pwc-archive/papers-with-abstracts huggingface dataset:
Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping
Authors: Metodi P. Yankov, Ognjen Jovanovic, Darko Zibar, Francesco Da Ros
Date: 2023-07-19 00:00:00

A many-to-one mapping geometric constellation shaping scheme is proposed with a fixed modulation format, fixed FEC engine and rate adaptation with an arbitrarily small step. An autoencoder is used to optimize the labelings and constellation points' positions.
Radar-Based Noncontact Measurement of Heartbeat of Humans and Chimpanzees Using Millimeter-Wave Radar with Topology Method
Authors: Itsuki Iwata, Takuya Sakamoto, Takuya Matsumoto, Satoshi Hirata
Date: 2023-07-19 00:00:00

This study proposes a method to determine the filter parameters required for the topology method, which is a radar-based noncontact method for measurement of heart inter-beat intervals. The effectiveness of the proposed method is evaluated by performing radar measurements involving both human participants and chimpanzee subjects. The proposed method is designed to enable setting of the filter cutoff frequency to eliminate respiratory components while maintaining the higher harmonics of the heartbeat components. Measurements using a millimeter-wave radar system and a reference contact-type electrocardiogram sensor demonstrate that the smallest errors that occur when measuring heart inter-beat intervals using the proposed method can be as small as 4.43 and 2.55 ms for humans and chimpanzees, respectively. These results indicate the possibility of using noncontact physiological measurements to monitor both humans and chimpanzees.
Improved Label Design for Timing Synchronization in OFDM Systems against Multi-path Uncertainty
Authors: Chaojin Qing, Shuhai Tang, Na Yang, Chuangui Rao, Jiafan Wang
Date: 2023-07-19 00:00:00

Timing synchronization (TS) is vital for orthogonal frequency division multiplexing (OFDM) systems, which makes the discrete Fourier transform (DFT) window start at the inter-symbol-interference (ISI)-free region. However, the multi-path uncertainty in wireless communication scenarios degrades the TS correctness. To alleviate this degradation, we propose a learning-based TS method enhanced by improving the design of training label. In the proposed method, the classic cross-correlator extracts the initial TS feature for benefiting the following machine learning. Wherein, the network architecture unfolds one classic cross-correlation process. Against the multi-path uncertainty, a novel training label is designed by representing the ISI-free region and especially highlighting its approximate midpoint. Therein, the closer to the region boundary of ISI-free the smaller label values are set, expecting to locate the maximum network output in ISI-free region with a high probability. Then, to guarantee the correctness of labeling, we exploit the priori information of line-of-sight (LOS) to form a LOS-aided labeling. Numerical results confirm that, the proposed training label effectively enhances the correctness of the proposed TS learner against the multi-path uncertainty.
A method to assess Granger causality, isolation and autonomy in the time and frequency domains: theory and application to cerebrovascular variability
Authors: Laura Sparacino, Yuri Antonacci, Chiara Barà, Angela Valenti, Alberto Porta, Luca Faes
Date: 2023-07-18 00:00:00

Concepts of Granger causality (GC) and Granger autonomy (GA) are central to assess the dynamics of coupled physiologic processes. While causality measures have been already proposed and applied in time and frequency domains, measures quantifying self-dependencies are still limited to the time-domain formulation and lack of a clear spectral representation. We embed into the classical linear parametric framework for computing GC from a driver random process X to a target process Y a measure of Granger Isolation (GI) quantifying the part of the dynamics of Y not originating from X, and a new spectral measure of GA assessing frequency-specific patterns of self-dependencies in Y. The measures are illustrated in theoretical simulations and applied to time series of mean arterial pressure and cerebral blood flow velocity obtained in subjects prone to develop postural syncope and healthy controls. Simulations show that GI is complementary to GC but not trivially related to it, while GA reflects the regularity of the internal dynamics of the analyzed target process. In the application to cerebrovascular interactions, spectral GA quantified the physiological response to postural stress of slow cerebral blood flow oscillations, while spectral GC and GI detected an altered response to postural stress in subjects prone to syncope, likely related to impaired cerebral autoregulation. The new spectral measures of GI and GA are useful complements to GC for the analysis of interacting oscillatory processes, and detect physiological and pathological responses to postural stress which cannot be traced in the time domain. The assessment of causality, isolation and autonomy opens new perspectives for the analysis of coupled biological processes in both physiological and clinical investigations.
A Dual Band Printed F Antenna using a Trap with small band separation
Authors: Prasad Samudrala, Justin Jose, Amit Kulkarni
Date: 2023-07-18 00:00:00

