Signal Processing
Communication in a Fractional World: MIMO MC-OTFS Precoder Prediction
Evan Allen, Karim Said, Robert Calderbank et al. (+1 more)
As 6G technologies advance, international bodies and regulatory agencies are intensifying efforts to extend seamless connectivity especially for high-mobility scenarios such as Mobile Ad-Hoc Networks (MANETs) types such as Vehicular Ad-Hoc Networks (VANETs) and Flying Ad-Hoc Networks (FANETs). For these environments to be considered for long term adoption and use they must support Multiple-Input-Multiple- (
MIMO) technology, rapidly fluctuating channel conditions in these environments place a heavy burden on traditional time-frequency CSI feedback schemes required for
MIMO precoding. This motivates a shift toward delay-Doppler representations like those employed by Orthogonal Time-Frequency Space(OTFS) modulation, which offers greater stability under mobility. We derive an expression for the variation over time in the OTFS I/O relationship. We then use this to create a physics informed complex exponential basis expansion model prediction framework that maximizes the usefulness of outdated Channel State Information (CSI) in the presence of integer and fractional delay-Doppler channels and facilitates high mobility
MIMO communication.
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All authors Evan Allen, Karim Said, Robert Calderbank, Lingjia Liu
Systems and Control
MDP-based Energy-aware Task Scheduling for Battery-less IoT
Shahab Jahanbazi, Mateen Ashraf and Onel L. A. López
Battery-less Internet of Things (IoT) devices rely on ambient energy harvesting and therefore require scheduling policies that jointly account for energy intermittency and hard timing constraints. This challenge is especially acute in periodic monitoring applications, where a sensing–computing–transmitting task chain must be completed within each reporting cycle. In this paper, we formulate this problem within a setting characterized by independently and identically distributed (i.i.d.) energy arrivals as a long-term average-reward Markov decision process (MDP) that explicitly captures capacitor-voltage
evolution, task ordering, permissible start windows, and safe-execution requirements. We further propose rewards that promote reliable task completion while penalizing risky low-energy execution. We prove that the considered MDP is unichain and that the optimal stationary policy has a threshold structure, which leads to an optimal stationary threshold-based (OSTB) scheduler. To account for more realistic energy sources, we additionally study a correlated harvesting model based on a finite-state Markov process and show that the proposed framework can be applied to this richer setting under conservative sufficient conditions. Finally, numerical results show that OSTB outperforms representative baselines in terms of long-term full-chain completion rate, power failures, and latency, particularly when harvested energy is scarce.
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Systems and Control
A Simultaneous ECG-PCG Acquisition System with Real-Time Burst-Adaptive Noise Cancellation
Avishka Herath, Malith Jayalath, Kumudu Kaushalya et al. (+7 more)
Cardiac auscultation is an essential clinical skill, requiring excellent hearing to distinguish subtle differences in timing and pitch of heart sounds. However, diagnosing solely from these sounds is often challenging due to interference from surrounding noise, and the information may be limited. Most of the existing solutions that adaptively cancel external noise are either non-real-time or computationally intensive, making them unsuitable for implementation in a portable system. This work proposes an end-to-end system with a real-time adaptive noise cancellation pipeline integrated into a device that simultaneously acquires electrocardiogram (ECG) and phonocardiogram (PCG) signals. We employ a burst-adaptive normalized least mean square
algorithm that adjusts its adaptation in response to high-energy, non-stationary hospital noise. The
algorithm's performance was initially assessed using
datasets with artificially induced noise. Subsequently, the complete end-to-end system was validated using real-world hospital recordings captured with the dual-modality device. For ECG and PCG signals recorded from the device in noisy hospital settings, the proposed system achieved signal-to-noise ratio improvements of 30.32 dB and 37.01 dB, respectively. Furthermore, complexity analysis confirms the pipeline's suitability for embedded implementation. These results demonstrate the system's effectiveness in enabling reliable and accessible cardiac screening in noisy hospital environments typical of resource-constrained settings.
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All authors Avishka Herath, Malith Jayalath, Kumudu Kaushalya, Sanjana Kapukotuwa, Chathuni Wijegunawardena, Pahan Mendis, Kithmin Wickremasinghe, Duminda Samarasinghe, Wageesha N. Manamperi, Chamira U. S. Edussooriya
Systems and Control
Maximal Load Shedding Verification for Neural Network Models of AC Line Switching
Samuel Chevalier, Duncan Starkenburg, Robert Parker et al. (+1 more)
Solving for globally optimal line switching decisions in AC transmission grids can be intractability slow. Machine learning (ML) models, meanwhile, can be trained to predict near-optimal decisions at a fraction of the speed. Verifying the performance and impact of these ML models on network operation, however, is a critically important step prior to their actual deployment. In this paper, we train a Neural Network (NN) to solve the optimal power shutoff line switching problem. To assess the worst-case load shedding induced by this model, we propose a bilevel attacker-defender verification approach that finds the NN line switching decisions that cause the highest quantity of network load shedding. Solving this problem to global optimality is challenging (due to AC power flow and NN nonconvexities), so our approach exploits a convex relaxation of the AC physics, combined with a local NN search, to find a guaranteed lower bound on worst–case load shedding. These under-approximation bounds are solved via MathOptAI.jl. We
benchmark against a random sampling approach, and we find that our optimization-based approach always finds larger load shedding. Test results are collected on multiple PGLib test cases and on trained NN models which contain more than 10 million model parameters.
