Signal Processing · Cross-listed from cs.IT
EML-AirComp: Layered Over-the-Air Computation from a Single Nomographic Gate
Onur Günlü
Over-the-air computation (AirComp) exploits multiple-access superposition to compute functions of distributed data without separately decoding all terminal messages. We study a reusable two-input AirComp gate for the exp-minus-log (EML) operation $\eml(u,v)=\exp(u)-\log(v)$, $v>0$. Thus, all internal nodes of a prescribed real-admissible EML tree reuse one gate type, avoiding node-specific nonlinear gate designs. Given an explicit EML tree whose intermediate logarithm arguments remain positive on a given compact domain, we derive additive white Gaussian noise (AWGN) and coherent flat fading implementations under peak-power constraints. We then characterize the number of gate evaluations, the dependency depth, evaluation latency, node-wise feasibility, deterministic error propagation, positivity preservation, and a high-probability AWGN error bound for the complete tree. A four-terminal two-hop example gives explicit positivity and end-to-end error conditions, and a digital interface propagates quantization and gate errors across the tree.
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Signal Processing
Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
Mohd Halim Mohd Noor and Abdulrahman M. A. Baraka
Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings. However, these supervised learning models rely on large amount of labeled data, which require labor-intensive collection and meticulous annotation. To address these challenges, this paper proposes a Joint
Embedding Predictive Architecture framework tailored for sensor-based HAR, designed to learn robust and generalizable representations from unlabeled
datasets. The proposed framework features an encoder designed to explicitly model both the fine-grained local temporal representations within individual window and the long-term temporal sequence of adjacent windows. Furthermore, we introduce an improved Variance-Invariance-Covariance Regularization (VICReg) objective function that incorporates computationally lightweight norm term to stabilize the JEPA pre-training phase. This term balances variance, invariance and covariance constraints to prevent representation collapse. The proposed HAR-JEPA framework is evaluated using two
benchmark continuously performed activity
datasets. The results show that high-quality representations are successfully learned by the proposed framework. Furthermore, the representations learned by HAR-JEPA demonstrates superior generalization on minority, high variance transitional activities such as sit-to-stand and sit-to-lie where supervised learning tend to overfit due to limited support.
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Systems and Control
The Internet of Things for Smart Manufacturing: A Review
Hui Yang, Soundar Kumara, Satish Bukkapatnam et al. (+1 more)
The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global
market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a cyber-physical system tightly integrating manufacturing enterprises in the physical world with virtual enterprises in cyberspace. To a great extent, realizing the full potential of cyber-physical systems depends on the development of new methodologies on the Internet of Manufacturing Things (IoMT) for data-enabled engineering innovations. This paper presents a review of the IoT technologies and systems that are the drivers and foundations of data-driven innovations in smart manufacturing. We discuss the
evolution of internet from computer networks to human networks to the latest era of smart and connected networks of manufacturing things (e.g., materials, sensors, equipment, people, products, and supply chain). In addition, we present a new framework that leverages IoMT and cloud computing to develop a virtual machine network. We further extend our review to IoMT cybersecurity issues that are of paramount importance to businesses and operations, as well as IoT and smart manufacturing policies that are laid out by governments around the world for the future of smart factory. Finally, we present the challenges and opportunities arising from IoMT. We hope this work will help catalyze more in-depth investigations and multi-disciplinary research efforts to advance IoMT technologies.
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All authors Hui Yang, Soundar Kumara, Satish Bukkapatnam, Fugee Tsung
Systems and Control · Cross-listed from math.DS
A Globally Asymptotically Stable Planar Homogeneous Polynomial Vector Field With No Polynomial Lyapunov Function
Jun Liu and Maxwell Fitzsimmons
We disprove the
conjecture that every globally asymptotically stable homogeneous
polynomial vector field admits a homogeneous
polynomial Lyapunov function. The counterexample is a planar homogeneous cubic
polynomial vector field with integer coefficients. It admits no positive definite homogeneous
polynomial with nonpositive Lie derivative and, more strongly, no real-analytic Lyapunov function even locally. Nevertheless, it has an explicit degree-two homogeneous Lyapunov function that is radially unbounded, continuously differentiable everywhere, and smooth away from the origin. We also provide a machine-checked Lean 4 formalization of the main result.
