Signal Processing
Reconstructing Patched or Partial Holograms to allow for Whole Slide Imaging with a Self-Referencing Holographic Microscope
Philip Groult, Julia D. Sistermanns, Ellen Emken et al. (+2 more)
The last decade has seen significant advances in computer-aided diagnostics for cytological screening, mainly through the improvement and integration of scanning techniques such as whole slide imaging (WSI) and the combination with deep learning. Simultaneously, new imaging techniques such as quantitative phase imaging (QPI) are being developed to capture richer
cell information with less sample preparation. So far, the two worlds of WSI and QPI have not been combined. In this work, we present a reconstruction
algorithm which makes whole slide imaging of cervical smears possible by using a self-referencing three-wave digital
holographic microscope. Since a WSI is constructed by combining multiple patches, the
algorithm is adaptive and can be used on partial holograms and patched holograms. We present the
algorithm for a single shot hologram, the adaptations to make it flexible to various inputs and show that the
algorithm performs well for the tested epithelial
cells. This is a preprint of our paper, which has been accepted for publication in 2026 IEEE International Symposium on Biomedical Imaging (ISBI).
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All authors Philip Groult, Julia D. Sistermanns, Ellen Emken, Oliver Hayden, Wolfgang Utschick
Audio and Speech Processing
A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering
Zhengding Luo, Haozhe Ma, Boxiang Wang et al. (+3 more)
The Filtered-x Normalized Least Mean Square (FxNLMS)
algorithm suffers from slow convergence and a risk of divergence, although it can achieve low steady-state errors after sufficient adaptation. In contrast, the Generative Fixed-Filter Active Noise Control (GFANC) method offers fast response speed, but its lack of adaptability may lead to large steady-state errors. This paper proposes a hybrid GFANC-FxNLMS
algorithm to leverage the complementary advantages of both approaches. In the hybrid GFANC-FxNLMS
algorithm, GFANC provides a frame-level control filter as an initialization for FxNLMS, while FxNLMS performs continuous adaptation at the sampling rate. Small variations in the GFANC-generated filter may repeatedly reinitialize FxNLMS, interrupting its adaptation process and destabilizing the system. An online clustering module is introduced to avoid unnecessary re-initializations and improve system stability. Simulation results show that the proposed
algorithm achieves fast response, very low steady-state error, and high stability, requiring only one pre-trained broadband filter.
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All authors Zhengding Luo, Haozhe Ma, Boxiang Wang, Ziyi Yang, Dongyuan Shi, Woon-Seng Gan
Audio and Speech Processing · Cross-listed from cs.SD
PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation
Jaekwon Im, Natalia Polouliakh and Taketo Akama
Dance-to-music generation aims to generate music that is aligned with dance movements. Existing approaches typically rely on body motion features extracted from a single human dancer and limited dance-to-music
datasets, which restrict their performance and applicability to real-world scenarios involving multiple dancers and non-human dancers. In this paper, we propose PF-D2M, a universal
diffusion-based dance-to-music generation model that incorporates visual features extracted from dance
videos. PF-D2M is trained with a progressive training strategy that effectively addresses data scarcity and generalization challenges. Both objective and subjective evaluations show that PF-D2M achieves state-of-the-art performance in dance-music
alignment and music quality.
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Signal Processing
Time-Varying Rician K-factor in Measured Vehicular Channels at cmWave and mmWave Bands
Faruk Pasic, Markus Hofer, Thomas Zemen et al. (+2 more)
Future vehicular communication systems will integrate millimeter wave (mmWave) technology to enhance data transmission rates. To investigate the propagation effects and small-scale fading differences between mmWave and conventional centimeter wave (cmWave) bands, multi-band channel measurements have to be conducted. One key parameter to characterize small-scale fading is the Rician K-factor. In this paper, we analyze the time-varying K-factor of vehicle-to-infrastructure (V2I) channels across multiple frequency bands, measured in an urban street environment. Specifically, we investigate three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of 31.25 {\mu}s. Furthermore, we analyze the relationship between K-factor and root-mean-square (RMS) delay spread. We show that the Ricean K-factor is similar at different frequency bands and that is correlated with the RMS delay spread.
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All authors Faruk Pasic, Markus Hofer, Thomas Zemen, Andreas F. Molisch, Christoph F. Mecklenbräuker
Signal Processing
Performance Analysis of Digital Beamforming mmWave MIMO with Low-Resolution DACs/ADCs
Faruk Pasic, Mariam Mussbah, Stefan Schwarz et al. (+3 more)
Future wireless communications will rely on multiple-input multiple-output (
MIMO) beamforming operating at millimeter wave (mmWave) frequency bands to deliver high data rates. To support flexible spatial processing and meet the demands of latency critical applications, it is essential to use fully digital mmWave
MIMO beamforming, which relies on accurate channel estimation. However, ensuring power efficiency in fully digital mmWave
MIMO systems requires the use of low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). The reduced resolution of these quantizers introduces distortion in both transmitted and received signals, ultimately degrading system performance. In this paper, we investigate the channel estimation performance of mmWave
MIMO systems employing fully digital beamforming with low-resolution quantization, under practical system constraints. We evaluate the system performance in terms of spectral efficiency (SE) and energy efficiency (EE). Simulation results demonstrate that a moderate quantization resolutions of 4-bit per DAC/ADC offers a favorable trade-off between energy consumption and achievable data rate.
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All authors Faruk Pasic, Mariam Mussbah, Stefan Schwarz, Markus Rupp, Fredrik Tufvesson, Christoph F. Mecklenbräuker
Signal Processing
Separable Delay And Doppler Estimation In Passive Radar
Mats Viberg, Daniele Gerosa, Tomas McKelvey et al. (+2 more)
In passive radar, a network of distributed sensors exploit signals from so-called Illuminators-of-Opportunity to detect and localize targets. We consider the case where the IO signal is available at each receiver node through a reference channel, whereas target returns corrupted by interference are collected in a separate surveillance channel. The problem formulation is similar to an active radar that uses a noise-like waveform, or an integrated sensing and communication application. The available data is first split into batches of manageable size. In the direct approach, the target's time-delay and Doppler parameters are estimated jointly by incoherently combining the batch-wise data. We propose a new method to estimate the time-delay separately, thus avoiding a costly 2-D search. Our approach is designed for slowly moving targets, and the accuracy of the time-delay estimate is similar to that of the full batch-wise 2-D method. Given the time-delay, the coherency between batches can be restored when estimating the Doppler parameter. Thereby, the separable approach is found to yield superior Doppler estimates over a wide parameter range. In addition to reducing computational complexity, the proposed separable estimation technique also significantly reduces the communication overhead in a distributed radar setting.
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All authors Mats Viberg, Daniele Gerosa, Tomas McKelvey, Patrik Dammert, Thomas Eriksson
Signal Processing
Dual-Mapping Sparse Vector Transmission for Short Packet URLLC
Yanfeng Zhang, Xu Zhu, Jinkai Zheng et al. (+5 more)
Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding
algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.
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All authors Yanfeng Zhang, Xu Zhu, Jinkai Zheng, Weiwei Yang, Xianhua Yu, Haiyong Zeng, Yujie Liu, Yong Liang Guan
Systems and Control
Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidents
Shiqi Wei, Qiqing Wang and Kaidi Yang
Adaptive traffic signal control (TSC) has demonstrated strong effectiveness in managing dynamic traffic flows. However, conventional methods often struggle when unforeseen traffic incidents occur (e.g., accidents and road maintenance), which typically require labor-intensive and inefficient manual interventions by traffic police officers. Large
Language Models (LLMs) appear to be a promising solution thanks to their remarkable
reasoning and generalization capabilities. Nevertheless, existing works often propose to replace existing TSC systems with LLM-based systems, which can be (i) unreliable due to the inherent
hallucinations of LLMs and (ii) costly due to the need for system replacement. To address the issues of existing works, we propose a hierarchical framework that augments existing TSC systems with LLMs, whereby a virtual traffic police
agent at the upper level dynamically fine-tunes selected parameters of signal controllers at the lower level in response to real-time traffic incidents. To enhance domain-specific reliability in response to unforeseen traffic incidents, we devise a self-refined traffic
language retrieval system (TLRS), whereby
retrieval-augmented generation is employed to draw knowledge from a tailored traffic
language database that encompasses traffic conditions and controller operation principles. Moreover, we devise an LLM-based verifier to update the TLRS continuously over the
reasoning process. Our results show that LLMs can serve as trustworthy virtual traffic police officers that can adapt conventional TSC methods to unforeseen traffic incidents with significantly improved operational efficiency and reliability.
