One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat produ...One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary,for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis.Fourier Transform Infrared Spectroscopy(FTIR)is used in this work to identify lard adulteration in cow,lamb,and chicken samples.A simplified extraction method was implied to obtain the lipids from pure and adulterated meat.Adulterated samples were obtained by mixing lard with chicken,lamb,and beef with different concentrations(10%–50%v/v).Principal component analysis(PCA)and partial least square(PLS)were used to develop a calibration model at 800–3500 cm^(−1).Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken,lamb,and beef samples.The corresponding FTIR peaks for the lard have been observed at 1159.6,1743.4,2853.1,and 2922.5 cm−1,which differentiate chicken,lamb,and beef samples.The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration(RMSEC)and root mean square error prediction(RMSEP)with an accuracy of 84.6%.Even the tiniest fat adulteration up to 10%can be reliably discovered using this methodology.展开更多
Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly...Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.展开更多
Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and avail...Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.展开更多
文摘One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness.The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary,for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis.Fourier Transform Infrared Spectroscopy(FTIR)is used in this work to identify lard adulteration in cow,lamb,and chicken samples.A simplified extraction method was implied to obtain the lipids from pure and adulterated meat.Adulterated samples were obtained by mixing lard with chicken,lamb,and beef with different concentrations(10%–50%v/v).Principal component analysis(PCA)and partial least square(PLS)were used to develop a calibration model at 800–3500 cm^(−1).Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken,lamb,and beef samples.The corresponding FTIR peaks for the lard have been observed at 1159.6,1743.4,2853.1,and 2922.5 cm−1,which differentiate chicken,lamb,and beef samples.The wavenumbers offer the highest determination coefficient R2 value of 0.846 and lowest root mean square error of calibration(RMSEC)and root mean square error prediction(RMSEP)with an accuracy of 84.6%.Even the tiniest fat adulteration up to 10%can be reliably discovered using this methodology.
基金the ADEK Award for Research Excellence (AARE19-245)2019.
文摘Long Range Wide Area Network (LoRaWAN) in the Internet ofThings (IoT) domain has been the subject of interest for researchers. Thereis an increasing demand to localize these IoT devices using LoRaWAN dueto the quickly growing number of IoT devices. LoRaWAN is well suited tosupport localization applications in IoTs due to its low power consumptionand long range. Multiple approaches have been proposed to solve the localizationproblem using LoRaWAN. The Expected Signal Power (ESP) basedtrilateration algorithm has the significant potential for localization becauseESP can identify the signal’s energy below the noise floor with no additionalhardware requirements and ease of implementation. This research articleoffers the technical evaluation of the trilateration technique, its efficiency,and its limitations for the localization using LoRa ESP in a large outdoorpopulated campus environment. Additionally, experimental evaluations areconducted to determine the effects of frequency hopping, outlier removal, andincreasing the number of gateways on localization accuracy. Results obtainedfrom the experiment show the importance of calculating the path loss exponentfor every frequency to circumvent the high localization error because ofthe frequency hopping, thus improving the localization performance withoutthe need of using only a single frequency.
基金The research work was supported by UTP-Universitas Telkom,Indonesia International Collaborative Research Funding(ICRF)015ME0-153 and Center for Graduate Studies(CGS),Universiti Teknologi PETRONAS(UTP),Perak,Malaysia.
文摘Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud services.However,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm.Furthermore,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud services.Moreover,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches.In this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and password.The underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers.The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.
