Papers

With the rapid development of diffusion models, style transfer has made remarkable progress. However, flexible and localized style editing for scene text remains an unsolved challenge. Although existing scene text editing methods have achieved text region editing, they are typically limited to content replacement and simple styles, which lack the ability of free-style transfer. In this paper, we introduce SceneTextStylizer, a novel training-free diffusion-based framework for flexible and high-fidelity style transfer of text in scene images. Unlike prior approaches that either perform global style transfer or focus solely on textual content modification, our method enables prompt-guided style transformation specifically for text regions, while preserving both text readability and stylistic consistency. To achieve this, we design a feature injection module that leverages diffusion model inversion and self-attention to transfer style features effectively. Additionally, a region control mechanism is introduced by applying a distance-based changing mask at each denoising step, enabling precise spatial control. To further enhance visual quality, we incorporate a style enhancement module based on the Fourier transform to reinforce stylistic richness. Extensive experiments demonstrate that our method achieves superior performance in scene text style transformation, outperforming existing state-of-the-art methods in both visual fidelity and text preservation.

We propose a novel framework for estimating the parameters of an aggregated distributed energy resources (DER A) model. First, we introduce a rigorous method to determine whether all model parameters are estimable. When they are not, our approach identifies the subset of parameters that can be estimated. The proposed framework offers new insights into the number and specific parameters that can be reliably estimated based on commonly available measurements. It also highlights the limitations of calibrating such models. Second, we introduce a Kalman filtering method to calibrate the DER A model. Since we account for nonlinear effects such as saturation and deadbands, we develop a specific mechanism to handle smoothing functions within the Kalman filter. Specifically, we consider the extended and the unscented Kalman filter. We demonstrate the effectiveness of the proposed framework on a modified IEEE 34-node distribution feeder with inverter- based resources. Our findings align with the North American Electric Reliability Corporation's parameterization guideline and underscore the importance of model calibration in accurately capturing the collective dynamics of distributed energy resources installed on distribution systems.

Ambisonics is a method for capturing and rendering a sound field accurately, assuming that the acoustics of the playback room does not significantly influence the sound field. However, in practice, the acoustics of the playback room may lead to a noticeable degradation in sound quality. We propose a recording and rendering method based on Ambisonics that utilizes a perceptually-motivated approach to compensate for the reverberation of the playback room. The recorded direct and reverberant sound field components in the spherical harmonics (SHs) domain are spectrally and spatially compensated to preserve the relevant auditory cues including the direction of arrival of the direct sound, the spectral energy of the direct and reverberant sound components, and the Interaural Coherence (IC) across each auditory band. In contrast to the conventional Ambisonics, a flexible number of Ambisonics channels can be used for audio rendering. Listening test results show that the proposed method provides a perceptually accurate rendering of the originally recorded sound field, outperforming both conventional Ambisonics without compensation and even ideal Ambisonics rendering in a simulated anechoic room. Additionally, subjective evaluations of listeners seated at the center of the loudspeaker array demonstrate that the method remains robust to head rotation and minor displacements.

Electricity storage is used for intertemporal price arbitrage and for ancillary services that balance unforeseen supply and demand fluctuations via frequency regulation. We present an optimization model that computes bids for both arbitrage and frequency regulation and ensures that storage operators can honor their market commitments at all times for all fluctuation signals in an uncertainty set inspired by market rules. This requirement, initially expressed by an infinite number of nonconvex functional constraints, is shown to be equivalent to a finite number of deterministic constraints. The resulting formulation is a mixed-integer bilinear program that admits mixed-integer linear relaxations and restrictions. Empirical tests on European electricity markets show a negligible optimality gap between the relaxation and the restriction. The model can account for intraday trading and, with a solution time of under 5 seconds, may serve as a building block for more complex trading strategies. Such strategies become necessary as battery capacity exceeds the demand for ancillary services. In a backtest from 1 July 2020 through 30 June 2024 joint market participation more than doubles profits and almost halves energy output compared to no FCR participation.

