Signal Processing
AIRMap: AI-Generated Radio Maps for Wireless Digital Twins
Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa et al. (+4 more)
Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast
radio-map estimation, along with an automated pipeline for creating the largest
radio-map
dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based
radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time
radio map estimation in wireless digital twins.
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All authors Ali Saeizadeh, Miead Tehrani-Moayyed, Davide Villa, J. Gordon Beattie Jr., Pedram Johari, Stefano Basagni, Tommaso Melodia
Audio and Speech Processing · Cross-listed from cs.SD
A Penny for Your Thoughts: Decoding Speech from Inexpensive Brain Signals
Quentin Auster, Kateryna Shapovalenko, Chuang Ma et al. (+1 more)
We explore whether neural networks can decode
brain activity into
speech by mapping
EEG recordings to audio representations. Using
EEG data recorded as subjects listened to natural
speech, we train a model with a contrastive CLIP loss to align
EEG-derived
embeddings with
embeddings from a pre-trained
transformer-based
speech model. Building on the state-of-the-art
EEG decoder from Meta, we introduce three architectural modifications: (i) subject-specific
attention layers (+0.15% WER improvement), (ii) personalized spatial
attention (+0.45%), and (iii) a dual-path RNN with
attention (-1.87%). Two of the three modifications improved performance, highlighting the promise of personalized architectures for
brain-to-
speech decoding and applications in
brain-computer interfaces.
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All authors Quentin Auster, Kateryna Shapovalenko, Chuang Ma, Demaio Sun
Systems and Control
Hybrid ILM-NILM Smart Plug System
Dániel István Németh and Kálmán Tornai
Electrical load classification is generally divided into intrusive and non-intrusive approaches, both having their limitations and advantages. With the non-intrusive approach, controlling appliances is not possible, but the installation cost of a single measurement device is cheap. In comparison, intrusive, smart plug-based solutions offer individual appliance control, but the installation cost is much higher. There have been very few approaches aiming to combine these methods. In this paper we show that extending a smart plug-based solution to multiple loads per plug can reduce control granularity in favor of lowering the system's installation costs. Connecting various loads to a Smart Plug through an extension cord is seldom considered in the literature, even though it is common in households. This scenario is also handled by the hybrid load classification solution presented in this paper.
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Image and Video Processing · Cross-listed from cs.CV
Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?
Zhiqi Qi, Runxin Zhao, Hanyang Zhuang et al. (+2 more)
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal
LiDAR placement for infrastructure, the profound impact of differing
LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection and overall environmental understanding. To systematically investigate these differences in infrastructure-based contexts, we introduce the "InfraLiDARs'
Benchmark," a novel
dataset meticulously collected in the CARLA simulation environment using concurrently operating infrastructure-based
LiDARs exhibiting both scanning paradigms. Leveraging this
benchmark, we conduct a comprehensive statistical analysis of the respective
LiDAR scanning abilities and evaluate the impact of these distinct patterns on the performance of various leading 3D object detection
algorithms. Our findings reveal that non-repetitive scanning
LiDAR and the 128-line repetitive
LiDAR were found to exhibit comparable detection performance across various scenarios. Despite non-repetitive
LiDAR's limited perception range, it's a cost-effective option considering its low price. Ultimately, this study provides insights for setting up roadside perception system with optimal
LiDAR scanning patterns and compatible
algorithms for diverse roadside applications, and publicly releases the "InfraLiDARs'
Benchmark"
dataset to foster further research.
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All authors Zhiqi Qi, Runxin Zhao, Hanyang Zhuang, Chunxiang Wang, Ming Yang
Audio and Speech Processing · Cross-listed from cs.SD
Cross-Corpus Validation of Speech Emotion Recognition in Urdu using Domain-Knowledge Acoustic Features
Unzela Talpur, Zafi Sherhan Syed, Muhammad Shehram Shah Syed et al. (+1 more)
Speech Emotion Recognition (SER) is a key affective computing technology that enables emotionally intelligent artificial intelligence. While SER is challenging in general, it is particularly difficult for low-resource
languages such as Urdu. This study investigates Urdu SER in a cross-corpus setting, an area that has remained largely unexplored. We employ a cross-corpus evaluation framework across three different Urdu emotional
speech datasets to test model generalization. Two standard domain-knowledge based acoustic feature sets, eGeMAPS and ComParE, are used to represent
speech signals as feature vectors which are then passed to Logistic Regression and Multilayer Perceptron classifiers. Classification performance is assessed using unweighted average recall (UAR) whilst considering class-label imbalance. Results show that Self-corpus validation often overestimates performance, with UAR exceeding cross-corpus evaluation by up to 13%, underscoring that cross-corpus evaluation offers a more realistic measure of model robustness. Overall, this work emphasizes the importance of cross-corpus validation for Urdu SER and its implications contribute to advancing affective computing research for underrepresented
language communities.
