices in use, capable of producing around 80 ZB (zettabytes) of data. So data acquisition, processing, communication, and visualization are necessary from a functional standpoint. Indicating sensors & data converters are the key components for IoT-based applications. The efficiency of such applications is truly measured in terms of latency, power, and resolution of data converters motivating designers to perform efficiently. Sensors capture and covert physical features from their chosen environment into detectable quantities. Data converter gives meaningful information and connects the real analog world to the digital component of the devices. The received data is interpreted and analyzed with the digital processing circuitry. Ultimately, it is used as information by a network of internet-connected smart devices. Because IoT technologies are adaptable to nearly any technology that may provide its operational activity and environmental conditions. But the challenges occur with power consumption as the complete IoT framework is battery operated and replacing a battery is a daunting task. So the goal of this chapter is to unveil the requirements to design energy-efficient data converters for IoT applications.
Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System
Authors: Zhengyu Wang, Wei Yang, Tiebin Mi, Robert Caiming Qiu
Date: 2022-11-03 00:00:00

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight (LOS) links and reflect the uplink pilot signal to sensors. To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm. The grid-based DoA estimation is modeled as a joint-sparse recovery problem considering the grid bias, and the Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The corresponding Cram\'er-Rao lower bound (CRLB) is derived to evaluate the estimation. Then, the beampattern at the RIS is optimized to maximize the signal-to-noise (SNR) at sensors according to the estimated angles. Numerical results show that the proposed alternating optimization algorithm can achieve lower estimation error compared to benchmarks of random beamforming design.
A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks
Authors: Cem Ates Musluoglu, Alexander Bertrand
Date: 2022-11-02 00:00:00

Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centralized way would lead to high energy and communication costs. The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve these signal fusion problems in a distributed fashion, which reduces the communication and energy costs in the network. The DASF framework assumes that there is a common goal across the nodes, i.e., the optimal filter is shared across the network. However, many applications require a node-specific objective, while all these node-specific objectives are still related via a common latent data model. In this work, we propose the DANSF algorithm which builds upon the DASF framework, and extends it to allow for node-specific spatial filtering problems.
Emerging Prototyping Activities in Joint Radar-Communications
Authors: Bhavani Shankar M. R., Kumar Vijay Mishra, Mohammad Alaee-Kerahroodi
Date: 2022-11-02 00:00:00

The previous chapters have discussed the canvas of joint radar-communications (JRC), highlighting the key approaches of radar-centric, communications-centric and dual-function radar-communications systems. Several signal processing and related aspects enabling these approaches including waveform design, resource allocation, privacy and security, and intelligent surfaces have been elaborated in detail. These topics offer comprehensive theoretical guarantees and algorithms. However, they are largely based on theoretical models. A hardware validation of these techniques would lend credence to the results while enabling their embrace by industry. To this end, this chapter presents some of the prototyping initiatives that address some salient aspects of JRC. We describe some existing prototypes to highlight the challenges in design and performance of JRC. We conclude by presenting some avenues that require prototyping support in the future.
Improving Performance of Higher-Order Codirectional Raman Amplifiers Using Phase-Modulated Signals -- Functional Principle
Authors: Lutz Rapp
Date: 2022-11-02 00:00:00

Existing unrepeatered submarine links are increasingly upgraded to the most advanced modulation format currently available for commercial applications. Quite often the use of third-order codirectional Raman amplifiers is necessary. Power fluctuations of the involved high-power pump induces phase shifts in phase modulated signals via the nonlinear Kerr effects. An approach for reducing the impact of this effect on system performance is explained in this document.
Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities
Authors: Ali Behravan, Vijaya Yajnanarayana, Musa Furkan Keskin, Hui Chen, Deep Shrestha, Traian E. Abrudan, Tommy Svensson, Kim Schindhelm, Andreas Wolfgang, Simon Lindberg, Henk Wymeersch
Date: 2022-11-02 00:00:00

Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing. These will be utilized not only for conventional location-aware services and for enhancing communication performance, but also to support new use case families with extreme performance requirements. This paper presents a unified vision from stakeholders across the value chain in terms of both opportunities and challenges for 6G positioning and sensing, as well as use cases, performance requirements, and gap analysis. Combined, this motivates the technical advances in 6G and guides system design.
Soft-Output Signal Detection for Cetacean Vocalizations Using Spectral Entropy, K-Means Clustering and the Continuous Wavelet Transform
Authors: Marco W. Rademan, Daniel J. Versfeld, Johan A. du Preez
Date: 2022-11-02 00:00:00
Tasks: Specificity

