Systems and Control
Efficient CNN Inference on Ultra-Low-Power MCUs via Saturation-Aware Convolution
Shiming Li, Luca Mottola, Yuan Yao et al. (+1 more)
Quantized CNN inference on ultra-low-power MCUs incurs unnecessary computations in
neurons that produce saturated output values. These values are too extreme and are eventually clamped to the boundaries allowed by the
neuron. Often times, the
neuron can save time by only producing a value that is extreme enough to lead to the clamped result, instead of completing the computation, yet without introducing any error. Based on this, we present saturation-aware convolution: an inference technique whereby we alter the order of computations in convolution kernels to induce earlier saturation, and value checks are inserted to omit unnecessary computations when the intermediate result is sufficiently extreme. Our experimental results display up to 24% inference time saving on a Cortex-M0+ MCU, with zero impact on accuracy.
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All authors Shiming Li, Luca Mottola, Yuan Yao, Stefanos Kaxiras
Systems and Control · Cross-listed from cs.LG
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström et al. (+2 more)
Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited data, relevant for, e.g., model-based control. Recent work incorporates
Hamiltonian dynamics into Gaussian Processes (GPs) to obtain uncertainty-quantifying, energy-consistent models, but these methods rely on – rarely available – velocity or momentum data. In this paper, we study dynamics learning using
Hamiltonian GPs and focus on learning solely from input-output data, without relying on velocity or momentum measurements. Adopting a non-conservative formulation, energy exchange with the environment, e.g., through external forces or dissipation, can be captured. We provide a fully
Bayesian scheme for estimating probability densities of unknown hidden states, GP hyperparameters, as well as structural hyperparameters, such as damping coefficients. The proposed method is evaluated in a nonlinear simulation case study and compared to a state-of-the-art approach that relies on momentum measurements.
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All authors Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström, Thomas B. Schön, Thomas Seel
Systems and Control
Privacy-Preserving Cramér-Rao Lower Bound
Jieming Ke, Jimin Wang and Ji-Feng Zhang
This paper establishes the
privacy-preserving Cramer-Rao lower bound (CRLB) theory, characterizing the fundamental limit of identification accuracy under
privacy constraint for general
stochastic obfuscation mechanisms. An identifiability criterion under
privacy constraint is derived by using Fisher information
matrix as the
privacy metric. In the identifiable case, a precise
privacy-preserving CRLB is established with an explicit expression, which quantifies the
privacy cost without unspecified constant factors. Considering computational efficiency, recursive formulas are developed to compute the
privacy-preserving CRLB for multi-measurement systems, reducing the computational burden caused by direct high-dimensional
matrix inversion. To demonstrate the tightness of the lower bound, a Gaussian-mechanism-based
privacy-preserving recursive least-squares
algorithm is shown to strictly attain the bound under Gaussian measurement noises, and a maximum-likelihood-based
privacy-preserving identification
algorithm is proposed to attain the bound in the sense of convergence rates under non-Gaussian measurement noises. Experimental results are provided to demonstrate the
privacy-preserving CRLB and show the effectiveness of the proposed
algorithms.
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Systems and Control · Cross-listed from cs.RO
Force-Safe Environment Maps and Real-Time Detection for Soft Robot Manipulators
Akua K. Dickson, Juan C. Pacheco Garcia and Andrew P. Sabelhaus
Soft
robot manipulators have the potential for deployment in delicate environments to perform complex manipulation tasks. However, existing obstacle detection and avoidance methods do not consider limits on the forces that manipulators may exert upon contact with delicate obstacles. This work introduces a framework that maps force safety criteria from task space (i.e. positions along the
robot's body) to configuration space (i.e. the
robot's joint angles) and enables real-time force safety detection. We incorporate limits on allowable environmental contact forces for given task-space obstacles, and map them into configuration space (C-space) through the manipulator's forward kinematics. This formulation ensures that configurations classified as safe are provably below the maximum force thresholds, thereby allowing us to determine force-safe configurations of the soft
robot manipulator in real-time. We validate our approach in simulation and hardware experiments on a two-segment pneumatic soft
robot manipulator. Results demonstrate that the proposed method accurately detects force safety during interactions with deformable obstacles, thereby laying the foundation for real-time safe planning of soft manipulators in delicate, cluttered environments.
