Impact of Marine Locomotion Constraints on a Bio-inspired Aerial-Aquatic Wing:Experimental Performance Verification

This paper describes the design, fabrication, experimental testing and performance optimization of the morphology of a flapping wing for use on a robot capable of aerial and aquatic modes of locomotion. The focus of the optimization studies is that of wing design for aquatic propulsion. Inspiration for the research stems from numerous avian species which use a flapping wing for the dual purpose of locomotion (propulsion) in both air and water. The main aim of this research is to determine optimal kinematic parameters for marine locomotion that maximize nondimensionalized performance measures (e.g., propulsive efficiency), derived from analysis of avian wing morphing mechanisms that balance competing demands of both aerial and aquatic movement. Optimization of the kinematic parameters enables the direct comparison between outstretched (aerial) and retracted (aquatic) wing morphologies and permits trade-off studies in the design space for future robotic vehicles. Static foils representing the wing in both an extended and retracted orientation have been manufactured and subsequently subjected to testing over a range of kinematics. Details of the purpose built 2 degree-of-freedom (dof) flapping mechanism are presented. The gathered results enable validation of previously developed numerical models as well as quantifying achievable performance measures. This research focuses on the mechanical propulsive efficiencies and thrust coefficients as key performance measures whilst simultaneously considering the required mechanical input torques and the associated thrust produced.</jats:p

Optimal Power Flow with Hybrid Distributed Generators and Unified Controller

Optimal power flow (OPF) study is conducted on a power system to achieve one of the following objectives: cost/loss minimization or available transfer capability (ATC) calculation in a deregulated environment. Distributed generation (DG) is a small source of electric power conversion from non-conventional energy sources and Hybrid DGs which often the most cost-effective and reliable way to produce power. The optimality of control variables and minimum value of objective functions in OPF study would definitely change when DGs are interconnected to the grid. The change would be respect to the location, quantity and combination of power injection by DGs. On the other hand, FACTS controllers are effective in utilizing the existing of transmission network which is very important especially in a deregulated system. Unified power flow controller (UPFC), a second generation FACTS controller, is well known for minimizing the cost of generation/losses with a good voltage profile as well as for ATC improvement. This paper conducts a detailed OPF study on a 9 bus system for the above mentioned three objectives to analyze the effect of DGs with and without UPFC. From the results, it is found that hybrid DGs along with UPFC yields better performance in many aspects

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Introduction: Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods: This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson’s disease patients. Results: Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion: Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study’s findings can inform future design iterations of neural decoders for adaptive DBS and BCI

Caveolin-3 Microdomain: Arrhythmia Implications for Potassium Inward Rectifier and Cardiac Sodium Channel

In human cardiac ventricular myocytes, caveolin-3 functions as a scaffolding and regulatory protein for signaling molecules and compartmentalizes ion channels. Our lab has recently explored this sub-cellular microdomain and found that potassium inward rectifier Kir2.x is found in association with caveolin-3. The three cardiac Kir2.x isoforms (Kir2.1, Kir2.2, and Kir2.3) are the molecular correlates of IK1 in the heart, of which Kir2.1 is the dominant isoform in the ventricle. Kir2.1 channels assemble with Kir2.2 and Kir2.3 forming hetero-tetramers that modulate IK1. IK1 sets the resting membrane potential and assists with terminal phase 3 ventricular repolarization. In our studies using native human ventricular tissue, Kir2.x co-localizes with caveolin-3 and significance of the association between Kir2.x and caveolin-3 is emphasized in relation to mutations in the gene which encodes caveolin-3, CAV3, associated with Long QT Syndrome 9 (LQT9). LQT9-associated CAV3 mutations cause decreased current density in Kir2.1 and Kir2.2 as homomeric and heteromeric channels, which affects repolarization and membrane potential stability. A portion of Kir2.1 cardiac localization parallels that of the cardiac sodium channel (Nav1.5). This may have implications for Long QT9 in which CAV3 mutations cause an increase in the late current of Nav1.5 (INa−L) via nNOS mediated nitrosylation of Nav1.5. In iPS-CMs, expression of LQT9 CAV3 mutations resulted in action potential duration (APD) prolongation and early-after depolarizations (EADs), supporting the arrhythmogenicity of LQT9. To evaluate the combined effect of the CAV3 mutants on INa−L and IK1, we studied both ventricular and Purkinje myocyte mathematical modeling. Interestingly, mathematical ventricular myocytes, similar to iPS-CMs, demonstrated EADs but no sustained arrhythmia. In contrast, Purkinje modeling demonstrated delayed-after depolarizations (DADs) driven mechanism for sustained arrhythmia, dependent on the combined loss of IK1 and gain of INa−L. This finding changes the overall assumed arrhythmia phenotype for LQT9. In future studies, we are exploring caveolar micro-domain disruption in heart failure and how this effects Kir2.x and Nav1.5. Here we review the caveolae cardiac microdomain of Kir2.x and Nav1.5 and explore some of the downstream effects of caveolin-3 and caveolae disruption in specific clinical scenarios

Formulation of a new gradient descent MARG orientation algorithm: case study on robot teleoperation

We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computa- tional efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is pro- ven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demon- strating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measure- ment and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field

A selective synthesis of taon nanoparticles and their comparative study of photoelectrochemical properties

A simplified ammonolysis method for synthesizing single phase TaON nanoparticles is presented and the resulting photoelectrochemical properties are compared and contrasted with as-synthesized Ta2O5 and Ta3N5. The protocol for partial nitridation of Ta2O5 (synthesis of TaON) offers a straightforward simplification over existing methods. Moreover, the present protocol offers extreme reproducibility and enhanced chemical safety. The morphological characterization of the as-synthesized photocatalysts indicate spherical nanoparticles with sizes 30, 40, and 30 nm Ta2O5, TaON, and Ta3N5 with the absorbance onset at ~320 nm, 580 nm, and 630 nm respectively. The photoactivity of the catalysts has been examined for the degradation of a representative cationic dye methylene blue (MB) using xenon light. Subsequent nitridation of Ta2O5 yields significant increment in the conversion (ζ: Ta2O5 95% ζ for a lower (0.1 g) loading and with a lamp with lower Ultraviolet (UV) content. Improved Photoelectrochemical performance is noted after a series of chronoamperometry (J/t), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS) measurements. Finally, stability experiments performed using recovered and treated photocatalyst show no loss of photoactivity, suggesting the photocatalysts can be successfully recycled

Littoral undersea warfare: a case study in process modelling for functionality and introperability of complex systems

The goal of this investigation is to demonstrate the application of a process modelling approach to architect a System of Systems (SoS) capable of conducting Anti-Submarine Warfare (ASW) operations projecting to the year 2025. Process modelling is a methodology for architectural analysis for complex systems whose operation is characterised by ‘processes’ whose sequential execution may be scaled-up to understand overall system behaviour. It is ideally suited to address complexity and interoperability issues of an ASW SoS. New contributions of this work include the successful implementation of a process modelling approach to architect an ASW SoS and a cohesive set of results analysing its operation with future projections to the year 2025. We believe this work may serve as a foundation for future systems engineering research addressing interoperability and performance of complex systems whose function is closely tied to time-dependent processes, with particular application to military and security systems

Fusion of enhanced and synthetic vision system images for runway and horizon detection

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications