Signal Processing
Computing on Dirty Paper: Interference-Free Integrated Communication and Computing
Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, David González G. et al. (+1 more)
Inspired by Costa's pioneering work on dirty paper coding (DPC), this paper proposes a novel scheme for integrated communication and computing (ICC), named Computing on Dirty Paper, whereby the transmission of discrete data symbols for communication, and over-the-air computation (AirComp) of nomographic functions can be achieved simultaneously over common multiple-access channels. In particular, the proposed scheme allows for the integration of communication and computation in a manner that is asymptotically interference-free, by precanceling the computing symbols at the transmitters (TXs) using DPC principles. A simulation-based assessment of the proposed ICC scheme under a single-input multiple-output (SIMO) setup is also offered, including the evaluation of performance for data detection, and of mean-squared-error (MSE) performance for function computation, over a block of symbols. The results validate the proposed method and demonstrate its ability to significantly outperform state-of-the-art (SotA) ICC schemes in terms of both bit error rate (BER) and MSE.
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All authors Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, David González G., Carlo Fischione
Systems and Control
Coordinated Car-following Using Distributed MPC
Di Shen, Qi Dai and Suzhou Huang
Within the modeling framework of Markov games, we propose a series of
algorithms for coordinated car-following using distributed model predictive control (DMPC). Instead of tracking prescribed feasible trajectories, driving policies are solved directly as outcomes of the DMPC optimization given the driver's perceivable states. The coordinated solutions are derived using the best response dynamics via iterated self-play, and are facilitated by direct negotiation using inter-
agent or
agent-infrastructure communication. These solutions closely approximate either Nash
equilibrium or centralized optimization. By re-parameterizing the action sequence in DMPC as a curve along the planning horizon, we are able to systematically reduce the original DMPC to very efficient grid searches such that the optimal solution to the original DMPC can be well executed in real-time. Within our modeling framework, it is natural to cast traffic control problems as mechanism design problems, in which all
agents are endogenized on an equal footing with full incentive compatibility. We show how traffic efficiency can be dramatically improved while keeping stop-and-go phantom waves tamed at high vehicle densities. Our approach can be viewed as an alternative way to formulate coordinated adaptive cruise control (CACC) without an explicit platooning (or with all vehicles in the traffic system treated as a single extended platoon). We also address the issue of linear stability of the associated discrete-time traffic dynamics and demonstrate why it does not always tell the full story about the traffic stability.
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Signal Processing
Wearable and Ultra-Low-Power Fusion of EMG and A-Mode US for Hand-Wrist Kinematic Tracking
Giusy Spacone, Sebastian Frey, Mattia Orlandi et al. (+5 more)
Hand gesture recognition based on biosignals has shown strong potential for developing intuitive human-machine interaction strategies that closely mimic natural human behavior. In particular, sensor fusion approaches have gained
attention for combining complementary information and overcoming the limitations of individual sensing modalities, thereby enabling more robust and reliable systems. Among them, the fusion of surface electromyography (EMG) and A-mode ultrasound (US) is very promising. However, prior solutions rely on power-hungry platforms unsuitable for multi-day use and are limited to discrete gesture classification. In this work, we present an ultra-low-power (sub-50 mW) system for concurrent acquisition of 8-channel EMG and 4-channel A-mode US signals, integrating two state-of-the-art platforms into fully wearable, dry-contact armbands. We propose a framework for continuous tracking of 23 degrees of freedom (DoFs), 20 for the hand and 3 for the wrist, using a kinematic glove for ground-truth labeling. Our method employs lightweight encoder-decoder architectures with multi-task learning to simultaneously estimate hand and wrist joint angles. Experimental results under realistic sensor repositioning conditions demonstrate that EMG-US fusion achieves a root mean squared error of $10.6^\circ\pm2.0^\circ$, compared to $12.0^\circ\pm1^\circ$ for EMG and $13.1^\circ\pm2.6^\circ$ for US, and a R$^2$ score of $0.61\pm0.1$, with $0.54\pm0.03$ for EMG and $0.38\pm0.20$ for US.
