pwc-archive/papers-with-abstracts huggingface dataset:
Modelling and Control of Spatial Behaviours in Multi-Agent Systems with Applications to Biology and Robotics
Authors: Andrea Giusti
Date: 2024-12-30 00:00:00

Large-Scale Multi-Agent Systems (LS-MAS) consist of several autonomous components, interacting in a non-trivial way, so that the emerging behaviour of the ensemble depends on the individual dynamics of the components and their reciprocal interactions. These models can describe a rich variety of natural systems, as well as artificial ones, characterised by unparalleled scalability, robustness, and flexibility. Indeed, a crucial objective is devising efficient strategies to model and control the spatial behaviours of LS-MAS to achieve specific goals. However, the inherent complexity of these systems and the wide spectrum of their emerging behaviours pose significant challenges. The overarching goal of this thesis is, therefore, to advance methods for modelling, analyzing and controlling the spatial behaviours of LS-MAS, with applications to cellular populations and swarm robotics. The thesis begins with an overview of the existing Literature, and is then organized into two distinct parts. In the context of swarm robotics, Part I deals with distributed control algorithms to spatially organize agents on geometric patterns. The contribution is twofold, encompassing both the development of original control algorithms, and providing a novel formal analysis, which allows to guarantee the emergence of specific geometric patterns. In Part II, looking at the spatial behaviours of biological agents, experiments are carried out to study the movement of microorganisms and their response to light stimuli. This allows the derivation and parametrization of mathematical models that capture these behaviours, and pave the way for the development of innovative approaches for the spatial control of microorganisms. The results presented in the thesis were developed by leveraging formal analytical tools, simulations, and experiments, using innovative platforms and original computational frameworks.
Automated monitoring of bee colony movement in the hive during winter season
Authors: Rostyslav Koroliuk, Vyacheslav Nykytyuk, Vitaliy Tymoshchuk, Veronika Soyka, Dmytro Tymoshchuk
Date: 2025-01-02 00:00:00
Tasks: PICO, Raspberry Pi 5

In this study, we have experimentally modelled the movement of a bee colony in a hive during the winter season and developed a monitoring system that allows tracking the movement of the bee colony and honey consumption. The monitoring system consists of four load cells connected to the RP2040 controller based on the Raspberry Pi Pico board, from which data is transmitted via the MQTT protocol to the Raspberry Pi 5 microcomputer via a Wi-Fi network. The processed data from the Raspberry Pi 5 is recorded in a MySQL database. The algorithm for finding the location of the bee colony in the hive works correctly, the trajectory of movement based on the data from the sensors repeats the physical movement in the experiment, which is an imitation of the movement of the bee colony in real conditions. The proposed monitoring system provides continuous observation of the bee colony without adversely affecting its natural activities and can be integrated with various wireless data networks. This is a promising tool for improving the efficiency of beekeeping and maintaining the health of bee colonies.
Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS
Authors: Hany Ragab, Sidney Givigi, Aboelmagd Noureldin
Date: 2024-12-31 00:00:00
Tasks: Autonomous Driving, Diagnostic, State Estimation

Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as well as the types of sensors used to measure wheel speed, differs across vehicles. These differences result in varying error characteristics that must be accounted for in navigation and autonomy applications. This paper addresses this gap by examining the diverse speed-sensing technologies employed in standard automotive systems and alternative techniques used in advanced systems designed for higher levels of autonomy, such as Advanced Driver Assistance Systems (ADAS), Autonomous Driving (AD), or surveying applications. We propose a method to identify the type of speed sensor in a vehicle and present strategies for accurately modeling its error characteristics. To validate our approach, we collected and analyzed data from three long real road trajectories conducted in urban environments in Toronto and Kingston, Ontario, Canada. The results underscore the critical role of integrating multiple sensor modalities to achieve more accurate speed estimation, thus improving automotive navigation state estimation, particularly in GNSS-denied environments.
Sensitivity of Room Impulse Responses in Changing Acoustic Environment
Authors: Karolina Prawda
Date: 2025-01-02 00:00:00
Tasks: Object Tracking, Sensitivity