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All authors Unzela Talpur, Zafi Sherhan Syed, Muhammad Shehram Shah Syed, Abbas Shah Syed
Signal Processing
Joint optimization of microphone array geometry, sensor directivity pattern, and beamforming parameters for linear superarrays
Yuanhang Qian, Xueqin Luo, Jilu Jin et al. (+3 more)
Linear superarrays (LSAs) have been proposed to address the limited steering capability of conventional linear differential microphone arrays (LDMAs) by integrating omnidirectional and directional microphones, enabling more flexible beamformer designs. However, existing approaches remain limited because array geometry and element directivity, both critical to beamforming performance, are not jointly optimized. This paper presents a generalized LSA optimization framework that simultaneously optimizes array geometry, element directivity, and the beamforming filter to minimize the approximation error between the designed beampattern and an ideal directivity pattern (IDP) over the full frequency band and all steering directions within the region of interest. The beamformer is derived by approximating the IDP using a Jacobi-Anger series expansion, while the array geometry and element directivity are optimized via a genetic
algorithm. Simulation results show that the proposed optimized array achieves lower approximation error than conventional LSAs across the target frequency band and steering range. Additionally, its directivity factor and white noise gain demonstrate more stable and improved performance across frequencies and steering angles.
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All authors Yuanhang Qian, Xueqin Luo, Jilu Jin, Gongping Huang, Jingdong Chen, Jacob Benesty
Audio and Speech Processing
See the Speaker: Crafting High-Resolution Talking Faces from Speech with Prior Guidance and Region Refinement
Jinting Wang, Jun Wang, Hei Victor Cheng et al. (+1 more)
Unlike existing methods that rely on source images as appearance references and use source
speech to generate motion, this work proposes a novel approach that directly extracts information from the
speech, addressing key challenges in
speech-to-talking face. Specifically, we first employ a
speech-to-face portrait generation stage, utilizing a
speech-conditioned
diffusion model combined with statistical facial prior and a sample-adaptive weighting module to achieve high-quality portrait generation. In the subsequent
speech-driven talking face generation stage, we embed expressive dynamics such as lip movement, facial expressions, and eye movements into the latent space of the
diffusion model and further optimize lip synchronization using a region-enhancement module. To generate high-resolution outputs, we integrate a pre-trained
Transformer-based discrete codebook with an image rendering network, enhancing
video frame details in an end-to-end manner. Experimental results demonstrate that our method outperforms existing approaches on the HDTF, VoxCeleb, and AVSpeech
datasets. Notably, this is the first method capable of generating high-resolution, high-quality talking face
videos exclusively from a single
speech input.
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All authors Jinting Wang, Jun Wang, Hei Victor Cheng, Li Liu
Audio and Speech Processing · Cross-listed from cs.SD
GACA-DiT: Diffusion-based Dance-to-Music Generation with Genre-Adaptive Rhythm and Context-Aware Alignment
Jinting Wang, Chenxing Li and Li Liu
Dance-to-music (D2M) generation aims to automatically compose music that is rhythmically and temporally aligned with dance movements. Existing methods typically rely on coarse rhythm
embeddings, such as global motion features or binarized joint-based rhythm values, which discard fine-grained motion cues and result in weak rhythmic
alignment. Moreover, temporal mismatches introduced by feature downsampling further hinder precise synchronization between dance and music. To address these problems, we propose GACA-DiT, a
diffusion transformer-based framework with two novel modules for rhythmically consistent and temporally aligned music generation. First, a genre-adaptive rhythm extraction module combines multi-scale temporal wavelet analysis and spatial phase histograms with adaptive joint weighting to capture fine-grained, genre-specific rhythm patterns. Second, a context-aware temporal
alignment module resolves temporal mismatches using learnable context queries to align music latents with relevant dance rhythm features. Extensive experiments on the AIST++ and TikTok
datasets demonstrate that GACA-DiT outperforms state-of-the-art methods in both objective metrics and human evaluation. Project page: https://beria-moon.github.io/GACA-DiT/.