Trap antenna is well known method and has many applications. With this method, trap(s) are used on antenna to block currents of some frequencies and so electrically divide the antenna into multiple segments and thus one antenna can work on multiple frequencies. In this paper, trap antenna method is used to design a dual band Sub GHz printed F-antenna. The antenna is printed on FR4 board to achieve low cost solution. The two bands are 865-870 MHz and 902-928 MHz. The challenge of this design is that the frequency separation of the two bands is very small. In this case, and also the extra section for low frequency band is too small. Then, the influence of trap LC component variation due to tolerance to the two resonant frequencies is big, and so it is difficult to achieve good in band return loss within the LC tolerance. This is the main difficulty of this design. The problem is solved by placing the low band section away from the end of the antenna.
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks
Authors: Sudhanshu Arya, Yifeng Peng, Jingda Yang, Ying Wang
Date: 2023-07-18 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

In this paper, we present a novel distributed UAVs beam reforming approach to dynamically form and reform a space-selective beam path in addressing the coexistence with satellite and terrestrial communications. Despite the unique advantage to support wider coverage in UAV-enabled cellular communications, the challenges reside in the array responses' sensitivity to random rotational motion and the hovering nature of the UAVs. A model-free reinforcement learning (RL) based unified UAV beam selection and tracking approach is presented to effectively realize the dynamic distributed and collaborative beamforming. The combined impact of the UAVs' hovering and rotational motions is considered while addressing the impairment due to the interference from the orbiting satellites and neighboring networks. The main objectives of this work are two-fold: first, to acquire the channel awareness to uncover its impairments; second, to overcome the beam distortion to meet the quality of service (QoS) requirements. To overcome the impact of the interference and to maximize the beamforming gain, we define and apply a new optimal UAV selection algorithm based on the brute force criteria. Results demonstrate that the detrimental effects of the channel fading and the interference from the orbiting satellites and neighboring networks can be overcome using the proposed approach. Subsequently, an RL algorithm based on Deep Q-Network (DQN) is developed for real-time beam tracking. By augmenting the system with the impairments due to hovering and rotational motion, we show that the proposed DQN algorithm can reform the beam in real-time with negligible error. It is demonstrated that the proposed DQN algorithm attains an exceptional performance improvement. We show that it requires a few iterations only for fine-tuning its parameters without observing any plateaus irrespective of the hovering tolerance.
A Blender-based channel simulator for FMCW Radar
Authors: YuAn Liu, Moein Ahmadi, Johann Fuchs, Mohammad Alaee-Kerahroodi, M. R. Bhavani Shankar
Date: 2023-07-18 00:00:00

Radar simulation is a promising way to provide data-cube with effectiveness and accuracy for AI-based approaches to radar applications. This paper develops a channel simulator to generate frequency-modulated continuous-wave (FMCW) waveform multiple inputs multiple outputs (MIMO) radar signals. In the proposed simulation framework, an open-source animation tool called Blender is utilized to model the scenarios and render animations. The ray tracing (RT) engine embedded can trace the radar propagation paths, i.e., the distance and signal strength of each path. The beat signal models of time division multiplexing (TDM)-MIMO are adapted to RT outputs. Finally, the environment-based models are simulated to show the validation.
Successive Linear Approximation VBI for Joint Sparse Signal Recovery and Dynamic Grid Parameters Estimation
Authors: Wenkang Xu, An Liu, Bingpeng Zhou, MinJian Zhao
Date: 2023-07-18 00:00:00
Tasks: Bayesian Inference, compressed sensing

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed sensing (CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have double-loop iterations, where the inner loop (E-step) obtains a Bayesian estimation of sparse signals and the outer loop (M-step) obtains a point estimation of dynamic grid parameters. This leads to a slow convergence rate. Furthermore, each iteration of the E-step involves a complicated matrix inverse in general. To overcome these drawbacks, we first propose a successive linear approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of both sparse signals and dynamic grid parameters. Besides, we simplify the matrix inverse operation based on the majorization-minimization (MM) algorithmic framework. In addition, we extend our proposed algorithm from an independent sparse prior to more complicated structured sparse priors, which can exploit structured sparsity in specific applications to further enhance the performance. Finally, we apply our proposed algorithm to solve two practical application problems in wireless communications and verify that the proposed algorithm can achieve faster convergence, lower complexity, and better performance compared to the state-of-the-art EM-based methods.
Radar-Based Estimation of Human Body Orientation Using Respiratory Features and Hierarchical Regression Model
Authors: Wenxu Sun, Shunsuke Iwata, Yuji Tanaka, Takuya Sakamoto
Date: 2023-07-18 00:00:00
Tasks: regression