Underwater acoustic monitoring systems record many hours of audio data for marine research, making fast and reliable non-causal signal detection paramount. Such detectors assist in reducing the amount of labor required for signal annotations, which often contain large portions devoid of signals. Cetacean vocalization detection based on spectral entropy is investigated as a means of vocalization discovery. Previous techniques using spectral entropy (SE) mostly consider time-frequency enhancement of the entropy measure, and utilize the STFT as its time-frequency (TF) decomposition. SE methods also requires the user to set a detection threshold manually, which call for knowledge of the produced entropy measures. This paper considers median filtering as a simple, effective way to provide temporal stabilization to the entropy measure, and considers the CWT as an alternative TF decomposition. K-means clustering is used to determine the threshold required to accurately separate the signal/no-signal entropy measures, resulting in a one-dimensional, two-class classification problem. The class means are used to perform pseudo-probabilistic soft class assignment, which is a useful metric in algorithmic development. The effect of median filtering, signal-to-noise ratio and the chosen TF decomposition are investigated. The proposed method shows a significant improvement in detection accuracy and specificity, while also providing a more interpretable detection threshold setting via soft class assignment.
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Authors: Sundeep Prabhakar Chepuri, Nir Shlezinger, Fan Liu, George C. Alexandropoulos, Stefano Buzzi, Yonina C. Eldar
Date: 2022-11-02 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands. However, establishing wireless connections at these high frequencies is quite challenging, mainly due to the penetrating pathloss that prevents reliable communication and sensing. Another emerging technology for next-generation wireless systems is reconfigurable intelligent surfaces (RISs), which are capable of modifying harsh propagation environments. RISs are the focus of growing research and industrial attention, bringing forth the vision of smart and programmable signal propagation environments. In this article, we provide a tutorial-style overview of the applications and benefits of RISs for sensing functionalities in general, and for ISAC systems in particular. We highlight the potential advantages when fusing these two emerging technologies, and identify for the first time that: i) joint sensing and communications designs are most beneficial when the channels referring to these operations are coupled, and that ii) RISs offer means for controlling this beneficial coupling. The usefulness of RIS-aided ISAC goes beyond the individual obvious gains of each of these technologies in both performance and power efficiency. We also discuss the main signal processing challenges and future research directions which arise from the fusion of these two emerging technologies.
Product Graph Learning from Multi-attribute Graph Signals with Inter-layer Coupling
Authors: Chenyue Zhang, Yiran He, Hoi-To Wai
Date: 2022-11-02 00:00:00
Tasks: Attribute, Graph Learning

This paper considers learning a product graph from multi-attribute graph signals. Our work is motivated by the widespread presence of multilayer networks that feature interactions within and across graph layers. Focusing on a product graph setting with homogeneous layers, we propose a bivariate polynomial graph filter model. We then consider the topology inference problems thru adapting existing spectral methods. We propose two solutions for the required spectral estimation step: a simplified solution via unfolding the multi-attribute data into matrices, and an exact solution via nearest Kronecker product decomposition (NKD). Interestingly, we show that strong inter-layer coupling can degrade the performance of the unfolding solution while the NKD solution is robust to inter-layer coupling effects. Numerical experiments show efficacy of our methods.
Particle Filter based Massive MIMO Channel Estimation
Authors: Anu T. S., Tara Raveendran
Date: 2022-11-02 00:00:00

Massive multiple-input multiple-output (MIMO) communication systems have drawn significant interest recently in next-generation wireless communications. The use of a large number of antennas in massive MIMO makes the estimation of channel state information very challenging. Accurate channel state information is essential in capitalizing the advantages of the massive MIMO technology. This paper proposes the application of the Ensemble Square Root Filter (EnSRF) and a variant of EnSRF, namely a Particle wise Update version of the Ensemble Square Root Filter (PUEnSRF) to estimate the time-selective frequency-flat fading channel coefficients in the massive MIMO scenario. Simulation results clearly indicate the remarkably superior accuracy and filter convergence of PUEnSRF estimates as compared to the conventional particle filters.
Dual-Polarized Massive MIMO-RSMA Networks: Tackling Imperfect SIC
Authors: Arthur Sousa de Sena, Pedro H. J. Nardelli, Daniel Benevides da Costa, Petar Popovski, Constantinos B. Papadias, Merouane Debbah
Date: 2022-11-02 00:00:00

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users' data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users' messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.
Genomics Data Analysis via Spectral Shape and Topology
Authors: Erik J. Amézquita, Farzana Nasrin, Kathleen M. Storey, Masato Yoshizawa
Date: 2022-11-02 00:00:00
Tasks: Dimensionality Reduction

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper and differential gene expression. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-SNE. Although Mapper shows promise in analyzing high-dimensional data, building tools to statistically analyze Mapper graphical structures is limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis.