Conventional machine models of water trash collection is here enhanced with the integration of sprinkler system. This enhanced model for water trash collection combines conventional methods integrated with a sprinkler...
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To achieve ultra-reliable and low-latency communication (URLLC) and support high density of wireless connections simultaneously, the sixth-generation industrial Internet of things (6G-IIoT) necessitates an expansion o...
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A compact filtering antenna system with wide-angle scanning is proposed for vehicle to infrastructure(V2I) communication which would handle complex communication scenarios. In this work, a wide beam filtering antenna ...
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A compact filtering antenna system with wide-angle scanning is proposed for vehicle to infrastructure(V2I) communication which would handle complex communication scenarios. In this work, a wide beam filtering antenna is realized by using some inductive resistance structures such as metal pins and pillars, and capacitive structures such as slots, parasitical patches to produce the radiation nulls at two sides of the operating frequency band and improve the impedance matching in the passband. Meanwhile, the wide beam capability is also realized by the above structure. Furthermore, two H-and E-plane linear arrays are designed for the beam scanning capability with filtering characteristics based on the proposed antenna. To verify the proposed design concept, a prototype is fabricated and measured. The measurement and simulation agree well, demonstrating an excellent filtering characteristic with the operating frequency band from 3.18 to 3.45 GHz(about 8.1%), the high total efficiency of about 88%, and 3-d B-beamwidth of more than 100° and 120° in the above two arrays, respectively. Additionally, the proposed arrays can realize the beam scanning up to the coverage of 112° and 120° with a lower gain reduction and a good filtering characteristic, respectively.
A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical *** basic challenge experienced while designing WSN is in increasing the network lifetime and use of low *** sensor nodes are resour...
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A Wireless Sensor Network(WSN)is constructed with numerous sensors over geographical *** basic challenge experienced while designing WSN is in increasing the network lifetime and use of low *** sensor nodes are resource constrained in nature,novel techniques are essential to improve lifetime of nodes in *** energy is considered as an important resource for sensor node which are battery powered *** WSN,energy is consumed mainly while data is being transferred among nodes in the *** research works are carried out focusing on preserving energy of nodes in the network and made network to live ***,this network is threatened by attacks like vampire attack where the network is loaded by fake ***,Dual Encoding Recurrent Neural network(DERNNet)is proposed for classifying the vampire nodes s node in the ***,the Grey Wolf Optimization(GWO)algorithm helps for transferring the data by determining best solutions to optimally select the aggregation points;thereby maximizing battery/lifetime of the network *** proposed method is evaluated with three standard approaches namely Knowledge and Intrusion Detection based Secure Atom Search Routing(KIDSASR),Risk-aware Reputation-based Trust(RaRTrust)model and Activation Function-based Trusted Neighbor Selection(AF-TNS)in terms of various *** existing methods may lead to wastage of energy due to vampire attack,which further reduce the lifetime and increase average energy consumed in the ***,the proposed DERNNet method achieves 31.4%of routing overhead,23%of end-to-end delay,78.6%of energy efficiency,94.8%of throughput,28.2%of average latency,92.4%of packet delivery ratio,85.2%of network lifetime,and 94.3%of classification accuracy.
The attention mechanism can extract task-relevant vital information while suppressing less important information, playing an increasingly critical role in deep feature representation for semantic segmentation. This re...
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Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of servi...
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Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.
As the world becomes more and more competitive, the number of people experiencing stress, anxiety, and other mental health problems is rapidly rising. In today’s society, stress and pressure are affecting everyone, i...
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Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlie...
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As cloud data centres expand and provide more services, they consume more energy and cause challenges for the environment. To address this, there is a focus on energy-saving scheduling approaches in cloud computing. T...
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As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empi...
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As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.
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