Physically interpretable models are essential for next-generation industrial systems, as these representations enable effective control, support design validation, and provide a foundation for monitoring strategies. The aim of this paper is to develop a system identification framework for estimating modal models of complex multivariable mechanical systems from frequency response data. To achieve this, a two-step structured identification algorithm is presented, where an additive model is first estimated using a refined instrumental variable method and subsequently projected onto a modal form. The developed identification method provides accurate, physically-relevant, minimal-order models, for both generally-damped and proportionally damped modal systems. The effectiveness of the proposed method is demonstrated through experimental validation on a prototype wafer-stage system, which features a large number of spatially distributed actuators and sensors and exhibits complex flexible dynamics.

Direction-of-arrival (DoA) estimation with leaky-wave antennas (LWAs) offers a compact and cost-effective alternative to conventional antenna arrays but remains challenging in the presence of coherent sources. To address this issue, we propose a spatially filtered sparse Bayesian learning (SF-SBL) framework. Firstly, the field of view (FoV) is divided into angular sectors according to the frequency beam-scanning property of LWAs, and Bayesian inverse problems are then solved within each sector to improve efficiency and reduce computational cost. Both on-grid SBL and off-grid SBL formulations are developed. Simulation results show that the proposed approach achieves robust and accurate DoA estimation, even with coherent sources.

We present a comprehensive two-layer Voronoi coverage control approach for coordinating hybrid aerial-ground robot teams in hazardous material emergency response scenarios. Traditional Voronoi coverage control methods face three critical limitations in emergency contexts: heterogeneous agent capabilities with vastly different velocities, clustered initial deployment configurations, and urgent time constraints requiring rapid response rather than eventual convergence. Our method addresses these challenges through a decoupled two-layer architecture that separately optimizes aerial and ground robot positioning, with aerial agents delivering ground sensors via airdrop to high-priority locations. We provide detailed implementation of bounded Voronoi cell computation, efficient numerical integration techniques for importance-weighted centroids, and robust control strategies that prevent agent trapping. Simulation results demonstrate an 88% reduction in response time, achieving target sensor coverage (18.5% of initial sensor loss) in 25 seconds compared to 220 seconds for ground-only deployment. Complete implementation code is available at https://github.com/dHutchings/ME292B.

As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, designed to adjust the detection threshold ({\epsilon}) in real-time. The threshold is updated based on the recursive mean and standard deviation of displacement errors between GPS and in-vehicle sensors data, but only at instances classified as non-anomalous. Furthermore, an initial threshold, determined from 120,000 clean data samples, ensures the capability to identify even subtle and gradual GPS spoofing attempts from the beginning. To assess the performance of the proposed method, five different subsets from the real-world Honda Research Institute Driving Dataset (HDD) are selected to simulate both large and small magnitude GPS spoofing attacks. The modified algorithm effectively identifies turn-by-turn, stop, overshoot, and multiple small biased spoofing attacks, achieving detection accuracies of 98.621%, 99.960.1%, 99.880.1%, and 98.380.1%, respectively. This work provides a substantial advancement in enhancing the security and safety of AVs against GPS spoofing threats.

Random telegraph signal (RTS) analysis is increasingly important for characterizing meaningful temporal fluctuations in physical, chemical, and biological systems. The simplest RTS arises from discrete stochastic switching events between two binary states, quantified by their transition amplitude and dwell times in each state. Quantitative analysis of RTSs provides valuable insights into microscopic processes such as charge trapping in semiconductors. However, analyzing RTS becomes considerably complex when signals exhibit multi-level structures or are corrupted by background white or pink noise. To address these challenges and support high-throughput RTS characterization, we propose a modular, training-free signal processing pipeline that integrates adaptive dual-tree complex wavelet transform (DTCWT) denoising with a lightweight Bayesian digitization strategy. The adaptive DTCWT denoiser incorporates autonomous parameter selection rules for its decomposition level and thresholds, optimizing white noise suppression without manual tuning. Our Bayesian digitizer formulates RTS level assignment as a probabilistic latent-state inference problem incorporating temporal regularization without iterative optimization, effectively resolving binary trap states even under residual notorious background pink noise. Quantitative benchmarking on large synthetic datasets with known ground truth demonstrates improved RTS reconstruction accuracy, trap-state resolution, and dwell-time estimation across diverse noise regimes and multi-trap scenarios, while achieving up to 83x speedups over classical and neural baselines. Qualitative validation on experimental RTS data when no ground truth is available illustrates practical usability and flexibility for real-time or large-scale analysis in real measurement settings.

Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies – Mean Spatial-Lag Autocorrelation and Spatial Smoothing – for its HDC pipeline, and then reframes DoA estimation as a pattern recognition problem. This approach leverages HDC's inherent robustness to noise and its transparent algebraic operations to bypass the expensive matrix decompositions and "black-box" nature of classical and deep learning methods, respectively. Our evaluation demonstrates that HYPERDOA achieves ~35.39% higher accuracy than state-of-the-art methods in low-SNR, coherent-source scenarios. Crucially, it also consumes ~93% less energy than competing neural baselines on an embedded NVIDIA Jetson Xavier NX platform. This dual advantage in accuracy and efficiency establishes HYPERDOA as a robust and viable solution for mission-critical applications on edge devices.

This paper introduces Bhasha-Rupantarika, a light and efficient multilingual translation system tailored through algorithm-hardware codesign for resource-limited settings. The method investigates model deployment at sub-octet precision levels (FP8, INT8, INT4, and FP4), with experimental results indicating a 4.1x reduction in model size (FP4) and a 4.2x speedup in inference speed, which correlates with an increased throughput of 66 tokens/s (improvement by 4.8x). This underscores the importance of ultra-low precision quantization for real-time deployment in IoT devices using FPGA accelerators, achieving performance on par with expectations. Our evaluation covers bidirectional translation between Indian and international languages, showcasing its adaptability in low-resource linguistic contexts. The FPGA deployment demonstrated a 1.96x reduction in LUTs and a 1.65x decrease in FFs, resulting in a 2.2x enhancement in throughput compared to OPU and a 4.6x enhancement compared to HPTA. Overall, the evaluation provides a viable solution based on quantisation-aware translation along with hardware efficiency suitable for deployable multilingual AI systems. The entire codes [https://github.com/mukullokhande99/Bhasha-Rupantarika/] and dataset for reproducibility are publicly available, facilitating rapid integration and further development by researchers.

Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DERs under PEM with fixed packet durations and only turn-ON requests. Different recent efforts to enable variable packet lengths have shown an increase in available flexibility and ramping capability, but have not been modeled in aggregate, which limits systematic analyses. To address this issue, this paper presents a new aggregate bin-based (macro) model of PEM loads that incorporates both turn-ON and turn-OFF request features, enabling the model to accurately characterize the capability of the fleet of DERs to track a power reference signal, population temperature dynamics, aggregate request rates, and variable packet lengths. Simulation-based validation is performed against an agent-based (micro) model to evaluate robustness and quantify model accuracy. Finally, the distribution of variable packet lengths from macro-model simulations are applied to inform past work on PEM with randomized packet lengths

Just Noticeable Distortion (JND)-guided pre-filter is a promising technique for improving the perceptual compression efficiency of image coding. However, existing methods are often computationally expensive, and the field lacks standardized benchmarks for fair comparison. To address these challenges, this paper introduces a twofold contribution. First, we develop and open-source FJNDF-Pytorch, a unified benchmark for frequency-domain JND-Guided pre-filters. Second, leveraging this platform, we propose a complete learning framework for a novel, lightweight Convolutional Neural Network (CNN). Experimental results demonstrate that our proposed method achieves state-of-the-art compression efficiency, consistently outperforming competitors across multiple datasets and encoders. In terms of computational cost, our model is exceptionally lightweight, requiring only 7.15 GFLOPs to process a 1080p image, which is merely 14.1% of the cost of recent lightweight network. Our work presents a robust, state-of-the-art solution that excels in both performance and efficiency, supported by a reproducible research platform. The open-source implementation is available at https://github.com/viplab-fudan/FJNDF-Pytorch.