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All authors Samuel Chevalier, Duncan Starkenburg, Robert Parker, Noah Rhodes
Signal Processing
Nonlinear Stacked Intelligent Surfaces for Wireless Systems
Omran Abbas, Abdullah Zayat, Lo{\i}c Markley et al. (+1 more)
Stacked intelligent surfaces (SIS) are a promising technology for next-generation wireless systems, offering an opportunity to enhance communication performance with low power consumption. Typically, an SIS is modelled as a surface that imparts phase shifts on impinging electromagnetic signals to achieve desired communication objectives. However, this mode of operation results in a linear SIS, which limits its applicability to linear operations. To unlock further SIS potential, we propose a nonlinear SIS that can mimic the behaviour of nonlinear neural networks. We discuss the feasibility and potential of this idea and propose a nonlinear SIS unit
cell with a step-like response. To evaluate the system-level performance of nonlinear SIS, we present a case study where SIS structures are optimized to minimize the symbol error rate (SER) in an
MIMO system with SIS deployed at both the transmitter and receiver sides using only statistical channel information. We demonstrate that a nonlinear SIS can improve communication reliability compared to a linear SIS by forming complex signal patterns across the SIS surface, which provide higher diversity against noise disturbances, while still allowing the receiver to discern these patterns. Finally, we outline several potential applications of nonlinear SIS in wireless communication scenarios.
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All authors Omran Abbas, Abdullah Zayat, Lo{\i}c Markley, Anas Chaaban
Image and Video Processing · Cross-listed from cs.CV
Explainable Detection of AI-Generated Images with Artifact Localization Using Faster-Than-Lies and Vision-Language Models for Edge Devices
Aryan Mathur, Asaduddin Ahmed, Pushti Amit Vasoya et al. (+3 more)
The increasing realism of AI-generated imagery poses challenges for verifying visual authenticity. We present an explainable image authenticity detection system that combines a lightweight convolutional classifier ("Faster-Than-Lies") with a
Vision-
Language Model (Qwen2-VL-7B) to classify, localize, and explain artifacts in 32x32 images. Our model achieves 96.5% accuracy on the extended CiFAKE
dataset augmented with
adversarial perturbations and maintains an inference time of 175ms on 8-core CPUs, enabling deployment on local or edge devices. Using autoencoder-based reconstruction error maps, we generate artifact localization heatmaps, which enhance interpretability for both humans and the VLM. We further categorize 70 visual artifact types into eight semantic groups and demonstrate explainable text generation for each detected anomaly. This work highlights the feasibility of combining visual and linguistic
reasoning for interpretable authenticity detection in low-resolution imagery and outlines potential cross-domain applications in forensics, industrial inspection, and social media moderation.
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All authors Aryan Mathur, Asaduddin Ahmed, Pushti Amit Vasoya, Simeon Kandan Sonar, Yasir Z, Madesh Kuppusamy
Systems and Control · Cross-listed from math.OC
Switching Network System Identification via Convex Optimizations
Kaito Iwasaki, Anthony Bloch and Maani Ghaffari
This paper introduces a convex optimization framework for identifying switched network systems, in which both the node dynamics and the underlying graph
topology switch between a finite number of configurations. Building on our recent convex identification method for general switching systems, we extend the formulation to structured network systems where each mode corresponds to a distinct adjacency
matrix. We show that both the continuous node dynamics and binary network
topologies can be identified from sampled state-velocity data by solving a sequence of convex programs. The proposed framework provides a unified and scalable way to recover piecewise network structures from data without a prior knowledge of mode labels at each state. Numerical results on diffusively coupled oscillators demonstrate accurate recovery of both mode dynamics and switching graphs.