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Audio and Speech Processing · Cross-listed from cs.SD
Oral Tradition-Encoded NanyinHGNN: Integrating Nanyin Music Preservation and Generation through a Pipa-Centric Dataset
Jianbing Xiahou, Weixi Zhai and Xu Cui
We propose NanyinHGNN, a heterogeneous graph network model for generating Nanyin instrumental music. As a UNESCO-recognized intangible cultural heritage, Nanyin follows a heterophonic tradition centered around the pipa, where core melodies are notated in traditional notation while ornamentations are passed down orally, presenting challenges for both preservation and contemporary innovation. To address this, we construct a Pipa-Centric MIDI
dataset, develop NanyinTok as a specialized tokenization method, and convert symbolic sequences into graph structures using a Graph Converter to ensure that key musical features are preserved. Our key innovation reformulates ornamentation generation as the creation of ornamentation nodes within a heterogeneous graph. First, a graph neural network generates melodic outlines optimized for ornamentations. Then, a rule-guided system informed by Nanyin performance practices refines these outlines into complete ornamentations without requiring explicit ornamentation annotations during training. Experimental results demonstrate that our model successfully generates authentic heterophonic ensembles featuring four traditional instruments. These findings validate that integrating domain-specific knowledge into model architecture can effectively mitigate data scarcity challenges in computational ethnomusicology.
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Systems and Control · Cross-listed from cs.NE
Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses
Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares et al. (+11 more)
The deployment of AI on edge computing devices faces significant challenges related to energy consumption and functionality. These devices could greatly benefit from
brain-inspired learning mechanisms, allowing for real-time adaptation while using low-power. In-
memory computing with nanoscale resistive
memories may play a crucial role in enabling the execution of AI workloads on these edge devices. In this study, we introduce voltage-dependent synaptic plasticity (VDSP) as an efficient approach for unsupervised and local learning in memristive synapses based on Hebbian principles. This method enables online learning without requiring complex pulse-shaping
circuits typically necessary for spike-timing-dependent plasticity (STDP). We show how VDSP can be advantageously adapted to three types of memristive devices (TiO$_2$, HfO$_2$-based metal-
oxide filamentary synapses, and HfZrO$_4$-based ferroelectric tunnel junctions (FTJ)) with disctinctive switching characteristics. System-level simulations of spiking neural networks incorporating these devices were conducted to validate unsupervised learning on MNIST-based pattern recognition tasks, achieving state-of-the-art performance. The results demonstrated over 83% accuracy across all devices using 200
neurons. Additionally, we assessed the impact of device variability, such as switching thresholds and ratios between high and low resistance state levels, and proposed mitigation strategies to enhance robustness.
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All authors Nikhil Garg, Ismael Balafrej, Joao Henrique Quintino Palhares, Laura Bégon-Lours, Davide Florini, Donato Francesco Falcone, Tommaso Stecconi, Valeria Bragaglia, Bert Jan Offrein, Jean-Michel Portal, Damien Querlioz, Yann Beilliard, Dominique Drouin, Fabien Alibart
Signal Processing · Cross-listed from cs.LG
HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series
Simon A. Lee, Cyrus Tanade, Hao Zhou et al. (+13 more)
Wearable sensors provide abundant physiological time series, yet the principles governing their predictive utility remain unclear. We hypothesize that temporal resolution is a fundamental axis of representation learning, with different clinical and behavioral outcomes relying on structure at distinct scales. To test this resolution hypothesis, we introduce HiMAE (Hierarchical Masked Autoencoder), a self supervised framework that combines masked autoencoding with a hierarchical convolutional encoder decoder. HiMAE produces multi resolution
embeddings that enable systematic evaluation of which temporal scales carry predictive signal, transforming resolution from a hyperparameter into a probe for interpretability. Across classification, regression, and generative
benchmarks, HiMAE consistently outperforms state of the art foundation models that collapse scale, while being orders of magnitude smaller. HiMAE is an efficient representation learner compact enough to run entirely on watch, achieving sub millisecond inference on smartwatch class CPUs for true edge inference. Together, these contributions position HiMAE as both an efficient self supervised learning method and a discovery tool for scale sensitive structure in wearable health.
