Signal Processing
Resilient Cell-Free Massive MIMO Networks
Junbin Yu, Tianyu Lu, Mohammadali Mohammadi et al. (+1 more)
This paper proposes a novel optimization framework for enhancing the security resilience of
cell-free massive multiple-input multiple-output (CF-mMIMO) networks with multi-
antenna access points (APs) and protective partial zero-forcing (PPZF) under active eavesdropping. Based on the main principles of absorption, adaptation, and recovery, we formulate a security-aware resilience metric to quantify the system performance during and after a security outage. A multi-user service priority-aware power allocation problem is formulated to minimize the mean squared error (MSE) between real-time and desired security efficiency, thereby enabling a trade-off between the target user's secrecy performance and multi-user quality of service (QoS). To solve this non-convex problem, a security-aware iterative
algorithm based on the successive convex approximation (SCA) is employed. The proposed
algorithm determines the optimal power allocation strategy by balancing solution quality against recovery time. At each iteration, it evaluates the overall resilience score and selects the strategy that achieves the highest value. Simulation results confirm that the proposed framework significantly improves the resilience of CF-mMIMO networks, allowing flexible adaptation between rapid recovery and high-quality recovery, depending on system requirements.
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All authors Junbin Yu, Tianyu Lu, Mohammadali Mohammadi, Michail Matthaiou
Signal Processing
BRISC: A Dataset of Channel Measurements at 5 GHz With a Reflective Intelligent Surface
Mattia Piana, Giovanni Angelo Alghisi, Anna Valeria Guglielmi et al. (+3 more)
We introduce the broadband reconfigurable intelligent surface (RIS) channel (BRISC)
dataset. The
dataset comprises measurements of channel state information (CSI) collected at 5.530 GHz using a 256-element RIS with binary states. In the measurement campaign, the transmitter and receiver are two software-defined
radios (SDRs), phase-synchronized via an OctoClock, where the transmitter (receiver) is equipped with one (two)
antenna(s). To manage complexity, the RIS elements are grouped into blocks of different sizes, where all elements within a block share the same state. CSIs have been captured for multiple a) transmitter positions (and fixed receiver location), b) RIS element-grouping granularity, and c) RIS configurations. Furthermore, we calibrated the parameters of state-of-the-art RIS channel models to fit the measured CSI. With approximately 10 000 configurations explored per transmitting position, BRISC serves as a robust
benchmark in communication applications.
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All authors Mattia Piana, Giovanni Angelo Alghisi, Anna Valeria Guglielmi, Giovanni Perin, Francesco Gringoli, Stefano Tomasin
Signal Processing
Distributed Continuous Aperture Arrays for Multiuser SWIPT
Muhammad Zeeshan Mumtaz, Mohammadali Mohammadi, Hien Quoc Ngo et al. (+1 more)
This paper proposes a distributed continuous aperture array (D CAPA) to support simultaneous wireless information and power transfer (SWIPT) to multiple information users (IUs) and energy users (EUs). Each metasurface supports continuous surface currents that radiate electromagnetic (EM) waves for information and energy transmission to the users. These waves propagate through continuous EM channels characterized by the dyadic Green function. We formulate a system power consumption (PC) minimization problem subject to spectral efficiency and energy harvesting quality of service (QoS) requirements, where the QoS requirements are derived under the equal power allocation (EPA) scheme. An efficient two layer optimization
algorithm is developed to solve this problem by optimizing the power allocation subject to the QoS violation penalties using augmented Lagrangian transformation. Our numerical results show that well optimized current distributions over each metasurface in the proposed D CAPA achieve up to 65% and 61% reductions in overall system PC compared to the EPA and colocated CAPA (C CAPA) cases, while maintaining the same total aperture size and transmission power.
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All authors Muhammad Zeeshan Mumtaz, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou
Systems and Control · Cross-listed from math.OC
Robustness certificates in data-driven non-convex optimization with additively-uncertain constraints
Alexander J Gallo, Massimiliano Zoggia, Alessandro Falsone et al. (+2 more)
We consider decision-making problems that are formulated as non-convex optimization programs where uncertainty enters the constraints through an additive term, independent of the decision variables, and robustness is imposed using a finite data-set, according to the scenario robust optimization paradigm. By exploiting the structure of the constraints, we show that both a priori and a posteriori distribution-free probabilistic robustness certificates for a possibly sub-optimal solution to the resulting data-driven optimization problem can be obtained with minimal computational effort. Building on these results, we also discuss a one-shot and an incremental procedure to determine the size of the data-set so as to guarantee a user-chosen robustness level. Notably, both the a posteriori robustness assessment and incremental data-set sizing do not require to solve the non-convex scenario program. A comparative analysis performed on the unit commitment problem using real data reveals a limited increase in conservativeness with a significant computational saving with respect to the application of scenario theory results for general, non necessarily structured, non-convex problems.
