pwc-archive/papers-with-abstracts huggingface dataset:
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization
Authors: Jie Wang, Zhicong Chen, Haodong Zhou, Lin Li, Qingyang Hong
Date: 2023-06-26 00:00:00
Tasks: Clustering, Community Detection, Graph Generation, speaker-diarization, Speaker Diarization

The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings and lack of digging potential relationships between speakers in a session. We propose a novel graph-based clustering approach called Community Detection Graph Convolutional Network (CDGCN) to improve the performance of the speaker diarization system. The CDGCN-based clustering method consists of graph generation, sub-graph detection, and Graph-based Overlapped Speech Detection (Graph-OSD). Firstly, the graph generation refines the local linkages among speech segments. Secondly the sub-graph detection finds the optimal global partition of the speaker graph. Finally, we view speaker clustering for overlap-aware speaker diarization as an overlapped community detection task and design a Graph-OSD component to output overlap-aware labels. By capturing local and global information, the speaker diarization system with CDGCN clustering outperforms the traditional Clustering-based Speaker Diarization (CSD) systems on the DIHARD III corpus.
Farthest Streamline Sampling for the Uniform Distribution of Forearm Muscle Fiber Tracts from Diffusion Tensor Imaging
Authors: Yang Li, Shihan Ma, Jiamin Zhao, Qing Li, Xinjun Sheng
Date: 2023-06-24 00:00:00

Background: Diffusion tensor imaging (DTI) has been used to characterize forearm muscle architecture. Since only uniform sampling is performed for seed points rather than fiber tracts, the tracts may be unevenly distributed in the muscle volume. Purpose: To reconstruct uniformly distributed fiber tracts in human forearm by filtering the tracts from DTI. Assessment: Farthest streamline sampling (FSS) was proposed for filtering and compared with two conventional methods, i.e., two-dimensional sampling and three-dimensional sampling. The uniform coverage performance of the methods was evaluated by streamline coverage (SC) and the coefficient of variation of streamline density (SDCV). Architectural parameters were calculated for 17 forearm muscles. Anatomical correctness was verified by 1. visually assessing the fiber orientation, 2. checking whether the architectural parameters were within physiological ranges, and 3. classifying the architectural types. Results: FSS had the highest SC (0.93+0.04) and the lowest SDCV (0.34+0.06) among the three methods (P<0.05). FSS reduced the sampling of long tracts (10% reduction in fiber length, P<0.05), and the architectural parameters were within physiological ranges (two parameters with P<0.05). The fiber orientation of the tractography was visually consistent with that of the cadaveric specimen. The architectural types of 16 muscles were correctly classified, except for the palmaris longus, which had a linear arrangement of fiber endpoints (R2=0.95+0.02, P<0.001). Data Conclusion: FSS reconstructed more muscle regions and uniformly distributed fiber tracts. The tracts were anatomically correct, indicating the validity of fiber tracts. Key Words: diffusion tensor imaging; forearm muscles; architectural properties
INR-MDSQC: Implicit Neural Representation Multiple Description Scalar Quantization for robust image Coding
Authors: Trung Hieu Le, Xavier Pic, Marc Antonini
Date: 2023-06-24 00:00:00
Tasks: Quantization

Multiple Description Coding (MDC) is an error-resilient source coding method designed for transmission over noisy channels. We present a novel MDC scheme employing a neural network based on implicit neural representation. This involves overfitting the neural representation for images. Each description is transmitted along with model parameters and its respective latent spaces. Our method has advantages over traditional MDC that utilizes auto-encoders, such as eliminating the need for model training and offering high flexibility in redundancy adjustment. Experiments demonstrate that our solution is competitive with autoencoder-based MDC and classic MDC based on HEVC, delivering superior visual quality.
Motion Plane Adaptive Motion Modeling for Spherical Video Coding in H.266/VVC
Authors: Andy Regensky, Christian Herglotz, André Kaup
Date: 2023-06-23 00:00:00
Tasks: Motion Compensation

Motion compensation is one of the key technologies enabling the high compression efficiency of modern video coding standards. To allow compression of spherical video content, special mapping functions are required to project the video to the 2D image plane. Distortions inevitably occurring in these mappings impair the performance of classical motion models. In this paper, we propose a novel motion plane adaptive motion modeling technique (MPA) for spherical video that allows to perform motion compensation on different motion planes in 3D space instead of having to work on the - in theory arbitrarily mapped - 2D image representation directly. The integration of MPA into the state-of-the-art H.266/VVC video coding standard shows average Bj{\o}ntegaard Delta rate savings of 1.72\% with a peak of 3.37\% based on PSNR and 1.55\% with a peak of 2.92\% based on WS-PSNR compared to VTM-14.2.
Improving Spherical Image Resampling through Viewport-Adaptivity
Authors: Andy Regensky, Viktoria Heimann, Ruoyu Zhang, André Kaup
Date: 2023-06-23 00:00:00
Tasks: SSIM

