Papers

The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-location channel measurements and constructs a structured channel charting with location-indexed PCFs. Furthermore, we propose a low-complexity algorithm to acquire beam domain statistical CSI (sCSI) using the PCFs in the channel charting. The resulting sCSI can be directly employed as prior information for channel estimation. Simulation results show that the proposed method delivers sCSI performance comparable to traditional online probing techniques, and the generated sCSI can serve as reliable prior knowledge to significantly enhance the accuracy of channel estimation. These results validate the proposed PCF as a powerful and versatile tool for channel acquisition and system design of the next-generation mobile communication.

In this work, we introduce SingFox, a comprehensive and large-scale dataset specifically designed to support robust evaluation of singing deepfake detection and source tracing systems. SingFox is divided into six distinct tracks (T1–T6), each targeting a unique form of novelty, ranging from language diversity (global and Indian) to genre-specific music and alternative fake generation methods. The dataset encompasses over 113,802 audio clips across 20 languages, totaling more than 126.32 hours of audio data and featuring 1,150 singers. Each track is designed to emulate real-world scenarios and evaluate how reliably models perform under different conditions, thereby assessing their robustness. SingFox aims to foster reproducibility and accelerate research in singing deepfake detection by providing a reliable benchmark for both the singfake detection task and the source verification task (model explainability). Experimental results show a highest accuracy of 77.84% in cross-dataset evaluation settings. All code and resources required to reproduce the dataset are publicly available at https://github.com/Arth-Shah/SingFox.

Early detection of cognitive impairment relies on neuropsychological tests to minimize subjectivity by assessing multiple cognitive domains. Speech-based evaluation can support diagnostics and improve accessibility, but transcription errors and the omission of nonverbal subtests (e.g., motor skills) limit accuracy. Beyond conventional test scores, speech-derived features can provide additional insights into cognitive status. This study investigates the speech-based evaluation of the German "Syndrom-Kurz-Test," a standardized dementia screening test comprising verbal and motor subtests. We train models that integrate transcript-derived scores and Whisper embeddings per verbal subtest to reduce scoring errors. To compensate for missing motor subtests, we then leverage these fused representations to approximate expert overall ratings. Despite omitting subtests, our models strongly correlate with expert ratings and efficiently and accurately discriminate between cognitive status groups.

In this paper, we propose diffusion warm initialization as a simple yet effective approach for a range of audio-to-audio transformation tasks. To illustrate the generality of the approach, we demonstrate its use in timbre transfer, MIDI-to-Real synthesis, and multiple audio enhancement tasks. We conduct a detailed empirical analysis on timbre transfer to investigate the role of the initialization time $t_\text{init}$. The effect of $t_\text{init}$ is evaluated using pitch-based Jaccard Distance and Fréchet Audio Distance to quantify faithfulness to the input signal and alignment with the target distribution. Our results provide practical guidance for selecting $t_\text{init}$ and show that, once properly chosen, a single pretrained diffusion model combined with warm initialization can support multiple transformation objectives without task-specific training or conditioning. Despite its simplicity, this approach already achieves competitive results when compared with more complex pipelines designed specifically for these tasks. We further observe that warm initialization does not necessarily require explicit noise injection, as the guide signal itself can often serve as a valid initialization state for the backward diffusion process. Together, these findings show that warm initialization provides a simple and effective framework that serves as a fundamental building block for more complex audio transformation pipelines.

Spaceborne synthetic aperture radar (SAR) provides coherent microwave imagery suitable for maritime infrastructure monitoring under illumination-independent and weather-independent acquisition conditions. An academic conference-style analysis is presented for SAR amplitude and geocoded multitemporal data over Tianjin Port, China. The processing chain includes amplitude visualization, radiometric scaling, view-direction interpretation, range and azimuth resolution assessment, speckle reduction, amplitude-based change mapping, GeoTIFF export for geographic inspection, and interferometric coherence estimation. Histogram-guided display limits improve the interpretability of the complex SAR magnitude images, while zoomed inspection of shadows and bright layover responses supports qualitative interpretation of illumination geometry. A two-dimensional Fourier analysis is used to characterize dominant spectral content and to estimate an approximate range resolution of 0.42 m and an azimuth angular separation of 0.19 degrees under the available image-coordinate calibration. Multitemporal master and slave images are subsequently compared through filtered amplitude differences and coherence maps computed with multiple spatial averaging windows. The results highlight the relevance of SAR amplitude and coherence products for detecting structural and surface-condition variations in dense port environments characterized by vessels, storage tanks, quay structures, industrial yards, and water-land transitions.