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Image and Video Processing · Cross-listed from q-bio.QM
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger, Jonas Bohn, Dasha Trofimova et al. (+13 more)
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential of the gut-liver axis for predicting colorectal neoplasia through liver-derived radiomic features extracted from routine CT images as a novel opportunistic screening approach. In this retrospective study, we analyzed data from 1,997 patients who underwent colonoscopy and abdominal CT. Patients either had no colorectal neoplasia (n=1,189) or colorectal neoplasia (n_total=808; adenomas n=423, CRC n=385). Radiomics features were extracted from 3D liver
segmentations using the Radiomics Processing ToolKit (RPTK), which performed feature extraction, filtering, and classification. The
dataset was split into training (n=1,397) and test (n=600) cohorts. Five machine learning models were trained with 5-fold cross-validation on the 20 most informative features, and the best model ensemble was selected based on the validation AUROC. The best radiomics-based XGBoost model achieved a test AUROC of 0.810, clearly outperforming the best clinical-only model (test AUROC: 0.457). Subclassification between colorectal cancer and adenoma showed lower accuracy (test AUROC: 0.674). Our findings establish proof-of-concept that liver-derived radiomics from routine abdominal CT can predict colorectal neoplasia. Beyond offering a pragmatic, widely accessible adjunct to CRC screening, this approach highlights the gut-liver axis as a novel biomarker source for opportunistic screening and sparks new mechanistic hypotheses for future translational research.
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All authors Anna Hinterberger, Jonas Bohn, Dasha Trofimova, Nicolas Knabe, Julia Dettling, Tobias Norajitra, Fabian Isensee, Johannes Betge, Stefan O. Schönberg, Dominik Nörenberg, Sergio Grosu, Sonja Loges, Ralf Floca, Jakob Nikolas Kather, Klaus Maier-Hein, Freba Grawe
Systems and Control · Cross-listed from math.OC
From Zonal to Nodal Capacity Expansion Planning: Spatial Aggregation Impacts on a Realistic Test-Case
Elizabeth Glista, Bernard Knueven and Jean-Paul Watson
Solving power system capacity expansion planning (CEP) problems at realistic spatial resolutions is computationally challenging. Thus, a common practice is to solve CEP over zonal models with low spatial resolution rather than over full-scale nodal power networks. Due to improvements in solving large-scale
stochastic mixed integer programs, these computational limitations are becoming less relevant, and the assumption that zonal models are realistic and useful approximations of nodal CEP is worth revisiting. This work is the first to conduct a systematic computational study on the assumption that spatial aggregation can reasonably be used for ISO-scale CEP. By considering a realistic, large-scale test network based on the state of California with over 8,000 buses, we find that well-designed small spatial aggregations can yield good approximations but that coarser zonal models may result in large distortions of investment decisions, e.g., capacity under-investment of up to 41% for the lowest resolution model considered.
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Image and Video Processing
Revising Second Order Terms in Deep Animation Video Coding
Konstantin Schmidt and Thomas Richter
First Order Motion Model is a generative model that animates human heads based on very little motion information derived from keypoints. It is a promising solution for
video communication because first it operates at very low bitrate and second its computational complexity is moderate compared to other learning based
video codecs. However, it has strong limitations by design. Since it generates facial animations by warping source-images, it fails to recreate
videos with strong head movements. This works concentrates on one specific kind of head movements, namely head rotations. We show that replacing the Jacobian transformations in FOMM by a global rotation helps the system to perform better on items with head-rotations while saving 40% to 80% of bitrate on P-frames. Moreover, we apply state-of-the-art normalization techniques to the discriminator to stabilize the
adversarial training which is essential for generating visually appealing
videos. We evaluate the performance by the learned metics LPIPS and DISTS to show the success our optimizations.
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Image and Video Processing
KongNet: A Multi-headed Deep Learning Model for Detection and Classification of Nuclei in Histopathology Images
Jiaqi Lv, Esha Sadia Nasir, Kesi Xu et al. (+4 more)
Accurate detection and classification of
nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel,
cell-type-specialised decoders. Through multi-task learning, each decoder jointly predicts
nuclei centroids,
segmentation masks, and contours, aided by Spatial and Channel Squeeze-and-Excitation (SCSE)
attention modules and a composite loss function. We validate KongNet in three Grand Challenges. The proposed model achieved first place on track 1 and second place on track 2 during the MONKEY Challenge. Its lightweight variant (KongNet-Det) secured first place in the 2025 MIDOG Challenge. KongNet pre-trained on the MONKEY
dataset and fine-tuned on the PUMA
dataset ranked among the top three in the PUMA Challenge without further optimisation. Furthermore, KongNet established state-of-the-art performance on the publicly available PanNuke and CoNIC
datasets. Our results demonstrate that the specialised multi-decoder design is highly effective for
nuclei detection and classification across diverse tissue and stain types. The pre-trained model weights along with the inference code have been publicly released to support future research.