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Systems and Control · Cross-listed from cs.LG
PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal et al. (+6 more)
Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists. The complexity of multi-
agent interactions demands continuous, real-time monitoring systems capable of anticipating conflicts before they escalate into crashes. We present PRISA, a modular infrastructure
LiDAR framework leveraging
privacy-preserving, low-light-robust roadside sensors for long-term traffic observation and real-time risk detection at the edge. The framework comprises two core components: a sensing and perception layer and a plug-and-play risk assessment module. The latter automatically curates site-specific training data from accumulated perception outputs to train a trajectory prediction model without manual annotation. It then deploys the trained model for continuous motion
forecasting and dual surrogate safety evaluation, using Time-to-Collision (TTC) for longitudinal conflicts and Predicted Post-Encroachment Time (PPET) for crossing and VRU-involved interactions. PRISA is evaluated on the public R-LiViT
dataset and deployed on an NVIDIA Jetson AGX Thor at a live signalized intersection in Chattanooga, Tennessee. PPET-based assessment operates at 194~ms end-to-end latency over a 2.4-second predictive horizon, with TTC-based detection and perception remaining within real-time constraints, demonstrating practical feasibility for proactive multi-
agent intersection safety monitoring.
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All authors Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal, Truc Phuong Nguyen, Austin Harris, Toru Hirano, Mina Sartipi, Yunfei Xu, Hoang H. Nguyen
Systems and Control · Cross-listed from cs.CV
CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception
Tam Bang, Hoang H. Nguyen, Lei Cheng et al. (+6 more)
Reliable roadside perception of vulnerable road users (VRUs) remains challenging under occlusions, variable lighting, and diverse weather conditions, particularly under strict edge-computing and latency constraints. Existing multi-sensor fusion systems rely on cloud or server-grade infrastructure, creating a deployment gap at real-world intersections. We present CLIFE, an edge-native camera-
LiDAR fusion framework that integrates targetless online calibration and lightweight late-fusion tracking entirely on a single embedded device, without cloud offloading. CLIFE adaptively refines camera-
LiDAR alignment on demand and performs multi-sensor fusion and track association with O(N log N) per-frame cost. We deploy CLIFE across 12 signalized intersections in Chattanooga and conduct an in-depth evaluation at a representative intersection using synchronized camera-
LiDAR data that spans diverse daytime, nighttime, and weather conditions. Our experiments demonstrate that the fusion architecture substantially enhances the perceptual range and robustness of the individual sensors under varied environmental and traffic conditions. The late-fusion core operates at 53.2 FPS on the Jetson AGX Thor, ensuring high throughput for real-time intersection-scale applications. By centering perception at the edge, CLIFE provides a deployable foundation for downstream safety applications, while reducing bandwidth and calibration overhead for agencies operating multi-intersection corridors.
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All authors Tam Bang, Hoang H. Nguyen, Lei Cheng, Lihao Guo, Siyang Cao, Hussam Abubakr, Tianya Zhang, Austin Harris, Mina Sartipi
Signal Processing
A Kalman Filter-Assisted Data-Predictive SAR ADC With Reduced Switching Energy for Low-Power Applications
Xiyuan Feng, Yuxiang Zhao, Jie Xiong et al. (+7 more)
The proliferation of Internet of Things (IoT) devices and wearable health monitors has created an urgent demand for ultra-low-power analog-to-digital converters (ADCs). Successive approximation register (SAR) ADCs are widely used in such applications, yet their energy efficiency remains constrained by the sequential bit-by-bit switching of the capacitive DAC (CDAC). The high-weight most significant bit (MSB) transitions dominate the total switching energy, and the rigid N -cycle conversion flow imposes a hard lower bound on latency per sample.This paper presents a Kalman filter-assisted data-predictive SAR ADC that replaces the first four comparator-driven decisions with a recursive state
estimator. The Kalman filter predicts the 4 MSBs from the complete conversion history before each cycle begins, enabling simultaneous parallel switching of the MSB capacitors. This eliminates redundant CDAC transitions, shortens the quantization cycle by four clock periods, and reduces switching energy by approximately 50%. An optimized 4-bit MSB switching scheme further suppresses residual switching at the hardware level. The ADC, designed in a 180-nm CMOS process, supports configurable dual-mode operation, toggling between a conventional mode and the Kalman-driven predictive mode for robustness under erratic inputs. At 20 MS/s and a 1.8-V supply, the predictive mode reduces total power consumption by 50.3% (from 1.96 mW to 0.975 mW), with a measured SNR/SFDR of 57.88/74.51 dB at 504 kHz, confirming its suitability for energy-constrained wireless sensor networks.
