pwc-archive/papers-with-abstracts huggingface dataset:
Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)
Authors: Siddhant Gautam, Angqi Li, Nicole Seiberlich, Jeffrey A. Fessler, Saiprasad Ravishankar
Date: 2025-01-16 00:00:00

Accelerated MRI involves collecting partial k-space measurements to reduce acquisition time, patient discomfort, and motion artifacts, and typically uses regular undersampling patterns or hand-designed schemes. Recent works have studied population-adaptive sampling patterns that are learned from a group of patients (or scans) based on population-specific metrics. However, such a general sampling pattern can be sub-optimal for any specific scan since it may lack scan or slice adaptive details. To overcome this issue, we propose a framework for jointly learning scan-adaptive Cartesian undersampling patterns and a corresponding reconstruction model from a training set. We use an alternating algorithm for learning the sampling patterns and reconstruction model where we use an iterative coordinate descent (ICD) based offline optimization of scan-adaptive k-space sampling patterns for each example in the training set. A nearest neighbor search is then used to select the scan-adaptive sampling pattern at test time from initially acquired low-frequency k-space information. We applied the proposed framework (dubbed SUNO) to the fastMRI multi-coil knee and brain datasets, demonstrating improved performance over currently used undersampling patterns at both 4x and 8x acceleration factors in terms of both visual quality and quantitative metrics. The code for the proposed framework is available at https://github.com/sidgautam95/adaptive-sampling-mri-suno.
A scalable event-driven spatiotemporal feature extraction circuit
Authors: Hugh Greatorex, Michele Mastella, Ole Richter, Madison Cotteret, Willian Soares Girão, Ella Janotte, Elisabetta Chicca
Date: 2025-01-17 00:00:00

Event-driven sensors, which produce data only when there is a change in the input signal, are increasingly used in applications that require low-latency and low-power real-time sensing, such as robotics and edge devices. To fully achieve the latency and power advantages on offer however, similarly event-driven data processing methods are required. A promising solution is the TDE: an event-based processing element which encodes the time difference between events on different channels into an output event stream. In this work we introduce a novel TDE implementation on CMOS. The circuit is robust to device mismatch and allows the linear integration of input events. This is crucial for enabling a high-density implementation of many TDEs on the same die, and for realising real-time parallel processing of the high-event-rate data produced by event-driven sensors.
Joint Active and Passive Beamforming Optimization for Beyond Diagonal RIS-aided Multi-User Communications
Authors: Xiaohua Zhou, Tianyu Fang, Yijie Mao
Date: 2025-01-17 00:00:00

Benefiting from its capability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide additional design flexibility via interactions between RIS elements, beyond-diagonal RIS (BD-RIS) has attracted considerable research interests recently. However, due to the symmetric and unitary passive beamforming constraint imposed on BD-RIS, existing joint active and passive beamforming optimization algorithms for BD-RIS either exhibit high computational complexity to achieve near optimal solutions or rely on heuristic algorithms with substantial performance loss. In this paper, we address this issue by proposing an efficient optimization framework for BD-RIS assisted multi-user multi-antenna communication networks. Specifically, we solve the weighted sum rate maximization problem by introducing a novel beamforming optimization algorithm that alternately optimizes active and passive beamforming matrices using iterative closed-form solutions. Numerical results demonstrate that our algorithm significantly reduces computational complexity while ensuring a sub-optimal solution.
Implementing Finite Impulse Response Filters on Quantum Computers
Authors: Aishwarya Majumdar, Bojko N. Bakalov, Dror Baron, YuAn Liu
Date: 2025-01-17 00:00:00

While signal processing is a mature area, its connections with quantum computing have received less attention. In this work, we propose approaches that perform classical discrete-time signal processing using quantum systems. Our approaches encode the classical discrete-time input signal into quantum states, and design unitaries to realize classical concepts of finite impulse response (FIR) filters. We also develop strategies to cascade lower-order filters to realize higher-order filters through designing appropriate unitary operators. Finally, a few directions for processing quantum states on classical systems after converting them to classical signals are suggested for future work.
Informativity Conditions for Multiple Signals: Properties, Experimental Design, and Applications
Authors: Ao Cao, Fuyong Wang
Date: 2025-01-17 00:00:00
Tasks: Experimental Design, LEMMA

