As tomorrow's machines become more general and autonomous, building resiliency into these systems is of critical importance. High-gain observers aggressively respond to errors and guarantee semi-global stability. Supervisory shared control architectures demonstrate the utility of a human-on-the-loop approach to enhance controllers via configurable parameters. Here, we investigate how a cascade high-gain observer-based controller designed to achieve leader-follower tracking can be modified to allow for human intervention, with the specific objective of maintaining performance goals while minimizing control saturation and aggressiveness. We perform envelope-straining experiments that suggest an adjustment of the high-gain parameter before the saturation limits is a better strategy to minimize aggression and thus energy cost.

This paper presents a compact and low-profile slotted waveguide antenna (SWA), designed to achieve dual-frequency performance within the Ku-band, targeting low-earth-orbit (LEO) satellite applications. This study introduces an approach by parameter sweeping of the slot offset in the SWA design, revealing a new optimal slot offset that significantly outperforms the traditional Stevenson method for calculating uniform slot offsets. Additionally, this investigation modifies the conventional calculations for the slot length and width, leading to the development of a new SWA configuration that achieves dual-frequency performance within the Ku band for the first time, overcoming the limitations of the conventional SWAs. The SWA features 10 longitudinal slots with optimized uniform offsets, lengths, and widths on the broad wall of the waveguide, operating at 14.3 GHz and 16.7 GHz. It achieves high performance with total antenna efficiency exceeding 99% and gains exceeding 15 dBi. The proposed SWA exhibits linear polarization with low side lobe levels (SLLs), and low reflection coefficients. Two prototypes, weighing 24 g and 22 g respectively, were fabricated using 3D printing with different methods, measured with a Vector Network Analyzer (VNA), and compared with the electromagnetic simulation results. The proposed SWA enhances the antenna's adaptability for dual-frequency applications within the Ku band, making it particularly effective for the frequency variations of LEO satellite systems.

Stochastic Computing has turned out to be a very suitable hardware design approach for Morphological Neural Networks (MNNs), given its easiness in implementing maxima, minima and products through simple logic gates that perform bitwise operations. This work enhances a previous MNN design by using approximate binary adders, reducing the required hardware resources of adders which are the most area-consuming part of the MNN. The experimental results show a minimal decrease in accuracy, a reduction in circuit area and increases in speed and energy efficiency. The proposed approach has been validated on a 180nm UMC CMOS chip for the MNIST experiment, showing highly competitive performance and energy efficiency values with respect other works present in the literature.

Vehicle-to-vehicle dynamic wireless charging (V2V-DWC) is a contemporary electrified transportation technology in which a dedicated charging vehicle supplies power to a user vehicle. This technology is gaining popularity due to the slow progress in energy storage technology and the limited availability of plug-in charging stations. The concept of V2V wireless power transfer offers a means for EVs to replenish their batteries during a journey. Literature shows that the concept has been empirically validated through analysis and experimentation, however, the issue of misalignment has not been addressed from this perspective yet. For optimal power transfer in V2V, the coils need to be in perfect alignment. Lateral Misalignment (LTM) arises when these coils are not properly aligned which results in undesirable energy loss. Furthermore, there is still a deficiency in the development of appropriate controllers for V2V misalignment problem solution. This paper aims to develop Neural Network based Adaptive Fuzzy Logic Controller (ANFIS), to mitigate the misalignment problem of V2V-DWC system. The paper delves into a comparative analysis of the proposed controller with the existing Fuzzy Logic Controller (FLC) to assess their effectiveness for different degrees of LTMs. The effectiveness of the proposed ANFIS controller has been assessed using simulations in MATLAB/Simulink alongside experimental tests. Results show that proposed ANFIS outperforms the FLC in both simulated and experimental scenarios in solving the V2V misalignment issue.

