500 research outputs found
Analysis of Dynamic Brain Imaging Data
Modern imaging techniques for probing brain function, including functional
Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging,
and magnetoencephalography, generate large data sets with complex content. In
this paper we develop appropriate techniques of analysis and visualization of
such imaging data, in order to separate the signal from the noise, as well as
to characterize the signal. The techniques developed fall into the general
category of multivariate time series analysis, and in particular we extensively
use the multitaper framework of spectral analysis. We develop specific
protocols for the analysis of fMRI, optical imaging and MEG data, and
illustrate the techniques by applications to real data sets generated by these
imaging modalities. In general, the analysis protocols involve two distinct
stages: `noise' characterization and suppression, and `signal' characterization
and visualization. An important general conclusion of our study is the utility
of a frequency-based representation, with short, moving analysis windows to
account for non-stationarity in the data. Of particular note are (a) the
development of a decomposition technique (`space-frequency singular value
decomposition') that is shown to be a useful means of characterizing the image
data, and (b) the development of an algorithm, based on multitaper methods, for
the removal of approximately periodic physiological artifacts arising from
cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files.
Originally submitted to the neuro-sys archive which was never publicly
announced (was 9804003
Data Leakage in Isolated Virtualized Enterprise Computing Systems
Virtualization and cloud computing have become critical parts of modern enterprise computing infrastructure. One of the benefits of using cloud infrastructure over in-house computing infrastructure is the offloading of security responsibilities. By hosting one’s services on the cloud, the responsibility for the security of the infrastructure is transferred to a trusted third party. As such, security of customer data in cloud environments is of critical importance. Side channels and covert channels have proven to be dangerous avenues for the leakage of sensitive information from computing systems. In this work, we propose and perform two experiments to investigate side and covert channel possibilities in virtual, enterprise environments. The first experiment is centered around the use of sensor data available via Intelligent Platform Management Interface, an open standard for out-of-band management often shipped with enterprise-level servers. We show how power-related sensors available with minimal user privilege over IPMI can be correlated with the levels of CPU stress of a virtual machine on a server. This leads to our second experiment, in which we demonstrate a power analysis approach for establishing a covert channel for the exfiltration of data from a network-isolated virtual machine on a server rack. By applying the concept of power analysis more broadly to the power consumption of an entire server rack, rather than individual hardware components, we find that basic patterns in system load can be clearly identified using signal processing techniques, demonstrating the potential for establishing a covert channel
Cross-Layer Optimization of Network Performance over MIMO Wireless Mobile Channels
In the information theory, the channel capacity states the maximum amount of in formation which can be reliably transmitted over the communication channel. In the specific case of multiple-input multiple-output (MIMO) wireless systems, it is well recognized that the instantaneous capacity of MIMO systems is a random Gaussian process. Time variation of the capacity leads to the outages at instances when it falls below the transmission rate. The frequency of such events is known as outage probability.
The cross-layer approach proposed in this work focuses on the effects of MIMO capacity outages on the network performance, providing a joint optimization of the MIMO communication system. For a constant rate transmission, the outage prob ability sensibly affects the amount of information correctly received at destination. Theoretically, the limit of the ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity variation. With an accu rate channel state information, the capacity evolution can be predicted by a suitable autoregressive model based on the capacity time correlation. Taking into consider ation the joint effects of channel outage at the physical layer and buffer overflow at the medium access control (MAC) layer, the optimal transmission strategy is derived analytically through the Markov decision processes (MDP) theory. The adaptive pol icy obtained by MDP is optimal and maximizes the amount of information correctly received at the destination MAC layer (throughput of the system). Analytical results demonstrate the significant improvements of the optimal variable rate strategy com pared to a constant transmission rate strategy, in terms of both system throughput and probability of data los
Cross-Layer Optimization of Network Performance over MIMO Wireless Mobile Channels
In the information theory, the channel capacity states the maximum amount of information which can be reliably transmitted over the communication channel. In the specific case of multiple-input multiple-output (MIMO) wireless systems, it is well recognized that the instantaneous capacity of MIMO systems is a random Gaussian process. Time variation of the capacity leads to the outages at instances when it falls below the transmission rate. The frequency of such events is known as outage probability. The cross-layer approach proposed in this work focuses on the effects of MIMO capacity outages on the network performance, providing a joint optimization of the MIMO communication system. For a constant rate transmission, the outage probability sensibly affects the amount of information correctly received at destination. Theoretically, the limit of the ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity variation. With an accurate channel state information, the capacity evolution can be predicted by a suitable autoregressive model based on the capacity time correlation. Taking into consideration the joint effects of channel outage at the physical layer and buffer overflow at the medium access control (MAC) layer, the optimal transmission strategy is derived analytically through the Markov decision processes (MDP) theory. The adaptive policy obtained by MDP is optimal and maximizes the amount of information correctly received at the destination MAC layer (throughput of the system). Analytical results demonstrate the significant improvements of the optimal variable rate strategy compared to a constant transmission rate strategy, in terms of both system throughput and probability of data loss
Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences
Compressive sensing (CS) has recently emerged as a framework for efficiently
capturing signals that are sparse or compressible in an appropriate basis.
While often motivated as an alternative to Nyquist-rate sampling, there remains
a gap between the discrete, finite-dimensional CS framework and the problem of
acquiring a continuous-time signal. In this paper, we attempt to bridge this
gap by exploiting the Discrete Prolate Spheroidal Sequences (DPSS's), a
collection of functions that trace back to the seminal work by Slepian, Landau,
and Pollack on the effects of time-limiting and bandlimiting operations. DPSS's
form a highly efficient basis for sampled bandlimited functions; by modulating
and merging DPSS bases, we obtain a dictionary that offers high-quality sparse
approximations for most sampled multiband signals. This multiband modulated
DPSS dictionary can be readily incorporated into the CS framework. We provide
theoretical guarantees and practical insight into the use of this dictionary
for recovery of sampled multiband signals from compressive measurements
Design and Performance Analysis of Hardware Realization of 3GPP Physical Layer for 5G Cell Search
5G Cell Search (CS) is the first step for user equipment (UE) to initiate the
communication with the 5G node B (gNB) every time it is powered ON. In cellular
networks, CS is accomplished via synchronization signals (SS) broadcasted by
gNB. 5G 3rd generation partnership project (3GPP) specifications offer a
detailed discussion on the SS generation at gNB but a limited understanding of
their blind search, and detection is available. Unlike 4G, 5G SS may not be
transmitted at the center of carrier frequency and their frequency location is
unknown to UE. In this work, we demonstrate the 5G CS by designing 3GPP
compatible hardware realization of the physical layer (PHY) of the gNB
transmitter and UE receiver. The proposed SS detection explores a novel
down-sampling approach resulting in a significant reduction in complexity and
latency. Via detailed performance analysis, we analyze the functional
correctness, computational complexity, and latency of the proposed approach for
different word lengths, signal-to-noise ratio (SNR), and down-sampling factors.
We demonstrate the complete CS functionality on GNU Radio-based RFNoC framework
and USRP-FPGA platform. The 3GPP compatibility and demonstration on hardware
strengthen the commercial significance of the proposed work
- …