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Signal Processing
Adaptive Non-Uniform Sampling of Bandlimited Signals via Algorithm-Encoder Co-Design
Kaluguri Yashaswini, Anshu Arora and Satish Mulleti
We propose an adaptive non-uniform sampling framework for bandlimited signals based on an
algorithm-encoder co-design perspective. By revisiting the convergence analysis of iterative reconstruction
algorithms for non-uniform measurements, we derive a local, energy-based sufficient condition that governs reconstruction behavior as a function of the signal and derivative energies within each sampling interval. Unlike classical approaches that impose a global Nyquist-type bound on the inter-sample spacing, the proposed condition permits large gaps in slowly varying regions while enforcing denser sampling only where the signal exhibits rapid temporal variation. Building on this theoretical insight, we design a variable-bias, variable-threshold integrate-and-fire time encoding machine (VBT-IF-TEM) whose firing mechanism is explicitly shaped to enforce the derived local convergence condition. To ensure robustness, a shifted-signal formulation is introduced to suppress excessive firing in regions where the magnitude of the signal amplitude is close to zero or the local signal energy approaches zero. Using the proposed encoder, an analog signal is discretely represented by time encodings and signal averages, enabling perfect reconstruction via a standard iterative
algorithm even when the local sampling rate falls below the Nyquist rate. Simulation results on synthetic signals and experiments on ultrasonic guided-wave and ECG signals demonstrate that the proposed framework achieves substantial reductions in sampling density compared to uniform sampling and conventional IF-TEMs, while maintaining accurate reconstruction. The results further highlight a controllable tradeoff between sampling density, reconstruction accuracy, and convergence behavior, which can be navigated through adaptive parameter selection.
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Signal Processing
Joint Pilot and Unknown Data-based Localization for OFDM Opportunistic Radar Systems
Mathieu Reniers, Martin Willame, Jérôme Louveaux et al. (+1 more)
Integrating Sensing and Communications (
ISAC) has emerged as a promising paradigm for Sixth Generation (6G) and Wi-Fi 7 networks, with the communication-centric approach being particularly attractive due to its compatibility with current standards. Typical communication signals comprise both deterministic known pilot signals and random unknown data payloads. Most existing approaches either rely solely on pilots for positioning, thereby ignoring the radar information present in the received data symbols that constitute the majority of each frame, or rely on data decisions, which bounds positioning performance to that of the communication system. To overcome these limitations, we propose a novel method that extracts positioning information from data payloads without decoding them. We consider an opportunistic scenario in which communication signals from a user are captured by a passive radar equipped with a uniform linear array of
antennas. We show that, in this setting, the estimation can be efficiently implemented using Fast
Fourier Transforms. Finally, we demonstrate superior localization performance compared to existing methods in the literature through numerical simulations.
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All authors Mathieu Reniers, Martin Willame, Jérôme Louveaux, Luc Vandendorpe
Signal Processing · Cross-listed from cs.IT
Recursive Flow: A Generative Framework for MIMO Channel Estimation
Zehua Jiang, Fenghao Zhu, Chongwen Huang et al. (+5 more)
Channel estimation is a fundamental challenge in massive multiple-input multiple-output systems, where estimation accuracy governs the spectral efficiency and link reliability. In this work, we introduce Recursive Flow (RC-Flow), a novel solver that leverages pre-trained flow matching priors to robustly recover channel state information from noisy, under-determined measurements. Different from conventional open-loop generative models, our approach establishes a closed-loop refinement framework via a serial restart mechanism and anchored trajectory rectification. By synergizing flow-consistent prior directions with data-fidelity proximal projections, the proposed RC-Flow achieves robust channel reconstruction and delivers state-of-the-art performance across diverse noise levels, particularly in noise-dominated scenarios. The framework is further augmented by an adaptive dual-scheduling strategy, offering flexible management of the trade-off between convergence speed and reconstruction accuracy. Theoretically, we analyze the Jacobian spectral radius of the recursive operator to prove its global asymptotic stability. Numerical results demonstrate that RC-Flow reduces inference latency by two orders of magnitude while achieving a 2.7 dB performance gain in low signal-to-noise ratio regimes compared to the score-based baseline.