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All authors Alexander J Gallo, Massimiliano Zoggia, Alessandro Falsone, Maria Prandini, Simone Garatti
Signal Processing · Cross-listed from cs.LG
Scaling Vision Transformers: Evaluating DeepSpeed for Image-Centric Workloads
Huy Trinh, Rebecca Ma, Zeqi Yu et al. (+1 more)
Vision Transformers (ViTs) have demonstrated remarkable potential in image processing tasks by utilizing self-
attention mechanisms to capture global relationships within data. However, their scalability is hindered by significant computational and
memory demands, especially for large-scale models with many parameters. This study aims to leverage DeepSpeed, a highly efficient distributed training framework that is commonly used for
language models, to enhance the scalability and performance of ViTs. We evaluate intra- and inter-node training efficiency across multiple GPU configurations on various
datasets like CIFAR-10 and CIFAR-100, exploring the impact of distributed data parallelism on training speed, communication overhead, and overall scalability (strong and weak scaling). By systematically varying software parameters, such as batch size and gradient accumulation, we identify key factors influencing performance of distributed training. The experiments in this study provide a foundational basis for applying DeepSpeed to image-related tasks. Future work will extend these investigations to deepen our understanding of DeepSpeed's limitations and explore strategies for optimizing distributed training pipelines for
Vision Transformers.
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All authors Huy Trinh, Rebecca Ma, Zeqi Yu, Tahsin Reza
Signal Processing
Optimal QAM Constellation for Over-the-Air Computation in the Presence of Heavy-Tailed Channel Noise
Saeed Razavikia, Deniz Gündüz and Carlo Fischione
Over-the-air computation (OAC) enables low-latency aggregation over multiple-access channels (MACs) by exploiting the superposition property of the wireless medium to compute functions efficiently in distributed networks. A critical but often overlooked challenge is that electromagnetic interference in practical
radio channels frequently exhibits heavy-tailed behavior, causing strong impulsive noise that severely degrades computation performance. This work studies digital OAC with QAM-based signaling under heavy-tailed interference modeled by a Cauchy distribution (lacking a finite second moment). We seek QAM-like constellations that minimize the mean-squared error (MSE) of sum aggregation subject to an average-power constraint. The problem is formulated as a constrained optimization, whose solution yields unique optimality conditions. Numerical results confirm the effectiveness of the proposed design. Notably, the framework extends naturally to nomographic functions, broader constellation families, and alternative noise models.
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Image and Video Processing
Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
Yubin Ge, Yongsong Huang and Xiaofeng Liu
Report-supervised (RSuper) learning seeks to alleviate the need for dense tumor voxel labels with constraints derived from radiology reports (e.g., volumes, counts, sizes, locations). In MRI studies of
brain tumors, however, we often involve multi-parametric scans and substructures. Here, fine-grained modality/parameter-wise reports are usually provided along with global findings and are correlated with different substructures. Moreover, the reports often describe only the largest lesion and provide qualitative or uncertain cues (“mild,” “possible”). Classical RSuper losses (e.g., sum volume consistency) can over-constrain or hallucinate unreported findings under such incompleteness, and are unable to utilize these hierarchical findings or exploit the priors of varied lesion types in a merged
dataset. We explicitly parse the global quantitative and modality-wise qualitative findings and introduce a unified, one-sided, uncertainty-aware formulation (MS-RSuper) that: (i) aligns modality-specific qualitative cues (e.g., T1c enhancement, FLAIR edema) with their corresponding substructures using existence and absence losses; (ii) enforces one-sided lower-bounds for partial quantitative cues (e.g., largest lesion size, minimal multiplicity); and (iii) adds extra- vs. intra-axial anatomical priors to respect cohort differences. Certainty
tokens scale penalties; missing cues are down-weighted. On 1238 report-labeled BraTS-MET/MEN scans, our MS-RSuper largely outperforms both a sparsely-supervised baseline and a naive RSuper method.
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Signal Processing
Cell-Free Massive MIMO-Assisted SWIPT Using Stacked Intelligent Metasurfaces
Thien Duc Hua, Mohammadali Mohammadi, Hien Quoc Ngo et al. (+1 more)
This study explores a next-generation multiple access (NGMA) framework for
cell-free massive
MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL
algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.
