In this paper, the outage perfor- mance of a cognitive relaying network over Nakagami-m fading channels, employing simultaneous wireless information and power transfer （SWIPT） technology is analyzed and evaluated. T...In this paper, the outage perfor- mance of a cognitive relaying network over Nakagami-m fading channels, employing simultaneous wireless information and power transfer （SWIPT） technology is analyzed and evaluated. The operation of this network is considered in conjunction with the convention- al decode-and-forward （DF） and incremental DF （IDF） protocols. For the conventional DF protocol, it is assumed that there is no direct link between the secondary transmitter （S） and the secondary destination （D）, while （for both protocols） after harvesting energy, the relay node （R） always helps to forward the resulting signal to D. However, for the IDF protocol, R assists in relaying S＇s information to D only when the direct communication between S and D has failed. Furthermore, for both DF and IDF protocols, we assume there is no power supply for R, and R harvests energy from the transmitted signal of S. We derive exact ana- lytical expressions for the outage probability at D in DF and IDF protocols, respectively, in terms of the bivariate Meijer＇s G-function. Performance evaluation results obtained by means of Monte-Carlo simulations are also provided and have validated the correctness of the oroDosed analysis.展开更多
The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity...The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity of the radio spectrum. The traditional command and control model (Static allocation) of spectrum allocation policy allows for severe spectrum underutilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a hybrid access scheme based on CSMA/CA and TDMA MAC protocols for use in the TV bands. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that, the proposed schemes protect the primary user from harmful Interference from the secondary user. In terms of delay, it was found that packet arrival rates, data rates and the number of secondary users have significant effects on delay.展开更多
Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and cla...Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,InceptionV3,and MobileNetV2 accuracy are observed to be 92.59%,83.38%,and 90.16%,respectively.The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices.展开更多
The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators...The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a dynamic spectrum access scheme for use in the TV bands which uses cognitive radio techniques to determine the spectrum availability. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that the proposed scheme protects the primary users from harmful interference from the secondary users. In comparison with the 802.11 MAC protocol, the scheme improves spectrum utilization by about 27% while limiting the interference imposed on the primary receiver.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.61472343)China Postdoctoral Science Foundation(Grant No.2014M56074)
文摘In this paper, the outage perfor- mance of a cognitive relaying network over Nakagami-m fading channels, employing simultaneous wireless information and power transfer （SWIPT） technology is analyzed and evaluated. The operation of this network is considered in conjunction with the convention- al decode-and-forward （DF） and incremental DF （IDF） protocols. For the conventional DF protocol, it is assumed that there is no direct link between the secondary transmitter （S） and the secondary destination （D）, while （for both protocols） after harvesting energy, the relay node （R） always helps to forward the resulting signal to D. However, for the IDF protocol, R assists in relaying S＇s information to D only when the direct communication between S and D has failed. Furthermore, for both DF and IDF protocols, we assume there is no power supply for R, and R harvests energy from the transmitted signal of S. We derive exact ana- lytical expressions for the outage probability at D in DF and IDF protocols, respectively, in terms of the bivariate Meijer＇s G-function. Performance evaluation results obtained by means of Monte-Carlo simulations are also provided and have validated the correctness of the oroDosed analysis.
文摘The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity of the radio spectrum. The traditional command and control model (Static allocation) of spectrum allocation policy allows for severe spectrum underutilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a hybrid access scheme based on CSMA/CA and TDMA MAC protocols for use in the TV bands. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that, the proposed schemes protect the primary user from harmful Interference from the secondary user. In terms of delay, it was found that packet arrival rates, data rates and the number of secondary users have significant effects on delay.
文摘Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,InceptionV3,and MobileNetV2 accuracy are observed to be 92.59%,83.38%,and 90.16%,respectively.The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices.
文摘The growing demand for wireless services coupled with the limited availability of suitable electromagnetic spectrum is increasing the need for more efficient RF spectrum utilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a dynamic spectrum access scheme for use in the TV bands which uses cognitive radio techniques to determine the spectrum availability. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that the proposed scheme protects the primary users from harmful interference from the secondary users. In comparison with the 802.11 MAC protocol, the scheme improves spectrum utilization by about 27% while limiting the interference imposed on the primary receiver.
