Systems and Control · Cross-listed from physics.soc-ph
Resilience of coupled systems under deep uncertainty and dynamic complexity: An integrative literature review
Jannie Coenen, Vítor Vasconcelos, Heiman Wertheim et al. (+8 more)
Resilience in coupled systems is increasingly critical in addressing global challenges such as
climate change and pandemics. These systems show unpredictable behaviour due to dynamic complexity and deep uncertainty across spatiotemporal scales. Despite growing interest, few studies systematically integrate both concepts when assessing resilience. This paper conducts an integrative review of 102 English-
language publications to identify gaps in current approaches. Findings reveal that most papers address lower levels of uncertainty and rarely consider dynamic complexity and deep uncertainty simultaneously, which limits the effectiveness of resilience strategies. To advance systems research, we propose a conceptual framework and practical tools to support researchers and decision-makers in evaluating and improving resilience. The paper also outlines future research directions for more robust, adaptive, and integrative resilience assessments.
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All authors Jannie Coenen, Vítor Vasconcelos, Heiman Wertheim, Marcel Olde Rikkert, Sophie Hadjisotiriou, Vittorio Nespeca, Tom Oreel, Rick Quax, Etiënne Rouwette, Vincent Marchau, Hubert Korzilius
Systems and Control · Cross-listed from cs.RO
A Formal Modular Synthesis Approach for the Coordination of 3-D Robotic Construction with Multi-robots
Marcelo Rosa, José E. R. Cury and Fabio L. Baldissera
In this paper, we deal with the problem of coordinating multiple
robots to build 3-D structures. This problem consists of a set of mobile
robots that interact with each other in order to autonomously build a predefined 3-D structure. Our approach is based on Supervisory Control Theory, and it allows us to synthesize from models that represent a single
robot and the target structure a correct-by-construction reactive controller, called supervisor. When this supervisor is replicated for the other
robots, then the target structure can be completed by all
robots
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Signal Processing
Efficient Precoding for LEO Satellites: A Low-Complexity Matrix Inversion Method via Woodbury Matrix Identity and arSVD
Mohammad Momani, Thomas Delamotte and Andreas Knopp
The increasing deployment of massive active
antenna arrays in low Earth orbit (LEO) satellites necessitates computationally efficient and adaptive precoding techniques to mitigate dynamic channel variations and enhance spectral efficiency. Regularized zero-forcing (RZF) precoding is widely used in multi-user
MIMO systems; however, its real-time implementation is limited by the computationally intensive inversion of the Gram
matrix. In this work, we develop a low-complexity framework that integrates the Woodbury (WB) formula with adaptive randomized singular value decomposition (arSVD) to efficiently update the Gram
matrix inverse as the satellite moves along its orbit. By leveraging low-rank perturbations, the WB formula reduces inversion complexity, while arSVD dynamically extracts dominant singular components, further enhancing computational efficiency. Monte Carlo simulations demonstrate that the proposed method achieves computational savings of up to 61% compared to conventional RZF precoding with full
matrix inversion, while incurring only a modest degradation in sum-rate performance. These results demonstrate that WB-arSVD offers a scalable and efficient solution for next-generation satellite communications, facilitating real-time deployment in power-constrained environments.
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Audio and Speech Processing · Cross-listed from cs.SD
Pseudo-Cepstrum: Pitch Modification for Mel-Based Neural Vocoders
Nikolaos Ellinas, Alexandra Vioni, Panos Kakoulidis et al. (+8 more)
This paper introduces a cepstrum-based pitch modification method that can be applied to any mel-spectrogram representation. As a result, this method is compatible with any mel-based vocoder without requiring any additional training or changes to the model. This is achieved by directly modifying the cepstrum feature space in order to shift the harmonic structure to the desired target. The spectrogram magnitude is computed via the pseudo-inverse mel transform, then converted to the cepstrum by applying DCT. In this domain, the cepstral peak is shifted without having to estimate its position and the modified mel is recomputed by applying IDCT and mel-filterbank. These pitch-shifted mel-spectrogram features can be converted to
speech with any compatible vocoder. The proposed method is validated experimentally with objective and subjective metrics on various state-of-the-art neural vocoders as well as in comparison with traditional pitch modification methods.
