pwc-archive/papers-with-abstracts huggingface dataset:
Autoregressive Coefficients based Intelligent Protection of Transmission Lines Connected to Type-3 Wind Farms
Authors: Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Om P. Malik
Date: 2023-10-05 00:00:00

Protective relays can mal-operate for transmission lines connected to doubly fed induction generator (DFIG) based large capacity wind farms (WFs). The performance of distance relays protecting such lines is investigated and a statistical model based intelligent protection of the area between the grid and the WF is proposed in this article. The suggested method employs an adaptive fuzzy inference system to detect faults based on autoregressive (AR) coefficients of the 3-phase currents selected using minimum redundancy maximum relevance algorithm. Deep learning networks are used to supervise the detection of faults, their subsequent localization, and classification. The effectiveness of the scheme is evaluated on IEEE 9-bus and IEEE 39-bus systems with varying fault resistances, fault inception times, locations, fault types, wind speeds, and transformer connections. Further, the impact of factors like the presence of type-4 WFs, double circuit lines, WF capacity, grid strength, FACTs devices, reclosing on permanent faults, power swings, fault during power swings, voltage instability, load encroachment, high impedance faults, evolving and cross-country faults, close-in and remote-end faults, CT saturation, sampling rate, data window size, synchronization error, noise, and semi-supervised learning are considered while validating the proposed scheme. The results show the efficacy of the suggested method in dealing with various system conditions and configurations while protecting the transmission lines that are connected to WFs.
Graph-based Simultaneous Localization and Bias Tracking
Authors: Alexander Venus, Erik Leitinger, Stefan Tertinek, Florian Meyer, Klaus Witrisal
Date: 2023-10-04 00:00:00
Tasks: Position

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.
Passive Respiration Detection via mmWave Communication Signal Under Interference
Authors: Kehan Wu, Renqi Chen, Haiyu Wang, Chenqing Ji, Jiayuan Zhu, Guang Wu
Date: 2023-10-05 00:00:00

Recent research has highlighted the detection of human respiration rate using commodity WiFi devices. Nevertheless, these devices encounter challenges in accurately discerning human respiration amidst the prevailing human motion interference encountered in daily life. To tackle this predicament, this paper introduces a passive sensing and communication system designed specifically for respiration detection in the presence of robust human motion interference. Operating within the 60.48 GHz band, the proposed system aims to detect human respiration even when confronted with substantial human motion interference within close proximity. Subsequently, a neural network is trained using the collected data by us to enable human respiration detection. The experimental results demonstrate a consistently high accuracy rate over 90\% of the human respiration detection under interference, given an adequate sensing duration. Finally, an empirical model is derived analytically to achieve the respiratory rate counting in 10 seconds.
Dynamic Changes of Brain Network during Epileptic Seizure
Authors: Atefeh Khoshkhahtinat, Hoda Mohammadzade
Date: 2023-10-04 00:00:00

Epilepsy is a neurological disorder identified by sudden and recurrent seizures, which are believed to be accompanied by distinct changes in brain dynamics. Exploring the dynamic changes of brain network states during seizures can pave the way for improving the diagnosis and treatment of patients with epilepsy. In this paper, the connectivity brain network is constructed using the phase lag index (PLI) measurement within five frequency bands, and graph-theoretic techniques are employed to extract topological features from the brain network. Subsequently, an unsupervised clustering approach is used to examine the state transitions of the brain network during seizures. Our findings demonstrate that the level of brain synchrony during the seizure period is higher than the pre-seizure and post-seizure periods in the theta, alpha, and beta bands, while it decreases in the gamma bands. These changes in synchronization also lead to alterations in the topological features of functional brain networks during seizures. Additionally, our results suggest that the dynamics of the brain during seizures are more complex than the traditional three-state model (pre-seizure, seizure, and post-seizure) and the brain network state exhibits a slower rate of change during the seizure period compared to the pre-seizure and post-seizure periods.
Dual mode multispectral imaging system for food and agricultural product quality estimation
Authors: Darsha Udayanga, Ashan Serasinghe, Supun Dassanayake, Roshan Godaliyadda, H. M. V. R. Herath, M. P. B. Ekanayake, H. L. P. Malshan
Date: 2023-10-04 00:00:00