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All authors Jiaqi Lv, Esha Sadia Nasir, Kesi Xu, Mostafa Jahanifar, Brinder Singh Chohan, Behnaz Elhaminia, Shan E Ahmed Raza
Audio and Speech Processing · Cross-listed from cs.SD
ISA-Bench: Benchmarking Instruction Sensitivity for Large Audio Language Models
Bohan Li, Wenbin Huang, Yuhang Qiu et al. (+7 more)
Large Audio
Language Models (LALMs), which couple acoustic perception with large
language models (LLMs) to extract and understand diverse information from audio, have attracted intense interest from both academic and industrial communities. However, existing LALMs are highly sensitive to how instructions are phrased, affecting both (i) instruction-following rates and (ii) task performance. Yet, no existing
benchmarks offer a systematic and comprehensive evaluation of this sensitivity. We introduce ISA-Bench, a dynamic
benchmark evaluating instruction sensitivity for LALMs along three axes: instruction description, output format, and task composition. We assess recent open-source and proprietary LALMs using ISA-Bench, profiling both compliance and accuracy under controlled instruction variations. Experimental results reveal that even state-of-the-art LALMs suffer significant instruction sensitivity, leading to degraded performance on fundamental audio understanding tasks. To mitigate this issue, we fine-tune Qwen2-Audio on a specifically constructed complex instruction-variant
dataset, achieving a marked improvement in instruction-following performance. However, this also induces nontrivial catastrophic forgetting: the model loses some previously mastered task capabilities when exposed to new instruction styles. Our
benchmark provides a standardized basis for assessing and improving instruction sensitivity in LALMs, underscoring the need for instruction-robust audio understanding in real-world pipelines.
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All authors Bohan Li, Wenbin Huang, Yuhang Qiu, Yiwei Guo, Hankun Wang, Zhihan Li, Jing Peng, Ziyang Ma, Xie Chen, Kai Yu
Systems and Control
Towards Stochastic (N-1)-Secure Redispatch
Oleksii Molodchyk, Hendrik Drögehorn, Martin Lindner et al. (+2 more)
The intermittent nature of renewable power availability is one of the major sources of uncertainty in power systems. While
markets can guarantee that the demand is covered by the available generation, transmission system operators have to often intervene via economic redispatch to ensure that the physical constraints of the network are satisfied. To account for uncertainty, the underlying optimal power flow (OPF) routines have to be modified. Recently,
polynomial chaos expansion (PCE) has been suggested in the literature as a tool for
stochastic OPF problems. However, the usage of PCE-based methods in security-constrained OPF for (N-1)-secure operations has not yet been explored. In this paper, we propose a procedure that iteratively solves a PCE-overloaded
stochastic OPF problem by including line outage constraints until an (N-1)-secure solution is achieved. We demonstrate the efficacy of our method by comparing it with a Monte-Carlo simulation on a 118-bus example system.
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All authors Oleksii Molodchyk, Hendrik Drögehorn, Martin Lindner, Mario Kendziorski, Timm Faulwasser
Audio and Speech Processing
SoulX-Podcast: Towards Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity
Hanke Xie, Haopeng Lin, Wenxiao Cao et al. (+17 more)
Recent advances in text-to-
speech (TTS) synthesis have significantly improved
speech expressiveness and naturalness. However, most existing systems are tailored for single-speaker synthesis and fall short in generating coherent multi-speaker conversational
speech. This technical report presents SoulX-Podcast, a system designed for podcast-style multi-turn, multi-speaker dialogic
speech generation, while also achieving state-of-the-art performance in conventional TTS tasks. To meet the higher naturalness demands of multi-turn spoken dialogue, SoulX-Podcast integrates a range of paralinguistic controls and supports both Mandarin and English, as well as several Chinese dialects, including Sichuanese, Henanese, and Cantonese, enabling more personalized podcast-style
speech generation. Experimental results demonstrate that SoulX-Podcast can continuously produce over 90 minutes of conversation with stable speaker timbre and smooth speaker transitions. Moreover, speakers exhibit contextually adaptive prosody, reflecting natural rhythm and intonation changes as dialogues progress. Across multiple evaluation metrics, SoulX-Podcast achieves state-of-the-art performance in both monologue TTS and multi-turn conversational
speech synthesis.
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All authors Hanke Xie, Haopeng Lin, Wenxiao Cao, Dake Guo, Wenjie Tian, Jun Wu, Hanlin Wen, Ruixuan Shang, Hongmei Liu, Zhiqi Jiang, Yuepeng Jiang, Wenxi Chen, Ruiqi Yan, Jiale Qian, Yichao Yan, Shunshun Yin, Ming Tao, Xie Chen, Lei Xie, Xinsheng Wang
Audio and Speech Processing · Cross-listed from cs.SD
Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization
Bernardo Torres, Manuel Moussallam and Gabriel Meseguer-Brocal
Audio autoencoders learn useful, compressed audio representations, but their non-linear latent spaces prevent intuitive algebraic manipulation such as mixing or scaling. We introduce a simple training methodology to induce linearity in a high-compression Consistency Autoencoder (CAE) by using data augmentation, thereby inducing homogeneity (equivariance to scalar gain) and additivity (the decoder preserves addition) without altering the model's architecture or loss function. When trained with our method, the CAE exhibits linear behavior in both the encoder and decoder while preserving reconstruction fidelity. We test the practical utility of our learned space on music source composition and separation via simple latent arithmetic. This work presents a straightforward technique for constructing structured latent spaces, enabling more intuitive and efficient audio processing.
