Systems and Control · Cross-listed from cs.CR
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
Jianzhu Yao, Hongxu Su, Taobo Liao et al. (+4 more)
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs. Users lack recourse against service downgrades (model swaps, quantization, graph rewrites, or discrepancies like altered ad
embeddings). Verifying outputs is hard because floating-point(FP) execution on heterogeneous accelerators is inherently nondeterministic. Existing approaches are either impractical for real FP neural networks or reintroduce vendor trust. We present TAO: a Tolerance Aware Optimistic verification protocol that accepts outputs within principled operator-level acceptance regions rather than requiring bitwise equality. TAO combines two error models: (i) sound per-operator IEEE-754 worst-case bounds and (ii) tight empirical percentile profiles calibrated across hardware. Discrepancies trigger a Merkle-anchored, threshold-guided dispute game that recursively partitions the computation graph until one operator remains, where adjudication reduces to a lightweight theoretical-bound check or a small honest-majority vote against empirical thresholds. Unchallenged results finalize after a challenge window, without requiring trusted hardware or deterministic kernels. We implement TAO as a PyTorch-compatible runtime and a contract layer currently deployed on Ethereum Holesky testnet. The runtime instruments graphs, computes per-operator bounds, and runs unmodified vendor kernels in FP32 with negligible overhead (0.3% on Qwen3-8B). Across CNNs,
Transformers and
diffusion models on A100, H100, RTX6000, RTX4090, empirical thresholds are $10^2-10^3$ times tighter than theoretical bounds, and bound-aware
adversarial attacks achieve 0% success. Together, TAO reconciles scalability with verifiability for real-world heterogeneous ML compute.
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All authors Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath
Signal Processing
Generalized Pinching-Antenna Systems: A Tutorial on Principles, Design Strategies, and Future Directions
Yanqing Xu, Jingjing Cui, Yongxu Zhu et al. (+8 more)
Pinching-
antenna systems have emerged as a novel and transformative flexible-
antenna architecture for next-generation wireless networks. They offer unprecedented flexibility and spatial reconfigurability by enabling dynamic positioning and activation of radiating elements along a signal-guiding medium (e.g., dielectric waveguides), which is not possible with conventional fixed
antenna systems. In this paper, we introduce the concept of generalized pinching
antenna systems, which retain the core principle of creating localized radiation points on demand, but can be physically realized in a variety of settings. These include implementations based on dielectric waveguides, leaky coaxial cables, surface-wave guiding structures, and other types of media, employing different feeding methods and activation mechanisms (e.g., mechanical, electronic, or hybrid). Despite differences in their physical realizations, they all share the same inherent ability to form, reposition, or deactivate radiation sites as needed, enabling user-centric and dynamic coverage. We first describe the underlying physical mechanisms of representative generalized pinching-
antenna realizations and their associated wireless channel models, highlighting their unique propagation and reconfigurability characteristics compared with conventional
antennas. Then, we review several representative pinching-
antenna system architectures, ranging from single- to multiple-waveguide configurations, and discuss advanced design strategies tailored to these flexible deployments. Furthermore, we examine their integration with emerging wireless technologies to enable synergistic, user-centric solutions. Finally, we identify key open research challenges and outline future directions, charting a pathway toward the practical deployment of generalized pinching
antennas in next-generation wireless networks.
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All authors Yanqing Xu, Jingjing Cui, Yongxu Zhu, Zhiguo Ding, Tsung-Hui Chang, Robert Schober, Vincent W.S. Wong, Octavia A. Dobre, George K. Karagiannidis, H. Vincent Poor, Xiaohu You
Audio and Speech Processing · Cross-listed from physics.soc-ph
Musical consonance: a review of theory and evidence on perception and preference of auditory roughness in humans and other animals
John M. McBride
The origins of consonance in human music has long been contested, and today there are three primary hypotheses: aversion to roughness, preference for harmonicity, and learned preferences from cultural exposure. While the evidence is currently insufficient to disentangle the contributions of these hypotheses, I propose several reasons why roughness is an especially promising area for future study. The aim of this review is to summarize and critically evaluate roughness theory and models, experimental data, to highlight areas that deserve further research. I identify 2 key areas: There are fundamental issues with the definition and interpretation of results due to tautology in the definition of roughness, and the lack of independence in empirical measurements. Despite extensive model development, there are many duplications and models have issues with data quality and overfitting. Future theory development should aim for model simplicity, and extra assumptions, features and parameters should be evaluated systematically. Model evaluation should aim to maximise the breadth of stimuli that are predicted.