Multispectral imaging coupled with Artificial Intelligence, Machine Learning and Signal Processing techniques work as a feasible alternative for laboratory testing, especially in food quality control. Most of the recent related research has been focused on reflectance multispectral imaging but a system with both reflectance, transmittance capabilities would be ideal for a wide array of specimen types including solid and liquid samples. In this paper, a device which includes a dedicated reflectance mode and a dedicated transmittance mode is proposed. Dual mode operation where fast switching between two modes is facilitated. An innovative merged mode is introduced in which both reflectance and transmittance information of a specimen are combined to form a higher dimensional dataset with more features. Spatial and temporal variations of measurements are analyzed to ensure the quality of measurements. The concept is validated using a standard color palette and specific case studies are done for standard food samples such as turmeric powder and coconut oil proving the validity of proposed contributions. The classification accuracy of standard color palette testing was over 90% and the accuracy of coconut oil adulteration was over 95%. while the merged mode was able to provide the best accuracy of 99% for the turmeric adulteration. A linear functional mapping was done for coconut oil adulteration with an R2 value of 0.9558.
Dark Side of HAPS Systems: Jamming Threats towards Satellites
Authors: Hadil Otay, Khaled Humadi, Gunes Karabulut Kurt
Date: 2023-10-04 00:00:00

Securing satellite communication networks is imperative in the rapidly evolving landscape of advanced telecommunications, particularly in the context of 6G advancements. This paper establishes a secure low earth orbit (LEO) satellite network paradigm to address the challenges of the evolving 6G era, with a focus on enhancing communication integrity between satellites and ground stations. Countering the threat of jamming, which can disrupt vital communication channels, is a key goal of this work. In particular, this paper investigates the performance of two LEO satellite communication scenarios under the presence of jamming attacker. In the first scenario, we consider a system that comprises one transmitting satellite, a receiving ground station, and a high altitude platform station (HAPS) acting as a jammer. The HAPS disrupts communication between the satellite and the ground station, impeding signal transmission. The second scenario involves two satellites, one is the transmitter while the other works as a relay, accompanied by a ground station, and a jamming HAPS. In this scenario, the transmitting satellite sends data to the ground station using two different paths, i.e., direct and indirect transmission paths, with a relay satellite acting as an intermediary in the case of indirect transmission. For both scenarios, we study the system security by developing mathematical frameworks to investigate the outage effect resulting from the jamming signals orchestrated by the HAPS. Our results show that the satellite cooperation in the second scenario improves the system's security since the extreme jamming effect occurs only when both links are simultaneously disturbed.
Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)
Authors: Haotian Zhang, Shijian Gao, Xiang Cheng, Liuqing Yang
Date: 2023-10-04 00:00:00

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios with adverse environment conditions, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach.
Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands
Authors: Haotian Liu, Zhiqing Wei, Fengyun Li, Yuewei Lin, Hanyang Qu, Huici Wu, Zhiyong Feng
Date: 2023-10-04 00:00:00
Tasks: compressed sensing, Integrated sensing and communication, ISAC

As a promising key technology of 6th generation (6G) mobile communication system, integrated sensing and communication (ISAC) technology aims to make full use of spectrum resources to enable the functional integration of communication and sensing. The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands. However, the conventional sensing algorithms over non-continuous spectrum bands have disadvantages such as reduced peak-to-side lobe ratio (PSLR) and degraded anti-noise performance. Facing this challenge, we propose a high-precision ISAC signal processing algorithm based on compressed sensing (CS) in this paper. By integrating the resource block group (RBG) configuration information in 5th generation new radio (5G NR) and channel information matrices, we can dynamically and accurately obtain power estimation spectra. Moreover, we employ the fast iterative shrinkage-thresholding algorithm (FISTA) to address the reconstruction problem and utilize K-fold cross validation (KCV) to obtain optimal parameters. Simulation results show that the proposed algorithm has lower sidelobes or even zero sidelobes compared with conventional sensing algorithms. Meanwhile, compared with the improved 2D FFT algorithm and conventional 2D FFT algorithm, the proposed algorithms in this paper have a maximum improvement of 54.66 % and 84.36 % in range estimation accuracy, and 41.54 % and 97.09 % in velocity estimation accuracy, respectively.
Enabling Energy-Efficiency in Massive-MIMO: A Scalable Low-Complexity Decoder for Generalized Quadrature Spatial Modulation
Authors: Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Osvaldo Gonsa
Date: 2023-10-04 00:00:00
Tasks: Decoder