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All authors Thien Duc Hua, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou
Audio and Speech Processing
Training-Free Intelligibility-Guided Observation Addition for Noisy ASR
Haoyang Li, Changsong Liu, Wei Rao et al. (+3 more)
Automatic
speech recognition (ASR) degrades severely in noisy environments. Although
speech enhancement (SE) front-ends effectively suppress background noise, they often introduce artifacts that harm recognition. Observation addition (OA) addressed this issue by fusing noisy and SE enhanced
speech, improving recognition without modifying the parameters of the SE or ASR models. This paper proposes an intelligibility-guided OA method, where fusion weights are derived from intelligibility estimates obtained directly from the backend ASR. Unlike prior OA methods based on trained neural predictors, the proposed method is training-free, reducing complexity and enhances generalization. Extensive experiments across diverse SE-ASR combinations and
datasets demonstrate strong robustness and improvements over existing OA baselines. Additional analyses of intelligibility-guided switching-based alternatives and frame versus utterance-level OA further validate the proposed design.
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All authors Haoyang Li, Changsong Liu, Wei Rao, Hao Shi, Sakriani Sakti, Eng Siong Chng
Signal Processing
Timing Recovery and Sequence Detection for Integrate-and-Fire Time Encoding Receivers
Neil Irwin Bernardo
Recent advances in neuromorphic signal processing have introduced time encoding machines as a promising alternative to conventional uniform sampling for low-power communication receivers. In this paradigm, analog signals are converted into event timings by an integrate-and-fire
circuit, allowing information to be represented through spike times rather than amplitude samples. While event-driven sampling eliminates the need for a fixed-rate clock, receivers equipped with integrate-and-fire time encoding machines, called time encoding receivers, often assume perfect symbol synchronization, leaving the problem of symbol timing recovery unresolved. This paper presents a joint timing recovery and data detection framework for integrate-and-fire time encoding receivers. The log-likelihood function is derived to capture the dependence between firing times, symbol timing offset, and transmitted sequence, leading to a maximum likelihood formulation for joint timing estimation and sequence detection. A practical two-stage receiver is developed, consisting of a timing recovery
algorithm followed by a zero-forcing detector. Simulation results demonstrate accurate symbol timing offset estimation and improved symbol error rate performance compared to existing time encoding receivers.
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Signal Processing
Synapse-Inspired Energy Networks: A Neuromorphic Approach to Microgrid Protection without Communication Links
Saurabh Prabhakar, Bijaya Ketan Panigrahi, Frede Blaabjerg et al. (+1 more)
Traditional protection systems for microgrids, which rely on high fault currents and continuous communication, struggle to keep up with the changing dynamics and cybersecurity concerns of decentralized networks. In this study, we introduce a novel biologically inspired protection system based on neuromorphic principles, where each distributed energy resource (DER) functions as a simple
neuron. These
neurons process local changes in voltage, current signals, and converting them into spike patterns that represent the severity of disturbances. Just as
neurons communicate via synapses in biological systems, we exploit transmission cables to coordinate between DERs, enabling them to share information and respond to faults collectively. Fault detection and
circuit breaker activation are driven by a First-To-Spike (FTTS) mechanism, similar to the concept of traveling wave protection, but without needing GPS synchronization or communication links. A key innovation is the ability to use the timing of spikes to locally determine the nature of a fault, offering an intelligent, adaptive response to disturbances. Performance shows tripping latency of 10-58 ms, surpassing conventional relays and even traveling-wave methods (60 ms), while maintaining detection accuracy above 98% and spatial selectivity over 97%, enabling real-time, communication-free, scalable protection for plug-and-play microgrids.
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All authors Saurabh Prabhakar, Bijaya Ketan Panigrahi, Frede Blaabjerg, Subham Sahoo
Signal Processing · Cross-listed from cs.LG
Hierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
Anupam Sharma, Harish Katti, Prajwal Singh et al. (+2 more)
An electroencephalogram (
EEG) records the spatially averaged electrical activity of
neurons in the
brain, measured from the human scalp. Prior studies have explored
EEG-based classification of objects or concepts, often for passive viewing of briefly presented image or
video stimuli, with limited classes. Because
EEG exhibits a low signal-to-noise ratio, recognizing fine-grained representations across a large number of classes remains challenging; however, abstract-level object representations may exist. In this work, we investigate whether
EEG captures object representations across multiple hierarchical levels, and propose episodic analysis, in which a Machine Learning (ML) model is evaluated across various, yet related, classification tasks (episodes). Unlike prior episodic
EEG studies that rely on fixed or randomly sampled classes of equal cardinality, we adopt hierarchy-aware episode sampling using WordNet to generate episodes with variable classes of diverse hierarchy. We also present the largest episodic framework in the
EEG domain for detecting observed text from
EEG signals in the PEERS
dataset, comprising $931538$
EEG samples under $1610$ object labels, acquired from $264$ human participants (subjects) performing controlled cognitive tasks, enabling the study of neural dynamics underlying perception, decision-making, and performance monitoring. We examine how the semantic abstraction level affects classification performance across multiple learning techniques and architectures, providing a comprehensive analysis. The models tend to improve performance when the classification categories are drawn from higher levels of the hierarchy, suggesting sensitivity to abstraction. Our work highlights abstraction depth as an underexplored dimension of
EEG decoding and motivates future research in this direction.
