pwc-archive/papers-with-abstracts huggingface dataset:
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution
Authors: Kristian Fischer, Christian Herglotz, André Kaup
Date: 2023-08-12 00:00:00
Tasks: 4k, Image Super-Resolution, Super-Resolution

Coding 4K data has become of vital interest in recent years, since the amount of 4K data is significantly increasing. We propose a coding chain with spatial down- and upscaling that combines the next-generation VVC codec with machine learning based single image super-resolution algorithms for 4K. The investigated coding chain, which spatially downscales the 4K data before coding, shows superior quality than the conventional VVC reference software for low bitrate scenarios. Throughout several tests, we find that up to 12 % and 18 % Bjontegaard delta rate gains can be achieved on average when coding 4K sequences with VVC and QP values above 34 and 42, respectively. Additionally, the investigated scenario with up- and downscaling helps to reduce the loss of details and compression artifacts, as it is shown in a visual example.
The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow
Authors: Xiaoxiao Hu, Kit Ian Kou, Cuiming Zou, Dong Cheng
Date: 2023-08-12 00:00:00
Tasks: Edge Detection, Image Quality Assessment, Optical Flow Estimation

This paper introduces the idea of the color Clifford Hardy signal, which can be used to process color images. As a complex analytic function's high-dimensional analogue, the color Clifford Hardy signal inherits many desirable qualities of analyticity. A crucial tool for getting the color and structural data is the local feature representation of a color image in the color Clifford Hardy signal. By looking at the extended Cauchy-Riemann equations in the high-dimensional space, it is possible to see the connection between the different parts of the color Clifford Hardy signal. Based on the distinctive and important local amplitude and local phase generated by the color Clifford Hardy signal, we propose five methods to identify the edges of color images with relation to a certain color. To prove the superiority of the offered methodologies, numerous comparative studies employing image quality assessment criteria are used. Specifically by using the multi-scale structure of the color Clifford Hardy signal, the proposed approaches are resistant to a variety of noises. In addition, a color optical flow detection method with anti-noise ability is provided as an example of application.
Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform
Authors: Kaili Jiang, Kailun Tian, HanCong Feng, Junyu Yuan, Bin Tang
Date: 2023-08-14 00:00:00

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods.
Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver
Authors: Kaili Jiang, Kailun Tian, HanCong Feng, Yuxin Zhao, Dechang Wang, Sen Cao, Jian Gao, Xuying Zhang, Yanfei Li, Junyu Yuan, Ying Xiong, Bin Tang
Date: 2023-08-14 00:00:00

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions.
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
Authors: Kaili Jiang, Dechang Wang, Kailun Tian, HanCong Feng, Yuxin Zhao, Sen Cao, Jian Gao, Xuying Zhang, Yanfei Li, Junyu Yuan, Ying Xiong, Bin Tang
Date: 2023-08-14 00:00:00

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications.
Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks
Authors: Anton Krause, Mohd Danish Khursheed, Philipp Schulz, Friedrich Burmeister, Gerhard Fettweis
Date: 2023-08-14 00:00:00
Tasks: Anomaly Detection

The increasing relevance of resilience in wireless connectivity for Industry 4.0 stems from the growing complexity and interconnectivity of industrial systems, where a single point of failure can disrupt the entire network, leading to significant downtime and productivity losses. It is thus essential to constantly monitor the network and identify any anomaly such as a jammer. Hereby, technologies envisioned to be integrated in 6G, in particular joint communications and sensing (JCAS) and accurate indoor positioning of transmitters, open up the possibility to build a digital twin (DT) of the radio environment. This paper proposes a new approach for anomaly detection in wireless networks enabled by such a DT which allows to integrate contextual information on the network in the anomaly detection procedure. The basic approach is thereby to compare expected received signal strengths (RSSs) from the DT with measurements done by distributed sensing units (SUs). Employing simulations, different algorithms are compared regarding their ability to infer from the comparison on the presence or absence of an anomaly, particular a jammer. Overall, the feasibility of anomaly detection using the proposed approach is demonstrated which integrates in the ongoing research on employing DTs for comprehensive monitoring of wireless networks.
3D Localization and Tracking Methods for Multi-Platform Radar Networks
Authors: Angela Marino, Giovanni Soldi, Domenico Gaglione, Augusto Aubry, Paolo Braca, Antonio De Maio, Peter Willett
Date: 2023-08-14 00:00:00
Tasks: Single Particle Analysis

Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario.
Kernel Based Reconstruction for Generalized Graph Signal Processing
Authors: Xingchao Jian, Wee Peng Tay, Yonina C. Eldar
Date: 2023-08-14 00:00:00

In generalized graph signal processing (GGSP), the signal associated with each vertex in a graph is an element from a Hilbert space. In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem. By devising an appropriate kernel, we show that this problem has a solution that can be evaluated in a distributed way. We interpret the problem and solution using both deterministic and Bayesian perspectives and link them to existing graph signal processing and GGSP frameworks. We then provide an online implementation via random Fourier features. Under the Bayesian framework, we investigate the statistical performance under the asymptotic sampling scheme. Finally, we validate our theory and methods on real-world datasets.
Novel Nonlinear Neural-Network Layers for High Performance and Generalization in Modulation-Recognition Applications
Authors: John A. Snoap, Dimitrie C. Popescu, Chad M. Spooner
Date: 2023-08-14 00:00:00
Tasks: Classification

The paper presents a novel type of capsule network (CAP) that uses custom-defined neural network (NN) layers for blind classification of digitally modulated signals using their in-phase/quadrature (I/Q) components. The custom NN layers of the CAP are inspired by cyclostationary signal processing (CSP) techniques and implement feature extraction capabilities that are akin to the calculation of the cyclic cumulant (CC) features employed in conventional CSP-based approaches to blind modulation classification and signal identification. The classification performance and the generalization abilities of the proposed CAP are tested using two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently, and numerical results obtained reveal that the proposed CAP with novel NN feature extraction layers achieves high classification accuracy while also outperforming alternative deep learning (DL)-based approaches for signal classification in terms of both classification accuracy and generalization abilities.
Detection of presence and number of persons by a Wi-Fi signal: a practical RSSI-based approach
Authors: Dario Jukić, Silvije Domazet, Ante Ivanko, David Raca, Siniša Nikolić, Marin Knežević, Filip Jović, Nenad Raca, Hrvoje Buljan
Date: 2023-08-13 00:00:00

We present experimental results and theoretical methods for the precise determination of the presence and the number of persons in an observed area by using Wi-Fi signals. Our setup does not require active cooperation of persons present in the Wi-Fi field, and relies only on the Received Signal Strength Indicator (RSSI), which is read by the detectors. We first show that the standard deviation of the measured RSSI data can be used as a practical tool to establish the presence of a person (or more persons) with high precision, in particular when the signal source is inside the measurement room. For the more difficult problem of counting the number of persons, we have employed machine learning algorithms to analyze data collected on nine different detectors and up to nine people present in our experiment. We have achieved excellent results (prediction accuracy of $98 \%$ and above) for counting already with only few detectors utilized in the analysis. While generalizations to nontrivial indoor geometries (such as odd shapes, more rooms, and greater size) may be of interest in some applications, where additional care with respect to positioning od detectors can be needed (or even placing additional detectors), this approach may be useful due to its conceptual practicality and solid prediction results.
Symbol-level Integrated Sensing and Communication enabled Multiple Base Stations Cooperative Sensing
Authors: Zhiqing Wei, Ruizhong Xu, Zhiyong Feng, Huici Wu, Ning Zhang, Wangjun Jiang, Xiaoyu Yang
Date: 2023-08-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the support of integrated sensing and communication (ISAC) technology, mobile communication system will integrate the function of wireless sensing, thereby facilitating new intelligent applications such as smart city and intelligent transportation. Due to the limited sensing accuracy and sensing range of single base station (BS), multi-BS cooperative sensing can be applied to realize high-accurate, long-range and continuous sensing, exploiting the specific advantages of large-scale networked mobile communication system. This paper proposes a cooperative sensing method suitable to mobile communication systems, which applies symbol-level sensing information fusion to estimate the location and velocity of target. With the demodulation symbols obtained from the echo signals of multiple BSs, the phase features contained in the demodulation symbols are used in the fusion procedure, which realizes cooperative sensing with the synchronization level of mobile communication system. Compared with the signal-level fusion in the area of distributed aperture coherence-synthetic radars, the requirement of synchronization is much lower. When signal-to-noise ratio (SNR) is -5 dB, it is evaluated that symbol-level multi-BS cooperative sensing effectively improves the accuracy of distance and velocity estimation of target. Compared with single-BS sensing, the accuracy of distance and velocity estimation is improved by 40% and 72%, respectively. Compared with data-level multi-BS cooperative sensing based on maximum likelihood (ML) estimation, the accuracy of location and velocity estimation is improved by 12% and 63%, respectively. This work may provide a guideline for the design of multi-BS cooperative sensing system to exploit the widely deployed networked mobile communication system.
On-line Parameter Estimation of the Polarization Curve of a Fuel Cell with Guaranteed Convergence Properties: Theoretical and Experimental Results
Authors: Carlo Beltran, Alexey Bobtsov, Romeo Ortega, Diego Langarica-Cordoba, Rafael Cisneros, Luis H. Diaz-Saldierna
Date: 2023-08-12 00:00:00
Tasks: Fault Detection, parameter estimation