Generalized quadrature spatial modulation (GQSM) schemes are known to achieve high energy- and spectral- efficiencies by modulating information both in transmitted symbols and in coded combinatorial activations of subsets of multiple transmit antennas. A challenge of the approach is, however, the decoding complexity which scales with the efficiency of the scheme. In order to circumvent this bottleneck and enable high-performance and feasible GQSM in massive multiple-input multiple-output (mMIMO) scenarios, we propose a novel decoding algorithm which enjoys a complexity order that is independent of the combinatorial factor. This remarkable feature of the proposed decoder is a consequence of a novel vectorized Gaussian belief propagation (GaBP) algorithm, here contributed, whose message passing (MP) rules leverage both pilot symbols and the unit vector decomposition (UVD) of the GQSM signal structure. The effectiveness of the proposed UVD-GaBP method is illustrated via computer simulations including numerical results for systems of a size never before reported in related literature (up to 32 transmit antennas), which demonstrates the potential of the approach in paving the way towards high energy and spectral efficiency for wireless systems in a truly mMIMO setting.
Implementation of hyperspectral inversion algorithms on FPGA: Hardware comparison using High Level Synthesis
Authors: El Mehdi Abdali, Daniele Picone, Mauro Dalla-Mura, Stéphane Mancini
Date: 2023-10-03 00:00:00
Tasks: High-Level Synthesis

Hyperspectral imaging is gathering significant attention due to its potential in various domains such as geology, agriculture, ecology, and surveillance. However, the associated processing algorithms, which are essential for enhancing output quality and extracting relevant information, are often computationally intensive and have to deal with substantial data volumes. Our focus lies on reconfigurable hardware, particularly recent FPGAs. While FPGA design can be complex, High Level Synthesis (HLS) workflows have emerged as a solution, abstracting low-level design intricacies and enhancing productivity. Despite successful prior efforts using HLS for hyperspectral imaging acceleration, we lack a comprehensive research to benchmark various algorithms and architectures within a unified framework. This study aims to quantitatively evaluate performance across different inversion algorithms and design architectures, providing insights for optimal trade-offs for specific applications. We apply this analysis to the case study of spectrum reconstruction processed from interferometric acquisitions taken by Fourier transform spectrometers.
On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring
Authors: Sulaiman Aburakhia, Abdallah Shami
Date: 2023-10-03 00:00:00
Tasks: Denoising

Vibration-based condition monitoring (VBCM) is widely utilized in various applications due to its non-destructive nature. Recent advancements in sensor technology, the Internet of Things (IoT), and computing have enabled the facilitation of reliable distributed VBCM where sensor nodes are deployed at multiple locations and connected wirelessly to monitoring centers. However, sensor nodes are typically constrained by limited power resources, necessitating control over the peak-to-average power ratio (PAPR) of the generated vibration signals. Effective control of PAPR is crucial to prevent nonlinear distortion and reduce power consumption within the node. Additionally, avoiding nonlinear distortion in the vibration signal and preserving its waveform is essential to ensure the reliability of condition monitoring. This paper conducts an in-depth analysis of the PAPR of vibration signals in VBCM systems, evaluates the impact of nonlinear power amplification on the system performance, and proposes a lightweight autoencoder-based signal companding scheme to control the PAPR to improve power efficiency and mitigate the impact of nonlinear distortion. The proposed scheme employs a lightweight reconstruction autoencoder with a compression-based activation function in the source to compress the vibration signals and avoid increasing the average power of the compressed signal. In the destination, the proposed scheme uses a denoising-expansion autoencoder to expand the compressed signals while minimizing noise enhancement during the expansion process. The experimental results demonstrate the effectiveness of the proposed companding scheme in preventing nonlinear distortion, improving the efficiency of power amplification in the source, and restoring the PAPR characteristics in the destination while avoiding the undesired effect of noise expansion.
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
Authors: Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang, Shuguang Cui, Xuemin Shen, Ping Zhang
Date: 2023-10-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications.
Managing the Impact of Sensor's Thermal Noise in Machine Learning for Nuclear Applications
Authors: Issam Hammad
Date: 2023-10-02 00:00:00
Tasks: Sensor Fusion