Recent studies highlight the importance of persistently exciting condition in single signal sequence for model identification and data-driven control methodologies. However, maintaining prolonged excitation in control signals introduces significant challenges, as continuous excitation can reduce the lifetime of mechanical devices. In this paper, we introduce three informativity conditions for various types of multi-signal data, each augmented by weight factors. We explore the interrelations between these conditions and their rank properties in linear time-invariant systems. Furthermore, we introduce open-loop experimental design methods tailored to each of the three conditions, which can synthesize the required excitation conditions either offline or online, even in the presence of limited information within each signal segment. We demonstrate the effectiveness of these informativity conditions in least-squares identification. Additionally, all three conditions can extend Willems' fundamental lemma and are utilized to assess the properties of the system. Illustrative examples confirm that these conditions yield satisfactory outcomes in both least-squares identification and the construction of data-driven controllers.
Complex-Valued Neural Networks for Ultra-Reliable Massive MIMO
Authors: Pedro Benevenuto Valadares, Jonathan Aguiar Soares, Kayol Mayer, Dalton Soares Arantes
Date: 2025-01-16 00:00:00
Tasks: Decoder

In the evolving landscape of 5G and 6G networks, the demands extend beyond high data rates, ultra-low latency, and extensive coverage, increasingly emphasizing the need for reliability. This paper proposes an ultra-reliable multiple-input multiple-output (MIMO) scheme utilizing quasi-orthogonal space-time block coding (QOSTBC) combined with singular value decomposition (SVD) for channel state information (CSI) correction, significantly improving performance over QOSTBC and traditional orthogonal STBC (OSTBC) when analyzing spectral efficiency. Although QOSTBC enhances spectral efficiency, it also increases computational complexity at the maximum likelihood (ML) decoder. To address this, a neural network-based decoding scheme using phase-transmittance radial basis function (PT-RBF) architecture is also introduced to manage QOSTBC's complexity. Simulation results demonstrate improved system robustness and performance, making this approach a potential candidate for ultra-reliable communication in next-generation networks.
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data
Authors: Nirit Alkalay, Roy Orfaig, Ben-Zion Bobrovsky
Date: 2025-01-08 00:00:00
Tasks: Autonomous Driving, Instance Segmentation, Motion Estimation, Scene Understanding, Segmentation, Semantic Segmentation

4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics, combining semantic and instance segmentation with temporal consistency. Current methods, like 4D-PLS and 4D-STOP, use a tracking-by-detection methodology, employing deep learning networks to perform semantic and instance segmentation on each frame. To maintain temporal consistency, large-size instances detected in the current frame are compared and associated with instances within a temporal window that includes the current and preceding frames. However, their reliance on short-term instance detection, lack of motion estimation, and exclusion of small-sized instances lead to frequent identity switches and reduced tracking performance. We address these issues with the NextStop1 tracker, which integrates Kalman filter-based motion estimation, data association, and lifespan management, along with a tracklet state concept to improve prioritization. Evaluated using the LiDAR Segmentation and Tracking Quality (LSTQ) metric on the SemanticKITTI validation set, NextStop demonstrated enhanced tracking performance, particularly for small-sized objects like people and bicyclists, with fewer ID switches, earlier tracking initiation, and improved reliability in complex environments. The source code is available at https://github.com/AIROTAU/NextStop
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge
Authors: Qing Wu
Date: 2025-01-06 00:00:00
Tasks: 3D Semantic Segmentation, Autonomous Vehicles, Data Augmentation, Diversity, Point Cloud Segmentation, Segmentation, Semantic Segmentation

3D semantic segmentation is one of the most crucial tasks in driving perception. The ability of a learning-based model to accurately perceive dense 3D surroundings often ensures the safe operation of autonomous vehicles. However, existing LiDAR-based 3D semantic segmentation databases consist of sequentially acquired LiDAR scans that are long-tailed and lack training diversity. In this report, we introduce MixSeg3D, a sophisticated combination of the strong point cloud segmentation model with advanced 3D data mixing strategies. Specifically, our approach integrates the MinkUNet family with LaserMix and PolarMix, two scene-scale data augmentation methods that blend LiDAR point clouds along the ego-scene's inclination and azimuth directions. Through empirical experiments, we demonstrate the superiority of MixSeg3D over the baseline and prior arts. Our team achieved 2nd place in the 3D semantic segmentation track of the 2024 Waymo Open Dataset Challenge.
Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task
Authors: Avalon Campbell-Cousins, Federica Guazzo, Mark Bastin, Mario A. Parra, Javier Escudero
Date: 2025-01-16 00:00:00
Tasks: Diagnostic

Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Brain networks are known to exhibit community structure at local, meso, and global scale. However, modularity is limited as a metric to a global scale describing the overall strength of community structure, overlooking important variations in community structure at node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity (nQ) captures both mesoscale and local-scale changes in modularity. We hypothesized that nQ would illuminate granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored nQ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. While limited by sample size, changes in nQ for individual regions of interest (ROIs) in our fMRI networks were predominantly observed in visual, limbic, and paralimbic systems in the brain, aligning with known AD trajectories and linked to amyloid-$\beta$ and tau deposition. Furthermore, observed changes in white-matter microstructure in our DTI networks in parietal and frontal regions may compliment studies of white-matter integrity in poor memory binders. Additionally, nQ clearly differentiated MCI from MCI converters indicating that nQ may be sensitive to this key turning point of AD. Our findings demonstrate the utility of nQ as a measure of localized group structure, providing novel insights into task and disease-related variability at the node level. Given the widespread application of modularity as a global measure, nQ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines.
Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning
Authors: Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria, Wei Chen
Date: 2025-01-17 00:00:00
Tasks: Decision Making, Model Predictive Control, quantile regression, Time Series, Uncertainty Quantification

Digital Twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control strategies for autonomous systems. However, achieving real-time decision-making in Digital Twins considering uncertainty necessitates an efficient uncertainty quantification (UQ) approach and optimization driven by accurate predictions of system behaviors, which remains a challenge for learning-based methods. This paper presents a simultaneous multi-step robust model predictive control (MPC) framework that incorporates real-time decision-making with uncertainty awareness for Digital Twin systems. Leveraging a multistep ahead predictor named Time-Series Dense Encoder (TiDE) as the surrogate model, this framework differs from conventional MPC models that provide only one-step ahead predictions. In contrast, TiDE can predict future states within the prediction horizon in a one-shot, significantly accelerating MPC. Furthermore, quantile regression is employed with the training of TiDE to perform flexible while computationally efficient UQ on data uncertainty. Consequently, with the deep learning quantiles, the robust MPC problem is formulated into a deterministic optimization problem and provides a safety buffer that accommodates disturbances to enhance constraint satisfaction rate. As a result, the proposed method outperforms existing robust MPC methods by providing less-conservative UQ and has demonstrated efficacy in an engineering case study involving Directed Energy Deposition (DED) additive manufacturing. This proactive while uncertainty-aware control capability positions the proposed method as a potent tool for future Digital Twin applications and real-time process control in engineering systems.
Actively Coupled Sensor Configuration and Planning in Unknown Dynamic Environments
Authors: Prakash Poudel, Jeffrey DesRoches, Raghvendra V. Cowlagi
Date: 2025-01-17 00:00:00

We address the problem of path-planning for an autonomous mobile vehicle, called the ego vehicle, in an unknown andtime-varying environment. The objective is for the ego vehicle to minimize exposure to a spatiotemporally-varying unknown scalar field called the threat field. Noisy measurements of the threat field are provided by a network of mobile sensors. Weaddress the problem of optimally configuring (placing) these sensors in the environment. To this end, we propose sensor reconfiguration by maximizing a reward function composed of three different elements. First, the reward includes an informa tion measure that we call context-relevant mutual information (CRMI). Unlike typical sensor placement techniques that maxi mize mutual information of the measurements and environment state, CRMI directly quantifies uncertainty reduction in the ego path cost while it moves in the environment. Therefore, the CRMI introduces active coupling between the ego vehicle and the sensor network. Second, the reward includes a penalty on the distances traveled by the sensors. Third, the reward includes a measure of proximity of the sensors to the ego vehicle. Although we do not consider communication issues in this paper, such proximity is of relevance for future work that addresses communications between the sensors and the ego vehicle. We illustrate and analyze the proposed technique via numerical simulations.
Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids
Authors: Cassia Nunes Almeida, Arun Narayanan, Hafiz Majid Hussain, Pedro H. J. Nardelli
Date: 2025-01-17 00:00:00
Tasks: Management

In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.
Decomposition and Quantification of SOTIF Requirements for Perception Systems of Autonomous Vehicles
Authors: Ruilin Yu, Cheng Wang, Yuxin Zhang, Fuming Zhao
Date: 2025-01-17 00:00:00
Tasks: Autonomous Vehicles

