pwc-archive/papers-with-abstracts huggingface dataset:
Class-Incremental Learning for Sound Event Localization and Detection
Authors: Ruchi Pandey, Manjunath Mulimani, Archontis Politis, Annamaria Mesaros
Date: 2024-11-19 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Sound Event Localization and Detection

This paper investigates the feasibility of class-incremental learning (CIL) for Sound Event Localization and Detection (SELD) tasks. The method features an incremental learner that can learn new sound classes independently while preserving knowledge of old classes. The continual learning is achieved through a mean square error-based distillation loss to minimize output discrepancies between subsequent learners. The experiments are conducted on the TAU-NIGENS Spatial Sound Events 2021 dataset, which includes 12 different sound classes and demonstrate the efficacy of proposed method. We begin by learning 8 classes and introduce the 4 new classes at next stage. After the incremental phase, the system is evaluated on the full set of learned classes. Results show that, for this realistic dataset, our proposed method successfully maintains baseline performance across all metrics.
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions
Authors: Hui-Peng Du, Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
Date: 2024-11-19 00:00:00
Tasks: Denoising, Speech Enhancement

This paper proposes a novel neural denoising vocoder that can generate clean speech waveforms from noisy mel-spectrograms. The proposed neural denoising vocoder consists of two components, i.e., a spectrum predictor and a enhancement module. The spectrum predictor first predicts the noisy amplitude and phase spectra from the input noisy mel-spectrogram, and subsequently the enhancement module recovers the clean amplitude and phase spectrum from noisy ones. Finally, clean speech waveforms are reconstructed through inverse short-time Fourier transform (iSTFT). All operations are performed at the frame-level spectral domain, with the APNet vocoder and MP-SENet speech enhancement model used as the backbones for the two components, respectively. Experimental results demonstrate that our proposed neural denoising vocoder achieves state-of-the-art performance compared to existing neural vocoders on the VoiceBank+DEMAND dataset. Additionally, despite the lack of phase information and partial amplitude information in the input mel-spectrogram, the proposed neural denoising vocoder still achieves comparable performance with the serveral advanced speech enhancement methods.
OceanLens: An Adaptive Backscatter and Edge Correction using Deep Learning Model for Enhanced Underwater Imaging
Authors: Rajini Makam, Dhatri Shankari T M, Sharanya Patil, Suresh Sundram
Date: 2024-11-20 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, SSIM

Underwater environments pose significant challenges due to the selective absorption and scattering of light by water, which affects image clarity, contrast, and color fidelity. To overcome these, we introduce OceanLens, a method that models underwater image physics-encompassing both backscatter and attenuation-using neural networks. Our model incorporates adaptive backscatter and edge correction losses, specifically Sobel and LoG losses, to manage image variance and luminance, resulting in clearer and more accurate outputs. Additionally, we demonstrate the relevance of pre-trained monocular depth estimation models for generating underwater depth maps. Our evaluation compares the performance of various loss functions against state-of-the-art methods using the SeeThru dataset, revealing significant improvements. Specifically, we observe an average of 65% reduction in Grayscale Patch Mean Angular Error (GPMAE) and a 60% increase in the Underwater Image Quality Metric (UIQM) compared to the SeeThru and DeepSeeColor methods. Further, the results were improved with additional convolution layers that capture subtle image details more effectively with OceanLens. This architecture is validated on the UIEB dataset, with model performance assessed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. OceanLens with multiple convolutional layers achieves up to 12-15% improvement in the SSIM.
Deep Learning-Based Prediction of PET Amyloid Status Using Multi-Contrast MRI
Authors: Donghoon Kim, Jon Andre Ottesen, Ashwin Kumar, Brandon C. Ho, Elsa Bismuth, Christina B. Young, Elizabeth Mormino, Greg Zaharchuk
Date: 2024-11-18 00:00:00
Tasks: Specificity