This study proposes an accurate method to estimate human body orientation using a millimeter-wave radar system. Body displacement is measured from the phase of the radar echo, which is analyzed to obtain features associated with the fundamental and higher-order harmonic components of the quasi-periodic respiratory motion. These features are used in body-orientation estimation invoking a novel hierarchical regression model in which a logistic regression model is adopted in the first step to determine whether the target person is facing forwards or backwards; a pair of ridge regression models are employed in the second step to estimate body-orientation angle. To evaluate the performance of the proposed method, respiratory motions of five participants were recorded using three millimeter-wave radar systems; cross-validation was also performed. The average error in estimating body orientation angle was 38.3$^\circ$ and 23.1$^\circ$ using respectively a conventional method with only the fundamental frequency component and our proposed method, indicating an improvement in accuracy by factor 1.7 when using the proposed method. In addition, the coefficient of correlation between the actual and estimated body-orientation angles using the conventional and proposed methods are 0.74 and 0.91, respectively. These results show that by combining the characteristic features of the fundamental and higher-order harmonics from the respiratory motion, the proposed method offers better accuracy.
Matrix Profile based Anomaly Detection in Streaming Gait Data for Fall Prevention
Authors: Branislav Gerazov, Elena Hadzieva, Andrei Krivosei, Fiorella Ines Soto Sanchez, Jakob Rostovski, Alar Kuusik, Mahtab Alam
Date: 2023-07-18 00:00:00
Tasks: Anomaly Detection

The automatic detection of gait anomalies can lead to systems that can be used for fall detection and prevention. In this paper, we present a gait anomaly detection system based on the Matrix Profile (MP) algorithm. The MP algorithm is exact, parameter free, simple and efficient, making it a perfect candidate for on the edge deployment. We propose a gait anomaly detection system that is able to adapt to an individual's gait pattern and successfully detect anomalous steps with short latency. To evaluate the system we record a small database of enacted anomalous steps. The results show the system outperforms a more complex Neural Network baseline.
Recent Developments in Contactless Monitoring Vital Signs Using Radar Devices
Authors: Gabriel Beltrão, Udo Schroeder
Date: 2023-07-18 00:00:00

Continuous monitoring of vital signs is of paramount importance. These critical physiological parameters play a crucial role in the early detection of conditions that affect the well-being of a patient. However, conventional contact-based devices are inappropriate for long-term monitoring. Besides mobility restrictions, they can cause epidermal damage, and even lead to pressure necrosis. In this paper we present a selection of recent works towards enabling the contactless monitoring of vital signs using radar devices. The selected contributions are threefold: an algorithm for recovering the chest wall movements from radar signals; a random body movement and interference mitigation technique; and a robust and accurate adaptive estimation framework. These contributions were tested in different scenarios, spanning ideal simulation settings, real data collected while imitating common working conditions in an office environment, and a complete validation with premature babies in a critical care environment.
Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars
Authors: Lulu Liu, Runwei Guan, Fei Ma, Jeremy Smith, Yutao Yue
Date: 2023-07-18 00:00:00
Tasks: Autonomous Vehicles, Denoising, GPU

With its small size, low cost and all-weather operation, millimeter-wave radar can accurately measure the distance, azimuth and radial velocity of a target compared to other traffic sensors. However, in practice, millimeter-wave radars are plagued by various interferences, leading to a drop in target detection accuracy or even failure to detect targets. This is undesirable in autonomous vehicles and traffic surveillance, as it is likely to threaten human life and cause property damage. Therefore, interference mitigation is of great significance for millimeter-wave radar-based target detection. Currently, the development of deep learning is rapid, but existing deep learning-based interference mitigation models still have great limitations in terms of model size and inference speed. For these reasons, we propose Radar-STDA, a Radar-Spatial Temporal Denoising Autoencoder. Radar-STDA is an efficient nano-level denoising autoencoder that takes into account both spatial and temporal information of range-Doppler maps. Among other methods, it achieves a maximum SINR of 17.08 dB with only 140,000 parameters. It obtains 207.6 FPS on an RTX A4000 GPU and 56.8 FPS on an NVIDIA Jetson AGXXavier respectively when denoising range-Doppler maps for three consecutive frames. Moreover, we release a synthetic data set called Ra-inf for the task, which involves 384,769 range-Doppler maps with various clutters from objects of no interest and receiver noise in realistic scenarios. To the best of our knowledge, Ra-inf is the first synthetic dataset of radar interference. To support the community, our research is open-source via the link \url{https://github.com/GuanRunwei/rd_map_temporal_spatial_denoising_autoencoder}.
A Hybrid Optimization and Deep RL Approach for Resource Allocation in Semi-GF NOMA Networks
Authors: Duc-Dung Tran, Vu Nguyen Ha, Symeon Chatzinotas, Ti Ti Nguyen
Date: 2023-07-18 00:00:00
Tasks: Management