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All authors Zehua Jiang, Fenghao Zhu, Chongwen Huang, Richeng Jin, Zhaohui Yang, Xiaoming Chen, Zhaoyang Zhang, Mérouane Debbah
Signal Processing
Bistatic ISAC: Practical Challenges and Solutions
Lucas Giroto, Marcus Henninger, Alexander Felix et al. (+7 more)
This article presents and discusses challenges and solutions for practical issues in bistatic integrated sensing and communication (
ISAC) in 6G networks. Considering orthogonal frequency-division multiplexing as the adopted waveform, a discussion on system design aiming to achieve both a desired sensing key performance indicators and limit the impact of hardware impairments is presented. In addition, signal processing techniques to enable over-the-air synchronization and generation of periodograms with range, Doppler shift, and angular information are discussed. Simulation results are then presented for a cellular-based
ISAC scenario considering system parameterization compliant to current 5G and, finally, a discussion on open challenges for future deployments is presented.
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All authors Lucas Giroto, Marcus Henninger, Alexander Felix, Maximilian Bauhofer, Taewon Jeong, Umut Utku Erdem, Stephan ten Brink, Thomas Zwick, Benjamin Nuss, Silvio Mandelli
Systems and Control · Cross-listed from cs.RO
DualShield: Safe Model Predictive Diffusion via Reachability Analysis for Interactive Autonomous Driving
Rui Yang, Lei Zheng, Ruoyu Yao et al. (+1 more)
Diffusion models have emerged as a powerful approach for
multimodal motion planning in autonomous driving. However, their practical deployment is typically hindered by the inherent difficulty in enforcing vehicle dynamics and a critical reliance on accurate predictions of other
agents, making them prone to safety issues under uncertain interactions. To address these limitations, we introduce DualShield, a planning and control framework that leverages Hamilton-Jacobi (HJ) reachability value functions in a dual capacity. First, the value functions act as proactive guidance, steering the
diffusion denoising process towards safe and dynamically feasible regions. Second, they form a reactive safety shield using control barrier-value functions (CBVFs) to modify the executed actions and ensure safety. This dual mechanism preserves the rich exploration capabilities of
diffusion models while providing principled safety assurance under uncertain and even
adversarial interactions. Simulations in challenging unprotected U-turn scenarios demonstrate that DualShield significantly improves both safety and task efficiency compared to leading methods from different planning paradigms under uncertainty.
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All authors Rui Yang, Lei Zheng, Ruoyu Yao, Jun Ma
Audio and Speech Processing · Cross-listed from cs.SD
Bridging the Perception Gap: A Lightweight Coarse-to-Fine Architecture for Edge Audio Systems
Hengfan Zhang, Yueqian Lin, Hai Helen Li et al. (+1 more)
Deploying Audio-
Language Models (Audio-LLMs) on edge infrastructure exposes a persistent tension between perception depth and computational efficiency. Lightweight local models tend to produce passive perception - generic summaries that miss the subtle evidence required for multi-step audio
reasoning - while indiscriminate cloud offloading incurs unacceptable latency, bandwidth cost, and
privacy risk. We propose CoFi-
Agent (Tool-Augmented Coarse-to-Fine
Agent), a hybrid architecture targeting edge servers and gateways. It performs fast local perception and triggers conditional forensic refinement only when uncertainty is detected. CoFi-
Agent runs an initial single-pass on a local 7B Audio-LLM, then a cloud controller gates difficult cases and issues lightweight plans for on-device tools such as temporal re-listening and local ASR. On the MMAR
benchmark, CoFi-
Agent improves accuracy from 27.20% to 53.60%, while achieving a better accuracy-efficiency trade-off than an always-on investigation pipeline. Overall, CoFi-
Agent bridges the perception gap via tool-enabled, conditional edge-cloud collaboration under practical system constraints.
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All authors Hengfan Zhang, Yueqian Lin, Hai Helen Li, Yiran Chen
Audio and Speech Processing
Distributed Multichannel Active Noise Control with Asynchronous Communication
Junwei Ji, Dongyuan Shi, Boxiang Wang et al. (+3 more)
Distributed multichannel active noise control (DMCANC) offers effective noise reduction across large spatial areas by distributing the computational load of centralized control to multiple low-cost nodes. Conventional DMCANC methods, however, typically assume synchronous communication and require frequent data exchange, resulting in high communication overhead. To enhance efficiency and adaptability, this work proposes an asynchronous communication strategy where each node executes a weight-constrained filtered-x LMS (WCFxLMS)
algorithm and independently requests communication only when its local noise reduction performance degrades. Upon request, other nodes transmit the weight difference between their local control filter and the center point in WCFxLMS, which are then integrated to update both the control filter and the center point. This design enables nodes to operate asynchronously while preserving cooperative behavior. Simulation results demonstrate that the proposed asynchronous communication DMCANC (ACDMCANC) system maintains effective noise reduction with significantly reduced communication load, offering improved scalability for heterogeneous networks.