Changes in room acoustics, such as modifications to surface absorption or the insertion of a scattering object, significantly impact measured room impulse responses (RIRs). These changes can affect the performance of systems used in echo cancellation and active acoustics and support tasks such as navigation and object tracking. Recognizing and quantifying such changes is, therefore, critical for advancing technologies based on room acoustics. This study introduces a method for analyzing acoustic environment changes by evaluating the similarity of consecutively recorded RIRs. Short-time coherence is employed to characterize modifications, including changes in wall absorption or the presence of a moving person in the room. A sensitivity rating is further used to quantify the magnitude of these changes. The results clearly differentiate between types of modifications -- atmospheric variation, changes in absorption, and human presence. The methods described provide a novel approach to analyzing and interpreting room acoustics, emphasizing RIR similarity and extracting information from temporal and spectral signal properties.
VoiceRestore: Flow-Matching Transformers for Speech Recording Quality Restoration
Authors: Stanislav Kirdey
Date: 2025-01-01 00:00:00

We present VoiceRestore, a novel approach to restoring the quality of speech recordings using flow-matching Transformers trained in a self-supervised manner on synthetic data. Our method tackles a wide range of degradations frequently found in both short and long-form speech recordings, including background noise, reverberation, compression artifacts, and bandwidth limitations - all within a single, unified model. Leveraging conditional flow matching and classifier free guidance, the model learns to map degraded speech to high quality recordings without requiring paired clean and degraded datasets. We describe the training process, the conditional flow matching framework, and the model's architecture. We also demonstrate the model's generalization to real-world speech restoration tasks, including both short utterances and extended monologues or dialogues. Qualitative and quantitative evaluations show that our approach provides a flexible and effective solution for enhancing the quality of speech recordings across varying lengths and degradation types.
Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge
Authors: Catarina Botelho, David Gimeno-Gómez, Francisco Teixeira, John Mendonça, Patrícia Pereira, Diogo A. P. Nunes, Thomas Rolland, Anna Pompili, Rubén Solera-Ureña, Maria Ponte, David Martins de Matos, Carlos-D. Martínez-Hinarejos, Isabel Trancoso, Alberto Abad
Date: 2024-12-30 00:00:00

This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach, encompassing both knowledge-based acoustic and text-based feature sets, as well as LLM-based macrolinguistic descriptors, pause-based acoustic biomarkers, and multiple neural representations (e.g., LongFormer, ECAPA-TDNN, and Trillson embeddings). Combining these feature sets with different classifiers resulted in a large pool of models, from which we selected those that provided the best balance between train, development, and individual class performance. Our results show that our best performing systems correspond to combinations of models that are complementary to each other, relying on acoustic and textual information from all three clinical tasks.
DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition
Authors: Alexander Polok, Dominik Klement, Martin Kocour, Jiangyu Han, Federico Landini, Bolaji Yusuf, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget
Date: 2024-12-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

Speaker-attributed automatic speech recognition (ASR) in multi-speaker environments remains a significant challenge, particularly when systems conditioned on speaker embeddings fail to generalize to unseen speakers. In this work, we propose Diarization-Conditioned Whisper (DiCoW), a novel approach to target-speaker ASR that leverages speaker diarization outputs as conditioning information. DiCoW extends the pre-trained Whisper model by integrating diarization labels directly, eliminating reliance on speaker embeddings and reducing the need for extensive speaker-specific training data. Our method introduces frame-level diarization-dependent transformations (FDDT) and query-key biasing (QKb) techniques to refine the model's focus on target speakers while effectively handling overlapping speech. By leveraging diarization outputs as conditioning signals, DiCoW simplifies the workflow for multi-speaker ASR, improves generalization to unseen speakers and enables more reliable transcription in real-world multi-speaker recordings. Additionally, we explore the integration of a connectionist temporal classification (CTC) head to Whisper and demonstrate its ability to improve transcription efficiency through hybrid decoding. Notably, we show that our approach is not limited to Whisper; it also provides similar benefits when applied to the Branchformer model. We validate DiCoW on real-world datasets, including AMI and NOTSOFAR-1 from CHiME-8 challenge, as well as synthetic benchmarks such as Libri2Mix and LibriCSS, enabling direct comparisons with previous methods. Results demonstrate that DiCoW enhances the model's target-speaker ASR capabilities while maintaining Whisper's accuracy and robustness on single-speaker data.
Smoothing traffic flow through automated vehicle control with optimal parameter selection
Authors: Shian Wang, Jose Acedo Aguilar, Miguel Velez-Reyes
Date: 2025-01-02 00:00:00