Wrist photoplethysmogram (PPG) signals are widely used for physiological monitoring in consumer devices. However, the PPG is highly susceptible to noise, which can reduce the accuracy of monitored parameters. The aim of this study was to identify factors which influence PPG signal quality. Data from the Aurora-BP dataset were used, consisting of reflectance wrist PPG signals measured from 1,155 subjects of varying ages and health statuses. Measurements were acquired in supine, sitting, and standing postures, and with the sensor held at different heights. Three signal quality metrics were calculated: the signal-to-noise ratio (SNR), the perfusion index (PI), and the template-matching correlation coefficient (TMCC). When comparing between postures with the sensor held at a natural height, quality was greatest in the supine position (SNR: 18.7 dB), followed by sitting with the arm resting in the lap (13.9 dB), and lowest whilst standing with the arm hanging alongside (9.1 dB) (p<0.001). Signal quality increased as the arm was raised to heart height: whilst sitting, quality was lowest with the arm alongside the body (10.7 dB), and increased when the sensor was held in the lap (13.9 dB) and at heart height (16.3 dB) (p<0.001). Similar trends were observed for the TMCC and PI. Findings were mixed for the influence of participant characteristics on signal quality. The SNR and TMCC, but not the PI, increased with age. The SNR decreased at darker skin tones when controlling for PPG DC amplitude, although this association did not hold for other metrics. In conclusion, this study identified the impacts of posture and sensor height on signal quality, with highest qualities observed in the supine posture and with the sensor at heart height. It also highlights the importance of adjusting LED light intensity to maintain signal quality across skin tones.

Climatic changes, which are increasingly responsible for the increase in the incidence and intensity of natural calamities, demand prompt and precise response and recovery actions. This research aims at determining how AI computer vision methodologies can be applied to aerial and satellite images related to a disastrous management system. We study different AI techniques that can be used in damage assessment, resource allocation, and recovery planning. This paper reviews data sources starting with aerial imagery from drones, to aircraft and satellite imagery, and encompasses all the diversity in sensors and resolutions. Key AI techniques brought into discussion include CNNs, semantic segmentation, and logistics optimization models. Key challenges identified are data quality, model generalization, and ethics. Applications in real-world settings are illustrated through case studies including rapid damage assessment following hurricane Maria, monitoring the Australian bushfires, and optimization of resources allocation during the COVID-19 pandemic. The survey outlines proposed future research directions with an emphasis on real-time analysis, integration of IoT and edge computing, improvement of model interpretability, and collaborative platforms. Surveying creates demands for continuous innovation and collaboration and underlines improvement for enhanced disaster preparedness and resilience.

This paper presents the design and implementation of a motion sensor circuit, demonstrating the integration of digital logic components to create a responsive system. The circuit's operation is based on the principle of motion-induced infrared detection. The IR sensor emits infrared radiation, which, upon detecting motion, triggers the CD-4017 IC to advance its outputs, sequentially activating the Triac BT136 and illuminating an LED. The paper elaborates on the theory of operation, the practical procedures for building the circuit, the cost analysis, challenges, and precautions. The results of the experiment confirm the circuit's functionality, demonstrating reliable motion sensing and response. This project not only provides hands-on experience in digital logic circuits but also underscores the significance of meticulous component selection, careful assembly, and safety in electronics projects. Building this motion sensor circuit offers valuable insights into motion detection technology with practical applications in security and automation systems.

We explore the innovative application of multimodal large language models (LLMs) in formation control of unmanned aerial vehicle (UAV) swarms by using image and text as input. To validate the feasibility of using LLMs in UAVs, in the first stage, we pre-train a large language model (LLM) using a dataset of 5000 samples for a single UAV, achieving a success rate of 78.0% in command extraction. This demonstrates the feasibility of using LLMs to control UAVs. Subsequently, we extend this framework to a UAV swarm, where the dataset is expanded to 20,000 samples, and the swarm LLM is pre-trained using this dataset. We utilize the GPT-4 multimodal LLM to process images captured by the leading UAV, which provides the UAV swarm with the capabilities of environmental recognition and semantic understanding. The pre-trained model achieves a success rate of 82.7% in command extraction. Specifically, for image understanding using multimodal LLM, we innovatively employ GPT-4 to process images captured by a leading UAV in the swarm, enabling visual feature-based formation planning with a planning success rate of 83.8%. Fianlly, based on our real-world experiments, LLMs exhibit outstanding performance in addressing both individual and collective UAV planning problems, demonstrating the extensive potential application of LLMs in multi-agent formation control and collective swarm tasks. Our experimental video is uploaded to https://youtu.be/WWQToKf8iyM.