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All authors Junwei Ji, Dongyuan Shi, Boxiang Wang, Ziyi Yang, Haowen Li, Woon-Seng Gan
Systems and Control
Bridging Qualitative Rubrics and AI: A Binary Question Framework for Criterion-Referenced Grading in Engineering
Lili Chen, Winn Wing-Yiu Chow, Stella Peng et al. (+2 more)
PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.
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All authors Lili Chen, Winn Wing-Yiu Chow, Stella Peng, Bencheng Fan, Sachitha Bandara
Systems and Control
Design, Modelling, and Control of Magnetic Ball Suspension System
Sampson E. Nwachukwu
This paper presents the modeling, control design, and performance analysis of a
Magnetic Ball Suspension System (MBSS), a nonlinear and inherently unstable electromechanical system used in various precision applications. The system's primary objective is to levitate a steel ball using electromagnetic force without physical contact, thereby eliminating frictional losses. A comprehensive state-space model was developed, capturing both the mechanical and electrical dynamics. The
equilibrium points of the system were determined through feedback linearization using the Jacobian
matrix. To ensure system stability, controllability and observability analyses were conducted, confirming that state feedback and observer-based control strategies could be effectively implemented. Three distinct control methods were explored: pole placement-based state feedback control, full-order observer design, and optimal state feedback control using the Linear Quadratic Regulator (LQR). Each control strategy was validated through Simulink simulations for both linearized and nonlinear models. Simulation results demonstrated that the linearized system consistently achieved desired performance with minimal oscillations, whereas the nonlinear system exhibited significant transient oscillations before stabilization. The full-order observer enhanced estimation accuracy, enabling effective control where direct state measurement was impractical. The LQR-based control offered improved robustness and minimized control effort, though its performance was comparable to standard state feedback in some cases.
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Audio and Speech Processing · Cross-listed from cs.SD
Qwen3-TTS Technical Report
Hangrui Hu, Xinfa Zhu, Ting He et al. (+13 more)
In this report, we present the Qwen3-TTS series, a family of advanced
multilingual, controllable, robust, and streaming text-to-
speech models. Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control, allowing both the creation of entirely novel voices and fine-grained manipulation over the output
speech. Trained on over 5 million hours of
speech data spanning 10
languages, Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis, coupled with two
speech tokenizers: 1) Qwen-TTS-Tokenizer-25Hz is a single-codebook codec emphasizing semantic content, which offers seamlessly integration with Qwen-Audio and enables streaming waveform reconstruction via a block-wise DiT. 2) Qwen-TTS-Tokenizer-12Hz achieves extreme bitrate reduction and ultra-low-latency streaming, enabling immediate first-packet emission ($97\,\mathrm{ms}$) through its 12.5 Hz, 16-layer multi-codebook design and a lightweight causal ConvNet. Extensive experiments indicate state-of-the-art performance across diverse objective and subjective
benchmark (e.g., TTS
multilingual test set, InstructTTSEval, and our long
speech test set). To facilitate community research and development, we release both tokenizers and models under the Apache 2.0 license.
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All authors Hangrui Hu, Xinfa Zhu, Ting He, Dake Guo, Bin Zhang, Xiong Wang, Zhifang Guo, Ziyue Jiang, Hongkun Hao, Zishan Guo, Xinyu Zhang, Pei Zhang, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin
Signal Processing
Does 6G Need a New Waveform: Comparing Zak-OTFS with CP-OFDM
Imran Ali Khan, Saif Khan Mohammed, Ronny Hadani et al. (+6 more)
Across the world, there is growing interest in new waveforms, Zak-OTFS in particular, and over-the-air implementations are starting to appear. The choice between OFDM and Zak-OTFS is not so much a choice between waveforms as it is an architectural choice between preventing inter-carrier interference (ICI) and embracing ICI. In OFDM, once the Input-Output (I/O) relation is known, equalization is relatively simple, at least when there is no ICI. However, in the presence of ICI the I/O relation is non-predictable and its acquisition is non-trivial. In contrast, equalization is more involved in Zak-OTFS due to inter-symbol-interference (ISI), however the I/O relation is predictable and its acquisition is simple. {Zak-OTFS exhibits superior performance in doubly-spread 6G use cases with high delay/Doppler channel spreads (i.e., high mobility and/or large
cells), but architectural choice is governed by the typical use case, today and in the future. What is typical depends to some degree on geography, since large delay spread is a characteristic of large
cells which are the rule rather than the exception in many important wireless
markets.} This paper provides a comprehensive performance comparison of cyclic prefix OFDM (CP-OFDM) and Zak-OTFS across the full range of 6G propagation environments. The performance results provide insights into the fundamental architectural choice.