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Signal Processing
Location-Informed Interference Suppression Precoding Methods for Distributed Massive MIMO Systems
Emiel Vanspranghels, Raquel Marina Noguera Oishi, Franco Minucci et al. (+1 more)
The
evolution of mobile networks towards user-centric
cell-free distributed Massive
MIMO configurations requires the development of novel signal processing techniques. More specifically, digital precoding
algorithms have to be designed or adopted to enable distributed operation. Future deployments are expected to improve coexistence between cellular generations, and between mobile networks and incumbent services such as radar. In dense
cell-free deployments, it might also not be possible to have full channel state information for all users at all
antennas. To leverage location information in a dense deployment area, we suggest and investigate several algorithmic alterations on existing precoding methods, aimed at location-informed interference suppression, for usage in existing and emerging systems where user locations are known. The proposed
algorithms are derived using a theoretical channel model and validated and numerically evaluated using an empirical
dataset containing channel measurements from an indoor distributed Massive
MIMO testbed. When dealing with measured CSI, the impact of the hardware, in addition to the location-based channel, needs to be compensated for. We propose a method to calibrate the hardware and achieve measurement-based evaluation of our location-based interference suppression
algorithms. The results demonstrate that the proposed methods allow location-based interference suppression without explicit CSI knowledge at the transmitter, under certain realistic network conditions.
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All authors Emiel Vanspranghels, Raquel Marina Noguera Oishi, Franco Minucci, Sofie Pollin
Image and Video Processing · Cross-listed from cs.CV
Automatic segmentation of colorectal liver metastases for ultrasound-based navigated resection
Tiziano Natali, Karin A. Olthof, Niels F.M. Kok et al. (+3 more)
Introduction: Accurate intraoperative delineation of colorectal liver metastases (CRLM) is crucial for achieving negative resection margins but remains challenging using intraoperative ultrasound (iUS) due to low contrast, noise, and operator dependency. Automated
segmentation could enhance precision and efficiency in ultrasound-based
navigation workflows. Methods: Eighty-five tracked 3D iUS volumes from 85 CRLM patients were used to train and evaluate a 3D U-Net implemented via the nnU-Net framework. Two variants were compared: one trained on full iUS volumes and another on cropped regions around tumors.
Segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), Hausdorff Distance (HDist.), and Relative Volume Difference (RVD) on retrospective and prospective
datasets. The workflow was integrated into 3D Slicer for real-time intraoperative use. Results: The cropped-volume model significantly outperformed the full-volume model across all metrics (AUC-ROC = 0.898 vs 0.718). It achieved median DSC = 0.74, recall = 0.79, and HDist. = 17.1 mm comparable to semi-automatic
segmentation but with ~4x faster execution (~ 1 min). Prospective intraoperative testing confirmed robust and consistent performance, with clinically acceptable accuracy for real-time
surgical guidance. Conclusion: Automatic 3D
segmentation of CRLM in iUS using a cropped 3D U-Net provides reliable, near real-time results with minimal operator input. The method enables efficient, registration-free ultrasound-based
navigation for hepatic surgery, approaching expert-level accuracy while substantially reducing manual workload and procedure time.
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All authors Tiziano Natali, Karin A. Olthof, Niels F.M. Kok, Koert F.D. Kuhlmann, Theo J.M. Ruers, Matteo Fusaglia
Systems and Control
Voltage-Independent Active-Power Droop Coefficient for Enhanced Andronov-Hopf Oscillator Grid-Forming Inverters
Hamed Rezazadeh, Mohammad Monfared, Meghdad Fazeli et al. (+1 more)
In recent years, virtual oscillator control, particularly the Andronov-
Hopf oscillator (AHO), has received widespread
attention for controlling grid-forming (GFM) inverters due to their superior dynamic response. However, traditional AHO systems feature droop coefficients that are dependent on the oscillator voltage amplitude, limiting their ability to maintain consistent grid support during disturbances and resulting in power-sharing inaccuracies. This paper presents an enhanced AHO (EAHO) strategy, where the active power droop coefficient is no longer a function of the voltage amplitude and retains the key dynamic benefits of the original AHO. The EAHO improves both frequency and voltage support and ensures accurate power sharing with other GFM inverters in grid-connected and stand-alone modes. Extensive comparative and small-signal analyses, alongside experimental validation on 2.5 kVA single-phase inverters, confirm the EAHO's improved steady-state performance, enhanced active and reactive power support, and stable operation under varying grid conditions.