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All authors Xiyuan Feng, Yuxiang Zhao, Jie Xiong, Dian Lin, Yunlei Zhong, Wei Liu, Zhongheng Ji, Ruiyu Tian, Chenhao Zhuo, Yue Yin
Systems and Control · Cross-listed from cs.CY
A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance
Andrea Ferrario
AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Existing approaches remain either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect multidimensional trustworthiness evidence with governance decisions. We propose a lightweight methodology centered on trustworthiness level functions: auditable rules that map measured trustworthiness profiles to governance-relevant levels. The methodology separates the underlying trustworthiness evidence from the governance rule used to interpret it and treats that rule as a lifecycle governance object. The rule may remain expert-defined or, when available evidence warrants empirical learning, be approximated by an interpretable candidate model. An AI lifecycle governance procedure embeds this choice in explicit decision gates for determining whether learning should be attempted and whether a learned candidate should become operative. The resulting rule supports lifecycle monitoring through level transitions, boundary margins, and profile drift, with explicit human responsibilities for validation, approval, and reassessment. We illustrate the methodology on synthetic AI lifecycle scenarios involving degradation, shocks, updates, heterogeneous monitoring cadences, and system comparison. Our methodology does not replace expert or legal judgment, but makes the governance interpretation of trustworthiness evidence more explicit, auditable, and contestable over time.
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Systems and Control · Cross-listed from cs.SE
Comparison of Energy System Optimization Software and Evaluation of Selected Frameworks
Pedro Caixeta, David Gawron, Hüseyin K. Çakmak et al. (+1 more)
Optimizing energy systems is a crucial step toward achieving a carbon-neutral future, with software tools playing a major role in the process. However, selecting the most suitable tool for specific optimization challenges can be complex, given the diverse objectives and requirements of various energy systems. In this study, we aim to address this issue by evaluating five preselected software tools-REMix, MTRESS, COMANDO, OEMOF, and HOMER PRO-to identify the scenarios for which they are most suitable. To achieve this, we conducted an extensive review of literature, documentation, tutorials, and example models, and developed a set of comparison criteria that were subsequently used to evaluate these tools. Our analysis suggests that REMix is particularly effective for scenarios where investment path optimization is required. COMANDO excels in systems with atypical components. OEMOF is the best-suited open-source tool for standard optimization problems. HOMER PRO is recommended for users seeking rapid synthesis optimization, especially those with limited programming experience. Due to difficulties in obtaining sufficient information on MTRESS, we were unable to complete the analysis for this tool.
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All authors Pedro Caixeta, David Gawron, Hüseyin K. Çakmak, Haozhen Cheng
Audio and Speech Processing
Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos
Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar et al. (+19 more)
We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large
language model (AV-LLM) for joint understanding and
reasoning over audio, images, and long-form
videos. Unlike prior AV-LLMs that primarily focus on short clips, AV-Flamingo is designed for understanding and
reasoning over long and complex real-world (audio-visual)
videos. To support this, we make three key contributions: (i) Audio-Visual-Skills, a large-scale collection of real-world
videos with ~7M caption and question-answer training instances designed to emphasize temporal, compositional, and cross-modal audio-visual
reasoning; (ii) a novel three-stage curriculum that progressively trains the model from short-range perception to long-horizon multi-event
reasoning; and (iii) Temporal Audio-Visual Interleaved Chain-of-Thought, a
reasoning framework that explicitly grounds intermediate
reasoning steps to timestamps in long audio-visual streams, improving temporal
alignment and interpretability. Extensive experiments across 15+ audio-visual, omni-modal, audio, and
vision benchmarks show that AV-Flamingo outperforms similarly sized open models by clear margins and remains highly competitive with, and in some cases surpasses, much larger open-weight and closed models, particularly on long and complex real-world audio-visual understanding and
reasoning tasks. Beyond
benchmark performance, AV-Flamingo exhibits strong real-world utility and transfers well to unseen tasks, highlighting its robustness and generalization ability.
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All authors Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar, Lasha Koroshinadze, Nishit Anand, Siddharth Gururani, Hanrong Ye, Pritam Biswas, Yuanhang Su, Ehsan Hosseini-Asl, Sang-gil Lee, Zhifeng Kong, Jaehyeon Kim, Sungwon Kim, S Sakshi, Ramani Duraiswami, Dinesh Manocha, Andrew Tao, Mohammad Shoeybi, Bryan Catanzaro, Ming-Yu Liu, Wei Ping
Audio and Speech Processing · Cross-listed from cs.CL
Controlling Implicit Shortcut Reliance in L2 Spoken English Auto-markers
Shilin Gao, Mark J. F. Gales and Kate M. Knill
Increasingly,
speech and
language processing tasks take either audio or text directly rather than extracting features from these as the input to the classifier or regressor. Often these systems make use of complex, for example
transformer-based, processes that have the ability to derive highly non-linear mappings between the input and the output. Unfortunately these systems can also learn ”shortcuts” where the classifier is overly reliant on particular aspects of the input to yield the output. For the task of
language proficiency assessment, this over-reliance can enable learners to increase their score by exploiting the shortcut rather than improving their ability. This paper introduces a novel training criterion that is able to reduce the classifier's reliance on shortcuts, thus for example limiting this option for malpractice in
language assessment. This process is illustrated on two forms of assessment system, one based on the audio the other on the
speech recognition text. The results show that, for both systems, there is higher correlations with features that could be exploited for malpractice than expected from the human reference, indicating an over-reliance on these features. By introducing the modified training criterion, this correlation can be reduced to be closer to the reference correlation.