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All authors Anupam Sharma, Harish Katti, Prajwal Singh, Shanmuganathan Raman, Krishna Miyapuram
Signal Processing
Continuous-Time Analysis of AFDM: Pulse-Shaping, Fundamental Bounds and Impact of Hardware Impairments
Michele Mirabella, Hyeon Seok Rou, Pasquale Di Viesti et al. (+2 more)
Affine frequency division multiplexing (AFDM) has recently emerged as a resilient waveform candidate for high-mobility next-generation wireless systems. However, current literature mostly focuses on discrete time (DT) models, often overlooking effects and hardware non-idealities of actual continuous time (CT) signal generation. In this paper, we bridge this gap by developing a CT-analytical framework based on the affine
Fourier series (AFS) representation, which allows us to demonstrate that strictly bandlimited pulses and subcarrier suppression strategies are essential to maintain the multicarrier structure of the transmitted signal. In addition, we derive the analytical power spectral density of AFDM and compare its spectral characteristics in comparison with those of other multicarrier schemes, taking into account the impact of realistic truncated pulse-shaping. Furthermore, we analyze the sensitivity of the CT model to phase noise, carrier frequency offset, and sampling jitter, providing a theoretical analysis of communication performance. Finally, we derive closed-form Cramér-Rao bounds for channel parameter estimation, showing that the chirped modulation peculiar of AFDM increases estimation variance but enables the resolution of Doppler ambiguities. Our findings lay the necessary theoretical and practical foundations for the implementation of AFDM in realistic wireless transceivers.
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All authors Michele Mirabella, Hyeon Seok Rou, Pasquale Di Viesti, Giuseppe Thadeu Freitas de Abreu, Giorgio Matteo Vitetta
Signal Processing
Symbol-Aware Precoder Design for Physical-Layer Anonymous Communications
Yu Li, Milad Tatar Mamaghani, Xiangyun Zhou et al. (+1 more)
Physical-layer characteristics, such as channel state information (CSI) and transmitter noise induced by hardware impairments, are often uniquely associated with a transmitter. This paper investigates transmitter anonymity at the physical layer from a signal design perspective. We consider an anonymous communication problem where the receiver should reliably decode the signal from the transmitter but should not make use of the signal to infer the transmitter's identity.Transmitter anonymity is quantified using a Kullback-Leibler divergence (KLD)-based metric, which enables the formulation of explicit anonymity constraints in the precoder design.We then propose an anonymous symbol-level precoding strategy that preserves reliable communication under spatial multiplexing while preventing transmitter identification. The proposed framework employs a partitioned equal-gain combining (P-EGC) scheme that leverages receiver diversity without requiring transmitter-specific CSI. Simulation results demonstrate anonymity-reliability tradeoffs across different signal-to-noise ratios (SNRs) and numbers of data streams. Moreover, the results reveal opposite trends of anonymity with respect to transmitter-dependent noise variations in the low-SNR and high-SNR regimes.
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All authors Yu Li, Milad Tatar Mamaghani, Xiangyun Zhou, Nan Yang
Signal Processing
FGFRFT: Fast Graph Fractional Fourier Transform via Exact Spectral Splitting and Fourier-Series Approximation
Ziqi Yan, Mingzhi Wang, Sen Shi et al. (+4 more)
The graph fractional
Fourier transform (GFRFT) for
unitary graph
Fourier transform (GFT)
matrices can be interpreted through the scalar function $e^{j\alpha\theta}$ on the unit circle. Under the principal branch, its
Fourier-series representation encounters an intrinsic obstruction at the spectral point $\lambda=-1$ for non-integer orders. To address this issue, we propose a fast graph fractional
Fourier transform (FGFRFT) based on exact spectral splitting: the $\lambda=-1$ component is treated exactly, and the complementary component is approximated by a truncated
Fourier series in integer powers of the GFT
matrix. This construction yields an offline–online implementation that reduces the online complexity of repeated operator updates from $O(N^3)$ to $O(2LN^2)$ for truncation order $L$, while preserving differentiability with respect to the transform order. We further derive truncation-error bounds, approximate unitarity and additivity, and reconstruction-error bounds. Experiments on approximation accuracy, transform-order learning, image denoising, and point-cloud denoising show that FGFRFT provides substantial online acceleration while remaining close to the exact GFRFT under the tested settings.