Identifying amyloid-beta positive patients is crucial for determining eligibility for Alzheimer's disease (AD) clinical trials and new disease-modifying treatments, but currently requires PET or CSF sampling. Previous MRI-based deep learning models for predicting amyloid positivity, using only T1w sequences, have shown moderate performance. We trained deep learning models to predict amyloid PET positivity and evaluated whether multi-contrast inputs improve performance. A total of 4,058 exams with multi-contrast MRI and PET-based quantitative amyloid deposition were obtained from three public datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies 3 (OASIS3), and the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4). Two separate EfficientNet models were trained for amyloid positivity prediction: one with only T1w images and the other with both T1w and T2-FLAIR images as network inputs. The area under the curve (AUC), accuracy, sensitivity, and specificity were determined using an internal held-out test set. The trained models were further evaluated using an external test set. In the held-out test sets, the T1w and T1w+T2FLAIR models demonstrated AUCs of 0.62 (95% CI: 0.60, 0.64) and 0.67 (95% CI: 0.64, 0.70) (p = 0.006); accuracies were 61% (95% CI: 60%, 63%) and 64% (95% CI: 62%, 66%) (p = 0.008); sensitivities were 0.88 and 0.71; and specificities were 0.23 and 0.53, respectively. The trained models showed similar performance in the external test set. Performance of the current model on both test sets exceeded that of the publicly available model. In conclusion, the use of multi-contrast MRI, specifically incorporating T2-FLAIR in addition to T1w images, significantly improved the predictive accuracy of PET-determined amyloid status from MRI scans using a deep learning approach.
SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
Authors: Jiayu Huo, Sebastien Ourselin, Rachel Sparks
Date: 2024-11-13 00:00:00
Tasks: Decoder, Image-to-Image Translation, Translation

Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field. However, current approaches based on Convolutional Neural Networks (CNNs) and Transformers often fail to capture fine-grain semantic features, resulting in suboptimal image quality. To address this challenge, we propose SAM-I2I, a novel image-to-image translation framework based on the Segment Anything Model 2 (SAM2). SAM-I2I utilizes a pre-trained image encoder to extract multiscale semantic features from the source image and a decoder, based on the mask unit attention module, to synthesize target modality images. Our experiments on multi-contrast MRI datasets demonstrate that SAM-I2I outperforms state-of-the-art methods, offering more efficient and accurate medical image translation.
Mutual Information-oriented ISAC Beamforming Design for Large Dimensional Antenna Array
Authors: Shanfeng Xu, Yanshuo Cheng, Siqiang Wang, Xinyi Wang, Zhong Zheng, Zesong Fei
Date: 2024-11-20 00:00:00
Tasks: Integrated sensing and communication, ISAC

Existing integrated sensing and communication (ISAC) beamforming design were mostly designed under perfect instantaneous channel state information (CSI), limiting their use in practical dynamic environments. In this paper, we study the beamforming design for multiple-input multiple-output (MIMO) ISAC systems based on statistical CSI, with the weighted mutual information (MI) comprising sensing and communication perspectives adopted as the performance metric. In particular, the operator-valued free probability theory is utilized to derive the closed-form expression for the weighted MI under statistical CSI. Subsequently, an efficient projected gradient ascent (PGA) algorithm is proposed to optimize the transmit beamforming matrix with the aim of maximizing the weighted MI.Numerical results validate that the derived closed-form expression matches well with the Monte Carlo simulation results and the proposed optimization algorithm is able to improve the weighted MI significantly. We also illustrate the trade-off between sensing and communication MI.
A CSI Feedback Framework based on Transmitting the Important Values and Generating the Others
Authors: Zhilin Du, Zhenyu Liu, Haozhen Li, Shilong Fan, Xinyu Gu, Lin Zhang
Date: 2024-11-20 00:00:00

The application of deep learning (DL)-based channel state information (CSI) feedback frameworks in massive multiple-input multiple-output (MIMO) systems has significantly improved reconstruction accuracy. However, the limited generalization of widely adopted autoencoder-based networks for CSI feedback challenges consistent performance under dynamic wireless channel conditions and varying communication overhead constraints. To enhance the robustness of DL-based CSI feedback across diverse channel scenarios, we propose a novel framework, ITUG, where the user equipment (UE) transmits only a selected portion of critical values in the CSI matrix, while a generative model deployed at the BS reconstructs the remaining values. Specifically, we introduce a scoring algorithm to identify important values based on amplitude and contrast, an encoding algorithm to convert these values into a bit stream for transmission using adaptive bit length and a modified Huffman codebook, and a Transformer-based generative network named TPMVNet to recover the untransmitted values based on the received important values. Experimental results demonstrate that the ITUG framework, equipped with a single TPMVNet, achieves superior reconstruction performance compared to several high-performance autoencoder models across various channel conditions.
Efficient Localization with Base Station-Integrated Beyond Diagonal RIS
Authors: Mahmoud Raeisi, Hui Chen, Henk Wymeersch, Ertugrul Basar
Date: 2024-11-20 00:00:00