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Systems and Control
Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem
Marco C. Campi and Simone Garatti
This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is meant to highlight a methodology of broad applicability. The example concerns an aircraft traveling from an origin point to a destination point in the presence of uncertain crosswinds and a low-connectivity zone that should be avoided. The MPC policy is parameterized by two hyperparameters, which are selected from data by the P2L procedure. Starting from a
dataset of 400 wind realizations, also called scenarios, P2L identifies a final compression set containing only two informative scenarios. The resulting MPC policy avoids the low-connectivity zone on all available scenarios and, according to the P2L theory, satisfies a probabilistic risk bound of $4.8\%$ at confidence level $1-10^{-5}$, where the risk is the probability of entering the low-connectivity zone in a future flight under a new wind realization not included in the sample.
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Image and Video Processing · Cross-listed from cs.LG
Physics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting
Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju et al. (+2 more)
Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions. Conventional short-range
forecasting with high-resolution numerical weather prediction requires frequent data assimilation, model initialization, and
spin-up, introducing computational latency. Machine learning provides an alternative by learning storm
evolution directly from high-frequency observations and producing forecasts quickly after training. This is particularly relevant for Mumbai, India, where monsoon convection, land-sea interactions, and localized intense rainfall make short-term prediction difficult. Here, we develop a compact radar-only nowcasting framework that combines multi-elevation reflectivity, Doppler radial velocity, and radial-velocity-gradient proxy features within an encoder-decoder U-Net. Using the most recent radar volume scan, the model predicts 12 future composite reflectivity fields at 7.5-min intervals up to 90 min lead time. The derived velocity magnitude, divergence-like, directional-shear, and vorticity-like channels represent kinematic signatures associated with convergence and boundary interactions without requiring full wind-field
retrieval. A high-reflectivity
attention module improves sensitivity to convective cores, and physics-guided attribution examines whether the learned sensitivities are meteorologically meaningful. The model is trained using Mumbai Doppler radar observations from May to August 2023 and evaluated on temporally independent events. At 90 min lead time, Critical Success Index values are 0.437, 0.332, and 0.193 for $\geq$10, $\geq$20, and $\geq$30 dBZ thresholds, respectively. Compared with persistence, the model gives lower RMSE and higher spatial correlation at longer lead times. Once trained, it runs on a standard computer, generating nowcasts within seconds for real-time use.
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All authors Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju, Sushma Nair, Subimal Ghosh
Systems and Control
Network-Induced Strategic Communication in Opinion Dynamics
Hassan Munif, Anthony Couthures, Vineeth S. Varma et al. (+2 more)
Classical opinion dynamics typically assume a fixed mapping from private opinions to public signals, such as linear exchange, saturated signaling, or discrete public actions. In this paper, we show that these communication mappings can be derived from a strategic communication game played on a weighted influence network. Each
agent acts as a receiver estimating its neighbors' states and as a sender broadcasting a public signal to influence its audience. We prove that the network's effect on a sender is summarized by a scalar, network-induced exaggeration factor, and the network game decouples into independent scalar cheap talk problems. The communication rules then emerge as behavioral regimes of one model: aligned incentives recover linear averaging; persuasive senders facing naive receivers produce saturated signaling; and persuasive senders facing
Bayesian receivers cannot credibly reveal their opinions, so
equilibrium communication becomes an interval quantizer, providing a game-theoretic foundation for continuous-opinion discrete-action (CODA) dynamics. Embedded in repeated opinion updates, the resulting strategic-CODA dynamics preserve opinion clustering and exclude extremism. The model predicts that a speaker exaggerates more when their audience is less influenced by them, and that under strong exaggeration, credible public expression collapses to a binary stance even while private opinions remain continuous.
