146,077 research outputs found
Optimal Parameter Extraction Scheme of Current Sources and Bias Dependent Elements for HBT by searching the whole unknown Parameter Space
New analytical expressions for the dynamic resistance,transconductance,base-collector internal capacitance,and base-emitter internal capacitance are derived.And a new scheme,to extract the current source parameters,thermal parameter,and small signal parameters at multiple bias points on the normal active region,is developed.The proposed parameter extraction method is robust and very fast.Based on these equations, we propose a new scheme to find out the optimal solution by searching for a full-unknown parameter space.The search space corresponds to 1.17x10 8 points on the error surface, and it takes 12.6 hours to get an optimal model parameters using a 2GHz-desktop PC.This scheme is helpful for the modeling of HBT excluding the local minimum problem in the gradient optimization method and the inaccuracies in the direct extraction method
Different Techniques and Algorithms for Biomedical Signal Processing
This paper is intended to give a broad overview of the complex area of biomedical and their use in signal processing. It contains sufficient theoretical materials to provide some understanding of the techniques involved for the researcher in the field. This paper consists of two parts: feature extraction and pattern recognition. The first part provides a basic understanding as to how the time domain signal of patient are converted to the frequency domain for analysis. The second part provides basic for understanding the theoretical and practical approaches to the development of neural network models and their implementation in modeling biological syste
Inferring models of bacterial dynamics toward point sources
Experiments have shown that bacteria can be sensitive to small variations in
chemoattractant (CA) concentrations. Motivated by these findings, our focus
here is on a regime rarely studied in experiments: bacteria tracking point CA
sources (such as food patches or even prey). In tracking point sources, the CA
detected by bacteria may show very large spatiotemporal fluctuations which vary
with distance from the source. We present a general statistical model to
describe how bacteria locate point sources of food on the basis of stochastic
event detection, rather than CA gradient information. We show how all model
parameters can be directly inferred from single cell tracking data even in the
limit of high detection noise. Once parameterized, our model recapitulates
bacterial behavior around point sources such as the "volcano effect". In
addition, while the search by bacteria for point sources such as prey may
appear random, our model identifies key statistical signatures of a targeted
search for a point source given any arbitrary source configuration
Seqgan: sequence generative adversarial nets with policy gradient
As a new way of training generative models, Generative Adversarial
Net (GAN) that uses a discriminative model to guide
the training of the generative model has enjoyed considerable
success in generating real-valued data. However, it has limitations
when the goal is for generating sequences of discrete
tokens. A major reason lies in that the discrete outputs from
the generative model make it difficult to pass the gradient update
from the discriminative model to the generative model.
Also, the discriminative model can only assess a complete
sequence, while for a partially generated sequence, it is nontrivial
to balance its current score and the future one once
the entire sequence has been generated. In this paper, we propose
a sequence generation framework, called SeqGAN, to
solve the problems. Modeling the data generator as a stochastic
policy in reinforcement learning (RL), SeqGAN bypasses
the generator differentiation problem by directly performing
gradient policy update. The RL reward signal comes from
the GAN discriminator judged on a complete sequence, and
is passed back to the intermediate state-action steps using
Monte Carlo search. Extensive experiments on synthetic data
and real-world tasks demonstrate significant improvements
over strong baselines
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