Sensors such as accelerometers, magnetometers, and gyroscopes are frequently utilized to perform measurements in nuclear power plants. For example, accelerometers are used for vibration monitoring of critical systems. With the recent rise of machine learning, data captured from such sensors can be used to build machine learning models for predictive maintenance and automation. However, these sensors are known to have thermal noise that can affect the sensor's accuracy. Thermal noise differs between sensors in terms of signal-to-noise ratio (SNR). This thermal noise will cause an accuracy drop in sensor-fusion-based machine learning models when deployed in production. This paper lists some applications for Canada Deuterium Uranium (CANDU) reactors where such sensors are used and therefore can be impacted by the thermal noise issue if machine learning is utilized. A list of recommendations to help mitigate the issue when building future machine learning models for nuclear applications based on sensor fusion is provided. Additionally, this paper demonstrates that machine learning algorithms can be impacted differently by the issue, therefore selecting a more resilient model can help in mitigating it.
DISCO Might Not Be Funky: Random Intelligent Reflective Surface Configurations That Attack
Authors: Huan Huang, Lipeng Dai, Hongliang Zhang, Chongfu Zhang, Zhongxing Tian, Yi Cai, A. Lee Swindlehurst, Zhu Han
Date: 2023-10-01 00:00:00

Emerging intelligent reflective surfaces (IRSs) significantly improve system performance, but also pose a significant risk for physical layer security (PLS). Unlike the extensive research on legitimate IRS-enhanced communications, in this article we present an adversarial IRS-based fully-passive jammer (FPJ). We describe typical application scenarios for Disco IRS (DIRS)-based FPJ, where an illegitimate IRS with random, time-varying reflection properties acts like a "disco ball" to randomly change the propagation environment. We introduce the principles of DIRS-based FPJ and overview existing investigations of the technology, including a design example employing one-bit phase shifters. The DIRS-based FPJ can be implemented without either jamming power or channel state information (CSI) for the legitimate users (LUs). It does not suffer from the energy constraints of traditional active jammers, nor does it require any knowledge of the LU channels. In addition to the proposed jamming attack, we also propose an anti-jamming strategy that requires only statistical rather than instantaneous CSI. Furthermore, we present a data frame structure that enables the legitimate access point (AP) to estimate the DIRS-jammed channels' statistical characteristics in the presence of the DIRS jamming. Typical cases are discussed to show the impact of the DIRS-based FPJ and the feasibility of the anti-jamming precoder (AJP). Moreover, we outline future research directions and challenges for the DIRS-based FPJ and its anti-jamming precoding to stimulate this line of research and pave the way for practical applications.
Sequential Monte Carlo Graph Convolutional Network for Dynamic Brain Connectivity
Authors: Fengfan Zhao, Ercan Engin Kuruoglu
Date: 2023-10-01 00:00:00
Tasks: Functional Connectivity, Graph Neural Network

An increasingly important brain function analysis modality is functional connectivity analysis which regards connections as statistical codependency between the signals of different brain regions. Graph-based analysis of brain connectivity provides a new way of exploring the association between brain functional deficits and the structural disruption related to brain disorders, but the current implementations have limited capability due to the assumptions of noise-free data and stationary graph topology. We propose a new methodology based on the particle filtering algorithm, with proven success in tracking problems, which estimates the hidden states of a dynamic graph with only partial and noisy observations, without the assumptions of stationarity on connectivity. We enrich the particle filtering state equation with a graph Neural Network called Sequential Monte Carlo Graph Convolutional Network (SMC-GCN), which due to the nonlinear regression capability, can limit spurious connections in the graph. Experiment studies demonstrate that SMC-GCN achieves the superior performance of several methods in brain disorder classification.