9,709 research outputs found
Utilizing Deep Neural Networks for Brain–Computer Interface-Based Prosthesis Control
Limb amputations affect a significant portion of the world’s population every year. The necessity for these operations can be associated with related health conditions or a traumatic event. Currently, prosthetic devices intended to alleviate the burden of amputation lack many of the premier features possessed by their biological counterparts. The foremost of these features are agility and tactile function. In an effort to address the former, researchers here investigate the fundamental connection between agile finger movement and brain signaling. In this study each subject was asked to move his or her right index finger in sync with a time-aligned finger movement demonstration while each movement was labeled and the subject’s brain waves were recorded via a single-channel electroencephalograph. This data was subsequently used to train and test a deep neural network in an effort to classify each subject’s intention to rest and intention to extend his or her right index finger. On average, the employed model yielded an accuracy of 63.3%, where the most predictable subject’s movements were classified with an accuracy of 70.5%
Improving Receiver Performance of Diffusive Molecular Communication with Enzymes
This paper studies the mitigation of intersymbol interference in a diffusive
molecular communication system using enzymes that freely diffuse in the
propagation environment. The enzymes form reaction intermediates with
information molecules and then degrade them so that they cannot interfere with
future transmissions. A lower bound expression on the expected number of
molecules measured at the receiver is derived. A simple binary receiver
detection scheme is proposed where the number of observed molecules is sampled
at the time when the maximum number of molecules is expected. Insight is also
provided into the selection of an appropriate bit interval. The expected bit
error probability is derived as a function of the current and all previously
transmitted bits. Simulation results show the accuracy of the bit error
probability expression and the improvement in communication performance by
having active enzymes present.Comment: 13 pages, 8 figures, 1 table. To appear in IEEE Transactions on
Nanobioscience (submitted January 22, 2013; minor revision October 16, 2013;
accepted December 4, 2013
Using Dimensional Analysis to Assess Scalability and Accuracy in Molecular Communication
In this paper, we apply dimensional analysis to study a diffusive molecular
communication system that uses diffusing enzymes in the propagation environment
to mitigate intersymbol interference. The enzymes bind to information molecules
and then degrade them so that they cannot interfere with the detection of
future transmissions at the receiver. We determine when it is accurate to
assume that the concentration of information molecules throughout the receiver
is constant and equal to that expected at the center of the receiver. We show
that a lower bound on the expected number of molecules observed at the receiver
can be arbitrarily scaled over the environmental parameters, and generalize how
the accuracy of the lower bound is qualitatively impacted by those parameters.Comment: 6 pages, 2 figures, will be presented at the 3rd IEEE International
Workshop on Molecular and Nanoscale Communications (MoNaCom 2013) in
Budapest, Hungar
Optimal Receiver Design for Diffusive Molecular Communication With Flow and Additive Noise
In this paper, we perform receiver design for a diffusive molecular
communication environment. Our model includes flow in any direction, sources of
information molecules in addition to the transmitter, and enzymes in the
propagation environment to mitigate intersymbol interference. We characterize
the mutual information between receiver observations to show how often
independent observations can be made. We derive the maximum likelihood sequence
detector to provide a lower bound on the bit error probability. We propose the
family of weighted sum detectors for more practical implementation and derive
their expected bit error probability. Under certain conditions, the performance
of the optimal weighted sum detector is shown to be equivalent to a matched
filter. Receiver simulation results show the tradeoff in detector complexity
versus achievable bit error probability, and that a slow flow in any direction
can improve the performance of a weighted sum detector.Comment: 14 pages, 7 figures, 1 appendix. To appear in IEEE Transactions on
NanoBioscience (submitted July 31, 2013, revised June 18, 2014, accepted July
7, 2014
Diffusive Molecular Communication with Disruptive Flows
In this paper, we study the performance of detectors in a diffusive molecular
communication environment where steady uniform flow is present. We derive the
expected number of information molecules to be observed in a passive spherical
receiver, and determine the impact of flow on the assumption that the
concentration of molecules throughout the receiver is uniform. Simulation
results show the impact of advection on detector performance as a function of
the flow's magnitude and direction. We highlight that there are disruptive
flows, i.e., flows that are not in the direction of information transmission,
that lead to an improvement in detector performance as long as the disruptive
flow does not dominate diffusion and sufficient samples are taken.Comment: 7 pages, 1 table, 5 figures. Will be presented at the 2014 IEEE
International Conference on Communications (ICC) in Sydney, Australia, on
September 12, 201
On the Statistics of Reaction-Diffusion Simulations for Molecular Communication
A molecule traveling in a realistic propagation environment can experience
stochastic interactions with other molecules and the environment boundary. The
statistical behavior of some isolated phenomena, such as dilute unbounded
molecular diffusion, are well understood. However, the coupling of multiple
interactions can impede closed-form analysis, such that simulations are
required to determine the statistics. This paper compares the statistics of
molecular reaction-diffusion simulation models from the perspective of
molecular communication systems. Microscopic methods track the location and
state of every molecule, whereas mesoscopic methods partition the environment
into virtual containers that hold molecules. The properties of each model are
described and compared with a hybrid of both models. Simulation results also
assess the accuracy of Poisson and Gaussian approximations of the underlying
Binomial statistics.Comment: 6 pages, 1 table, 10 figures. Submitted to the 2nd ACM International
Conference on Nanoscale Computing and Communication (ACM NANOCOM 2015) on May
16, 201
Bounds on Distance Estimation via Diffusive Molecular Communication
This paper studies distance estimation for diffusive molecular communication.
The Cramer-Rao lower bound on the variance of the distance estimation error is
derived. The lower bound is derived for a physically unbounded environment with
molecule degradation and steady uniform flow. The maximum likelihood distance
estimator is derived and its accuracy is shown via simulation to perform very
close to the Cramer-Rao lower bound. An existing protocol is shown to be
equivalent to the maximum likelihood distance estimator if only one observation
is made. Simulation results also show the accuracy of existing protocols with
respect to the Cramer-Rao lower bound.Comment: 7 pages, 5 figures, 1 table. Will be presented at the 2014 IEEE
Global Communications Conference (GLOBECOM) in Austin, TX, USA, on December
9, 201
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