pwc-archive/papers-with-abstracts huggingface dataset:
Irregularity-Aware Bandlimited Approximation for Graph Signal Interpolation
Authors: Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
Date: 2023-12-14 00:00:00

In most work to date, graph signal sampling and reconstruction algorithms are intrinsically tied to graph properties, assuming bandlimitedness and optimal sampling set choices. However, practical scenarios often defy these assumptions, leading to suboptimal performance. In the context of sampling and reconstruction, graph irregularities lead to varying contributions from sampled nodes for interpolation and differing levels of reliability for interpolated nodes. The existing GFT-based methods in the literature make bandlimited signal approximations without considering graph irregularities and the relative significance of nodes, resulting in suboptimal reconstruction performance under various mismatch conditions. In this paper, we leverage the GFT equipped with a specific inner product to address graph irregularities and account for the relative importance of nodes during the bandlimited signal approximation and interpolation process. Empirical evidence demonstrates that the proposed method outperforms other GFT-based approaches for bandlimited signal interpolation in challenging scenarios, such as sampling sets selected independently of the underlying graph, low sampling rates, and high noise levels.
Enhanced EEG-Based Mental State Classification : A novel approach to eliminate data leakage and improve training optimization for Machine Learning
Authors: Maxime Girard, Rémi Nahon, Enzo Tartaglione, Van-Tam Nguyen
Date: 2023-12-14 00:00:00
Tasks: EEG

In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML). Our method proposes an optimized training method by introducing a validation set and a refined standardization process to rectify data leakage shortcomings observed in preceding studies. Furthermore, we establish novel benchmark figures for various models, including random forest and deep neural networks.
Multi-cell mMIMO IRS Systems With Impairments and Aging: Phase Optimization and Receiver Design
Authors: Rakesh Munagala, Dheeraj Naidu Amudala, Rohit Budhiraja
Date: 2023-12-14 00:00:00

We consider the uplink of a hardware-impaired intelligent reflective surfaces (IRS) aided multi-cell massive multiple-input multiple-output (mMIMO) system with mobile user equipments, whose channel age with time. For this system, we analyze the distortion due to hardware impairments, and the effect of channel aging, and propose a novel distortion-and-aging-aware MMSE (DAA-MMSE) receiver that not only provides a higher spectral efficiency (SE) than conventional maximal ratio and distortion-unaware MMSE (DU-MMSE) receivers, but also reduces the pilot overhead. By considering non-ideal hardware and spatially-correlated Rician fading channels with phase shifts, we also derive the SE lower bound for this system. We show that the SE gain of the DAA-MMSE receiver over DU-MMSE receiver increases with hardware impairments, and channel aging. Along with DAA-MMSE receiver, the IRS is also shown to reduce the pilot overhead in a mMIMO system with channel aging.
Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas
Authors: Natalie Lang, Yaela Gabay, Nir Shlezinger, Tirza Routtenberg, Yasaman Ghasempour, George C. Alexandropoulos, Yonina C. Eldar
Date: 2023-12-14 00:00:00

Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications. An alternative antenna technology, which can achieve low cost and power efficient THz signaling, is based on leaky wave antennas (LWAs). In this paper, we explore the usage of the LWAs for wideband downlink multi-user THz communications. We propose a model for LWA-aided communication systems that faithfully captures the antenna operations. We that LWAs yield frequency-dependent beams, where the equivalent wideband channel induces dependence between angle, frequency, and spectral lobe width. We identify the LWAs inherent frequency-selective beamsteering capabilities as motivating multi-band THz communications that deviate from conventional orthogonal frequency-division, and employ non-identical subbands. Then, we propose an alternating optimization algorithm for jointly optimizing the LWA configuration along with the spectral division and power allocation to maximize the achievable sum-rate. Our numerical results show that a single LWA can generate diverse beampatterns, exhibiting performance comparable to costly fully digital MIMO. Interestingly, we demonstrate that allowing transmission with non-identical subbands leverages the characteristics of LWA-based channels compared to uniform division, yielding improved beamsteering that translate to higher rates.
Simulation-based Evaluation of Indoor Positioning Systems in Connected Aircraft Cabins
Authors: Paul Schwarzbach, Jonas Ninnemann, Hagen Ußler, Oliver Michler, Michael Schultz
Date: 2023-12-14 00:00:00

