The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular networks.This scenario affects the qual...The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular networks.This scenario affects the quality of service(QoS)of vehicle and non-vehicle users.Nowadays,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly accessible.Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks.However,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task.This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs.Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision.However,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available.The present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions.The proposed mechanism is decentralized,where each vehicle makes the prediction by itself.This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios.The MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading ratio(i.e.,above 90%)than the existing mechanisms for several tested scenarios.展开更多
In this paper,we presented the development of a navigation control system for a sailboat based on spiking neural networks(SNN).Our inspiration for this choice of network lies in their potential to achieve fast and low...In this paper,we presented the development of a navigation control system for a sailboat based on spiking neural networks(SNN).Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware.To train our system,we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator.Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies,depending on the sailing scenario conditions.We presented the mathematical definition of the problem,the operation scheme of the simulation environment,the spiking neural network controllers,and the control strategy used.As a result,we obtained 425 SNN-based controllers that completed the proposed navigation task,indicating that the simulation environment and the implemented control strategy work effectively.Finally,we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.展开更多
The influence of single and double layered gold(Au)nanocrystals(NC),embedded in SiO_(2) matrix,on the electrical characteristics of metal–oxide–semiconductor(MOS)structures is reported in this communication.The size...The influence of single and double layered gold(Au)nanocrystals(NC),embedded in SiO_(2) matrix,on the electrical characteristics of metal–oxide–semiconductor(MOS)structures is reported in this communication.The size and position of the NCs are varied and study is made using Sentaurus TCAD simulation tools.In a single NC-layered MOS structure,the role of NCs is more prominent when they are placed closer to SiO_(2)/Si-substrate interface than to SiO_(2)/Al–gate interface.In MOS structures with larger NC dots and double layered NCs,the charge storage capacity is increased due to charging of the dielectric in the presence of NCs.Higher breakdown voltage and smaller leakage current are also obtained in the case of dual NC-layered MOS device.A new phenomenon of smearing out of the capacitance–voltage curve is observed in the presence of dual NC layer indicating generation of interface traps.An internal electric field developed between these two charged NC layers is expected to generate such interface traps at the SiO_(2)/Si interface.展开更多
基金This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular networks.This scenario affects the quality of service(QoS)of vehicle and non-vehicle users.Nowadays,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly accessible.Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks.However,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task.This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs.Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision.However,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available.The present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions.The proposed mechanism is decentralized,where each vehicle makes the prediction by itself.This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios.The MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading ratio(i.e.,above 90%)than the existing mechanisms for several tested scenarios.
基金supported by the University of Antioquia with project PRG2017-16182by the Colombia Scientific Program within the framework of the call Ecosistema Científico(Contract No.FP44842-218-2018).
文摘In this paper,we presented the development of a navigation control system for a sailboat based on spiking neural networks(SNN).Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware.To train our system,we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator.Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies,depending on the sailing scenario conditions.We presented the mathematical definition of the problem,the operation scheme of the simulation environment,the spiking neural network controllers,and the control strategy used.As a result,we obtained 425 SNN-based controllers that completed the proposed navigation task,indicating that the simulation environment and the implemented control strategy work effectively.Finally,we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.
文摘The influence of single and double layered gold(Au)nanocrystals(NC),embedded in SiO_(2) matrix,on the electrical characteristics of metal–oxide–semiconductor(MOS)structures is reported in this communication.The size and position of the NCs are varied and study is made using Sentaurus TCAD simulation tools.In a single NC-layered MOS structure,the role of NCs is more prominent when they are placed closer to SiO_(2)/Si-substrate interface than to SiO_(2)/Al–gate interface.In MOS structures with larger NC dots and double layered NCs,the charge storage capacity is increased due to charging of the dielectric in the presence of NCs.Higher breakdown voltage and smaller leakage current are also obtained in the case of dual NC-layered MOS device.A new phenomenon of smearing out of the capacitance–voltage curve is observed in the presence of dual NC layer indicating generation of interface traps.An internal electric field developed between these two charged NC layers is expected to generate such interface traps at the SiO_(2)/Si interface.