We introduce a rank-one Riemannian cometric update inducing a modification of the Riemannian metric that makes specific directions of motion cheaper to travel along. We establish basic completeness properties of this reward metric, and give an explicit characterization of its Levi–Civita connection. We propose a preconditioned trajectory-tracking strategy by adding the connection-difference term to a standard intrinsic PD control, and illustrate the construction on a connection control-affine system on the Special Euclidean group with a maze navigation experiment. When the nominal trajectory is an integral curve of the vector field used to define the reward metric, our methodology improves the overall tracking, which is demonstrated through simulation results.

This paper constructs a connection map on the second-order tangent bundle induced by a linear connection on the base manifold and uses it to define a generalized Sasaki metric. The associated geodesic equations are derived, and jet-constrained variational problems are shown to yield Riemannian quintics in tension. The construction is then specialized to rigid body attitude dynamics with first-order actuator dynamics, producing an intrinsic higher-order trajectory model on the rotation group. Numerical simulations compare quintics in tension with Riemannian cubics as nominal trajectories and show modest reductions in actuator-relevant cost with comparable tracking performance.

Tissue motion correction through image registration is essential for ultrasound localization microscopy (ULM). Parametric image registration is commonly formulated as an optimization problem where motion parameters are iteratively updated to maximize image similarity, and used optimization algorithms typically rely on gradient information, the explicit evaluation of which can become computationally demanding. This work investigates Extremum Seeking Control (ESC) as an alternative to explicit derivative evaluation in image registration. By obtaining descent information via integrating perturbed and demodulated image similarity metric across iterations, ESC avoids differentiation of the image similarity metric with respect to motion parameters in each iteration. The classical ESC, whose optimization behavior approximates that of classical gradient descent (GD), is first compared with GD for affine image registration using simulated ground-truth motions derived from a beating ex vivo porcine heart dataset. The results show that ESC achieves registration accuracy and convergence behavior comparable to GD while reducing per-iteration computational cost by approximately 3.5-fold. ESC is subsequently employed in a two-stage motion correction pipeline, where affine registration compensates for global tissue motion and B-spline registration corrects residual local deformation. The proposed method is applied to ULM imaging of a beating ex vivo porcine heart and achieves a spatial resolution of 219 um, substantially below the half-wavelength diffraction limit of 321 um associated with 2.4 MHz diverging-wave imaging. These results demonstrate that ESC provides an effective alternative to explicit derivative evaluation in ULM image registration, enabling accurate motion correction and high-quality super-resolution imaging.

This work investigates the application of a domain-shift aware neural network for regression tasks aimed at estimating unbalance masses in rotating shafts under varying operating conditions. Experimental data were collected from a test rig in which a primary shaft, equipped with a flange carrying unbalanced masses, was driven at different rotational speeds, while a secondary shaft could be optionally activated to introduce domain discrepancy. The unbalance masses were positioned at a fixed radial distance, and the dynamic response of the system was recorded using triaxial accelerometers. The inverse problem of mass estimation is formulated within a domain adaptation framework, where the network is trained with a maximum mean discrepancy strategy to align feature representations across source and target distributions. The results demonstrate the effectiveness of explicitly addressing domain shift in improving prediction accuracy, especially when the system's physical behavior and sources of domain discrepancy are not fully known and fall outside the training conditions. These findings highlight the potential of domain-shift aware models for regression tasks in Structural Health Monitoring.