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All authors Imran Ali Khan, Saif Khan Mohammed, Ronny Hadani, Ananthanarayanan Chockalingam, Robert Calderbank, Anton Monk, Shachar Kons, Shlomo Rakib, Yoav Hebron
Signal Processing · Cross-listed from cs.LG
Neural Nonlinear Shrinkage of Covariance Matrices for Minimum Variance Portfolio Optimization
Liusha Yang, Siqi Zhao and Shuqi Chai
This paper introduces a neural network-based nonlinear shrinkage
estimator of covariance
matrices for the purpose of minimum variance portfolio optimization. It is a hybrid approach that integrates statistical estimation with machine learning. Starting from the Ledoit-Wolf (LW) shrinkage
estimator, we decompose the LW covariance
matrix into its
eigenvalues and eigenvectors, and apply a lightweight
transformer-based neural network to learn a nonlinear
eigenvalue shrinkage function. Trained with portfolio risk as the loss function, the resulting precision
matrix (the inverse covariance
matrix)
estimator directly targets portfolio risk minimization. By conditioning on the sample-to-dimension ratio, the approach remains scalable across different sample sizes and asset universes. Empirical results on stock daily returns from Standard & Poor's 500 Index (S&P500) demonstrate that the proposed method consistently achieves lower out-of-sample realized risk than
benchmark approaches. This highlights the promise of integrating structural statistical models with data-driven learning.
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Audio and Speech Processing · Cross-listed from cs.SD
DeepASMR: LLM-Based Zero-Shot ASMR Speech Generation for Anyone of Any Voice
Leying Zhang, Tingxiao Zhou, Haiyang Sun et al. (+2 more)
While modern Text-to-
Speech (TTS) systems achieve high fidelity for read-style
speech, they struggle to generate Autonomous Sensory Meridian Response (ASMR), a specialized, low-intensity
speech style essential for relaxation. The inherent challenges include ASMR's subtle, often unvoiced characteristics and the demand for zero-shot speaker adaptation. In this paper, we introduce DeepASMR, the first framework designed for zero-shot ASMR generation. We demonstrate that a single short snippet of a speaker's ordinary, read-style
speech is sufficient to synthesize high-fidelity ASMR in their voice, eliminating the need for whispered training data from the target speaker. Methodologically, we first identify that discrete
speech tokens provide a soft factorization of ASMR style from speaker timbre. Leveraging this insight, we propose a two-stage pipeline incorporating a Large
Language Model (LLM) for content-style encoding and a flow-matching acoustic decoder for timbre reconstruction. Furthermore, we contribute DeepASMR-DB, a comprehensive 670-hour English-Chinese multi-speaker ASMR
speech corpus, and introduce a novel evaluation protocol integrating objective metrics, human listening tests, LLM-based scoring and unvoiced
speech analysis. Extensive experiments confirm that DeepASMR achieves state-of-the-art naturalness and style fidelity in ASMR generation for anyone of any voice, while maintaining competitive performance on normal
speech synthesis.
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All authors Leying Zhang, Tingxiao Zhou, Haiyang Sun, Mengxiao Bi, Yanmin Qian
Signal Processing
Amalgamated CHIRP and OFDM for ISAC
Pankaj Kumar, Mohammed El-Hajjar, Ibrahim A. Hemadeh et al. (+4 more)
Integrated Sensing and Communication (
ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the chirp waveforms to improve both the communication and sensing performance within an
ISAC framework. Hence, a new architecture is proposed that utilizes the conventional communication framework while leveraging the parameters sensed at the receiver (Rx) for enhancing the communication performance. We demonstrate that the affine addition of OFDM and chirp signals results in a near constant-envelope OFDM waveform, which effectively reduces the peak-to-average power ratio (PAPR), a key limitation of traditional OFDM systems. Using the OFDM framework for sensing in the conventional fashion requires the allocation of some resources for sensing, which in turn reduces communication performance. As a remedy, the proposed affine amalgam facilitates sensing through the chirp waveform without consuming communication resources, thereby preserving communication efficiency. Furthermore, a novel technique of integrating the chirp signal into the OFDM framework at the slot-level is proposed to enhance the accuracy of range estimation. The results show that the OFDM signal incorporated with chirp has better autocorrelation properties, improved root mean square error (RMSE) of range and velocity, and lower PAPR. Finally, we characterize the trade-off between communications and sensing performance.