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Signal Processing · Cross-listed from cs.DC
Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems
Fatemeh Zahra Safaeipour, Jacob Chakareski and Morteza Hashemi
Mobile edge devices (e.g., AR/VR headsets) typically need to complete timely inference tasks while operating with limited on-board computing and energy resources. In this paper, we investigate the problem of collaborative inference in wireless edge networks, where energy-constrained edge devices aim to complete inference tasks within given deadlines. These tasks are carried out using neural networks, and the edge device seeks to optimize inference performance under energy and delay constraints. The inference process can be split between the edge device and an edge server, thereby achieving collaborative inference over wireless networks. We formulate an inference utility optimization problem subject to energy and delay constraints, and propose a novel solution called Bayes-Split-Edge, which leverages
Bayesian optimization for collaborative split inference over wireless edge networks. Our solution jointly optimizes the transmission power and the neural network split point. The Bayes-Split-Edge framework incorporates a novel hybrid acquisition function that balances inference task utility, sample efficiency, and constraint violation penalties. We evaluate our approach using the VGG19 model on the ImageNet-Mini
dataset, and Resnet101 on Tiny-ImageNet, and real-world mMobile wireless channel
datasets. Numerical results demonstrate that Bayes-Split-Edge achieves up to 2.4x reduction in evaluation cost compared to standard
Bayesian optimization and achieves near-linear convergence. It also outperforms several baselines, including CMA-ES, DIRECT, exhaustive search, and Proximal Policy Optimization (PPO), while matching exhaustive search performance under tight constraints. These results confirm that the proposed framework provides a sample-efficient solution requiring maximum 20 function evaluations and constraint-aware optimization for wireless split inference in edge computing systems.
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Systems and Control
An Error-Based Safety Buffer for Safe Adaptive Control (Extended Version)
Peter A. Fisher, Johannes Autenrieb and Anuradha M. Annaswamy
We consider the problem of adaptive control of a class of feedback linearizable plants with matched parametric uncertainties whose states are accessible, subject to state constraints, which often arise due to safety considerations. In this paper, we combine adaptation and control barrier functions into a real-time control architecture that guarantees stability, ensures control performance, and remains safe even with the parametric uncertainties. Two problems are considered, differing in the nature of the parametric uncertainties. In both cases, the control barrier function is assumed to have an arbitrary relative degree. In addition to guaranteeing stability, it is proved that both the control objective and safety objective are met with near-zero conservatism. No excitation conditions are imposed on the command signal. Simulation results demonstrate the non-conservatism of all of the theoretical developments.
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Signal Processing
Joint Uplink and Downlink Resource Allocation and Antenna Activation for Pinching Antenna Systems
Shreya Khisa, Ali Amhaz, Mohamed Elhattab et al. (+2 more)
In this paper, we explore a novel joint uplink and downlink framework utilizing a pinching
antenna system (PASS). We consider two waveguides, one dedicated to transmission and one to reception, and both of them are connected to a base station (BS). Each type of waveguide consists of several pinching
antennas (PAs) in some preconfigured positions. In this framework, we assume the BS can serve downlink and uplink user equipments (UEs) at the same time using the same spectrum resources through the presented PASS. In this aspect, we formulate a sum rate optimization problem that jointly optimizes the
antenna activation factor, the BS transmit power, and the UE's transmit power, subject to power budget constraints for the BS and the UEs, as well as minimum rate requirements for the UEs. The formulated problem is highly non-convex and difficult to solve directly. Hence, we divide the main problem into two sub-problems: the
antenna activation sub-problem and the power allocation sub-problem. Then, we solve the
antenna activation problem utilizing a distance and spatial correlation-based
algorithm. Meanwhile, the resource allocation problem is solved using a successive convex approximation (SCA)-based
algorithm. Numerical results show that our proposed framework can achieve around 60-90% performance gains over its time division duplex (TDD) where the uplink and downlink transmissions are served in different orthogonal time slots.
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All authors Shreya Khisa, Ali Amhaz, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine
Signal Processing
Randomized Space-Time Stacked Intelligent Metasurfaces for Massive Multiuser Downlink Connectivity
Donatella Darsena, Ivan Iudice, Vincenzo Galdi et al. (+1 more)
Stacked intelligent metasurfaces (SIMs) represent a key enabler for next-generation wireless networks, offering beamforming gains while significantly reducing
radio-frequency chain requirements. In conventional space-only SIM architectures, the rate of reconfigurability of the SIM is equal to the inverse of the channel coherence time. This paper investigates a novel beamforming strategy for massive downlink connectivity using a randomized space-time (ST) coded SIM. In addition to conventional space-only metasurface layers, the proposed design integrates a ST metasurface layer at the input stage of the SIM that introduces random time variations over each channel coherence time interval. These artificial time variations enable opportunistic user scheduling and exploitation of multiuser diversity under slow channel dynamics. To mitigate the prohibitive overhead associated with full channel state information at the transmitter (CSIT), we propose a partial-CSIT-based beamforming scheme that leverages randomized steering vectors and limited user-side feedback based on signal quality measurements. Numerical results demonstrate that the proposed ST-SIM architecture achieves satisfactory sum-rate performance while significantly reducing CSIT acquisition and feedback overhead, thereby enabling scalable downlink connectivity in dense networks.
