Papers

This study presents an AI enhanced IoT framework for predictive maintenance and affordability optimization in smart microgrids using a Digital Twin modeling approach. The proposed system integrates real time sensor data, machine learning based fault prediction, and cost aware operational analytics to improve reliability and energy efficiency in distributed microgrid environments. By synchronizing physical microgrid components with a virtual Digital Twin, the framework enables early detection of component degradation, dynamic load management, and optimized maintenance scheduling. Experimental evaluations demonstrate improved predictive accuracy, reduced operational downtime, and measurable cost savings compared to baseline microgrid management methods. The findings highlight the potential of Digital Twin driven IoT architectures as a scalable solution for next generation intelligent and affordable energy systems.

This paper investigates the synthesis of controllers for displacement-based formation control in the presence of bounded disturbances, specifically focusing on uncertainties originating from measurement noise. While the literature frequently addresses such problems using stochastic frameworks, this work proposes a deterministic methodology grounded in set-theoretic concepts. By leveraging the principles of set invariance, we adapt the theory of ultimate boundedness to the specific dynamics of displacement-based formations. This approach provides a rigorous method for analyzing the system's behavior under persistent disturbances. Furthermore, this set-theoretic framework allows for the optimized selection of the proposed control law parameters to guarantee pre-specified performance bounds. The efficacy of the synthesized controller is demonstrated in the challenging application of maintaining tight formations in a multi-obstacles environment.

Compute-in-memory (CIM) techniques are widely employed in energy-efficient artificial intelligent (AI) processors. They alleviate power and latency bottlenecks caused by extensive data movements between compute and storage units. To extend these benefits to Transformer, this brief proposes a digital CIM macro to compute attention score. To eliminate dynamic matrix multiplication (MM), we reconstruct the computation as static MM using a combined QK-weight matrix, so that inputs can be directly fed to a single CIM macro to obtain the score results. However, this introduces a new challenge of 2-input static MM. The computation is further decomposed into four groups of bit-serial logical and addition operations. This allows 2-input to directly activate the word line via AND gate, thus realizing 2-input static MM with minimal overhead. A hierarchical zero-value bit skipping mechanism is introduced to prioritize skipping zero-value bits in the 2-input case. This mechanism effectively utilizes data sparsity of 2-input, significantly reducing redundant operations. Implemented in a 65-nm process, the 0.35 mm2 macro delivers 42.27 GOPS at 1.24 mW, yielding 34.1 TOPS/W energy and 120.77 GOPS/mm2 area efficiency. Compared to CPUs and GPUs, it achieves ~25x and ~13x higher efficiency, respectively. Against other Transformer-CIMs, it demonstrates at least 7x energy and 2x area efficiency gains, highlighting its strong potential for edge intelligence.

As the relevance of control systems capable of dealing with multiple objectives rises (e.g. being economic while maintaining a certain performance), multi-objective Switched Model Predictive Control combines all the advantages of Model Predictive Control while dealing with multiple objectives. We propose two novel frameworks, a nominal and a robust framework to guarantee recursive feasibility of each Model Predictive Controller under arbitrary switching and assure asymptotic stability of the closed-loop system applying the nominal framework and Input-to-State stability using the robust framework. The presented frameworks employ methods from switched systems, enabling the utilization of a supervisor control instance which allows for complex objectives and multi-objective control. Our numerical example confirms the superior performance of our proposed frameworks compared to a standard Model Predictive Control approach.

Non-intrusive load monitoring (NILM) is an advanced load monitoring technique that uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances. However, real-world NILM deployment still faces major challenges, including overfitting, low model generalization, and disaggregating a large number of appliances operating at the same time. To address these challenges, this work proposes an end-to-end framework for the NILM classification task, which consists of high-frequency labeled data, a feature extraction method, and a lightweight neural network. Within this framework, we introduce a novel feature extraction method that fuses Independent Component Analysis (ICA) and Principal Component Analysis (PCA) features. Moreover, we propose a lightweight architecture for multi-label NILM classification (Fusion-ResNet). The proposed feature-based model achieves a higher $F1$ score on average and across different appliances compared to state-of-the-art NILM classifiers while minimizing the training and inference time. Finally, we assessed the performance of our model against baselines with a varying number of simultaneously active devices. Results demonstrate that Fusion-ResNet is relatively robust to stress conditions with up to 15 concurrently active appliances.

