pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Detection and Classification of Corona Infection (COVID-19) from X-ray Images Using Convolution Neural Network
Authors: Kinjal A Patel, Tanvi Goswami
Date: 2024-03-09 00:00:00
Tasks: Binary Classification, Diagnostic

The novel coronavirus universally known as the COVID-19 outbreak arises at the end of 2019 in one of the East Asian countries and it is subjected to widespread discussion and debate. There are almost 200 countries affected across the globe by COVID-19 and it has ruined many lives and the global economy. The virus is spreading very rapidly at the pace of around 10 fold in less than a month. Also, in the case of COVID- 19 it is critical to detect the infection as it employs various symptoms which may differ from person to person. Hence, diagnosis in starting stage and treatment are very much important for such type of infectious disease. The chest x-ray is one of the primary techniques among blood tests and Computed Tomography contributes a major role in the early diagnosis of COVID-19. There is a rising need for automated and auxiliary diagnostic tools for early diagnosis, as there are no accurate and truthful automated tool kits on hand. In this research study, we have designed a Convolution Neural Network architecture a deep net for the classification of x-ray images of chest among two classes: COVID-19 or Non-COVID- 19 infection. The anticipated model is expected to provide accurate diagnostic results and produced classification accuracy of 99%, 100%, and 100% with 70%-30%,75%-25% and 80%-20% train-test data split respectively, for the binary classification of the x-ray image to be COVID-19 or Non-COVID-19 infection category. We have designed the CNN with optimized parameters with 3 convolution layers and optimized number of filters in each layer.
HermEIS: A Parallel Multichannel Approach to Rapid Spectral Characterization of Neural MEAs
Authors: Akwasi Akwaboah, Ralph Etienne-Cummings
Date: 2024-03-12 00:00:00

The promise of increasing channel counts in high density ($> 10^4$) neural Microelectrode Arrays (MEAs) for high resolution recording comes with the curse of developing faster characterization strategies for concurrent acquisition of multichannel electrode integrities over a wide frequency spectrum. To circumvent the latency associated with the current multiplexed technique for impedance acquisition, it is common practice to resort to the single frequency impedance measurement (i.e. $Z_{1 \text{kHz}}$). This, however, does not offer sufficient spectral impedance information crucial for determining the capacity of electrodes at withstanding slow and fast-changing stimulus and recordings. In this work, we present \textit{HermEIS}, a novel approach that leverages single cycle in-phase and quadrature signal integrations for reducing the massive data throughput characteristic of such high density acquisition systems. As an initial proof-of-concept, we demonstrate over $6$ decades of impedance bandwidth ($5\times10^{-2} - 5\times10^{4}\text{ Hz}$) in a parallel $4$-channel potentiostatic setup composed of a custom PCB with off-the-shelf electronics working in tandem with an FPGA.
Enhancing Physical Layer Security in Dual-Function Radar-Communication Systems with Hybrid Beamforming Architecture
Authors: Lingyun Xu, Bowen Wang, Huiyong Li, Ziyang Cheng
Date: 2024-03-12 00:00:00

In this letter, we investigate enhancing the physical layer security (PLS) for the dual-function radar-communication (DFRC) system with hybrid beamforming (HBF) architecture, where the base station (BS) achieves downlink communication and radar target detection simultaneously. We consider an eavesdropper intercepting the information transmitted from the BS to the downlink communication users with imperfectly known channel state information. Additionally, the location of the radar target is also imperfectly known by the BS. To enhance PLS in the considered DFRC system, we propose a novel HBF architecture, which introduces a new integrated sensing and security (I2S) symbol. The secure HBF design problem for DFRC is formulated by maximizing the minimum legitimate user communication rate subject to radar signal-to-interference-plus-noise ratio, eavesdropping rate, hardware and power constraints. To solve this non-convex problem, we propose an alternating optimization based method to jointly optimize transmit and receive beamformers. Numerical simulation results validate the effectiveness of the proposed algorithm and show the superiority of the proposed I2S-aided HBF architecture for achieving DFRC and enhancing PLS.
Experimental analysis of the TRC benchmark system
Authors: Arati Bhattu, Svenja Hermann, Nidhal Jamia, Florian Müller, Maren Scheel, Christoph Schwingshackl, H. Nevzat Özgüven, Malte Krack
Date: 2024-03-12 00:00:00