This paper proposes a novel approach to design of Nonlinear Model Predictive Control (NMPC) schemes based on Finite-Gain Stability (FGS) concepts. The proposed formulation considers the case where the plant is affected by unknown but bounded disturbances, which renders difficult the classical Lyapunov-based analysis/design. Based on FGS conditions for a closed-loop system, we develop a systematic NMPC design methodology, allowing us to choose the relevant NMPC parameters that lead to closed-loop FGS and provide a satisfactory tracking performance, also for the case of time-varying reference signals. A simulated example is presented to demonstrate the effectiveness of our framework, concerned with lateral/longitudinal control of an automated vehicle.

The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data-center energy management mainly relies on temporal and spatial workload shifting and campus-level energy asset scheduling, but it usually treats LLM inference demand as an aggregate load. As a result, these approaches fail to exploit the internal characteristics of LLM serving and therefore overlook the flexibility offered by LLM-specific techniques such as model quantization. To unlock this flexibility, this paper proposes a quantization-enabled energy management framework for grid-responsive LLM inference data centers. First, a quantization-to-power model is established to map each model–quantization configuration to a compact set of dispatchable parameters. Second, a two-stage quantization-enabled DR model is developed to account for model instance switching, request routing, and precision selection. Third, a multi-campus co-optimization method is introduced for DR participation by integrating grid-side electricity and carbon signals with the quantization-enabled DR model. Case studies show that the proposed framework reduces total data-center operating cost by 34.3% without curtailing served token volume, validating model quantization as an effective flexibility lever for grid-responsive LLM data-center energy management.

In this article, we address the problem of spectrum scarcity in cellular networks (CNs). We propose a backup channel (BuC) for cellular users (CUs) located in the same macro-cell under the control of a single macro base station (eNB). This BuC operates in television white space and is detected by the CUs through a cognitive radio energy-detection channel-sensing technique with a certain probability of success. When all regular channels with the cellular eNB are occupied, the CUs within the same coverage area of the macro eNB can utilize the sensed BuC to establish a controlled out-of-band device-to-device link for communication. The BuC bypasses the eNB for data communication and reduces the burden on the core of the CN. This leads to improved cellular eNB capacity. In the proposed system model, each CU and eNB is equipped with two antennas for communication in two separate bands, i.e., cellular and TV bands. Simulations show significant reductions in the blocking probability and probability of call delay.

We propose a method for extending the depth-of-field (DoF) to construct high-fidelity neural radiance fields (NeRF) – an emerging technique for rendering photorealistic novel views from a dataset of images captured at different viewpoints, based on implicit neural representations. The trade-off between DoF and light quantity is inherent not only in conventional cameras but also in NeRF, since the datasets used by NeRF are captured by these cameras. To address this issue, we introduce a coded aperture placed at the camera pupil, preserving spatial frequency components under defocused conditions. We develop a camera model incorporating coded apertures into NeRF, allowing direct input of coded images and enabling the generation of novel views with an extended DoF. We validate the proposed method, termed extended DoF-NeRF (EDoF-NeRF), through simulations and experiments, demonstrating its superior performance compared to conventional aperture cameras.

This letter proposes a learn-to-optimize (LTO) architecture for distributed optimal power flow (D-OPF) as the nexus between data-driven and model-based methods. By unfolding alternating direction method of multipliers (ADMM) into a deep neural network (NN) and embedding differentiable optimization layers, our architecture realizes near-instantaneous interpretable distributed decision-making. For mainstream relaxed formulations of D-OPF, the decisions from our architecture achieve comparable optimality with that of state-of-the-art solvers and excelled feasibility compared with existing data-driven approaches. Comparative case studies underpin the effectiveness of our architecture regarding the optimality and feasibility.

Cooling-limited exothermic semi-batch reactors require coordinated feed and cooling control to shorten batch time while maintaining the prescribed temperature. We develop a theoretical basis for advanced regulatory control (ARC) design by combining minimum-time and local safety analyses. Minimum-time analysis leads to an economic valve position control structure that adjusts feed using the temperature control system's cooling request, while cooling regulates temperature. Local safety analysis specifies the controller form and tuning conditions for reducing feed during cooling overload and restoring it as capacity becomes available. We also provide guidelines for industrial implementation and tuning. A reduced benchmark verifies the analytical tuning conditions, and an industrial-scale polymerization model evaluates the design. In simulations with parameter mismatch and unmodeled reaction dynamics, ARC achieves batch times comparable to those of parameter adaptive nonlinear model predictive control, using regulatory feedback without online nonlinear optimization.