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All authors Giusy Spacone, Sebastian Frey, Mattia Orlandi, Pierangelo Maria Rapa, Victor Kartsch, Simone Benatti, Luca Benini, Andrea Cossettini
Systems and Control · Cross-listed from cs.RO
SPARC: Spine with Prismatic And Revolute Compliance for Faster Quadrupedal Bounding
Yue Wang and Yanran Xu
Quadruped mammals coordinate sagittal spinal bending with axial extension and compression during dynamic locomotion. Yet most robotic quadrupeds use rigid trunks, passively compliant spines with fixed properties, or actively controlled spines that track prescribed trajectories. Whether actively regulated spinal compliance can support faster dynamic locomotion remains unclear. We present SPARC, a compact 1.26-kg, 3-DoF sagittal-plane spine that combines revolute and prismatic motion with independently tunable task-space stiffness and damping. A floating-base impedance controller renders the desired task-space compliance, and benchtop tests show that the fitted axial stiffness matches commanded values within 1.5%. We integrate SPARC into an 8-DoF quadruped and evaluate it across 97 bounding trials under three spine configurations: impedance-controlled SPARC, the same SPARC module held near a fixed pose using position control, and a lightweight rigid spine. Impedance-controlled SPARC reaches 1.029 m/s, compared with 0.769 m/s for position-controlled SPARC and 0.673 m/s for the rigid spine. Impedance-controlled SPARC reaches higher speeds with larger axial motion and greater mechanical power exchange, while at matched speed it has a higher electrical cost of transport than the rigid spine, revealing an energetic trade-off. Code and hardware are available at: https://github.com/YueWang996/sparc
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Audio and Speech Processing · Cross-listed from cs.SD
Multi-bit Audio Watermarking
Luca A. Lanzendörfer, Kyle Fearne, Florian Grötschla et al. (+1 more)
We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given any 44.1 kHz stereo music snippet, our method performs per-audio gradient optimization to add imperceptible perturbations in the latent space of a pretrained audio VAE, guided by a combined message and perceptual loss. The watermark can then be extracted using a pretrained CLAP model. We evaluate 16-bit watermarking on MUSDB18-HQ against AudioSeal, WavMark, and SilentCipher across common filtering, noise, compression, resampling, cropping, and regeneration attacks. Our approach attains the best average bit error rates, while preserving perceptual quality, demonstrating an efficient,
dataset-free path to imperceptible audio watermarking.
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All authors Luca A. Lanzendörfer, Kyle Fearne, Florian Grötschla, Roger Wattenhofer
Audio and Speech Processing · Cross-listed from cs.SD
Exploring Resolution-Wise Shared Attention in Hybrid Mamba-U-Nets for Improved Cross-Corpus Speech Enhancement
Nikolai Lund Kühne, Jesper Jensen, Jan Østergaard et al. (+1 more)
Recent advances in
speech enhancement have shown that models combining Mamba and
attention mechanisms yield superior cross-corpus generalization performance. At the same time, integrating Mamba in a U-Net structure has yielded state-of-the-art enhancement performance, while reducing both model size and computational complexity. Inspired by these insights, we propose RWSA-MambaUNet, a novel and efficient hybrid model combining Mamba and multi-head
attention in a U-Net structure for improved cross-corpus performance. Resolution-wise shared
attention (RWSA) refers to layerwise
attention-sharing across corresponding time- and frequency resolutions. Our best-performing RWSA-MambaUNet model achieves state-of-the-art generalization performance on two out-of-domain test sets. Notably, our smallest model surpasses all baselines on the out-of-domain DNS 2020 test set in terms of PESQ, SSNR, and ESTOI, and on the out-of-domain EARS-WHAM_v2 test set in terms of SSNR, ESTOI, and SI-SDR, while using less than half the model parameters and a fraction of the FLOPs.
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All authors Nikolai Lund Kühne, Jesper Jensen, Jan Østergaard, Zheng-Hua Tan
Audio and Speech Processing
Clustering of Acoustic Environments with Variational Autoencoders for Hearing Devices
Luan Vinícius Fiorio, Ivana Nikoloska, Wim van Houtum et al. (+1 more)
Traditional acoustic environment classification relies on: i) classical signal processing
algorithms, which are unable to extract meaningful representations of high-dimensional data; or on ii) supervised learning, limited by the availability of labels. Knowing that human-imposed labels do not always reflect the true structure of acoustic scenes, we explore the potential of (unsupervised) clustering of acoustic environments using variational autoencoders (VAEs). We employ a VAE model for categorical latent clustering with a Gumbel-Softmax reparameterization which can operate with a time-context windowing scheme for lower
memory requirements, tailored for real-world hearing device scenarios. Additionally, general adaptations on VAE architectures for audio clustering are also proposed. The approaches are validated through the clustering of spoken digits, a simpler task where labels are meaningful, and urban soundscapes, where the recordings present strong overlap in time and frequency. While all variational methods succeeded when clustering spoken digits, only the proposed model achieved effective clustering performance on urban acoustic scenes, given its categorical nature.