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Systems and Control · Cross-listed from cs.LG
Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks
Faezeh Dehghan Tarzjani and Bhaskar Krishnamachari
The p-persistent CSMA protocol is central to random-access MAC analysis, but predicting saturation throughput in heterogeneous multi-hop wireless networks remains a hard problem. Simplified models that assume a single, shared interference domain can underestimate throughput by 48-62% in sparse
topologies. Exact Markov-chain analyses are accurate but scale exponentially in computation time, making them impractical for large networks. These computational barriers motivate structural machine learning approaches like GNNs for scalable throughput prediction in general network
topologies. Yet off-the-shelf GNNs struggle here: a standard GCN yields 63.94% normalized mean absolute error (NMAE) on heterogeneous networks because symmetric normalization conflates a node's direct interference with higher-order, cascading effects that pertain to how interference propagates over the network graph. Building on these insights, we propose the Decoupled Graph Convolutional Network (D-GCN), a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects. D-GCN replaces mean aggregation with learnable
attention, yielding interpretable, per-neighbor contribution weights while capturing complex multihop interference patterns. D-GCN attains 3.3% NMAE, outperforms strong baselines, remains tractable even when exact analytical methods become computationally infeasible, and enables gradient-based network optimization that achieves within 1% of theoretical optima.
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Systems and Control · Cross-listed from cs.ET
Laser Fault Injection in Memristor-Based Accelerators for AI/ML and Neuromorphic Computing
Muhammad Faheemur Rahman and Wayne Burleson
Memristive crossbar arrays (MCA) are emerging as efficient building blocks for in-
memory computing and neuromorphic hardware due to their high density and parallel analog
matrix-vector multiplication capabilities. However, the physical properties of their nonvolatile
memory elements introduce new attack surfaces, particularly under fault injection scenarios. This work explores
Laser Fault Injection as a means of inducing analog perturbations in MCA-based architectures. We present a detailed threat model in which adversaries target memristive
cells to subtly alter their physical properties or outputs using
laser beams. Through HSPICE simulations of a large MCA on 45 nm CMOS tech. node, we show how
laser-induced photocurrent manifests in output current distributions, enabling differential fault analysis to infer internal weights with up to 99.7% accuracy, replicate the model, and compromise computational integrity through targeted weight alterations by approximately 143%.
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Systems and Control
Resource-Aware Stealthy Attacks in Vehicle Platoons
Ali Eslami and Mohammad Pirani
Connected and Autonomous Vehicles (CAVs) are transforming modern transportation by enabling cooperative applications such as vehicle platooning, where multiple vehicles travel in close formation to improve efficiency and safety. However, the heavy reliance on inter-vehicle communication makes platoons highly susceptible to attacks, where even subtle manipulations can escalate into severe physical consequences. While existing research has largely focused on defending against attacks, far less
attention has been given to stealthy adversaries that aim to covertly manipulate platoon behavior. This paper introduces a new perspective on the attack design problem by demonstrating how attackers can guide platoons toward their own desired trajectories while remaining undetected. We outline conditions under which such attacks are feasible, analyze their dependence on communication
topologies and control protocols, and investigate the resources required by the attacker. By characterizing the resources needed to launch stealthy attacks, we address system vulnerabilities and informing the design of resilient countermeasures. Our findings reveal critical weaknesses in current platoon architectures and anomaly detection mechanisms and provide methods to develop more secure and trustworthy CAV systems.
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Systems and Control
Belief Space Control of Safety-Critical Systems Under State-Dependent Measurement Noise
Rohan Walia, Mitchell Black, Andrew Schoer et al. (+1 more)
Safety-critical control is imperative for deploying autonomous systems in the real world. Control Barrier Functions (CBFs) offer strong safety guarantees when accurate system and sensor models are available. However, widely used additive, fixed-noise models are not representative of complex sensor modalities with state-dependent error characteristics. Although CBFs have been designed to mitigate uncertainty using fixed worst-case bounds on measurement noise, this approach can lead to overly-conservative control. To solve this problem, we extend the Belief Control Barrier Function (BCBF) framework to accommodate state-dependent measurement noise via the Generalized Extended Kalman Filter (GEKF)
algorithm, which models measurement noise as a linear function of the state. Using the original BCBF framework as baseline, we demonstrate the performance of the BCBF-GEKF approach through simulation results on a 1D single integrator setpoint tracking scenario and 2D unicycle kinematics trajectory tracking scenario. Our results confirm that the BCBF-GEKF approach offers less conservative control with greater safety.
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All authors Rohan Walia, Mitchell Black, Andrew Schoer, Kevin Leahy
Systems and Control
DiffOPF: Diffusion Solver for Optimal Power Flow
Milad Hoseinpour and Vladimir Dvorkin
The optimal power flow (OPF) is a multi-valued, non-convex mapping from loads to dispatch setpoints. The variability of system parameters (e.g., admittances,
topology) further contributes to the multiplicity of dispatch setpoints for a given load. Existing deep learning OPF solvers are single-valued and thus fail to capture the variability of system parameters unless fully represented in the feature space, which is prohibitive. To solve this problem, we introduce a
diffusion-based OPF solver, termed DiffOPF, that treats OPF as a conditional sampling problem. The solver learns the joint distribution of loads and dispatch setpoints from operational history, and returns the marginal dispatch distributions conditioned on loads. Unlike single-valued solvers, DiffOPF enables sampling statistically credible warm starts with favorable cost and constraint satisfaction trade-offs. We explore the sample complexity of DiffOPF to ensure the OPF solution within a prescribed distance from the optimization-based solution, and verify this experimentally on power system
benchmarks.