Power system benchmarks usually evaluate numerical solvers, prediction models, or sequential controllers. These benchmarks are necessary, but they do not directly test whether a Large Language Model (LLM) agent can execute an engineering workflow: inspect a grid case, select tools, call simulators, screen contingencies, propose admissible mitigations, validate results, and produce an auditable evidence trail. This paper introduces PowerAgentBench-SS, a steady-state benchmark framework for evaluating tool-using agents in power system operation and planning studies. The benchmark exposes public case data, action constraints, a tool API, and a validation budget to an agent, while a hidden evaluator recomputes physical validity and scores the submitted report. We define the agent interface, tool contract, evidence log, and risk-sensitive metrics, including submitted recall, evidence-backed recall, found recall, false-safe penalties, severity regret, residual violation score, action cost, tool-use efficiency, and workflow diagnostics. To make the framework concrete, we instantiate the protocol in a reproducible DC thermal N-2 contingency-search pilot on deterministic IEEE 39-bus operating-point variants, with scripted baselines, an LLM JSON-command adapter, three locally hosted Ollama LLM agents, and one OpenAI API agent. The results show why solver-only or answer-only evaluation is insufficient: agents are distinguished not only by top-contingency discovery, but also by validation-budget use, explicit submission, type coercions, duplicate validations, evidence-backed reporting, and mitigation behavior.

A rotatable antenna (RA)-enhanced secure integrated sensing and communications system is investigated, where an RA-based transceiver simultaneously communicates with legitimate users and senses a target that is regarded as a potential eavesdropper. Under imperfect eavesdropping channel state information (CSI), a max-min data rate optimization problem is formulated by jointly optimizing the transmit beamforming, artificial noise (AN) covariance matrix, and transmit/receive boresights of RAs, subject to the maximum information leakage and minimum sensing power constraints. To address the highly non-convex problem, the information leakage and sensing power constraints are transformed into convex ones via S-Procedure method and Cauchy-Schwarz inequality, respectively. Subsequently, an alternating optimization algorithm is developed to decompose the reformulated problem into two subproblems. In particular, the transmit beamforming and AN covariance matrix are optimized by utilizing successive convex approximation and semi-definite relaxation methods, while the RA boresights are obtained by invoking the particle swarm optimization. Simulation results show that the RA-based scheme significantly outperforms the benchmarks, and offers enhanced robustness against imperfect CSI with the increase of the maximum rotation range.

We derive a one-dimensional model for heat transfer in a moving fluid incorporating Fourier conduction, an exponentially decaying memory term, and advection under thermally insulated boundary conditions. We numerically construct a bounded state feedback law driving the closed-loop solution to zero exponentially with decay rate at least $\omega>0$ for every initial state, i.e., we solve the $\omega$-stabilization problem. We explicitly describe the eigenvalues of the state operator $A$, a subset of which converges to a finite negative accumulation point that sets the upper bound on the achievable decay rate. Since $A$ lacks compact resolvent, we show that the spectrum is the closure of its eigenvalues, each of finite algebraic multiplicity, and use this to verify stabilizability. For $\omega$ below the accumulation bound, the problem is solvable provided the control operator $B$ satisfies a non-orthogonality condition. To compute gains, we formulate an LQR problem and solve finite-dimensional approximations: for each $n$ we construct $A_n$, $B_n$ approximating $A$, $B$ and solve the associated algebraic Riccati equation for a gain $K_n$. We show that, for all sufficiently large $n$, $K_n$ can be chosen so every eigenvalue of $A_n+B_nK_n$ satisfies $\operatorname{Re}\lambda<-\omega$, and we establish stabilizability of $(A_n+\omega I,B_n)$ uniformly in $n$. Hence, for large $n$, these gains solve the $\omega$-stabilization problem for the original system. We validate the results numerically with an example.

