<i>Signal Theory and Random Processes</i> by Harry Urkowitz

Urkowitz, H.; Kelly, James G.

doi:10.1121/1.391172

Cited by 35 publications

(59 citation statements)

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“…It has been previously shown that image intensities in magnitude MRI images follow a Rician distribution (14). When the signal-to-noise ratio (SNR) in such an image exceeds 3.0, however, the noise approaches an additive Gaussian random variable with a variance equal to that of the underlying image.…”

Section: Contrast-to-noise Behaviormentioning

confidence: 99%

Magnitude image CSPAMM reconstruction (MICSR)

NessAiver

Prince

2003

Magnetic Resonance in Med

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Image reconstruction of tagged cardiac MR images using complementary spatial modulation of magnetization (CSPAMM) requires the subtraction of two complex datasets to remove the untagged signal. Although the resultant images typically have sharper and more persistent tags than images formed without complementary tagging pulses, handling the complex data is problematic and tag contrast still degrades significantly during diastole. This article presents a magnitude image CSPAMM reconstruction (MICSR) method that is simple to implement and produces images with improved contrast and tag persistence. The MICSR method uses only magnitude images -i.e., no complex data -but yields tags with zero mean, sinusoidal profiles. A trinary display of MICSR images emphasizes their long tag persistence and demonstrates a novel way to visualize myocardial deformation. MICSR contrast and contrast-to-noise ratios (CNR) were evaluated using simulations, a phantom, and two normal volunteers. Tag contrast 1000 msec after the R wave trigger was 3.0 times better with MICSR than with traditional CSPAMM reconstruction techniques, while CNRs were 2.0 times better.Magn Reson Med 50:331-342, 2003.

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Section: Contrast-to-noise Behaviormentioning

confidence: 99%

Magnitude image CSPAMM reconstruction (MICSR)

NessAiver

Prince

2003

Magnetic Resonance in Med

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“…) [10]. Jammers lower the SINR conditions at the adversary sensors, thereby inducing errors to their localization system.…”

Section: Collaborative Jamming For Location Privacy Protectionmentioning

confidence: 99%

“…We briefly explain how the jamming noise affects the location privacy of nodes, which is measured by the accuracy that the adversary can achieve on the location of target nodes. The accuracy of location estimations of adversary localization systems using TOA measurements depends on the Signal to Interference and Noise Ratio (SINR) of the received signal and Non-Line Of Sight (NLOS) signal components [10]. Hence, we use the CRLB, || √ σû||[m], on the target location, u = {x, y}, as a privacy measure at given topology and SINR conditions where σû is the CRLB on u.…”

Section: Collaborative Jamming For Location Privacy Protectionmentioning

confidence: 99%

Multi-node coordinated jamming for location privacy protection

Gruteser

2011

2011 - MILCOM 2011 Military Communications Conference

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Abstract-In wireless sensor networks, adversaries can easily threaten the location privacy of sensor nodes using ordinary localization techniques. By collecting basic physical layer information, such as signal strength and time of arrival, adversaries can precisely locate the target nodes. Although basic anti-localization techniques based on power control and directional antennas are believed to mitigate such localization attacks in the radio physical layer, there has been little research on location privacy protection techniques by obfuscating physical layer information. In this paper, we propose a novel jamming technique that prevents adversaries from locating target nodes by mixing the jamming signal from neighboring nodes. We introduce this beneficial use of jamming technique, which uses single-or multiple-jammers, to improve location privacy while providing similar link throughputs. In addition, we introduce the Multi Cooperator Power Control (MCPC) algorithm to control jamming noise power, which further increases location privacy by actively controlling the strength of jamming noise.

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“…The Cramer-Rao is an unbiased estimator for the lower bound variance of TOF measurements defined by equation 1 [12]. The variance (TOF time error) is defined as σ 2 T OF , β f [Hz] is the spectral bandwidth of the received signal, n is the number of averaged TOF measurements and SNR is the energy per bit divided by the noise power (E b /N 0 ).…”

Section: Cramer-rao Lower Bound For Time-of-flight Ranging Estimatesmentioning

confidence: 99%

Radio frequency (RF) time-of-flight ranging for wireless sensor networks

Thorbjornsen

White

Brown

et al. 2010

Meas. Sci. Technol.

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Abstract. Position information of nodes within Wireless Sensor Networks (WSNs) is often a requirement in order to make use of the data recorded by the sensors themselves. On deployment the nodes normally have no prior knowledge of their position and thus a locationing mechanism is required to determine their positions. In this paper, we describe a method to determine the point-to-point range between sensor nodes as part of the locationing process. A two-way Time-of-Flight (TOF) ranging scheme is presented using narrow-band RF. The frequency difference between the transceivers involved with the point-to-point measurement is used to obtain a sub-clock TOF phase offset measurement in order to achieve high resolution TOF measurements. The ranging algorithm has been developed and prototyped on a TI CC2430 development kit with no additional hardware being required. Performance results have been obtained for the line-of-sight (LOS), non-line-of-sight (NLOS) and indoor condition. Accuracy is typically better than 7.0m RMS for the LOS condition over 250.0m and 15.8m RMS for the NLOS condition over 120.0m using a one-hundred sample average. Indoor accuracy is measured to 1.7m RMS using a 1000 sample average over 8.0m. Ranging error is linear and does not increase with increased transmitter-receiver distance. Our TOA ranging scheme demonstartes a novel system where resolution and accuracy are time dependent in comparison to alternative frequency dependent methods using narrowband RF.

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Signal Theory and Random Processes by Harry Urkowitz

Cited by 35 publications

References 0 publications

Magnitude image CSPAMM reconstruction (MICSR)

Magnitude image CSPAMM reconstruction (MICSR)

Multi-node coordinated jamming for location privacy protection

Radio frequency (RF) time-of-flight ranging for wireless sensor networks

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