pwc-archive/papers-with-abstracts huggingface dataset:
Soft Label Coding for End-to-end Sound Source Localization With Ad-hoc Microphone Arrays
Authors: Linfeng Feng, Yijun Gong, Xiao-Lei Zhang
Date: 2023-04-15 00:00:00
Tasks: Quantization, Sound Source Localization

Recently, an end-to-end two-dimensional sound source localization algorithm with ad-hoc microphone arrays formulates the sound source localization problem as a classification problem. The algorithm divides the target indoor space into a set of local areas, and predicts the local area where the speaker locates. However, the local areas are encoded by one-hot code, which may lose the connections between the local areas due to quantization errors. In this paper, we propose a new soft label coding method, named label smoothing, for the classification-based two-dimensional sound source location with ad-hoc microphone arrays. The core idea is to take the geometric connection between the classes into the label coding process.The first one is named static soft label coding (SSLC), which modifies the one-hot codes into soft codes based on the distances between the local areas. Because SSLC is handcrafted which may not be optimal, the second one, named dynamic soft label coding (DSLC), further rectifies SSLC, by learning the soft codes according to the statistics of the predictions produced by the classification-based localization model in the training stage. Experimental results show that the proposed methods can effectively improve the localization accuracy.
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference
Authors: Haotian Wu, Nitish Mital, Krystian Mikolajczyk, Deniz Gündüz
Date: 2023-04-17 00:00:00
Tasks: Contrastive Learning, Image Retrieval, Retrieval, Semantic Communication

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel. We propose a semantic non-orthogonal multiple access (NOMA) communication paradigm, in which extracted features from each device are mapped directly to channel inputs, which are then added over-the-air. We propose a novel contrastive learning (CL)-based semantic communication (CL-SC) paradigm, aiming to exploit signal correlations to maximize the retrieval accuracy under a total bandwidth constraints. Specifically, we treat noisy correlated signals as different augmentations of a common identity, and propose a cross-view CL algorithm to optimize the correlated signals in a coarse-to-fine fashion to improve retrieval accuracy. Extensive numerical experiments verify that our method achieves the state-of-the-art performance and can significantly improve retrieval accuracy, with particularly significant gains in low signla-to-noise ratio (SNR) and limited bandwidth regimes.
Lightweight and Interpretable Left Ventricular Ejection Fraction Estimation using Mobile U-Net
Authors: Meghan Muldoon, Naimul Khan
Date: 2023-04-17 00:00:00

Accurate LVEF measurement is important in clinical practice as it identifies patients who may be in need of life-prolonging treatments. This paper presents a deep learning based framework to automatically estimate left ventricular ejection fraction from an entire 4-chamber apical echocardiogram video. The aim of the proposed framework is to provide an interpretable and computationally effective ejection fraction prediction pipeline. A lightweight Mobile U-Net based network is developed to segment the left ventricle in each frame of an echocardiogram video. An unsupervised LVEF estimation algorithm is implemented based on Simpson's mono-plane method. Experimental results on a large public dataset show that our proposed approach achieves comparable accuracy to the state-of-the-art while being significantly more space and time efficient (with 5 times fewer parameters and 10 times fewer FLOPS).
LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization
Authors: Zhangxing Bian, Jiayang Zhong, Yanglong Lu, Charles R. Hatt, Nicholas S. Burris
Date: 2023-04-15 00:00:00
Tasks: Auxiliary Learning

Landmark detection is a critical component of the image processing pipeline for automated aortic size measurements. Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal training can estimate the location of unseen landmarks from limited examples, we proposed an auxiliary learning task to learn the implicit topology of aortic landmarks through a CNN-based network. Specifically, we created a network to predict the location of missing landmarks from the visible ones by minimizing the Implicit Topology loss in an end-to-end manner. The proposed learning task can be easily adapted and combined with Unet-style backbones. To validate our method, we utilized a dataset consisting of 207 CTAs, labeling four landmarks on each aorta. Our method outperforms the state-of-the-art Unet-style architectures (ResUnet, UnetR) in terms of localization accuracy, with only a light (#params=0.4M) overhead. We also demonstrate our approach in two clinically meaningful applications: aortic sub-region division and automatic centerline generation.
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
Authors: Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu, Hairong Zheng, Shanshan Wang
Date: 2023-04-15 00:00:00
Tasks: Federated Learning, Image Reconstruction