This paper introduces a novel approach to efficient localization in next-generation communication systems through a base station (BS)-enabled passive beamforming utilizing beyond diagonal reconfigurable intelligent surfaces (BD-RISs). Unlike conventional diagonal RISs (D-RISs), which suffer from limited beamforming capability, a BD-RIS provides enhanced control over both phase and amplitude, significantly improving localization accuracy. By conducting a comprehensive Cram\'er-Rao lower bound (CRLB) analysis across various system parameters in both near-field and far-field scenarios, we establish the BD-RIS structure as a competitive alternative to traditional active antenna arrays. Our results reveal that BD-RISs achieve near active antenna arrays performance in localization precision, overcoming the limitations of D-RISs and underscoring its potential for high-accuracy positioning in future communication networks. This work envisions the use of BD-RIS for enabling passive beamforming-based localization, setting the stage for more efficient and scalable localization strategies in sixth-generation networks and beyond.
EEG Signal Denoising Using pix2pix GAN: Enhancing Neurological Data Analysis
Authors: Haoyi Wang, Xufang Chen, Yue Yang, Kewei Zhou, Meining Lv, Dongrui Wang, Wenjie Zhang
Date: 2024-11-20 00:00:00
Tasks: Denoising, EEG, Electromyography (EMG)

Electroencephalography (EEG) is essential in neuroscience and clinical practice, yet it suffers from physiological artifacts, particularly electromyography (EMG), which distort signals. We propose a deep learning model using pix2pixGAN to remove such noise and generate reliable EEG signals. Leveraging the EEGdenoiseNet dataset, we created synthetic datasets with controlled EMG noise levels for model training and testing across a signal-to-noise ratio (SNR) from -7 to 2. Our evaluation metrics included RRMSE and Pearson's CC, assessing both time and frequency domains, and compared our model with others. The pix2pixGAN model excelled, especially under high noise conditions, showing significant improvements in lower RRMSE and higher CC values. This demonstrates the model's superior accuracy and stability in purifying EEG signals, offering a robust solution for EEG analysis challenges and advancing clinical and neuroscience applications.
Coexistence of Real-Time Source Reconstruction and Broadband Services Over Wireless Networks
Authors: Anup Mishra, Nikolaos Pappas, Čedomir Stefanović, Onur Ayan, Xueli An, Yiqun Wu, Petar Popovski, Israel Leyva-Mayorga
Date: 2024-11-20 00:00:00

Achieving a flexible and efficient sharing of wireless resources among a wide range of novel applications and services is one of the major goals of the sixth-generation of mobile systems (6G). Accordingly, this work investigates the performance of a real-time system that coexists with a broadband service in a frame-based wireless channel. Specifically, we consider real-time remote tracking of an information source, where a device monitors its evolution and sends updates to a base station (BS), which is responsible for real-time source reconstruction and, potentially, remote actuation. To achieve this, the BS employs a grant-free access mechanism to serve the monitoring device together with a broadband user, which share the available wireless resources through orthogonal or non-orthogonal multiple access schemes. We analyse the performance of the system with time-averaged reconstruction error, time-averaged cost of actuation error, and update-delivery cost as performance metrics. Furthermore, we analyse the performance of the broadband user in terms of throughput and energy efficiency. Our results show that an orthogonal resource sharing between the users is beneficial in most cases where the broadband user requires maximum throughput. However, sharing the resources in a non-orthogonal manner leads to a far greater energy efficiency.
Experimental Assessment of Human Blockage at sub-THz and mmWave Frequency Bands
Authors: Juan E. Galeote-Cazorla, Alejandro Ramírez-Arroyo, José-María Molina-García-Pardo, María-Teresa Martínez-Inglés, Juan F. Valenzuela Valdés
Date: 2024-11-20 00:00:00

The fifth generation (5G) of mobile communications relies on extremely high data transmissions using a large variety of frequency bands, such as FR1 (sub-6 GHz) and FR2 (mmWave). Future mobile communications envisage using electromagnetic spectrum beyond FR2, i.e. above 100 GHz, known as sub-THz band. These new frequencies open up challenging scenarios where communications shall rely on a major contribution such as the line-of-sight (LoS) component. To the best of the authors' knowledge, for the first time in literature this work studies the human blockage effects over an extremely wide frequency band from 75 GHz to 215 GHz given: (i) the distance between the blocker and the antennas and (ii) the body orientation. Furthermore, the obtained results are modeled with the classical path loss models and compared to 3GPP alternatives. The average losses increase from 42 dB to 56 dB when frequency rises from 75 GHz to 215 GHz. In terms of distance, a 18 dB increment in the received power is found when the Tx--Rx separation is increased from 1 m to 2.5 m. Finally, the blocker orientation induces variations of up to 4.6 dB.
Coexistence of Radar and Communication with Rate-Splitting Wireless Access
Authors: Anup Mishra, Israel Leyva-Mayorga, Petar Popovski
Date: 2024-11-20 00:00:00
Tasks: Joint Radar-Communication