Federated learning (FL) in energy-harvesting (EH) networks is challenged by intermittent and stochastic energy arrivals that lead to unstable device participation across training rounds, and by high communication costs under limited energy budgets, reducing overall training efficiency. This paper studies FL under a slot-based EH model and proposes EH-FedSAG, a server-memory-based variance-reduced method. We compare EH-FedSAG with vanilla EH-FedAvg under the same multi-channel orthogonal multiple-access uplink model and within a unified simulation framework that captures battery charging, local computation cost, and transmission cost under different energy-arrival probabilities. Performance is assessed in terms of test accuracy over training rounds for both homogeneous and heterogeneous data distributions. The results show that EH-FedSAG consistently achieves higher test accuracy than EH-FedAvg in the considered settings, while exhibiting substantially lower training variance. The advantage of EH-FedSAG is more pronounced under scarce energy availability and non-independent/identically-distributed data.

Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks. Among various approaches, localized statistical channel modeling (LSCM), which models the channel multipath angular power spectrum (APS) from the reference signal received power (RSRP) measurement, has emerged as a state-of-the-art method tailored for efficient network optimization. However, despite its effectiveness, LSCM cannot predict APS at the vast majority of locations where no measurements are available, which significantly restricts its applicability in large-scale, real-world scenarios. To address this challenge, we present point-cloud-assisted tangent Gaussian splatting (PC-TGS), the first framework to extrapolate APS to unmeasured outdoor grids by integrating sparse radio measurements with dense LiDAR-based geometry. PC-TGS represents environmental scatterers as anisotropic 3D Gaussians, initialized and refined through a relaxed-mean reparameterization of the raw point cloud. A tangent-plane projection accurately maps each Gaussian into the local angular domain, while a depth-aware electromagnetic splatting process aggregates their contributions. To ensure practical deployment, we derive a closed-form Gaussian-weighted average (GWA) for APS bin integration and provide a provable error bound. { Evaluations on a LiDAR-scanned city-scale dataset (5M points, 6,310 RSRP samples) demonstrate that PC-TGS achieves better APS and RSRP prediction performance compared to state-of-the-art baselines and faster inference time for APS extrapolation task. These results highlight the potential of PC-TGS to enable geometry-aware and data-efficient channel prediction in large-scale wireless digital twins.

Learning unsupervised representations of medical imaging cohorts can reveal anatomically meaningful prototypes without expert labels, which are often noisy and fail to capture true pathological heterogeneity. However, existing deep latent-variable models estimate Gaussian mixture priors via Euclidean averaging, producing prototypes that drift off the curved data manifold and degenerate as the number of sub-populations grows. We propose a manifold-anchored variational framework built on a geometry-aware Expectation-Maximization (EM) algorithm, whose M-step selects each sub-population prototype as the graph medoid with the highest diffusion centrality on a heat-kernel-weighted latent graph, ensuring that every prototype remains on-manifold. A Dirichlet energy regularizer enforces geometric smoothness of the latent space, and a per-sub-population uncertainty score enables label-free quality assessment. The manifold-anchored EM is a general-purpose geometric tool that extends standard EM and applies readily to other latent-variable models beyond this setting. On cardiac scar and brain MRI benchmarks, our framework attains the highest accuracy among all compared methods, produces the sharpest prototypes reported to date, and remains stable at large sub-population counts where all baselines degenerate. Code and implementation details are available at https://github.com/jr-xing/On-Manifold-Variational-Learning-with-Heat-Kernel-Priors.

Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness. Automatic utterance-level assessment of dysarthric speech can support scalable speech monitoring and therapy-related analysis. Yet training such systems is bottlenecked by the scarcity of clinically annotated dysarthric speech. This work proposes to augment dysarthric speech assessment using data from speech synthesis evaluations, specifically human-annotated utterances with Mean Opinion Score (MOS) labels from the QualiSpeech corpus. Experiments show that fine-tuning on speech synthesis assessment data consistently improves performance on both intelligibility and naturalness prediction, while joint training yields gains primarily on naturalness. These results suggest that synthesis artifacts and dysarthric speech share perceptual commonalities, and speech synthesis evaluation corpora offer a practical augmentation source that reduces reliance on scarce clinical annotations.