Increasing attention is given to the upper mid-band or Frequency Range 3 (FR3), from 7 to 24 GHz, in the research towards sixth-generation (6G) networks. Promises of offering large data rates at favorable propagation conditions are leading to novel FR3 base station (BS) architectures, with up to thousands of antenna elements and radio-frequency (RF) chains. This work investigates the power consumption of prospective FR3 BSs and its relation to the delivered data rates. We model the power consumed by digital and analog signal processing, power amplifiers (PAs), and supply and cooling during four phases (data, signaling, micro-sleep, and idle) in downlink and uplink. Hybrid partially-connected beamforming is compared to fully-digital one. Results show that, for BS arrays with $1024$ antennas at $30\%$ of load, the PA consumes most of the power when $64$ or less RF chains are utilized, while the digital and analog processing consumption takes over when the number of RF chains is $512$ or more. The digital plus analog processing consumes $2\times$ to $4\times$ more than the PA for fully-digital beamforming. Hybrid beamforming achieves $1.3$ Gbit/s/user in downlink while improving the energy efficiency by $1.4\times$ compared to fully-digital beamforming.

This paper proposes an integrated sensing and communication (ISAC) system covert waveform design method for complex clutter environments, with the core objective of maximizing the signal-to-clutter-plus-noise ratio (SCNR). The design achieves efficient clutter suppression while meeting the covertness requirement through joint optimization of the transmit waveform and receive filter, enabling cooperative radar detection and wireless communication. This study presents key innovations that explicitly address target Doppler shift uncertainty, significantly enhancing system robustness against Doppler effects. To ensure communication reliability, the method incorporates phase difference constraints between communication signal elements in the waveform design, along with energy constraint, covert constraint, and peak-to-average power ratio (PAPR) constraint. The original non-convex optimization problem is transformed into a tractable convex optimization form through convex optimization technique. Simulation results demonstrate that the optimized waveform not only satisfies the covertness requirement in complex clutter environment, but also achieves superior target detection performance. It also ensures reliable communication and confirms the effectiveness of propose method.

Accurate channel prediction and effective beamforming are essential for low Earth orbit (LEO) satellite communications to enhance system capacity and enable high-speed connectivity. Most existing channel prediction and predictive beamforming methods are limited by model generalization capabilities and struggle to adapt to time-varying wireless propagation environments. Inspired by the remarkable generalization and reasoning capabilities of large language models (LLMs), this work proposes an LLM-based channel prediction framework, namely CPLLM, to forecast future channel state information (CSI) for LEO satellites based on historical CSI data. In the proposed CPLLM, a dedicated CSI encoder is designed to map raw CSI data into the textual embedding space, effectively bridging the modality gap and enabling the LLM to perform reliable reasoning over CSI data. Additionally, a CSI decoder is introduced to simultaneously predict CSI for multiple future time slots, substantially reducing the computational burden and inference latency associated with the inherent autoregressive decoding process of LLMs. Then, instead of training the LLM from scratch, we adopt a parameter-efficient fine-tuning strategy, i.e., LoRA, for CPLLM, where the pretrained LLM remains frozen and trainable low-rank matrices are injected into each Transformer decoder layer to enable effective fine-tuning. Furthermore, we extend CPLLM to directly generate beamforming strategies for future time slots based on historical CSI data, namely BFLLM. This extended framework retains the same architecture as CPLLM, while introducing a dedicated beamforming decoder to output beamforming strategies. Finally, extensive simulation results validate the effectiveness of the proposed approaches in channel prediction and predictive beamforming for LEO satellite communications.

The study is anchored to the principles of Nearly-Zero Energy Building (NZEB). It aimed to transform the Tarlac State University Gymnasium into a facility with energy-efficient equipment to contribute to reducing carbon footprints by integrating a solar PV system as its renewable energy source. The researchers found out that the electrical infrastructure of the Gym was outdated, and the lighting was not energy efficient, and there were too few convenience or power outlets. There was also insufficient cooling equipment to maintain a comfortable temperature. Analysis shows that the payback period is within the average range, making it a cost-effective investment for the University. Aside from the cost of the PV System, adherence to engineering design standards will mean additional costs to replace the metal halides with LED high bay lamps, installation of additional air conditioning units, and provision of additional convenience outlets. These additional costs should be considered when evaluating the feasibility of the project. It is recommended that the integrity of the existing roof system of the Gymnasium be considered. The total cost of putting up the whole electrical system, including new lighting, cooling, and convenience loads, must be calculated to determine the total cost of implementing the whole NZEB project. Other factors in the economic evaluation may be considered to determine a more stringent result.