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All authors Nikolaos Ellinas, Alexandra Vioni, Panos Kakoulidis, Georgios Vamvoukakis, Myrsini Christidou, Konstantinos Markopoulos, Junkwang Oh, Gunu Jho, Inchul Hwang, Aimilios Chalamandaris, Pirros Tsiakoulis
Audio and Speech Processing · Cross-listed from cs.HC
Poster: Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning
Xuefu Dong, Liqiang Xu, Lixing He et al. (+6 more)
Silent
speech interface (SSI) enables hands-free input without audible vocalization, but most SSI systems do not verify speaker identity. We present HEar-ID, which uses consumer active noise-canceling earbuds to capture low-frequency "whisper" audio and high-frequency ultrasonic reflections. Features from both streams pass through a shared encoder, producing
embeddings that feed a contrastive branch for user authentication and an SSI head for silent spelling recognition. This design supports decoding of 50 words while reliably rejecting impostors, all on commodity earbuds with a single model. Experiments demonstrate that HEar-ID achieves strong spelling accuracy and robust authentication.
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All authors Xuefu Dong, Liqiang Xu, Lixing He, Zengyi Han, Ken Christofferson, Yifei Chen, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Systems and Control
From Liability to Asset: A Three-Mode Grid-Forming Control Framework for Centralized Data Center UPS Systems
Mohamed Shamseldein
AI workloads are turning large data centers into highly dynamic power-electronic loads; fault-time behavior and workload pulsing can stress weak-grid points of interconnection. This paper proposes a centralized medium-voltage (MV) uninterruptible power supply (UPS) control architecture implemented as three operating modes: Mode 1 regulates a DC stiff bus and shapes normal-operation grid draw, Mode 2 enforces current-limited fault-mode P–Q priority with UPS battery energy storage system (UPS-BESS) buffering and a rate-limited post-fault "soft return," and Mode 3 optionally provides droop-based fast frequency response via grid-draw modulation. Fundamental-frequency averaged dq simulations (50 MW block, short-
circuit ratio (SCR) = 1.5, 0.5 p.u. three-phase dip for 150~ms) show zero unserved information-technology (IT) energy (0.00000 MWh vs.0.00208 MWh for a momentary-cessation
benchmark), a 0.57 p.u. peak inverter current (vs. 1.02 p.u. for a synchronous-reference-frame phase-locked loop (SRF-PLL) low-voltage ride-through (LVRT) baseline), a nonzero mean fault-window grid draw of 0.20~p.u. (vs.approx 0 for momentary cessation), and an improved settled point-of-common-coupling (PCC) voltage minimum of 0.79 p.u. after one cycle (vs. 0.66 p.u.). A forced-oscillation case study applies a 1 Hz pulsed load (+/- 0.25 p.u.) and shows that the normal-operation shaping filters the oscillation seen by the grid while the UPS-BESS buffers the pulsing component.
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Signal Processing
Robust 6G OFDM High-Mobility Communications Using Delay-Doppler Superimposed Pilots
Mauro Marchese and Pietro Savazzi
In this work, a novel receiver architecture for orthogonal frequency division multiplexing (OFDM) communications in 6G high-mobility scenarios is developed. In particular, a delay-Doppler superimposed pilot (SP) scheme is used for channel estimation (CE) by adding a single pilot in the delay-Doppler domain. Unlike previous research on delay-Doppler superimposed pilots in OFDM systems, intercarrier interference (ICI) effects, fractional delays, and Doppler shifts are considered. Consequently, a disjoint fractional delay-Doppler estimation
algorithm is derived, and a reduced-complexity equalization method based on the Landweber iteration, which exploits intrinsic channel structure, is proposed. Simulation results reveal that the proposed receiver architecture achieves robust communication performance across various mobility conditions, with speeds of up to 1000 km/h, and increases the effective throughput compared to existing methods.