This paper presents a transformer-based three- transmission-line (Tline) series Doherty power amplifier (PA) implemented in 65-nm CMOS, targeting broadband K/Ka-band applications. By integrating an impedance-scaling network into the output matching structure, the design enables effective load modulation and reduced impedance transformation ratio (ITR) at power back-off when employing stacked cascode transistors. The PA demonstrates a -3-dB small-signal gain bandwidth from 22 to 32.5 GHz, a saturated output power (Psat) of 21.6 dBm, and a peak power-added efficiency (PAE) of 39%. At 6dB back-off, the PAE remains above 24%, validating its suitability for high- efficiency mm-wave phased-array transmitters in next-generation wireless systems.

Volumetric ultrasound imaging faces a fundamental trade-off among image quality, frame rate, and hardware complexity. This study introduces three-dimensional Null Subtraction Imaging (3D NSI), a nonlinear beamforming framework that addresses this trade-off by combining computationally efficient null-subtraction process with multiplexing-aware sparse aperture designs on matrix arrays. We evaluate three apodization configurations: a fully addressed circular aperture and two Fermat's spiral sparse apertures. To overcome channel-sharing constraints common in matrix arrays multiplexed with low-channel-count ultrasound systems, we propose a spiral "no-reuse" apodization that enforces non-overlapping element sets across transmit-receive events. This design resolves multiplexing conflicts and enables up to a 16-fold increase in acquisition volume rate using only 240 active elements on a 1024-element probe. In computer simulations and tissue-mimicking phantom experiments, 3D NSI achieved an average improvement of 36% in azimuthal and elevational resolutions, along with an approximately 20% higher contrast ratio, compared to the conventional Delay-and-Sum (DAS) beamformer under matched transmit/receive configurations. When implemented with the spiral no-reuse aperture, the 3D NSI framework achieved over 1000 volumes per second with a computational load less than three times that of DAS, making it a practical solution for real-time 4D imaging.

This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure (AoAs/AoDs) using a Gaussian Mixture Model (GMM). Furthermore, a low-resolution analog-to-digital converter (ADC) is employed at each RF chain, which is crucial for wideband THz massive MIMO systems to reduce power consumption and hardware complexity, given the high sampling rates and large number of antennas involved. The quantized MU THz MIMO model is linearized using the popular Bussgang decomposition followed by BGSR based channel learning framework that results in sparsity across different subcarriers, where each subcarrier has its unique dictionary matrix. Next, the Bayesian Cramér Rao Bound (BCRB) is devised for bounding the normalized mean square error (NMSE) performance. Extensive simulations were performed to assess the performance improvements achieved by the proposed BGSR method compared to other sparse estimation techniques. The metrics considered for quantifying the performance improvements include the NMSE and bit error rate (BER).

Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often employed to enhance feature representations, yet conventional uniform averaging overlooks differences in trial informativeness and can degrade representational quality. We introduce a weighted bootstrapping approach that prioritizes more reliable trials to generate higher-quality augmented samples. In a Sentence Evaluation paradigm, weights were computed from relative ERP differences and applied during probabilistic sampling and averaging. Across conditions, weighted bootstrapping improved decoding accuracy relative to unweighted (from 68.35% to 71.25% at best), demonstrating that emphasizing reliable trials strengthens representational quality. The results demonstrate that reliability-based augmentation yields more robust and discriminative EEG representations. The code is publicly available at https://github.com/lyricists/NeuroBootstrap.

Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introduce DCA-LUT, the first deep learning framework for purple fringing removal. Inspired by the physical root of the problem, the spatial misalignment of RGB color channels due to lens dispersion, we introduce a novel Chromatic-Aware Coordinate Transformation (CA-CT) module, learning an image-adaptive color space to decouple and isolate fringing into a dedicated dimension. This targeted separation allows the network to learn a precise “purple fringe channel", which then guides the accurate restoration of the luminance channel. The final color correction is performed by a learned 5D Look-Up Table (5D LUT), enabling efficient and powerful% non-linear color mapping. To enable robust training and fair evaluation, we constructed a large-scale synthetic purple fringing dataset (PF-Synth). Extensive experiments in synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in purple fringing removal.

Monitoring battery health is essential for ensuring safe and efficient operation. However, there is an inherent trade-off between assessment speed and diagnostic depth-specifically, between rapid overall health estimation and precise identification of internal degradation states. Capturing detailed internal battery information efficiently remains a major challenge, yet such insights are key to understanding the various degradation mechanisms. To address this, we develop a parameterized physics-informed neural network (P-PINNSPM) over the key aging-related parameter space for a single particle model. The model can accurately predict internal battery variables across the parameter space and identifies internal parameters in about 30 seconds-achieving a 47x speedup over the finite volume method-while maintaining high accuracy. These parameters improve the battery state-of-health (SOH) estimation accuracy by at least 60.61%, compared to models without parameter incorporation. Moreover, they enable extrapolation to unseen SOH levels and support robust estimation across diverse charging profiles and operating conditions. Our results demonstrate the strong potential of physics-informed machine learning to advance real-time, data-efficient, and physics-aware battery management systems.

Operational near-real-time monitoring of Earth's surface deformation using Interferometric Synthetic Aperture Radar (InSAR) requires processing algorithms that efficiently incorporate new acquisitions without reprocessing historical archives. We present sequential phase linking approach using compressed single-look-complex images (SLCs) capable of producing surface displacement estimates within hours of the time of a new acquisition. Our key algorithmic contribution is a mini-stack reference scheme that maintains phase consistency across processing batches without adjusting or re-estimating previous time steps, enabling straightforward operational deployment. We introduce online methods for persistent and distributed scatterer identification that adapt to temporal changes in surface properties through incremental amplitude statistics updates. The processing chain incorporates multiple complementary metrics for pixel quality that are reliable for small SLC stack sizes, and an L1-norm network inversion to limit propagation of unwrapping errors across the time series. We use our algorithm to produce OPERA Surface Displacement from Sentinel-1 product, the first continental-scale surface displacement product over North America. Validation against GPS measurements and InSAR residual analysis demonstrates millimeter-level agreement in velocity estimates in varying environmental conditions. We demonstrate our algorithm's capabilities with a successful recovery of meter-scale co-eruptive displacement at Kilauea volcano during the 2018 eruption, as well as detection of subtle uplift at Three Sisters volcano, Oregon – a challenging environment for C-band InSAR due to dense vegetation and seasonal snow. We have made all software available as open source libraries, providing a significant advancement to the open scientific community's ability to process large InSAR data sets in a cloud environment.

The emotional and structural experience of music remains a significant accessibility challenge for the deaf and hard of hearing community. This paper introduces MUSTEM (Multisensorial Emotional Translation), a novel system designed to translate music into a rich, coherent, and scientifically-grounded sensory experience. We present a dual-modality approach addressing this challenge through two interconnected components. First, a low-cost, portable hardware prototype that performs real-time audio analysis, mapping distinct frequency bands (sub-bass, bass, mid-range, treble) to a four-channel vibrotactile system, allowing users to feel the music's rhythmic and foundational structure. Second, to overcome the processing limitations of embedded hardware, we developed a high-fidelity software simulation that demonstrates the full potential of the visual translation. This assistive dashboard decodes musical components - such as rhythm, harmony, and frequency spectrum - into an intuitive and educational visual interface. MUSTEM offers a comprehensive framework for sensory substitution, presenting a viable and accessible pathway for the deaf community to experience music not just as vibration, but as a structured, substantiated and emotionally resonant visual and tactile language. Preliminary feedback from seven deaf users suggests the system's spatial vibrotactile mapping is perceptible and engaging. All source code and hardware designs are released as open-source. Video demonstrations and open-source code are available on the project's official channel.