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All authors Simon A. Lee, Cyrus Tanade, Hao Zhou, Juhyeon Lee, Megha Thukral, Minji Han, Rachel Choi, Md Sazzad Hissain Khan, Baiying Lu, Migyeong Gwak, Mehrab Bin Morshed, Viswam Nathan, Md Mahbubur Rahman, Li Zhu, Subramaniam Venkatraman, Sharanya Arcot Desai
Signal Processing · Cross-listed from cs.LG
SPIRE: Shared-Private Representation Learning for Continuous Multi-Region LFP under Perturbation
Rahil Soroushmojdehi, Sina Javadzadeh, Mehrnaz Asadi et al. (+1 more)
Learning shared and region-specific representations from continuous multi-region local field potentials (LFPs) is challenging because neural signals are nonlinear, autocorrelated, spectrally structured, and temporally misaligned. We introduce SPIRE (Shared-Private Inter-Regional Encoder), a nonlinear, time-resolved framework that separates multi-region LFP into shared and region-specific latent trajectories using region-specific sequence encoders, constrained temporal
alignment, shared-only reconstruction, and variance regularization. On synthetic
benchmarks with known ground-truth factors, SPIRE preferentially recovers shared and private information despite nonlinear mixing, time-varying delays, and spectral confounding. On held-out human GPi-STN LFP, shared representations strongly predict activity across regions, while cross-region prediction from private representations remains near a structured temporal null, supporting functional rather than strict statistical separation. We then apply representations trained on off-stimulation recordings to deep
brain stimulation (DBS) data. Stimulation induces larger changes and more strongly encodes stimulation condition in shared than private representations. A matched CTAE analysis reproduces the shared-over-private effect, while retaining substantially less stimulation information. These findings suggest that DBS-related changes are preferentially expressed in neural representations shared across recorded regions.
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All authors Rahil Soroushmojdehi, Sina Javadzadeh, Mehrnaz Asadi, Terence D.Sanger
Signal Processing
Hybrid Liquid Neural Network-Random Finite Set Filtering for Robust Maneuvering Object Tracking
Minti Liu, Qinghua Guo, Cao Zeng et al. (+3 more)
This work addresses the problem of tracking maneuvering objects with complex motion patterns, a task in which conventional methods often struggle due to their reliance on predefined motion models. We integrate a data-driven
liquid neural network (LNN) into the random finite set (RFS) framework, leading to two LNN-RFS filters. By learning continuous-time dynamics directly from data, the LNN enables the filters to adapt to complex, nonlinear motion and achieve accurate tracking of highly maneuvering objects in clutter. This hybrid approach preserves the inherent multi-object tracking strengths of the RFS framework while improving flexibility and robustness. Simulation results on challenging maneuvering scenarios demonstrate substantial gains of the proposed hybrid approach in tracking accuracy.
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All authors Minti Liu, Qinghua Guo, Cao Zeng, Yanguang Yu, Jun Li, Ming Jin
Image and Video Processing · Cross-listed from cs.IT
Resi-VidTok: An Efficient and Decomposed Progressive Tokenization Framework for Ultra-Low-Rate and Lightweight Video Transmission
Zhenyu Liu, Yi Ma, Rahim Tafazolli et al. (+1 more)
Real-time transmission of
video over wireless networks remains highly challenging, even with advanced deep models, particularly under severe channel conditions such as limited bandwidth and weak connectivity. In this paper, we propose Resi-VidTok, a Resilient Tokenization-Enabled framework designed for ultra-low-rate and lightweight
video transmission that delivers strong robustness while preserving perceptual and semantic fidelity on commodity digital hardware. By reorganizing spatio–temporal content into a discrete, importance-ordered
token stream composed of key
tokens and refinement
tokens, Resi-VidTok enables progressive encoding, prefix-decodable reconstruction, and graceful quality degradation under constrained channels. A key contribution is a resilient 1D tokenization pipeline for
video that integrates differential temporal
token coding, explicitly supporting reliable recovery from incomplete
token sets using a single shared framewise decoder–without auxiliary temporal extractors or heavy generative models. Furthermore, stride-controlled frame sparsification combined with a lightweight decoder-side interpolator reduces transmission load while maintaining motion continuity. Finally, a channel-adaptive source–channel coding and modulation scheme dynamically allocates rate and protection according to
token importance and channel condition, yielding stable quality across adverse SNRs. Evaluation results indicate robust visual and semantic consistency at channel bandwidth ratios (CBR) as low as 0.0004 and real-time reconstruction at over 30 fps, demonstrating the practicality of Resi-VidTok for energy-efficient, latency-sensitive, and reliability-critical wireless applications.
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All authors Zhenyu Liu, Yi Ma, Rahim Tafazolli, Zhi Ding
Systems and Control · Cross-listed from cs.RO
Defect Mitigation for Robot Arm-based Additive Manufacturing Utilizing Intelligent Control and IOT
Matsive Ali, Blake Gassen and Sen Liu
This paper presents an integrated robotic fused deposition modeling additive manufacturing system featuring closed-loop thermal control and intelligent in-situ defect correction using a 6-degree of freedom robotic arm and an Oak-D camera. The
robot arm end effector was modified to mount an E3D hotend thermally regulated by an IoT microcontroller, enabling precise temperature control through real-time feedback. Filament extrusion system was synchronized with robotic motion, coordinated via ROS2, ensuring consistent deposition along complex trajectories. A
vision system based on OpenCV detects layer-wise defects position, commanding autonomous re-extrusion at identified sites. Experimental validation demonstrated successful defect mitigation in printing operations. The integrated system effectively addresses challenges real-time quality assurance. Inverse kinematics were used for motion planning, while homography transformations corrected camera perspectives for accurate defect localization. The intelligent system successfully mitigated surface anomalies without interrupting the print process. By combining real-time thermal regulation, motion control, and intelligent defect detection & correction, this architecture establishes a scalable and adaptive robotic additive manufacturing framework suitable for aerospace, biomedical, and industrial applications.