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All authors Hamed Rezazadeh, Mohammad Monfared, Meghdad Fazeli, Saeed Golestan
Image and Video Processing
Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing
Yang Lin, Claudio Bruschini and Edoardo Charbon
Single-
photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and histogram-based processing, leading to significant data transfer and processing challenges. Previous work based on recurrent neural networks has realized histogram-free processing. To further address these limitations, we propose a novel paradigm that eliminates TDCs by integrating in-sensor spike encoders. This approach enables preprocessing of
photon arrival events in the sensor while significantly compressing data, reducing complexity, and maintaining real-time edge processing capabilities. A dedicated spike encoder folds multiple
laser repetition periods, transforming phase-based spike trains into density-based spike trains optimized for spiking neural network processing and training via backpropagation through time. As a proof of concept, we introduce Transporter, a 128$\times$4 SPAD sensor with a per-pixel D flip-flop ring-based spike encoder, designed for intelligent active time-resolved imaging. This work demonstrates a path toward more efficient, neuromorphic SPAD imaging systems with reduced data overhead and enhanced real-time processing.
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Signal Processing · Cross-listed from quant-ph
Adaptive Entanglement-Aware Routing for Satellite Quantum Networks under Orbital and Atmospheric Variability
Dhrumil Bhatt and Vidushi Kumar
The expansion of satellite-based
quantum networks requires adaptive routing mechanisms that can sustain
entanglement under dynamic orbital and atmospheric conditions. Conventional schemes, often tailored to static or idealised
topologies, fail to capture the combined effects of orbital motion, fading, and trust variability in inter-satellite links. This work proposes an adaptive
entanglement-aware routing framework that jointly accounts for orbital geometry, atmospheric attenuation, and multi-parameter link evaluation. The routing metric integrates fidelity, trust, and key-rate weights to maintain connectivity and mitigate loss from
turbulence and fading. Monte Carlo simulations across multiple orbital densities ($\rho = 10^{-6}$~km$^{-3}$) and environmental regimes, standard atmosphere, strong
turbulence, and clear-sky LEO show up to a 275% improvement in key generation rate and a 15% increase in effective
entanglement fidelity over existing adaptive methods. The framework achieves sub-linear path-length scaling with network size and remains robust for fading variances up to $\sigma_{\mathrm{fade}}=0.1$, demonstrating strong potential for future global
quantum constellations.
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Systems and Control
Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk et al. (+1 more)
Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for large-scale or online applications. We propose a neural-operator framework for surrogate modeling of power system components, using Deep Operator Networks (DeepONets) to learn mappings from system states and time-varying inputs to full trajectories without step-by-step integration. To enhance generalization and data efficiency, we introduce Physics-Informed DeepONets (PI-DeepONets), which embed the residuals of governing equations into the training loss. Our results show that DeepONets, and especially PI-DeepONets, achieve accurate predictions under diverse scenarios, providing over 30 times speedup compared to high-order ODE solvers. Benchmarking against Physics-Informed Neural Networks (PINNs) highlights superior stability and scalability. Our results demonstrate neural operators as a promising path toward real-time, physics-aware simulation of power system dynamics.
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All authors Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis
Signal Processing
Millimeter-Scale Absolute Carrier Phase-Based Localization in Multi-Band Systems
Andrea Bedin, Joerg Widmer, Melanny Davila et al. (+2 more)
Localization is a key feature of future Sixth Generation (6G) net-works with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, rendering them impractical for more wide-spread deployment. In this paper, we present the first system that enables low-complexity and low-bandwidth absolute 3D localization with millimeter-level accuracy in generic wireless networks. It performs a carrier phase-based wireless localization refinement of an initial location estimate based on successive location-likelihood optimization across multiple bands. Unlike previous phase unwrapping methods, our solution is one-shot. We evaluate its performance collecting ~350, 000 measurements, showing an improvement of more than one order of magnitude over classical localization techniques. Finally, we will open-source the low-cost, modular FR3 front-end that we developed for the experimental campaign.
