9,078 research outputs found
The role of Dnmt3a2 in regulating neuronal gene transcription, cognitive and locomotor functions
Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks
Energy harvester based cognitive radio is a promising solution to address the
shortage of both spectrum and energy. Since the spectrum access and power
consumption patterns are interdependent, and the power value harvested from
certain environmental sources are spatially correlated, the new power dimension
could provide additional information to enhance the spectrum sensing accuracy.
In this paper, the Markovian behavior of the primary users is considered, based
on which we adopt a hidden input Markov model to specify the primary vs.
secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and
power (harvested) sensing scheme to improve the primary user detection
performance, which is also capable of estimating the primary transmit power
level. Theoretical and simulated results demonstrate the effectiveness of the
proposed scheme, in term of the performance gain achieved by considering the
new power dimension. To the best of our knowledge, this is the first work to
jointly consider the spectrum and power dimensions for the cognitive primary
user detection problem
In Brief: Myeloid-derived suppressor cells in cancer
The role of myeloid-derived suppressor cells (MDSCs) in cancer development has become clear over recent years, and MDSC targeting is an emerging opportunity for enhancing the effectiveness of current anticancer therapies. As MDSCs are not only able to limit anti-tumour T-cell responses, but also to promote tumour angiogenesis and invasion, their monitoring has prognostic and predictive value. Herein, we review the key features of MDSCs in cancer promotion
Learning-Based Distributed Detection-Estimation in Sensor Networks with Unknown Sensor Defects
We consider the problem of distributed estimation of an unknown deterministic
scalar parameter (the target signal) in a wireless sensor network (WSN), where
each sensor receives a single snapshot of the field. We assume that the
observation at each node randomly falls into one of two modes: a valid or an
invalid observation mode. Specifically, mode one corresponds to the desired
signal plus noise observation mode (\emph{valid}), and mode two corresponds to
the pure noise mode (\emph{invalid}) due to node defect or damage. With no
prior information on such local sensing modes, we introduce a learning-based
distributed procedure, called the mixed detection-estimation (MDE) algorithm,
based on iterative closed-loop interactions between mode learning (detection)
and target estimation. The online learning step re-assesses the validity of the
local observations at each iteration, thus refining the ongoing estimation
update process. The convergence of the MDE algorithm is established
analytically. Asymptotic analysis shows that, in the high signal-to-noise ratio
(SNR) regime, the MDE estimation error converges to that of an ideal
(centralized) estimator with perfect information about the node sensing modes.
This is in contrast to the estimation performance of a naive average consensus
based distributed estimator (without mode learning), whose estimation error
blows up with an increasing SNR.Comment: 15 pages, 2 figures, submitted to TS
A class of \v{S}id\'ak-type tests based on maximal precedence and exceedance statistic
A class of nonparametric two-sample tests has been proposed in this article.
As a generalization of the original \v{S}id\'aks' test, the proposed test
statistic is developed as the sum of the maximal precedence and maximal
exceedance statistics. Unlike the \v{S}id\'ak-type precedence-exceedance test
and the maximal precedence test, the proposed test is suitable for a two-sided
alternative while being free from any parametric assumption. Exact distribution
of the test statistic is obtained under the null as well as under the Lehmann
alternative. Power value comparison has been carried out that shows the
competency of the proposed test as a useful alternative to a number of existing
tests based on precedence-exceedance statistics. Real-life example is provided
to illustrate the application of the proposed test
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