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All authors Hassan Munif, Anthony Couthures, Vineeth S. Varma, Samson Lasaulce, Tamer Başar
Image and Video Processing
Robust Monitoring of Arc Welding Processes: A Generalizable Framework with DVAE and Particle Filter
Yue Cao, Hai Lin and YuMing Zhang
Arc welding processes are essential for continuous fabrication but prone to disturbances that impair weld quality, making real-time monitoring critical yet difficult due to complex visual patterns and nonlinear, time-varying dynamics. Deep learning shows promise but faces scalability limits because of its dependence on large labeled
datasets and application-specific tuning. We explore whether a unified approach can characterize major arc welding processes across applications and improve scalability through consistent state monitoring. This paper introduces a robust and generalizable monitoring framework for arc welding. It combines unsupervised deep latent representation learning, which extracts compact features from weld pool images, with
Bayesian filtering to handle persistent and fluctuating disturbances such as arc radiation and specular reflections. Specifically, a Dynamic Variational Autoencoder (DVAE), consisting of a CNN-based encoder-decoder and an LSTM-based transition model, jointly learns latent representations and their
evolution under control inputs. For robust real-time inference, a specialized Particle Filter (PF) propagates the latent and LSTM hidden states, preserving process history while suppressing sensor noise. This design is well suited to welding's slow and inertial dynamics. Validation on GTAW and GMAW without process-specific tuning demonstrates the framework's generalizability and robustness.
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Systems and Control
Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
Josef Hoppe, Sarra Bouchkati, Farah Nasr et al. (+7 more)
Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.
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All authors Josef Hoppe, Sarra Bouchkati, Farah Nasr, Jonathan Krapp, Alexander Och, Maximilian Wirth, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Michael T. Schaub
Systems and Control · Cross-listed from cs.RO
A Morphing-Designed Hexarotor Prototype combining Practical Resilience and Efficiency
Murat Bronz, Mahmoud Hamandi, Elgiz Baskaya et al. (+2 more)
This work demonstrates experimentally the existence of a hexarotor prototype, termed Opti-Hexa, that simultaneously achieves practical resilience to single-propeller failures and energy efficiency comparable to a standard
Star-shaped prototype with the same size, weight, hardware and software. Leveraging a novel open-source morphing platform, we investigate the trade-offs across a continuous range of geometries by varying the angles between adjacent propellers. We study practical efficiency through a data-fitted empirical power model and evaluate practical resilience by comparing the position accuracy and rotational kinetic energy during failure to those observed under nominal hovering conditions. Our experiments confirm the existence of a
geometric viability region for this specific morphing platform, where resilience is ensured without the aerodynamic efficiency losses typically associated with practically resilient designs found in the state of the art. The complete hardware and software of the morphing platform are released to support further research.
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All authors Murat Bronz, Mahmoud Hamandi, Elgiz Baskaya, Chiara Gabellieri, Antonio Franchi
Signal Processing
Inertial Human Motion Capture: From Biomechanics to Recent Sensor Fusion Methods and Back
Manon Kok, Ive Weygers, Hassan Osman et al. (+4 more)
Inertial measurement units (IMUs) are a promising means to capture human motion, yet obtaining meaningful biomechanical quantities from IMU measurements remains non-trivial. This tutorial-style review focuses on kinematics and introduces four key aspects (inertial human motion capture objective, environmental conditions, subject & attributes, and motion characteristics) to determine how to translate biomechanical problems into adequate formulations for the fusion of inertial sensor measurements. We identify three fundamental challenges for kinematics estimation from IMUs: IMUs do not provide direct information about the joint angle, IMUs do not measure their own orientation, and real-world environments and dynamics compromise sensor reliability. Though there exist widely-used methods to overcome these challenges, they suffer from severe limitations in real-life applications, e.g., the need for sensor-to-segment calibration, and the fact that
magnetic field disturbances degrade joint angle accuracy. The full potential for many use-cases hence remains untapped in terms of accuracy and reliability. We share insights into recently proposed methods, e.g. exploiting the human body's kinematic chain constraints, having the potential to overcome these limitations. We also present guiding questions related to the four key aspects and illustrate their use for navigating the methodological landscape for the use-case of lower-extremity joint angle estimation, for which we share open-access code and compare the traditional workflow with three alternatives. Our aim is to bridge the gap between the sensor fusion community developing methods for human motion capture and the biomechanics community in need of accurate, easy-to-use, and reliable methods to study human motion outside of the laboratory.
