A programmable arbitrary waveform generator for creation of experimental defibrillation shocks is described. The system is capable of delivering shocks for internal defibrillation via 10 channels at 1000 Volts and 30 ...
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A programmable arbitrary waveform generator for creation of experimental defibrillation shocks is described. The system is capable of delivering shocks for internal defibrillation via 10 channels at 1000 Volts and 30 Amps. A microcontroller driven system that can receive waveform commands from a laptop was designed to be able to deliver shocks to any combination of electrodes. Waveforms are controllable down to 100 microsecond intervals and each channel is capable of serving as anode or cathode. This system can be used to verify predictions for defibrillation waveform efficacy as predicted by modeling efforts or to test new experimental waveforms tuned to parameters from an individual subject.
Expressions for the electromagnetic power extinguished by a scatterer in a half-space are derived via the generalized optical theorem. The power is seen to be related to the structure of the scattering object. Applica...
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Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the org...
Information technology is providing manufacturers with additional flexibility with regard to their supply chain network choices. Our research studies supply chain network organization structures categorized by the organic and mechanistic management control structures. The structural impacts on cost and fill rate performance are studied in two-echelon and two-supply-chain network organization models under different market coordination conditions using system dynamic simulations. Our results show significant effects of demand and network structural factors, and their interactions, on these measures. As demand becomes dynamic, the cooperative interaction model, where supply chains cooperate to satisfy customer demand, is found to have better system performance than the competitive supply chain model. The analysis also suggests that increasing the responsiveness at the downstream plant is particularly important to the overall system performance improvement.
Recent years witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control requires many problems to be solved because of the many degrees of f...
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Recent years witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control requires many problems to be solved because of the many degrees of freedom and nonlinearity in their dynamics. The so-called open loop walking with offline trajectory generation is one of the control approaches in the literature. There are various difficulties involved in this approach, the most important one being the difficulty in tuning the gait parameters. This paper proposes an online fuzzy adaptation scheme for one of the trajectory parameters in the offline generated walking pattern. A fuzzy identifier system, represented as a three-layer feed-forward neural network is employed to compute the parameter as a function of time in simulations. Fuzzy system parameters are adapted via back-propagation. Virtual torsional springs are attached to the trunk center of the biped. The torque generated by the springs serve as the criterion for the tuning and they help maintaining a stable and a longer walk which is necessary for the online tuning process. 3D simulation and animation techniques are employed for a 12-DOF biped robot to test the proposed adaptive method.
Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function e...
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ISBN:
(纸本)0262201526
Synapses are a critical element of biologically-realistic, spike-based neural computation, serving the role of communication, computation, and modification. Many different circuit implementations of synapse function exist with different computational goals in mind. In this paper we describe a new CMOS synapse design that separately controls quiescent leak current, synaptic gain, and time-constant of decay. This circuit implements part of a commonly-used kinetic model of synaptic conductance. We show a theoretical analysis and experimental data for prototypes fabricated in a commercially-available 1.5μm CMOS process.
Integrated, low-power, low-noise CMOS neural amplifiers have recently grown in importance as large microelectrode arrays have begun to be practical. With an eye to a future where thousands of signals must be transmitt...
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Integrated, low-power, low-noise CMOS neural amplifiers have recently grown in importance as large microelectrode arrays have begun to be practical. With an eye to a future where thousands of signals must be transmitted over a limited bandwidth link or be processed in situ, we are developing low-power neural amplifiers with integrated pre-filtering and measurements of the spike signal to facilitate spike-sorting and data reduction prior to transmission to a data-acquisition system. We have fabricated a prototype circuit in a commercially-available 1.5 /spl mu/m, 2-metal, 2-poly CMOS process that occupies approximately 91,000 square /spl mu/m. We report circuit characteristics for a 1.5 V power supply, suitable for single cell battery operation. In one specific configuration, the circuit bandpass filters the incoming signal from 22 Hz to 6.7 kHz while providing a gain of 42.5 dB. With an amplifier power consumption of 0.8 /spl mu/W, the rms input-referred noise is 20.6 /spl mu/V.
This paper extends our prior work on multi-modal image registration based on the a priori knowledge of the joint intensity distribution that we expect to obtain, and Kullback-Leibler distance. This expected joint dist...
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We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi, yi) a distribution over (xi, yi) is specified. In parti...
We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi, yi) a distribution over (xi, yi) is specified. In particular, we derive a robust formulation when the distribution is given by a normal distribution. It leads to Second Order Cone programming formulation. Our method is applied to the problem of missing data, where it outperforms direct imputation.
This paper extends our prior work on multi-modal image registration based on the a priori knowledge of the joint intensity distribution that we expect to obtain, and Kullback-Leibler distance. This expected joint dist...
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