The increasing integration of energy storage systems (ESSs) into power grids has necessitated effective real-time control strategies under uncertain and volatile electricity prices. An important problem of model predictive control of ESSs is identifying the minimum forecast horizon needed to exactly simulate the globally optimal control trajectory. Existing methods in the literature provide only sufficient conditions and might ignore real-world inconsistencies in control actions. In this paper, we introduce a trajectory-alignment-based definition of the minimum forecast horizon and propose an algorithm that identifies the minimum planning horizon for which all rolling-horizon control decisions match those of the full-horizon global optimization. Using real price data from the bidding zone DK1 in Denmark of the Nord Pool day-ahead market and a realistic ESS model, we illustrate that $60$ hours of forecast horizon allows us to exactly simulate the global control sequence and economic outcomes. In addition, we illustrate that under other parameter configurations, no forecast horizon ensures full convergence, demonstrating the sensitivity of the existence of a forecast horizon to various parameters. Our findings provide an operationally significant framework for minimum forecast horizon detection in storage scheduling and pave the way for the analytical description of this important planning measure.

Autonomous robots operating in dynamic environments must balance global path optimality with real-time responsiveness to disturbances. This requires addressing a fundamental trade-off between computationally expensive global planning and fast local adaptation. Sampling-based planners such as RRT* produce near-optimal paths but struggle under perturbations, while dynamical systems approaches like SEDS enable smooth reactive behavior but rely on offline data-driven optimization. We introduce Sampling-Based Adaptive Motion Planning (SBAMP), a hybrid framework that combines RRT*-based global planning with an online, Lyapunov-stable SEDS-inspired controller that requires no pre-trained data. By integrating lightweight constrained optimization into the control loop, SBAMP enables stable, real-time adaptation while preserving global path structure. Experiments in simulation and on RoboRacer hardware demonstrate robust recovery from disturbances, reliable obstacle handling, and consistent performance under dynamic conditions.

This paper investigates a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) system with multiple information decoding (ID) and energy harvesting (EH) users. Under the considered system model, we formulate an optimization problem that maximizes the sum-rate of all ID users via the design of the TRIS transceiver's active beamforming. The design is constrained by per-antenna power limits at the TRIS transceiver and by the minimum harvested energy demand of all EH users. Due to the non-convexity of the objective function and the energy harvesting constraint, the sum-rate problem is difficult to tackle. To solve this challenging optimization problem, by leveraging the weighted minimum mean squared error (WMMSE) framework and the majorization-minimization (MM) method, we propose a second-order cone programming (SOCP)-based algorithm. Per-element power constraints introduce a large number of constraints, making the problem considerably more difficult. By applying the alternating direction method of multipliers (ADMM) method, we successfully develop an analytical, computationally efficient, and highly parallelizable algorithm to address this challenge. Numerical results are provided to validate the convergence and effectiveness of the proposed algorithms. Furthermore, the low-complexity algorithm significantly reduces computational complexity without performance degradation.

A novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered simultaneous wireless information and power transfer (SWIPT) framework is proposed. The sum-rate of the information decoding (ID) users is maximized by optimizing the TRIS transceiver's beamforming, subject to the energy harvesting (EH) users' quality-of-harvest and the per-antenna power constraints. To solve this non-convex problem, we develop an efficient optimization algorithm. First, the original problem is reformulated as a semi-definite programming (SDP) problem. The resulting SDP problem is then addressed using successive convex approximation (SCA) combined with a penalty-based method. Numerical results demonstrate the effectiveness of the algorithm.

Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability in practice. We introduce Implicit-MAP (ImMAP), a diffusion-based reconstruction framework that integrates the acquisition noise model directly into a maximum a posteriori (MAP) formulation. Specifically, we build on the stochastic ascent method of Kadkhodaie et al. and generalize it to handle MRI encoding operators and realistic measurement noise. Across both simulated and real noisy datasets, ImMAP consistently outperforms state-of-the-art deep learning (LPDSNet) and diffusion-based (DDS) methods. By clarifying the practical behavior and limitations of diffusion models under realistic noise conditions, ImMAP establishes a more reliable and interpretable

Recently, Wi-Fi signals have emerged as a powerful tool for contactless sensing. During the sensing process, obtaining target direction information can provide valuable contextual insights for various applications. Existing direction estimation methods typically rely on antenna arrays, which are costly and complex to deploy in real-world scenarios. In this paper, we present WiRainbow, a novel approach that enables single-antenna-based direction awareness for Wi-Fi sensing by leveraging the dispersion effect of frequency-scanning antennas (FSAs), which can naturally steer Wi-Fi subcarriers toward distinct angles during signal transmission. To address key challenges in antenna design and signal processing, we propose a coupled-resonator-based antenna architecture that significantly expands the narrow Field-of-View inherent in conventional FSAs, improving sensing coverage. Additionally, we develop a sensing signal-to-noise-ratio-based signal processing framework that reliably estimates target direction in multipath-rich environments. We prototype WiRainbow and evaluate its performance through benchmark experiments and real-world case studies, demonstrating its ability to achieve accurate, robust, and cost-effective direction awareness for diverse Wi-Fi sensing applications.

Decentralized air traffic management systems offer a scalable alternative to centralized control, but often assume high levels of cooperation. In practice, such assumptions frequently break down since agents, each representing an airspace sector, may operate independently and prioritize local objectives. We address the problem of sector overload in decentralized air traffic management by proposing a regulated decentralized protocol that models self-interested behaviors based on best response dynamics. Each agent adjusts the departure times of its assigned flights to reduce its own congestion, without requiring centralized joint optimization. A tunable cooperativeness factor models the degree to which each agent accounts for overload in other sectors, while a minimal admissibility rule prevents local updates from creating new overloads. We show that this regulatory restriction is sufficient to guarantee finite-time convergence of best response dynamics to a pure Nash equilibrium under partial cooperation. In addition, we identify a sufficient condition under which an overload-free solution corresponds to a global minimizer of the potential function. Numerical experiments using one month of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation, achieving performance close to a centralized global mixed-integer optimizer and exceeding a centralized first-come-first-served baseline while requiring substantially less computation than centralized optimization. We further evaluate departure delay and fairness to quantify the operational cost of overload mitigation.

In this paper, we propose a new Temporal MDS-Vision Transformer (T-MDS-ViT) for multiclass target classification using millimeter-wave FMCW radar micro-Doppler spectrograms. Specifically, we design a transformer-based architecture that processes stacked range-velocity-angle (RVA) spatiotemporal tensors via patch embeddings and cross-axis attention mechanisms to explicitly model the sequential nature of MDS data across multiple frames. The T-MDS-ViT exploits mobility-aware constraints in its attention layer correspondences to maintain separability under target overlaps and partial occlusions. Next, we apply an explainable mechanism to examine how the attention layers focus on characteristic high-energy regions of the MDS representations and their effect on class-specific kinematic features. We also demonstrate that our proposed framework is superior to existing CNN-based methods in terms of classification accuracy while achieving better data efficiency and real-time deployability.

