LTE Signal Detection Using Two-Stage Cooperative Compressive Sensing System

Author(s):  
Mustafa Mahdi Ali ◽  
Mokhalad Khaleel Alghrairi ◽  
Emad Hmood Salman
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Marcelo A. Pedroso ◽  
Lucas H. Negri ◽  
Marcos A. Kamizi ◽  
José L. Fabris ◽  
Marcia Muller

This work describes the development of a quasi-distributed real-time tactile sensing system with a reduced number of fiber Bragg grating-based sensors and reports its use with a reconstruction method based on differential evolution. The sensing system is comprised of six fiber Bragg gratings encapsulated in silicone elastomer to form a tactile sensor array with total dimensions of 60 × 80 mm, divided into eight sensing cells with dimensions of 20 × 30 mm. Forces applied at the central position of the sensor array resulted in linear response curves for the gratings, highlighting their coupled responses and allowing the application of compressive sensing. The reduced number of sensors regarding the number of sensing cells results in an undetermined inverse problem, solved with a compressive sensing algorithm with the aid of differential evolution method. The system is capable of identifying and quantifying up to four different loads at four different cells with relative errors lower than 10.5% and signal-to-noise ratio better than 12 dB.


2021 ◽  
Author(s):  
Qiuli Ma ◽  
Jeffrey Joseph Starns ◽  
David Kellen

We explored a two-stage recognition memory paradigm in which people first make single-item “studied”/“not studied” decisions and then have a chance to correct their errors in forced-choice trials. Each forced-choice trial included one studied word (“target”) and one non-studied word (“lure”) that received the same previous single-item response. For example, a “studied”-“studied” trial would have a target that was correctly called “studied” and a lure that was incorrectly called “studied.” The two-high-threshold (2HT) model and the unequal-variance signal detection (UVSD) model predict opposite effects of biasing the initial single-item responses on subsequent forced-choice accuracy. Results from two experiments showed that the bias effect is actually near zero and well out of the range of effects predicted by either model. Follow-up analyses showed that the model failures were not a function of experiment artifacts like changing memory states between the two types of recognition trials. Follow-up analyses also showed that the dual process signal detection (DPSD) model made better predictions for the forced-choice data than 2HT and UVSD models.


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