Four-channel bilateral control is a method for achieving remote control with force feedback and adjustment operability by synchronizing the positions and forces of two manipulators. This is expected to significantly improve the operability of the remote control in contact-rich tasks. Among these, 4-channel bilateral control on the Cartesian coordinate system is advantageous owing to its suitability for manipulators with different structures and because it allows the dynamics in the Cartesian coordinate system to be adjusted by adjusting the control parameters, thus achieving intuitive operability for humans. This paper proposes a 4-channel bilateral control method that achieves the desired dynamics by decoupling each dimension in the Cartesian coordinate system regardless of the scaling factor.

This study proposes a robust non-contact physiological sensing method using distributed radar systems to address the challenges arising from varying body orientations. Conventional physiological sensing methods using a single radar system suffer from performance degradation caused by variations in the relative geometry between the radar and the human body, particularly those caused by changes in body orientation. To help radar systems overcome this limitation, we propose a multivariate physiological variational mode decomposition method that extracts common respiratory and heart rates across multiple displacements of various body parts acquired from distributed radar systems. The proposed method incorporates harmonic constraints and a gap component tailored to the intrinsic nature of physiological signals into the model, thereby improving both estimation accuracy and robustness. Simultaneous measurements using four radar systems were performed on six participants under different participant-position and body-orientation conditions. Using the proposed method, we achieved success rates exceeding 90% for respiration and heartbeat estimation, which represent improvements of 19.8 and 25.3 percentage points, respectively, over rates using a conventional method based on a single radar system. Furthermore, we conducted simultaneous measurements of 16 participants using two radar systems in a multi-person scenario and successfully estimated both respiratory and heart rates with over 85% success rates. Our work contributes to the realization of practical radar-based non-contact physiological sensing by establishing a data fusion framework for distributed radar systems.

Traditional directed graph signal processing generally depends on fixed representation matrices, whose rigid structures limit the model's ability to adapt to complex graph topologies. To address this issue, this study employed the unified graph representation matrix (UGRM) to propose a generalized graph Fourier transform (UGRM-GFT) method based on singular value decomposition (SVD) for signal analysis on directed graphs and Cartesian product graphs. We defined UGRM-GFT for general directed graphs by introducing a parameterized UGRM that incorporates traditional representations such as the Laplacian matrix and adjacency matrix. The SVD is used to construct spectral transform pairs with both left and right singular vectors. We extended this approach to two types of UGRM-GFTs applied to directed Cartesian product graphs. UGRM-GFT-I performs SVD directly on the composite UGRM matrix of the two-dimensional graph structure, suitable for globally coupled graph signals. UGRM-GFT-II separately applies SVD to the UGRMs of the two-factor graphs and then combines the results, significantly reducing computational complexity while preserving spectral expressiveness. Theoretical analysis confirmed the monotonicity of the proposed method with respect to the parameters alpha and k embedded in the UGRM. Experimental results on real-world datasets demonstrated that the proposed method significantly outperforms traditional fixed-matrix approaches in denoising tasks, with a particular emphasis on signal-to-noise ratio and bandwidth efficiency.

Remote Direct Memory Access (RDMA) is a memory technology that allows remote devices to directly write to and read from each other's memory, bypassing components such as the CPU and operating system. This enables low-latency high-throughput networking, as required for many modern data centres, HPC applications and AI/ML workloads. However, baseline RDMA comprises a highly permissive weak memory model that is difficult to use in practice and has only recently been formalised. In this paper, we introduce the Library of Composable Objects (LOCO), a formally verified library for building multi-node objects on RDMA, filling the gap between shared memory and distributed system programming. LOCO objects are well-encapsulated and take advantage of the strong locality and the weak consistency characteristics of RDMA. They have performance comparable to custom RDMA systems (e.g. distributed maps), but with a far simpler programming model amenable to formal proofs of correctness. To support verification, we develop a novel modular declarative verification framework, called Mowgli, that is flexible enough to model multinode objects and is independent of a memory consistency model. We instantiate Mowgli with the RDMA memory model, and use it to verify correctness of LOCO libraries.

