pwc-archive/papers-with-abstracts huggingface dataset:
Connecting Image Inpainting with Denoising in the Homogeneous Diffusion Setting
Authors: Daniel Gaa, Vassillen Chizhov, Pascal Peter, Joachim Weickert, Robin Dirk Adam
Date: 2023-09-23 00:00:00
Tasks: Denoising, Image Compression, Image Denoising, Image Inpainting

While local methods for image denoising and inpainting may use similar concepts, their connections have hardly been investigated so far. The goal of this work is to establish links between the two by focusing on the most foundational scenario on both sides: the homogeneous diffusion setting. To this end, we study a denoising by inpainting (DbI) framework: It averages multiple inpainting results from different noisy subsets. We derive equivalence results between DbI on shifted regular grids and homogeneous diffusion filtering in 1D via an explicit relation between the density and the diffusion time. We also provide an empirical extension to the 2-D case. We present experiments that confirm our theory and suggest that it can also be generalized to diffusions with non-homogeneous data or non-homogeneous diffusivities. More generally, our work demonstrates that the hardly explored idea of data adaptivity deserves more attention: It can be as powerful as some popular models with operator adaptivity.
MBIR Training for a 2.5D DL network in X-ray CT
Authors: Obaidullah Rahman, Madhuri Nagare, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, Brian Nett, Jie Tang
Date: 2023-09-23 00:00:00

In computed tomographic imaging, model based iterative reconstruction methods have generally shown better image quality than the more traditional, faster filtered backprojection technique. The cost we have to pay is that MBIR is computationally expensive. In this work we train a 2.5D deep learning (DL) network to mimic MBIR quality image. The network is realized by a modified Unet, and trained using clinical FBP and MBIR image pairs. We achieve the quality of MBIR images faster and with a much smaller computation cost. Visually and in terms of noise power spectrum (NPS), DL-MBIR images have texture similar to that of MBIR, with reduced noise power. Image profile plots, NPS plots, standard deviation, etc. suggest that the DL-MBIR images result from a successful emulation of an MBIR operator.
Direct Iterative Reconstruction of Multiple Basis Material Images in Photon-counting Spectral CT
Authors: Obaidullah Rahman, Ken Sauer, Connor Evans, Ryan Roeder
Date: 2023-09-23 00:00:00
Tasks: Unity

In this work, we perform direct material reconstruction from spectral CT data using a model based iterative reconstruction (MBIR) approach. Material concentrations are measured in volume fractions, whose total is constrained by a maximum of unity. A phantom containing a combination of 4 basis materials (water, iodine, gadolinium, calcium) was scanned using a photon-counting detector. Iodine and gadolinium were chosen because of their common use as contrast agents in CT imaging. Scan data was binned into 5 energy (keV) levels. Each energy bin in a calibration scan was reconstructed, allowing the linear attenuation coefficient of each material for every energy to be estimated by a least-squares fit to ground truth in the image domain. The resulting $5\times 4$ matrix, for $5$ energies and $4$ materials, is incorporated into the forward model in direct reconstruction of the $4$ basis material images with spatial and/or inter-material regularization. In reconstruction from a subsequent low-concentration scan, volume fractions within regions of interest (ROIs) are found to be close to the ground truth. This work is meant to lay the foundation for further work with phantoms including spatially coincident mixtures of contrast materials and/or contrast agents in widely varying concentrations, molecular imaging from animal scans, and eventually clinical applications.
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Authors: Peiwen Jiang, Chao-Kai Wen, Xinping Yi, Xiao Li, Shi Jin, Jun Zhang
Date: 2023-09-23 00:00:00

Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics. While previous research has explored the use of FMs in semantic communications to improve semantic extraction and reconstruction, the impact of these models on different system levels, considering computation and memory complexity, requires further analysis. This study focuses on integrating FMs at the effectiveness, semantic, and physical levels, using universal knowledge to profoundly transform system design. Additionally, it examines the use of compact models to balance performance and complexity, comparing three separate approaches that employ FMs. Ultimately, the study highlights unresolved issues in the field that need addressing.
Heart rate measurement using the built-in triaxial accelerometer from a commercial digital writing device
Authors: Julie Payette, Fabrice Vaussenat, Sylvain G. Cloutier
Date: 2023-09-25 00:00:00