The Tribomechadynamics Research Challenge (TRC) was a blind prediction of the vibration behavior of a thin plate clamped on two sides using bolted joints. The first bending mode's natural frequency and damping ratio were requested as function of the amplitude, starting from the linear regime until high levels, where both frictional contact and nonlinear bending-stretching coupling become relevant. The predictions were confronted with experimental results in a companion paper; the present article addresses the experimental analysis of this benchmark system. Amplitude-dependent modal data was obtained from phase resonance and response controlled tests. An original variant of response controlled testing is proposed: Instead of a fixed frequency interval, a fixed phase interval is analyzed. This way, the high excitation levels required outside resonance, which could activate unwanted exciter nonlinearity, are avoided. Consistency of testing methods is carefully analyzed. Overall, these measures have permitted to gain high confidence in the acquired modal data. The different sources of the remaining uncertainty were further analyzed. A low reassembly-variability but a moderate time-variability were identified, where the latter is attributed to some thermal sensitivity of the system. Two nominally identical plates were analyzed, which both have an appreciable initial curvature, and a significant effect on the vibration behavior was found depending on whether the plate is aligned/misaligned with the support structure. Further, a 1:2 nonlinear modal interaction with the first torsion mode was observed, which only occurs in the aligned configurations.
Broadened-beam Uniform Rectangular Array Coefficient Design in LEO SatComs Under Quality of Service and Constant Modulus Constraints
Authors: Weiting Lin, Yuchieh Wu, Borching Su
Date: 2024-03-12 00:00:00

Satellite communications (SatComs) are anticipated to deliver global Internet access. Low Earth orbit (LEO) satellites (SATs) offer the advantage of higher downlink capacity due to their reduced link budget compared to medium Earth orbit (MEO) and geostationary Earth orbit (GEO) SATs. In this paper, beam broadening methods for uniform rectangular arrays (URAs) in LEO SatComs were studied. The proposed method is the first of its kind to jointly consider path loss variation from SAT to the user terminal (UT) due to the Earth's curvature to guarantee the quality of service (QoS), constant modulus constraints (CMCs) favored for maximizing power amplifier (PA) efficiency, and out-of-beam radiation suppression to avoid interference. A broadened-beam URA coefficient design problem is formulated and decomposed into two uniform linear array (ULA) design subproblems utilizing Kronecker product beamforming. With this decomposition, the number of beamforming coefficients that need to be optimized is significantly reduced compared to the original URA design problem. The non-convex ULA subproblems are addressed using the semidefinite relaxation (SDR) technique and a convex iterative algorithm. Simulation results reveal the advantages of the proposed method for suppressing the out-of-beam radiation and achieving the design criteria. In addition, channel capacity evaluations are carried out. It demonstrates that the proposed "broadened-beam" beamformers can offer capacities that are at least four times greater than those of beamformers employing an array steering vector when the beam transition time is considered. The proposed method holds potential for LEO SAT broadcasting applications, such as digital video broadcasting (DVB).
Analysis of Intelligent Reflecting Surface-Enhanced Mobility Through a Line-of-Sight State Transition Model
Authors: Haoyan Wei, Hongtao Zhang
Date: 2024-03-12 00:00:00
Tasks: Blocking

Rapid signal fluctuations due to blockage effects cause excessive handovers (HOs) and degrade mobility performance. By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflective surface (IRS) has the potential to address this issue. Due to the lack of introducing blocking effects, existing HO analyses cannot capture excessive HOs or exploit enhancements via IRSs. This paper proposes an LoS state transition model enabling analysis of mobility enhancement achieved by IRS-reconfigured LoS links, where LoS link blocking and reconfiguration utilizing IRS during user movement are explicitly modeled as stochastic processes. Specifically, the condition for blocking LoS links is characterized as a set of possible blockage locations, the distribution of available IRSs is thinned by the criteria for reconfiguring LoS links. In addition, BSs potentially handed over are categorized by probabilities of LoS states to enable HO decision analysis. By projecting distinct gains of LoS states onto a uniform equivalent distance criterion, mobility enhanced by IRS is quantified through the compact expression of HO probability. Results show the probability of dropping into non-LoS due to movement decreases by 70% when deploying IRSs with the density of 93/km$^2$, and HOs decrease by 57% under the optimal IRS distributed deployment parameter.
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement
Authors: Jianxin Xie, Bing Yao, Zheyu Jiang
Date: 2024-03-12 00:00:00
Tasks: Active Learning, Soil moisture estimation