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All authors Andrea Bedin, Joerg Widmer, Melanny Davila, Marco Canil, Rafael Ruiz
Signal Processing
Look Before Switch: Sensing-Assisted Handover in 5G NR V2I Networks
Yunxin Li, Fan Liu, Haoqiu Xiong et al. (+3 more)
Integrated Sensing and Communication (
ISAC) has emerged as a promising solution in addressing the challenges of high-mobility scenarios in 5G NR Vehicle-to-Infrastructure (V2I) communications. This paper proposes a novel sensing-assisted handover framework that leverages
ISAC capabilities to enable precise beamforming and proactive handover decisions. Two sensing-enabled handover triggering
algorithms are developed: a distance-based scheme that utilizes estimated spatial positioning, and a probability-based approach that predicts vehicle maneuvers using interacting multiple model extended Kalman filter (IMM-EKF) tracking. The proposed methods eliminate the need for uplink feedback and beam sweeping, thus significantly reducing signaling overhead and handover interruption time. A sensing-assisted NR frame structure and corresponding protocol design are also introduced to support rapid synchronization and access under vehicular mobility. Extensive link-level simulations using real-world map data demonstrate that the proposed framework reduces the average handover interruption time by over 50%, achieves lower handover rates, and enhances overall communication performance.
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All authors Yunxin Li, Fan Liu, Haoqiu Xiong, Zhenkun Wang, Narengerile, Christos Masouros
Image and Video Processing · Cross-listed from q-bio.QM
PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessing
Gregory Verghese, Anthony Baptista, Chima Eke et al. (+10 more)
The integration of artificial intelligence (AI) into pathology is advancing precision medicine by improving diagnosis, treatment planning, and patient outcomes. Digitised whole-slide images (WSIs) capture rich spatial and morphological information vital for understanding disease biology, yet their gigapixel scale and variability pose major challenges for standardisation and analysis. Robust preprocessing, covering tissue detection, tessellation, stain normalisation, and annotation parsing is critical but often limited by fragmented and inconsistent workflows. We present PySlyde, a lightweight, open-source Python toolkit built on OpenSlide to simplify and standardise WSI preprocessing. PySlyde provides an intuitive API for slide loading, annotation management, tissue detection, tiling, and feature extraction, compatible with modern pathology foundation models. By unifying these processes, it streamlines WSI preprocessing, enhances reproducibility, and accelerates the generation of AI-ready
datasets, enabling researchers to focus on model development and downstream analysis.
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All authors Gregory Verghese, Anthony Baptista, Chima Eke, Holly Rafique, Mengyuan Li, Fathima Mohamed, Ananya Bhalla, Lucy Ryan, Michael Pitcher, Enrico Parisini, Concetta Piazzese, Liz Ing-Simmons, Anita Grigoriadis
Signal Processing · Cross-listed from quant-ph
Performance Analysis of One- and Two-way DV-QKD with MIMO FSO Communication Systems
Sushil Kumar, Soumya P. Dash and George C. Alexandropoulos
This paper considers a multiple-input multiple-output (
MIMO) wireless system wherein two legitimate users attempt to exchange secret keys over free-space optical (FSO) channels. Novel frameworks for the use of the one- and two-way discrete-variable
quantum key distribution (DV-QKD) protocols, employing weak coherent pulses and decoy states, are presented. Focusing on the case where a
photon-number-splitting attack is adopted by the eavesdropper and the legitimate multi-
antenna receiver using threshold detection for the key extraction, novel expressions for the secret key rate and
quantum bit error rate for both one- and two-way protocols are derived. The performance gain with larger
MIMO configurations and the tradeoff between the performances with the one- and the two-way protocols with respect to the transmission distance of the legitimate FSO link are numerically assessed.
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Audio and Speech Processing
Synthesizing speech with selected perceptual voice qualities - A case study with creaky voice
Frederik Rautenberg, Fritz Seebauer, Jana Wiechmann et al. (+3 more)
The control of perceptual voice qualities in a text-to-
speech (TTS) system is of interest for applications where unmanipu- lated and manipulated
speech probes can serve to illustrate pho- netic concepts that are otherwise difficult to grasp. Here, we show that a TTS system, that is augmented with a global speaker attribute manipulation block based on normalizing flows1 , is capable of correctly manipulating the non-persistent, localized quality of creaky voice, thus avoiding the necessity of a, typi- cally unreliable, frame-wise creak predictor. Subjective listen- ing tests confirm successful creak manipulation at a slightly re- duced MOS score compared to the original recording.