In this paper, we address the problem of online parameter estimation of a Proton Exchange Membrane Fuel Cell (PEMFC) polarization curve, that is the static relation between the voltage and the current of the PEMFC. The task of designing this estimator -- even off-line -- is complicated by the fact that the uncertain parameters enter the curve in a highly nonlinear fashion, namely in the form of nonseparable nonlinearities. We consider several scenarios for the model of the polarization curve, starting from the standard full model and including several popular simplifications to this complicated mathematical function. In all cases, we derive separable regression equations -- either linearly or nonlinearly parameterized -- which are instrumental for the implementation of the parameter estimators. We concentrate our attention on on-line estimation schemes for which, under suitable excitation conditions, global parameter convergence is ensured. Due to these global convergence properties, the estimators are robust to unavoidable additive noise and structural uncertainty. Moreover, their on-line nature endows the schemes with the ability to track (slow) parameter variations, that occur during the operation of the PEMFC. These two features -- unavailable in time-consuming off-line data-fitting procedures -- make the proposed estimators helpful for on-line time-saving characterization of a given PEMFC, and the implementation of fault-detection procedures and model-based adaptive control strategies. Simulation and experimental results that validate the theoretical claims are presented.
3-Lead to 12-Lead ECG Reconstruction: A Novel AI-based Spatio-Temporal Method
Authors: Rahul LR, Albert Shaiju, Soumya Jana
Date: 2023-08-12 00:00:00

Diagnosis of cardiovascular diseases usually relies on the widely used standard 12-Lead (S12) ECG system. However, such a system could be bulky, too resource-intensive, and too specialized for personalized home-based monitoring. In contrast, clinicians are generally not trained on the alternative proposal, i.e., the reduced lead (RL) system. This necessitates mapping RL to S12. In this context, to improve upon traditional linear transformation (LT) techniques, artificial intelligence (AI) approaches like long short-term memory (LSTM) networks capturing non-linear temporal dependencies, have been suggested. However, LSTM does not adequately interpolate spatially (in 3D). To fill this gap, we propose a combined LSTM-UNet model that also handles spatial aspects of the problem, and demonstrate performance improvement. Evaluated on PhysioNet PTBDB database, our LSTM-UNet achieved a mean R^2 value of 94.37%, surpassing LSTM by 0.79% and LT by 2.73%. Similarly, for PhysioNet INCARTDB database, LSTM-UNet achieved a mean R^2 value of 93.91%, outperforming LSTM by 1.78% and LT by 12.17%.
Near-Field Integrated Sensing and Communication: Performance Analysis and Beamforming Design
Authors: Kaiqian Qu, Shuaishuai Guo, Nasir Saeed
Date: 2023-08-12 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

This paper explores the potential of near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of electromagnetic (EM) near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. We formulate the NFBF design problem as minimizing the weighted summation of radar and the communication beamforming errors under a total power constraint and solve this quadratically constrained quadratic programming (QCQP) problem using the least squares (LS) method. Moreover, the Cram\'er-Rao bound (CRB) for target parameter estimation is derived to verify the performance of NFBF. Furthermore, we also perform power minimization using convex optimization while ensuring the required communication and sensing quality-of-service (QoS). The simulation results show the influence of model mismatch on near-field ISAC and the performance gain of transmit beamforming from the additional distance dimension of near-field.
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Authors: Hamed Hojatian, Zoubeir Mlika, Jérémy Nadal, Jean-François Frigon, François Leduc-Primeau
Date: 2023-08-11 00:00:00

Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder~(FDP) and hybrid beamforming~(HBF) transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop an unsupervised deep learning method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, to ensure that the proposed method can be implemented in practice, we investigate the ability of the model to be trained exclusively using imperfect channel state information~(CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.
Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods
Authors: Thomas Kropfreiter, Florian Meyer, David F. Crouse, Stefano Coraluppi, Franz Hlawatsch, Peter Willett
Date: 2023-08-11 00:00:00

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.