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All authors Luan Vinícius Fiorio, Ivana Nikoloska, Wim van Houtum, Ronald M. Aarts
Image and Video Processing
GFSR-Net: Guided Focus via Segment-Wise Relevance Network for Interpretable Deep Learning in Medical Imaging
Jhonatan Contreras and Thomas Bocklitz
Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their
reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise Relevance Network (GFSR-Net), an approach designed to improve interpretability and reliability in medical imaging. GFSR-Net uses a small number of human annotations to approximate where a person would focus within an image intuitively, without requiring precise boundaries or exhaustive markings, making the process fast and practical. During training, the model learns to align its focus with these areas, progressively emphasizing features that carry diagnostic meaning. This guidance works across different types of natural and medical images, including chest X-rays, retinal scans, and dermatological images. Our experiments demonstrate that GFSR achieves comparable or superior accuracy while producing saliency maps that better reflect human expectations. This reduces the reliance on irrelevant patterns and increases confidence in automated diagnostic tools.
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Signal Processing · Cross-listed from cs.CV
Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou et al. (+1 more)
Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable-sized
dataset for training and testing. Four varieties of the YOLO model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.
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All authors Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Yen-Ting Liu
Audio and Speech Processing · Cross-listed from cs.SD
MelTok: 2D Tokenization for Single-Codebook Audio Compression
Jingyi Li, Zhiyuan Zhao, Zhisheng Zhang et al. (+6 more)
Large Audio
Language Models (LALMs) have emerged with strong performance across diverse audio understanding tasks and can be further enhanced by neural audio codecs. Transitioning from multi-layer residual vector quantizers to a single-layer quantizer has been shown to facilitate more efficient downstream
language models decoding. However, the ability of a single codebook to capture fine-grained acoustic details remains limited, as the frequency-variant nature of 1D tokenizers leads to redundancy. To address this issue, we propose MelTok, a two-dimensional (2D) tokenizer that effectively compresses acoustic details of 44.1 KHz audio into a single codebook. The tokenizer encodes audio into a more compact representation than one-dimensional tokenizers. Furthermore, to recover audio from mel-spectrogram
tokens, we propose a
token-based vocoder. Both objective and subjective evaluations demonstrate that MelTok achieves quality comparable to multi-codebook codecs and outperforms existing state-of-the-art neural codecs with a single codebook on high-fidelity audio reconstruction. By preserving acoustic details, MelTok offers a strong representation for downstream understanding tasks.
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All authors Jingyi Li, Zhiyuan Zhao, Zhisheng Zhang, Yunfei Liu, Lijian Lin, Ye Zhu, Jiahao Wu, Qiuqiang Kong, Yu Li
Audio and Speech Processing · Cross-listed from cs.SD
HRTFformer: A Spatially-Aware Transformer for Individual HRTF Upsampling in Immersive Audio Rendering
Xuyi Hu, Jian Li, Shaojie Zhang et al. (+4 more)
Individual Head-Related Transfer Functions (HRTFs) are starting to be introduced in many commercial immersive audio applications and are crucial for realistic spatial audio rendering. However, one of the main hesitations regarding their introduction is that creating individual HRTFs is impractical at scale due to the complexities of the HRTF measurement process. To mitigate this drawback, HRTF spatial upsampling has been proposed with the aim of reducing the measurements required. While prior work has seen success with different machine learning (ML) approaches, these models often struggle with long-range preservation of local spatial variation patterns across neighbouring source directions and generalization at high upsampling factors. In this paper, we propose a novel
transformer-based architecture for HRTF upsampling, leveraging the
attention mechanism to better capture spatial correlations across the HRTF sphere. Working in the spherical harmonic (SH) domain, our model learns to reconstruct high-resolution HRTFs from sparse input measurements with significantly improved accuracy. To enhance spatial coherence, we introduce a neighbour dissimilarity loss that promotes magnitude smoothness, yielding more realistic upsampling. We evaluate our method using both perceptual localization models and objective spectral distortion metrics. Experiments show that our model outperforms existing methods across several evaluation metrics in generating realistic, high-fidelity HRTFs.
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All authors Xuyi Hu, Jian Li, Shaojie Zhang, Stefan Goetz, Lorenzo Picinali, Ozgur B. Akan, Aidan O. T. Hogg
Audio and Speech Processing
SLAP: Learning Speaker and Health-Related Representations from Natural Language Supervision
Angelika Ando, Auguste Crabeil, Adrien Lesage et al. (+1 more)
Speech encodes paralinguistic information such as demographics, voice quality, and health. Yet no audio foundation model supports zero-shot or out-of-distribution (OOD) generalization to these tasks. We introduce SLAP (Speaker contrastive
Language-Audio Pretraining), the first model aligning
speech with natural
language descriptions of speaker and health metadata through contrastive learning. SLAP combines a
Vision Transformer audio encoder with text encoders, trained on more than 3400 hours across 9
datasets with diverse speaker annotations. We evaluated on 38 binary classification tasks spanning demographics, voice characteristics, and clinical assessments across 14
datasets in 7
languages. SLAP achieves 62.9% average F1 in zero-shot evaluation, a 48% relative improvement over CLAP (42.4%), while demonstrating strong OOD generalization to unseen
languages and clinical populations. When fine-tuned with linear probing, SLAP reaches 69.3% F1 overall and achieves best-in-class performance on health tasks (57.9% F1), surpassing larger foundation models.