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All authors Frederik Rautenberg, Fritz Seebauer, Jana Wiechmann, Michael Kuhlmann, Petra Wagner, Reinhold Haeb-Umbach
Signal Processing
Near-optimal Reconfigurable Intelligent Surface Configuration: Blind Beamforming with Sensing
Son Dinh-Van, Nam Phuong Tran and Matthew D. Higgins
Blind beamforming has emerged as a promising approach to configure reconfigurable intelligent surfaces (RISs) without relying on channel state information (CSI) or
geometric models, making it directly compatible with commodity hardware. In this paper, we propose a new blind beamforming
algorithm, so-called Blind Optimal RIS Beamforming with Sensing (BORN), that operates using only received signal strength (RSS). In contrast to existing methods that rely on majority-voting mechanisms, BORN exploits the intrinsic quadratic structure of the received signal-to-noise ratio (SNR). The
algorithm proceeds in two stages: sensing, where a quadratic model is estimated from RSS measurements, and optimization, where the RIS configuration is obtained using the estimated quadratic model. Our novelties are twofold. Firstly, we show for the first time, that BORN can achieve provable near-optimal performance using only $O(N \log_2(N))$ samples, where $N$ is the number of RIS elements. As a by-product of our analysis, we show that quadratic models are learnable under Rademacher feature distributions when the second-order coefficient
matrix is low-rank. This result, to our knowledge, has not been established in prior
matrix sensing literature. Secondly, extensive simulations and real-world field tests demonstrate that BORN achieves near-optimal performance, substantially outperforming state-of-the-art blind beamforming
algorithms, particularly in scenarios with a weak background channel such as non-line-of-sight (NLOS).
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Systems and Control
Predicting and forecasting reactivity and flux using long short-term memory models in pebble bed reactors during run-in
Ian Kolaja, Ludovic Jantzen, Tatiana Siaraferas et al. (+1 more)
Pebble bed reactor (PBR) operation presents unique advantages and challenges due to the ability to continuously change the fuel mixture and excess reactivity. Each operation parameter affects reactivity on a different timescale. For example, fuel insertion changes may take months to fully propagate, whereas control rod movements have immediate effects. In-core measurements are further limited by the high temperatures, intense
neutron flux, and dynamic motion of the fuel bed. In this study, long short-term
memory (LSTM) networks are trained to predict reactivity, flux profiles, and power profiles as functions of operating history and synthetic batch-level pebble measurements, such as discharge burnup distributions. The model's performance is evaluated using unseen temporal data, achieving an $R^2$ of 0.9914 on the testing set. The capability of the network to forecast reactivity responses to future operational changes is also examined, and its application for optimizing reactor running-in procedures is explored.
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All authors Ian Kolaja, Ludovic Jantzen, Tatiana Siaraferas, Massimiliano Fratoni
Systems and Control
Evaluating the Impact of a Load Admittance Approximation in Transient Stability-Constrained Optimal Power Flow
Alex Junior da Cunha Coelho, Araceli Hernandez and Luis Badesa
The Transient Stability-Constrained Optimal Power Flow (TSC-OPF) incorporates dynamic stability constraints into the OPF formulation to ensure secure and economical operation under disturbances. While discretizing system dynamics enables the use of nonlinear programming techniques, it significantly increases computational burden. To enhance scalability, many studies simplify the network by representing loads as constant admittances, allowing the use of Kron reduction. However, computing the Kron reduction outside the optimization requires a voltage-based assumption to convert loads from constant power to constant admittance. This paper proposes a practical voltage-based load admittance approximation and evaluates the errors it may introduce in rotor angle and speed deviation trajectories. Case studies on the WECC 9-bus system show that the proposed approach reproduces rotor dynamics consistent with time-domain simulations during the first few seconds while considerably reducing implementation effort and mitigating convergence issues. The proposed framework thus offers a simple and effective strategy for scalable TSC-OPF implementations.
