Easy and quick methods to quantify ethanol reliably in beverages are always important. In 2022, the Enzytec<sup>TM</sup> Liquid Ethanol test kit was approved as AOAC Official Method<sup>SM</sup>...Easy and quick methods to quantify ethanol reliably in beverages are always important. In 2022, the Enzytec<sup>TM</sup> Liquid Ethanol test kit was approved as AOAC Official Method<sup>SM</sup> 2017.07 Final Action after a collaborative study was conducted with different beverages such as kombucha, juices, and beer. During set-up of this collaborative test, small sized companies asked to include the RIDA<sup>®</sup>CUBE Ethanol/RIDA<sup>®</sup>CUBE SCAN device since it is easy to use, suitable for a few samples only and contains the identical reagents as the Enzytec<sup>TM</sup> Liquid system. It is applicable to quantify ethanol in diluted kombucha, fruit juices, and alcohol-free beer samples around 0.5% alcohol-by-volume within 12 min. The overall relative reproducibility standard deviation across a wide concentration range for kombucha, was calculated to be 6.29%. Analysis of juices and beer showed an overall higher variation with an estimated overall RSD(R) value by regression of 14.4%. The data obtained by this collaborative study show that the RIDA<sup>®</sup>CUBE Ethanol in combination with the RIDA<sup>®</sup>CUBE SCAN device is suitable to quantify ethanol from matrices representing important alcohol-free liquid food categories.展开更多
Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a r...Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.展开更多
Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typ...Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.展开更多
文摘Easy and quick methods to quantify ethanol reliably in beverages are always important. In 2022, the Enzytec<sup>TM</sup> Liquid Ethanol test kit was approved as AOAC Official Method<sup>SM</sup> 2017.07 Final Action after a collaborative study was conducted with different beverages such as kombucha, juices, and beer. During set-up of this collaborative test, small sized companies asked to include the RIDA<sup>®</sup>CUBE Ethanol/RIDA<sup>®</sup>CUBE SCAN device since it is easy to use, suitable for a few samples only and contains the identical reagents as the Enzytec<sup>TM</sup> Liquid system. It is applicable to quantify ethanol in diluted kombucha, fruit juices, and alcohol-free beer samples around 0.5% alcohol-by-volume within 12 min. The overall relative reproducibility standard deviation across a wide concentration range for kombucha, was calculated to be 6.29%. Analysis of juices and beer showed an overall higher variation with an estimated overall RSD(R) value by regression of 14.4%. The data obtained by this collaborative study show that the RIDA<sup>®</sup>CUBE Ethanol in combination with the RIDA<sup>®</sup>CUBE SCAN device is suitable to quantify ethanol from matrices representing important alcohol-free liquid food categories.
文摘Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.
基金Project(2019JJ40047)supported by the Hunan Provincial Natural Science Foundation of ChinaProject(kq2014057)supported by the Changsha Municipal Natural Science Foundation,China。
文摘Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.
