6,598 research outputs found
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems
Recent results in telecardiology show that compressed sensing (CS) is a
promising tool to lower energy consumption in wireless body area networks for
electrocardiogram (ECG) monitoring. However, the performance of current
CS-based algorithms, in terms of compression rate and reconstruction quality of
the ECG, still falls short of the performance attained by state-of-the-art
wavelet based algorithms. In this paper, we propose to exploit the structure of
the wavelet representation of the ECG signal to boost the performance of
CS-based methods for compression and reconstruction of ECG signals. More
precisely, we incorporate prior information about the wavelet dependencies
across scales into the reconstruction algorithms and exploit the high fraction
of common support of the wavelet coefficients of consecutive ECG segments.
Experimental results utilizing the MIT-BIH Arrhythmia Database show that
significant performance gains, in terms of compression rate and reconstruction
quality, can be obtained by the proposed algorithms compared to current
CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health
Informatic
Physical Layer Service Integration in 5G: Potentials and Challenges
High transmission rate and secure communication have been identified as the
key targets that need to be effectively addressed by fifth generation (5G)
wireless systems. In this context, the concept of physical-layer security
becomes attractive, as it can establish perfect security using only the
characteristics of wireless medium. Nonetheless, to further increase the
spectral efficiency, an emerging concept, termed physical-layer service
integration (PHY-SI), has been recognized as an effective means. Its basic idea
is to combine multiple coexisting services, i.e., multicast/broadcast service
and confidential service, into one integral service for one-time transmission
at the transmitter side. This article first provides a tutorial on typical
PHY-SI models. Furthermore, we propose some state-of-the-art solutions to
improve the overall performance of PHY-SI in certain important communication
scenarios. In particular, we highlight the extension of several concepts
borrowed from conventional single-service communications, such as artificial
noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These
techniques are shown to be effective in the design of reliable and robust
PHY-SI schemes. Finally, several potential research directions are identified
for future work.Comment: 12 pages, 7 figure
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