Eigenvalue-based techniques for continuous sensing model in MIMO CR networks

2013 ◽  
Author(s):  
Adoniran Judson Braga ◽  
Rausley A. A. de Souza ◽  
Dayan Adionel Guimaraes
Keyword(s):  
Sensing Model

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

2021 ◽  
Vol 13 (8) ◽  
pp. 1409
Author(s):  
Kun Song ◽  
Xichuan Liu ◽  
Taichang Gao ◽  
Peng Zhang
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Water Cycle ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Vapor Density ◽  
Measurement Resolution ◽  
Sensing Model ◽  
Numerical Weather Forecasting ◽  
Mean Square Errors

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

Deep AI Enabled Ubiquitous Wireless Sensing

2021 ◽  
Vol 54 (2) ◽  
pp. 1-35
Author(s):  
Chenning Li ◽  
Zhichao Cao ◽  
Yunhao Liu
Keyword(s):  
Great Success ◽  
Wireless Sensing ◽  
Ubiquitous Sensing ◽  
Sensing Systems ◽  
The Status ◽  
Sensing Model ◽  
High Level ◽  
Traditional Approaches ◽  
The Internet Of Things ◽  
High Level Feature

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

scholarly journals ISMA: Intelligent Sensing Model for Anomalies Detection in Cross Platform OSNs With a Case Study on IoT

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 3284-3301 ◽  
Author(s):  
Vishal Sharma ◽  
Ilsun You ◽  
Ravinder Kumar
Keyword(s):  
Sensing Model ◽  
Cross Platform

Application of an optical remote sensing model

1995 ◽  
Author(s):  
Charles R. Bostater, Jr. ◽  
Wei-ming Ma ◽  
Ted McNally ◽  
Manuel Gimond ◽  
A. P. Lamb
Keyword(s):  
Remote Sensing ◽  
Optical Remote Sensing ◽  
Remote Sensing Model ◽  
Sensing Model

scholarly journals Investigation of a Three-Dimensional Micro-Scale Sensing System Based on a Tapered Self-Assembly Four-Cores Fiber Bragg Grating Probe

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2824 ◽  
Author(s):  
Kunpeng Feng ◽  
Jiwen Cui ◽  
Xun Sun ◽  
Hong Dang ◽  
Tangjun Shi ◽  
...  
Keyword(s):  
Fiber Bragg Grating ◽  
Self Assembly ◽  
Three Dimensional ◽  
Critical Length ◽  
Bragg Grating ◽  
Manufacturing Method ◽  
Micro Scale ◽  
Sensing Model ◽  
Shift Point ◽  
Better Than

Three-dimensional micro-scale sensors are in high demand in the fields of metrology, precision manufacturing and industry inspection. To extend the minimum measurable dimension and enhance the accuracy, a tapered four-cores fiber Bragg grating (FBG) probe is proposed. The sensing model is built to investigate the micro-scale sensing characteristics of this method and the design of the tapered stylus is found to influence the accuracy. Therefore, a π/2 phase-shift point is introduced into the FBGs comprised in the probe to suppress spectrum distortion and improve accuracy. Then, the manufacturing method based on capillary self-assembly is proposed to form the probe and the critical length to form a square array for four cylindrical fibers is verified to be effective for the tapered fibers. Experimental results indicate that the design of the tapered stylus can extend the minimum measurable dimension by twofold and has nearly no influence on its sensitivity. The three-dimensional measurement repeatability is better than 31.1 nm and the stability is better than 200 nm within once measuring process. Furthermore, the measurement precision of the three-dimensional micro-scale measurement results is less than 150 nm. It would be widely used in measuring micro-scale features for industry inspection or metrology.

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