Engineering & Signals · Interdisciplinary & Society
Signal Processing
The newest papers in Signal Processing that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
429 papers, from 31 August 2026 to 5 November 2026: 360 arXiv preprints and 69 OpenAlex records.
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Newest papers, page 12
Real-Time Adaptive Filtering and the Boxcar Limit in Superconducting Qubit Readout
Control and readout systems for superconducting quantum computing support real-time control and high-fidelity qubit-state discrimination.PI-AMFM: Permutation-Invariant Learning for Variable-Cardinality AM-FM Mode Decomposition in Biomedical Signal Analysis
Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals.Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning
Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts, volume…MyoCodec: A Streaming Neural Codec for Electromyography
Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and token-based sequence modeling.LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective
As large language models (LLMs) evolve from standalone models into collaborative agents embedded in physical systems, their reasoning and execution are increasingly distributed across wireless edge…Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin
The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or…End-to-End Optical Semantic Communication over a Nonlinear WDM Fiber Link
Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an opportunity to trade bit-level fidelity for greater…Kinematic signatures of impairment: Detecting alcohol intoxication in e-scooter riders using sensor data and machine learning
Alcohol intoxication is a leading contributor to fatal and severe-injured e-scooterist crashes.Certificate-Governed CRT Sparse FFT: Verified Global-Label Candidate Construction under Explicit Decoding Models
We present a deterministic CRT-based sparse Fourier architecture for exact recovery of noiseless, on-grid, at-most-\(k\)-sparse spectra with an engineered transform length.Nonnegative DAG Learning via Concomitant Estimation
We study the problem of learning directed acyclic graphs (DAGs) with nonnegative edge weights from observational data.Hierarchical Clustering and Signal Denoising on Digraphs
In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix.Separating personal from population gains when calibrating EEG foundation models for new users
Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model.Cyclostationary Phase Conditioning for Medical Time Series Diffusion
Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying cycle phase.A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation
Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodontic diagnosis.Graph neural networks for sampling-invariant embeddings of organized signal sets
Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes.Fundamental Limits of Transferability and Equivariance in Algebraic Signal Models I: Finite Dimensions
We study the fundamental limits of transferability in algebraic signal processing through homomorphisms between algebraic signal models.Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learning
Everywhere learning provides a principled framework for training AI models under constraints that must hold throughout the data distribution.Plug-and-Play Methods Provably Converge Even with Improperly Trained Denoisers: Convergence by Architectural Design
Plug-and-Play (PnP) methods replace proximal operators with learned denoisers, which produce state-of-the-art reconstruction quality, but sacrifice the variational interpretation and convergence…Simulation-Free Learning of GP-SDEs from Irregular Observations
Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertainty quantification, but learning and inference…ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiograms
Multimodal large language models (MLLMs) can now interpret a standard ten-second, twelve-lead electrocardiogram (ECG) with clinically grounded, reward-verified reasoning.