pwc-archive/papers-with-abstracts huggingface dataset:
An Ontology-based Method to Identify Triggering Conditions for Perception Insufficiency of Autonomous Vehicles
Authors: Xingyu Xing, Tong Jia, Junyi Chen, Lu Xiong, Zhuoping Yu
Date: 2022-10-17 00:00:00
Tasks: Autonomous Vehicles

The autonomous vehicle (AV) is a safety-critical system relying on complex sensors and algorithms. The AV may confront risk conditions if these sensors and algorithms misunderstand the environment and situation, even though all components are fault-free. The ISO 21448 defined the safety of the intended functionality (SOTIF), aiming to enhance the AV's safety by specifying AV's development and validation process. As required in the ISO 21448, the triggering conditions, which may lead to the vehicle's functional insufficiencies, should be analyzed and verified. However, there is not yet a method to realize a comprehensive and systematic identification of triggering conditions so far. This paper proposed an analysis framework of triggering conditions for the perception system based on the propagation chain of events model, which consists of triggering source, influenced perception stage, and triggering effect. According to the analysis framework, ontologies of triggering source and perception stage were constructed, and the relationships between concepts in ontologies are defined. According to these ontologies, triggering conditions can be generated comprehensively and systematically. The proposed method was applied on an L3 autonomous vehicle, and 20 from 87 triggering conditions identified were tested in the field, among which eight triggering conditions triggered risky behaviors of the vehicle.
Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis
Authors: Paris A. Karakasis, Athanasios P. Liavas, Nicholas D. Sidiropoulos, Panagiotis G. Simos, Efrosini Papadaki
Date: 2022-10-16 00:00:00

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).
Combining band-frequency separation and deep neural networks for optoacoustic imaging
Authors: Martin G. Gonzalez, Matias Vera, Leonardo Rey Vega
Date: 2022-10-14 00:00:00
Tasks: Image Reconstruction

In this paper we consider the problem of image reconstruction in optoacoustic tomography. In particular, we devise a deep neural architecture that can explicitly take into account the band-frequency information contained in the sinogram. This is accomplished by two means. First, we jointly use a linear filtered back-projection method and a fully dense UNet for the generation of the images corresponding to each one of the frequency bands considered in the separation. Secondly, in order to train the model, we introduce a special loss function consisting of three terms: (i) a separating frequency bands term; (ii) a sinogram-based consistency term and (iii) a term that directly measures the quality of image reconstruction and which takes advantage of the presence of ground-truth images present in training dataset. Numerical experiments show that the proposed model, which can be easily trainable by standard optimization methods, presents an excellent generalization performance quantified by a number of metrics commonly used in practice. Also, in the testing phase, our solution has a comparable (in some cases lower) computational complexity, which is a desirable feature for real-time implementation of optoacoustic imaging.
Virtual-Reality based Vestibular Ocular Motor Screening for Concussion Detection using Machine-Learning
Authors: Khondker Fariha Hossain, Sharif Amit Kamran, Prithul Sarker, Philip Pavilionis, Isayas Adhanom, Nicholas Murray, Alireza Tavakkoli
Date: 2022-10-13 00:00:00
Tasks: Management

Sport-related concussion (SRC) depends on sensory information from visual, vestibular, and somatosensory systems. At the same time, the current clinical administration of Vestibular/Ocular Motor Screening (VOMS) is subjective and deviates among administrators. Therefore, for the assessment and management of concussion detection, standardization is required to lower the risk of injury and increase the validation among clinicians. With the advancement of technology, virtual reality (VR) can be utilized to advance the standardization of the VOMS, increasing the accuracy of testing administration and decreasing overall false positive rates. In this paper, we experimented with multiple machine learning methods to detect SRC on VR-generated data using VOMS. In our observation, the data generated from VR for smooth pursuit (SP) and the Visual Motion Sensitivity (VMS) tests are highly reliable for concussion detection. Furthermore, we train and evaluate these models, both qualitatively and quantitatively. Our findings show these models can reach high true-positive-rates of around 99.9 percent of symptom provocation on the VR stimuli-based VOMS vs. current clinical manual VOMS.
Learning Texture Transformer Network for Light Field Super-Resolution
Authors: Javeria Shabbir, M. Zeshan Alam, M. Umair Mukati
Date: 2022-10-09 00:00:00
Tasks: Super-Resolution

Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolution perspective image which serves as a reference image for the second module, i.e. TTSR, which in turn produces a high-resolution light field. The last module refines the spatial resolution by imposing a light field prior. The results demonstrate around 4 dB to 6 dB PSNR gain over a bicubically resized light field image
An Improved Multi-State Constraint Kalman Filter for Visual-Inertial Odometry
Authors: M. R. Abdollahi, Seid H. Pourtakdoust, M. H. Yoosefian Nooshabadi, H. N. Pishkenari
Date: 2022-10-04 00:00:00
Tasks: Pose Estimation

Fast pose estimation (PE) is of vital importance for successful mission performance of agile autonomous robots. Global Positioning Systems such as GPS and GNSS have been typically used in fusion with Inertial Navigation Systems (INS) for PE. However, the low update rate and lack of proper signals make their utility impractical for indoor and urban applications. On the other hand, Visual-Inertial Odometry (VIO) is gaining popularity as a practical alternative for GNSS/INS systems in GPS-denied environments. Among the many VIO-based methods, the Multi-State Constraint Kalman Filter (MSCKF) has received a greater attention due to its robustness, speed and accuracy. To this end, the high computational cost associated with image processing for real-time implementation of MSCKF on resource-constrained vehicles is still a challenging ongoing research. In this paper, an enhanced version of the MSCKF is proposed. To this aim, different feature marginalization and state pruning strategies are suggested that result in a much faster algorithm. The proposed algorithm is tested both on an open-source dataset and in real-world experiments for validation. It is demonstrated that the proposed Fast-MSCKF (FMSCKF) is about six times faster and at least 20% more accurate in final position estimation than the standard MSCKF algorithm.
A Closed-form Expression for the ISRS GN Model Supporting Distributed Raman Amplification
Authors: H. Buglia, M. Jarmolovicius, A. Vasylchenkova, E. Sillekens, R. I. Killey, P. Bayvel, L. Galdino
Date: 2022-10-17 00:00:00
Tasks: Form

A closed-form model for the nonlinear interference in distributed Raman amplified links is presented, the formula accounts for both forward and backward pumping.
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices
Authors: Lucas Drumetz, Alexandre Reiffers-Masson, Naoufal El Bekri, Franck Vermet
Date: 2022-10-17 00:00:00
Tasks: Numerical Integration

In many applications, one encounters signals that lie on manifolds rather than a Euclidean space. In particular, covariance matrices are examples of ubiquitous mathematical objects that have a non Euclidean structure. The application of Euclidean methods to integrate differential equations lying on such objects does not respect the geometry of the manifold, which can cause many numerical issues. In this paper, we propose to use Lie group methods to define geometry-preserving numerical integration schemes on the manifold of symmetric positive definite matrices. These can be applied to a number of differential equations on covariance matrices of practical interest. We show that they are more stable and robust than other classical or naive integration schemes on an example.
Waveform Design for Optimal PSL Under Spectral and Unimodular Constraints via Alternating Minimization
Authors: Chin-wei Huang, Li-Fu Chen, Borching Su
Date: 2022-10-16 00:00:00
Tasks: parameter estimation

In an active sensing system, waveforms with good auto-correlations are preferred for accurate parameter estimation. Furthermore, spectral compatibility is required to avoid mutual interference between devices as the electromagnetic environment becomes increasingly crowded. Waveforms should also be unimodular due to hardware limits. In this paper, a new approach to generating a unimodular sequence with an approximately optimal peak side-lobe level (PSL) in auto-correlation and adjustable stopband attenuation is proposed. The proposed method is based on alternating minimization (AM) and numerical results suggest that it outperforms existing methods in terms of PSL. We also develop a theoretical lower bound for the PSL minimization problem under spectral constraints and unimodular constraints, which can be used for the evaluation of the results in various works about this waveform design problem. It is observed in the numerical results that the PSL of the proposed algorithm is close to the derived lower bound.
Two-Timescale Transmission Design for RIS-Aided Cell-Free Massive MIMO Systems
Authors: Jianxin Dai, Jin Ge, Kangda Zhi, Cunhua Pan, Zaichen Zhang, Jiangzhou Wang, Xiaohu You
Date: 2022-10-16 00:00:00
Tasks: CPU