The conversion between different spherical image and video projection formats requires highly accurate resampling techniques in order to minimize the inevitable loss of information. Suitable resampling algorithms such as nearest neighbor, linear or cubic resampling are readily available. However, no generally applicable resampling technique exploits the special properties of spherical images so far. Thus, we propose a novel viewport-adaptive resampling (VAR) technique that takes the spherical characteristics of the underlying resampling problem into account. VAR can be applied to any mesh-to-mesh capable resampling algorithm and shows significant gains across all tested techniques. In combination with frequency-selective resampling, VAR outperforms conventional cubic resampling by more than 2 dB in terms of WS-PSNR. A visual inspection and the evaluation of further metrics such as PSNR and SSIM support the positive results.
MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities
Authors: Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz
Date: 2023-06-19 00:00:00
Tasks: Facial Expression Recognition, Management, RTE

The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective and ubiquitous monitoring systems. In this paper, we present MeciFace, an innovative wearable technology designed to monitor facial expressions and eating activities in real-time on-the-edge (RTE). MeciFace aims to provide a low-power, privacy-conscious, and highly accurate tool for promoting healthy eating behaviors and stress management. We employ lightweight convolutional neural networks as backbone models for facial expression and eating monitoring scenarios. The MeciFace system ensures efficient data processing with a tiny memory footprint, ranging from 11KB to 19 KB. During RTE evaluation, the system achieves an F1-score of < 86% for facial expression recognition and 94% for eating/drinking monitoring, for the RTE of unseen users (user-independent case).
Deep Unfolding Enabled Constant Modulus Waveform Design for Joint Communications and Sensing
Authors: Prashanth Krishnananthalingam, Nhan Thanh Nguyen, Markku Juntti
Date: 2023-06-26 00:00:00

Joint communications and sensing (JCAS) systems have recently emerged as a promising technology to utilize the scarce spectrum in wireless networks and to reuse the same hardware to save infrastructure costs. In practical JCAS systems, dual functional constant-modulus waveforms can be employed to avoid signal distortion in nonlinear power amplifiers. However, the designs of such waveforms are very challenging due to the nonconvex constant-modulus constraint. The conventional branch-and-bound (BnB) method can achieve optimal solution but at the cost of exponential complexity and long run time. In this paper, we propose an efficient deep unfolding method for the constant-modulus waveform design in a multiuser multiple-input multiple-output (MIMO) JCAS system. The deep unfolding model has a sparsely-connected structure and is trained in an unsupervised fashion. It achieves good communications-sensing performance tradeoff while maintaining low computational complexity and low run time. Specifically, our numerical results show that the proposed deep unfolding scheme achieves a similar achievable rate compared to the conventional BnB method with 30 times faster execution time.
A Dual-Band 28/38-GHz Power Amplifier With Inter-Band Suppression in 22-nm FD-SOI CMOS for Multi-Standard mm-Wave 5G Communications
Authors: Abbas Nasri, Alireza Yousefi, Reza Nikandish
Date: 2023-06-26 00:00:00

In this article, we present a dual-band 28/38-GHz power amplifier (PA) with inter-band suppression for millimeter-wave 5G communications. The dual-band operation is achieved using a center-tapped transformer network with an extra resonator which can provide optimum load impedance of the transistor in the two bands and synthesize a short-circuit between the two bands. This feature suppresses the PA signal emissions in the inter band, commonly allocated for other applications. A design procedure is developed for the proposed matching network including physical limits on the quality factor and the coupling coefficient of the transformer. The PA is designed using a 22-nm fully-depleted silicon-on-insulator (FD-SOI) CMOS process. The transistor stacking and a four-path transformer parallel-series power combining techniques are used to achieve high output power using the low-voltage process. The PA achieves simulated performance of 22.6/22.0 dBm saturated output power, 19.8/20.0 dBm output power at 1-dB gain compression, and 33/32 % maximum power-added efficiency (PAE) at 28/38 GHz. The inter-band suppression is 6 dB at 33 GHz.
A Unified Framework for Solving a General Class of Nonconvexly Regularized Convex Models
Authors: Yi Zhang, Isao Yamada
Date: 2023-06-26 00:00:00