Stop-and-go traffic waves are known for reducing the efficiency of transportation systems by increasing traffic oscillations and energy consumption. In this study, we develop an approach to synthesize a class of additive feedback controllers for automated vehicles (AVs) to smooth nonlinear mixed traffic flow, including both AVs and human-driven vehicles (HVs). Unlike recent explicit AV controllers that rely on strict assumptions such as time-varying equilibrium traffic speed, our proposed AV controller requires only local traffic information, such as inter-vehicle spacing and relative speed, which are readily available through AV onboard sensors. Essentially, it allows a controlled AV to track a subtler version of the perturbed speed profile resulting from its preceding vehicle, thereby enabling smoother traffic flow. Additionally, we provide a method for selecting the optimal control parameters to achieve traffic-smoothing effects efficiently. These unique features of the developed AV controller ensure much higher implementability. We demonstrate the effectiveness of the proposed approach through simulations of two distinct traffic scenarios with varying levels of oscillation. The results show that AVs using the proposed controller are capable of effectively reducing traffic oscillations and lowering vehicle fuel consumption by up to 46.78\% and 2.74\%, respectively, for a platoon of 10 vehicles. The traffic-smoothing effect of the controller is more pronounced at higher penetration rates of AVs. While the performance of the proposed approach is slightly less superior to that of the most recent additive AV controller, it offers greater implementability and provides an efficient method for selecting optimal control parameters.
Data-Driven Yet Formal Policy Synthesis for Stochastic Nonlinear Dynamical Systems
Authors: Mahdi Nazeri, Thom Badings, Sadegh Soudjani, Alessandro Abate
Date: 2025-01-02 00:00:00

The automated synthesis of control policies for stochastic dynamical systems presents significant challenges. A standard approach is to construct a finite-state abstraction of the continuous system, typically represented as a Markov decision process (MDP). However, generating abstractions is challenging when (1) the system's dynamics are nonlinear, and/or (2) we do not have complete knowledge of the dynamics. In this work, we introduce a novel data-driven abstraction technique for nonlinear Lipschitz continuous dynamical systems with additive stochastic noise that addresses both of these issues. As a key step, we use samples of the dynamics to learn the enabled actions and transition probabilities of the abstraction. We represent abstractions as MDPs with intervals of transition probabilities, known as interval MDPs (IMDPs). These abstractions enable the synthesis of policies for the concrete nonlinear system, with probably approximately correct (PAC) guarantees on the probability of satisfying a specified control objective. Our numerical experiments illustrate the effectiveness and robustness of our approach in achieving reliable control under uncertainty.
Temperature-Controlled Smart Charging for Electric Vehicles in Cold Climates
Authors: Grant Ruan, Munther A. Dahleh
Date: 2025-01-02 00:00:00