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All authors Manon Kok, Ive Weygers, Hassan Osman, Daniel Weber, Ruiyuan Li, Thomas Seel, Ajay Seth
Systems and Control · Cross-listed from cs.IT
A 140-GHz Direct Raised-Cosine Envelope-Shaping Transmitter with Integrated ILO Phase Shifter
Haoling Li and Najme Ebrahimi
A 140-GHz transmitter with direct raised-cosine-like envelope shaping and wide-range phase tuning is presented in 90-nm SiGe BiCMOS. The proposed architecture relaxes conventional baseband pulse shaping and high-speed digital-to-analog converters by directly synthesizing 3-level and 5-level RF envelope states that approximate a raised-cosine waveform, enabled by a 23-dB modulation dynamic range. Measured results demonstrate data rate up to 8 Gbpers with 34-dB sidelobe suppression. To support scalable phased-array applications, an injection-locked-oscillator phase-tuning path is also integrated, providing 22.5deg digital phase resolution together with continuous analog tuning over more than 360 deg. The transmitter achieves up to 2 dBm output power and combines direct RF envelope shaping with wide-range fine phase control for sub-THz wireless links
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Image and Video Processing · Cross-listed from cs.LG
DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging
Théophane Loloum, Fabien Vivodtzev, David Hébert et al. (+3 more)
This application paper presents DebrisTracer, a framework for the reliable tracking of debris in hypervelocity impact fast imaging. These noisy and highly specific
datasets capture the ejection of a large number of debris fragments after the impact of a projectile launched at hypervelocity into a target material. The reliable estimation of debris mass and speed distributions is of major importance in aerospace applications. We document how to extend an off-the-shelf
topology tracking framework based on critical point extraction and matching, in order to incorporate domain knowledge and physical assumptions. Our approach automatically produces an accurate and reliable debris tracking, enabling an interpretable visual analysis of this complex space-time phenomenon. Extensive experiments demonstrate the accuracy improvements provided by our approach over established tools used by domain experts in terms of physical validation, specifically via the prediction of the experimental ejected mass and crater depth profiles. We illustrate the utility of our approach across several use cases (with varying impact angles and physics). We show that our statistical summaries enable the visual identification of distinct regimes within the debris population, corroborating and refining prior expectations of domain experts. Our database and our C++ implementation are available at this address: https://github.com/tloloum/DebrisTracer.
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All authors Théophane Loloum, Fabien Vivodtzev, David Hébert, Baptiste Reynier, Michel Arrigoni, Julien Tierny
Systems and Control
Vessel Trajectory Prediction using COLREGs-aware Optimal Planning
David Kaikkonen, Fredrik Ljungberg and Erik Frisk
This paper presents a trajectory prediction method for marine vessels based on optimal planning. Crude initial trajectories respecting static obstacles are first generated using A*-search to provide a feasible warm start. In the second step, a numerical optimizer is used to ensure COLREG compliance. The prediction problem is posed as sequential trajectory planning from the perspective of each surrounding vessel, requiring only their current positions, velocities, and intended destinations as input. As the latter is included in AIS messages, this enables faster predictions than learning-based methods that typically require longer data histories. The proposed method is validated using real-world scenarios constructed from AIS data.
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Systems and Control
Dynamic Constraint Reconstruction Based Control Barrier Functions for Safety-Critical Control of High-Dimensional Manipulators
Bingsheng Zhang, Shen Wang, Qiang Wang et al. (+3 more)
Control barrier functions (CBFs) provide formal safety guarantees for constrained nonlinear systems, but their effectiveness relies on accurate system dynamics. In high-dimensional manipulators subject to unknown disturbances and model uncertainties, fixed safety constraints constructed from nominal dynamics may become inconsistent with the actual system behavior, leading to safety degradation or excessive conservatism. This paper proposes a dynamic constraint reconstruction based control barrier function (DCR-CBF) framework for safety-critical control of disturbed robotic manipulators. An extended state observer is employed to estimate lumped disturbances online, and the estimated disturbance is incorporated into high-order control barrier functions to reconstruct safety constraints according to the estimated true dynamics. To address estimation inaccuracies, a safety margin is introduced, and a sufficient condition is derived to guarantee forward invariance under bounded estimation errors. Simulation studies on a 4-DOF excavation manipulator demonstrate that the proposed DCR-CBF method achieves zero safety violation under strong unknown disturbances while significantly improving trajectory-tracking performance compared with standard and robust CBF methods.
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All authors Bingsheng Zhang, Shen Wang, Qiang Wang, Muguo Du, Donghai Shi, Xiaofeng Tao
Signal Processing
Multibit Quantized Precoding for MU-mMIMO
Getuar Rexhepi, Shreesal Shrestha, Christoph Studer et al. (+1 more)
We propose a novel multibit quantized precoding method for the downlink of multi-user massive
MIMO systems with low-resolution digital-to-analog converters. The new method, termed multibit quantized precoding (MQP), enforces the finite-alphabet constraint through an l0-norm penalty, approximated by a smooth surrogate so as to yield a reformulated problem, which is then convexized via fractional programming, ultimately extending quantized precoding beyond 1-bit alphabets. The regularization parameter of the proposed method is selected via a discrepancy principle integrated with graduated non-convexity continuation, resulting in a principled and reproducible hyperparameter tuning method and an efficient iterative
algorithm with a closed-form, least-squares-type update per iteration. In order to further reduce the computational complexity of the method, we include a Gaussian belief propagation (GaBP) step for turning the least-squares update in linear-time. Simulations performed for systems with different sizes demonstrate that both methods, namely the MQP with and without GaBP, achieve competitive or superior error-rate performance compared to state-of-the-art quantized precoding
algorithms under various channel conditions.