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All authors Ziqi Yan, Mingzhi Wang, Sen Shi, Feiyue Zhao, Manjun Cui, Yangfan He, Zhichao Zhang
Signal Processing
Segmented Continuous Optimization
Teymur Aghayev
Segmented curve fitting remains an essential approach for the comprehensive analysis of local patterns in non-stationary time-series data. However, traditional regression
algorithms primarily focus on linear or
polynomial functions, which can be insufficient for analyzing raw signals with oscillatory or transcendental behavior. In this paper, we propose Segmented Continuous Optimization (SCO), a framework that performs piecewise continuous curve fitting on various non-linear models, including trigonometric,
polynomial, and exponential. SCO presents a novel signal representation by optimizing a user-defined model in segments with $C^1$ continuity to properly analyze the data's local and global trends. The framework is tested for accuracy and efficiency across all included models. Finally, we provide examples using velocity and
EEG datasets to demonstrate the
algorithm's practical usage in examining signal patterns, optimized parameters, derivatives, and integrals of the final fit.
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Systems and Control
Fast-Response Balancing Capacity of Alkaline Electrolyzers
Marvin Dorn, Julian Hoffmann, André Weber et al. (+1 more)
The energy transition requires flexible technologies to maintain grid stability, and electrolyzers are playing an increasingly important role in meeting this need. While previous studies often question the dynamic capabilities of large-scale alkaline electrolyzer systems, we assess their potential to provide balancing services using real manufacturer data. Unlike common approaches, we propose the decoupling between the total electrolyzer power and a smaller fractions of power actually offered on balancing
markets. Adapting an existing methodology, we analyze alkaline electrolyzer systems and extend the assessment to Germany and Europe. Our results show that large-scale electrolyzers are technically capable of delivering fast-response balancing services, with significantly lower dynamic requirements than previously assumed. The planned electrolyzers in Germany could cover the entire balancing capacity
market, potentially saving around 13 % of their electricity costs, excluding energy balancing revenues. The decoupling also resolves part of the trade-off for electrolyzer manufacturers, enabling the design of less dynamic but more stable systems.
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All authors Marvin Dorn, Julian Hoffmann, André Weber, Veit Hagenmeyer
Audio and Speech Processing · Cross-listed from cs.SD
Geometric Analysis of Speech Representation Spaces: Topological Disentanglement and Confound Detection
Bipasha Kashyap and Pubudu N. Pathirana
Speech-based clinical tools are increasingly deployed in
multilingual settings, yet whether pathological
speech markers remain geometrically separable from accent variation remains unclear. Systems may misclassify healthy non-native speakers or miss pathology in
multilingual patients. We propose a four-metric clustering framework to evaluate
geometric disentanglement of emotional, linguistic, and pathological
speech features across six corpora and eight
dataset combinations. A consistent hierarchy emerges: emotional features form the tightest clusters (Silhouette 0.250), followed by pathological (0.141) and linguistic (0.077). Confound analysis shows pathological-linguistic overlap remains below 0.21, which is above the permutation null but bounded for clinical deployment. Trustworthiness analysis confirms
embedding fidelity and robustness of the
geometric conclusions. Our framework provides actionable guidelines for equitable and reliable
speech health systems across diverse populations.
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Systems and Control · Cross-listed from stat.ME
Hawkes Identification with a Prescribed Causal Basis: Closed-Form Estimators and Asymptotics
Xinhui Rong and Girish N. Nair
Driven by the recent surge in neural-inspired modeling, point processes have gained significant traction in systems and control. While the Hawkes process is the standard model for characterizing random event sequences with
memory, identifying its unknown kernels is often hindered by nonlinearity. Approaches using prescribed basis kernels have emerged to enable linear parameterization, yet they typically rely on iterative likelihood methods and lack rigorous analysis under model misspecification. This paper justifies a closed-form Least Squares identification framework for Hawkes processes with prescribed kernels. We guarantee
estimator existence via the almost-sure positive definiteness of the empirical Gram
matrix and prove convergence to the true parameters under correct specification, or to well-defined pseudo-true parameters under misspecification. Furthermore, we derive explicit Central Limit
Theorems for both regimes, providing a complete and interpretable asymptotic theory. We demonstrate these theoretical findings through comparative numerical simulations.