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Audio and Speech Processing · Cross-listed from cs.CL
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model
Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj et al. (+5 more)
Recent advances in spoken
language processing have led to substantial progress in phonetic tasks such as automatic
speech recognition (ASR), phone recognition (PR), grapheme-to-phoneme conversion (G2P), and phoneme-to-grapheme conversion (P2G). Despite their conceptual similarity, these tasks have largely been studied in isolation, each relying on task-specific architectures and
datasets. In this paper, we introduce POWSM (Phonetic Open Whisper-style
Speech Model), the first unified framework capable of jointly performing multiple phone-related tasks. POWSM enables seamless conversion between audio, text (graphemes), and phones, opening up new possibilities for universal and low-resource
speech processing. Our model outperforms or matches specialized PR models of similar size (Wav2Vec2Phoneme and ZIPA) while jointly supporting G2P, P2G, and ASR. Our training data, code and models are released to foster open science.
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All authors Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo, Kwanghee Choi, Jian Zhu, David Mortensen, Shinji Watanabe
Systems and Control
A Hamilton-Jacobi Reachability Framework with Soft Constraints for Safety-Critical Systems
Chams Eddine Mballo, Donggun Lee and Claire J. Tomlin
Traditional reachability methods provide formal guarantees of safety under bounded disturbances. However, they strictly enforce state constraints as inviolable, which can result in overly conservative or infeasible solutions in complex operational scenarios. Many constraints encountered in practice, such as bounds on battery state of charge in electric vehicles, recommended speed envelopes, and comfort constraints in passenger-carrying vehicles, are inherently soft. Soft constraints allow temporary violations within predefined safety margins to accommodate uncertainty and competing operational demands, albeit at a cost such as increased wear or higher operational expenses. This paper introduces a novel soft-constrained reachability framework that extends Hamilton-Jacobi reachability analysis for the formal verification of safety-critical systems subject to both hard and soft constraints. Specifically, the framework characterizes a subset of the state space, referred to as the soft-constrained reach-avoid set, from which the system is guaranteed to reach a desired set safely, under worst-case disturbances, while ensuring that cumulative soft-constraint violations remain within a user-specified budget. The framework comprises two principal components: (i) an augmented-state model with an auxiliary budget state that tracks soft-constraint violations, and (ii) a regularization-based approximation of the discontinuous Hamilton-Jacobi value function associated with the reach-avoid differential game studied herein. The effectiveness of the proposed framework is demonstrated through numerical examples involving the landing of a simple point-mass model and a fixed-wing aircraft executing an emergency descent, both under wind disturbances. The simulation results validate the framework's ability to simultaneously manage both hard and soft constraints in safety-critical settings
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Signal Processing
Optimizing Next Generation Wireless BAN with Prioritized Access for Heterogeneous Traffic
Shama Sidiqui and Indrakshi Dey
Efficient management of heterogeneous traffic with varying priorities is critical in Wireless Body Area Networks (WBANs). The priority mechanisms embedded in Media Access Control (MAC) schemes largely govern the performance of WBAN in terms of reliability, delay and energy efficiency. Minimizing the delay between packet generation and reception is critical for enhancing WBAN performance and associated health outcomes; however, delay optimization must be tailored to each traffic priority. In this work, we proposed a novel priority-based MAC protocol, Adaptive and Dynamic Polling MAC for Prioritized Traffic (ADP2-MAC), designed to support heterogeneous traffic in WBANs. The protocol utilizes a probabilistic approach to dynamically determine channel polling/listening intervals. ADP2-MAC not only identifies traffic arrival patterns to determine optimal polling intervals but also interrupts the transmission of lower-priority data when urgent packets are expected. The performance of ADP2-MAC has been compared with the MAC protocol for Variable Data Rates (MVDR) which supports heterogeneous traffic by assigning different data rates based on traffic priority. ADP2-MAC outperforms MVDR due to its use of probabilistic polling intervals and an interruption mechanism designed to efficiently handle urgent-priority data.