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All authors Getuar Rexhepi, Shreesal Shrestha, Christoph Studer, Giuseppe Thadeu Freitas de Abreu
Systems and Control
Gaussian behaviors and stochastic data-driven control
András Sasfi, Alberto Padoan, Ivan Markovsky et al. (+1 more)
We propose a
stochastic behavioral modeling framework, termed Gaussian behaviors, which augments a deterministic linear time-invariant (LTI) behavior with a Gaussian noise component. We show that this notion is a tractable subclass of
stochastic behaviors and encompasses classical parametric
stochastic LTI state-space system models as special cases. Analogously to deterministic LTI behaviors, the framework enables simple and tractable
stochastic data-driven control methods. To this end, we obtain a method for prediction by conditioning the Gaussian behavior on the known part of the trajectory, which is identified directly from the sample covariance of trajectory data. Building on this method, we develop predictive control formulations that optimize over feedforward or disturbance affine feedback policies. The resulting formulations are shown to be convex. We further derive a finite-sample confidence bound on the prediction accounting for both aleatoric and epistemic uncertainty, and incorporate it into a robust control method, for which a tractable convex upper bound is obtained. Within this framework, subspace predictive control is recovered when only the mean prediction is used, while data-enabled predictive control is shown to account for the prediction uncertainty in an optimistic fashion. Numerical case studies illustrate the benefits of the proposed methods.
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All authors András Sasfi, Alberto Padoan, Ivan Markovsky, Florian Dörfler
Systems and Control · Cross-listed from cs.IT
Strategic Persuasion Through Information Timeliness
Ahmet Bugra Gundogan and Melih Bastopcu
We study a dynamic strategic communication problem in which a sender controls the timing of truthful updates from binary continuous-time Markov sources. The receiver chooses between a zero-order-hold
estimator that follows the sender's updates and a prior-only default
estimator, aiming to maximize a weighted correct-estimation utility. In contrast, the sender seeks to persuade the receiver to estimate the state as 1, regardless of the true state. This misalignment leads to a Stackelberg game in which the sender, as the leader, commits to state-dependent Poisson update rates, and the receiver, as the follower, decides whether to follow the sender's messages. The sender maximizes the long-term average time that the receiver's estimate equals 1, subject to a conditional intensity budget and a participation constraint (PC) ensuring that following the sender's messages does not degrade the receiver's average utility relative to its prior information. For a single source, we show that the sender's optimal policy allocates a minimum state-0 update intensity to the undesired state-0, just enough to satisfy the PC, and the remaining budget to the desired state-1. For multiple sources with heterogeneous minimum state-0 update intensities, we develop a branch-and-bound
algorithm that typically avoids exhaustive search. Finally, we extend the solution to multiple receivers over dedicated channels. Our results show that controlling timeliness alone enables the sender to persuade the receiver and increase its utility.
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Signal Processing · Cross-listed from cs.IT
Current Should Not Sneak: Constrained Codes for Reliable Memristor Crossbar Arrays
Selahattin Kaan K{\i}rgeç, Yunus Alp B{\i}y{\i}koğlu and Ahmed Hareedy
The approach of squeezing more transistors in the same area in order to speed up computing is no longer effective. Currently, researchers and engineers are searching for novel solutions that offer faster computing. One of these solutions is to compute where you store, known as in-
memory computing. Resistive random access
memories (ReRAMs), which are based on memristor crossbar arrays, enable in-
memory computing. Moreover, ReRAMs offer large storage capacity associated with energy efficiency. In this work, we focus on storing digital data in memristor crossbar arrays. A critical challenge here is the sneak-path problem, occurring when there is a rectangle on the array with three low and one high resistances at the corners. The electric current in this case is prone to sneaking through the low-resistance path upon reading, which results in the high resistance data becoming erroneous. In this paper, we propose effective constrained coding solutions to the sneak-path problem after finding the expected number of sneak paths over a two-dimensional array given their circumferences. In particular, we adopt a literature model where $b$ rows on the crossbar array are read simultaneously while the others are grounded, and we design capacity-achieving non-binary constrained codes for the cases of $b=2$ and $b=3$. We focus more on the sneak paths with shorter circumferences as they are more detrimental. Here, GF refers to Galois field. Our GF$(4)$ codes, for $b=2$, and GF$(8)$ codes, for $b=3$, are a class of lexicographically-ordered constrained (LOCO) codes, and we call them resistive-LOCO (RES-LOCO) codes. RES-LOCO codes operate horizontally, and we also suggest a run-length-limited scheme for coding data on the crossbar array vertically to mitigate the sneak-path problem for $b=4$. We experimentally demonstrate the effectiveness of our RES-LOCO codes for various array setups.