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All authors Pankaj Kumar, Mohammed El-Hajjar, Ibrahim A. Hemadeh, Yasser Mestrah, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Signal Processing
An Iterated Hybrid Fast Parallel FIR Filter
Keshab K. Parhi
This paper revisits the design and optimization of parallel fast finite impulse response (FIR) filters using polyphase decomposition and iterated fast FIR
algorithms (FFAs). Parallel FIR filtering enhances computational efficiency and throughput in digital signal processing (DSP) applications by enabling the simultaneous processing of multiple input samples. We revisit a prior approach to design of fast parallel filter architectures by using the iterated FFA approach where the same primitive filter, such as 2-parallel, is iterated to design the fast parallel filter. In this paper, we present yet another novel iterated fast parallel FIR filter, referred to as the fast hybrid filter. The hybrid filter iterates a transposed 2-parallel fast FIR filter in all the inner layers and a direct-form 2-parallel fast FIR filter in the outermost layer, resulting in reduced hardware complexity. Such an iterated hybrid approach has not been presented before. We show that the hybrid fast parallel filters require less number of additions compared to prior approaches.
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Image and Video Processing
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Jieyun Bai, Yitong Tang, Zihao Zhou et al. (+36 more)
Accurate
segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first
benchmark for semi-supervised learning in cervical
segmentation, hosted at ISBI 2025. FUGC provides a
dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized
benchmark for cervical
segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.
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All authors Jieyun Bai, Yitong Tang, Zihao Zhou, Mahdi Islam, Musarrat Tabassum, Enrique Almar-Munoz, Hongyu Liu, Hui Meng, Nianjiang Lv, Bo Deng, Yu Chen, Zilun Peng, Yusong Xiao, Li Xiao, Nam-Khanh Tran, Dac-Phu Phan-Le, Hai-Dang Nguyen, Xiao Liu, Jiale Hu, Mingxu Huang, Jitao Liang, Chaolu Feng, Xuezhi Zhang, Lyuyang Tong, Bo Du, Ha-Hieu Pham, Thanh-Huy Nguyen, Min Xu, Juntao Jiang, Jiangning Zhang, Yong Liu, Md. Kamrul Hasan, Jie Gan, Zhuonan Liang, Weidong Cai, Yuxin Huang, Gongning Luo, Mohammad Yaqub, Karim Lekadir
Signal Processing
Distributed Uplink Anti-Jamming in LEO Mega-Constellations via Game-Theoretic Beamforming
Shizhen Jia, Mingjun Ying, Marco Mezzavilla et al. (+2 more)
Low-Earth-Orbit (LEO) satellite constellations have become vital in emerging commercial and defense Non-Terrestrial Networks (NTNs). However, their predictable orbital dynamics and exposed geometries make them highly susceptible to ground-based jamming. Traditional single-satellite interference mitigation techniques struggle to spatially separate desired uplink signals from nearby jammers, even with large
antenna arrays. This paper explores a distributed multi-satellite anti-jamming strategy leveraging the dense connectivity and high-speed inter-satellite links of modern LEO mega-constellations. We model the uplink interference scenario as a convex-concave game between a desired terrestrial transmitter and a jammer, each optimizing their spatial covariance
matrices to maximize or minimize achievable rate. We propose an efficient min-max solver combining alternating best-response updates with projected gradient descent, achieving fast convergence of the beamforming strategy to the Nash
equilibrium. Using realistic Starlink orbital geometries and Sionna ray-tracing simulations, we demonstrate that while close-proximity jammers can cripple single-satellite links, distributed satellite cooperation significantly enhances resilience, shifting the capacity distribution upward under strong interference.
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All authors Shizhen Jia, Mingjun Ying, Marco Mezzavilla, Theodore S. Rappaport, Sundeep Rangan