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the central processing unit (CPU) utilizes the maximum ratio combining (MRC) technology to perform fully centralized processing based on the instantaneous overall channel, which are the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form approximate expression of the uplink achievable rate for arbitrary numbers of access point (AP) antennas and RIS reflecting elements. Relying on the derived expressions, we theoretically analyze the benefits of deploying RIS into cell-free mMIMO systems and draw explicit insights. Then, based on the closed-form approximate rate expression under statistical CSI, we optimize the phase shifts of RISs based on the genetic algorithm (GA) to maximize the sum rate and minimum rate of users, respectively. Finally, the numerical results demonstrate the correctness of our derived expressions and the benefits of deploying large-size RISs into cell-free mMIMO systems. Also, we investigate the optimality and convergence behaviors of the GA to verify its effectiveness. To give more beneficial analysis, we give the closed-form expression of the energy efficiency and present numerical results to show the high energy efficiency of the system with the help of RISs. Besides, our results have revealed the benefits of distributed deployment of APs and RISs in the RIS-aided mMIMO system with cell-free networks.
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithm
Authors: Gwenevere Frank, Scott Makeig, Arnaud Delorme
Date: 2022-10-16 00:00:00
Tasks: blind source separation, EEG, Electroencephalogram (EEG)

Independent component analysis (ICA), is a blind source separation method that is becoming increasingly used to separate brain and non-brain related activities in electroencephalographic (EEG) and other electrophysiological recordings. It can be used to extract effective brain source activities and estimate their cortical source areas, and is commonly used in machine learning applications to classify EEG artifacts. Previously, we compared results of decomposing 13 71-channel scalp EEG datasets using 22 ICA and other blind source separation (BSS) algorithms. We are now making this framework available to the scientific community and, in the process of its release are testing a recent ICA algorithm (Picard) not included in the previous assay. Our test framework uses three main metrics to assess BSS performance: Pairwise Mutual Information (PMI) between scalp channel pairs; PMI remaining between component pairs after decomposition; and, the complete (not pairwise) Mutual Information Reduction (MIR) produced by each algorithm. We also measure the "dipolarity" of the scalp projection maps for the decomposed component, defined by the number of components whose scalp projection maps nearly match the projection of a single equivalent dipole. Within this framework, Picard performed similarly to Infomax ICA. This is not surprising since Picard is a type of Infomax algorithm that uses the L-BFGS method for faster convergence, in contrast to Infomax and Extended Infomax (runica) which use gradient descent. Our results show that Picard performs similarly to Infomax and, likewise, better than other BSS algorithms, excepting the more computationally complex AMICA. We have released the source code of our framework and the test data through GitHub.
Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security
Authors: Nanchi Su, Fan Liu, Christos Masouros
Date: 2022-10-15 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate the sensing-aided physical layer security (PLS) towards Integrated Sensing and Communication (ISAC) systems. A well-known limitation of PLS is the need to have information about potential eavesdroppers (Eves). The sensing functionality of ISAC offers an enabling role here, by estimating the directions of potential Eves to inform PLS. In our approach, the ISAC base station (BS) firstly emits an omni-directional waveform to search for potential Eves' directions by employing the combined Capon and approximate maximum likelihood (CAML) technique. Using the resulting information about potential Eves, we formulate secrecy rate expressions, that are a function of the Eves' estimation accuracy. We then formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the CRB with the aid of the artificial noise (AN), and minimize the CRB of targets'/Eves' estimation. By taking the possible estimation errors into account, we enforce a beampattern constraint with a wide main beam covering all possible directions of Eves. This implicates that security needs to be enforced in all these directions. By improving estimation accuracy, the sensing and security functionalities provide mutual benefits, resulting in improvement of the mutual performances with every iteration of the optimization, until convergence. Our results avail of these mutual benefits and reveal the usefulness of sensing as an enabler for practical PLS.
Compressive Sensing of ECG Signals using Plug-and-Play Regularization
Authors: Unni VS, Ruturaj Gavaskar, Kunal Narayan Chaudhury
Date: 2022-10-15 00:00:00
Tasks: Compressive Sensing, Data Compression

Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements remains a challenging problem. Traditional recovery methods are based on solving a regularized minimization problem, where a sparsity-promoting prior is used. In this paper, we propose an alternative iterative recovery algorithm based on the Plug-and-Play (PnP) method, which has recently become popular for imaging problems. In PnP, a powerful denoiser is used to implicitly perform regularization, instead of using hand-crafted regularizers; this has been found to be more successful than traditional methods. In this work, we use a PnP version of the Proximal Gradient Descent (PGD) algorithm for ECG recovery. To ensure mathematical convergence of the PnP algorithm, the signal denoiser in question needs to satisfy some technical conditions. We use a high-quality ECG signal denoiser fulfilling this condition by learning a Bayesian prior for small-sized signal patches. This guarantees that the proposed algorithm converges to a fixed point irrespective of the initialization. Importantly, through extensive experiments, we show that the reconstruction quality of the proposed method is superior to that of state-of-the-art methods.
Full-Duplex Transceivers for Next-Generation Wireless Communication Systems
Authors: Ian P. Roberts, Himal A. Suraweera
Date: 2022-10-14 00:00:00

