Inverse Scattering Using a Joint Norm-Based Regularization

Shah, Pratik; Khankhoje, Uday K.; Moghaddam, Mahta

doi:10.1109/tap.2016.2529641

Cited by 56 publications

(9 citation statements)

References 32 publications

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“…The problem with the use of l 2 -norm is that it produces extra smoothness and becomes drastically inefficient when being applied to domains with sharp variations, discontinuities, or sparse content. So, the sharpness provided by l 1 -norm was adopted [84]. l 1 -norm-based implementation received a huge support with the advent of Compressive Sensing (CS).…”

Section: Regularization and Optimization Schemesmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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Microwave imaging (MWI) is a non-ionizing, non-invasive and an upcoming affordable medical imaging modality. Over the last few decades, MWI has invited active research towards biomedical imaging, with special focus on breast tumor detection. After long years of intense research and clinical trials, a breast tumour monitoring unit based on MWI is finally entering clinical imaging scenarios. In this manuscript, the vast literature in MWI to date has been consolidated, and an indetail study of the state-of-the-art for breast tumor detection has been presented. The hurdles faced during clinical trials are discussed, and their possible solutions and future directions for a fast transition into clinical imaging have been presented. It is hoped that this paper can serve as a guide for MWI researchers and practitioners, especially those new to the field to comprehend the potential of MWI as a viable imaging tool for breast imaging.

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Section: Regularization and Optimization Schemesmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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show abstract

“…The decision module by itself belongs to DNN, containing two fully connected layers, which is backward connected to a softmax classifier to compute Q-values of M available actions. L2-norm regularization with weight decay of 0.001 is leveraged to avoid over-fitting [42]. Given an input χ, the Q-values of actions can be calculated by: (16) where f D (•) is the nonlinear function approximator in the decision process, ϑ D (•) is the set of parameters and v is related to the model scale.…”

Section: ) Q-network For Dcrqnmentioning

confidence: 99%

Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

Han

Lei

et al. 2019

IEEE Access

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So far, the handoff management involved in the wireless local area network (WLAN) has mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff mechanism generates noticeable delays during the handoff process, resulting in discontinuity of service, which is more evident in dense WLANs. Inspired by software-defined networking (SDN), prior works put forward many seamless handoff mechanisms to ensure service continuity. With respect to the handoff decision algorithm, when to trigger handoff and which access point to reconnect to, however, are still tricky problems. In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN frameworks. Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep Q-network. The proposed scheme enables the network to learn from actual users' behaviors and network status from scratch, adapting its learning in time-varying dense WLANs. Due to the temporal correlation property, the handoff decision is modeled as the Markov decision process (MDP). In the modeled MDP, the proposed scheme depends on the real-time network statistics at the time of decisions. Moreover, the convolutional neural network and the recurrent neural network are leveraged to extract fine-grained discriminative features. The numerical results through simulation demonstrate that DCRQN can effectively improve the data rate during the handoff process, outperforming the traditional handoff scheme.

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“…Consequently, proper inversion procedures need to be devised, to consider both these theoretical problems. To this end, several approaches have been proposed in the last years [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. They have been investigated in the context of Hilbert spaces, in most cases.…”

Section: Introductionmentioning

confidence: 99%

Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

et al. 2019

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An overview of the recent advancements in the development of microwave imaging procedures based on the exploitation of the regularization theory in Lebesgue spaces is reported in this paper. Such inversion schemes have been found to provide accurate results in several microwave imaging scenarios, thanks to the different geometrical properties that Lebesgue spaces can exhibit with respect to the more classical Hilbert ones. Moreover, the recent extension to the more general case of variable-exponent Lebesgue spaces is also addressed. Experimental results involving reference data are shown for supporting the theoretical description of the approaches.

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Inverse Scattering Using a Joint Norm-Based Regularization

Cited by 56 publications

References 32 publications

An Overview of Microwave Imaging for Breast Tumor Detection

An Overview of Microwave Imaging for Breast Tumor Detection

Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

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