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Image and Video Processing · Cross-listed from physics.optics
Optical Computation-in-Communication enables low-latency, high-fidelity perception in telesurgery
Rui Yang, Jiaming Hu, Jian-Qing Zheng et al. (+12 more)
Artificial intelligence (AI) holds significant promise for enhancing intraoperative perception and decision-making in telesurgery, where physical separation impairs sensory feedback and control. Despite advances in medical AI and
surgical robotics, conventional electronic AI architectures remain fundamentally constrained by the compounded latency from serial processing of inference and communication. This limitation is especially critical in latency-sensitive procedures such as endovascular interventions, where delays over 200 ms can compromise real-time AI reliability and patient safety. Here, we introduce an Optical Computation-in-Communication (OCiC) framework that reduces end-to-end latency significantly by performing AI inference concurrently with optical communication. OCiC integrates Optical Remote Computing Units (ORCUs) directly into the optical communication pathway, with each ORCU experimentally achieving up to 69 tera-operations per second per channel through spectrally efficient two-dimensional photonic convolution. The system maintains ultrahigh inference fidelity within 0.1% of CPU/GPU baselines on classification and coronary angiography
segmentation, while intrinsically mitigating cumulative error propagation, a longstanding barrier to deep optical network scalability. We validated the robustness of OCiC through outdoor dark fibre deployments, confirming consistent and stable performance across varying environmental conditions. When scaled globally, OCiC transforms long-haul fibre infrastructure into a distributed photonic AI fabric with exascale potential, enabling reliable, low-latency telesurgery across distances up to 10,000 km and opening a new optical frontier for distributed medical intelligence.
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All authors Rui Yang, Jiaming Hu, Jian-Qing Zheng, Yue-Zhen Lu, Jian-Wei Cui, Qun Ren, Yi-Jie Yu, John Edward Wu, Zhao-Yu Wang, Xiao-Li Lin, Dandan Zhang, Mingchu Tang, Christos Masouros, Huiyun Liu, Chin-Pang Liu
Systems and Control
Dual Detection Framework for Faults and Integrity Attacks in Cyber-Physical Control Systems
Xixing Xue, Dong Shen, Steven X. Ding et al. (+1 more)
Anomaly detection plays a vital role in the security and safety of cyber-physical control systems, and accurately distinguishing between different anomaly types is crucial for system recovery and mitigation. This study proposes a dual detection framework for anomaly detection and discrimination. By leveraging the dynamic characteristics of control loops and the stealthiness features of integrity attacks, the closed-loop stealthiness condition is first derived, and two dedicated detectors are designed and deployed on the controller side and the plant side, respectively, enabling joint plant fault and cyber attack detection. Moreover, by jointly analyzing the residual response of the two detectors corresponding to different anomalies, it is proved that the proposed method can distinguish between faults and integrity attacks due to the detectors' individual residual spaces. According to the detector's residual space, the fault and attack detection performance is further improved by a two-stage optimization scheme. Simulation results validate the effectiveness of the proposed approach.
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All authors Xixing Xue, Dong Shen, Steven X. Ding, Dong Zhao
Systems and Control
Multi-Period Sparse Optimization for Proactive Grid Blackout Diagnosis
Qinghua Ma, Reetam Sen Biswas, Denis Osipov et al. (+3 more)
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses (i.e. blackouts). For realistic extreme events that are correlated or share similar patterns, it is reasonable to expect that the dominant vulnerability or failure sources behind them share the same locations but with different severity. Early warning diagnosis that proactively identifies the key vulnerabilities responsible for a number of system collapses of interest can significantly enhance resilience. This paper proposes a multi-period sparse optimization method, enabling the discovery of persistent failure sources across a sequence of collapsed systems with increasing system stress, such as rising demand or worsening contingencies. This work defines persistency and efficiently integrates persistency constraints to capture the “hidden” evolving vulnerabilities.
Circuit-theory based power flow formulations and
circuit-inspired optimization heuristics are used to facilitate the scalability of the method. Experiments on
benchmark systems show that the method reliably tracks persistent vulnerability locations under increasing load stress, and solves with scalability to large systems (on average taking around 200 s per scenario on 2000+ bus systems).
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All authors Qinghua Ma, Reetam Sen Biswas, Denis Osipov, Guannan Qu, Soummya Kar, Shimiao Li
Systems and Control
Cyber-Resilient System Identification for Power Grid through Bayesian Integration
Shimiao Li, Guannan Qu, Bryan Hooi et al. (+3 more)
Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape. Advances in snapshot-based system identification approaches have enabled accurately estimating states and
topology from a snapshot of measurement data, under random bad data and
topology errors. However, modern interactive, targeted false data can stay undetectable to these methods, and significantly compromise estimation accuracy. This work advances system identification that combines snapshot-based method with time-series model via
Bayesian Integration, to advance cyber resiliency against both random and targeted false data. Using a distance-based time-series model, this work can leverage historical data of different distributions induced by changes in grid
topology and other settings. The normal system behavior captured from historical data is integrated into system identification through a
Bayesian treatment, to make solutions robust to targeted false data. We experiment on mixed random anomalies (bad data,
topology error) and targeted false data injection attack (FDIA) to demonstrate our method's 1) cyber resilience: achieving over 70% reduction in estimation error under FDIA; 2) anomalous data identification: being able to alarm and locate anomalous data; 3) almost linear scalability: achieving comparable speed with the snapshot-based baseline, both taking <1min per time tick on the large 2,383-bus system using a laptop CPU.