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Signal Processing · Cross-listed from cs.LG
Batch Normalization-Free Fully Integer Quantized Neural Networks via Progressive Tandem Learning
Pengfei Sun, Wenyu Jiang, Piew Yoong Chee et al. (+2 more)
Quantised neural networks (QNNs) shrink models and reduce inference energy through low-bit arithmetic, yet most still depend on a running statistics batch normalisation (BN) layer, preventing true integer-only deployment. Prior attempts remove BN by parameter folding or tailored initialisation; while helpful, they rarely recover BN's stability and accuracy and often impose bespoke constraints. We present a BN-free, fully integer QNN trained via a progressive, layer-wise
distillation scheme that slots into existing low-bit pipelines. Starting from a pretrained BN-enabled teacher, we use layer-wise targets and progressive compensation to train a student that performs inference exclusively with integer arithmetic and contains no BN operations. On ImageNet with AlexNet, the BN-free model attains competitive Top-1 accuracy under aggressive quantisation. The procedure integrates directly with standard quantisation workflows, enabling end-to-end integer-only inference for resource-constrained settings such as edge and embedded devices.
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All authors Pengfei Sun, Wenyu Jiang, Piew Yoong Chee, Paul Devos, Dick Botteldooren
Signal Processing · Cross-listed from physics.optics
Reconfigurable Silicon Photonics Extreme Learning Machine with Random Non-linearities as Neural Processor and Physical Unclonable Function
George Sarantoglou, Georgios Aias Karydis, Adonis Bogris et al. (+1 more)
An alternative extreme learning machine -ELM- paradigm is presented exploiting random non-linearities -RN, named RN-ELM, instead of a conventional fixed node non-linearity. This method is implemented on a hybrid neural engine, with the physical layer realized by an integrated silicon photonic mesh and the digital layer by a simple regression
algorithm. Non-linearities are intrinsically non-power depended and are generated through non-linear frequency to power mapping offered by optical filters. The numerical evaluation is based on an experimentally derived transfer function of an all-pass filter, implemented on a silicon reconfigurable photonic integrated chip -RPIC. RN-ELM is evaluated in a twofold manner; first as a machine learning scheme, where the expressivity offered by multiple, yet random, activation functions lead to a compact and highly simplified design with 5 optical filters, offering state-of-the-art performance in time-series prediction tasks with minimum hardware requirements. The second scenario entails its deployment as a physical unclonable function -PUF, for authentication applications directly in the physical layer. In this case, the random activation functions are associated with unavoidable, fabrication related waveguide imperfections that can act as hardware signatures. Numerical results reveal a probability of cloning as low as 10e-15, which corresponds to a highly secure authentication
token.
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All authors George Sarantoglou, Georgios Aias Karydis, Adonis Bogris, Charis Mesaritakis
Signal Processing
An active-set algorithm for spectral unmixing
Nils Foix-Colonier and Sébastien Bourguignon
Linear spectral unmixing under nonnegativity and sum-to-one constraints is a convex optimization problem for which many
algorithms were proposed. In practice, especially for supervised unmixing (i.e., with a large dictionary), solutions tend to be sparse due to the nonnegativity of the abundances, thereby motivating the use of an active-set solver. Given the problem specific features, it seems advantageous to design a dedicated
algorithm in order to gain computational performance compared to generic solvers. In this paper, we propose to derive such a specific
algorithm, while extending the nonnegativity constraints to broader minimum abundance constraints.
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Systems and Control · Cross-listed from cond-mat.soft
Machine Learning-based Optimal Control for Colloidal Self-Assembly
Andres Lizano-Villalobos, Fangyuan Ma, Wentao Tang et al. (+2 more)
Achieving precise control of colloidal self-assembly into specific patterns remains a longstanding challenge due to the complex process dynamics. Recently, machine learning-based state representation and reinforcement learning-based control strategies have started to accumulate popularity in the field, showing great potential in achieving an automatable and generalizable approach to producing patterned colloidal assembly. In this work, we adopted a machine learning-based optimal control framework, combining unsupervised learning and graph convolutional neural work for state observation with deep reinforcement learning-based optimal control policy calculation, to provide a data-driven control approach that can potentially be generalized to other many-body self-assembly systems. With Brownian dynamics simulations, we demonstrated its superior performance as compared to traditional order parameter-based state description, and its efficacy in obtaining ordered 2-dimensional spherical colloidal self-assembly in an electric field-mediated system with an actual success rate of 97%.