The manuscript discusses the increasing use of location-aware radio communication systems to support operational processes for the demanding aircraft cabin environment. In this context, the challenges for evaluation and integration of radio-based localization systems in the connected cabin are specifically addressed by proposing a hybrid deterministic and stochastic simulation approach, including both model-based ray-tracing and empirical residual simulation. The simulation approach is detailed in the manuscript and a methodology for applying and evaluating localization methods based on obtained geometric relations is conducted. This can also be used as a data generation and validation tool for data-driven localization methods, which further increase the localization accuracy and robustness. The derived location information can in return be used in order to perform operational prediction and optimization for efficient and sustainable passenger handling. A dataset derived from the introduced simulation platform is publicly available.
Multi-IRS Aided Mobile Edge Computing for High Reliability and Low Latency Services
Authors: Elie El Haber, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine, Kim Khoa Nguyen
Date: 2023-12-14 00:00:00
Tasks: Edge-computing

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective surfaces (IRS) have recently emerged as a technology capable of enhancing the quality of the signals in the radio access network, where passive reflecting elements can be tuned to improve the uplink or downlink signals. Harnessing the IRS's potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices' energy consumption considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a suboptimal solution based on alternating optimization where we divide the problem into sub-problems which are then solved separately. Specifically, the offloading decision is solved through a matching game algorithm, and then the IRS phase shifts and resource allocation optimizations are solved in an alternating fashion using the Difference of Convex approach. The obtained results demonstrate the gains both in energy and network resources and highlight the IRS's influence on the design of the MEC parameters.
Coexistence of Satellite-borne Passive Radiometry and Terrestrial NextG Wireless Networks in the Restricted L-Band
Authors: Mohammad Koosha, Nicholas Mastronarde
Date: 2023-12-13 00:00:00

The rapid growth of active wireless communications technologies has fostered research on spectrum coexistence worldwide. One idea that is gaining attention is sharing frequency bands solely devoted to passive applications, such as passive remote sensing. One such option is the 27 MHz L-band spectrum from 1.400 to 1.427 GHz. Active wireless transmissions are prohibited in this passive band due to radio regulations aimed at preventing Radio Frequency Interference (RFI) on highly sensitive passive radiometry instruments. The Soil Moisture Active Passive (SMAP) satellite, launched by the National Aeronautics and Space Administration (NASA), is a recent space-based remote sensing mission that passively scans the Earth's electromagnetic emissions in this 27 MHz band to assess soil moisture on a global scale periodically. In this paper, leveraging stochastic geometry, we assess the feasibility of using this passive band for the downlink (base station to user equipment) of a large-scale terrestrial NextG wireless network exposed to SMAP, while ensuring that the error induced on SMAP's measurements due to RFI is below a given threshold. Although the methodology established here is based on SMAP's specifications, it is adaptable to various passive sensing satellites regardless of their orbits or operating frequencies.
Score-based diffusion priors for multi-target detection
Authors: Alon Zabatani, Shay Kreymer, Tamir Bendory
Date: 2023-12-13 00:00:00

Multi-target detection (MTD) is the problem of estimating an image from a large, noisy measurement that contains randomly translated and rotated copies of the image. Motivated by the single-particle cryo-electron microscopy technology, we design data-driven diffusion priors for the MTD problem, derived from score-based stochastic differential equations models. We then integrate the prior into the approximate expectation-maximization algorithm. In particular, our method alternates between an expectation step that approximates the expected log-likelihood and a maximization step that balances the approximated log-likelihood with the learned log-prior. We show on two datasets that adding the data-driven prior substantially reduces the estimation error, in particular in high noise regimes.
Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms
Authors: Saad Ahmed Jamal
Date: 2023-12-05 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Earth Observation, parameter estimation

Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of Earth Observation. It is a remote sensing technology that utilizes laser beams to measure distances and create detailed three-dimensional representations of objects and environments. The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Overall shape of signal provides important information about properties of water body. However, the shape of FWL is unexplored as most LiDAR software work on point cloud by utilizing the maximum value within the waveform. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. However, these methods suffer from limitations in accuracy. Depth estimation through inverse modeling provides only approximate values and does not account for variations in surface properties, while the regression approach for the attenuation coefficient is only able to generalize a value through several data points which lacks precision and may lead to significant errors in estimation. Additionally, there is currently no established modeling method available for predicting bottom reflectance. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis. By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance. Performance of model was validated by testing it on real LiDAR data. In future, more data availability would enable more accuracy and reliability of such models.
Performance-Based Optimization of 2D Reinforced Concrete Moment Frames through Pushover Analysis and ABC Optimization Algorithm
Authors: Saba Faghirnejad
Date: 2023-12-04 00:00:00

Conducting nonlinear pushover analysis typically demands intricate and resource-intensive computational attempts, and involves a process that is highly iterative and necessary for satisfying design-defined and also requirements of codes in performance-based design. A computer-based technique is presented for reinforced concrete (RC) buildings in this study, incorporating optimization numerical approaches, techniques of optimality criteria and pushover analysis to seismic design automatically the pushover drift performance. The optimal design based on the performance of concrete beams, columns and shear walls in concrete moment frames is presented using the artificial bee colony optimization algorithm. The design is applied to three frames such as a 4-story, an 8-story and a 12-story. These structures are designed to minimize the overall weight while satisfying the levels of performance include Life Safety (L-S), Collapse Prevention (C-P), and Immediate Occupancy (I-O). To achieve this goal, three main steps are performed. In the first step, optimization codes are implemented in MATLAB software, and the OpenSees software is used for nonlinear static analysis of the structure. By solving the optimization problem, several top designs are obtained for each frame and shear wall. Pushover analysis is performed considering the constraints of relative displacement and plastic hinge rotation based on the nonlinear provisions of FEMA356 code to achieve each levels of performance. Following this, convergence, pushover, and drift history curves are plotted for each frame, and selecting the best design for each frame ultimately occurs. The results demonstrate the algorithm's performance is desirable for the structure to achieve selecting the best design and lower weight.
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
Authors: Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, Brandon Westover
Date: 2023-12-03 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG)

In electroencephalogram (EEG) recordings, the presence of interictal epileptiform discharges (IEDs) serves as a critical biomarker for seizures or seizure-like events.Detecting IEDs can be difficult; even highly trained experts disagree on the same sample. As a result, specialists have turned to machine-learning models for assistance. However, many existing models are black boxes and do not provide any human-interpretable reasoning for their decisions. In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses. We introduce ProtoEEGNet, a model that achieves state-of-the-art accuracy for IED detection while additionally providing an interpretable justification for its classifications. Specifically, it can reason that one EEG looks similar to another ''prototypical'' EEG that is known to contain an IED. ProtoEEGNet can therefore help medical professionals effectively detect IEDs while maintaining a transparent decision-making process.
Livestock feeding behaviour: A review on automated systems for ruminant monitoring
Authors: José Chelotti, Luciano Martinez-Rau, Mariano Ferrero, Leandro Vignolo, Julio Galli, Alejandra Planisich, H. Leonardo Rufiner, Leonardo Giovanini
Date: 2023-12-03 00:00:00

Livestock feeding behaviour is an influential research area for those involved in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Despite the developments accomplished in the last decade, there is still much to do and learn about the methods for measuring and analysing livestock feeding behaviour. Automated monitoring systems mainly use motion, acoustic, and image sensors to collect animal behavioural data. The performance evaluation of existing methods is a complex task and direct comparisons between studies are difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. To the best of our knowledge, this work represents the first tutorial-style review on the analysis of the feeding behaviour of ruminants, emphasising the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies (i.e. based on movement, sound, images/videos, and pressure) and the main techniques to measure and analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potentiality of automated monitoring systems to provide valuable information that improves our understanding of livestock feeding behaviour. The relevance of these systems is increasingly important due to their impact on production systems and research. Finally, the paper closes by discussing future challenges and opportunities in livestock feeding behaviour monitoring.
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution
Authors: Dongdong Li, Zhongliang Zeng, Zhe Wang, Hai Yang
Date: 2023-12-03 00:00:00
Tasks: channel selection, EEG, Emotion Recognition, Person Identification, Spatial Interpolation, Super-Resolution