In this paper, we propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs), built on an open architecture that leverages open interfaces and artificial intelligence (AI)-based functionalities. This approach advances interoperability, flexibility, and intelligence in next-generation telecommunications. First, we provide a concise overview of the state-of-the-art architectures for Open-RAN and AI-RAN, highlighting key network functions and infrastructure elements. Next, we introduce our integrated AIO-RAN-NTN blueprint, emphasizing how internal and air interfaces from AIO-RAN and the 3rd Generation Partnership Project (3GPP) can be applied to emerging environments such as NTNs. To examine the impact of mobility on AIO-RAN, we implement a testbed transmission using the OpenAirInterface platform for a standalone (SA) New Radio (NR) 5G system. We then train an AI model on realistic data to forecast key performance indicators (KPIs). Our experiments demonstrate that the AIO-based SA architecture is sensitive to mobility, even at low speeds, but this limitation can be mitigated through AI-driven KPI forecasting.

Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotations for clinical applications like sleep staging and seizure detection. While current EEG SSL methods predominantly use masked reconstruction strategies like masked autoencoders (MAE) that capture local temporal patterns, position prediction pretraining remains underexplored despite its potential to learn long-range dependencies in neural signals. We introduce PAirwise Relative Shift or PARS pretraining, a novel pretext task that predicts relative temporal shifts between randomly sampled EEG window pairs. Unlike reconstruction-based methods that focus on local pattern recovery, PARS encourages encoders to capture relative temporal composition and long-range dependencies inherent in neural signals. Through comprehensive evaluation on various EEG decoding tasks, we demonstrate that PARS-pretrained transformers consistently outperform existing pretraining strategies in label-efficient and transfer learning settings, establishing a new paradigm for self-supervised EEG representation learning.

Ultrasound-based risk stratification of thyroid nodules is a critical clinical task, but it suffers from high inter-observer variability. While many deep learning (DL) models function as "black boxes," we propose a fully automated, two-stage framework for interpretable malignancy prediction. Our method achieves interpretability by forcing the model to focus only on clinically relevant regions. First, a TransUNet model automatically segments the thyroid nodule. The resulting mask is then used to create a region of interest around the nodule, and this localised image is fed directly into a ResNet-18 classifier. We evaluated our framework using 5-fold cross-validation on a clinical dataset of 349 images, where it achieved a high F1-score of 0.852 for predicting malignancy. To validate its performance, we compared it against a strong baseline using a Random Forest classifier with hand-crafted morphological features, which achieved an F1-score of 0.829. The superior performance of our DL framework suggests that the implicit visual features learned from the localised nodule are more predictive than explicit shape features alone. This is the first fully automated end-to-end pipeline for both detecting thyroid nodules on ultrasound images and predicting their malignancy.

In safety-critical control systems, ensuring both safety and feasibility under sampled-data implementations is crucial for practical deployment. Existing Control Barrier Function (CBF) frameworks, such as High-Order CBFs (HOCBFs), effectively guarantee safety in continuous time but may become unsafe when executed under zero-order-hold (ZOH) controllers due to inter-sampling effects. Moreover, they do not explicitly handle finite-time reach-and-remain requirements or multiple simultaneous constraints, which often lead to conflicts between safety and reach-and-remain objectives, resulting in feasibility issues during control synthesis. This paper introduces Sampling-Aware Control Barrier Functions (SACBFs), a unified framework that accounts for sampling effects and high relative-degree constraints by estimating and incorporating Taylor-based upper bounds on barrier evolution between sampling instants. The proposed method guarantees continuous-time forward invariance of safety and finite-time reach-and-remain sets under ZOH control. To further improve feasibility, a relaxed variant (r-SACBF) introduces slack variables for handling multiple constraints realized through time-varying CBFs. Simulation studies on a unicycle robot demonstrate that SACBFs achieve safe and feasible performance in scenarios where traditional HOCBF methods fail.