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All authors Donatella Darsena, Ivan Iudice, Vincenzo Galdi, Francesco Verde
Signal Processing · Cross-listed from cs.CV
Quality-controlled registration of urban MLS point clouds reducing drift effects by adaptive fragmentation
Marco Antonio Ortiz Rincon, Yihui Yang and Christoph Holst
This study presents a novel workflow designed to efficiently and accurately register large-scale mobile
laser scanning (MLS) point clouds to a target model point cloud in urban street scenarios. This workflow specifically targets the complexities inherent in urban environments and adeptly addresses the challenges of integrating point clouds that vary in density, noise characteristics, and occlusion scenarios, which are common in bustling city centers. Two methodological advancements are introduced. First, the proposed Semi-sphere Check (SSC) preprocessing technique optimally fragments MLS trajectory data by identifying mutually orthogonal planar surfaces. This step reduces the impact of MLS drift on the accuracy of the entire point cloud registration, while ensuring sufficient
geometric features within each fragment to avoid local minima. Second, we propose Planar Voxel-based Generalized Iterative Closest Point (PV-GICP), a fine registration method that selectively utilizes planar surfaces within voxel partitions. This pre-process strategy not only improves registration accuracy but also reduces computation time by more than 50% compared to conventional point-to-plane ICP methods. Experiments on real-world
datasets from Munich's inner city demonstrate that our workflow achieves sub-0.01 m average registration accuracy while significantly shortening processing times. The results underscore the potential of the proposed methods to advance automated 3D urban modeling and updating, with direct applications in urban planning, infrastructure management, and dynamic city monitoring.
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Audio and Speech Processing
Evaluation of Spherical Wavelet Framework in Comparsion with Ambisonics
Ş. Ekmen and H. Lee
Recently, the Spherical Wavelet Framework (SWF) was proposed to combine the benefits of Ambisonics and Object-Based Audio (OBA) by utilising highly localised basis functions. SWF can enhance the sweet-spot area and reduce localisation blur while still enabling a sparse representation of the complete sound field, making storage and transmission more efficient. Initial vector analysis and listening test of SWF have shown promising results; however, these findings are limited to very specific conditions and do not include perceptual metrics. The present study investigates SWF in greater detail, comparing it with Ambisonics. The comparison was carried out using IACC, ITD, and ILD estimations, as well as listening tests with ecologically valid sound sources. Various reproduction layouts: regular polyhedron, t-design, and Lebedev grid with their corresponding Ambisonics orders and channel counts were evaluated. Results indicate that SWF is rated significantly more similar to the reference than Ambisonics is, in terms of overall spatial and timbral fidelity; however, it is considerably dependent on the subdivison of the sphere. Moreover, it cannot natively represent a wave arriving at a continuous direction. Possible solutions are proposed.
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Systems and Control
IoT-Driven Smart Management in Broiler Farming: Simulation of Remote Sensing and Control Systems
Sandra Coello Suarez, V. Sanchez Padilla, Ronald Ponguillo-Intriago et al. (+1 more)
Parameter monitoring and control systems are crucial in the industry as they enable automation processes that improve productivity and resource optimization. These improvements also help to manage environmental factors and the complex interactions between multiple inputs and outputs required for production management. This paper proposes an automation system for broiler management based on a simulation scenario that involves sensor networks and embedded systems. The aim is to create a transmission network for monitoring and controlling broiler temperature and feeding using the Internet of Things (IoT), complemented by a dashboard and a cloud-based service database to track improvements in broiler management. We look forward this work will serve as a guide for stakeholders and entrepreneurs in the animal production industry, fostering sustainable development through simple and cost-effective automation solutions. The goal is for them to scale and integrate these recommendations into their existing operations, leading to more efficient decision-making at the management level.