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Systems and Control
Day-Ahead Forecasting of Largest Single Infeed/Outfeed on the Irish Power Grid: A Generative Artificial Intelligence Approach
Amir Moshari, Mo Cloonan, Taulant Kerci et al. (+9 more)
This paper presents a generative artificial intelligence (Gen AI) approach for
forecasting, at a day-ahead stage, the largest single infeed (LSI) and largest single outfeed (LSO) on the Irish power system to assist in reserve dimensioning. Developed collaboratively between EirGrid, the electric transmission system operator (TSO) for Ireland, and GridZero.ai using the GridZero.ai platform, the system delivers accurate forecasts up to 38 hours ahead of real-time using limited data available before the day-ahead and intra-day energy
market gate closure timings. Initial performance demonstrates an accuracy with a mean absolute percentage error (MAPE) that is only 1.1% higher than the results possible using full
market data (8-hours ahead). Thus, if this approach is integrated into operational systems and such high levels of accuracy are maintained, reserve procurement costs could be significantly reduced. The results also demonstrate the practicality and extensibility of AI-powered resource planning for TSOs.
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All authors Amir Moshari, Mo Cloonan, Taulant Kerci, Zhi Li, Colm Gaffney, Chotiya Mahittigul, Manuel Hurtado, Simon Tweed, Bryan Murray, Michael Walsh, Eoin Kennedy, Ritesh Madan
Systems and Control
Scalable Supervisory HVAC Control for Linear Objectives
W. Grant Dierking, Arash J. Khabbazi, Levi D. Reyes Premer et al. (+1 more)
Advanced control of heating, ventilation, and air-conditioning (HVAC) systems can substantially reduce energy costs and pollution. However, real-world adoption of popular
algorithms among researchers, such as model predictive control and reinforcement learning, remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free supervisory controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require, at most, two easily-estimable thermal parameters, forecasts of energy prices and occupant temperature preferences over a prediction horizon, and an indoor temperature measurement. In residential cooling simulations, both controllers perform essentially as well under traditional time-invariant electricity pricing as an omniscient optimal controller with exact model information and perfect forecasts, and attain up to 86.6% of the omniscient cost savings under increasingly prevalent time-varying pricing. These results suggest that simple, structure-exploiting controllers may capture most of the attainable value of advanced supervisory HVAC control with linear objectives, while avoiding the data, modeling, tuning, and computational burdens that hinder real-world deployment.
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All authors W. Grant Dierking, Arash J. Khabbazi, Levi D. Reyes Premer, Kevin J. Kircher
Systems and Control · Cross-listed from quant-ph
Maximal quantum leakage: operational interpretation and quantum channel analysis
Shuixin Xiao, Zijia Zhao, Jingge Zhu et al. (+1 more)
Maximal
quantum leakage quantifies
privacy against adversaries with arbitrary intentions. In this work, we prove that computing this leakage is equivalent to minimum-error
quantum state discrimination with equal priors. This establishes a computable operational interpretation, addressing the previous difficulty in computing maximal
quantum leakage. We further analyze the impact of collective measurements on multiple copies of a state, demonstrating that leakage increases monotonically with the number of copies, which leads to explicitly characterizing the maximal leakage in the asymptotic limit. Extending this framework to
quantum channels, we develop an iterative
algorithm for the jointly designing of input states and measurements. Numerical examples involving collective measurements and the maximal channel leakage demonstrate our theoretical findings.
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All authors Shuixin Xiao, Zijia Zhao, Jingge Zhu, Farhad Farokhi
Systems and Control
Converging Safety and Security: IO-Link Wireless and OPC UA over 5G under prEN 50742
Henry Beuster, Thomas Robert Doebbert and Gerd Scholl
The integration of wireless communication technologies in industrial automation offers greater flexibility, but also exposes safety systems to a broader threat vector. Emerging regulations, such as the draft standard prEN 50742, mandate the convergence of functional safety and cybersecurity by requiring cryptographic security mechanisms directly in safety-critical communication. This paper presents an empirical evaluation of this safety-security convergence across a complete control chain, spanning from an IO-Link Wireless Safety device to a PLC via an OPC UA backbone. We measure the latencies and jitter of different Safety-Related Security Levels under prEN 50742 over Ethernet, Wi-Fi 6, and private 5G. Our results reveal that while cryptographic execution time is negligible, the resulting frame payload expansion severely restricts wireless fieldbus capacity, reducing the maximum number of devices per IO-Link Wireless track from 8 to 2. Furthermore, we demonstrate that, despite higher average latency, a private 5G provides sufficiently deterministic latency characteristics to preserve functional safety watchdog margins, unlike unlicensed Wi-Fi 6.
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