The battery performance and lifespan of electric vehicles (EVs) degrade significantly in cold climates, requiring a considerable amount of energy to heat up the EV batteries. This paper proposes a novel technology, namely temperature-controlled smart charging, to coordinate the heating/charging power and reduce the total energy use of a solar-powered EV charging station. Instead of fixing the battery temperature setpoints, we analyze the thermal dynamics and inertia of EV batteries, and decide the optimal timing and proper amount of energy allocated for heating. In addition, a temperature-sensitive charging model is formulated with consideration of dynamic charging rates as well as battery health. We further tailor acceleration algorithms for large-scale EV charging, including the reduced-order dual decomposition and vehicle rescheduling. Simulation results demonstrate that the proposed temperature-controlled smart charging is superior in capturing the flexibility value of EV batteries and making full use of the rooftop solar energy. The proposed model typically achieves a 12.5--18.4% reduction in the charging cost and a 0.4--6.8% drop in the overhead energy use for heating.
Learning-Based Stable Optimal Guidance for Spacecraft Close-Proximity Operations
Authors: Kun Wang, Roberto Armellin, Adam Evans, Harry Holt, Zheng Chen
Date: 2025-01-02 00:00:00

Machine learning techniques have demonstrated their effectiveness in achieving autonomy and optimality for nonlinear and high-dimensional dynamical systems. However, traditional black-box machine learning methods often lack formal stability guarantees, which are critical for safety-sensitive aerospace applications. This paper proposes a comprehensive framework that combines control Lyapunov functions with supervised learning to provide certifiably stable, time- and fuel-optimal guidance for rendezvous maneuvers governed by Clohessy-Wiltshire dynamics. The framework is easily extensible to nonlinear control-affine systems. A novel neural candidate Lyapunov function is developed to ensure positive definiteness. Subsequently, a control policy is defined, in which the thrust direction vector minimizes the Lyapunov function's time derivative, and the thrust throttle is determined using minimal required throttle. This approach ensures that all loss terms related to the control Lyapunov function are either naturally satisfied or replaced by the derived control policy. To jointly supervise the Lyapunov function and the control policy, a simple loss function is introduced, leveraging optimal state-control pairs obtained by a polynomial maps based method. Consequently, the trained neural network not only certifies the Lyapunov function but also generates a near-optimal guidance policy, even for the bang-bang fuel-optimal problem. Extensive numerical simulations are presented to validate the proposed method.
Cyber-physical Defense for Heterogeneous Multi-agent Systems Against Exponentially Unbounded Attacks on Signed Digraphs
Authors: Yichao Wang, Mohamadamin Rajabinezhad, Yi Zhang, Shan Zuo
Date: 2025-01-02 00:00:00

Cyber-physical systems (CPSs) are subjected to attacks on both cyber and physical spaces. In reality, the attackers could launch exponentially unbounded false data injection (EU-FDI) attacks, which are more destructive and could lead to the system's collapse or instability. Existing literature generally addresses bounded attack signals and/or bounded-first-order-derivative attack signals, which exposes the CPSs to significant threats. In contrast, this paper proposes a fully-distributed attack-resilient bi-layer defense framework to address the bipartite output containment problem for heterogeneous multi-agent systems on signed digraphs, in the presence of EU-FDI attacks on both cyber-physical layer (CPL) and observer layer (OL). First, we design attack-resilient dynamic compensators that utilize data communicated on the OL to estimate the convex combinations of the states and negative states of the leaders. The attack-resilient compensators address the EU-FDI attacks on the OL and guarantee the uniformly ultimately bounded (UUB) estimation of the leaders' states. Then, by using the compensators' states, fully-distributed attack-resilient controllers are designed on the CPL to further address the EU-FDI attacks on the actuators. Rigorous mathematical proof based on Lyapunov stability analysis is provided, establishing the theoretical soundness of the proposed bi-layer resilient defense framework, by preserving the UUB consensus and stability against EU-FDI attacks on both CPL and OL. Finally, a comparative case study for heterogeneous multi-agent systems validate the enhanced resilience of the proposed defense strategies.
Defense Strategies for Autonomous Multi-agent Systems: Ensuring Safety and Resilience Under Exponentially Unbounded FDI Attacks
Authors: Yichao Wang, Mohamadamin Rajabinezhad, Dimitra Panagou, Shan Zuo
Date: 2025-01-01 00:00:00
Tasks: Collision Avoidance

