This paper presents a numerical model of a morphing wing supporting the development of a biologically inspired vehicle capable of aerial and aquatic of locomotion. The model draws inspiration from the seabird Uria aalge, the common guillemot. It is implemented within a parametric study associated with aerial and aquatic performance, specifically aiming at minimizing energy of locomotion. The implications of varying wing geometry and kinematic parameters are investigated and presented in the form of nested performance charts. Trends within both the aquatic and aerial model are discussed highlighting the implications of parameter variation on the power requirements associated with both mediums. Conflicts of geometric parameter selection are contrasted between the aerial and aquatic model, as well as other trends that impact the design of concept vehicles with this capability. The model has been validated by implementing a heuristic optimization of its key parameters under conditions akin to those of the actual bird; optimal parameters output by the model correlate to the actual behaviour of the guillemot.

The goal of this paper is to introduce a new strategy to accurately classify event-related potentials (ERPs), recorded using dense electrode arrays, into predefined brain activity categories. The challenge is to exploit the enhanced spatial information offered by dense arrays while overcoming the significant increase in the dimensionality problem introduced by the large increase in the number of channels. These conflicting objectives are achieved by introducing a spatiotemporal-array model to observe the dense-array ERP amplitude variations across channels and time, simultaneously. To account for latency variations and EEG noise in the array elements, each spatiotemporal element in the array is initially modeled as a Gaussian random variable. A two-step process that uses the Kolmogrov-Smirnov test and the Lilliefors test is formulated to select the array elements that have different Gaussian densities across all ERP categories. Selecting spatiotemporal elements that fit the assumed model and also statistically differ across the ERP categories not only ensures high classification accuracies but also decreases the dimensionality significantly. The selection is dynamic in the sense that selecting spatiotemporal-array elements corresponds to selecting ERP samples of different channels at different time instants. Each selected array element is classified using a univariate Gaussian classifier, and the resulting decisions are fused into a decision fusion vector that is classified using a discrete Bayes classifier. By converting an inherently multivariate classification problem into a simpler problem involving only univariate classifications, the dimensionality problem that plagues the design of practical multivariate ERP classifiers is circumvented. Consequently, classifiers can be designed to classify the ERPs that are unique to an individual without having to collect a prohibitively large ERP dataset from him/her. The application of the resulting dynamic-channel-selection-based classification strategy is demonstrated by designing and testing classifiers for eight subjects using ERPs from a Stroop color test and it is shown that the strategy yields high classification accuracies. Finally, it is noted that because of the generalized formulation of the strategy, it can be applied to various other problems involving the classification of multivariate signals acquired from multiple identical or multiple heterogeneous sensors.

Robot teleoperation systems have been limited in their utility due to the need for operator motion, lack of portability and limitation to singular input modalities. In this article, the design and construction of a dual-mode human machine interface system for robot teleoperation addressing all these issues is presented. The interface is capable of directing robotic devices in response to tongue movement and/or speech without insertion of any device in the vicinity of the oral cavity. The interface is centered on the unique properties of the human ear as an acoustic output device. Specifically, we present: (1) an analysis of the sensitivity of human ear canals as acoustic output device; (2) the design of a new sensor for monitoring airflow in the aural canal; (3) pattern recognition procedures for recognition of both speech and tongue movement by monitoring aural flow across several human test subjects; and (4) a conceptual design and simulation of the machine interface system. We believe this work will lay the foundation for a new generation of human machine interface systems for all manner of robotic applications.

The focus of this research is to address the criticality and vulnerability of commercial shipping in the Straits of Mallacca by designing and evaluating competing systems architectures that could provide sufficient maritime domain protection. The category of primary concern was the introduction of a weapon of mass destruction (WMD) in a cargo container. The Maritime Domain Protection (MDP) physical architecture alternatives combined five separate systems: 1) a land-based cargo inspection system, 2) a sensor system, 3) a C3I (command and control, communications, and intelligence) system, 4) a force response system, and 5) a sea-based cargo inspection system. Individual models for each system were developed and combined into an overarching integrated architecture model to evaluate overall performance. Study results based on current technology showed that while solutions were found to effectively reduce risk in the WMD threat scenario, effective suppression came at great expense and included the participation of commercial shipping companies. A range of alternative cost-effective solutions were also found, but with limited performance. Future work involves using the developed architecture as a test bed for evaluating the overall impact and effectiveness of new technologies and research (such as smart containers) on MDP and homeland security.