Gaussian segmentation is usually posed as transferring object knowledge from 2D foundation models into a 3D representation. This leaves a fundamental question unanswered: how much object structure is already encoded by a trained gaussian scene? We investigate this question with GS-IntSeg, an intrinsic, mask-free, and training-free method that constructs partitions using only: gaussian geometry, opacity, spherical-harmonic radiance, and deformation trajectories. On the dynamic Neu3D and HyperNeRF datasets, GS-IntSeg obtains a mean of 0.677 mIoU across multi-view and monocular scenes without masks, external features, or segmentation training. This intrinsic formulation also enables GS-IntSeg to require approximately 2.5 minutes per HyperNeRF scene on a consumer RTX 5080 to construct its partitions, over 10x faster than SAM-based methods that require mask generation, feature rendering, and other stages. These results suggest that gaussians alone can approach mask-supervised performance in gaussian scenes segmentation. While a gap remains between intrinsic and foundation model based methods, to our knowledge, GS-IntSeg is the first mask-free approach to gaussian segmentation, motivating a re-evaluation of the assumption that gaussian scene segmentation must be based on external masks and pointing toward an alternative faster, more generalizable segmentation approach.

Since the inception of electrical recording for phonograph records in 1924, records have been intentionally cut with a non-uniform frequency response to maximize the information density on a disc and to improve the signal-to-noise ratio. To reproduce a nominally flat signal within the available bandwidth, the effects of this cutting curve must be undone by applying an inverse curve on playback. Until 1953, with the introduction of what has become known as the RIAA curve, the playback curve required for any particular disc could vary by record company and over time. As a consequence, anyone seeking to hear or restore the information on a disc must have access to equipment that is capable of implementing multiple playback equalizations. This correction may be accomplished with either analog hardware or digital processing. The digital approach has the advantages of reduced cost and expanded versatility, but requires a transformation from continuous time, where the original curves are defined, to discrete time. This transformation inevitably comes with some deviations from the continuous-time response near the Nyquist frequency. There are many established methods for discretizing continuous-time filters, and these vary in performance, computational cost, and inherent latency. In this work, several methods for performing this transformation are explored in the context of phonograph playback equalization, and the performance of each approach is quantified. This work is intended as a resource for anyone developing systems for digital playback equalization or similar applications that require approximating the response of a continuous-time filter digitally.

The GOOSE 2D Fine-Grained Semantic Segmentation Challenge at the ICRA 2026 Workshop on Field Robotics evaluates dense semantic segmentation of off-road imagery over a fine-grained taxonomy of 64 classes and 11 evaluated non-void coarse categories. We present the first-place solution to this challenge. Our solution comprises two complementary improvements: (a) a network-level design that combines a self-supervised DINOv3 ViT-L/16 backbone, a ViT-Adapter, and a Mask2Former mask-classification decoder, together with a coarse-category auxiliary loss on the global [CLS] token; and (b) an inference-time aggregation strategy based on multi-scale and horizontal-flip test-time augmentation and an ensemble of the top three checkpoints selected using Codabench scores. Our method achieves an official composite score of 76.57%, consisting of 69.32% fine-class mIoU and 83.81% category-level mIoU, and ranks first on the final phase leaderboard: www.codabench.org/competitions/14257/#/results-tab.