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All authors Andres Lizano-Villalobos, Fangyuan Ma, Wentao Tang, Wei Sun, Xun Tang
Audio and Speech Processing
BEST-STD2.0: Balanced and Efficient Speech Tokenizer for Spoken Term Detection
Anup Singh, Vipul Arora and Kris Demuynck
Fast and accurate spoken content
retrieval is vital for applications such as voice search. Query-by-Example Spoken Term Detection (STD) involves retrieving matching segments from an audio database given a spoken query.
Token-based STD systems, which use discrete
speech representations, enable efficient search but struggle with robustness to noise and reverberation, and with inefficient
token utilization. We address these challenges by proposing a noise and reverberation-augmented training strategy to improve tokenizer robustness. In addition, we introduce optimal transport-based regularization to ensure balanced
token usage and enhance
token efficiency. To further speed up
retrieval, we adopt a TF-IDF-based search mechanism. Empirical evaluations demonstrate that the proposed method outperforms STD baselines across various distortion levels while maintaining high search efficiency.
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Systems and Control
Economic versus energetic model predictive control of a cold production plant with thermal energy storage
Manuel G. Satué, Manuel R. Arahal, Luis F. Acedo et al. (+1 more)
Economic model predictive control has been proposed as a means for solving the unit loading and unit allocation problem in multi-chiller cooling plants. The adjective economic stems from the use of financial cost due to electricity consumption in a time horizon, such is the loss function minimized at each sampling period. The energetic approach is rarely encountered. This article presents for the first time a comparison between the energetic optimization objective and the economic one. The comparison is made on a cooling plant using air-cooled water chillers and a cold storage system. Models developed have been integrated into Simscape, and non-convex mixed optimization methods used to achieve optimal control trajectories for both energetic and economic goals considered separately. The results over several scenarios, and in different seasons, support the consideration of the energetic approach despite the current prevalence of the economic one. The results are dependent on the electric season and the available tariffs. In particular, for the high electric season and considering a representative tariff, the results show that an increment of about 2.15% in energy consumption takes place when using the economic approach instead of the energetic one. On the other hand, a reduction in cost of 2.94% is achieved.
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All authors Manuel G. Satué, Manuel R. Arahal, Luis F. Acedo, Manuel G. Ortega
Systems and Control
Optimal chiller loading including transients
Manuel R. Arahal, Manuel G. Satué and Manuel G. Ortega
Scheduling and loading of chillers in a multi-chiller plant is considered. A new framework is introduced considering an extended set of independent variables for the optimization problem of energy consumption. In this way the number of decision variables is increased, providing extra degrees of freedom to optimize cooling plant operation. The dynamic effects due to transients arising from switching on and off of units are usually not considered in the literature dealing with Optimal Chiller Loading/Sequencing which is restricted to the static case. In this paper, these effects are treated in a way that results in a manageable optimization problem. A Simultaneous Perturbation
Stochastic Approximation solution is deployed for the problem and the proposed method is compared with a similar but static approach showing the benefits in terms of reduced energy consumption.
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Systems and Control
Contraction Analysis of Filippov Solutions in Multi-Modal Piecewise Smooth Systems
Zonglin Liu, Kyra Borchhardt and Olaf Stursberg
This paper provides conditions to ensure contractive behavior of Filippov solutions generated by multi-modal piecewise smooth (PWS) systems. These conditions are instrumental in analyzing the asymptotic behavior of PWS systems, such as convergence towards an
equilibrium point or a limit cycle. The work is motivated by a known principle for contraction analysis of bimodal PWS systems which ensures that the flow dynamics of each mode and the sliding dynamics on the switching
manifold are contracting. This approach is extended first to PWS systems with multiple non-intersecting switching
manifolds in Rn, and then to two intersecting switching
manifolds in R2. Numerical examples are provided to validate the theoretical findings, along with a discussion on extensions to more general intersecting switching
manifolds in Rn.
