Relevant bibliographies by topics / Selection network (SN)

Academic literature on the topic 'Selection network (SN)'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Selection network (SN)"

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Höfinger, Gerhard, and Stefan Brunner. "Network-based Simulation in Water Construction – a Flexible Tool for Equipment Selection." SNE Simulation Notes Europe 26, no. 1 (March 2016): 55–58. http://dx.doi.org/10.11128/sne.26.sn.10329.

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He, Yixin, Daosen Zhai, Dawei Wang, Xiao Tang, and Ruonan Zhang. "A Relay Selection Protocol for UAV-Assisted VANETs." Applied Sciences 10, no. 23 (December 7, 2020): 8762. http://dx.doi.org/10.3390/app10238762.

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In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead.
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Deepa, C., B. Latha, and I. Jenish. "Development and Performance Evaluation of a New Energy-Efficient Double Cluster-Head Routing (EEDCR) Protocol for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/5041153.

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Utilization of energy and the lifetime increment are the big issues in designing of routing algorithms for wireless sensor networks (WSNs). Many routing algorithms have been developed by various researchers to achieve energy efficiency and to improve the lifetime of the network. But, the way to route the information from the sensor node (SN) to the base station (BS) and vice versa is an important issue, because of resource constraints. In this paper, we have proposed a low energy consumed, cluster-based routing protocol named an energy-efficient and double cluster-head routing (EEDCR) protocol, to increase the network lifetime and minimize the end-to-end delay. Selection of the cluster head (CH) is depending on random selection method, energy method, the total number of nodes, and its energy level. The performances of developed protocol were assessed using simulations and found that it provides successful outcomes in terms of energy-efficient and lifetime increment in routing for WSNs.
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Nasimifar, Mahdi, Senthilmurugan Thyagarajan, Sarah Chaudhari, and Nadarajah Sivaneswaran. "Pavement Structural Capacity from Traffic Speed Deflectometer for Network Level Pavement Management System Application." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 2 (January 19, 2019): 456–65. http://dx.doi.org/10.1177/0361198118825122.

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Structural number (SN) represents the structural capacity of a flexible pavement system to sustain anticipated traffic and is among the structural indices most commonly used by pavement design engineers in the U.S. Effective structural number (SNeff) is an indicator of structural capacity of in-service pavement sections and is conventionally estimated from nondestructive testing (NDT) device data such as falling weight deflectometers (FWDs) using methods such as suggested by AASHTO. In addition to pavement design, structural condition is a critical input for the selection of maintenance and rehabilitation strategies in pavement management system (PMS) application. However, use of SN in network level application has not been practical because of limitations of FWD such as stop-and-go operation, lane closures, and low testing frequency. The traffic speed deflectometer (TSD), a continuous deflection device, has recently been gaining worldwide application as a reliable NDT device for network level PMS applications. The objective of this study is to develop a practical approach to compute and utilize SN of in-service flexible pavements from TSD data for network level PMS applications. The study is based on the fundamental that, for the same pavement, SNeff from the TSD using the proposed method should be in good agreement with SNeff from the FWD using AASHTO method. The developed method was field validated with TSD and FWD data collected at in-service pavement sections. In addition, the use of structural number ratio, defined as a ratio of SNeff to required SN, in network level prioritization of structural capacity improvements was illustrated.
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Xiao, Xingxing, and Haining Huang. "A Clustering Routing Algorithm Based on Improved Ant Colony Optimization Algorithms for Underwater Wireless Sensor Networks." Algorithms 13, no. 10 (October 1, 2020): 250. http://dx.doi.org/10.3390/a13100250.

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Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.
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Orlandi, Andrea, and Alice Mado Proverbio. "Left-Hemispheric Asymmetry for Object-Based Attention: an ERP Study." Brain Sciences 9, no. 11 (November 8, 2019): 315. http://dx.doi.org/10.3390/brainsci9110315.

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It has been shown that selective attention enhances the activity in visual regions associated with stimulus processing. The left hemisphere seems to have a prominent role when non-spatial attention is directed towards specific stimulus features (e.g., color, spatial frequency). The present electrophysiological study investigated the time course and neural correlates of object-based attention, under the assumption of left-hemispheric asymmetry. Twenty-nine right-handed participants were presented with 3D graphic images representing the shapes of different object categories (wooden dummies, chairs, structures of cubes) which lacked detail. They were instructed to press a button in response to a target stimulus indicated at the beginning of each run. The perception of non-target stimuli elicited a larger anterior N2 component, which was likely associated with motor inhibition. Conversely, target selection resulted in an enhanced selection negativity (SN) response lateralized over the left occipito-temporal regions, followed by a larger centro-parietal P300 response. These potentials were interpreted as indexing attentional selection and categorization processes, respectively. The standardized weighted low-resolution electromagnetic tomography (swLORETA) source reconstruction showed the engagement of a fronto-temporo-limbic network underlying object-based visual attention. Overall, the SN scalp distribution and relative neural generators hinted at a left-hemispheric advantage for non-spatial object-based visual attention.
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Tsvetanov, Filip Atanasov, and Martin Pandurski. "Selection of Protocols for Integration of Sensory Data Networks in Cloud Structures." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 09 (July 11, 2022): 29–40. http://dx.doi.org/10.3991/ijoe.v18i09.31321.