Recently, several nonconvex sparse regularizers which can preserve the convexity of the cost function have received increasing attention. This paper proposes a general class of such convexity-preserving (CP) regularizers, termed partially smoothed difference-of-convex (pSDC) regularizer. The pSDC regularizer is formulated as a structured difference-of-convex (DC) function, where the landscape of the subtrahend function can be adjusted by a parameterized smoothing function so as to attain overall-convexity. Assigned with proper building blocks, the pSDC regularizer reproduces existing CP regularizers and opens the way to a large number of promising new ones. With respect to the resultant nonconvexly regularized convex (NRC) model, we derive a series of overall-convexity conditions which naturally embrace the conditions in previous works. Moreover, we develop a unified framework based on DC programming for solving the NRC model. Compared to previously reported proximal splitting type approaches, the proposed framework makes less stringent assumptions. We establish the convergence of the proposed framework to a global minimizer. Numerical experiments demonstrate the power of the pSDC regularizers and the efficiency of the proposed DC algorithm.
Generalized Graph Signal Sampling by Difference-of-Convex Optimization
Authors: Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono
Date: 2023-06-26 00:00:00

We propose a desigining method of a flexible sampling operator for graph signals via a difference-of-convex (DC) optimization algorithm. A fundamental challenge in graph signal processing is sampling, especially for graph signals that are not bandlimited. In order to sample beyond bandlimited graph signals, there are studies to expand the generalized sampling theory for the graph setting. Vertex-wise sampling and flexible sampling are two main strategies to sample graph signals. Recovery accuracy of existing vertex-wise sampling methods is highly dependent on specific vertices selected to generate a sampled graph signal that may compromise the accurary especially when noise is generated at the vertices. In contrast, a flexible sampling mixes values at multiple vertices to generate a sampled signal for robust sampling; however, existing flexible sampling methods impose strict assumptions and aggressive relaxations. To address these limitations, we aim to design a flexible sampling operator without such strict assumptions and aggressive relaxations by introducing DC optimization. By formulating the problem of designing a flexible sampling operator as a DC optimization problem, our method ensures robust sampling for graph signals under arbitrary priors based on generalized sampling theory. We develop an efficient solver based on the general double-proximal gradient DC algorithm, which guarantees convergence to a critical point. Experimental results demonstrate the superiority of our method in sampling and recovering beyond bandlimited graph signals compared to existing approaches.
Faster-Than-Nyquist Symbol-Level Precoding for Wideband Integrated Sensing and Communications
Authors: Zihan Liao, Fan Liu, Ang Li, Christos Masouros
Date: 2023-06-26 00:00:00
Tasks: ISAC, parameter estimation

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework significantly enhances communication throughput while preserving satisfactory sensing performance.
Calibration of Wideband LFM Radars based on Sliding Window Algorithm
Authors: Hyung-Woo Kim, Jin-woo Kim, Jin-ha Kim, Jaeyoung Choi, Sangpyo Hong, Byungkwan Kim
Date: 2023-06-26 00:00:00

This paper addresses the challenges of wideband signal beamforming in radar systems and proposes a new calibration method. Due to operating conditions, the frequency dependent characteristics of the system can be changed, and amplitude, phase, and time delay error can be generated. The proposed method is based on the concept of sliding window algorithm for linear frequency modulated (LFM) signals. To calibrate the frequency-dependent errors from transceiver and the time delay error from true time delay elements, the proposed method utilizes the characteristic of the LFM signal. The LFM signal changes its frequency linearly with time, and the frequency domain characteristics of the hardware are presented in time. Therefore, by applying matched filter to a part of the LFM signal, the frequency dependent characteristics can be monitored and calibrated. The proposed method is compared with the conventional matched filter based calibration results and verified by simulation results and beampatterns. Since the proposed method utilizes LFM signal as calibration tone, the proposed method can be applied to any beamforming systems, not limited to LFM radars.
A New Optimal Subpattern Assignment (OSPA) Metric for Multi-target Filtering
Authors: Tuyet Vu
Date: 2023-06-26 00:00:00