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.
Comparison of HRV Indices of ECG and BCG Signals
Authors: Kavya Remesh, Job Chunkath
Date: 2023-07-18 00:00:00
Tasks: Electrocardiography (ECG), Heart Rate Variability

Electrocardiography (ECG) plays a significant role in diagnosing heart-related issues, it provides, accurate, fast, and dependable insights into crucial parameters like QRS complex duration, the R-R interval, and the occurrence, amplitude, and duration of P, R, and T waves. However, utilizing ECG for prolonged monitoring poses challenges as it necessitates connecting multiple electrodes to the patient's body. This can be discomforting and disruptive, hampering the attainment of uninterrupted recordings. Ballistocardiography (BCG) emerges as a promising substitute for ECG, presenting a non-invasive technique for recording the heart's mechanical activity. BCG signals can be captured using sensors positioned beneath the bed, thereby providing enhanced comfort and convenience for long-term monitoring of the subject. In a recent study, researchers compared the heart rate variability (HRV) indices derived from simultaneously acquired ECG and BCG signals. Encouragingly, the BCG signal yielded satisfactory results similar to those obtained from ECG, implying that BCG holds potential as a viable alternative for prolonged monitoring. The findings of this study carry substantial implications for the advancement of innovative, non-invasive methods in monitoring heart health. BCG showcases the ability to offer a more comfortable and convenient alternative to ECG while retaining its capacity to deliver accurate and reliable cardiac information concerning a patient's condition.
On intermediate Marginals in Martingale Optimal Transportation
Authors: Julian Sester
Date: 2023-07-19 00:00:00

We study the influence of additional intermediate marginal distributions on the value of the martingale optimal transport problem. From a financial point of view, this corresponds to taking into account call option prices not only, as usual, for those call options where the respective future maturities coincide with the maturities of some exotic derivative but also additional maturities and then to study the effect on model-independent price bounds for the exotic derivative. We characterize market settings, i.e., combinations of the payoff of exotic derivatives, call option prices and marginal distributions that guarantee improved price bounds as well as those market settings that exclude any improvement. Eventually, we showcase in numerous examples that the consideration of additional price information on vanilla options may have a considerable impact on the resultant model-independent price bounds.
Rough PDEs for local stochastic volatility models
Authors: Peter Bank, Christian Bayer, Peter K. Friz, Luca Pelizzari
Date: 2023-07-18 00:00:00

In this work, we introduce a novel pricing methodology in general, possibly non-Markovian local stochastic volatility (LSV) models. We observe that by conditioning the LSV dynamics on the Brownian motion that drives the volatility, one obtains a time-inhomogeneous Markov process. Using tools from rough path theory, we describe how to precisely understand the conditional LSV dynamics and reveal their Markovian nature. The latter allows us to connect the conditional dynamics to so-called rough partial differential equations (RPDEs), through a Feynman-Kac type of formula. In terms of European pricing, conditional on realizations of one Brownian motion, we can compute conditional option prices by solving the corresponding linear RPDEs, and then average over all samples to find unconditional prices. Our approach depends only minimally on the specification of the volatility, making it applicable for a wide range of classical and rough LSV models, and it establishes a PDE pricing method for non-Markovian models. Finally, we present a first glimpse at numerical methods for RPDEs and apply them to price European options in several rough LSV models.
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Authors: Diego E. Kleiman, Hassan Nadeem, Diwakar Shukla
Date: 2023-07-18 00:00:00

Molecular Dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long timescales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased and path-sampling methods. In this perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds, rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by machine learning techniques as well as their shortcomings.
Scalable Deep Learning for RNA Secondary Structure Prediction
Authors: Jörg K. H. Franke, Frederic Runge, Frank Hutter
Date: 2023-07-14 00:00:00
Tasks: Deep Learning, Prediction

The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques. In this work, we present the RNAformer, a lean deep learning model using axial attention and recycling in the latent space. We gain performance improvements by designing the architecture for modeling the adjacency matrix directly in the latent space and by scaling the size of the model. Our approach achieves state-of-the-art performance on the popular TS0 benchmark dataset and even outperforms methods that use external information. Further, we show experimentally that the RNAformer can learn a biophysical model of the RNA folding process.