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All authors Angelika Ando, Auguste Crabeil, Adrien Lesage, Rachid Riad
Systems and Control
A TSO-DSO Coordination Framework via Analytical Representation and Monetization of PQV-Based Distribution System Flexibility
Burak Dindar, Can Berk Saner, Hüseyin Kemal Çakmak et al. (+1 more)
As the role of distribution system (DS) flexibility in transmission system operator (TSO) network management becomes increasingly vital, data
privacy concerns hinder seamless interoperability. The notion of the feasible operating region (FOR), defined in the PQ domain, has emerged as a promising
privacy-preserving approach. However, effectively leveraging FOR in TSO operations remains challenging due to three key factors: its accurate determination in large-scale, meshed DS networks; its tractable analytical representation; and its economic valuation. In the present paper, we propose a novel AC optimal power flow (OPF)-based method to construct a three-dimensional PQV-FOR, explicitly accounting for voltage variability and diverse flexibility-providing unit (FPU) characteristics. The construction process employs a two-stage sampling strategy that combines bounding box projection and Fibonacci direction techniques to efficiently capture the FOR. We then introduce an implicit
polynomial fitting approach to analytically represent the FOR. Furthermore, we derive a quadratic cost function over the PQV domain to monetize the FOR. Thus, the proposed framework enables single-round TSO-DSO coordination: the DSO provides an analytical FOR and cost model; the TSO determines operating point at the point of common coupling (PCC) within the FOR-based AC-OPF; and the DSO computes FPU dispatch by solving its local OPF, without computationally intensive disaggregation or iterative coordination. Case studies on meshed DS with up to 533 buses, integrated into TS, demonstrates the method's efficiency compared to standard AC-OPF. On average, the proposed approach yields negligible cost deviations of at most 0.058% across test cases, while reducing computation times by up to 58.11%.
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All authors Burak Dindar, Can Berk Saner, Hüseyin Kemal Çakmak, Veit Hagenmeyer
Signal Processing
NGGAN: Noise Generation GAN Based on the Practical Measurement Dataset for Narrowband Powerline Communications
Ying-Ren Chien, Po-Heng Chou, You-Jie Peng et al. (+3 more)
To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative
adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering
circuits of a commercial NB-PLC modem to build a realistic
dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured
datasets, we conduct both quantitative and qualitative analyses. The training
datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fréchet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.
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All authors Ying-Ren Chien, Po-Heng Chou, You-Jie Peng, Chun-Yuan Huang, Hen-Wai Tsao, Yu Tsao
Audio and Speech Processing
Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification
Oğuzhan Kurnaz, Jagabandhu Mishra, Tomi H. Kinnunen et al. (+1 more)
Spoofing-robust speaker verification (SASV) combines the tasks of speaker and spoof detection to authenticate speakers under
adversarial settings. Many SASV systems rely on fusion of speaker and spoof cues at
embedding, score or decision levels, based on independently trained subsystems. In this study, we respect similar modularity of the two subsystems, by integrating their outputs using trainable back-end classifiers. In particular, we explore various approaches for directly optimizing the back-end for the recently-proposed SASV performance metric (a-DCF) as a training objective. Our experiments on the ASVspoof 5
dataset demonstrate two important findings: (i) nonlinear score fusion consistently improves a-DCF over linear fusion, and (ii) the combination of weighted cosine scoring for speaker detection with SSL-AASIST for spoof detection achieves state-of-the-art performance, reducing min a-DCF to 0.196 and SPF-EER to 7.6%. These contributions highlight the importance of modular design, calibrated integration, and task-aligned optimization for advancing robust and interpretable SASV systems.
