An analysis of Newton's method in wireless systems using Gabor frames

Masud, Usman; Baig, Muhammad Iram

doi:10.1109/inmic.2012.6511480

Cited by 3 publications

(12 citation statements)

References 14 publications

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“…We present here a single tap channel model as described in [35,39]. If n and q represent the time and delay dimensions, respectively, then the channel mapping is defined as below,…”

Section: Formulation Of the Wireless Channel Modelmentioning

confidence: 99%

“…As follows from [35,36], we have to find a prototype function g[n] that minimizes the mean square error per TF coefficient in dispersive channels. Mathematically,…”

Section: Formulation Of the Wireless Channel Modelmentioning

confidence: 99%

“…From the research work in wireless systems, this simple approximation is only optimum for non-dispersive channels [35][36][37]. Orthogonality of Gabor atoms will be seriously distorted by dispersive channel.…”

Section: Introductionmentioning

confidence: 99%

“…The work started a couple of years ago when theoretical principles were initially laid down for the foundation of a wireless channel at a project [35,39]. Numerous approaches were explored in terms of the software implementation [40].…”

Section: Introductionmentioning

confidence: 99%

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Dual mode spectroscopic biomedical sensor: Technical considerations for the wireless testbed*

et al. 2020

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A real time implementation of a spectroscopy based dual mode sensor necessitates a timely and reliable transmission of information to and from the sensor. This becomes one of the pivotal roles of the sensor as the wireless transmission must be timely, accurate and reliable. For this purpose, our project aims to use a Gabor expansion model with a single-tap time-frequency channel model. This protocol has been planned to be used in a biomedical sensor system that aims for the detection of human breath for patients above 60. For this mechanism to work in dispersive channels, error introduced by this model needs to be minimized by optimizing Gabor prototype frames. We attempt to reduce this error via Newton method for a Wide-Sense Stationary Uncorrelated Scattering (WSSUS) environment. Therefore, numerous factors need to be considered, and have been presented. The sensitivity of this sensor setup in terms of least amount of discernible absorption coefficient is 8.37 × 10 − 5 mm − 1 , and we attempt to maintain this value via our wireless approach. The suggested solution provides an error within a threshold of 1 to 2% in case of our results. In this manner, the sanctity of information going to and from the human body is tantamount to be highly accurate that highlights the efficiency of this scheme in our biomedical sensor.

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“…We present here a single tap channel model as described in [35,39]. If n and q represent the time and delay dimensions, respectively, then the channel mapping is defined as below,…”

Section: Formulation Of the Wireless Channel Modelmentioning

confidence: 99%

“…As follows from [35,36], we have to find a prototype function g[n] that minimizes the mean square error per TF coefficient in dispersive channels. Mathematically,…”

Section: Formulation Of the Wireless Channel Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual mode spectroscopic biomedical sensor: Technical considerations for the wireless testbed*

et al. 2020

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“…But there exits two disadvantages, falling into local minimum easily and having a slow convergence. For these problems many researchs scholars proposed a lot of solutions that including Conjugate gradient, Newton, Causs-Newton, Levenberg-Marguard methods etc [1] [2] [3] [4]. Also the problems above have a great improved.…”

Section: Introductionmentioning

confidence: 99%

PCA-PSO-BP Neural Network Application in IDS

Shi

Yong-sheng²,

Lv³

2015

Proceedings of the 2015 International Power, Electronics and Materials Engineering Conference

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BP neural network has two disadvantages, one is to fall into local minimum value easily; the other is the slow convergence. We propose in this paper an approach, including three main operations. Firstly, the algorithm of particle swarm optimization (PSO) is applied to improve back propagation (BP) neural network. Secondly, principal components analysis (PCA) method is used to deal with the original information. Thirdly, after optimization of BP neural network, we employ it into the intrusion detection system. The simulation results reveal that the new proposed BP neural network is superior to the traditional BP neural network.

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PCA-based PSO-BP neural network optimization algorithm

Shi

Tang

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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An analysis of Newton's method in wireless systems using Gabor frames

Cited by 3 publications

References 14 publications

Dual mode spectroscopic biomedical sensor: Technical considerations for the wireless testbed*

Dual mode spectroscopic biomedical sensor: Technical considerations for the wireless testbed*

PCA-PSO-BP Neural Network Application in IDS

PCA-based PSO-BP neural network optimization algorithm

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