Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update dec...
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ISBN:
(纸本)9781424441211
Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.
Many animals are known to maintain an internal estimate of their orientation in the environment. In the absence of external sensory cues, this estimate inevitably exhibits drift. When sensory information is available,...
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Many animals are known to maintain an internal estimate of their orientation in the environment. In the absence of external sensory cues, this estimate inevitably exhibits drift. When sensory information is available, associations between sensory landmarks and the internal estimate can be used to correct for drift. In this paper we present a neuromorphic system to model such associations between sensory landmarks in the environment (as provided by sonar) and the activity of a hardware-based head direction cell system (HDS) that continuously integrates angular velocity signals to maintain an estimate of the orientation. These associations are shown to correct for drift errors that are encountered in the HDS.
We consider the centralized detection of an intruder, whose location is modeled as uniform across a specified set of points, using an optimally placed team of sensors. These sensors make conditionally independent obse...
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ISBN:
(纸本)9781457718175
We consider the centralized detection of an intruder, whose location is modeled as uniform across a specified set of points, using an optimally placed team of sensors. These sensors make conditionally independent observations. The local detectors at the sensors are also assumed to be identical, with detection probability (P D ) and false alarm probability (P F ). We formulate the problem as an N-ary hypothesis testing problem, jointly optimizing the sensor placement and detection policies at the fusion center. We prove that uniform sensor placement is never strictly optimal when the number of sensors (M) equals the number of placement points (N). We prove that for N 2 >; N 1 >; M, where N 1 , N 2 are number of placement points, the framework utilizing M sensors and N 1 placement points has the same optimal placement structure as the one utilizing M sensors and N 2 placement points. For M ≤ 6 and for fixed P D (or P F ), increasing P F (or P D ) leads to optimal placements that are higher in the majorization-based partial order. For M >; 6, this result does not necessarily hold and we provide a simple counterexample. It is conjectured that the set of optimal placements for a given (M, N) can always be placed on a majorization-based partial order.
Brain-Machine Interface (BMI) decoding algorithms are often trained offline, but this paradigm ignores both the non-stationarity of neural signals and the feedback that exists in online, closed-loop control. To addres...
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Brain-Machine Interface (BMI) decoding algorithms are often trained offline, but this paradigm ignores both the non-stationarity of neural signals and the feedback that exists in online, closed-loop control. To address these problems, we have developed an Adaptive Kalman Filter (AKF), a Kalman filter variant that adaptively updates its model parameters during training. For a Kalman filter decoder, batch retraining methods require completely re-estimating the parameter matrices from sufficient data to perform regression accurately, even if only small changes are necessary. Conversely, the AKF is designed to update the decoder parameters continuously and more intelligently. We simulated a population of 41 neurons learning to control a 2D computer cursor. The AKF yielded significantly faster skill acquisition and better robustness to perturbation and neuron loss than a standard Kalman filter with periodic batch retraining.
Digital breast tomosynthesis is a novel breast cancer detection technique by allowing the reconstruction of arbitrary planes in the breast from a set of limited-angle projection images acquired at different view angle...
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Digital breast tomosynthesis is a novel breast cancer detection technique by allowing the reconstruction of arbitrary planes in the breast from a set of limited-angle projection images acquired at different view angles with a particular tube geometry setup. In this paper, filtered backprojection (FBP) was optimized as the reconstruction method with a parallel imaging breast tomosynthesis system. The slice thickness filter (profile filter) with task-adapted parameters was applied to computer simulated data, in order to investigate the main effects of this kind of filter regarding spatial resolution and artifacts in the reconstructed results.
Variable speed wind turbine with full scale converter or so-called Type-D wind turbine generator worldwide installation has been significantly increased in the last few years. Voltage sag in the grid side may cause th...
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Variable speed wind turbine with full scale converter or so-called Type-D wind turbine generator worldwide installation has been significantly increased in the last few years. Voltage sag in the grid side may cause the wind turbine to be disconnected from the grid. In this paper, effect of STATCOM on the low voltage ride through capability of Type-D wind turbine during voltage sag in the grid side is studied. Simulation is carried out using MATLAB/Simulink software. Results show that STATCOM can significantly improve the voltage profile at the Point of Common Coupling (PCC) and improve the capability of Type-D wind turbine and prevents it from being disconnected from the grid during certain level of voltage sag in the grid side.
The synthesis and second-order nonlinear optical (NLO) properties of a new family of optically nonlinear coumarin dye-containing polymers is reported. Polymerizable coumaromethacrylate (CMA) monomers having various al...
The synthesis and second-order nonlinear optical (NLO) properties of a new family of optically nonlinear coumarin dye-containing polymers is reported. Polymerizable coumaromethacrylate (CMA) monomers having various alkyl spacers (C2 to C5) between the polymerizable group and the chromophore were prepared and copolymerized with methylmethacrylate (MMA) and other vinyl monomers. The CMA copolymers had high chromophoric content (up to 18 × 1020) and glass transitions as high as 132°C. Spin cast films were prepared on BK7 substrates and oriented by corona-onset poling at elevated temperatures (COPET). The poled CMA copolymer films were characterized for spectroscopic absorption and second harmonic properties. A comparison of the nonlinear optical properties of poled CMA-MMA copolymers to poled guest-host systems is made. Relative to guest/host systems, CMA-MMA copolymer films exhibit larger and much more stable second-order nonlinear optical properties.
Observation of light emission from porous Si has demonstrated that the optical properties of Si can be drastically altered by the quantum size effects. We have investigated the improvement of absorption properties of ...
Observation of light emission from porous Si has demonstrated that the optical properties of Si can be drastically altered by the quantum size effects. We have investigated the improvement of absorption properties of Si material by forming a porous Si layer. Shallow-junction commercial crystalline as well as polycrystalline Si solar cells without anti-reflective coatings have been processed into porous Si solar cells by a wet chemical etching technique. Our best results have demonstrated more than 15% improvement in short-circuit current with no change in open-circuit voltage. The performance of the porous Si solar cells has been found to be sensitive to the porous layer thickness. The efficiency can be reduced when the porous layer is relatively deep, presumably due to the penetration of pores through the shallow junction. We believe porous Si can be optimized for photovoltaic applications by properly controlling its porosity and thickness.
A wearable device was developed to study the human grasping behavior. This device includes a custom-made glove with conductors at finger tips to detect the finger contact signals, a simple electronic circuit to enhanc...
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A wearable device was developed to study the human grasping behavior. This device includes a custom-made glove with conductors at finger tips to detect the finger contact signals, a simple electronic circuit to enhance the signal quality, and a real-time data acquisition system to synchronize the acquisition of the finger contact signals with obtaining the optical marker signals in a motion capture system. A preliminary study shows that the new device is capable of detecting the moments of finger-object contacts with better resolution of time than the existing motion capture camera system by three orders of magnitude. This new device will be intensively used in experiments to understand human grasping.
It has been a challenge to achieve high efficiency organic photovoltaics (OPV) that absorb long wavelength solar radiation without incurring unacceptable reductions in open circuit voltage (Voc) or charge separation e...
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