scholarly journals A Novel Device-Free Counting Method Based on Channel Status Information

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3981 ◽  
Author(s):  
Junhuai Li ◽  
Pengjia Tu ◽  
Huaijun Wang ◽  
Kan Wang ◽  
Lei Yu
Keyword(s):  
Spatial Diversity ◽  
Operating Conditions ◽  
Frequency Diversity ◽  
Counting Method ◽  
Widespread Application ◽  
Crowd Counting ◽  
Novel Device ◽  
Counting Approach ◽  
Status Information ◽  
Device Free

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread application. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed. The wavelet domain denoising is introduced to mitigate environment noise. Furthermore, the amplitude or phase covariance matrix is extracted as the eigenmatrix. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. At the same experimental environment, the accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

scholarly journals A Novel Device-Free Counting Method Based on Channel Status Information

2018 ◽  
Author(s):  
Junhuai Li ◽  
Pengjia Tu ◽  
Huaijun Wang ◽  
Kan Wang ◽  
Lei Yu
Keyword(s):  
Spatial Diversity ◽  
Operating Conditions ◽  
Frequency Diversity ◽  
Counting Method ◽  
Crowd Counting ◽  
Crowd Management ◽  
Novel Device ◽  
Counting Approach ◽  
Status Information ◽  
Device Free

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread deployment. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed, which could mitigate environment noise through wavelet transform and extract the amplitude or phase covariance matrix as the feature vector. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. The accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

scholarly journals Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqi Tang ◽  
Zhisong Pan ◽  
Xingyu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Regression Method ◽  
Low Rank ◽  
Counting Method ◽  
Direct Integral ◽  
Crowd Counting ◽  
Counting Methods ◽  
Density Map

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.

scholarly journals Exploiting Fine-Grained Subcarrier Information for Device-Free Localization in Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3110 ◽  
Author(s):  
Yan Guo ◽  
Dongping Yu ◽  
Ning Li
Keyword(s):  
Compressive Sensing ◽  
Estimation Algorithm ◽  
Surface Model ◽  
Frequency Diversity ◽  
Fine Grained ◽  
Area Of Interest ◽  
Radio Signals ◽  
Device Free ◽  
Target Locations ◽  
Spatial Sparsity

Device-free localization (DFL) that aims to localize targets without carrying any electronic devices is addressed as an emerging and promising research topic. DFL techniques estimate the locations of transceiver-free targets by analyzing their shadowing effects on the radio signals that travel through the area of interest. Recently, compressive sensing (CS) theory has been applied in DFL to reduce the number of measurements by exploiting the inherent spatial sparsity of target locations. In this paper, we propose a novel CS-based multi-target DFL method to leverage the frequency diversity of fine-grained subcarrier information. Specifically, we build the dictionaries of multiple channels based on the saddle surface model and formulate the multi-target DFL as a joint sparse recovery problem. To estimate the location vector, an iterative location vector estimation algorithm is developed under the multitask Bayesian compressive sensing (MBCS) framework. Compared with the state-of-the-art CS-based multi-target DFL approaches, simulation results validate the superiority of the proposed algorithm.

Operating Conditions and System Considerations for a Reversible Solid Oxide Cell System for Electrical Energy Storage

2014 ◽  
Author(s):  
Christopher H. Wendel ◽  
Pejman Kazempoor ◽  
Robert J. Braun
Keyword(s):  
Energy Storage ◽  
Thermal Management ◽  
Management Strategies ◽  
Electrical Energy ◽  
Cell System ◽  
Operating Conditions ◽  
Solid Oxide ◽  
System Level ◽  
Widespread Application ◽  
Electrical Energy Storage