Soil moisture is a crucial hydrological state variable that has significant importance to the global environment and agriculture. Precise monitoring of soil moisture in crop fields is critical to reducing agricultural drought and improving crop yield. In-situ soil moisture sensors, which are buried at pre-determined depths and distributed across the field, are promising solutions for monitoring soil moisture. However, high-density sensor deployment is neither economically feasible nor practical. Thus, to achieve a higher spatial resolution of soil moisture dynamics using a limited number of sensors, we integrate a physics-based agro-hydrological model based on Richards' equation in a physics-constrained deep learning framework to accurately predict soil moisture dynamics in the soil's root zone. This approach ensures that soil moisture estimates align well with sensor observations while obeying physical laws at the same time. Furthermore, to strategically identify the locations for sensor placement, we introduce a novel active learning framework that combines space-filling design and physics residual-based sampling to maximize data acquisition potential with limited sensors. Our numerical results demonstrate that integrating Physics-constrained Deep Learning (P-DL) with an active learning strategy within a unified framework--named the Physics-constrained Active Learning (P-DAL) framework--significantly improves the predictive accuracy and effectiveness of field-scale soil moisture monitoring using in-situ sensors.
Graph learning methods to extract empathy supporting regions in a naturalistic stimuli fMRI
Authors: Sasanka GRS, Ayushi Agrawal, Santosh Nannuru, Kavita Vemuri
Date: 2024-03-11 00:00:00
Tasks: Clustering, Graph Clustering, Graph Learning

Functional MRI (fMRI) research, employing naturalistic stimuli like movies, explores brain network interactions in complex cognitive processes such as empathy. The empathy network encompasses multiple brain areas, including the Insula, PFC, ACC, and parietal regions. Our novel processing pipeline applies graph learning methods to whole-brain timeseries signals, incorporating high-pass filtering, voxel-level clustering, and windowed graph learning with a sparsity-based approach. The study involves two short movies shown to 14 healthy volunteers, considering 54 regions extracted from the AAL Atlas. The sparsity-based graph learning consistently outperforms, achieving over 88% accuracy in capturing emotion contagion variations. Temporal analysis reveals a gradual induction of empathy, supported by the method's effectiveness in capturing dynamic connectomes through graph clustering. Edge-weight dynamics analysis underscores sparsity-based learning's superiority, while connectome-network analysis highlights the pivotal role of the Insula, Amygdala, and Thalamus in empathy. Spectral filtering analysis emphasizes the band-pass filter's significance in isolating regions linked to emotional and empathetic processing during empathy HIGH states. Key regions like Amygdala, Insula, and Angular Gyrus consistently activate, supporting their critical role in immediate emotional responses. Strong similarities across movies in graph cluster labels, connectome-network analysis, and spectral filtering-based analyses reveal robust neural correlates of empathy. These findings advance our understanding of empathy-related neural dynamics and identify specific regions in empathetic responses, offering insights for targeted interventions and treatments associated with empathetic processing.
Multiobject Tracking for Thresholded Cell Measurements
Authors: Thomas Kropfreiter, Jason L. Williams, Florian Meyer
Date: 2024-03-11 00:00:00

In many multiobject tracking applications, including radar and sonar tracking, after prefiltering the received signal, measurement data is typically structured in cells. The cells, e.g., represent different range and bearing values. However, conventional multiobject tracking methods use so-called point measurements. Point measurements are provided by a preprocessing stage that applies a threshold or detector and breaks up the cell's structure by converting cell indexes into, e.g., range and bearing measurements. We here propose a Bayesian multiobject tracking method that processes measurements that have been thresholded but are still cell-structured. We first derive a likelihood function that systematically incorporates an adjustable detection threshold which makes it possible to control the number of cell measurements. We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood function for cell measurements. Furthermore, we establish a link to the conventional point measurement model by deriving the likelihood function for point measurements with amplitude information (AM) and discuss the PMB filter that uses point measurements with AM. Our numerical results demonstrate the advantages of the proposed method that relies on thresholded cell measurements compared to the conventional multiobject tracking based on point measurements with and without AM.
User Tracking and Direction Estimation Codebook Design for IRS-Assisted mmWave Communication
Authors: Moritz Garkisch, Sebastian Lotter, Gui Zhou, Vahid Jamali, Robert Schober
Date: 2024-03-11 00:00:00

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak likelihood-based direction estimation. Using the direction information, the user's trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our numerical results reveal a lower direction estimation error of the proposed UT algorithm when employing our designed codebook compared to codebooks from the literature. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform two benchmark schemes based on direct phase shift optimization and hierarchical codebook search, respectively, via computer simulations.
One-Bit Target Detection in Collocated MIMO Radar with Colored Background Noise
Authors: Yu-Hang Xiao, David Ramírez, Lei Huang, Xiao Peng Li, Hing Cheung So
Date: 2024-03-11 00:00:00