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Systems and Control
Grid-Mind: An LLM-Orchestrated Multi-Fidelity Agent for Automated Connection Impact Assessment
Mohamed Shamseldein
Large
language models (LLMs) have demonstrated remarkable tool-use capabilities, yet their application to power system operations remains largely unexplored. This paper presents Grid-Mind, a domain-specific LLM
agent that interprets natural-
language interconnection requests and autonomously orchestrates multi-fidelity power system simulations. The LLM-first architecture positions the
language model as the central decision-making entity, employing an eleven-tool registry to execute Connection Impact Assessment (CIA) studies spanning steadystate power flow, N-1 contingency analysis, transient stability, and electromagnetic transient screening. A violation inspector grounds every decision in quantitative simulation outputs, while a three-layer anti-
hallucination defence mitigates numerical fabrication risk through forced capacity-tool routing and post-response grounding validation. A
prompt-level self-correction mechanism extracts distilled lessons from
agent failures, yielding progressive accuracy improvements without model retraining. End-to-end evaluation on 50 IEEE 118-bus scenarios (DeepSeek-V3, 2026-02-23) achieved 84.0% tool-selection accuracy and 100% parsing accuracy. A separate 56-scenario self-correction suite passed 49 of 56 cases (87.5%) with a mean score of 89.3. These results establish a reproducible baseline for continued refinement while maintaining auditable, simulation-grounded decision support.
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Signal Processing · Cross-listed from cs.IT
Efficient Solvers for Coupling-Aware Beamforming in Continuous Aperture Arrays
Geonhee Lee, Kwonyeol Park, Hyeongjun Park et al. (+2 more)
In continuous aperture arrays (CAPAs), careful consideration of the underlying physics is essential, among which electromagnetic (EM) mutual coupling plays a critical role in beamforming performance. Building on a physically consistent mutual coupling model, the beamforming design is formulated as a functional optimization whose optimality condition leads to a Fredholm integral equation. The incorporation of the coupling model, however, substantially increases computational complexity, necessitating efficient and accurate integral equation solvers. In this letter, we propose two efficient solvers: 1) a coordinate-transformation-based kernel approximation that preserves the operator structure while alleviating discretization demands, and 2) a direct lower-upper (LU)-based solver that stably handles the Nyström-discretized system. Numerical results demonstrate improved accuracy and reduced computational overhead compared to conventional methods, with the LU-based solver emerging as an efficient and scalable solution for large-scale CAPA optimization via offline factorization.
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All authors Geonhee Lee, Kwonyeol Park, Hyeongjun Park, Jinwoo An, Junil Choi
Audio and Speech Processing · Cross-listed from cs.SD
Quantifying Dimensional Independence in Speech: An Information-Theoretic Framework for Disentangled Representation Learning
Bipasha Kashyap, Björn W. Schuller and Pubudu N. Pathirana
Speech signals encode emotional, linguistic, and pathological information within a shared acoustic channel; however, disentanglement is typically assessed indirectly through downstream task performance. We introduce an information-theoretic framework to quantify cross-dimension statistical dependence in handcrafted acoustic features by integrating bounded neural mutual information (MI) estimation with non-parametric validation. Across six corpora, cross-dimension MI remains low, with tight estimation bounds ($< 0.15$ nats), indicating weak statistical coupling in the data considered, whereas Source–Filter MI is substantially higher (0.47 nats). Attribution analysis, defined as the proportion of total MI attributable to source versus filter components, reveals source dominance for emotional dimensions (80%) and filter dominance for linguistic and pathological dimensions (60% and 58%, respectively). These findings provide a principled framework for quantifying dimensional independence in
speech.
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Signal Processing
Provable orbit recovery over SO(3) from the non-uniform second moment
Tamir Bendory, Dan Edidin, Josh Katz et al. (+2 more)
We study the recovery of an unknown three-dimensional band-limited signal from multiple noisy observations that are randomly rotated by latent elements of SO(3), where the rotations are drawn from an unknown, non-uniform distribution. Because the rotations are unobserved, only the signal orbit under the rotation group can be recovered. We show that the signal orbit and the rotation distribution are jointly identifiable from the first and second moments. This yields an improved high-noise sample complexity that scales quadratically with the noise variance, rather than cubically as in the uniform-rotation case. We further develop a provable, computationally efficient reconstruction
algorithm that recovers the 3-D signal by successively solving a sequence of well-conditioned linear systems. The
algorithm is validated through extensive numerical experiments. Our results provide a principled and tractable framework for high-noise 3-D orbit recovery, with potential relevance to cryo-electron microscopy and cryo-electron
tomography modeling, where molecules are observed in unknown orientations.