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Signal Processing
Average and Worst-case Analysis of MIMO Beamforming Loss due to Hardware Impairments
Xuan Chen, Matthieu Crussière and Luc Le Magoarou
In this paper, we investigate the impact of hardware impairments in
antenna arrays on the beamforming performance of multi-input multi-output (
MIMO) communication systems. We consider two types of imperfections: per-element gain mismatches and inter-element spacing deviations. We analytically determine the impairment configurations that result in the worst-case degradation. In addition, the analytical expression of the average-case performance is also derived for comparison. Simulation and theoretical results show that the average SNR degradation remains relatively limited, whereas the worst-case scenarios can exhibit substantially higher losses. These findings provide clear insight into the robustness limits of
MIMO systems under practical hardware imperfections.
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Systems and Control · Cross-listed from cs.RO
TAPOM: Task-Space Topology-Guided Motion Planning for Manipulating Elongated Object in Cluttered Environments
Zihao Li, Yiming Zhu, Zhe Zhong et al. (+2 more)
Robotic manipulation in complex, constrained spaces is vital for widespread applications but challenging, particularly when navigating narrow passages with elongated objects. Existing planning methods often fail in these low-clearance scenarios due to the sampling difficulties or the local minima. This work proposes
Topology-Aware Planning for Object Manipulation (TAPOM), which explicitly incorporates task-space topological analysis to enable efficient planning. TAPOM uses a high-level analysis to identify critical pathways and generate guiding keyframes, which are utilized in a low-level planner to find feasible configuration space trajectories. Experimental validation demonstrates significantly high success rates and improved efficiency over state-of-the-art methods on low-clearance manipulation tasks. This approach offers broad implications for enhancing manipulation capabilities of
robots in complex real-world environments.
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All authors Zihao Li, Yiming Zhu, Zhe Zhong, Qinyuan Ren, Yijiang Huang
Systems and Control
Design and Implementation of a Cloud Computing Security Assessment Model Based on Hierarchical Analysis and Fuzzy Comprehensive Evaluation
Yihong Zou
At the rapid pace of technological
evolution, the emerging cloud computing technology has promoted the digitalization and business innovation of the enterprise in all industries due to its advantages of data storage and service mode. Nevertheless, given the swift progress in cloud computing services, the security problems have gradually appeared. The data breach and cyber attack happen frequently, which cause huge losses to enterprises and individuals. These issues have gradually become important constraints for the popularization of cloud computingTo tackle the problems outlined above, this paper constructs a security evaluation framework for cloud computing services, integrating the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation method. By applying this scientific and systematic methodology, the framework enables enterprises and individuals to better apprehend the security posture of cloud services, thereby fostering the healthy
evolution of the entire industry.
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Signal Processing
Fundamental Models and Signal Processing for Movable Antenna-Enhanced Wireless Communications and Sensing
Zhenyu Xiao, Xiangyu Pi, Songqi Cao et al. (+4 more)
Movable
antenna (MA) has been recognized as a promising technology for performance enhancement in wireless communication and sensing systems by exploiting the spatial degrees of freedom (DoFs) in flexible
antenna movement. However, the integration of MAs into next-generation wireless networks still faces design challenges due to the paradigm shift from conventional fixed-position
antennas (FPAs) to MAs, which motivates this paper to provide a comprehensive overview of the models, scenarios, and signal processing techniques for MA-enhanced wireless networks. First, we introduce several efficient methods to realize flexible
antenna movement. Next, channel models based on field response and spatial correlation are presented to characterize the channel variations with respect to MA movement. Then, we discuss the advantages and challenges of applying MAs to typical application scenarios of wireless communications and sensing. Moreover, we show the signal processing techniques for MA-enhanced communication and sensing systems, including channel acquisition and
antenna position optimization. Finally, we highlight promising research directions to inspire future investigations.
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All authors Zhenyu Xiao, Xiangyu Pi, Songqi Cao, Lipeng Zhu, Zhen Gao, Xiang-Gen Xia, Rui Zhang
Image and Video Processing
J-SGFT: Joint Spatial and Graph Fourier Domain Learning for Point Cloud Attribute Deblocking
Muhammad Talha, Qi Yang, Zhu Li et al. (+2 more)
Point clouds (PC) are essential for AR/VR and autonomous driving but challenge compression schemes with their size, irregular sampling, and sparsity. MPEG's Geometry-based Point Cloud Compression (GPCC) methods successfully reduce bitrate; however, they introduce significant blocky artifacts in the reconstructed point cloud. We introduce a novel multi-scale postprocessing framework that fuses graph-
Fourier latent attribute representations with sparse convolutions and channel-wise
attention to efficiently deblock reconstructed point clouds. Against the GPCC TMC13v14 baseline, our approach achieves BD-rate reduction of 18.81% in the Y channel and 18.14% in the joint YUV on the 8iVFBv2
dataset, delivering markedly improved visual fidelity with minimal overhead.