The performance of polarized multiple-input-multiple-output (MIMO) systems can be significantly influenced by the antenna's cross-polar discrimination (AXPD) depending on the channel characteristics in communication scenarios utilizing radiating cables. Vertically polarized radiating cables, commonly used in these settings, often suffer from low AXPD due to inherent design constraints that compromise polarization purity, thereby limiting the system's ability to effectively manage multiple data streams. This study proposes a novel approach to enhance the AXPD of vertically polarized radiating cables for MIMO applications. The proposed design utilizes a recticoax cross-section as the feeder structure, optimized through the two-dimensional finite difference (2D-FD) method, which allows for precise modeling and improvement of the electromagnetic properties of the cable. The research specifically examines a scaled version of the enhanced cable design, tailored for operation within the 1700 to 4200 MHz frequency range— a spectrum critical for modern wireless communications. Simulation results indicate a significant improvement in AXPD, suggesting that the proposed design could considerably enhance the performance of MIMO systems using vertically polarized radiating cables. This advancement addresses key limitations of current designs, offering new opportunities for improving polarization performance and, consequently, the overall efficiency of MIMO communication systems in complex environments.

The need for additional capacity in motorway networks during periods of high demand is unavoidable if congestion is to be prevented. Increasing capacity by building new roads is often infeasible, leaving operation-based traffic control measures as the primary approach to exploit the existing infrastructure. In this paper, the novel concept of dynamic lane configuration is introduced, which opens a new avenue in motorway traffic control that harnesses the infrastructure for traffic improvement. The lateral capacity of the existing motorway infrastructure is under-utilized due to lanes that are much wider than the vehicle's width. Dynamic lane configuration suggests that while current wide lanes ensure safety during high-speed driving, lower speed limits can be actively imposed during times of high traffic demand, allowing the lane width to be reduced, thanks to the reduced required lateral gap between vehicles at lower longitudinal speeds. By narrowing the lanes prior to congestion, it is possible to reclaim wasted space and add lanes to the road, leading to a dynamic capacity increase during the operation. This dynamic infrastructure layout with demand-responsive lane configuration during operation bridges the traffic management and infrastructure design. A model-based optimal control approach is developed to model the dynamic lane configuration and to define the times and locations of changing lane configuration. The proposed approach is tested in a simulation environment on two different motorway networks, each with a different configuration and demand profile. The promising results indicate the potential of the proposed approach in congestion mitigation and reducing travel time.

This paper proposes a bank of symbol-level correlators (SLCs) derived in accordance with joint maximum likelihood estimation (JMLE) to test for the presence of individual user equipments (UEs). A fully digital, extended, fractionally spaced, two-dimensional (2D) array of SLCs is addressed to estimate both residual frequency errors (RFEs) and residual time errors (RTEs) simultaneously for multiple active UEs. The proposed technique exploits multi-rate signal processing to decimate (i.e., downsample) the received signal for reducing subsequent computational complexity and to interpolate (i.e., upsample) in the frequency domain to enhance the accuracy of RFE estimation. Coherent accumulation of the squared magnitudes of SLC outputs further improves the signal-to-interference-plus-noise ratio (SINR). The proposed approach offers flexibility and practicality, enabling highly accurate estimation of RFEs and RTEs over extended ranges beyond one subcarrier spacing and beyond one symbol duration, respectively, while achieving precision at the fractional levels of a subcarrier spacing and a symbol duration. Simulation results confirm effectiveness of the proposed technique.

Accurate control of continuum robots is challenging, especially in the presence of external disturbances. To address this issue, reinforcement learning (RL) has been increasingly investigated in continuum robot control due to its online policy updating capability. However, the gap of Sim2Real transfer caused by inaccurate modeling results in RL implementation a dilemma. In this article, we propose a safety-critical Real2Sim2Real online RL framework, where the Real2Sim transfer is firstly achieved by the identification of a continuum robot using the Koopman operator. We further improve the training efficiency by introducing an imperfect demonstration into the RL framework. The offline policy is trained in simulations and then tested on a real continuum robot platform. During tests, the tracking performance is influenced by the hysteresis effect, which cannot be captured by the Koopman operator. This results in a millimeter-level tracking root mean square error (RMSE). To address this issue, we online update the policy through continuous learning, and the RMSE of the online controller outperforms the offline controller by 89.16% in free space and 85.70% under external payload, respectively.