The quantity that defines the behavior of a dynamic range compressor is the time-varying gain applied to the signal as a function of the input level. However, models of these devices are typically evaluated using proxy metrics because isolating the gain reduction signal from the audio input-output data included in existing datasets creates an ill-conditioned inverse problem. It is unclear how accurately these metrics describe the behavior the model is tasked with emulating, particularly as waveform-based metrics can be influenced by secondary effects introduced by analog processing and capture, even when those effects are inaudible. We investigate a method of evaluation in which the gain-reduction signal produced by a model is measured directly against a gain-reduction control voltage signal produced by the hardware. To evaluate the efficacy of this metric as a learning objective, a gray-box model is trained using loss computed directly over the gain control signals alongside two models trained using common proxy losses. The models trained using proxy losses did not achieve parity with models trained directly on the gain control signal when evaluated with respect to the underlying control trajectory, and the waveform-domain metrics assigned similar errors to models that were clearly separated by the direct metric. To facilitate further exploration of this method of evaluation, we present a Solid State Logic bus compressor dataset that includes the gain control voltage signal captured alongside the audio output.

Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities. MCI detection from spontaneous speech is promising for scalable screening. However, learned models often exploit demographic cues correlated with labels, resulting in a large performance gap across subgroups. We present a multimodal framework that combines (i) cross-model fusion between modalities (speech, text, and image), and (ii) unlearning using gradient reversal that discourages the shared embedding from encoding task-irrelevant demographic attributes. Evaluated on the multilingual benchmarks TAUKADIAL and PREPARE, our method outperforms the state-of-the-art multilingual and multimodal baseline in MCI classification while substantially reducing the performance gap across patient subgroups (sex and language). We further analyze transfer across datasets, showing that demographic unlearning helps learn more robust representations for MCI detection.

An experimental investigation of neural image classification on the CIFAR-10 benchmark is presented through fully connected and convolutional network formulations. The analysis emphasizes the complete learning pipeline: image vectorization, normalization, one-hot class encoding, supervised loss minimization, learning-rate selection, mini-batch training, convolutional feature extraction, max-pooling, and validation-based generalization assessment. A convolutional architecture with six convolutional layers and three max-pooling stages is evaluated for ten training epochs using a batch size of 128 and an Adam optimizer with a learning rate of 0.001. The validation accuracy reaches approximately 74.77%, while the validation loss begins to increase after the middle of training despite continued reduction in training loss. The resulting behavior illustrates the practical difference between representation learning and memorization, and it provides a compact experimental baseline for future studies on regularization, data augmentation, deeper architectures, and reproducible image-classification education.

Reference-based adaptive interference cancellation is evaluated for stereo audio recordings corrupted by real train noise and environmental background. The observed signal is modeled as a clean stereo program contaminated by an additive disturbance generated by an external acoustic source through unknown propagation paths. A second stereo recording, representing another filtered observation of the same physical noise source, is used as the reference input of a multi-reference recursive least-squares (RLS) estimator. The estimated train-interference component is subtracted from the noisy audio and followed by a finite-impulse-response low-pass postfilter. Three 74.01 s real audio sequences sampled at 11.025 kHz are processed under identical algorithmic parameters. Since clean ground truth is not available, performance is assessed with no-reference indicators: waveform behavior, Welch spectral estimates, RMS change, and residual normalized correlation with the reference. With 30 taps per reference channel, 15 anti-causal taps, and forgetting factor 0.999, the maximum reference correlation is reduced from 0.386–0.832 before processing to 0.011–0.016 after processing. The corresponding correlation-ratio reduction is approximately 30.6–34.1 dB, while the output RMS decreases by 1.8–4.8 dB depending on section and stereo channel. The results demonstrate that real train interference, including environmental acoustic effects, can be substantially attenuated when a correlated reference recording is available.

Unmanned aerial vehicle (UAV)-mounted base stations are highly susceptible to wind disturbances such as gusts and turbulence, which induce positional drift and degrade communication link quality, particularly in emergency scenarios. To address this challenge, we propose a DRL-based framework for wind-resilient trajectory adjustment and positioning based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The method models wind as a stochastic kinematic perturbation, avoiding complex aerodynamic modeling, thereby enabling the TD3 agent to learn adaptive control policies that maintain optimal coverage footprints. By prioritizing user-centric performance metrics under turbulent conditions, the proposed architecture ensures continuous service availability despite external disruptions. Simulation results demonstrate that the TD3-based approach effectively compensates for wind-induced displacements and outperforms benchmark methods, including Proximal Policy Optimization (PPO), in terms of throughput stability and robustness in windy environments.