One-bit sampling has emerged as a promising technique in multiple-input multiple-output (MIMO) radar systems due to its ability to significantly reduce data volume and processing requirements. Nevertheless, current detection methods have not adequately addressed the impact of colored noise, which is frequently encountered in real scenarios. In this paper, we present a novel detection method that accounts for colored noise in MIMO radar systems. Specifically, we derive Rao's test by computing the derivative of the likelihood function with respect to the target reflectivity parameter and the Fisher information matrix, resulting in a detector that takes the form of a weighted matched filter. To ensure the constant false alarm rate (CFAR) property, we also consider noise covariance uncertainty and examine its effect on the probability of false alarm. The detection probability is also studied analytically. Simulation results demonstrate that the proposed detector provides considerable performance gains in the presence of colored noise.
UAV-Enabled Asynchronous Federated Learning
Authors: Zhiyuan Zhai, Xiaojun Yuan, Xin Wang, Huiyuan Yang
Date: 2024-03-11 00:00:00
Tasks: Federated Learning

To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). However, there are some critical challenges for FL deployment. One major challenge called straggler issue severely limits FL's coverage where the device with the weakest channel condition becomes the bottleneck of the model aggregation performance. Besides, the huge uplink communication overhead compromises the effectiveness of FL, which is particularly pronounced in large-scale systems. To address the straggler issue, we propose the integration of an unmanned aerial vehicle (UAV) as the parameter server (UAV-PS) to coordinate the FL implementation. We further employ over-the-air computation technique that leverages the superposition property of wireless channels for efficient uplink communication. Specifically, in this paper, we develop a novel UAV-enabled over-the-air asynchronous FL (UAV-AFL) framework which supports the UAV-PS in updating the model continuously to enhance the learning performance. Moreover, we conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning performance. Based on this, a unified communication-learning problem is formulated to maximize asymptotical learning performance by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results demonstrate that the proposed scheme achieves substantially learning efficiency improvement compared with the state-of-the-art approaches.
Ray Launching-Based Computation of Exact Paths with Noisy Dense Point Clouds
Authors: Niklas Vaara, Pekka Sangi, Miguel Bordallo López, Janne Heikkilä
Date: 2024-03-11 00:00:00

Point clouds have been a recent interest for ray tracing-based radio channel characterization, as sensors such as RGB-D cameras and laser scanners can be utilized to generate an accurate virtual copy of a physical environment. In this paper, a novel ray launching algorithm is presented, which operates directly on noisy point clouds acquired from sensor data. It produces coarse paths that are further refined to exact paths consisting of reflections and diffractions. A commercial ray tracing tool is utilized as the baseline for validating the simulated paths. A significant majority of the baseline paths is found. The robustness to noise is examined by artificially applying noise along the normal vector of each point. It is observed that the proposed method is capable of adapting to noise and finds similar paths compared to the baseline path trajectories with noisy point clouds. This is prevalent especially if the normal vectors of the points are estimated accurately. Lastly, a simulation is performed with a reconstructed point cloud and compared against channel measurements and the baseline paths. The resulting paths demonstrate similarity with the baseline path trajectories and exhibit an analogous pattern to the aggregated impulse response extracted from the measurements. Code available at https://github.com/nvaara/NimbusRT
Integrated SWIPT Receiver with Memory Effects: Circuit Analysis and Information Detection
Authors: Eleni Demarchou, Zulqarnain Bin Ashraf, Dieff Vital, Besma Smida, Constantinos Psomas, Ioannis Krikidis
Date: 2024-03-11 00:00:00

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit which accounts for the memory introduced by the capacitor. The provided expressions are also validated through circuit simulations on ADS. Then, the half-wave rectifier is used as an integrated simultaneous wireless information and power transfer receiver where the circuit's output is used for decoding information based on amplitude modulation. We investigate the bit error rate performance based on two detection schemes: (i) symbol-by-symbol maximum likelihood (ML); and (ii) ML sequence detection (MLSD). We show that the symbol period is critical due to the intersymbol interference induced by circuit. Our results reveal that MLSD is necessary towards improving the error probability and achieving higher data rates.
Extreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
Authors: Junbin Liu, Ya Liu, Wing-Kin Ma, Mingjie Shao, Anthony Man-Cho So
Date: 2024-03-11 00:00:00
Tasks: Constrained Clustering, Graph Matching

In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providing exact penalization results. In this second part of the study, we continue our error bound analysis for the cases of partial permutation matrices, size-constrained assignment matrices and non-negative semi-orthogonal matrices. We develop new error bounds and penalized formulations for these three cases, and the new formulations possess good structures for building computationally efficient algorithms. Moreover, we provide numerical results to demonstrate our framework in a variety of applications such as the densest k-subgraph problem, graph matching, size-constrained clustering, non-negative orthogonal matrix factorization and sparse fair principal component analysis.