Wearable devices are on the rise. Smart watches and phones, fitness trackers or smart textiles now provide unprecedented access to our own personal data. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor the heart's and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heartrate when they are attached to the chest. They can also help filter out some noise in ECG signal from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen), with a standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is done with eight volunteers, writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685x10$^{-3}$ comparing the DigiPen's computed ${\Delta}$t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates from the pen accurately correlate with the reference ECG signals.
Joint RIS Phase Profile Design and Power Allocation for Parameter Estimation in Presence of Eavesdropping
Authors: Erfan Mehdipour Abadi, Ayda Nodel Hokmabadi, Sinan Gezici
Date: 2023-09-25 00:00:00
Tasks: parameter estimation

We consider secure transmission of a deterministic complex-valued parameter vector from a transmitter to an intended receiver in the presence of an eavesdropper in a reconfigurable intelligent surface (RIS)-integrated environment. We aim to jointly optimize the RIS phase profile and the power allocation matrix at the transmitter to enhance the estimation accuracy at the intended receiver while limiting that at the eavesdropper. We utilize the trace of the Fisher information matrix (FIM), equivalently, the average Fisher information, as the estimation accuracy metric, and obtain its closed form expression for the intended receiver and the eavesdropper. Accordingly, the joint RIS phase profile and power allocation problem is formulated, and it is solved via alternating optimization. When the power allocation matrix is fixed during alternating optimization, the optimal RIS phase profile design problem is formulated as a non-convex problem and it is solved via semidefinite relaxation and rank reduction. When the RIS phase profile is fixed, a linear programming formulation is obtained for optimal power allocation. Via simulations, the effects of RIS phase design and power allocation are illustrated individually and jointly. Moreover, extensions are provided by considering the presence of line of sight paths in the environment and the availability of RIS elements with adjustable magnitudes.
Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance
Authors: Rashed Hasan Ratul, Muhammad Iqbal, Tabinda Ashraf, Jen-Yi Pan, Yi-Han Wang, Shao-Yu Lien
Date: 2023-09-25 00:00:00

A potential candidate technology for the development of future 6G networks has been recognized as Reconfigurable Intelligent Surface (RIS). However, due to the variation in radio link quality, traditional passive RISs only accomplish a minimal signal gain in situations with strong direct links between user equipment (UE) and base station (BS). In order to get over this fundamental restriction of smaller gain, the idea of active RISs might be a suitable solution. In contrast to current passive RIS, which simply reflects and directs signals without any additional amplification, active RISs have the ability to enhance reflected signals by the incorporation of amplifiers inside its elements. However, with additional amplifiers, apart from the relatively complex attributes of RIS-assisted arrangements, the additional energy consumption of such technologies is often disregarded. So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement. The objective of this work is to provide a primary idea of a three-layer hybrid RIS-assisted configuration that is responsive to both active and passive RIS, as well as an additional dormant or inactive state. The single RIS structure should be capable of adjusting its overall configuration in response to fluctuations in transmit power and radio link quality. Furthermore, our fabricated passive RIS-assisted structure verifies a portion of the proposed idea, with simulations highlighting its advantages over standalone passive or active RIS-assisted technologies.
Beam Squint Assisted User Localization in Near-Field Integrated Sensing and Communications Systems
Authors: Hongliang Luo, Feifei Gao, Wanmai Yuan, Shun Zhang
Date: 2023-09-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been regarded as a key technology for 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) array with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of true-time-delay lines (TTDs), the range and trajectory of the beam squint in near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the beam sweeping overhead as compared to the conventional time domain beam search based approach. Furthermore, we utilize the phase difference of the maximum power subcarriers received by the user at different frequencies in several times beam sweeping to obtain a more accurate distance estimation result, ultimately realizing high accuracy and low beam sweeping overhead user localization. Simulation results demonstrate the effectiveness of the proposed schemes.
Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection
Authors: Jin Lu, Guojie Peng, Weichuan Zhang, Changming Sun
Date: 2023-09-25 00:00:00
Tasks: State Estimation

Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency.
Online Resource Allocation for Semantic-Aware Edge Computing Systems
Authors: Yihan Cang, Ming Chen, Zhaohui Yang, Yuntao Hu, Yinlu Wang, Chongwen Huang, Zhaoyang Zhang
Date: 2023-09-25 00:00:00
Tasks: Edge-computing