Deep learning-based methods have achieved encouraging performances in the field of magnetic resonance (MR) image reconstruction. Nevertheless, to properly learn a powerful and robust model, these methods generally require large quantities of data, the collection of which from multiple centers may cause ethical and data privacy violation issues. Lately, federated learning has served as a promising solution to exploit multi-center data while getting rid of the data transfer between institutions. However, high heterogeneity exists in the data from different centers, and existing federated learning methods tend to use average aggregation methods to combine the client's information, which limits the performance and generalization capability of the trained models. In this paper, we propose a Model-based Federated learning framework (ModFed). ModFed has three major contributions: 1) Different from the existing data-driven federated learning methods, model-driven neural networks are designed to relieve each client's dependency on large data; 2) An adaptive dynamic aggregation scheme is proposed to address the data heterogeneity issue and improve the generalization capability and robustness the trained neural network models; 3) A spatial Laplacian attention mechanism and a personalized client-side loss regularization are introduced to capture the detailed information for accurate image reconstruction. ModFed is evaluated on three in-vivo datasets. Experimental results show that ModFed has strong capability in improving image reconstruction quality and enforcing model generalization capability when compared to the other five state-of-the-art federated learning approaches. Codes will be made available at https://github.com/ternencewu123/ModFed.
Robust thalamic nuclei segmentation from T1-weighted MRI
Authors: Julie P. Vidal, Lola Danet, Patrice Péran, Jérémie Pariente, Meritxell Bach Cuadra, Natalie M. Zahr, Emmanuel J. Barbeau, Manojkumar Saranathan
Date: 2023-04-14 00:00:00
Tasks: Segmentation

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. Here, we introduce Histogram-based polynomial synthesis (HIPS), a fast preprocessing step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation. HIPS was incorporated into our Thalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts, scanner manufacturers, and field strength. HIPS-synthesized images improved intra-thalamic contrast and thalamic boundaries, and their segmentations yielded significantly better mean Dice, lower percentage of volume error, and lower standard deviations compared to both the CNN method and T1w-THOMAS. Finally, using THOMAS, HIPS-synthesized images were as effective as WMn images for identifying thalamic nuclei atrophy in alcohol use disorders subjects relative to healthy controls, with a higher area under the ROC curve compared to T1w-THOMAS (0.79 vs 0.73).
Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning'
Authors: Milad Sikaroudi, Mehdi Afshari, Abubakr Shafique, Shivam Kalra, H. R. Tizhoosh
Date: 2023-04-07 00:00:00
Tasks: Binarization, Image Retrieval, whole slide images

Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The authors call their method 'self-supervised image search for histology', short SISH. We express our concerns that SISH is an incremental modification of Yottixel, has used MinMax binarization but does not cite the original works, and is based on a misnomer 'self-supervised image search'. As well, we point to several other concerns regarding experiments and comparisons performed by Chen et al.
Fault Tolerant FPGA Implementation on Redundancy Techniques and ECG Denoising
Authors: Sathvik Reddy O, Sakthivel SM
Date: 2023-04-17 00:00:00
Tasks: Denoising, ECG Denoising

As more the communications and signal process we use in the today life the more we intend to develop more reliable devices which gives fewer errors due to transient fault, So we use a technique called 5-modular redundancy to generate fewer errors. 5-Modular redundancy is an approach to increasing the reliability of hardware systems constructed from component devices that are subject to failure. In digital signal processing and many other important daily life subjects FIR digital filters are used and they alike performing multiplications, complex computations and selecting desired frequency for different applications. FIR filters comprise of multipliers, adders, delay units. FIR filters has been chosen because it offers more steadiness and simple execution because of its limited length and no feedback within the circuit. Fir filter is most important for the signal processing and also used in daily life basis. The noise reduction can be performed by denoising of the signals of all the implementations .The proposed FPGA methods use Xilinx Vivado EDA.
Wideband Effects on Near-Field Pose Estimation of Target-Lodged RIS
Authors: Dario Tagliaferri
Date: 2023-04-17 00:00:00
Tasks: Pose Estimation