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All authors Shimiao Li, Guannan Qu, Bryan Hooi, Vyas Sekar, Soummya Kar, Larry Pileggi
Image and Video Processing
Image-based Facial Rig Inversion
Tianxiang Yang, Marco Volino, Armin Mustafa et al. (+2 more)
We present an image-based rig inversion framework that leverages two modalities: RGB appearance and RGB-encoded normal maps. Each modality is processed by an independent Hiera
transformer backbone, and the extracted features are fused to regress 102 rig parameters derived from the Facial Action Coding System (FACS). Experiments on synthetic and scanned
datasets demonstrate that the method generalizes to scanned data, producing faithful reconstructions.
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All authors Tianxiang Yang, Marco Volino, Armin Mustafa, Greg Maguire, Robert Kosk
Image and Video Processing
Invited Paper: BitMedViT: Ternary-Quantized Vision Transformer for Medical AI Assistants on the Edge
Mikolaj Walczak, Uttej Kallakuri, Edward Humes et al. (+2 more)
Vision Transformers (ViTs) have demonstrated strong capabilities in interpreting complex medical imaging data. However, their significant computational and
memory demands pose challenges for deployment in real-time, resource-constrained mobile and wearable devices used in clinical environments. We introduce, BiTMedViT, a new class of Edge ViTs serving as medical AI assistants that perform structured analysis of medical images directly on the edge. BiTMedViT utilizes ternary- quantized linear layers tailored for medical imaging and com- bines a training procedure with multi-query
attention, preserving stability under ternary weights with low-precision activations. Furthermore, BiTMedViT employs task-aware
distillation from a high-capacity teacher to recover accuracy lost due to extreme quantization. Lastly, we also present a pipeline that maps the ternarized ViTs to a custom CUDA kernel for efficient
memory bandwidth utilization and latency reduction on the Jetson Orin Nano. Finally, BiTMedViT achieves 86% diagnostic accuracy (89% SOTA) on MedMNIST across 12
datasets, while reducing model size by 43x,
memory traffic by 39x, and enabling 16.8 ms inference at an energy efficiency up to 41x that of SOTA models at 183.62 GOPs/J on the Orin Nano. Our results demonstrate a practical and scientifically grounded route for extreme-precision medical imaging ViTs deployable on the edge, narrowing the gap between algorithmic advances and deployable clinical tools.
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All authors Mikolaj Walczak, Uttej Kallakuri, Edward Humes, Xiaomin Lin, Tinoosh Mohsenin
Image and Video Processing
DeDelayed: Deleting Remote Inference Delay via On-Device Correction
Dan Jacobellis, Mateen Ulhaq, Fabien Racapé et al. (+2 more)
Video comprises the vast majority of bits that are generated daily, and is the primary signal driving current innovations in robotics, remote sensing, and wearable technology. Yet, the most powerful
video understanding models are too expensive for the resource-constrained platforms used in these applications. One approach is to offload inference to the cloud; this gives access to GPUs capable of processing high-resolution
videos in real time. But even with reliable, high-bandwidth communication channels, the combined latency of
video encoding, model inference, and round-trip communication prohibits use for certain real-time applications. The alternative is to use fully local inference; but this places extreme constraints on computational and power costs, requiring smaller models and lower resolution, leading to degraded accuracy. To address these challenges, we propose Dedelayed, a real-time inference system that divides computation between a remote model operating on delayed
video frames and a local model with access to the current frame. The remote model is trained to make predictions on anticipated future frames, which the local model incorporates into its prediction for the current frame. The local and remote models are jointly optimized with an autoencoder that limits the transmission bitrate required by the available downlink communication channel. We evaluate Dedelayed on the task of real-time streaming
video segmentation using the BDD100k driving
dataset. For a round trip delay of 100 ms, Dedelayed improves performance by 6.4 mIoU compared to fully local inference and 9.8 mIoU compared to remote inference – an equivalent improvement to using a model ten times larger. We release our training code, pretrained models, and python library at https://github.com/InterDigitalInc/dedelayed .