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All authors Oğuzhan Kurnaz, Jagabandhu Mishra, Tomi H. Kinnunen, Cemal Hanilçi
Audio and Speech Processing · Cross-listed from cs.SD
SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment
Yuxun Tang, Lan Liu, Wenhao Feng et al. (+5 more)
Singing voice generation progresses rapidly, yet evaluating singing quality remains a critical challenge. Human subjective assessment, typically in the form of listening tests, is costly and time consuming, while existing objective metrics capture only limited perceptual aspects. In this work, we introduce SingMOS-Pro, a
dataset for automatic singing quality assessment. Building on our preview version SingMOS, which provides only overall ratings, SingMOS-Pro extends the annotations of the additional data to include lyrics, melody, and overall quality, offering broader coverage and greater diversity. The
dataset contains 7,981 singing clips generated by 41 models across 12
datasets, spanning from early systems to recent state-of-the-art approaches. Each clip is rated by at least five experienced annotators to ensure reliability and consistency. Furthermore, we investigate strategies for effectively utilizing MOS data annotated under heterogeneous standards and
benchmark several widely used evaluation methods from related tasks on SingMOS-Pro, establishing strong baselines and practical references for future research. The
dataset is publicly available at https://huggingface.co/
datasets/TangRain/SingMOS-Pro.
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All authors Yuxun Tang, Lan Liu, Wenhao Feng, Yiwen Zhao, Jionghao Han, Yifeng Yu, Jiatong Shi, Qin Jin
Systems and Control · Cross-listed from math.OC
Robust MPC for Large-scale Linear Systems
Georg Schildbach
State-of-the-art approaches of Robust Model Predictive Control (MPC) are restricted to linear systems of relatively small scale, i.e., with no more than about 5 states. The main reason is the computational burden of determining a robust positively invariant (RPI) set, whose complexity suffers from the curse of dimensionality. The recently proposed approach of Deadbeat Robust Model Predictive Control (DRMPC) is the first that does not rely on an RPI set. Yet it comes with the full set of essential system theoretic guarantees. DRMPC is hence a viable option, in particular, for large-scale systems. This paper introduces a detailed design procedure for DRMPC. It is shown that the optimal control problem generated for DRMPC has exactly the same computational complexity as Nominal MPC. A numerical study validates its applicability to randomly generated large-scale linear systems of various dimensions.
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Signal Processing
Performance Optimization for Movable Antenna Enhanced MISO-OFDM Systems
Ruixi Feng, Weidong Mei, Lele Lu et al. (+4 more)
Movable
antenna (MA) technology offers a flexible approach to enhancing wireless channel conditions by adjusting
antenna positions within a designated region. While most existing works focus on narrowband MA systems, this paper investigates MA position optimization for an MA-enhanced multiple-input single-output (MISO) orthogonal frequency-division multiplexing (OFDM) system. This problem appears to be particularly challenging due to the frequency-flat nature of MA positioning, which should accommodate the channel conditions across different subcarriers. To overcome this challenge, we discretize the movement region into a multitude of sampling points, thereby converting the continuous position optimization problem into a discrete point selection problem. Although this problem is combinatorial, we develop an efficient partial enumeration
algorithm to find the optimal solution using a branch-and-bound framework, where a graph-theoretic method is incorporated to effectively prune suboptimal solutions. In the low signal-to-noise ratio (SNR) regime, a simplified graph-based
algorithm is also proposed to obtain the optimal MA positions without the need for enumeration. Simulation results reveal that the proposed
algorithm outperforms conventional fixed-position
antennas (FPAs), while narrowband-based
antenna position optimization can achieve near-optimal performance.
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All authors Ruixi Feng, Weidong Mei, Lele Lu, Xin Wei, Zhi Chen, Zhen Gao, Boyu Ning
Signal Processing
Closed-form Single UAV-aided Emitter Localization and Trajectory Design Using Doppler and TOA Measurements
Samaneh Motie, Hadi Zayyani, Mohammad Salman et al. (+1 more)
In this paper, a single Unmanned-Aerial-Vehicle (
UAV)-aided localization
algorithm which uses both Doppler and Time of Arrival (ToA) measurements is presented. In contrast to Doppler-based localization
algorithms which are based on non-convex functions, exploiting ToA measurements in a Least-Square (LS) Doppler-based cost function, leads to a quadratic convex function whose minimizer lies on a line. Utilizing the ToA measurements in addition to the linear equation of minimizer, a closed form solution is obtained for the emitter location using a constrained LS optimization. In addition, a trajectory design of the
UAV is provided which has also closed-form solution. Simulation experiments demonstrate the effectiveness of the proposed
algorithm in comparison to some others in the literature.