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Signal Processing
Next-Generation MAC Technique for Priority Handling in Industrial Cyber-Physical Systems
Anwar Ahmed Khan, Farid Nait-Abdesselam and Indrakshi Dey
Next Generation Media Access Control (NGMA) techniques have been designed to support diverse applications with heterogeneous priorities. In industrial cyber-physical systems (CPS), the number of connected devices and systems is expected to grow significantly, demanding dependable and
prompt network services. In this work, we present a novel scheme, Dynamic Fragmentation-MAC (DyFrag-MAC) that offers dynamic, differentiated channel access to the traffic of various priorities. DyFrag-MAC works on fragmenting the data of normal priority in order to support early delivery of urgent priority data. In prior work, urgent priority data either had to wait for the complete transmission of lower-priority packets or relied on multi-channel protocols to gain access. We compared the proposed fragmentation scheme with FROG-MAC and industrial Deterministic and Synchronous Multi-channel Extension (i-DSME). FROG-MAC fragmented the lower priority packets, but did not adjust the fragment size dynamically, whereas i-DSME utilized multiple channels and adaptive contention mechanisms; both protocols lack the ability to preempt ongoing lower-priority transmissions. Hence, the performance evaluation in terms of average delay and throughput reveals better performance of DyFRAG-MAC for the heterogeneous traffic.
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Systems and Control
Delay Tolerant Control for Autonomous Driving Using CDOB
Xincheng Cao, Haochong Chen, Levent Guvenc et al. (+1 more)
With the rapid growth of autonomous vehicle technologies, effective path-tracking control has become a critical component in ensuring safety and efficiency in complex traffic scenarios. When a high level decision making
agent generates a collision free path, a robust low level controller is required to precisely follow this trajectory. However, connected autonomous vehicles (CAV) are inherently affected by communication delays and computation delays, which significantly degrade the performance of conventional controllers such as PID or other more advanced controllers like disturbance observers (DOB). While DOB-based designs have shown effectiveness in rejecting disturbances under nominal conditions, their performance deteriorates considerably in the presence of unknown time delays. To address this challenge, this paper proposes a delay-tolerant communication disturbance observer (CDOB) framework for path-tracking control in delayed systems. The proposed CDOB compensates for the adverse effects of time delays, maintaining accurate trajectory tracking even under uncertain and varying delay conditions. It is shown through a simulation study that the proposed control architecture maintains close
alignment with the reference trajectory across various scenarios, including single lane change, double-= lane change, and Elastic Band generated collision avoidance paths under various time delays. Simulation results further demonstrate that the proposed method outperforms conventional approaches in both tracking accuracy and delay robustness, making it well suited for autonomous driving applications.
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All authors Xincheng Cao, Haochong Chen, Levent Guvenc, Bilin Aksun-Guvenc
Signal Processing
A Cylindrical Nanowire Array-Based Flexure-FET Receiver for Molecular Communication
Dilara Aktas and Ozgur B. Akan
Molecular communication (MC) enables biocompatible and energy-efficient information transfer through chemical signaling, forming a foundational paradigm for emerging applications in the Internet of Nano Things (IoNT) and intrabody healthcare systems. The realization of this
vision critically depends on developing advanced receiver architectures that merge nanoscale communication and networking techniques with bio-cyber interfaces, ensuring energy-efficient, reliable, and low-complexity modulation and detection while maintaining biocompatibility. To address these challenges, the Flexure-FET MC receiver was introduced as a mechanically transducing design capable of detecting both charged and neutral molecular species. In this study, we present a cylindrical nanowire array-based Flexure-FET MC receiver that enhances design versatility and scalability through distributed electromechanical coupling in a suspended-gate configuration. The proposed array architecture offers additional
geometric degrees of freedom, including nanowire radius, length, spacing, and array size, providing a flexible framework that can be tailored to advanced MC scenarios. An analytical end-to-end model is developed to characterize the system's electromechanical response, noise behavior, and information-theoretic performance, including signal-to-noise ratio (SNR) and channel capacity. The results reveal the strong interdependence between geometry, electromechanical dynamics, and molecular binding processes, enabling tunable control over sensitivity, noise characteristics, and communication capacity. The enhanced structural tunability and array configuration of the proposed design provide a flexible foundation for future mixture-based and spatially modulated MC systems, paving the way toward scalable and multifunctional receiver architectures within the IoNT framework.