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Systems and Control
Using Seminorms To Analyze Contraction of Switched Systems With Only Non-Contracting Modes
Edwin Baum, Zonglin Liu, Yuzhen Qin et al. (+1 more)
This paper investigates contraction properties of switched dynamical systems for the case that all modes are non-contracting, thereby extending existing results that require at least one mode to be contracting. Leveraging the property that unstable systems may still exhibit stable behavior within certain subspaces, conditions are provided which ensure contracting
evolution within a given subspace of the state space of the switched system. These conditions are derived using the concepts of seminorms and semi-contracting systems. Then, by selecting a set of subspaces whose corresponding seminorms form a separating family of the state space, and by verifying whether a given mode is contracting in each subspace, conditions on the activation time of each mode are provided by which contraction on the complete state space is guaranteed. Numerical examples are presented for illustration.
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All authors Edwin Baum, Zonglin Liu, Yuzhen Qin, Olaf Stursberg
Systems and Control
Closed Loop Reference Optimization for Extrusion Additive Manufacturing
Rawan Hoteit, Andrea Balestra, Nathan Mingard et al. (+2 more)
Various defects occur during material extrusion additive manufacturing processes that degrade the quality of the 3D printed parts and lead to significant material waste. This motivates feedback control of the extrusion process to mitigate defects and prevent print failure. We propose a linear quadratic regulator (LQR) for closed-loop control with force feedback to provide accurate width tracking of the extruded filament. Furthermore, we propose preemptive optimization of the reference force given to the LQR that accounts for the performance of the LQR and generates the optimal reference for the closed loop extrusion dynamics and machine constraints. Simulation results demonstrate the improved tracking performance and response time. Experiments on a Fused Filament Fabrication 3D printer showcase a root mean square error improvement of 39.57% compared to tracking the unmodified reference as well as an 83.7% shorter settling time.
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All authors Rawan Hoteit, Andrea Balestra, Nathan Mingard, Efe C. Balta, John Lygeros
Audio and Speech Processing
Learning Filters in Feedback Delay Networks from Noisy Room Impulse Responses
Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht et al. (+1 more)
Recursion is a fundamental concept in the design of filters and audio systems. In particular, artificial reverberation systems that use delay networks depend on recursive paths to control both echo density and the decay rate of modal components. The differentiable digital signal processing framework has shown promise in automatically tuning recursive and non-recursive elements using gradient-based optimization with perceptually or physically motivated loss functions, such as energy decay or spectrogram differences. These representations are highly sensitive to model mismatches, which can lead to spurious loss minima. In particular, discrepancies in background noise can result in inaccurate attenuation estimates. This paper addresses the problem of tuning recursive attenuation filters of a feedback delay network when targets are noisy. We analyze the loss profile associated with different optimization objectives and propose a method that explicitly models noise, improving the accuracy of the estimated attenuation filters under low signal-to-noise conditions. We demonstrate the effectiveness of the proposed approach through statistical analysis on both synthetic and real target data. Furthermore, we identify the sensitivity of attenuation filter parameters tuning to perturbations in frequency-independent parameters. These findings provide practical guidelines for more robust and reproducible gradient-based optimization of feedback delay networks.
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All authors Gloria Dal Santo, Karolina Prawda, Sebastian J. Schlecht, Vesa Välimäki
Signal Processing
CPMamba: Selective State Space Models for MIMO Channel Prediction in High-Mobility Environments
Sheng Luo, Jiashu Xie, Yueling Che et al. (+4 more)
Channel prediction is a key technology for improving the performance of various functions such as precoding, adaptive modulation, and resource allocation in
MIMO-OFDM systems. Especially in high-mobility scenarios with fast time-varying channels, it is crucial for resisting channel aging and ensuring communication quality. However, existing methods suffer from high complexity and the inability to accurately model the temporal variations of channels. To address this issue, this paper proposes CPMamba – an efficient channel prediction framework based on the selective state space model. The proposed CPMamba architecture extracts features from historical channel state information (CSI) using a specifically designed feature extraction and
embedding network and employs stacked residual Mamba modules for temporal modeling. By leveraging an input-dependent selective mechanism to dynamically adjust state transitions, it can effectively capture the long-range dependencies between the CSIs while maintaining a linear computational complexity. Simulation results under the 3GPP standard channel model demonstrate that CPMamba achieves state-of-the-art prediction accuracy across all scenarios, along with superior generalization and robustness. Compared to existing baseline models, CPMamba reduces the number of parameters by approximately 50 percent while achieving comparable or better performance, thereby significantly lowering the barrier for practical deployment.