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All authors Samaneh Motie, Hadi Zayyani, Mohammad Salman, Hasan Abu Hilal
Signal Processing
Composite Generalized Quadratic Noise Modulation via Signal Addition: Towards Higher Dimensional Noise Modulations
Hadi Zayyani, Mohammad Salman, Felipe A. P. de Figueiredo et al. (+1 more)
This letter proposes superposing two Generalized Quadratic Noise Modulators (GQNM) by simply adding their outputs. It creates a 16-ary noise modulator that resembles QAM modulators in classical communication. It modulates the information bits on four different means and four different variances. It could also be applied to reach higher-order modulations than 16-ary schemes by adding the outputs of more than two modulators, which is not discussed in detail in this letter and left for future work. By selecting the parameters necessary for satisfying the theoretical distinguishability conditions provided in the paper, we can reach better performances in comparison to the Kirchhoff-Law Johnson Noise (KLJN) modulator and the GQNM modulator, which is verified by the simulations. The better result in terms of smaller Bit Error Probability (BEP) is achieved by increasing the complexity in the modulator, the transmitter, and the detectors in the receiver.
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All authors Hadi Zayyani, Mohammad Salman, Felipe A. P. de Figueiredo, Rausley A. A. de Souza
Signal Processing
Exactly or Approximately Wasserstein Distributionally Robust Estimation According to Wasserstein Radii Being Small or Large
Xiao Ding, Enbin Song, Dunbiao Niu et al. (+2 more)
This paper primarily considers the robust estimation problem under Wasserstein distance constraints on the parameter and noise distributions in the linear measurement model with additive noise, which can be formulated as an infinite-dimensional nonconvex minimax problem. We prove that the existence of a saddle point for this problem is equivalent to that for a finite-dimensional minimax problem, and give a counterexample demonstrating that the saddle point may not exist. Motivated by this observation, we present a verifiable necessary and sufficient condition whose parameters can be derived from a convex problem and its dual. Additionally, we also introduce a simplified sufficient condition, which intuitively indicates that when the Wasserstein radii are small enough, the saddle point always exists. In the absence of the saddle point, we solve an finite-dimensional nonconvex minimax problem, obtained by restricting the
estimator to be linear. Its optimal value establishes an upper bound on the robust estimation problem, while its optimal solution yields a robust linear
estimator. Numerical experiments are also provided to validate our theoretical results.
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All authors Xiao Ding, Enbin Song, Dunbiao Niu, Zhujun Cao, Qingjiang Shi
Systems and Control · Cross-listed from cs.RO
Dual-Mode Magnetic Continuum Robot for Targeted Drug Delivery
Wendu Zhang, Heng Wang, Shuangyi Wang et al. (+1 more)
Magnetic continuum
robots (MCRs) enable minimally invasive
navigation through tortuous anatomical channels, yet axially magnetized designs have largely been limited to bending-only motion. To expand deformation capabilities, this paper presents a simple assembly that embeds permanent magnets radially within the catheter wall, allowing a single externally steered permanent magnet to independently induce either bending or torsion. A physics-based formulation together with finite-element analysis establishes the actuation principles, and benchtop experiments validate decoupled mode control under practical fields. Building on this, a dual-layer blockage mechanism consisting of outer grooves and inner plates leverages torsional shear to achieve on-demand drug release. Finally, an in-phantom intervention experiment demonstrates end-to-end operation: lumen following by bending for target approach, followed by twist-activated release at the site. The resulting compact, cable-free platform combines versatile deformation with precise payload delivery, indicating strong potential for next-generation, site-specific therapies.
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All authors Wendu Zhang, Heng Wang, Shuangyi Wang, Yuanrui Huang
Signal Processing
3D 8-Ary Noise Modulation Using Bayesian- and Kurtosis-based Detectors
Hadi Zayyani, Felipe A. P. de Figueiredo, Mohammad Salman et al. (+1 more)
This paper presents a novel three-dimensional (3D) 8-ary noise modulation scheme that introduces a new dimension: the mixture probability of a Mixture of Gaussian (MoG) distribution. This proposed approach utilizes the dimensions of mean and variance, in addition to the new probability dimension. Within this framework, each transmitted symbol carries three bits, each corresponding to a distinct sub-channel. For detection, a combination of specialized detectors is employed: a simple threshold based detector for the first sub-channel bit (modulated by the mean), a Maximum-Likelihood (ML) detector for the second sub-channel bit (modulated by the variance), a Kurtosis-based, Jarque-Bera (JB) test, and
Bayesian Hypothesis (BHT)-based detectors for the third bit (modulated by the MoG probability). The Kurtosis- and JB-based detectors specifically distinguish between Gaussian (or near-Gaussian) and non-Gaussian MoG distributions by leveraging higher-order statistical measures. The Bit Error Probabilities (BEPs) are derived for the threshold-, Kurtosis-, and BHT-based detectors. The optimum threshold for the Kurtosis-based detector is also derived in a tractable manner. Simulation results demonstrate that a comparably low BEP is achieved for the third sub-channel bit relative to existing two-dimensional (2D) schemes. Simultaneously, the proposed scheme increases the data rate by a factor of 1.5 and 3 compared to the Generalized Quadratic noise modulator and the classical binary KLJN noise modulator, respectively. Furthermore, the Kurtosis-based detector offers a low-complexity solution, achieving an acceptable BEP of approximately 0.06.