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All authors Muhammad Talha, Qi Yang, Zhu Li, Anique Akhtar, Geert Van Der Auwera
Signal Processing
PECL: A Heterogeneous Parallel Multi-Domain Network for Radar-Based Human Activity Recognition
Jiuqi Yan, Chendong Xu and Dongyu Liu
Radar systems are increasingly favored for medical applications because they provide non-intrusive monitoring with high
privacy and robustness to lighting conditions. However, existing research typically relies on single-domain radar signals and overlooks the temporal dependencies inherent in human activity, which complicates the classification of similar actions. To address this issue, we designed the Parallel-EfficientNet-CBAM-LSTM (PECL) network to process data in three complementary domains: Range-Time, Doppler-Time, and Range-Doppler. PECL combines a channel-spatial
attention module and temporal units to capture more features and dynamic dependencies during action sequences, improving both accuracy and robustness. The experimental results show that PECL achieves an accuracy of 96.16% on the same
dataset, outperforming existing methods by at least 4.78%. PECL also performs best in distinguishing between easily confused actions. Despite its strong performance, PECL maintains moderate model complexity, with 23.42M parameters and 1324.82M FLOPs. Its parameter-efficient design further reduces computational cost.
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Image and Video Processing
UHDRes: Ultra-High-Definition Image Restoration via Dual-Domain Decoupled Spectral Modulation
S. Zhao, W. Lu, B. Wang et al. (+3 more)
Ultra-high-definition (UHD) images often suffer from severe degradations such as blur, haze, rain, or low-light conditions, which pose significant challenges for image restoration due to their high resolution and computational demands. In this paper, we propose UHDRes, a novel lightweight dual-domain decoupled spectral modulation framework for UHD image restoration. It explicitly models the amplitude spectrum via lightweight spectrum-domain modulation, while restoring phase implicitly through spatial-domain refinement. We introduce the spatio-spectral fusion mechanism, which first employs a multi-scale context aggregator to extract local and global spatial features, and then performs spectral modulation in a decoupled manner. It explicitly enhances amplitude features in the frequency domain while implicitly restoring phase information through spatial refinement. Additionally, a shared gated feed-forward network is designed to efficiently promote feature interaction through shared-parameter convolutions and adaptive gating mechanisms. Extensive experimental comparisons on five public UHD
benchmarks demonstrate that our UHDRes achieves the state-of-the-art restoration performance with only 400K parameters, while significantly reducing inference latency and
memory usage. The codes and models are available at https://github.com/Zhao0100/UHDRes.
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All authors S. Zhao, W. Lu, B. Wang, T. Wang, K. Zhang, H. Zhao
Signal Processing · Cross-listed from q-bio.NC
Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors
Assma Habadi, Milos Zefran, Lijuan Yin et al. (+4 more)
Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshots of activity. Wearable sensors offer an attractive alternative by continuously monitoring physiological signals. This study investigated whether physiological data can accurately predict scores on established cognitive tests. Research Design and Methods: We recorded physiological signals from 23 older adults completing three NIH Toolbox Cognitive Battery tests, which assess working
memory, processing speed, and
attention. The Empatica EmbracePlus, a wearable device, measured blood volume pulse, skin conductance, temperature, and movement. Statistical features were extracted using wavelet-based and
segmentation methods. We then applied supervised learning and validated predictions via cross-validation, hold-out testing, and bootstrapping. Results: Our models showed strong performance with Spearman's \rho of 0.73-0.82 and mean absolute errors of 0.14-0.16, significantly outperforming a naive mean predictor. Sensor roles varied: heart-related signals combined with movement and temperature best predicted working
memory, movement paired with skin conductance was most informative for processing speed, and heart in tandem with skin conductance worked best for
attention. Discussion and Implications: These findings suggest that wearable sensors paired with AI tools such as supervised learning and feature engineering can noninvasively track specific cognitive functions in older adults, enabling continuous monitoring. Our study demonstrates how AI can be leveraged when the data sample is small. This approach may support remote assessments and facilitate clinical interventions.
