Device-Free Localization via Sparse Coding with a Generalized Thresholding Algorithm

2021 ◽  
Author(s):  
Qin CHENG ◽  
Linghua ZHANG ◽  
Bo XUE ◽  
Feng SHU ◽  
Yang YU
Keyword(s):  
Sparse Coding ◽  
Thresholding Algorithm ◽  
Device Free

scholarly journals An Accurate and Efficient Device-Free Localization Approach Based on Sparse Coding in Subspace

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 61782-61799 ◽  
Author(s):  
Huakun Huang ◽  
Haoli Zhao ◽  
Xiang Li ◽  
Shuxue Ding ◽  
Lingjun Zhao ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Device Free ◽  
Localization Approach

scholarly journals Towards understanding residual and dilated dense neural networks via convolutional sparse coding

2020 ◽  
Author(s):  
Zhiyang Zhang ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Sparse Coding ◽  
State Of The Art ◽  
Theoretical Interpretation ◽  
Theoretical Understanding ◽  
Dilated Convolution ◽  
Thresholding Algorithm ◽  
Propagation Rule ◽  
Special Case ◽  
Iterative Soft Thresholding

Abstract Convolutional neural network (CNN) and its variants have led to many state-of-the-art results in various fields. However, a clear theoretical understanding of such networks is still lacking. Recently, a multilayer convolutional sparse coding (ML-CSC) model has been proposed and proved to equal such simply stacked networks (plain networks). Here, we consider the initialization, the dictionary design and the number of iterations to be factors in each layer that greatly affect the performance of the ML-CSC model. Inspired by these considerations, we propose two novel multilayer models: the residual convolutional sparse coding (Res-CSC) model and the mixed-scale dense convolutional sparse coding (MSD-CSC) model. They are closely related to the residual neural network (ResNet) and the mixed-scale (dilated) dense neural network (MSDNet), respectively. Mathematically, we derive the skip connection in the ResNet as a special case of a new forward propagation rule for the ML-CSC model. We also find a theoretical interpretation of dilated convolution and dense connection in the MSDNet by analyzing the MSD-CSC model, which gives a clear mathematical understanding of each. We implement the iterative soft thresholding algorithm and its fast version to solve the Res-CSC and MSD-CSC models. The unfolding operation can be employed for further improvement. Finally, extensive numerical experiments and comparison with competing methods demonstrate their effectiveness.

scholarly journals Block-Sparse Coding Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

2020 ◽  
pp. 1-1
Author(s):  
Lingjun Zhao ◽  
Huakun Huang ◽  
Chunhua Su ◽  
Shuxue Ding ◽  
Huawei Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sparse Coding ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Device Free

scholarly journals Multi-targets device-free localization based on sparse coding in smart city

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985822
Author(s):  
Min Zhao ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Guangchao Xu
Keyword(s):  
Sparse Coding ◽  
Smart City ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Localization Accuracy ◽  
Threshold Algorithm ◽  
Single Target ◽  
Iterative Shrinkage ◽  
Device Free

With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city.

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

2021 ◽  
pp. 147592172110448
Author(s):  
Han Zhang ◽  
Jing Lin ◽  
Jiadong Hua ◽  
Tong Tong
Keyword(s):  
Sparse Coding ◽  
Lamb Wave ◽  
Structural Damage ◽  
Damage Identification ◽  
Lamb Waves ◽  
Damage Localization ◽  
Coding Method ◽  
Data Driven Approach ◽  
Band Pass ◽  
Thresholding Algorithm

Lamb wave-based damage identification and localization methods hold the potential for nondestructive evaluation and structural health monitoring. Dispersive and multimodal characteristics lead to complicated Lamb wave signals that are challenging to be analyzed. Deep learning architectures could identify damage-related features effectively. Convolutional neural network (CNN) is a promising architecture that has been widely applied in recent years. However, this data-driven approach still lacks a certain degree of physical interpretability and requires a large number of parameters. In this article, an interpretable Lamb wave convolutional sparse coding (LW-CSC) method is proposed for structural damage identification and localization. First, toneburst signals at different center frequencies are considered in the first convolutional layer. The network convolves the waveforms with a set of parametrized functions that implement band-pass filters, which performs more physical interpretability compared to conventional CNN model. Subsequently, the damage features are extracted according to the multi-layer iterative soft thresholding algorithm for multi-layer CSC model, which could realize a deeper network without adding parameters unlike CNN. Finally, Lamb wave-based damage localization is visualized using an imaging algorithm. The experimental results demonstrate that the proposed method not only enables improvement of the classification accuracy but also achieves structural damage localization accurately.

scholarly journals Improved Sparse Coding Algorithm with Device-Free Localization Technique for Intrusion Detection and Monitoring

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 637 ◽  
Author(s):  
Huakun Huang ◽  
Zhaoyang Han ◽  
Shuxue Ding ◽  
Chunhua Su ◽  
Lingjun Zhao
Keyword(s):  
Intrusion Detection ◽  
Sparse Coding ◽  
State Of The Art ◽  
Optimization Method ◽  
Living Room ◽  
Full Potential ◽  
Wireless Devices ◽  
Proximal Operator ◽  
Art Methods ◽  
Device Free

Device-free localization (DFL) locates target in a wireless sensors network (WSN) without equipping with wireless devices or tags, which is an emerging technology in the fields of intrusion detection and monitoring. In order to achieve an accurate result of DFL, the conventional works adopt l 1 norm as a regularizer to take the full potential of sparsity for locating targets. Contrasting to the previous works, we exploit the l 2 , 1 norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding algorithm with proximal operator (ISCPO). Compared with the state-of-the-art methods that adopt l 1 norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution. Experimental results on our real testbeds of indoor DFL show that, in scenarios of living room and corridor, the proposed approach can achieve high localization accuracies of about 100% and 90%, respectively. In addition, the proposed ISCPO algorithm outperforms the compared state-of-the-art methods and has a more robust performance in challenged environments for target localization.

History Aware Device-free Passive (DfP) Localisation

2011 ◽  
Vol 16 (3-4) ◽  
Author(s):  
Gabriel Deak ◽  
Kevin Curran ◽  
Joan Condell
Keyword(s):  
Device Free
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