In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semanticaware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying longterm delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.
NoncovANM: Gridless DOA Estimation for LPDF System
Authors: Yangying Zhao, Peng Chen, Zhenxin Cao, Xianbin Wang
Date: 2023-09-25 00:00:00

Direction of arrival (DOA) estimation is an important research in the area of array signal processing, and has been studied for decades. High resolution DOA estimation requires large array aperture, which leads to the increase of hardware cost. Besides, high accuracy DOA estimation methods usually have high computational complexity. In this paper, the problem of decreasing the hardware cost and algorithm complexity is addressed. First, considering the ability of flexible controlling the electromagnetic waves and low-cost, an intelligent reconfigurable surface (IRS)-aided low-cost passive direction finding (LPDF) system is developed, where only one fully functional receiving channel is adopted. Then, the sparsity of targets direction in the spatial domain is exploited by formulating an atomic norm minimization (ANM) problem to estimate the DOA. Traditionally, solving ANM problem is complex and cannot be realized efficiently. Hence, a novel nonconvex-based ANM (NC-ANM) method is proposed by gradient threshold iteration, where a perturbation is introduced to avoid falling into saddle points. The theoretical analysis for the convergence of the NC-ANM method is also given. Moreover, the corresponding Cram\'er-Rao lower bound (CRLB) in the LPDF system is derived, and taken as the referred bound of the DOA estimation. Simulation results show that the proposed method outperforms the compared methods in the DOA estimation with lower computational complexity in the LPDF system.
DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
Authors: Zhimin Chen, Peng Chen, Le Zheng, Yudong Zhang
Date: 2023-09-25 00:00:00

Reconfigurable intelligent surface (RIS) or intelligent reflecting surface (IRS) has been an attractive technology for future wireless communication and sensing systems. However, in the practical RIS, the mutual coupling effect among RIS elements, the reflection phase shift, and amplitude errors will degrade the RIS performance significantly. This paper investigates the two-dimensional direction-of-arrival (DOA) estimation problem in the scenario using a practical RIS. After formulating the system model with the mutual coupling effect and the reflection phase/amplitude errors of the RIS, a novel DNNDANM method is proposed for the DOA estimation by combining the deep neural network (DNN) and the decoupling atomic norm minimization (DANM). The DNN step reconstructs the received signal from the one with RIS impairments, and the DANM step exploits the signal sparsity in the two-dimensional spatial domain. Additionally, a semi-definite programming (SDP) method with low computational complexity is proposed to solve the atomic minimization problem. Finally, both simulation and prototype are carried out to show estimation performance, and the proposed method outperforms the existing methods in the two-dimensional DOA estimation with low complexity in the scenario with practical RIS.
Non-Uniform Sampling Reconstruction for Symmetrical NMR Spectroscopy by Exploiting Inherent Symmetry
Authors: Enping Lin, Ze Fang, Yuqing Huang, Yu Yang, Zhong Chen
Date: 2023-09-24 00:00:00
Tasks: compressed sensing

Symmetrical NMR spectroscopy constitutes a vital branch of multidimensional NMR spectroscopy, providing a powerful tool for the structural elucidation of biological macromolecules. Non-Uniform Sampling (NUS) serves as an effective strategy for averting the prohibitive acquisition time of multidimensional NMR spectroscopy by only sampling a few points according to NUS sampling schedules and reconstructing missing points via algorithms. However, current sampling schedules are unable to maintain the accurate recovery of cross peaks that are weak but important. In this work, we propose a novel sampling schedule termed as SCPG (Symmetrical Copy Poisson Gap) and employ CS (Compressed Sensing) methods for reconstruction. We theoretically prove that the symmetrical constraint, apart from sparsity, is implicitly implemented when SCPG is combined with CS methods. The simulated and experimental data substantiate the advantage of SCPG over state-of-the-art 2D Woven PG in the NUS reconstruction of symmetrical NMR spectroscopy.
Detection of Ghost Targets for Automotive Radar in the Presence of Multipath
Authors: Le Zheng, Jiamin Long, Marco Lops, Fan Liu, Xueyao Hu
Date: 2023-09-24 00:00:00
Tasks: compressed sensing, Philosophy

Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows forming a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, H0 say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, H1 say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, wherein the unknown parameters are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. An extensive performance analysis is finally offered in order to validate the proposed solution.
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
Authors: An Chen, Wenbo Xu, Liyang Lu, Yue Wang
Date: 2023-09-24 00:00:00
Tasks: Compressive Sensing

Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.