This study presents a novel method for visualizing the time-dependent development of a scientific discipline using UMAP (Uniform Manifold Approximation and Projection) and word embeddings. By encoding the abstracts of scholarly articles into a high-dimensional space and then projecting them into a 3D space, we demonstrate how the evolution of research interests and topics in a specific field can be mapped over time. This approach o↵ers new insights into the dynamics of scholarly discourse and the emergence and disappearance of research themes.

This study presents a constrained optimizationbased neuro-adaptive controller (CoNAC) for uncertain Euler-Lagrange systems subject to weight norm and input constraints. A deep neural network (DNN) is employed to approximate the ideal stabilizing control law, compensating for lumped system uncertainties while addressing both types of constraints. The weight adaptation laws are formulated through a constrained optimization problem, ensuring first-order optimality conditions at steady state. The controller's stability is rigorously analyzed using Lyapunov theory, guaranteeing bounded tracking errors and DNN weights. Numerical simulations comparing CoNAC with three benchmark controllers demonstrate its effectiveness in tracking error regulation and satisfaction of constraints.

Flexure bearings provide precise, low-maintenance operation but have a limited range of motion compared to conventional bearings. Here we introduce a new class of bearing – the metamorphic flexure bearing – that retains the advantages of precision, low wear, and low hysteresis over its limited flexure-bearing range, but provides an extended range of motion as needed. This extended range of motion is achieved via a position-activated transition to a conventional sliding or rolling bearing. To demonstrate the operating principles of this new class of bearing, we describe, design, assemble, and test a linear-motion metamorphic flexure bearing using three categorically-different transition mechanisms: a compression spring, a constant-force spring, and a pair of magnetic catches. This design paradigm has the potential to provide various benefits, such as reduced wear, reduced downtime, cost savings, and increased safety, in areas ranging from precision manufacturing to healthcare robotics to biomedical implants.

This paper demonstrates a method for capturing, analyzing, and replicating FM radio station signals. Using RTL-SDR and CUBIC-SDR software, signals are transmitted to a PC, converted to .wav, and processed in MATLAB via Fast Fourier Transform. Simulated transmission through an FM transmitter replicates original conditions, providing comparison of Fourier analysis graphs for signal equivalence. This systematic approach ensures accurate signal replication, contributing to advancements in signal processing and communication technology.

This study presents a novel chipless complementary split-ring resonator (CSRR) loaded substrate integrated waveguide (SIW) tag, coupled with an ultra-wideband coplanar antenna, for real-time monitoring of lubricant evaporation on lubricant-infused surfaces (LISs). The sensor introduces a new method for assessing the stability of the lubricant layer, a critical factor for the efficient operation of these coatings. Its design and functionality have been validated through simulations and measurements of the evaporation rate of two different lubricants with different vapor pressures, demonstrating high sensitivity in detecting lubricant evaporation. This sensor represents a significant advancement in biomaterial development and testing, providing a real-time solution to ensure the effectiveness of lubricant-infused coatings, which is essential for minimizing complications like non-specific adhesion of blood components and bacteria.

The blockchain space is evolving rapidly, yet regulatory compliance and oversight remain significant challenges. This paper presents a comprehensive framework for embedding legal protocols into blockchain smart contracts, ensuring compliance with regulatory standards across diverse sectors such as finance, supply chain, and healthcare. By executing protocols mandated by legal authorities, blockchain applications can achieve new levels of conformity, transparency, and accountability. The concept introduces innovative routers for real-time monitoring, automated legal enforcement, and a hybrid legal-tech infrastructure that strengthens trust in decentralized financial ecosystems. These protocols are designed to function similarly to ERC standards, offering a standardized and scalable approach to regulatory compliance across multiple blockchain platforms. Furthermore, the importance of regulatory protocols for virtual machines (VMs) is emphasized, with particular focus on ensuring secure and compliant operation in decentralized environments. The proposed framework promotes proactive compliance, enhanced transparency, and improved trust in blockchain-based systems. This paper explores the technical and legal implications, potential challenges, benefits, and future adoption strategies for this revolutionary approach, aiming to align the blockchain ecosystem with the requirements of modern regulatory frameworks.