Ensuring the safety of autonomous vehicles (AVs) is paramount before they can be introduced to the market. More specifically, securing the Safety of the Intended Functionality (SOTIF) poses a notable challenge; while ISO 21448 outlines numerous activities to refine the performance of AVs, it offers minimal quantitative guidance. This paper endeavors to decompose the acceptance criterion into quantitative perception requirements, aiming to furnish developers with requirements that are not only understandable but also actionable. This paper introduces a risk decomposition methodology to derive SOTIF requirements for perception. More explicitly, for subsystemlevel safety requirements, we define a collision severity model to establish requirements for state uncertainty and present a Bayesian model to discern requirements for existence uncertainty. For component-level safety requirements, we proposed a decomposition method based on the Shapley value. Our findings indicate that these methods can effectively decompose the system-level safety requirements into quantitative perception requirements, potentially facilitating the safety verification of various AV components.
Hybrid Parallel Collaborative Simulation Framework Integrating Device Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment
Authors: Qingyuan Shi, Chijie Zhuang, Jiapeng Liu, Bo Lin, Xiyu Peng, Dan Wu, Zhicheng Liu, Rong Zeng
Date: 2025-01-17 00:00:00
Tasks: Blocking

Optimizing high-performance power electronic equipment, such as power converters, requires multiscale simulations that incorporate the physics of power semiconductor devices and the dynamics of other circuit components, especially in conducting Design of Experiments (DoEs), defining the safe operating area of devices, and analyzing failures related to semiconductor devices. However, current methodologies either overlook the intricacies of device physics or do not achieve satisfactory computational speeds. To bridge this gap, this paper proposes a Hybrid-Parallel Collaborative (HPC) framework specifically designed to analyze the Partial Differential Algebraic Equation (PDAE) modeled power electronic equipment, integrating the device physics and circuit dynamics. The HPC framework employs a dynamic iteration to tackle the challenges inherent in solving the coupled nonlinear PDAE system, and utilizes a hybrid-parallel computing strategy to reduce computing time. Physics-based system partitioning along with hybrid-process-thread parallelization on shared and distributed memory are employed, facilitating the simulation of hundreds of partial differential equations (PDEs)-modeled devices simultaneously without compromising speed. Experiments based on the hybrid line commutated converter and reverse-blocking integrated gate-commutated thyristors are conducted under 3 typical real-world scenarios: semiconductor device optimization for the converter; converter design optimization; and device failure analysis. The HPC framework delivers simulation speed up to 60 times faster than the leading commercial software, while maintaining carrier-level accuracy in the experiments. This shows great potential for comprehensive analysis and collaborative optimization of devices and electronic power equipment, particularly in extreme conditions and failure scenarios.
Minimum-Time Sequential Traversal by a Team of Small Unmanned Aerial Vehicles in an Unknown Environment with Winds
Authors: Jeffrey A. DesRoches, Raghvendra V. Cowlagi
Date: 2025-01-17 00:00:00

We consider the problem of transporting multiple packages from an initial location to a destination location in a windy urban environment using a team of SUAVs. Each SUAV carries one package. We assume that the wind field is unknown, but wind speed can be measured by SUAVs during flight. The SUAVs fly sequentially one after the other, measure wind speeds along their trajectories, and report the measurements to a central computer. The overall objective is to minimize the total travel time of all SUAVs, which is in turn related to the number of SUAV traversals through the environment. For a discretized environment modeled by a graph, we describe a method to estimate wind speeds and the time of traversal for each SUAV path. Each SUAV traverses a minimum-time path planned based on the current wind field estimate. We study cases of static and time-varying wind fields with and without measurement noise. For each case, we demonstrate via numerical simulation that the proposed method finds the optimal path after a minimal number of traversals.
Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles
Authors: Yeyson A. Becerra-Mora, José Ángel Acosta, Ángel Rodríguez Castaño
Date: 2025-01-17 00:00:00

At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to autonomously replicate the same tasks. However, the inherent nonlinearity of ship trajectories and environmental perturbations as wind or marine currents make it difficult to learn a model and its respective control. We therefore present a data-driven learning and control methodology for USV, which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the Sontag's universal formula. Our approach is capable to learn the nonlinear dynamics as well as guarantee the convergence toward the target with a robust controller. Real data have been collected through experiments with a vessel at the port of Ceuta. The complex trajectories followed by an expert have been learned including the robust controller. The effect of the controller over noise/perturbations are presented, a measure of error is used to compare estimates and real data trajectories, and finally, an analysis of computational complexity is performed.