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Systems and Control
Decentralized Merging Control of Connected and Automated Vehicles to Enhance Safety and Energy Efficiency using Control Barrier Functions
Shreshta Rajakumar Deshpande and Mrdjan Jankovic
This paper presents a decentralized Control Barrier Function (CBF) based approach for highway merging of Connected and Automated Vehicles (CAVs). In this control
algorithm, each "host" vehicle negotiates with other
agents in a control zone of the highway network, and enacts its own action, to perform safe and energy-efficient merge maneuvers. It uses predictor-corrector loops within the robust CBF setting for negotiation and to reconcile disagreements that may arise. There is no explicit order of vehicles and no priority. A notable feature is absence of gridlocks due to instability of the inter-
agent system. Results from Monte Carlo simulations show significant improvement in the system-wide energy efficiency and traffic flow compared to a first-in-first-out approach, as well as enhanced robustness of the proposed decentralized controller compared to its centralized counterpart.
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Systems and Control · Cross-listed from cs.NI
Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty
Mehrshad Eskandarpour and Hossein Soleimani
Efficient mobility management and load balancing are critical to sustaining Quality of Service (QoS) in dense, highly dynamic 5G
radio access networks. We present a deep reinforcement learning framework based on Proximal Policy Optimization (PPO) for autonomous, QoS-aware load balancing implemented end-to-end in a lightweight, pure-Python simulation environment. The control problem is formulated as a Markov Decision Process in which the
agent periodically adjusts
Cell Individual Offset (CIO) values to steer user-
cell associations. A multi-objective reward captures key performance indicators (aggregate throughput, latency, jitter, packet loss rate, Jain's
fairness index, and handover count), so the learned policy explicitly balances efficiency and stability under user mobility and noisy observations. The PPO
agent uses an actor-critic neural network trained from trajectories generated by the Python simulator with configurable mobility (e.g., Gauss-Markov) and
stochastic measurement noise. Across 500+ training episodes and stress tests with increasing user density, the PPO policy consistently improves KPI trends (higher throughput and
fairness, lower delay, jitter, packet loss, and handovers) and exhibits rapid, stable convergence. Comparative evaluations show that PPO outperforms rule-based ReBuHa and A3 as well as the learning-based CDQL baseline across all KPIs while maintaining smoother learning dynamics and stronger generalization as load increases. These results indicate that PPO's clipped policy updates and advantage-based training yield robust, deployable control for next-generation RAN load balancing using an entirely Python-based toolchain.
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Image and Video Processing
Dipole-lets: a new multiscale decomposition for MR phase and quantitative susceptibility mapping
Ignacio Contreras-Zúñiga, Mathias Lambert, Benjamín Palacios et al. (+2 more)
Nondipolar phase contributions can generate severe streaking artifacts during quantitative susceptibility mapping (QSM) inversion. We propose Dipole-lets, a dipole-adapted multiscale decomposition designed to identify phase components associated with streaking artifacts before and during susceptibility inversion. Dipole- lets combine an undecimated radial decomposition with an angular partition based on proximity to the magic cone. The resulting coefficients emphasize phase components near the magic cone, where nondipolar contributions may become relatively prominent. This information was incorporated into QSM reconstruction through a data- driven fidelity weight and a Dipole-let-based regularizer that models an additional nondipolar phase component. The methods were evaluated using modified simulated QSM data and an in vivo
dataset. The data-driven weighting reduced streaking artifacts while preserving anatomical detail and improved quantitative reconstruction metrics compared with magnitude-weighted TV. The Dipole-let regularizer achieved streaking suppression comparable to L1-QSM and lower reconstruction error in the modified simulated
dataset. In vivo experiments further demonstrated the applicability of the proposed approach to data with strong susceptibility-induced phase perturbations. Dipole-lets provide a multiscale representation for characterizing phase components associated with streaking artifacts and incorporating this information into QSM reconstruction, reducing streaking while preserving relevant susceptibility structures, and providing a flexible basis for future QSM reconstruction methods.
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All authors Ignacio Contreras-Zúñiga, Mathias Lambert, Benjamín Palacios, Cristian Tejos, Carlos Milovic
Audio and Speech Processing · Cross-listed from cs.SD
STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
Zihan Liu, Zhikang Niu, Qiuyang Xiao et al. (+10 more)
Despite rapid progress in Multi-modal Large
Language Models and Large Audio-
Language Models, existing audio
benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual
reasoning. We formalize audio 4D intelligence that is defined as
reasoning over sound dynamics in time and 3D space, and introduce
STAR-Bench to measure it.
STAR-Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative regimes) with a Holistic Spatio-Temporal
Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior
benchmarks where caption-only answering reduces accuracy slightly,
STAR-Bench induces far larger drops (-31.5% temporal, -35.2% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and
reasoning. Our
STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world.