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All authors Tamir Bendory, Dan Edidin, Josh Katz, Shay Kreymer, Nir Sharon
Signal Processing
On Channel Estimation for Group-Connected Beyond Diagonal RIS Assisted Multi-User MIMO Communication
Rui Wang, Junyuan Gao, Shuowen Zhang et al. (+2 more)
Beyond diagonal reconfigurable intelligent surface (BD-RIS) architectures offer superior beamforming gain over conventional diagonal RISs. However, the channel estimation overhead is the main hurdle for reaping the above gain in practice. This letter addresses this issue for group-connected BDRIS aided uplink communication from multiple multi-
antenna users to one multi-
antenna base station (BS). We first reveal that within each BD-RIS group, the cascaded channel associated with one user
antenna and one BD-RIS element is a scaled version of that associated with any other user
antenna and BD-RIS element due to the common RIS-BS channel. This insight drastically reduces the dimensionality of the channel estimation problem. Building on this property, we propose an efficient two-phase channel estimation protocol. In the first phase, the reference cascaded channels for all groups are estimated in parallel based on common received signals while determining the scaling coefficients for a single reference
antenna. In the second phase, the scaling coefficients for all remaining user
antennas are estimated. Numerical results demonstrate that our proposed framework achieves substantially lower estimation error with fewer pilot signals compared to state-of-the-art
benchmark schemes.
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All authors Rui Wang, Junyuan Gao, Shuowen Zhang, Bruno Clerckx, Liang Liu
Image and Video Processing
Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications
Yida Lin, Bing Xue, Mengjie Zhang et al. (+2 more)
Accurate per-branch 3D reconstruction is a prerequisite for autonomous
UAV-based tree
pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance
segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch
topology.
Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a
UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous
pruning tool positioning. All code and processed data are publicly released to facilitate further
UAV forestry research.
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All authors Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green
Signal Processing
Comparing Implicit Neural Representations and B-Splines for Continuous Function Fitting from Sparse Samples
Hongze Yu, Yun Jiang and Jeffrey A. Fessler
Continuous signal representations are naturally suited for inverse problems, such as
magnetic resonance imaging (MRI) and computed
tomography, because the measurements depend on an underlying physically continuous signal. While classical methods rely on predefined analytical bases like B-splines, implicit neural representations (INRs) have emerged as a powerful alternative that use coordinate-based networks to parameterize continuous functions with implicitly defined bases. Despite their empirical success, direct comparisons of their intrinsic representation capabilities with conventional models remain limited. This preliminary empirical study compares a positional-encoded INR with a cubic B-spline model for continuous function fitting from sparse random samples, isolating the representation capacity difference by only using coefficient-domain Tikhonov regularization. Results demonstrate that, under oracle hyperparameter selection, the INR achieves a lower normalized root-mean-squared error, yielding sharper edge transitions and fewer oscillatory artifacts than the oracle-tuned B-spline model. Additionally, we show that a practical bilevel optimization framework for INR hyperparameter selection based on measurement data split effectively approximates oracle performance. These findings empirically support the superior representation capacity of INRs for sparse data fitting.
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Audio and Speech Processing · Cross-listed from cs.LG
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
Ming Li, Yong-Jin Liu, Fang Liu et al. (+6 more)
Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular
emotions in controlled laboratory settings. Real-world human emotional experiences, by contrast, are often characterized by the simultaneous presence of multiple affective states, spurring recent interest in mixed
emotion recognition as an
emotion distribution learning problem. Current approaches, however, often neglect the valence consistency and structured correlations inherent among coexisting
emotions. To address this limitation, we propose a
Memory-guided Prototypical Co-occurrence Learning (MPCL) framework that explicitly models
emotion co-occurrence patterns. Specifically, we first fuse multi-modal signals via a multi-scale associative
memory mechanism. To capture cross-modal semantic relationships, we construct
emotion-specific prototype
memory banks, yielding rich physiological and behavioral representations, and employ prototype relation
distillation to ensure cross-modal
alignment in the latent prototype space. Furthermore, inspired by human cognitive
memory systems, we introduce a
memory retrieval strategy to extract semantic-level co-occurrence associations across
emotion categories. Through this bottom-up hierarchical abstraction process, our model learns affectively informative representations for accurate
emotion distribution prediction. Comprehensive experiments on two public
datasets demonstrate that MPCL consistently outperforms state-of-the-art methods in mixed
emotion recognition, both quantitatively and qualitatively.