Electrical energy storage (EES) is an important component of the future electric grid. Given that no other widely available technology meets all the EES requirements, reversible (or regenerative) solid oxide cells (ReSOCs) working in both fuel cell (power producing) and electrolysis (fuel producing) modes are envisioned as a technology capable of providing highly efficient and cost-effective EES. However, there are still many challenges from cell materials development to system level operation of ReSOCs that should be addressed before widespread application. One particular challenge of this novel system is establishing effective thermal management strategies to maintain the high conversion efficiency of the ReSOC. The system presented in this paper employs a thermal management strategy of promoting exothermic methanation in the ReSOC stack to offset the endothermic electrolysis reactions during charging mode (fuel producing) while also enhancing the energy density of the stored gases. Modeling and parametric analysis of an energy storage concept is performed using a thermodynamic system model coupled with a physically based ReSOC stack model. Results indicate that roundtrip efficiencies greater than 70% can be achieved at intermediate stack temperature (∼680°C) and pressure (∼20 bar). The optimal operating conditions result from a tradeoff between high stack efficiency and high parasitic balance of plant power.

scholarly journals Crowd Counting Method Based on Cross-Source Multi-View and Multi-Granularity Video Fusion

2020 ◽  
Author(s):  
Liu Bai ◽  
Cheng Wu ◽  
Yufeng Lin ◽  
Jin Zhang ◽  
Jie Sheng ◽  
...  
Keyword(s):  
High Altitude ◽  
Rapid Development ◽  
Estimation Methods ◽  
Surveillance Systems ◽  
Counting Method ◽  
Crowd Counting ◽  
Single View ◽  
Crowd Management ◽  
Video Fusion ◽  
Low Altitude

Abstract With the rapid growth of the world's population and the rapid development of urbanization, the issue of crowd gathering safety has aroused widespread concern in society. Extensive video surveillance systems provide rich data support for dense crowd management. Video-based crowd counting and density estimation methods are the core technologies to ensure the safety of crowd gathering. Different from single-view video analysis, cross-source multi-view multi-granularity video contains more cross-information. The complementary sharing of information is of great help to solve the problems such as occlusion in the current crowd counting. Therefore, this article proposes a crowd counting method based on cross-source multi-view and multi-granularity video distributed information fusion. By establishing a distributed structure from different cameras that matches low-altitude and high-altitude views, it uses fine-grained from low-altitude images. The high-resolution local information corrects and supplements the global information from high-altitude images, so as to calculate a more accurate and global number and density of people. This method is actually applied to the landmark building of Suzhou Life City Square. The changes in the number of people and the movement situation during the evacuation are analyzed and evaluated, and good results are obtained.

An Efficient Method for the Optimization of Viscoplastic Constitutive Model Constants

2010 ◽  
Author(s):  
James P. DeMarco ◽  
Erik A. Hogan ◽  
Calvin M. Stewart ◽  
Ali P. Gordon
Keyword(s):  
Constitutive Model ◽  
Random Noise ◽  
Constitutive Models ◽  
Optimization Method ◽  
Complex Stress ◽  
Operating Conditions ◽  
Widespread Application ◽  
Material Constants ◽  
Material Parameters ◽  
Critical Components

Constitutive modeling has proven useful in providing accurate predictions of material response in components subjected to a variety of operating conditions; however, the high number of experiments necessary to determine appropriate constants for a model can be prohibitive, especially for more expensive materials. Generally, up to twenty experiments simulating a range of conditions are needed to identify the material parameters for a model. In this paper, an automated process for optimizing the material constants of the Miller constitutive model for uniaxial modeling is introduced. The use of more complex stress, strain, and temperature histories than are traditionally used allows for the effects of all material parameters to be captured using significantly fewer tests. A graphical user interface known as uSHARP was created to implement the resulting method, which determines the material constants of a viscoplastic model using a minimum amount of experimental data. By carrying out successive finite element simulations and comparing the results to simulated experimental test data, both with and without random noise, the material constants were determined from 75% fewer experiments. The optimization method introduced here reduces the cost and time necessary to determine constitutive model constants through experimentation. Thus it allows for a more widespread application of advanced constitutive models in industry and for better life prediction modeling of critical components in high-temperature applications.

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