Towards practical applications of Electroencephalography (EEG), lightweight acquisition devices garner significant attention. However, EEG channel selection methods are commonly data-sensitive and cannot establish a unified sound paradigm for EEG acquisition devices. Through reverse conceptualisation, we formulated EEG applications in an EEG super-resolution (SR) manner, but suffered from high computation costs, extra interpolation bias, and few insights into spatiotemporal dependency modelling. To this end, we propose ESTformer, an EEG SR framework that utilises spatiotemporal dependencies based on the transformer. ESTformer applies positional encoding methods and a multihead self-attention mechanism to the space and time dimensions, which can learn spatial structural correlations and temporal functional variations. ESTformer, with the fixed mask strategy, adopts a mask token to upsample low-resolution (LR) EEG data in the case of disturbance from mathematical interpolation methods. On this basis, we designed various transformer blocks to construct a spatial interpolation module (SIM) and a temporal reconstruction module (TRM). Finally, ESTformer cascades the SIM and TRM to capture and model the spatiotemporal dependencies for EEG SR with fidelity. Extensive experimental results on two EEG datasets show the effectiveness of ESTformer against previous state-of-the-art methods, demonstrating the versatility of the Transformer for EEG SR tasks. The superiority of the SR data was verified in an EEG-based person identification and emotion recognition task, achieving a 2% to 38% improvement compared with the LR data at different sampling scales.
Semantic Synchronization for Enhanced Reliability in Communication Systems
Authors: Xiaoyi Liu, Haotai Liang, Chen Dong, Xiaodong Xu
Date: 2023-12-02 00:00:00
Tasks: Decoder, Semantic Communication, Template Matching

As a new communication paradigm, semantic communication has received widespread attention in communication fields. However, since the decoding of semantic signals relies on contextual knowledge, misalignment between the starting position of the semantic signal and the AI-based semantic decoder would prevent source signal recovery and reconstruction. To achieve more precise semantic communication, this study proposes an image-based semantic synchronization method leveraging intrinsic semantic features of image content. Specifically, a shared synchronized image (SyncImg) is encoded into a synchronization vector header at the transmitter and sent to the receiver. The receiver adopts a sliding window semantic decoder combined with classification and template matching methods to locate the synchronization point. Experimental results demonstrate that compared with traditional methods, the proposed method achieves a lower miss detected ratio (MDR) and root-mean-square error (RMSE) under low signal-to-noise ratios, realizing accurate synchronization of semantic signals across different devices.
Deep Learning Models for Arrhythmia Classification Using Stacked Time-frequency Scalogram Images from ECG Signals
Authors: Parshuram N. Aarotale, Ajita Rattani
Date: 2023-12-01 00:00:00
Tasks: Classification

Electrocardiograms (ECGs), a medical monitoring technology recording cardiac activity, are widely used for diagnosing cardiac arrhythmia. The diagnosis is based on the analysis of the deformation of the signal shapes due to irregular heart rates associated with heart diseases. Due to the infeasibility of manual examination of large volumes of ECG data, this paper aims to propose an automated AI based system for ECG-based arrhythmia classification. To this front, a deep learning based solution has been proposed for ECG-based arrhythmia classification. Twelve lead electrocardiograms (ECG) of length 10 sec from 45, 152 individuals from Shaoxing People's Hospital (SPH) dataset from PhysioNet with four different types of arrhythmias were used. The sampling frequency utilized was 500 Hz. Median filtering was used to preprocess the ECG signals. For every 1 sec of ECG signal, the time-frequency (TF) scalogram was estimated and stacked row wise to obtain a single image from 12 channels, resulting in 10 stacked TF scalograms for each ECG signal. These stacked TF scalograms are fed to the pretrained convolutional neural network (CNN), 1D CNN, and 1D CNN-LSTM (Long short-term memory) models, for arrhythmia classification. The fine-tuned CNN models obtained the best test accuracy of about 98% followed by 95% test accuracy by basic CNN-LSTM in arrhythmia classification.