Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model

The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation.

Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.

Primary User Protection Contour and No-Talk Zone Characterization for TV Whitespace Spectrum Reuse in Nigeria.

To encourage secondary spectrum access within the TV broadcast bands in Nigeria, the propagation properties of TV signals on the VHF and UHF frequency ranges were empirically studied through measurements carried from two TV stations. The Pathloss exponent for the VHF band was found to be 1.9 with a characterised Pathloss equation for VHF band computed as 𝑷𝑳 𝒅𝑩 = 𝟖𝟒. 𝟎𝟒 + 𝟏𝟗. 𝟎𝟑𝒍𝒐𝒈𝟏𝟎 𝒅 , where (𝒅) is the distance from the transmitter to the receiver. The UHF band Pathloss exponent was computed to be 1.8 with a Pathloss equation characterised as 𝑷𝑳 𝒅𝑩 = 𝟓𝟕. 𝟑𝟓 + 𝟏𝟕. 𝟗𝟔𝒍𝒐𝒈𝟏𝟎 𝒅 . The findings re-echoed the need for specific prediction model to accurately estimate the service coverage of TV stations and facilitate effective utilization of spatial TV white space as it was found that there were divergence in coverage prediction between the measured model and some of the conventional models. Using the protection view point, the protection contour in kilometers for TV signals propagating in the UHF band in Nigeria was characterized to be 𝒅𝒓𝒑 = 𝟏𝟎 𝑷𝒕+𝟑𝟐.𝟔𝟓 𝟏𝟕.𝟗𝟔 . . Where (𝒅𝒓𝒑) is the protection contour radius modeled as a function of the transmit power of the TV station in decibels with reference to one milliwatt (dBm) for co-channel and adjacent channel coverage. Similarly, the no-talk-zone in kilometers was characterized as a function of the transmit power of the secondary user device in dBm for co-channel usage to be 𝒅(𝒓𝒏−𝒓𝒑) = 𝒂𝒏𝒕𝒊𝒍𝒐𝒈[ 𝑷𝒔−𝟖𝟗.𝟕𝟔 𝟏𝟕.𝟗𝟔 ] modeled as a function of the secondary user transmit power 𝑷𝒔 . The separation distance in kilometers from the TV station to the possible secondary user transmitter beyond which no interference exist was computed to have a relationship equal to 𝒂𝒏𝒕𝒊𝒍𝒐𝒈 𝑷𝒕+𝑷𝒔−𝟓𝟕.𝟏𝟏 𝟏𝟕.𝟗𝟔 . This model will facilitate TVWS cochannel coexistence using the specified equation to determine the separation distances between television transmitters and secondary user transmitters.

4G Wireless network coverage prediction for operator “A” by using GIS in Jeddah City: a case study of Al Nuzhah district: التنبؤ بتغطية أبراج شبكة الاتصالات اللاسلكية 4G للمشغل "أ" باستخدام نظم المعلومات الجغرافية في مدينة جدة (دراسة حالة حي النزهة)

Communication systems depend on cell tower signals which affect the quality and efficiency of communications networks. In this paper, a prediction network coverage of 4G wireless network for operator “A” in Al Nuzhah city has been elaborated. The main objective is, firstly, to come up with prediction network maps showing the quality of communication networks in the study area and to identify areas with good and bad coverage, in order to maintain and improve coverage through relocating cell towers and antennas, increasing their number, or through installing new ones in bad coverage areas. Another objective is to compare this output with actual network coverage efficiency. In this study, GIS programs were adopted to handle, manage, process and analyze spatial and attribute data. GIS extensions were used to design communication networks such as the Mentum-Planet program through which prediction network coverage was calculated and represented. Cell towers and sectors data was first collected and then processed to generate the 4G coverage network prediction for operator "A" based on propagation model (Q9). Results were later compared to the network efficiency generated by the Drive Test. It was found that the signal strength was between -95 and -75 db. About 24.2% and 75.8% of the studied area had medium and excellent network coverage, respectively. The Drive Test showed areas of poor network coverage distributed throughout the study area. This research concluded that use of GIS in communications reduces cost and time of implementation. It is highly recommended to include building heights in the propagation model as it affects the spread of communication waves, to increase cell towers in poor coverage areas, and to modify the propagation model to ensure quality of service and efficient coverage.