The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

This paper theoretically and experimentally investigates an ac/dc converter for low-voltage vibrational energy harvesting systems. The circuit employs an active-diode-based voltage doubler, where the output is a dc voltage that is twice the amplitude of the input ac voltage. Analytical solutions for the steady-state open-circuit voltage are derived, capturing the effects of the activediode comparator hysteresis. It is shown that the hysteresis plays an important role in the rectification characteristics, circuit stability, and overall efficiency. Experimentally, the circuit is able to rectify input voltage amplitude as low as 5 mV and operates over a frequency range of 1 to 500 Hz, which spans most common mechanical vibrations. For input voltage amplitudes 250 mV or higher, the circuit exhibits >80% efficiency for a range of load resistances, delivering 0.1-10 mW of power. Additionally, the circuit successfully rectifies the voltage from a vibrational energy harvester having a highly irregular and time-varying voltage waveform.Index Terms-AC/DC converter, active diode, energy harvesting, voltage doubler.

This paper presents an input-powered energyharvesting interface circuit that eliminates standby power consumption by automatically shutting down when the ac input voltage amplitude is too low for successful energy reclamation. This feature eliminates the need for precharging the load and allows for indefinitely long intervals between energy harvesting cycles. The interface comprises two subcircuits: an input-powered ac/dc converter and an input-powered dc/dc boost converter with regulated output. The two subcircuits are separately fabricated in the ON Semi 3M-2P 0.5 μm CMOS process. The entire interface circuit starts up when the ac input amplitude exceeds 1 V and supplies a regulated dc output up to 3 V. When the input amplitude drops below 600 mV, the interface automatically enters standby mode and consumes no power. The system achieves a maximum efficiency of 60% with 1.5-V ac input amplitude and 3 V regulated dc output, delivering 3.9 mW of output power. The interface also functions properly in tests with an electrodynamic (magnetic) vibrational energy harvester.Index Terms-Ac/dc converter, active diode, boost converter, dc/dc converter, energy harvesting, input-powered, rectifier.

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Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf).Alternative solutions to ICA utilize higher order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this paper, we propose a new ICA algorithm motivated by the Maximum Entropy Principle (for estimating signal distributions).The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family, and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals, hence the name Minimax Mutual Information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher order cumulants.