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All authors Ming Li, Yong-Jin Liu, Fang Liu, Huankun Sheng, Yeying Fan, Yixiang Wei, Minnan Luo, Weizhan Zhang, Wenping Wang
Signal Processing · Cross-listed from cs.IT
Integer-Forcing Precoding: A Geometric Perspective and Low-Complexity Algorithm Design
Junren Qin, Fan Jiang, Tao Yang et al. (+3 more)
The joint coefficient–scaling design of integer-forcing (IF) precoding couples a full-rank integer coefficient
matrix with a positive diagonal scaling
matrix, resulting in a mixed discrete–continuous optimization problem. We first distinguish the original full-rank formulation and its finite-field invertibility requirement from the unimodular restriction studied in the subsequent analysis. Under this restriction, we formulate coefficient selection as the unimodular minimum-sum basis problem (UMSBP). By parameterizing the scaling
matrix with its normalized diagonal vector, we define a deterministic exact single-output UMSBP oracle whose preimages partition the scaling
manifold into disjoint coefficient-labelled regions. The restricted objective is consequently represented as the lower envelope of fixed-coefficient quadratic functions. Based on this formulation, we derive an exact decomposition into fixed-coefficient scaling optimization, coefficient-consistency verification, cross-region exploration, and candidate comparison. We prove that exact UMSBP evaluation is NP-hard through a
polynomial-time reduction from exact Euclidean CVP. For a fixed coefficient
matrix, we derive an equivalent convex formulation, its normalized stationarity condition, and a
matrix-balancing interpretation. Under the stated positivity and finite-projective-diameter condition
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All authors Junren Qin, Fan Jiang, Tao Yang, Shanxiang Lyu, Rongke Liu, Shi Jin
Signal Processing
Height-Dependent Spectrum Activity Measurements and Modeling: A Case Study with FM Radio Bands
Sung Joon Maeng, Amir Hossein Fahim Raouf, Ozgur Ozdemir et al. (+2 more)
The increasing demand for wireless connectivity necessitates advanced spectrum modeling to enable efficient spectrum sharing for next-generation aerial communications. While traditional models often overlook vertical variations in signal behavior, this paper proposes a height-dependent propagation model using a helikite-mounted software-defined
radio (SDR). We collected extensive measurement data across the 88 MHz to 6 GHz range in both urban and rural environments. As a case study to validate our methodology, we focus on the FM
radio band, which allows us to use publicly available transmitter locations and transmit power levels to facilitate comparisons between analytical with measurement results. We identify a clear transition from non-line-of-sight (NLoS) to line-of-sight (LoS) regimes at a specific altitude threshold and propose an altitude-dependent path loss model that incorporates this transition. Our results demonstrate that the proposed model significantly outperforms the standard free space path loss (FSPL) model in complex urban
topologies, providing a more accurate framework for altitude-aware spectrum prediction and management across emerging aerial wireless technologies and bands.
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All authors Sung Joon Maeng, Amir Hossein Fahim Raouf, Ozgur Ozdemir, İsmail Güvenç, Mihail L. Sichitiu
Systems and Control
IoT-Driven Building Energy Management Systems (BEMS) for Net Zero Energy Buildings: Concept, Integration and Future Directions
Haizum Hanim Ab Halim, Dalila Alias, Akmal Zaini Arsad et al. (+3 more)
Construction and operating of buildings is one of the major contributors to global greenhouse emissions. With the inefficient usage of energy due to human behavior and manual operation, the energy consumption of buildings is further increased. These challenges highlight the need for improved Building Energy Management Systems (BEMS) integrated with Internet of Things (IoT) and data driven intelligence to enhance energy-efficiency in a building and contribute to Net-Zero Energy Buildings (NZEB) targets. This paper offers four keys contributions: i) a systematic review of IoT enabled BEMS including components, network architecture and functional capabilities, ii) an evaluation of real-world BEMS
datasets to support Artificial Intelligence (AI) based predictive control, iii) an analysis of integration challenges related to interoperability, smart grids and net-zero energy strategies, and iv) a case study highlighting global best practices, performances outcomes, and lesson learned for scaling advanced BEMS solutions.
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All authors Haizum Hanim Ab Halim, Dalila Alias, Akmal Zaini Arsad, Lewis Tee Jen Looi, Rosdiadee Nordin, Denny Ng Kok Sum