10,049 research outputs found
Towards Continuous Subject Identification Using Wearable Devices and Deep CNNs
© 2020 IEEE. Subject identification has several applications. In transportation companies, knowing who is driving their vehicles might prevent theft or other ill-intended actions. On the other hand, privacy concerns, and the respective legislation, hinder the applicability of several traditional recognition techniques based on invasive technologies, such as video cameras. Moreover, in order to keep the driver's distractions to a minimum, this technologies must be non-disruptive, that is, they must be able to identify the subject seamlessly without them taking any action. In this context, we propose using deep learning applied to smart watch data for recognizing the person driving a vehicle based on a training set. Our proposal relies on the possibility of using transfer learning to avoid long training sessions for new drivers and to deliver a solution which can be deployed in practice. In this paper, we describe the convolutional neural network used in the solution and present results according to a real data-set collected by us, achieving accuracies ranging from 75 to 94%
Delay Minimizing User Association in Cellular Networks via Hierarchically Well-Separated Trees
We study downlink delay minimization within the context of cellular user
association policies that map mobile users to base stations. We note the delay
minimum user association problem fits within a broader class of network utility
maximization and can be posed as a non-convex quadratic program. This
non-convexity motivates a split quadratic objective function that captures the
original problem's inherent tradeoff: association with a station that provides
the highest signal-to-interference-plus-noise ratio (SINR) vs. a station that
is least congested. We find the split-term formulation is amenable to
linearization by embedding the base stations in a hierarchically well-separated
tree (HST), which offers a linear approximation with constant distortion. We
provide a numerical comparison of several problem formulations and find that
with appropriate optimization parameter selection, the quadratic reformulation
produces association policies with sum delays that are close to that of the
original network utility maximization. We also comment on the more difficult
problem when idle base stations (those without associated users) are
deactivated.Comment: 6 pages, 5 figures. Submitted on 2013-10-03 to the 2015 IEEE
International Conference on Communications (ICC). Accepted on 2015-01-09 to
the 2015 IEEE International Conference on Communications (ICC
Appendices for: Improper Signaling in Two-Path Relay Channels
This document contains the appendices for our work which is submitted to 2017 IEEE International Conference on Communications (ICC) Workshop on Full-Duplex Communications for Future Wireless Networks, Paris, France
Combating False Reports for Secure Networked Control in Smart Grid via Trustiness Evaluation
Smart grid, equipped with modern communication infrastructures, is subject to
possible cyber attacks. Particularly, false report attacks which replace the
sensor reports with fraud ones may cause the instability of the whole power
grid or even result in a large area blackout. In this paper, a trustiness
system is introduced to the controller, who computes the trustiness of
different sensors by comparing its prediction, obtained from Kalman filtering,
on the system state with the reports from sensor. The trustiness mechanism is
discussed and analyzed for the Linear Quadratic Regulation (LQR) controller.
Numerical simulations show that the trustiness system can effectively combat
the cyber attacks to smart grid.Comment: It has been submitted to IEEE International Conference on
Communications (ICC
Interference Alignment via Message-Passing
We introduce an iterative solution to the problem of interference alignment
(IA) over MIMO channels based on a message-passing formulation. We propose a
parameterization of the messages that enables the computation of IA precoders
by a min-sum algorithm over continuous variable spaces -- under this
parameterization, suitable approximations of the messages can be computed in
closed-form. We show that the iterative leakage minimization algorithm of
Cadambe et al. is a special case of our message-passing algorithm, obtained for
a particular schedule. Finally, we show that the proposed algorithm compares
favorably to iterative leakage minimization in terms of convergence speed, and
discuss a distributed implementation.Comment: Submitted to the IEEE International Conference on Communications
(ICC) 201
Coding for Memory with Stuck-at Defects
In this paper, we propose an encoding scheme for partitioned linear block
codes (PLBC) which mask the stuck-at defects in memories. In addition, we
derive an upper bound and the estimate of the probability that masking fails.
Numerical results show that PLBC can efficiently mask the defects with the
proposed encoding scheme. Also, we show that our upper bound is very tight by
using numerical results.Comment: 6 pages, 5 figures, IEEE International Conference on Communications
(ICC), Jun. 201
Lightweight Security for Network Coding
Under the emerging network coding paradigm, intermediate nodes in the network
are allowed not only to store and forward packets but also to process and mix
different data flows. We propose a low-complexity cryptographic scheme that
exploits the inherent security provided by random linear network coding and
offers the advantage of reduced overhead in comparison to traditional
end-to-end encryption of the entire data. Confidentiality is achieved by
protecting (or "locking") the source coefficients required to decode the
encoded data, without preventing intermediate nodes from running their standard
network coding operations. Our scheme can be easily combined with existing
techniques that counter active attacks.Comment: Proc. of the IEEE International Conference on Communications (ICC
2008), Beijing, China, May 200
Decoding the `Nature Encoded' Messages for Distributed Energy Generation Control in Microgrid
The communication for the control of distributed energy generation (DEG) in
microgrid is discussed. Due to the requirement of realtime transmission, weak
or no explicit channel coding is used for the message of system state. To
protect the reliability of the uncoded or weakly encoded messages, the system
dynamics are considered as a `nature encoding' similar to convolution code, due
to its redundancy in time. For systems with or without explicit channel coding,
two decoding procedures based on Kalman filtering and Pearl's Belief
Propagation, in a similar manner to Turbo processing in traditional data
communication systems, are proposed. Numerical simulations have demonstrated
the validity of the schemes, using a linear model of electric generator dynamic
system.Comment: It has been submitted to IEEE International Conference on
Communications (ICC
- …