A novel parameter extraction technique suitable f or short channel length lightly-doped-drain (LDD) MOSFET's is proposed which seg ments the total gate bias range,and executes the linear regression in every subs ...A novel parameter extraction technique suitable f or short channel length lightly-doped-drain (LDD) MOSFET's is proposed which seg ments the total gate bias range,and executes the linear regression in every subs ections,yielding the gate bias dependent parameters,such as effective channel le ngth,parasitic resistance,and mobility,etc.This method avoids the gate bias rang e optimization,and retains the accuracy and simplicity of linear regression.The extracted gate bias dependent parameters are implemented in the compact I-V model which has been proposed for deep submicron LDD MOSFET's.The good agreemen ts between simulations and measurements of the devices on 0.18μm CMOS technolo gy indicate the effectivity of this technique.展开更多
The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-hei...The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.展开更多
文摘A novel parameter extraction technique suitable f or short channel length lightly-doped-drain (LDD) MOSFET's is proposed which seg ments the total gate bias range,and executes the linear regression in every subs ections,yielding the gate bias dependent parameters,such as effective channel le ngth,parasitic resistance,and mobility,etc.This method avoids the gate bias rang e optimization,and retains the accuracy and simplicity of linear regression.The extracted gate bias dependent parameters are implemented in the compact I-V model which has been proposed for deep submicron LDD MOSFET's.The good agreemen ts between simulations and measurements of the devices on 0.18μm CMOS technolo gy indicate the effectivity of this technique.
基金supported by the Fundamental Research Funds for the Central Universities of China(Grant No.2013SCU11006)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Mapping and Geoinformation of China(Grant NO.DM2014SC02)the Key Laboratory of Geospecial Information Technology,Ministry of Land and Resources of China(Grant NO.KLGSIT201504)
文摘The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.
