Superlattices with varying GaN well widths (2, 3, 6, 9 nm) and fixed AlGaN barrier (8 nm) with high Al-content (x=0.65) were grown. Streaky RHEED patterns indicated 2D growth mode for the superlattices. XRD measuremen...Superlattices with varying GaN well widths (2, 3, 6, 9 nm) and fixed AlGaN barrier (8 nm) with high Al-content (x=0.65) were grown. Streaky RHEED patterns indicated 2D growth mode for the superlattices. XRD measurements showed multiple satellite peaks corresponding to uniform periodicity of the GaN/AlGaN pairs. The AlGaN barrier XRD peak also shifted with increasing well widths, while the GaN XRD peak was nominally unchanged. Room temperature photoluminescence experiments revealed peak emissions at energies lower than the bulk GaN energy gap. The large red shift with respect to the bulk gap is attributed to significant Stark effect for wide multiple quantum wells.展开更多
Fluorine doped tin oxide, SnO2:F, thin films were deposited by ultrasonic chemical spray starting from tin chloride and hydrofluoric acid. The physical characteristics of the films as a function of both water content ...Fluorine doped tin oxide, SnO2:F, thin films were deposited by ultrasonic chemical spray starting from tin chloride and hydrofluoric acid. The physical characteristics of the films as a function of both water content in the starting solution and substrate temperature were studied. The film structure was polycrystalline in all cases, showing that the intensity of (200) peak increased with the water content in the starting solution. The electrical resistivity decreased with the water content, reaching a minimum value, in the order of 8 × 10-4 Ωcm, for films deposited at 450℃ from a starting solution with a water content of 10 ml per 100 ml of solution;further increase in water content increased the corresponding resistivity. Optical transmittances of SnO2:F films were high, in the order of 75%, and the band gap values oscillated around 3.9 eV. SEM analysis showed uniform surface morphologies with different geometries depending on the deposition conditions. Composition analysis showed a stoichiometric compound with a [Sn/O] ratio around 1:2 in all samples. The presence of F into the SnO2 lattice was detected, within 2 at % respect to Sn.展开更多
Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every year.According to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 milli...Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every year.According to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in 2012.It is considered as the world’s largest human gathering.Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided.This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj.The image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech recognition.The core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee mages.The algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential network.Thus,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of KERNELS.The aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and normal.The utility of these results lies in producing appropriate signals for proving helpful in monitoring the pilgrims.Counting pilgrims from the photos will help the authorities to determine the number of people in certain areas.The results demonstrate the utility of agent-based modeling for Hajj pilgrims.展开更多
文摘Superlattices with varying GaN well widths (2, 3, 6, 9 nm) and fixed AlGaN barrier (8 nm) with high Al-content (x=0.65) were grown. Streaky RHEED patterns indicated 2D growth mode for the superlattices. XRD measurements showed multiple satellite peaks corresponding to uniform periodicity of the GaN/AlGaN pairs. The AlGaN barrier XRD peak also shifted with increasing well widths, while the GaN XRD peak was nominally unchanged. Room temperature photoluminescence experiments revealed peak emissions at energies lower than the bulk GaN energy gap. The large red shift with respect to the bulk gap is attributed to significant Stark effect for wide multiple quantum wells.
基金This work was partially supported by CONACyT under contract Number 166601.
文摘Fluorine doped tin oxide, SnO2:F, thin films were deposited by ultrasonic chemical spray starting from tin chloride and hydrofluoric acid. The physical characteristics of the films as a function of both water content in the starting solution and substrate temperature were studied. The film structure was polycrystalline in all cases, showing that the intensity of (200) peak increased with the water content in the starting solution. The electrical resistivity decreased with the water content, reaching a minimum value, in the order of 8 × 10-4 Ωcm, for films deposited at 450℃ from a starting solution with a water content of 10 ml per 100 ml of solution;further increase in water content increased the corresponding resistivity. Optical transmittances of SnO2:F films were high, in the order of 75%, and the band gap values oscillated around 3.9 eV. SEM analysis showed uniform surface morphologies with different geometries depending on the deposition conditions. Composition analysis showed a stoichiometric compound with a [Sn/O] ratio around 1:2 in all samples. The presence of F into the SnO2 lattice was detected, within 2 at % respect to Sn.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number QURDO001 titled“Intelligent Real-time Crowd Monitoring System Using Unmanned Aerial Vehicle(UAV)Video and Global Positioning Systems(GPS)Data”.
文摘Hajj as the Muslim holy pilgrimage,attracts millions of humans to Mecca every year.According to statists,the pilgrimage has attracted close to 2.5 million pilgrims in 2019,and at its peak,it has attracted over 3 million pilgrims in 2012.It is considered as the world’s largest human gathering.Safety makes one of the main concerns with regards to managing the large crowds and ensuring that stampedes and other similar overcrowding accidents are avoided.This paper presents a crowd management system using image classification and an alarm system for managing the millions of crowds during Hajj.The image classification system greatly relies on the appropriate dataset used to train the Convolutional neural network(CNN),which is the deep learning technique that has recently attracted the interest of the research community and industry in varying applications of image classification and speech recognition.The core building block of CNN is is a convolutional layer obtained by the getting CNN trained with patches bearing designated features of the trainee mages.The algorithm is implemented,using the Conv2D layers to activate the CNN as a sequential network.Thus,creating a 2D convolution layer having 64 filters and drop out of 0.5 makes the core of a CNN referred to as a set of KERNELS.The aim is to train the CNN model with mapped image data,and to make it available for use in classifying the crowd as heavily-crowded,crowded,semi-crowded,light crowded,and normal.The utility of these results lies in producing appropriate signals for proving helpful in monitoring the pilgrims.Counting pilgrims from the photos will help the authorities to determine the number of people in certain areas.The results demonstrate the utility of agent-based modeling for Hajj pilgrims.