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All authors Dan Jacobellis, Mateen Ulhaq, Fabien Racapé, Hyomin Choi, Neeraja J. Yadwadkar
Systems and Control
A 0.62 $\mu$W/sensor 82 fps Time-to-Digital Impedance Measurement IC with Unified Excitation/Readout Front-end for Large-Scale Piezo-Resistive Sensor Array
Jiayang Li, Qingyu Zhang, Sohmyung Ha et al. (+3 more)
This paper presents a fast impedance measurement IC for large-scale piezo-resistive sensor array. It features a unified differential time-to-digital demodulation architecture that readout impedance directly through the excitation
circuit. The proposed pre-saturation adaptive bias technique further improves power efficiency. The chip scans 253 sensors in 12.2 ms (82 fps) at 125 kHz, consuming 158 $\mu$W (7.5 nJ/sensor). With loads from 20 $\Omega$ to 500 k$\Omega$, it achieves 0.5% error and up to 71.1 dB SNR.
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All authors Jiayang Li, Qingyu Zhang, Sohmyung Ha, Andreas Demosthenous, Dai Jiang, Yu Wu
Audio and Speech Processing · Cross-listed from cs.CL
Closing the Gap Between Text and Speech Understanding in LLMs
Santiago Cuervo, Skyler Seto, Maureen de Seyssel et al. (+5 more)
Large
Language Models (LLMs) can be adapted to extend their text capabilities to
speech inputs. However, these
speech-adapted LLMs consistently underperform their text-based counterparts–and even cascaded pipelines–on
language understanding tasks. We term this shortfall the text-
speech understanding gap: the performance drop observed when a
speech-adapted LLM processes spoken inputs relative to when the original text-based LLM processes the equivalent text. Recent approaches to narrowing this gap either rely on large-scale
speech synthesis of text corpora, which is costly and heavily dependent on synthetic data, or on large-scale proprietary
speech datasets, which are not reproducible. As a result, there remains a need for more data-efficient alternatives for closing the text-
speech understanding gap. In this work, we analyze the gap as driven by two factors: (i) forgetting of text capabilities during adaptation, and (ii) cross-modal misalignment between
speech and text. Based on this analysis, we introduce SALAD–Sample-efficient
Alignment with Learning through Active selection and cross-modal
Distillation–which combines cross-modal
distillation with targeted synthetic data to improve
alignment while mitigating forgetting. Applied to 3B and 7B LLMs, SALAD achieves competitive performance with a strong open-weight model across broad-domain
benchmarks in knowledge,
language understanding, and
reasoning, while training on over an order of magnitude less
speech data from public corpora.
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All authors Santiago Cuervo, Skyler Seto, Maureen de Seyssel, Richard He Bai, Zijin Gu, Tatiana Likhomanenko, Navdeep Jaitly, Zakaria Aldeneh
Systems and Control · Cross-listed from quant-ph
Cryo-CMOS Antenna for Wireless Communications within a Quantum Computer Cryostat
Viviana Centritto, Ama Bandara, Heqi Deng et al. (+4 more)
Scaling
quantum computers from a few
qubits to large numbers remains one of the critical challenges in realizing practical
quantum advantage. Multi-core
quantum architectures have emerged as a promising solution, enabling scalability through distributed
quantum processing units (QPUs) interconnected via classical and
quantum links. However, the bottleneck of wired connections persists, as densely packed wired interconnects, both vertically across temperature stages and horizontally within the same layer, introduce spatial constraints, power dissipation, and latency, which could hinder performance as the number of QPUs increases. To overcome these limitations, this work proposes a cryo-compatible on-chip differential dipole
antenna operating at 28 GHz to enable short-range wireless communication within a
quantum computer cryostat. Temperature-dependent material properties are incorporated to accurately capture
antenna behavior at 4 K. Moreover, by
embedding the
antenna in a realistic cryostat structure, we evaluate the feasibility of
antenna operation within the cryogenic environment. The proposed
antenna achieves a reflection coefficient of -20.8 dB in free space and -18.38 dB within the cryostat, demonstrating efficient impedance matching.
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All authors Viviana Centritto, Ama Bandara, Heqi Deng, Masoud Babaie, Evgenii Vinogradov, Sergi Abadal, Eduard Alarcon
Systems and Control · Cross-listed from cs.RO
Efficient Force and Stiffness Prediction in Robotic Produce Handling with a Piezoresistive Pressure Sensor
Preston Fairchild, Claudia Chen and Xiaobo Tan
Properly handling delicate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing. Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product. In this work, a flexible pressure sensor that is both low cost and easy to fabricate is integrated with robotic grippers for working with produce of varying shapes, sizes, and stiffnesses. The sensor is successfully integrated with both a rigid robotic gripper, as well as a pneumatically actuated soft finger. Furthermore, an
algorithm is proposed for accelerated estimation of the steady-state value of the sensor output based on the transient response data, to enable real-time applications. The sensor is shown to be effective in incorporating feedback to correctly grasp objects of unknown sizes and stiffnesses. At the same time, the sensor provides estimates for these values which can be utilized for identification of qualities such as ripeness levels and bruising. It is also shown to be able to provide force feedback for objects of variable stiffnesses. This enables future use not only for produce identification, but also for tasks such as quality control and selective distribution based on ripeness levels.