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All authors Hadi Zayyani, Felipe A. P. de Figueiredo, Mohammad Salman, Rausley A. A. de Souza
Audio and Speech Processing · Cross-listed from cs.SD
Emotional Text-To-Speech Based on Mutual-Information-Guided Emotion-Timbre Disentanglement
Jianing Yang, Sheng Li, Takahiro Shinozaki et al. (+2 more)
Current emotional Text-To-
Speech (TTS) and style transfer methods rely on reference encoders to control global style or
emotion vectors, but do not capture nuanced acoustic details of the reference
speech. To this end, we propose a novel emotional TTS method that enables fine-grained phoneme-level
emotion embedding prediction while disentangling intrinsic attributes of the reference
speech. The proposed method employs a style disentanglement method to guide two feature extractors, reducing mutual information between timbre and
emotion features, and effectively separating distinct style components from the reference
speech. Experimental results demonstrate that our method outperforms baseline TTS systems in generating natural and emotionally rich
speech. This work highlights the potential of disentangled and fine-grained representations in advancing the quality and flexibility of emotional TTS systems.
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All authors Jianing Yang, Sheng Li, Takahiro Shinozaki, Yuki Saito, Hiroshi Saruwatari
Signal Processing
SEP Analysis of 1-Bit Quantized SIMO Systems with QPSK over Fading Channels
Amila Ravinath, Minhua Ding, Bikshapathi Gouda et al. (+2 more)
The average symbol error probability (SEP) of a 1-bit quantized single-input multiple-output (SIMO) system is analyzed under Rayleigh fading channels and quadrature phase-shift keying (QPSK) modulation. Previous studies have partially characterized the diversity gain for selection combining (SC). In this paper, leveraging a novel analytical method, an exact analytical SEP expression is derived for a 1-bit quantized SIMO system employing QPSK modulation at the transmitter and maximum ratio combining (MRC) at the receiver. The corresponding diversity and coding gains of a SIMO-MRC system are also determined. Furthermore, the diversity and coding gains of a 1-bit quantized SIMO-SC system are quantified for an arbitrary number of receive
antennas, thereby extending and complementing prior results.
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All authors Amila Ravinath, Minhua Ding, Bikshapathi Gouda, Italo Atzeni, Antti Tölli
Audio and Speech Processing · Cross-listed from cs.IR
TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
Seungheon Doh, Keunwoo Choi and Juhan Nam
While the recent developments in large
language models (LLMs) have successfully enabled generative recommenders with natural
language interactions, their recommendation behavior is limited, leaving other simpler yet crucial components such as metadata or attribute filtering underutilized in the system. We propose an LLM-based music recommendation system with tool calling to serve as a unified
retrieval-reranking pipeline. Our system positions an LLM as an end-to-end recommendation system that interprets user intent, plans tool invocations, and orchestrates specialized components: boolean filters (SQL), sparse
retrieval (BM25), dense
retrieval (
embedding similarity), and generative
retrieval (semantic IDs). Through tool planning, the system predicts which types of tools to use, their execution order, and the arguments needed to find music matching user preferences, supporting diverse modalities while seamlessly integrating multiple database filtering methods. We demonstrate that this unified tool-calling framework achieves competitive performance across diverse recommendation scenarios by selectively employing appropriate
retrieval methods based on user queries, envisioning a new paradigm for conversational music recommendation systems.
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Systems and Control · Cross-listed from cs.RO
Geometric Backstepping Control of Omnidirectional Tiltrotors Incorporating Servo-Rotor Dynamics for Robustness against Sudden Disturbances
Jaewoo Lee, Dongjae Lee, Jinwoo Lee et al. (+3 more)
This work presents a
geometric backstepping controller for a variable-tilt omnidirectional multirotor that explicitly accounts for both servo and rotor dynamics. Considering actuator dynamics is essential for more effective and reliable operation, particularly during aggressive flight maneuvers or recovery from sudden disturbances. While prior studies have investigated actuator-aware control for conventional and fixed-tilt multirotors, these approaches rely on linear relationships between actuator input and wrench, which cannot capture the nonlinearities induced by variable tilt angles. In this work, we exploit the cascade structure between the rigid-body dynamics of the multirotor and its nonlinear actuator dynamics to design the proposed backstepping controller and establish exponential stability of the overall system. Furthermore, we reveal parametric uncertainty in the actuator model through experiments, and we demonstrate that the proposed controller remains robust against such uncertainty. The controller was compared against a baseline that does not account for actuator dynamics across three experimental scenarios: fast translational tracking, rapid rotational tracking, and recovery from sudden disturbance. The proposed method consistently achieved better tracking performance, and notably, while the baseline diverged and crashed during the fastest translational trajectory tracking and the recovery experiment, the proposed controller maintained stability and successfully completed the tasks, thereby demonstrating its effectiveness.
