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Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses.

Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. Coherence is commonly used but neural activity does not follow its Gaussian assumption. The recently introduced mutual information in frequency (MIF) technique makes no model assumptions and measures non-Gaussian and nonlinear relationships. We develop a powerful MIF estimator optimized for correlating frequency coupling with task performance and other relevant task phenomena. In light of variance reduction afforded by multitaper spectral estimation, which is critical to precisely measuring such correlations, we propose a multitaper approach for MIF and compare its performance with coherence in simulations. Additionally, multitaper MIF and coherence are computed between macaque visual cortical recordings and their correlation with task performance is analyzed. Our multitaper MIF estimator produces low variance and performs better than all other estimators in simulated correlation analyses. Simulations further suggest that multitaper MIF captures more information than coherence. For the macaque data set, coherence and our new MIF estimator largely agree. Overall, we provide a new way to precisely estimate frequency coupling that sheds light on task performance and helps neuroscientists accurately capture correlations between coupling and task phenomena in general. Additionally, we make an MIF toolbox available for the first time.

Wearable haptic systems offer portable, private tactile communication to a human user. To date, advances in wearable haptic devices have typically focused on the optimization of haptic cue transmission using a single modality, or have combined two types of cutaneous feedbacks, each mapped to a particular parameter of the task. Alternatively, researchers have employed arrays of haptic tactile actuators to maximize information throughput to a user. However, when large cue sets are to be transmitted, such as those required to communicate language, perceptual interference between transmitted cues can decrease the efficacy of single-sensory systems, or require large footprints to ensure salient spatiotemporal cues are rendered to the user. In this paper, we present a wearable, multi-sensory haptic feedback system, MISSIVE (Multi-sensory Interface of Stretch, Squeeze, and Integrated Vibration Elements), that conveys multi-sensory haptic cues to the user's upper arm. We present experimental results that demonstrate that rendering haptic cues with multi-sensory components-specifically, lateral skin stretch, radial squeeze, and vibrotactile stimuli-improved perceptual distinguishability in comparison to similar cues with all-vibrotactile components. These results support the incorporation of diverse stimuli, both vibrotactile and nonvibrotactile, for applications requiring large haptic cue sets.

The following paper presents a framework for the optimal control of an electric warship using a load profile derived from an operational vignette. This framework consists of three key components: a reduced order model of an electric ship, a discretization of the resulting constitutive equations using an orthogonal spline collocation method, and an optimization engine to solve the resulting formulation. Once assembled, this control framework is validated through its application to a four zone model of a medium voltage DC (MVDC) electric ship using a load profile from an operational vignette

Objective. Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections. Approach. Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series. Main results. GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections. Significance. GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.

Electrical components used in LED lighting devices have a significant impact on the reliability of lighting systems. Understanding the reliability of LED drivers requires a knowledge of the intended use of the device including environmental (e.g., temperature, humidity), electrical (i.e., voltage quality and transients), and mechanical (i.e., vibration) stresses that the products would experience. In addition, knowledge of the susceptibility of key electronic components (e.g., capacitors, switching transistors, diodes, ICs, and linear components) to these stresses is also important in understanding overall product reliability. Although this information is difficult to determine for an electronic assembly such as the LED lighting driver, accelerated tests can help provide insights regarding likely failure modes and provide a basis to project reliability and product lifetime. In this chapter we have investigated common failure modes in LED drivers under accelerated testing conditions.

Primal---dual interior point methods and the HKM method in particular have been implemented in a number of software packages for semidefinite programming. These methods have performed well in practice on small to medium sized SDPs. However, primal---dual codes have had some trouble in solving larger problems because of the storage requirements and required computational effort. In this paper we describe a parallel implementation of the primal---dual method on a shared memory system. Computational results are presented, including the solution of some large scale problems with over 50,000 constraints.

This report describes an integrated approach for designing communication, sensing, and control systems for mobile distributed systems. Graph theoretic methods are used to analyze the input/output reachability and structural controllability and observability of a decentralized system. Embedded in each network node, this analysis will automatically reconfigure an ad hoc communication network for the sensing and control task at hand. The graph analysis can also be used to create the optimal communication flow control based upon the spatial distribution of the network nodes. Edge coloring algorithms tell us that the minimum number of time slots in a planar network is equal to either the maximum number of adjacent nodes (or degree) of the undirected graph plus some small number. Therefore, the more spread out that the nodes are, the fewer number of time slots are needed for communication, and the smaller the latency between nodes. In a coupled system, this results in a more responsive sensor network and control system. Network protocols are developed to propagate this information, and distributed algorithms are developed to automatically adjust the number of time slots available for communication. These protocols and algorithms must be extremely efficient and only updated as network nodes move. In addition,more » queuing theory is used to analyze the delay characteristics of Carrier Sense Multiple Access (CSMA) networks. This report documents the analysis, simulation, and implementation of these algorithms performed under this Laboratory Directed Research and Development (LDRD) effort.« less

The term multimedia implies the combination of many different forms of information including computer graphics, text, video and audio, along with data distribution mechanisms and storage systems that can provide such data with real time or interactive response [1]. Until recently, almost all systems were limited in their ability to manipulate video data.