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All authors Sheng Luo, Jiashu Xie, Yueling Che, Junmei Yao, Jian Tian, Daquan Feng, Kaishun Wu
Signal Processing
Fast Collaborative Inference via Distributed Speculative Decoding
Ce Zheng, Ke Zhang, Chen Sun et al. (+3 more)
Speculative decoding accelerates large
language model (LLM) inference by allowing a small draft model to predict multiple future
tokens for verification by a larger target model. In AI-native
radio access networks (AI-RAN), this enables device-edge collaborative inference but introduces significant uplink overhead, as existing distributed speculative decoding schemes transmit full vocabulary logits at every step. We propose a sparsify-then-sample strategy, Truncated Sparse Logits Transmission (TSLT), which transmits only the logits and indices of a truncated candidate set. We provide theoretical guarantees showing that the acceptance rate is preserved under TSLT. TSLT is further extended to multi-candidate case, where multiple draft candidates per step increase acceptance probability. Experiments show that TSLT significantly reduces uplink communication while maintaining end-to-end inference latency and model quality, demonstrating its effectiveness for scalable, communication-efficient distributed LLM inference in future AI-RAN systems.
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All authors Ce Zheng, Ke Zhang, Chen Sun, Wenqi Zhang, Qiong Liu, Angesom Ataklity Tesfay
Systems and Control
Black-Start Power Capacity Sizing and Control Strategy for an Islanded DFIG Wind-to-Hydrogen System
Bosen Yang, Kang Ma, Jin Lin et al. (+1 more)
This paper proposes a black-start method for an off-grid wind-to-hydrogen (W2H) system comprising a wind farm based on Doubly-Fed Induction Generators (DFIGs),
proton exchange membrane fuel
cells (PEMFCs) serving as the black-start power source, and a hydrogen production industry. The PEMFC is installed within the hydrogen industry to facilitate direct access to hydrogen fuel. Based on the microgrid
topology and black-start scheme, this study innovatively sizes the rated capacity of the PEMFC through power flow analysis. The capacity must be sufficient to charge passive components such as transmission lines and
transformers, provide rotor excitation, and supply wind turbine (WT) and electrolyzer (ELZ) auxiliaries during startup. The proposed system integrates wind-hydrogen coordinated control (WHCC) and hydrogen-storage coordinated control (HSCC). Under maximum power point tracking (MPPT) of the WTs, the ELZ follows power fluctuations to absorb wind output, ensuring stable voltage and frequency. Fixed-frequency control applied to either the DFIG or PEMFC converters enables DFIGs to retain conventional grid-following (GFL) operation, reducing converter development costs. For both control modes, this paper establishes the black-start sequence and formulates a comprehensive coordinated control strategy for the entire system. The entire control system is validated through simulations in MATLAB/Simulink. Results confirm that the calculated PEMFC capacity supports reliable black-start, while the black-start control strategy ensures smooth system self-startup. Furthermore, the coordinated control strategy maintains stable frequency and voltage under fluctuating wind power, demonstrating the practicality and robustness of the proposed approach.
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All authors Bosen Yang, Kang Ma, Jin Lin, Yonghua Song
Signal Processing
Simultaneous Secrecy and Covert Communications (SSACC) in Mobility-Aware RIS-Aided Networks
Yanyu Cheng, Yujian Hu, Haoran Liu et al. (+3 more)
In this paper, we propose a simultaneous secrecy and covert communications (SSACC) scheme in a reconfigurable intelligent surface (RIS)-aided network with a cooperative jammer. The scheme enhances communication security by maximizing the secrecy capacity and the detection error probability (DEP). Under a worst-case scenario for covert communications, we consider that the eavesdropper can optimally adjust the detection threshold to minimize the DEP. Accordingly, we derive closedform expressions for both average minimum DEP (AMDEP) and average secrecy capacity (ASC). To balance AMDEP and ASC, we propose a new performance metric and design an
algorithm based on generative
diffusion models (GDM) and deep reinforcement learning (DRL). The
algorithm maximizes data rates under user mobility while ensuring high AMDEP and ASC by optimizing power allocation. Simulation results demonstrate that the proposed
algorithm achieves faster convergence and superior performance compared to conventional deep deterministic policy gradient (DDPG) methods, thereby validating its effectiveness in balancing security and capacity performance.
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All authors Yanyu Cheng, Yujian Hu, Haoran Liu, Hua Zhong, Wei Wang, Pan Li
Image and Video Processing · Cross-listed from cs.CV
Learning High-Quality Initial Noise for Single-View Synthesis with Diffusion Models
Zhihao Zhang, Xuejun Yang, Weihua Liu et al. (+1 more)
Single-view novel view synthesis (NVS) models based on
diffusion models have recently attracted increasing
attention, as they can generate a series of novel view images from a single image
prompt and camera pose information as conditions. It has been observed that in
diffusion models, certain high-quality initial noise patterns lead to better generation results than others. However, there remains a lack of dedicated learning frameworks that enable NVS models to learn such high-quality noise. To obtain high-quality initial noise from random Gaussian noise, we make the following contributions. First, we design a discretized Euler inversion method to inject image semantic information into random noise, thereby constructing paired
datasets of random and high-quality noise. Second, we propose a learning framework based on an encoder-decoder network (EDN) that directly transforms random noise into high-quality noise. Experiments demonstrate that the proposed EDN can be seamlessly plugged into various NVS models, such as SV3D and MV-Adapter, achieving significant performance improvements across multiple
datasets. Code is available at: https://github.com/zhihao0512/EDN.
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All authors Zhihao Zhang, Xuejun Yang, Weihua Liu, Mouquan Shen
Systems and Control
Consensus Tracking of Perturbed Open Multi-Agent Systems with Repelling Antagonistic Interactions
Mengqi Xue, Yuchao Xiong and Yue Song
An open multi-
agent system (OMAS) features migrating
agents which produce a flexible network that is naturally switching and size-varying. Meanwhile,
agent migrations also make an OMAS prone to environmental adversities. In this work, we investigate the consensus tracking problem of OMASs suffering migration-induced adversities, including non-vanishing
agent dynamics/state perturbations and repelling antagonistic interactions among
agents, over an intermittently disconnected signed digraph. The OMAS is interpreted into a perturbed multi-mode multi-dimensional ($M^3D$) system in which unstable subsystems are created when repelling interactions dominate the cooperative ones in the network regardless of its connectivity. To handle the destabilizing effect brought by repelling interactions and non-vanishing perturbations, we extend the stability theory for $M^3D$ systems and apply it to the OMAS to show that ultimately bounded consensus tracking can be achieved if the network switching satisfies the piecewise average dwell time and activation time ratio conditions. Particularly, for vanishing perturbations, asymptotic tracking can be ensured under weaker switching conditions.
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Signal Processing · Cross-listed from cs.LG
BUILD with Precision: Bottom-Up Inference of Linear DAGs
Hamed Ajorlou, Samuel Rey, Gonzalo Mateos et al. (+2 more)
Learning the structure of directed acyclic graphs (DAGs) from observational data is a central problem in causal discovery, statistical signal processing, and machine learning. Under a linear Gaussian structural equation model (SEM) with equal noise variances, the problem is identifiable and we show that the ensemble precision
matrix of the observations exhibits a distinctive structure that facilitates DAG recovery. Exploiting this property, we propose BUILD (Bottom-Up Inference of Linear DAGs), a deterministic stepwise
algorithm that identifies leaf nodes and their parents, then prunes the leaves by removing incident edges to proceed to the next step, exactly reconstructing the DAG from the true precision
matrix. In practice, precision
matrices must be estimated from finite data, and ill-conditioning may lead to error accumulation across BUILD steps. As a mitigation strategy, we periodically re-estimate the precision
matrix (with less variables as leaves are pruned), trading off runtime for enhanced robustness. Reproducible results on challenging synthetic
benchmarks demonstrate that BUILD compares favorably to state-of-the-art DAG learning
algorithms, while offering an explicit handle on complexity.