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All authors Jaewoo Lee, Dongjae Lee, Jinwoo Lee, Hyungyu Lee, Yeonjoon Kim, H. Jin Kim
Image and Video Processing
Median2Median: Zero-shot Suppression of Structured Noise in Images
Jianxu Wang and Ge Wang
Image denoising is a fundamental problem in computer
vision and medical imaging. However, real-world images are often degraded by structured noise with strong anisotropic correlations that existing methods struggle to remove. Most data-driven approaches rely on large
datasets with high-quality labels and still suffer from limited generalizability, whereas existing zero-shot methods avoid this limitation but remain effective only for independent and identically distributed (i.i.d.) noise. To address this gap, we propose Median2Median (M2M), a zero-shot denoising framework designed for structured noise. M2M introduces a novel sampling strategy that generates pseudo-independent sub-image pairs from a single noisy input. This strategy leverages directional interpolation and generalized median filtering to adaptively exclude values distorted by structured artifacts. To further enlarge the effective sampling space and eliminate systematic bias, a randomized assignment strategy is employed, ensuring that the sampled sub-image pairs are suitable for Noise2Noise training. In our realistic simulation studies, M2M performs on par with state-of-the-art zero-shot methods under i.i.d. noise, while consistently outperforming them under correlated noise. These findings establish M2M as an efficient, data-free solution for structured noise suppression and mark the first step toward effective zero-shot denoising beyond the strict i.i.d. assumption.
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Signal Processing · Cross-listed from cs.IT
Next-Generation AI-Native Wireless Communications: MCMC-Based Receiver Architectures for Unified Processing
Xingyu Zhou, Le Liang, Jing Zhang et al. (+2 more)
The multiple-input multiple-output (
MIMO) receiver processing is a key technology for current and next-generation wireless communications. However, it faces significant challenges related to complexity and scalability as the number of
antennas increases. Artificial intelligence (AI), a cornerstone of next-generation wireless networks, offers considerable potential for addressing these challenges. This paper proposes an AI-driven, universal
MIMO receiver architecture based on Markov chain Monte Carlo (MCMC) techniques. Unlike existing AI-based methods that treat receiver processing as a black box, our MCMC-based approach functions as a generic
Bayesian computing engine applicable to various processing tasks, including channel estimation, symbol detection, and channel decoding. This method enhances the interpretability, scalability, and flexibility of receivers in diverse scenarios. Furthermore, the proposed approach integrates these tasks into a unified probabilistic framework, thereby enabling overall performance optimization. This unified framework can also be seamlessly combined with data-driven learning methods to facilitate the development of fully intelligent communication receivers.
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All authors Xingyu Zhou, Le Liang, Jing Zhang, Chao-Kai Wen, Shi Jin
Signal Processing · Cross-listed from cs.CV
NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
Roman Jacome, Romario Gualdrón-Hurtado, Leon Suarez et al. (+1 more)
Imaging inverse problems aim to recover high-dimensional signals from undersampled, noisy measurements, a fundamentally ill-posed task with infinite solutions in the null-space of the sensing operator. To resolve this ambiguity, prior information is typically incorporated through handcrafted regularizers or learned models that constrain the solution space. However, these priors typically ignore the task-specific structure of that null-space. In this work, we propose Non-Linear Projections of the Null-Space (NPN), a novel class of regularization that, instead of enforcing structural constraints in the image domain, promotes solutions that lie in a low-dimensional projection of the sensing
matrix's null-space with a neural network. Our approach has two key advantages: (1) Interpretability: by focusing on the structure of the null-space, we design sensing-
matrix-specific priors that capture information orthogonal to the signal components that are fundamentally blind to the sensing process. (2) Flexibility: NPN is adaptable to various inverse problems, compatible with existing reconstruction frameworks, and complementary to conventional image-domain priors. We provide theoretical guarantees on convergence and reconstruction accuracy when used within plug-and-play methods. Empirical results across diverse sensing
matrices demonstrate that NPN priors consistently enhance reconstruction fidelity in various imaging inverse problems, such as compressive sensing, deblurring, super-resolution, computed
tomography, and
magnetic resonance imaging, with plug-and-play methods, unrolling networks, deep image prior, and
diffusion models.
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All authors Roman Jacome, Romario Gualdrón-Hurtado, Leon Suarez, Henry Arguello