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All authors Zihan Liu, Zhikang Niu, Qiuyang Xiao, Zhisheng Zheng, Ruoqi Yuan, Yuhang Zang, Yuhang Cao, Xiaoyi Dong, Jianze Liang, Xie Chen, Leilei Sun, Dahua Lin, Jiaqi Wang
Image and Video Processing
Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry
Andreas Hauptmann, Leonid Kunyansky and Jenni Poimala
The inverse source problem arising in photoacoustic
tomography and in several other coupled-physics modalities is frequently solved by iterative
algorithms. Such
algorithms are based on the minimization of a certain cost functional. In addition, novel deep learning techniques are currently being investigated to further improve such optimization approaches. All such methods require multiple applications of the operator defining the forward problem, and of its adjoint. In this paper, we present new asymptotically fast
algorithms for numerical evaluation of the forward and adjoint operators, applicable in the circular acquisition geometry. For an $(n \times n)$ image, our
algorithms compute these operators in $\mathcal{O}(n^2 \log n)$ floating point operations. We demonstrate the performance of our
algorithms in numerical simulations, where they are used as an integral part of several iterative image reconstruction techniques: classic variational methods, such as non-negative least squares and total variation regularized least squares, as well as deep learning methods, such as learned primal dual. A Python implementation of our
algorithms and computational examples is available to the general public.
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Systems and Control · Cross-listed from cs.RO
Feature Matching-Based Gait Phase Prediction for Obstacle Crossing Control of Powered Transfemoral Prosthesis
Jiaxuan Zhang, Yuquan Leng, Yixuan Guo et al. (+1 more)
For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic
algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction
algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.
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All authors Jiaxuan Zhang, Yuquan Leng, Yixuan Guo, Chenglong Fu
Systems and Control · Cross-listed from cs.LG
Learning to Drive Safely with Hybrid Options
Bram De Cooman and Johan Suykens
Out of the many deep reinforcement learning approaches for autonomous driving, only few make use of the options (or skills) framework. That is surprising, as this framework is naturally suited for hierarchical control applications in general, and autonomous driving tasks in specific. Therefore, in this work the options framework is applied and tailored to autonomous driving tasks on highways. More specifically, we define dedicated options for longitudinal and lateral manoeuvres with embedded safety and comfort constraints. This way, prior domain knowledge can be incorporated into the learning process and the learned driving behaviour can be constrained more easily. We propose several setups for hierarchical control with options and derive practical
algorithms following state-of-the-art reinforcement learning techniques. By separately selecting actions for longitudinal and lateral control, the introduced policies over combined and hybrid options obtain the same expressiveness and flexibility that human drivers have, while being easier to interpret than classical policies over continuous actions. Of all the investigated approaches, these flexible policies over hybrid options perform the best under varying traffic conditions, outperforming the baseline policies over actions.
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Signal Processing · Cross-listed from cs.LG
Semi-supervised and unsupervised learning for health indicator extraction from guided waves in aerospace composite structures
James Josep Perry, Pablo Garcia-Conde Ortiz, George Konstantinou et al. (+3 more)
Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures, enabling efficient maintenance and operational safety. However, extracting reliable HIs remains challenging due to variability in material properties,
stochastic damage
evolution, and diverse damage modes. Manufacturing defects (e.g., disbonds) and in-service incidents (e.g., bird strikes) further complicate this process. This study presents a comprehensive data-driven framework that learns HIs via two learning approaches integrated with multi-domain signal processing. Because ground-truth HIs are unavailable, a semi-supervised and an unsupervised approach are proposed: (i) a diversity deep semi-supervised anomaly detection (Diversity-DeepSAD) approach augmented with continuous auxiliary labels used as hypothetical damage proxies, which overcomes the limitation of prior binary labels that only distinguish healthy and failed states while neglecting intermediate degradation, and (ii) a degradation-trend-constrained variational autoencoder (DTC-VAE), in which the monotonicity criterion is embedded via an explicit trend constraint. Guided waves with multiple excitation frequencies are used to monitor single-stiffener composite structures under fatigue loading. Time, frequency, and time-frequency representations are explored, and per-frequency HIs are fused via unsupervised ensemble learning to mitigate frequency dependence and reduce variance. Using fast
Fourier transform features, the augmented Diversity-DeepSAD model achieved 81.6% performance, while DTC-VAE delivered the most consistent HIs with 92.3% performance, outperforming existing baselines.
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All authors James Josep Perry, Pablo Garcia-Conde Ortiz, George Konstantinou, Cornelie Vergouwen, Edlyn Santha Kumaran, Morteza Moradi