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All authors Hamed Ajorlou, Samuel Rey, Gonzalo Mateos, Geert Leus, Antonio G. Marques
Systems and Control
Synchronization, Identification, and Signal Detection for Underwater Photon-Counting Communications With Input-Dependent Shot Noise
Fanghua Li, Xiaolin Zhou, Yongkang Chen et al. (+4 more)
Photon counting (PhC) is an effective detection technology for
underwater optical wireless communication (OWC) systems. The presence of signal-dependent Poisson shot noise and asynchronous multi-user interference (MUI) complicates the processing of received data signals, hindering the effective signal detection of PhC OWC systems. This paper proposes a novel iterative signal detection method in grant-free, multi-user,
underwater PhC OWC systems with signal-dependent Poisson shot noise. We first introduce a new synchronization
algorithm with a unique frame structure design.The
algorithm performs active user identification and transmission delay estimation. Specifically, the estimation is performed first on a user group basis and then at the individual user level with reduced complexity and latency.We also develop a nonlinear iterative multi-user detection (MUD)
algorithm that utilizes a detection window for each user to identify interfering symbols and estimate MUI on a slot-by-slot basis, followed by maximum a-posteriori probability detection of user signals.Simulations demonstrate that our scheme achieves bit error rates comparable to scenarios with transmission delays known and signal detection perfectly synchronized.
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All authors Fanghua Li, Xiaolin Zhou, Yongkang Chen, Wei Ni, Xin Wang, Dusit Niyato, Ekram Hossain
Signal Processing · Cross-listed from cs.ET
Feasibility of Radio Frequency Based Wireless Sensing of Lead Contamination in Soil
Yixuan Gao, Tanvir Ahmed, Mikhail Mohammed et al. (+2 more)
Widespread Pb (lead) contamination of urban soil significantly impacts food safety and public health and hinders city greening efforts. However, most existing technologies for measuring Pb are labor-intensive and costly. In this study, we propose SoilScanner, a
radio frequency-based wireless system that can detect Pb in soils. This is based on our discovery that the propagation of different frequency band
radio signals is affected differently by different salts such as NaCl and Pb(NO3)2 in the soil. In a controlled experiment, manually adding NaCl and Pb(NO3)2 in clean soil, we demonstrated that different salts reflected signals at different frequencies in distinct patterns. In addition, we confirmed the finding using uncontrolled field samples with a machine learning model. Our experiment results show that SoilScanner can classify soil samples into low-Pb and high-Pb categories (threshold at 200 ppm) with an accuracy of 72%, with no sample with > 500 ppm of Pb being misclassified. The results of this study show that it is feasible to build portable and affordable Pb detection and screening devices based on wireless technology.
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All authors Yixuan Gao, Tanvir Ahmed, Mikhail Mohammed, Zhongqi Cheng, Rajalakshmi Nandakumar
Image and Video Processing
Single-View Tomographic Reconstruction Using Learned Primal Dual
Sean Breckling, Matthew Swan, Keith D. Tan et al. (+6 more)
The Learned Primal Dual (LPD) method has shown promising results in various tomographic reconstruction modalities, particularly under challenging acquisition restrictions such as limited viewing angles or a limited number of views. We investigate the performance of LPD in a more extreme case: single-view tomographic reconstructions of axially-symmetric targets. This study considers two modalities: the first assumes low-divergence or parallel X-rays. The second models a cone-beam X-ray imaging testbed. For both modalities, training data is generated using closed-form integral transforms, or physics-based ray-tracing software, then corrupted with blur and noise. Our results are then compared against common numerical inversion methodologies.
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All authors Sean Breckling, Matthew Swan, Keith D. Tan, Derek Wingard, Brandon Baldonado, Yoohwan Kim, Ju-Yeon Jo, Evan Scott, Jordan Pillow