At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the per...At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.展开更多
The method opted for accuracy,and no existing studies are based on this method.A design and characteristic survey of a new small band gap semiconducting Single Wall Carbon Nano Tube(SWCNT)Field Effect Transistor as a ...The method opted for accuracy,and no existing studies are based on this method.A design and characteristic survey of a new small band gap semiconducting Single Wall Carbon Nano Tube(SWCNT)Field Effect Transistor as a photodetector is carried out.In the proposed device,better performance is achieved by increasing the diameter and introducing a new single halo(SH)doping in the channel length of the CNTFET device.This paper is a study and analysis of the performance of a Carbon Nano Tube Field Effect Transistor(CNTFET)as a photodetector using the self-consistent Poisson and Green function method.The 2D self-consistent Poisson and Green’s function method for various optical intensities and wavelength simulate this proposed photodetector.The performance study is based on the simulation of drain current,transconductance,sub-threshold swing,cut-off frequency,gain,directivity,and quantum efficiency under dark and illuminated conditions.These quantum simulation results show that cut-off frequency increases while there is an increase in diameter.The proposed SH-CNTFET provides better performance in terms of higher gain and directivity than conventional CNTFET(C-CNTFET).This device will be helpful in optoelectronic integrated circuits(OEIC)receivers due to its superior performance.展开更多
文摘At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.
文摘The method opted for accuracy,and no existing studies are based on this method.A design and characteristic survey of a new small band gap semiconducting Single Wall Carbon Nano Tube(SWCNT)Field Effect Transistor as a photodetector is carried out.In the proposed device,better performance is achieved by increasing the diameter and introducing a new single halo(SH)doping in the channel length of the CNTFET device.This paper is a study and analysis of the performance of a Carbon Nano Tube Field Effect Transistor(CNTFET)as a photodetector using the self-consistent Poisson and Green function method.The 2D self-consistent Poisson and Green’s function method for various optical intensities and wavelength simulate this proposed photodetector.The performance study is based on the simulation of drain current,transconductance,sub-threshold swing,cut-off frequency,gain,directivity,and quantum efficiency under dark and illuminated conditions.These quantum simulation results show that cut-off frequency increases while there is an increase in diameter.The proposed SH-CNTFET provides better performance in terms of higher gain and directivity than conventional CNTFET(C-CNTFET).This device will be helpful in optoelectronic integrated circuits(OEIC)receivers due to its superior performance.
