Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different cro...Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.展开更多
The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and storage.This paper investigates a different approach based on...The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and storage.This paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud environment.This Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and technology.An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed.This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in industry.An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.展开更多
Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the case.Most of the time,these caches are made of static random access memory cells.They take up a...Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the case.Most of the time,these caches are made of static random access memory cells.They take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several aspects.This is true for phones and tablets.Cache memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of power.The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture.Furthermore,power reduction techniques are applied over different blocks of cache memory architecture to optimize energy.The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.展开更多
Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by ag...Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil cultivation.There comes a demand for computer-related soil classification methods to support agriculturalists.This study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)technique.The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches.In the presented GBODL-ASC technique,three major processes are involved.At the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature vectors.For soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)model.The design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,respectively.To demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five classes.The simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.展开更多
基金supported by the Deanship of Research and Graduate Studies at the King Khalid University(RGP2/287/46)the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R733)+1 种基金the Princess Nourah bint Abdulrahman University Research Supporting Project(RSPD2025R787)the King Saud University,Saudi Arabia.
文摘Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.
基金This research project was supported by a grant from the“Research Center of College of Computer and Information Sciences”,Deanship of Scientific Research,King Saud University.
文摘The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and storage.This paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud environment.This Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and technology.An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed.This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in industry.An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.
基金Research General Direction funded this research at Universidad Santiago de Cali,Grant Number 01-2021 and APC was funded by 01-2021.
文摘Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the case.Most of the time,these caches are made of static random access memory cells.They take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several aspects.This is true for phones and tablets.Cache memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of power.The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture.Furthermore,power reduction techniques are applied over different blocks of cache memory architecture to optimize energy.The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R303)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.Research Supporting Project number(RSPD2023R787)+1 种基金King Saud University,Riyadh,Saudi ArabiaThis study is supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2023/R/1444).
文摘Soil classification is one of the emanating topics and major concerns in many countries.As the population has been increasing at a rapid pace,the demand for food also increases dynamically.Common approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil cultivation.There comes a demand for computer-related soil classification methods to support agriculturalists.This study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)technique.The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches.In the presented GBODL-ASC technique,three major processes are involved.At the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature vectors.For soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)model.The design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,respectively.To demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five classes.The simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.