The graph linear canonical transform (GLCT)-based filtering methods often optimize transform parameters and filters separately, which results in high computational costs and limited stability. To address this issue, this paper proposes a trainable joint optimization framework that combines GLCT parameters and Wiener filtering into an end-to-end learning process, allowing for synergistic optimization between transform domain construction and filtering operations. The proposed method not only eliminates the cumbersome grid search required by traditional strategies but also significantly enhances the flexibility and training stability of the filtering system. Experimental results on real-world graph data show the proposed method outperforms existing methods in denoising tasks, featuring superior denoising performance, higher robustness and lower computational complexity.

This paper proposes an efficient 3D avatar coding framework that leverages compact human priors and canonical-to-target transformation to enable high-quality 3D human avatar video compression at ultra-low bit rates. The framework begins by training a canonical Gaussian avatar using articulated splatting in a network-free manner, which serves as the foundation for avatar appearance modeling. Simultaneously, a human-prior template is employed to capture temporal body movements through compact parametric representations. This decomposition of appearance and temporal evolution minimizes redundancy, enabling efficient compression: the canonical avatar is shared across the sequence, requiring compression only once, while the temporal parameters, consisting of just 94 parameters per frame, are transmitted with minimal bit-rate. For each frame, the target human avatar is generated by deforming canonical avatar via Linear Blend Skinning transformation, facilitating temporal coherent video reconstruction and novel view synthesis. Experimental results demonstrate that the proposed method significantly outperforms conventional 2D/3D codecs and existing learnable dynamic 3D Gaussian splatting compression method in terms of rate-distortion performance on mainstream multi-view human video datasets, paving the way for seamless immersive multimedia experiences in meta-verse applications.

Rydberg atomic quantum receivers (RAQRs) have attracted significant attention in recent years due to their ultra-high sensitivity. Although capable of precisely detecting the amplitude and phase of weak signals, conventional RAQRs face inherent limitations in accurately receiving wideband RF signals, due to the discrete nature of atomic energy levels and their intrinsic instantaneous bandwidth constraints. These limitations hinder their direct application to multi-carrier communication and sensing. To address this issue, this paper proposes a multi-carrier Rydberg atomic quantum receiver (MC-RAQR) structure with five energy levels. We derive the amplitude and phase of the MC-RAQR and extract the baseband electrical signal for signal processing. In terms of multi-carrier communication and sensing, we analyze the channel capacity and accuracy of angle of arrival (AoA) and distance parameters, respectively. Numerical results validate our proposed model, showing that the MC-RAQR can achieve up to a bandwidth of 11.7 MHz, which is 17-fold larger than the conventional RAQRs. As a result, the channel capacity and the resolution for multi-target sensing are improved significantly. Specifically, the channel capacity of MC-RAQR is 110-fold and 2.8-fold larger than the classical RF receivers and RAQRs, respectively. For sensing performance, the RMSE of AoA estimation for MC-RAQR exhibits 7.6-fold reduction, compared with the conventional RAQRs. Furthermore, the RMSE of distance estimation is $634$-fold smaller than that of the root-CRB of classical RF receivers, showing the superior performance of the MC-RAQR. This demonstrates its compatibility with waveforms such as orthogonal frequency-division multiplexing (OFDM) and its significant advantages for multi-carrier signal reception.

Galilean symmetry is the natural symmetry of inertial motion that underpins Newtonian physics. Although rigid-body symmetry is one of the most established and fundamental tools in robotics, there appears to be no comparable treatment of Galilean symmetry for a robotics audience. In this paper, we present a robotics-tailored exposition of Galilean symmetry that leverages the community's familiarity with and understanding of rigid-body transformations and pose representations. Our approach contrasts with common treatments in the physics literature that introduce Galilean symmetry as a stepping stone to Einstein's relativity. A key insight is that the Galilean matrix Lie group can be used to describe two different pose representations, Galilean frames, that use inertial velocity in the state definition, and extended poses, that use coordinate velocity. We provide three examples where applying the Galilean matrix Lie-group algebra to robotics problems is straightforward and yields significant insights: inertial navigation above the rotating Earth, manipulator kinematics, and sensor data fusion under temporal uncertainty. We believe that the time is right for the robotics community to benefit from rediscovering and extending this classical material and applying it to modern problems.

Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down and lift-off events and holonomic constraints for each leg. This paper presents a unified reinforcement learning framework for generating versatile quadrupedal gaits by leveraging the intrinsic symmetries and velocity-period relationship of dynamic legged systems. We propose a symmetry-guided reward function design that incorporates temporal, morphological, and time-reversal symmetries. By focusing on preserved symmetries and natural dynamics, our approach eliminates the need for predefined trajectories, enabling smooth transitions between diverse locomotion patterns such as trotting, bounding, half-bounding, and galloping. Implemented on the Unitree Go2 robot, our method demonstrates robust performance across a range of speeds in both simulations and hardware tests, significantly improving gait adaptability without extensive reward tuning or explicit foot placement control. This work provides insights into dynamic locomotion strategies and underscores the crucial role of symmetries in robotic gait design.

Incremental stability is a property of dynamical systems that ensures the convergence of trajectories with respect to each other rather than a fixed equilibrium point or a fixed trajectory. In this paper, we introduce a related stability notion called incremental input-to-state practical stability ({\delta}-ISpS), ensuring safety guarantees. We also present a feedback linearization based control design scheme that renders a partially unknown system incrementally input-to-state practically stable and safe with formal guarantees. To deal with the unknown dynamics, we utilize Gaussian process regression to approximate the model. Finally, we implement the controller synthesized by the proposed scheme on a manipulator example

This paper presents a hybrid control framework with a risk-budgeted monitor for safety-certified autonomous driving. A sliding-window monitor tracks insufficient barrier residuals and triggers switching from a relaxed control barrier function (R-CBF) to a more conservative conditional value-at-risk CBF (CVaR-CBF) when the safety margin deteriorates. Two real-time triggers are considered: feasibility-triggered (FT), which activates CVaR-CBF when the R-CBF problem is reported infeasible, and quality-triggered (QT), which switches when the residual falls below a prescribed safety margin. The framework is evaluated with model predictive control (MPC) under vehicle localization noise and obstacle position uncertainty across multiple AV-pedestrian interaction scenarios with 1,500 Monte Carlo runs. In the most challenging case with 5 m pedestrian detection uncertainty, the proposed method achieves a 94–96% collision-free success rate over 300 trials while maintaining the lowest mean cross-track error (CTE = 3.2–3.6 m), indicating faster trajectory recovery after obstacle avoidance and a favorable balance between safety and performance.

In nature, signals often appear in the form of the superposition of multiple non-stationary signals. The overlap of signal components in the time-frequency domain poses a significant challenge for signal analysis. One approach to addressing this problem is to introduce an additional chirprate parameter and use the chirplet transform (CT) to elevate the two-dimensional time-frequency representation to a three-dimensional time-frequency-chirprate representation. From a certain point of view, the CT of a signal can be regarded as a windowed special linear canonical transform of that signal, undergoing a shift and a modulation. In this paper, we develop this idea to propose a novel windowed linear canonical transform (WLCT), which provides a new time-frequency-chirprate representation. We discuss four types of WLCTs. In addition, we use a special X-ray transform to further sharpen the time-frequency-chirprate representation. Furthermore, we derive the corresponding three-dimensional synchrosqueezed transform, demonstrating that the WLCTs have great potential for three-dimensional signal separation.

Image feature matching plays a vital role in many computer vision tasks. Although many image feature detection and matching techniques have been proposed over the past few decades, it is still time-consuming to match feature points in two images, especially for images with a large number of detected features. Feature spatial order can estimate the probability that a pair of features is correct. Since it is a completely independent concept from epipolar geometry, it can be used to complement epipolar geometry in guiding feature match in a target region so as to improve matching efficiency. In this paper, we integrate the concept of feature spatial order into a progressive matching framework. We use some of the initially matched features to build a computational model of feature spatial order and employs it to calculates the possible spatial range of subsequent feature matches, thus filtering out unnecessary feature matches. We also integrate it with epipolar geometry to further improve matching efficiency and accuracy. Since the spatial order of feature points is affected by image rotation, we propose a suitable image alignment method from the fundamental matrix of epipolar geometry to remove the effect of image rotation. To verify the feasibility of the proposed method, we conduct a series of experiments, including a standard benchmark dataset, self-generated simulated images, and real images. The results demonstrate that our proposed method is significantly more efficient and has more accurate feature matching than the traditional method.