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您的检索式：作者名="Mohammad R.Khosravi"
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| 1 | Mobile Multimedia Computing in Cyber-Physical Surveillance Services Through UAV-Borne Video-SAR:A Taxonomy of Intelligent Data Processing for IoMT-Enabled Radar Sensor Networks显示文摘This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar(Video-SAR)as a new mode of radar imaging for real-time remote sensing and surveillance.This research also considers new suggestions in the systematic design,research taxonomy,and future trends of radar data processing.Despite the conventional modes of SAR imaging,Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night(regardless of light sources)in all weathers.First,an introduction to Video-SAR is presented.Then,some specific properties of this imaging mode are reviewed.Particularly,this research covers one of the most important aspects of the Video-SAR systems,namely,the systematic design requirements,and also some new types of visual distortions which are different from the distortions,artifacts and noises observed in the conventional imaging radar.In addition,some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle(UAV)platforms,Internet of Multimedia Things(IoMT),Video-SAR data processing issues,and real-world applications are investigated. | Mohammad R.Khosravi Sadegh Samadi | 2022 | Tsinghua Science and Technology2022，27，2： | 2 |
| 2 | Efficient pipelined flow classification for intelligent data processing in IoT显示文摘The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps. | Seyed Navid Mousavi Fengping Chen Mahdi Abbasi Mohammad R.Khosravi Milad Rafiee | 2022 | Digital Communications and Networks2022，8，4： | 0 |
| 3 | Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes显示文摘Energy management in smart homes is one of the most critical problems for the Quality of Life(QoL)and preserving energy resources.One of the relevant issues in this subject is environmental contamination,which threatens the world's future.Green computing-enabled Artificial Intelligence(Al)algorithms can provide impactful solutions to this topic.This research proposes using one of the Recurrent Neural Network(RNN)algorithms known as Long Short-Term Memory(LSTM)to comprehend how it is feasible to perform the cloud/fog/edge-enabled prediction of the building's energy.Four parameters of power electricity,power heating,power cooling,and total power in an office/home in cold-climate cities are considered as our features in the study.Based on the collected data,we evaluate the LSTM approach for forecasting parameters for the next year to predict energy consumption and online monitoring of the model's performance under various conditions.Towards implementing the Al predictive algorithm,several existing tools are studied.The results have been generated through simulations,and we find them promisingforfutureapplications. | Omid Akbarzadeh Sahand Hamzehei Hani Attar Ayman Amer Nazanin Fasihihour Mohammad R.Khosravi Ahmed A.Solyman | 2024 | Tsinghua Science and Technology2024，29，1： | 0 |
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