This paper proposes and evaluates a new metric. This metric will overcome a limitation of the Optimal Subpattern Assignment (OSPA) metric mentioned by Schuhmacher et al.: the OSPA distance between two sets of points is insensitive to the the case where one is empty. This proposed metric called Complete OSPA (COSPA), retains all the advantages of the OSPA metric for evaluating the performance of multiple target filtering algorithms while also allowing separate control over the threshold of physical distance errors and cardinality errors.
Propagation Measurements and Coverage Analysis for mmWave and Sub-THz Frequency Bands with Transparent Reflectors
Authors: Ashwini Pondeycherry Ganesh, Wahab Khawaja, Ozgur Ozdemir, Ismail Guvenc, Hiroyuki Nomoto, Yasuaki Ide
Date: 2023-06-25 00:00:00

The emerging 5G and future 6G technologies are envisioned to provide higher bandwidths and coverage using millimeter wave (mmWave) and sub-Terahertz (THz) frequency bands. The growing demand for higher data rates using these bands can be addressed by overcoming high path loss, especially for non-line-of-sight (NLOS) scenarios. In this work, we investigate the use of passive transparent reflectors to improve signal coverage in an NLOS indoor scenario. Measurements are conducted to characterize the maximum reflectivity property of the transparent reflector using channel sounder equipment from NI. Flat and curved reflectors, each with a size of 16 inches by 16 inches, are used to study coverage improvements with different reflector shapes and orientations. The measurement results using passive metallic reflectors are also compared with the ray-tracing-based simulations, to further corroborate our inferences. The analysis reveals that the transparent reflector outperforms the metal reflector and increases the radio propagation coverage in all three frequencies of interest: 28~GHz, 39~GHz, and 120~GHz. Using transparent reflectors, there is an increase in peak received power that is greater than 5~dB for certain scenarios compared to metallic reflectors when used in flat mode, and greater than 3~dB when used in curved (convex) mode.
Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning
Authors: Rafid Umayer Murshed, Kazi Noshin, Md. Anu Zakaria, Md. Forkan Uddin, A. F. M. Saiful Amin, Mohammed Eunus Ali
Date: 2023-06-25 00:00:00
Tasks: Contrastive Learning, Graph Neural Network

Seismic intensity prediction from early or initial seismic waves received by a few seismic stations can enhance Earthquake Early Warning (EEW) systems, particularly in ground motion-based approaches like PLUM. While many operational EEW systems currently utilize point-source-based models that estimate the warning area based on magnitude and distance measures, direct intensity prediction offers a potential improvement in accuracy and reliability. In this paper, we propose a novel deep learning approach, Seismic Contrastive Graph Neural Network (SC-GNN), for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms from a few seismic stations. The SC-GNN consists of two key components: (i) a graph neural network (GNN) to propagate spatiotemporal information through a graph-like structure representing seismic station distribution and wave propagation, and (ii) a self-supervised contrastive learning component to train the network with larger time windows and enable predictions using shorter initial waveforms. The efficacy of our approach is demonstrated through experiments on three real-world seismic datasets, where it shows superior performance over existing techniques, including a significant reduction in mean squared error (MSE) and the lowest standard deviation of error, indicating its robustness, reliability, and strong positive relationship between predicted and actual values. Notably, the SC-GNN model maintains superior performance even with 5s input waveforms, making it especially suitable for enhancing EEW applications.
Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks
Authors: Priyanka Maity, Suraj Srivastava, Kunwar Pritiraj Rajput, Naveen K. D. Venkategowda, Aditya K. Jagannatham, Lajos Hanzo
Date: 2023-06-25 00:00:00
Tasks: Benchmarking, parameter estimation

Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs). More explicitly, efficient pre- and post-processing of the sensor observations and received signal are proposed for the minimum mean square error (MMSE) estimation of a parameter vector. The proposed techniques exploit the limited scattering nature of the mmWave MIMO channel for formulating the hybrid transceiver design framework as a multiple measurement vectors (MMV)-based sparse signal recovery problem. This is then solved using the iterative appealingly low-complexity simultaneous orthogonal matching pursuit (SOMP). Tailor-made designs are presented for WSNs operating under both total and per-sensor power constraints, while considering ideal noiseless as well as realistic noisy sensors. Furthermore, both the Bayesian Cramer-Rao lower bound and the centralized MMSE bound are derived for benchmarking the proposed decentralized estimation schemes. Our simulation results demonstrate the efficiency of the designs advocated and verify the analytical findings.
Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines
Authors: Xiang Cheng, Haotian Zhang, Jianan Zhang, Shijian Gao, Sijiang Li, Ziwei Huang, Lu Bai, Zonghui Yang, Xinhu Zheng, Liuqing Yang
Date: 2023-06-25 00:00:00
Tasks: ISAC

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.