This work investigates the coexistence of sensing and communication functionalities in a base station (BS) serving a communication user in the uplink and simultaneously detecting a radar target with the same frequency resources. To address inter-functionality interference, we employ rate-splitting (RS) at the communication user and successive interference cancellation (SIC) at the joint radar-communication receiver at the BS. This approach is motivated by RS's proven effectiveness in mitigating inter-user interference among communication users. Building on the proposed system model based on RS, we derive inner bounds on performance in terms of ergodic data information rate for communication and ergodic radar estimation information rate for sensing. Additionally, we present a closed-form solution for the optimal power split in RS that maximizes the communication user's performance. The bounds achieved with RS are compared to conventional methods, including spectral isolation and full spectral sharing with SIC. We demonstrate that RS offers a superior performance trade-off between sensing and communication functionalities compared to traditional approaches. Pertinently, while the original concept of RS deals only with digital signals, this work brings forward RS as a general method for including non-orthogonal access for sensing signals. As a consequence, the work done in this paper provides a systematic and parametrized way to effectuate non-orthogonal sensing and communication waveforms.
Enhanced average for event-related potential analysis using dynamic time warping
Authors: Mario Molina, Lorenzo J. Tardon, Ana M. Barbancho, Irene De-Torres, Isabel Barbancho
Date: 2024-11-20 00:00:00
Tasks: Dynamic Time Warping, EEG, ERP

Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental processes. Normally, they are considered on the basis of averages across a number of trials. However, there exist notable variability in latency jitter, jitter, and amplitude, across trials, and, also, across users; this causes the average ERP waveform to blur, and, furthermore, diminish the amplitude of underlying waves. For these reasons, a strategy is proposed for obtaining ERP waveforms based on dynamic time warping (DTW) to adapt, and adjust individual trials to the averaged ERP, previously calculated, to build an enhanced average by making use of these warped signals. At the sight of the experiments carried out on the behaviour of the proposed scheme using publicly available datasets, this strategy reduces the attenuation in amplitude of ERP components thanks to the reduction of the influence of variability of latency and jitter, and, thus, improves the averaged ERP waveforms.
Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks
Authors: Huan Huang, Hongliang Zhang, Jide Yuan, Luyao Sun, Yitian Wang, Weidong Mei, Boya Di, Yi Cai, Zhu Han
Date: 2024-11-20 00:00:00
Tasks: Quantization

Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a "DISCO" that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation
Authors: Md Niaz Imtiaz, Naimul Khan
Date: 2024-11-19 00:00:00
Tasks: Domain Adaptation, EEG, Electroencephalogram (EEG), Emotion Classification, Emotion Recognition, Unsupervised Domain Adaptation

Emotion recognition has significant potential in healthcare and affect-sensitive systems such as brain-computer interfaces (BCIs). However, challenges such as the high cost of labeled data and variability in electroencephalogram (EEG) signals across individuals limit the applicability of EEG-based emotion recognition models across domains. These challenges are exacerbated in cross-dataset scenarios due to differences in subject demographics, recording devices, and presented stimuli. To address these issues, we propose a novel approach to improve cross-domain EEG-based emotion classification. Our method, Gradual Proximity-guided Target Data Selection (GPTDS), incrementally selects reliable target domain samples for training. By evaluating their proximity to source clusters and the models confidence in predicting them, GPTDS minimizes negative transfer caused by noisy and diverse samples. Additionally, we introduce Prediction Confidence-aware Test-Time Augmentation (PC-TTA), a cost-effective augmentation technique. Unlike traditional TTA methods, which are computationally intensive, PC-TTA activates only when model confidence is low, improving inference performance while drastically reducing computational costs. Experiments on the DEAP and SEED datasets validate the effectiveness of our approach. When trained on DEAP and tested on SEED, our model achieves 67.44% accuracy, a 7.09% improvement over the baseline. Conversely, training on SEED and testing on DEAP yields 59.68% accuracy, a 6.07% improvement. Furthermore, PC-TTA reduces computational time by a factor of 15 compared to traditional TTA methods. Our method excels in detecting both positive and negative emotions, demonstrating its practical utility in healthcare applications. Code available at: https://github.com/RyersonMultimediaLab/EmotionRecognitionUDA