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Systems and Control
Channel Estimation under Large Doppler Shifts and Channel Aging in NOMA-Based Air-Ground Communications
Ayten Gürbüz, Giuseppe Caire and Michael Walter
This paper investigates a multiple
antenna system with non-orthogonal multiple access (NOMA) for the exchange of air traffic management data between commercial aircraft pilots and ground-based air traffic controllers. While NOMA techniques enhance spectral efficiency, their application to aircraft communications is challenged by the high speed of the aircraft (up to 214 m/s) and the long communication ranges (up to 250 km), resulting in significant Doppler shifts and low signal-to-noise ratios, respectively. To accurately assess these challenges, we employ a realistic geometry-based
stochastic air-ground channel model, derived from dedicated flight measurement campaigns. In this paper, multiple aircraft simultaneously transmit data to the ground station. We focus on the channel estimation problem at the ground station under high carrier frequency offsets and the effects of channel aging due to channel's time-varying nature. For the channel estimation problem, we compare the Zadoff-Chu sequences with time-division approach under varying carrier frequency offset pre-compensation accuracies at the aircraft transmitter. For the channel aging problem and performance evaluation of channel
estimators, we compute the outage probability for both the zero-forcing detector and the minimum mean squared error detector with successive interference cancellation. The results show that the favorable channel
estimator-detector combinations differ between the takeoff & landing phase and the enroute cruise phase of the flight, due to the distinct channel propagation characteristics of each phase.
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Systems and Control · Cross-listed from math.OC
Data-driven learning of feedback maps for explicit robust predictive control: an approximation theoretic view
Siddhartha Ganguly, Shubham Gupta and Debasish Chatterjee
We establish an
algorithm to learn feedback maps from data for a class of robust model predictive control (MPC) problems. The
algorithm accounts for the approximation errors due to the learning directly at the synthesis stage, ensuring recursive feasibility by construction. The optimal control problem consists of a linear noisy dynamical system, a quadratic stage and quadratic terminal costs as the objective, and convex constraints on the state, control, and disturbance sequences; the control minimizes and the disturbance maximizes the objective. We proceed via two steps – (a) Data generation: First, we reformulate the given minmax problem into a convex semi-infinite program and employ recently developed tools to solve it in an exact fashion on grid points of the state space to generate (state, action) data. (b) Learning approximate feedback maps: We employ a couple of approximation schemes that furnish tight approximations within preassigned uniform error bounds on the admissible state space to learn the unknown feedback policy. The stability of the closed-loop system under the approximate feedback policies is also guaranteed under a standard set of hypotheses. Two
benchmark numerical examples are provided to illustrate the results.
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Systems and Control
Quantifying the Impact of Missing Risk Markets for Decarbonized Power Systems with Long Duration Energy Storage
Andreas C. Makrides, Adam Suski and Elina Spyrou
The transition to a fully decarbonised electricity system depends on integrating new technologies that ensure reliability alongside sustainability. However, missing risk
markets hinder investment in reliability-enhancing technologies by exposing investors to revenue uncertainty. This study provides the first quantitative assessment of how missing risk
markets affect investment decisions in power systems that depend on long-duration energy storage (LDES) for reliability. We develop a two-stage
stochastic equilibrium model with risk-averse
market participants, which independently sizes power and energy capacity. We apply the method to a case study of a deeply decarbonised power system in Great Britain. The results show that incomplete risk
markets reduce social welfare, harm reliability, and discourage investment in LDES and other technologies with volatile revenue streams. Revenue
volatility leads to substantial risk premiums and higher financing costs for LDES, creating a barrier to its large-scale deployment. These findings demonstrate the importance of policy mechanisms that hedge revenue risk to lower the cost of capital and accelerate investment in reliability-enhancing, zero-carbon technologies
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Signal Processing
A Robust EDM Optimization Approach for 3D Single-Source Localization with Angle and Range Measurements
Mingyu Zhao, Qingna Li and Hou-Duo Qi
Accurate source localization in Multi-Platform Radar Networks (MPRNs) benefits from exploiting both range and angle measurements under robust estimation. In this paper, we propose a robust Euclidean distance
matrix (EDM) optimization model that simultaneously integrates range measurements, angle information, and the least absolute deviation ($\ell_1$-norm) criterion for the case of 3D single-source localization (3DSSL). A key theoretical contribution of this work is the rigorous reformulation of {existing} 3D angle measurements into simple box constraints on the Euclidean distances. Unlike previous approximations, we achieve this by reducing each of the 3D angle measurements to a two-dimensional nonlinear optimization problem, whose global minimum and maximum solutions can be characterized and utilized to get the lower and upper bounds of the distances from the unknown source to the sensors. To solve the resulting rank-constrained EDM problem, we develop an efficient
algorithm based on the majorization penalty method. Extensive numerical experiments confirm that the new EDM model significantly outperforms leading solvers in terms of localization accuracy and computational efficiency, particularly in low Signal-to-Noise Ratio (SNR) scenarios.