Neuromorphic engineering aims at designing computing and control systems inspired by the neurons and the brain. For the control community, neuromorphic control is an emerging topic that focuses on designing event-based spiking controllers in the form of spiking neural networks (SNNs). At present, systematic methods for designing and analyzing such controllers are lacking. Therefore in this paper we present a systematic approach for stabilizing linear time-invariant (LTI) systems using SNN-based controllers, in the form of a network of integrate-and-fire neurons, whose input is the measured output from the plant, and which generate spiking control signals. The new approach consists of a two-step emulation-based design procedure. In the first step, we establish conditions on the neuron parameters to ensure that the spiky signal generated by a pair of neurons emulates any continuous-time signal input to the neurons with arbitrary accuracy in terms of a special metric for spiky signals. In the second step, we propose a novel stability notion, called spiky-Input-to-State Stability (sISS) building on this metric, and prove that an asymptotically stable LTI system has this sISS property. By combining these steps, a certifiable practical stability property of the closed-loop system can be established. The approach is illustrated in a numerical case study.

This paper proposes a robust control strategy that integrates Iterative Learning Control (ILC) with a simple lateral neural network to enhance the trajectory tracking performance of a linear Lorentz force actuator under friction and model uncertainties. The ILC compensates for nonlinear friction effects, while the neural network estimates the nonlinear ILC effort for varying reference commands. By dynamically adjusting the ILC effort, the method adapts to time-varying friction, reduces errors at reference changes, and accelerates convergence. Compared to previous approaches using complex neural networks, this method simplifies online training and implementation, making it practical for real-time applications. Experimental results confirm its effectiveness in achieving precise tracking across multiple tasks with different reference trajectories.

This research presents a simple strategy for designing an exponentially tapered, triple-notched ultrawideband antenna. The antenna's microstrip line feed and radiating patch are matched using an exponential tapered transformer. This method inserts antenna notch elements, by cutting two inverted C-shaped slots in the radiating patch; frequency rejection can be achieved for WI-MAX and wireless LAN. The X-band is rejected by etching a U-shaped slot in the feedline. When embedding the notch elements, cross-coupling was minimized. The desired antenna was designed, simulated, and measured. The measured results and graphs show that our proposed design is reliable. This band notched antenna rejects 3.5 GHz (Wi-MAX band, 3.3 to 3.7 GHz), 5.5 GHz (WLAN 2 band, 5.15 to 5.825 GHz), and 7.5 GHz (for satellite downlink X - band-7.25 GHz to 7.75 GHz). The proposed antenna meets UWB design requirements.

Speech quality and intelligibility are significantly degraded in noisy environments. This paper presents a novel transformer-based learning framework to address the single-channel noise suppression problem for real-time applications. Although existing deep learning networks have shown remarkable improvements in handling stationary noise, their performance often diminishes in real-world environments characterized by non-stationary noise (e.g., dog barking, baby crying). The proposed dual-input acoustic-image feature fusion using a hybrid ViT framework effectively models both temporal and spectral dependencies in noisy signals. Designed for real-world audio environments, the proposed framework is computationally lightweight and suitable for implementation on embedded devices. To evaluate its effectiveness, four standard and commonly used quality measurements, namely PESQ, STOI, Seg SNR, and LLR, are utilized. Experimental results obtained using the Librispeech dataset as the clean speech source and the UrbanSound8K and Google Audioset datasets as the noise sources, demonstrate that the proposed method significantly improves noise reduction, speech intelligibility, and perceptual quality compared to the noisy input signal, achieving performance close to the clean reference.

Many promising applications of multimodal wearables require continuous sensing and heavy computation, yet users reject such devices due to privacy concerns. This paper shares our experiences building an ear-mounted voice-and-vision wearable that performs local AI inference using a paired smartphone as a trusted personal edge. We describe the hardware-software co-design of this privacy-preserving system, including challenges in integrating a camera, microphone, and speaker within a 30-gram form factor, enabling wake word-triggered capture, and running quantized vision-language and large-language models entirely offline. Through iterative prototyping, we identify key design hurdles in power budgeting, connectivity, latency, and social acceptability. Our initial evaluation shows that fully local multimodal inference is feasible on commodity mobile hardware with interactive latency. We conclude with design lessons for researchers developing embedded AI systems that balance privacy, responsiveness, and usability in everyday settings.