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The striving to obtain more detailed information about the environment and control various processes leads to an increase in the number of connected sensor devices in various industrial areas. The collected large amount of data can be analysed in real-time. The sensors that build up the WSN have limited hardware resources and cannot process large amounts of data. The integration between WSN and cloud structures is an excellent method for storing, processing, accessing data via the Internet and solves the issue of the limited capacity of WSN. The big challenge to designing the WSN - cloud systems is establishing a communication channel (through different protocols) between devices in the network and cloud platforms. This project executes/perform a real experiment on the XBee sensor network and the ThingSpeak cloud, and the data transmission between them is forwarded using different protocols (HTTP, HTTPS, MQTT and MQTT-SN). The influence of the parameters of the transmitted packet on the delay, the CPU, RAM load has been studied. The results give some advantages of MQTT over other protocols in terms of data rate, CPU and RAM load when working with XBee sensor modules and integration between WSN and cloud structures.
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Narayan, Vipul, and A. K. Daniel. "CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 2 (October 21, 2022): 159–78. http://dx.doi.org/10.14201/adcaij.27271.

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The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.
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Lo, Serigne N., Jiawen Ma, Richard A. Scolyer, Lauren E. Haydu, Jonathan R. Stretch, Robyn P. M. Saw, Omgo E. Nieweg, et al. "Improved Risk Prediction Calculator for Sentinel Node Positivity in Patients With Melanoma: The Melanoma Institute Australia Nomogram." Journal of Clinical Oncology 38, no. 24 (August 20, 2020): 2719–27. http://dx.doi.org/10.1200/jco.19.02362.

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PURPOSE For patients with primary cutaneous melanoma, the risk of sentinel node (SN) metastasis varies according to several clinicopathologic parameters. Patient selection for SN biopsy can be assisted by National Comprehensive Cancer Network (NCCN) and ASCO/Society of Surgical Oncology (SSO) guidelines and the Memorial Sloan Kettering Cancer Center (MSKCC) online nomogram. We sought to develop an improved online risk calculator using alternative clinicopathologic parameters to more accurately predict SN positivity. PATIENTS AND METHODS Data from 3,477 patients with melanoma who underwent SN biopsy at Melanoma Institute Australia (MIA) were analyzed. A new nomogram was developed by replacing body site and Clark level from the MSKCC model with mitotic rate, melanoma subtype, and lymphovascular invasion. The predictive performance of the new nomogram was externally validated using data from The University of Texas MD Anderson Cancer Center (n = 3,496). RESULTS The MSKCC model receiver operating characteristic curve had a predictive accuracy of 67.7% (95% CI, 65.3% to 70.0%). The MIA model had a predictive accuracy of 73.9% (95% CI, 71.9% to 75.9%), a 9.2% increase in accuracy over the MSKCC model ( P < .001). Among the 2,748 SN-negative patients, SN biopsy would not have been offered to 22.1%, 13.4%, and 12.4% based on the MIA model, the MSKCC model, and NCCN or ASCO/SSO criteria, respectively. External validation generated a C-statistic of 75.0% (95% CI, 73.2% to 76.7%). CONCLUSION A robust nomogram was developed that more accurately estimates the risk of SN positivity in patients with melanoma than currently available methods. The model only requires the input of 6 widely available clinicopathologic parameters. Importantly, the number of patients undergoing unnecessary SN biopsy would be significantly reduced compared with use of the MSKCC nomogram or the NCCN or ASCO/SSO guidelines, without losing sensitivity. An online calculator is available at www.melanomarisk.org.au .
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Uddin Ahmed Zihan, Zia, Mostafa A. Elseifi, Kevin Gaspard, and Zhongjie Zhang. "Development of a Structural Capacity Prediction Model Based on Traffic Speed Deflectometer Measurements." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (April 23, 2018): 315–25. http://dx.doi.org/10.1177/0361198118758058.

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The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model’s ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.
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Book chapters on the topic "Selection network (SN)"

1

Dhiman, Saloni, Deepti Kakkar, and Gurjot Kaur. "Performance Analysis of Multi-Hop Routing Protocol With Optimized Grid-Based Clustering for Wireless Sensor Network." In Advances in Computer and Electrical Engineering, 254–82. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1626-3.ch009.

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Wireless sensor networks (WSNs) consist of several sensor nodes (SNs) that are powered by battery, so their lifetime is limited, which ultimately affects the lifespan and hence performance of the overall networks. Till now many techniques have been developed to solve this problem of WSN. Clustering is among the effective technique used for increasing the network lifespan. In this chapter, analysis of multi-hop routing protocol based on grid clustering with different selection criteria is presented. For analysis, the network is divided into equal-sized grids where each grid corresponds to a cluster and is assigned with a grid head (GH) responsible for collecting data from each SN belonging to respective grid and transferring it to the base station (BS) using multi-hop routing. The performance of the network has been analyzed for different position of BS, different number of grids, and different number of SNs.
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