Wireless communication systems can be enhanced at the link level, in medium access, and at the network level when transceivers are equipped with full-duplex capability: the transformative ability to simultaneously transmit and receive over the same frequency spectrum. Effective methods to cancel self-interference are required to facilitate full-duplex operation, which we overview herein in the context of traditional radios, along with those in next-generation wireless networks. We highlight advances in self-interference cancellation that leverage machine learning, and we summarize key considerations and recent progress in full-duplex millimeter-wave systems and their application in integrated access and backhaul. We present example design problems and noteworthy findings from recent experimental research to introduce and motivate the advancement of full-duplex millimeter-wave systems. We conclude this chapter by forecasting the future of full-duplex and outlining important research directions that warrant further study.
Spatial and Statistical Modeling of Multi-Panel Millimeter Wave Self-Interference
Authors: Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath, Jeffrey G. Andrews
Date: 2022-10-14 00:00:00

Characterizing self-interference is essential to the design and evaluation of in-band full-duplex communication systems. Until now, little has been understood about this coupling in full-duplex systems operating at millimeter wave (mmWave) frequencies, and it has been shown that the highly-idealized models proposed for such do not align with practice. This work presents the first spatial and statistical model of mmWave self-interference backed by measurements, enabling engineers to draw realizations that exhibit the large-scale and small-scale spatial characteristics observed in our nearly 6.5 million measurements taken at 28 GHz. Core to our model is its use of system and model parameters having real-world meaning, which facilitates its extension to systems beyond our own phased array platform through proper parameterization. We demonstrate this by collecting nearly 13 million additional measurements to show that our model can generalize to two other system configurations. We assess our model by comparing it against actual measurements to confirm its ability to align spatially and in distribution with real-world self-interference. In addition, using both measurements and our model of self-interference, we evaluate an existing beamforming-based full-duplex mmWave solution to illustrate that our model can be reliably used to design new solutions and validate the performance improvements they may offer.
ANFIS-based prediction of power generation for combined cycle power plant
Authors: Mary Pa, Amin Kazemi
Date: 2022-10-07 00:00:00
Tasks: Time Series, Time Series Analysis

This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to predict the generated electrical power in a combined cycle power plant. The ANFIS architecture is implemented in MATLAB through a code that utilizes a hybrid algorithm that combines gradient descent and the least square estimator to train the network. The Model is verified by applying it to approximate a nonlinear equation with three variables, the time series Mackey-Glass equation and the ANFIS toolbox in MATLAB. Once its validity is confirmed, ANFIS is implemented to forecast the generated electrical power by the power plant. The ANFIS has three inputs: temperature, pressure, and relative humidity. Each input is fuzzified by three Gaussian membership functions. The first-order Sugeno type defuzzification approach is utilized to evaluate a crisp output. Proposed ANFIS is cable of successfully predicting power generation with extremely high accuracy and being much faster than Toolbox, which makes it a promising tool for energy generation applications.
Duality in optimal consumption--investment problems with alternative data
Authors: Kexin Chen, Hoi Ying Wong
Date: 2022-10-16 00:00:00
Tasks: Decision Making

This study investigates an optimal consumption--investment problem in which the unobserved stock trend is modulated by a hidden Markov chain that represents different economic regimes. In the classical approach, the hidden state is estimated from historical asset prices, but recent advancements in technology enable investors to consider alternative data in their decision-making. These include social media commentary, expert opinions, COVID-19 pandemic data, and GPS data, which originate outside of the standard sources of market data but are considered useful for predicting stock trends. We develop a novel duality theory for this problem and consider a jump-diffusion process for the alternative data series. This theory helps investors in identifying ``useful'' alternative data for dynamic decision-making by offering conditions to the filter equation that permit the use of a control approach based on the dynamic programming principle. We demonstrate an application for proving a unique smooth solution for a constant relative risk-averse agent once the distributions of the signals generated from alternative data satisfy a bounded likelihood ratio condition. In doing so, we obtain an explicit consumption--investment strategy that takes advantage of different types of alternative data that have not been addressed in the literature.