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Signal Processing
Radio over Fiber with Cascaded Structure: Algorithm for Uplink Positioning
Dexin Kong, Diana Pamela Moya Osorio and Erik G. Larsson
Recent advancements in
polymer microwave fiber (PMF) technology have created significant opportunities for robust, low-cost, and high-speed sub-terahertz (THz)
radio-over-fiber communications. Recognizing these potential benefits, this paper explores a novel
radio-over-fiber (RoF) structure that interconnects multiple
radio units (RUs) in cascade via fiber, envisioning its application in indoor scenarios. This structure creates a number of research opportunities when considering cascaded distortion effects introduced by non-linear power amplifiers (PAs) at the RUs and the propagation channel over the fiber. We propose maximum-likelihood and non-linear least-squares
algorithms to estimate the propagation distance along the RoF and the time-of-arrival between the RoF and the user equipment. For the case of linear PAs, we derive the Cramér-Rao lower bound to
benchmark the performance of the
estimators. Finally, we investigate the use of the system for uplink positioning. Our simulation results demonstrate that the proposed
estimators perform satisfactorily even with the cascaded effects of non-linear PAs, and that the deployment of this RoF structure can enable new cost-effective opportunities for high-resolution positioning in indoor scenarios. In the numerical evaluation, we also use measured PMF characteristics for high-density polyethylene fibers.
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Systems and Control
Physics-Informed Neural Network Modeling of Vehicle Collision Dynamics in Precision Immobilization Technique Maneuvers
Yangye Jiang, Jiachen Wang and Daofei Li
Accurate prediction of vehicle collision dynamics is crucial for advanced safety systems and post-impact control applications, yet existing methods face inherent trade-offs among computational efficiency, prediction accuracy, and data requirements. This paper proposes a dual Physics-Informed Neural Network framework addressing these challenges through two complementary networks. The first network integrates Gaussian Mixture Models with PINN architecture to learn impact force distributions from finite element analysis data while enforcing momentum conservation and energy consistency constraints. The second network employs an adaptive PINN with dynamic constraint weighting to predict post-collision vehicle dynamics, featuring an adaptive physics guard layer that prevents unrealistic predictions whil e preserving data-driven learning capabilities. The framework incorporates uncertainty quantification through time-varying parameters and enables rapid adaptation via fine-tuning strategies. Validation demonstrates significant improvements: the impact force model achieves relative errors below 15.0% for force prediction on finite element analysis (FEA)
datasets, while the vehicle dynamics model reduces average trajectory prediction error by 63.6% compared to traditional four-degree-of-freedom models in scaled vehicle experiments. The integrated system maintains millisecond-level computational efficiency suitable for real-time applications while providing probabilistic confidence bounds essential for safety-critical control. Comprehensive validation through FEA simulation, dynamic modeling, and scaled vehicle experiments confirms the framework's effectiveness for Precision Immobilization Technique scenarios and general collision dynamics prediction.
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Systems and Control
On the Flexibility Potential of a Swiss Distribution Grid: Opportunities and Limitations
Jan Brändle, Julie Rousseau, Pulkit Nahata et al. (+1 more)
The growing integration of distributed renewable generation and the electrification of heating and transportation are rapidly increasing the number of flexible devices within modern distribution grids. Leveraging the aggregated flexibility of these small-scale distributed resources is essential to maintaining future grid-wide stability. This work uses the Swiss distribution grid of Walenstadt as a case study to provide insights into the aggregated flexibility potential of distribution grids. It demonstrates that incorporating devices such as heat pumps and photovoltaic systems significantly enhances distribution grid flexibility. It investigates the time-varying nature of aggregated flexibility and highlights how it can vary seasonally. Furthermore, simulations of future scenarios reveal that aggregated flexibility does not increase linearly or monotonically with higher levels of flexible device penetration. This is primarily due to the overloading of individual feeders, which underscores the impact of grid
topology and network constraints on the aggregated flexibility potential.
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All authors Jan Brändle, Julie Rousseau, Pulkit Nahata, Gabriela Hug
Signal Processing
Geometry-Based Drift Compensation for Distributed Channel Sounding Measurements in Dynamic Drone Scenarios
Lorenz Mohr, Marc Miranda, Sebastian Semper et al. (+5 more)
Measured impulse responses obtained from a dynamic unmanned aerial vehicle (
UAV) channel sounding system exhibit effects attributable to time-varying carrier frequency offset (CFO) and sampling frequency offset (SFO). To correct the recorded data in post-processing, we extend existing geometry-based drift compensation
algorithms by an explicit line-of-sight (LoS) determination, combining a symbol-wise high-resolution parameter estimation (HRPE) in delay with a Kalman filter. This proposed extension facilitates the removal of rapidly varying synchronization mismatches from channel sounding measurements in rich multipath propagation scenarios. Furthermore, we propose using the relative residual power after subtraction of estimated multipath components as a metric for ground-truth-independent comparison of post-processing synchronization methods for recorded channel sounding data. The application of the proposed procedure shows that our approach outperforms existing post-processing compensation
algorithms, reducing the relative residual power by more than 5 dB and the delay-Doppler estimate root mean square errors (RMSEs) of a passive
UAV target by approximately 60 %.