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All authors Sandra Coello Suarez, V. Sanchez Padilla, Ronald Ponguillo-Intriago, Albert Espinal
Signal Processing
Uplink SCMA-empowered Uncoordinated Random Access for Future mMTC
Pengyu Gao, Qu Luo, Jing Zhu et al. (+3 more)
In this paper, a novel uncoordinated random access (URA) protocol is presented to address the pressing demand for massive connectivity with low access latency in future massive machine type communication (mMTC) scenarios. The proposed URA scheme integrates the classical slotted ALOHA (S-ALOHA) protocol with sparse code multiple access (SCMA) technique, referred to as SCMA-empowered URA. Specifically, active users randomly choose an SCMA codebook to access the communication network in an arbitrary time slot whenever they want without scheduling. However, due to the lack of central coordination in the proposed URA scheme, SCMA codebook collisions become inevitable, making decoding challenging and leading to increased access failures. To cope with the decoding issue, an interference-canceling (IC) first decoding strategy is proposed at the access point (AP), which can partially tackles collision problems, contributing to a higher system throughput. Taking the proposed IC-first decoding strategy into account, a closed-form theoretical expression of the throughput is derived. Moreover, to alleviate the throughput degradation under the congested user traffic, a user barring mechanism is introduced to manage the traffic load. Firstly, a closed-form expression of idle codebook probability is developed to help indicate the system state, i.e., congested or not. Then, in addition to the estimated real-time load, the AP adaptively adjusts the access probability and redistributes the actual access load. Finally, simulation results demonstrate that the proposed SCMA-empowered URA scheme enjoys higher maximum throughput, compared to the conventional orthogonal multiple access (OMA) based URA scheme. Moreover, the accuracy of the presented theoretical analysis and the effectiveness of the user barring mechanism are verified.
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All authors Pengyu Gao, Qu Luo, Jing Zhu, Gaojie Chen, Pei Xiao, Chuan Heng Foh
Systems and Control · Cross-listed from math.OC
Flexibility aggregation via set projection for distribution grids with multiple interconnections
Maísa Beraldo Bandeira, Alexander Engelmann and Timm Faulwasser
With the increasing number of flexible energy devices in distribution grids, coordination between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) becomes critical for optimal system operation. One form of coordination is to solve the overall system operation problem in a hierarchical way, computing Feasible Operational Regions (FORs) for the interconnection between TSO/DSO. Most methods for computing FORs rely on the assumption of only one interconnection point between TSO and DSOs, which is often violated in practice. In this work, we propose a method for computing FORs in distribution grids with multiple interconnection points to the transmission grid. We test our method in a grid with two interconnecting points and analyze the properties of the resulting high-dimensional FOR from a power systems perspective.
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Audio and Speech Processing
LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization
Máté Gedeon and Péter Mihajlik
We introduce LibriConvo, a synthetic conversational
speech corpus for speaker diarization and automatic
speech recognition (ASR), built by instantiating the previously proposed Speaker-Aware Simulated Conversation (SASC) framework in a
dataset and benchmarking setting. The main contribution of this paper is a corpus construction pipeline and
benchmark derived from that framework. To make the data more suitable for downstream ASR and diarization, conversational timing statistics are estimated from English CallHome using external voice activity detection, long pauses are compressed, LibriTTS utterances are grouped by book to improve local semantic continuity, and room impulse responses are selected with a spatial-plausibility heuristic. The resulting corpus contains 240.1 hours of audio across 1,496 dialogues involving 830 speakers, partitioned into speaker-disjoint train, validation, and test splits. We report baseline results for both diarization and ASR. On the test split, Sortformer outperforms the pyannote pipeline in diarization (11.1% vs.~24.4% DER). For ASR, a Fast Conformer-CTC XLarge model fine-tuned with Serialized Output Training achieves 7.29% WER and 6.97% cpWER, outperforming zero-shot Whisper-large-v3. These results position LibriConvo as a practical
benchmark for studying synthetic conversational
speech and for evaluating multi-speaker
speech processing systems.
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Image and Video Processing
Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Dirk Elias Schut, Adriaan Graas, Robert van Liere et al. (+1 more)
Deep learning has shown impressive results in reducing noise and artifacts in X-ray computed
tomography (CT) reconstruction. Self-supervised CT reconstruction methods are especially appealing for real-world applications because they require no ground truth training examples. However, these methods involve a simplified X-ray physics model during training, which may make inaccurate assumptions, for example, about scintillator blurring, the scanning geometry, or the distribution of the noise. As a result, they can be less robust to real-world imaging circumstances. In this paper, we review the model assumptions of six recent self-supervised CT reconstruction methods. Based on this, we combined concepts of the Robust Equivariant Imaging and Sparse2Inverse methods in a new self-supervised CT reconstruction method called Equivariance2Inverse that is robust to scintillator blurring and limited-angle data. We benchmarked Equivariance2Inverse and the existing methods on the real-world 2DeteCT
dataset and on synthetic data with and without scintillator blurring and a limited-angle scanning geometry. The results of our
benchmark show that methods that assume that the noise is pixel-wise independent do not perform well on data with scintillator blurring. Moreover, they show that when the distribution of objects is rotationally invariant, this invariance can be used to reduce artifacts in limited-angle reconstructions.