Reconfigurable intelligent surfaces (RISs) have been recently considered in sensing context to either improve localization performance or to extend the coverage in non-line-of-sight scenarios. While most of the literature considers RISs as statically placed in the environment, the usage of target-lodged RISs is relatively new and could be of interest where the target's reflectivity can/must be increased to improve its detection or parameters' estimation. This letter derives the Cram\'er-Rao bound (CRB) on the estimation of position and orientation (\textit{pose}) of a target-mounted RIS, in generic conditions: near-field, bistatic and wideband operation (i.e., when the wavefront across the RIS is curved and the employed sensing bandwidth is large enough to obtain a frequency-dependent RIS behavior). In particular, we focus on the wideband effect, that implies a \textit{pose-dependent filtering} on the impinging signal decreasing or increasing the CRB depending on the RIS size and the employed signal bandwidth.
Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning
Authors: Yuchao Chang, Wen Chen, Jun Li, Jianpo Liu, Haoran Wei, Zhendong Wang, Naofal Al-Dhahir
Date: 2023-04-17 00:00:00
Tasks: Deep Reinforcement Learning, Management, Q-Learning

Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs radiated power levels with constraints on the users traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs energy efficiency with the multiplicative complexity constraint is modeled to achieve nearoptimal performance by using a deep Q-network (DQN). In DQN,each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multiBSs energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively.
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
Authors: Itay Buchnik, Damiano Steger, Guy Revach, Ruud J. G. van Sloun, Tirza Routtenberg, Nir Shlezinger
Date: 2023-04-16 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applicability under complex settings, e.g., when tracking based on visual data, and the processing of high-dimensional signals often induces notable latency. These challenges can be treated by mapping the measurements into latent features obeying some postulated closed-form SS model, and applying the KF in the latent space. However, the validity of this approximated SS model may constitute a limiting factor. In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach. By gradually tackling the challenges in handling the observations model and the task, we develop Latent-KalmanNet, which implements tracking from high-dimensional measurements by leveraging data to jointly learn the KF along with the latent space mapping. Latent-KalmanNet combines a learned encoder with data-driven tracking in the latent space using the recently proposed-KalmanNet, while identifying the ability of each of these trainable modules to assist its counterpart via providing a suitable prior (by KalmanNet) and by learning a latent representation that facilitates data-aided tracking (by the encoder). Our empirical results demonstrate that the proposed Latent-KalmanNet achieves improved accuracy and run-time performance over both model-based and data-driven techniques by learning a surrogate latent representation that most facilitates tracking, while operating with limited complexity and latency.
sar reduction techniques for wearable application
Authors: Prithvi Raj, Ravi Prakash Dwivedi
Date: 2023-04-16 00:00:00

The biocompatible, noninvasive, lightweight, compact size and low manufacturing cost of the wearable antenna has attracted the researchers to work more in this field to bring the use of wearable antennas to the mainstream wearable devices industry. In recent years there has been great growth in the field of wearable electronics due to their unmatched functionalities and use cases, with the increased use of wearables there comes the concern about the safety in the usage of the antenna which directly points towards the SAR value of the particular antenna in use. This paper examines a wide range of antenna based on their SAR value and the techniques such as EBG, AMC, use of metamaterials, used to reduce the SAR for safe usage
Reconfigurable Intelligent Surface-Enabled Gridless DoA Estimation System for NLoS Scenarios
Authors: Jiawen Yuan, Shaodan Ma, Gong Zhang, Henry Leung
Date: 2023-04-16 00:00:00
Tasks: Computational Efficiency