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All authors Assma Habadi, Milos Zefran, Lijuan Yin, Woojin Song, Maria Caceres, Elise Hu, Naoko Muramatsu
Signal Processing
Channel Knowledge Map Construction: Recent Advances and Open Challenges
Zixiang Ren, Juncong Zhou, Jie Xu et al. (+5 more)
Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM construction is an important technical problem for its effective implementation. This article provides a comprehensive overview of recent advances in CKM construction. First, we examine classical interpolation-based CKM construction methods, highlighting their limitations in practical deployments. Next, we explore image processing and generative artificial intelligence (AI) techniques, which leverage feature extraction to construct CKMs based on environmental knowledge. Furthermore, we present emerging wireless radiance field (WRF) frameworks that exploit neural radiance fields or Gaussian splatting to construct high-fidelity CKMs from sparse measurement data. Finally, we outline various future research directions in real-time and cross-domain CKM construction, as well as cost-efficient deployment of CKMs.
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All authors Zixiang Ren, Juncong Zhou, Jie Xu, Ling Qiu, Yong Zeng, Han Hu, Juyong Zhang, Rui Zhang
Systems and Control
Strategic Decision-Making Under Uncertainty through Bi-Level Game Theory and Distributionally Robust Optimization
Jiachen Shen, Jian Shi, Lei Fan et al. (+4 more)
In strategic scenarios where decision-makers operate at different hierarchical levels, traditional optimization methods are often inadequate for handling uncertainties from incomplete information or unpredictable external factors. To fill this gap, we introduce a mathematical framework that integrates bi-level game theory with distributionally robust optimization (DRO), particularly suited for complex network systems. Our approach leverages the hierarchical structure of bi-level games to model leader-follower interactions while incorporating distributional robustness to guard against worst-case probability distributions. To ensure computational tractability, the Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level challenge into a more manageable single-level model, and the infinite-dimensional DRO problem is reformulated into a finite equivalent. We propose a generalized
algorithm to solve this integrated model. Simulation results validate our framework's efficacy, demonstrating that under high uncertainty, the proposed model achieves up to a 22% cost reduction compared to traditional
stochastic methods while maintaining a service level of over 90%. This highlights its potential to significantly improve decision quality and robustness in networked systems such as transportation and communication networks.
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All authors Jiachen Shen, Jian Shi, Lei Fan, Chenye Wu, Dan Wang, Choong Seon Hong, Zhu Han
Systems and Control
IoT and Predictive Maintenance in Industrial Engineering: A Data-Driven Approach
P. Vijaya Bharati, J.S.V. Siva Kumar, Sathish K Anumula et al. (+2 more)
Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning
algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.
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All authors P. Vijaya Bharati, J.S.V. Siva Kumar, Sathish K Anumula, P Vamshi Krishna, Sangam Malla
Systems and Control
Computationally Efficient Spline-Based Modeling of DER Dynamics for Voltage Stability in Active Distribution Networks
Shadrack T. Asiedu, Tara Aryal, Zongjie Wang et al. (+2 more)
The increasing integration of Distributed Energy Resources (DERs) into power systems necessitates the accurate representation of their dynamic behavior at the transmission level. Traditional electromagnetic transient models (EMT), while effective, face scalability challenges due to their reliance on detailed system information. Data-driven approaches, such as System Identification (SysID), offer a promising alternative by modeling system dynamics without detailed system knowledge. However, SysID and similar methods are computationally intensive, requiring the computation of complex ordinary differential equations (ODEs) or transfer functions estimation. This makes them less effective for real-time operation. We therefore propose a novel data-driven approach that simplifies the modeling of DERs dynamics by leveraging B-splines to transform discrete system data into continuous differentiable functions. This enables the estimation of lower order linear ordinary differential equations with simple linear regression to represent the underlying dynamics at a very low computational cost. Furthermore, the extracted dynamic equations are discretized by the backward Euler method for potential integration into discrete-time power dispatch models. Validation results indicate a goodness-of-fit (GoF) of 98.74%, comparable to the 99.03% GoF of the SysID method, yet, 4.8 times faster. Our proposed model's execution time of less than one minute makes it more suitable for real-time applications in power system operations.
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All authors Shadrack T. Asiedu, Tara Aryal, Zongjie Wang, Hossein Moradi Rekabdarkolaee, Timothy M. Hansen