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All authors Lorenz Mohr, Marc Miranda, Sebastian Semper, Julia Beuster, Carsten Andrich, Sebastian Giehl, Christian Schneider, Reiner S. Thomä
Image and Video Processing
Distribution-Aligned Representation Adaptation for DJSCC over Hybrid Wireless-Wired Networks
Jiangyuan Guo, Wei Chen, Yuxuan Sun et al. (+1 more)
Deep joint source-channel coding (DJSCC) has emerged as a robust alternative to traditional separate coding for communications through wireless channels. Existing DJSCC approaches focus primarily on point-to-point wireless communication scenarios, while neglecting end-to-end communication efficiency in hybrid wireless-wired networks such as 5G and 6G communication systems. Considerable redundancy in DJSCC symbols against wireless channels becomes inefficient for long-distance wired transmission. Furthermore, DJSCC symbols must adapt to the varying transmission rate of the wired network to avoid congestion. In this paper, we propose a novel framework designed for efficient wired transmission of DJSCC symbols within hybrid wireless-wired networks, namely Rate-Controllable Wired Adaptor (RCWA). RCWA achieves redundancy-aware coding for DJSCC symbols to improve transmission efficiency, which removes considerable redundancy present in DJSCC symbols for wireless channels and encodes only source-relevant information into bits. Moreover, we leverage the Lagrangian multiplier method to achieve controllable and continuous variable-rate coding, which can encode given features into expected rates, thereby minimizing end-to-end distortion while satisfying given constraints. Extensive experiments on diverse
datasets demonstrate the superior RD performance and robustness of RCWA compared to existing baselines, validating its potential for wired resource utilization in hybrid transmission scenarios. Specifically, our method can obtain peak signal-to-noise ratio gain of up to 0.7dB and 4dB compared to neural network-based methods and digital baselines on CIFAR-10
dataset, respectively.
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All authors Jiangyuan Guo, Wei Chen, Yuxuan Sun, Bo Ai
Image and Video Processing
Semantic Communication Enabled Holographic Video Processing and Transmission
Jingkai Ying, Zhiyuan Qi, Yulong Feng et al. (+4 more)
Holographic video communication is considered a paradigm shift in visual communications, becoming increasingly popular for its ability to offer immersive experiences. This article provides an overview of
holographic video communication and outlines the requirements of a
holographic video communication system. Particularly, following a brief review of semantic com- munication, an architecture for a semantic-enabled
holographic video communication system is presented. Key technologies, including semantic sampling, joint semantic-channel coding, and semantic-aware transmission, are designed based on the proposed architecture. Two related use cases are presented to demonstrate the performance gain of the proposed methods. Finally, potential research topics are discussed to pave the way for the realization of semantic-enabled
holographic video communications.
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All authors Jingkai Ying, Zhiyuan Qi, Yulong Feng, Zhijin Qin, Zhu Han, Rahim Tafazolli, Yonina C. Eldar
Signal Processing
Working Memory Functional Connectivity Analysis for Dementia Classification using EEG
Shivani Ranjan, Anant Jain, Robin Badal et al. (+5 more)
Background: Dementia, particularly Alzheimer's Disease (AD), is a progressive neurodegenerative disorder marked by cognitive decline. Early detection, especially at the Mild Cognitive Impairment (MCI) stage, is essential for timely intervention. Working
Memory (WM) impairment is a key early indicator of neurodegeneration, affecting higher cognitive processes. Electroencephalography (
EEG), with its high temporal resolution, offers a cost-effective method to assess
brain dynamics. This study investigates WM-related
EEG functional connectivity (FC) to identify
brain network alterations across dementia stages. Methods:
EEG signals were recorded from 24 participants (8 AD, 8 MCI, and 8 healthy controls) during WM tasks, including encoding, recall, and
retrieval stages. Data preprocessing involved noise reduction and feature extraction using Spherical and Head Harmonic Decomposition (SHD, HHD). FC was quantified using Cross-Plot Transition
Entropy (CPTE) and Phase Lag Index (PLI). Network metrics such as Degree and Eigenvector Centrality were analyzed using Support Vector Machine, Random Forest, and XGBoost classifiers. Results: The CPTE-based connectivity metrics outperformed the traditional PLI approach in differentiating dementia stages, attaining a peak classification accuracy of 97.53% during the
retrieval phase with the Random Forest model. A connectivity threshold of 0.5 was optimal for network discrimination. SHD and HHD features also demonstrated strong discriminative potential. AD subjects exhibited higher synchronization patterns during WM tasks than healthy controls. Conclusions: The integration of WM tasks with
EEG-based FC analysis provides a robust framework for dementia classification. The proposed CPTE-based approach offers a robust, scalable, non-invasive, and effective diagnostic tool for early detection and monitoring of neurodegenerative diseases.
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All authors Shivani Ranjan, Anant Jain, Robin Badal, Amit Kumar, Harshal Shende, Deepak Joshi, Pramod Yadav, Lalan Kumar