The conventional direction-of-arrival (DoA) estimation approaches only be effective when the line-of-sight (LoS) link exists, while in the case of the non-line-of-sight (NLoS) situation, the spatial angle can not be captured and thus the DoA estimation performance would be significantly degraded. To address this challenge, a novel reconfigurable intelligent surface (RIS)- enabled gridless DoA estimation approach is proposed, where the RIS can establish the virtual LoS link between the base station (BS) and the targets. For extracting the statistics of the signal, the RIS-enabled signal model in the covariance domain is proposed. Then we estimate the noise variance by constraining the Frobenius norm of the measurement error matrix to obtain the RIS-enabled covariance matrix free of noise nuisance. Additionally, we reconstruct the Hermitian Toeplitz matrix by addressing the atom norm minimization (ANM) problem. To ease the calculation burden, an efficient iterative approach finally is designed to solve the ANM problem via the alternating direction method of multipliers (ADMM). Numerical experiments validate the robustness of the proposed method against the benchmark in terms of computational efficiency and multi-source DoA estimation precision.
JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical
Authors: Jacopo Pegoraro, Jesus O. Lacruz, Tommy Azzino, Marco Mezzavilla, Michele Rossi, Joerg Widmer, Sundeep Rangan
Date: 2023-04-16 00:00:00

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development of joint communication and sensing technology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introduces time-varying timing and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.
Computationally-Efficient Synchrophasor Estimation: Delayed in-quadrature Interpolated DFT
Authors: César García Veloso, Mario Paolone, José María Maza Ortega
Date: 2023-04-15 00:00:00

The paper proposes a synchropahsor estimation (SE) algorithm that leverages the use of a delayed in-quadrature complex signal to mitigate the self-interference of the fundamental tone. The estimator, which uses a three-point IpDFT combined with a three-cycle Hanning window, incorporates a new detection mechanism to iteratively estimate and remove the effects caused by interfering tones within the out-of-band interference (OOBI) range. The main feature of the method is its ability to detect interfering tones with an amplitude lower than that adopted by the IEC/IEEE Std. 60255-118, this detection being notably challenging. Furthermore, it simultaneously satisfies all the accuracy requirements for the P and M phasor measurement unit (PMU) performance classes, while offering a reduction in the total computational cost compared to other state-of-the-art techniques. Despite an increase in total memory requirements, a preliminary analysis reveals its suitability for implementation on embedded devices.
Distributed detection of ARMA signals
Authors: João Domingos, João Xavier
Date: 2023-04-14 00:00:00

This paper considers a distributed detection setup where agents in a network want to detect a time-varying signal embedded in temporally correlated noise. The signal of interest is the impulse response of an ARMA (auto-regressive moving average) filter, and the noise is the output of yet another ARMA filter which is fed white Gaussian noise. For this extended problem setup, which can prompt novel behaviour, we propose a comprehensive solution. First, we extend the well-known running consensus detector (RCD) to this correlated setup; then, we design an efficient implementation of the RCD by exploiting the underlying ARMA structures; and, finally, we derive the theoretical asymptotic performance of the RCD in this ARMA setup. It turns out that the error probability at each agent exhibits one of two regimes: either (a) the error probability decays exponentially fast to zero or (b) it converges to a strictly positive error floor. While regime (a) spans staple results in large deviation theory, regime (b) is new in distributed detection and is elicited by the ARMA setup. We fully characterize these two scenarios: we give necessary and sufficient conditions, phrased in terms of the zero and poles of the underlying ARMA models, for the emergence of each regime, and provide closed-form expressions for both the decay rates of regime (a) and the positive error floors of regime (b). Our analysis also shows that the ARMA setup leads to two novel features: (1) the threshold level used in RCD can influence the asymptotics of the error probabilities and (2) some agents might be weakly informative, in the sense that their observations do not improve the asymptotic performance of RCD and, as such, can be safely muted to save sensing resources. Numerical simulations illustrate and confirm the theoretical findings.
Robust utility maximization with intractable claims
Authors: Yunhong Li, Zuo Quan Xu, Xun Yu Zhou
Date: 2023-04-14 00:00:00

We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market. The investor knows nothing about the claim other than its probability distribution, hence an ``intractable claim''. In view of the lack of necessary information about the claim, we consider a robust formulation to maximize her utility in the worst scenario. We apply the quantile formulation to solve the problem, expressing the quantile function of the optimal terminal investment income as the solution of certain variational inequalities of ordinary differential equations and obtaining the resulting optimal trading strategy. In the case of an exponential utility, the problem reduces to a (non-robust) rank--dependent utility maximization with probability distortion whose solution is available in the literature. The results can also be used to determine the utility indifference price of the intractable claim.