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All authors Dirk Elias Schut, Adriaan Graas, Robert van Liere, Tristan van Leeuwen
Audio and Speech Processing · Cross-listed from cs.SD
Low-Resource Audio Codec (LRAC): 2025 Challenge Description
Kamil Wojcicki, Yusuf Ziya Isik, Laura Lechler et al. (+8 more)
While recent neural audio codecs deliver superior
speech quality at ultralow bitrates over traditional methods, their practical adoption is hindered by obstacles related to low-resource operation and robustness to acoustic distortions. Edge deployment scenarios demand codecs that operate under stringent compute constraints while maintaining low latency and bitrate. The presence of background noise and reverberation further necessitates designs that are resilient to such degradations. The performance of neural codecs under these constraints and their integration with
speech enhancement remain largely unaddressed. To catalyze progress in this area, we introduce the 2025 Low-Resource Audio Codec Challenge, which targets the development of neural and hybrid codecs for resource-constrained applications. Participants are supported with a standardized training
dataset, two baseline systems, and a comprehensive evaluation framework. The challenge is expected to yield valuable insights applicable to both codec design and related downstream audio tasks.
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All authors Kamil Wojcicki, Yusuf Ziya Isik, Laura Lechler, Mansur Yesilbursa, Ivana Balić, Wolfgang Mack, Rafał Łaganowski, Guoqing Zhang, Yossi Adi, Minje Kim, Shinji Watanabe
Systems and Control
Payload trajectory tracking control for aerial transportation systems with cable length online optimization
Hai Yu, Zhichao Yang, Wei He et al. (+3 more)
Cable-suspended aerial transportation systems are employed extensively across various industries. The capability to flexibly adjust the relative position between the multirotor and the payload has spurred growing interest in the system equipped with variable-length cable, promising broader application potential. Compared to systems with fixed-length cables, introducing the variable-length cable adds a new degree of freedom. However, it also results in increased nonlinearity and more complex dynamic coupling among the multirotor, the cable and the payload, posing significant challenges in control design. This paper introduces a backstepping control strategy tailored for aerial transportation systems with variable-length cable, designed to precisely track the payload trajectory while dynamically adjusting cable length. Then, a cable length generator has been developed that achieves online optimization of the cable length while satisfying state constraints, thus balancing the multirotor's motion and cable length changes without the need for manual trajectory planning. The asymptotic stability of the closed-loop system is guaranteed through Lyapunov techniques and the growth restriction condition. Finally, simulation results confirm the efficacy of the proposed method in managing trajectory tracking and cable length adjustments effectively.
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All authors Hai Yu, Zhichao Yang, Wei He, Jianda Han, Yongchun Fang, Xiao Liang
Image and Video Processing · Cross-listed from cs.CR
Privacy-Preserving Semantic Communication over Wiretap Channels with Learnable Differential Privacy
Weixuan Chen, Qianqian Yang, Shuo Shao et al. (+3 more)
While semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information, it also raises critical
privacy concerns. Many existing secure SemCom approaches rely on restrictive or impractical assumptions, such as favorable channel conditions for the legitimate user or prior knowledge of the eavesdropper's model. To address these limitations, this paper proposes a novel secure SemCom framework for image transmission over wiretap channels, leveraging differential
privacy (DP) to provide approximate
privacy guarantees. Specifically, our approach first extracts disentangled semantic representations from source images using generative
adversarial network (GAN) inversion method, and then selectively perturbs private semantic representations with approximate DP noise. Distinct from conventional DP-based protection methods, we introduce DP noise with learnable pattern, instead of traditional white Gaussian or Laplace noise, achieved through
adversarial training of neural networks (NNs). This design mitigates the inherent non-invertibility of DP while effectively protecting private information. Moreover, it enables explicitly controllable security levels by adjusting the
privacy budget according to specific security requirements, which is not achieved in most existing secure SemCom approaches. Experimental results demonstrate that, compared with the previous DP-based method and direct transmission, the proposed method significantly degrades the reconstruction quality for the eavesdropper, while introducing only slight degradation in task performance. Under comparable security levels, our approach achieves an LPIPS advantage of 0.06-0.29 and an FPPSR advantage of 0.10-0.86 for the legitimate user compared with the previous DP-based method.
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All authors Weixuan Chen, Qianqian Yang, Shuo Shao, Shunpu Tang, Zhiguo Shi, Shui Yu
Signal Processing · Cross-listed from cs.LG
Grassmanian Interpolation of Low-Pass Graph Filters: Theory and Applications
Anton Savostianov, Michael T. Schaub and Benjamin Stamm
Low-pass graph filters are fundamental for signal processing on graphs and other non-Euclidean domains. However, the computation of such filters for parametric graph families can be prohibitively expensive as computation of the corresponding low-frequency subspaces, requires the repeated solution of an
eigenvalue problem. We suggest a novel
algorithm of low-pass graph filter interpolation based on Riemannian interpolation in normal coordinates on the Grassmann
manifold. We derive an error bound estimate for the subspace interpolation and suggest two possible applications for induced parametric graph families. First, we argue that the temporal
evolution of the node features may be translated to the evolving graph
topology via a similarity correction to adjust the homophily degree of the network. Second, we suggest a dot product graph family induced by a given static graph which allows to infer improved message passing scheme for node classification facilitated by the filter interpolation.
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