False data injection attacks pose a significant threat to autonomous multi-agent systems (MASs). Existing attack-resilient control strategies generally have strict assumptions on the attack signals and overlook safety constraints, such as collision avoidance. In practical applications, leader agents equipped with advanced sensors or weaponry span a safe region to guide heterogeneous follower agents, ensuring coordinated operations while addressing collision avoidance to prevent financial losses and mission failures. This letter addresses these gaps by introducing and solving the safety-aware and attack-resilient (SAAR) control problem under exponentially unbounded false data injection (EU-FDI) attacks. Specifically, a novel attack-resilient observer layer (OL) is first designed to defend against EU-FDI attacks on the OL. Then, an attack-resilient compensational signal is designed to mitigate the adverse effects caused by the EU-FDI attack on control input layer (CIL). Finally, a SAAR controller is designed by solving a quadratic programming (QP) problem integrating control barrier function (CBF) certified collision-free safety constraints. Rigorous Lyapunov-based stability analysis certifies the SAAR controller's effectiveness in ensuring both safety and resilience. This study also pioneers a three-dimensional (3D) simulation of the SAAR containment control problem for heterogeneous MASs, demonstrating its applicability in realistic multi-agent scenarios.
Observer-Based Data-Driven Consensus Control for Nonlinear Multi-Agent Systems against DoS and FDI attacks
Authors: Yi Zhang, Bin Lei, Mohamadamin Rajabinezhad, Caiwen Ding, Shan Zuo
Date: 2025-01-01 00:00:00

Existing data-driven control methods generally do not address False Data Injection (FDI) and Denial-of-Service (DoS) attacks simultaneously. This letter introduces a distributed data-driven attack-resilient consensus problem under both FDI and DoS attacks and proposes a data-driven consensus control framework, consisting of a group of comprehensive attack-resilient observers. The proposed group of observers is designed to estimate FDI attacks, external disturbances, and lumped disturbances, combined with a DoS attack compensation mechanism. A rigorous stability analysis of the approach is provided to ensure the boundedness of the distributed neighborhood estimation consensus error. The effectiveness of the approach is validated through numerical examples involving both leaderless consensus and leader-follower consensus, demonstrating significantly improved resilient performance compared to existing data-driven control approaches.
Advances in UAV Avionics Systems Architecture, Classification and Integration: A Comprehensive Review and Future Perspectives
Authors: Hashim A. Hashim
Date: 2025-01-01 00:00:00
Tasks: Autonomous Navigation, Collision Avoidance, Navigate

Avionics systems of an Unmanned Aerial Vehicle (UAV) or drone are the critical electronic components found onboard that regulate, navigate, and control UAV travel while ensuring public safety. Contemporary UAV avionics work together to facilitate success of UAV missions by enabling stable communication, secure identification protocols, novel energy solutions, multi-sensor accurate perception and autonomous navigation, precise path planning, that guarantees collision avoidance, reliable trajectory control, and efficient data transfer within the UAV system. Moreover, special consideration must be given to electronic warfare threats prevention, detection, and mitigation, and the regulatory framework associated with UAV operations. This review presents the role and taxonomy of each UAV avionics system while covering shortcomings and benefits of available alternatives within each system. UAV communication systems, antennas, and location communication tracking are surveyed. Identification systems that respond to air-to-air or air-to-ground interrogating signals are presented. UAV classical and more innovative power sources are discussed. The rapid development of perception systems improves UAV autonomous navigation and control capabilities. The paper reviews common perception systems, navigation techniques, path planning approaches, obstacle avoidance methods, and tracking control. Modern electronic warfare uses advanced techniques and has to be counteracted by equally advanced methods to keep the public safe. Consequently, this work presents a detailed overview of common electronic warfare threats and state-of-the-art countermeasures and defensive aids. UAV safety occurrences are analyzed in the context of national regulatory framework and the certification process. Databus communication and standards for UAVs are reviewed as they enable efficient and fast real-time data transfer.