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Study of the structural,ferroelectric,dielectric,and pyroelectric properties of the K0.5Na0.5NbO3 system doped with Li+,La3+,and Ti4+

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Journal of Advanced ceramics 2020年第3期9卷 329-338页

作者： c.montero-tavera M.D.DURRUTHY-RODRiGUEZ F.D.cORTES-VEGA J.M.YANEZ-LIMON center for Research and Advanced Studies of IPN Campus QueretaroMexico National Evangelical University Paseo de los Periodistas#54Ensanche MirafloresSanto DomingoDistrito NacionalRepiblica Dominicana

Pure *** sNbO3(KNN)and KNN doped with Lit(6%mole),Lat(1.66%,5%,6%mole),and Ti+t(10%mole)were prepared by mixture of oxides using high-energy milling and conventional solid-state *** effects of the dopant on the physical properties of pure KNN have been evaluated based on the structural,ferroelectric,pyroelectric,and dielectric *** XRD measurements show that KNN pure sample contains a mixture of monoclinic and orthorhombic crystalline phases,with a slightly higher concentration of monoclinic *** contrast,all doped samples show a higher concentration of the orthorhombic phase,as well as the presence of a secondary phase(K6Nb10.8O3o),also detected by Raman *** samples with a higher concentration of this secondary phase,also present greater dielectric losses and lower values of remnant *** dielectric measurements allowed us to detect temperatures of structural transitions(orthorhombic-tetragonal,O-T)previous to the ferroelectric paraelectric transition(tetragonal-cubic,T-c),and also in this set of samples,a direct correlation was found between the values of remnant polarization and the corresponding pyroelectric signal response.

关键词： K0.5Na0.5NbO3(KNN) lead-free solid-state reaction structural properties pyroelectric properties dielectric properties Li,La,Ti doping elements

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Internet of Things Enabled Energy Aware Metaheuristic clustering for Real Time Disaster Management

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computer Systems Science & Engineering 2023年第5期45卷 1561-1576页

作者： Riya Kumarasamy Santhanaraj Surendran Rajendran carlos Andres tavera Romero Sadish Sendil Murugaraj Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India center for Artificial Intelligence and Research(cAIR) Chennai Institute of TechnologyChennai600069India cOMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Emerging Technologies Guru Nanak Institute of TechnologyIbrahipatnamTelangana501506India

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless *** management in smart cities can be considered as a safety critical ***,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted *** and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT *** article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMcR-RTDM).The proposed EAMcR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster *** achieve this,EAMcR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-c)technique to elect cluster heads(cHs)and organize *** addition,enhanced cockroach swarm optimization(EcSO)based multihop routing(EcSO-MHR)approach was derived for optimal route *** YSGF-c and EcSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network *** design of YSGF-c and EcSO-MHR techniques for disaster management in IoT networks shows the novelty of the *** examining the improved outcomes of the EAMcR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different *** comparative outcomes highlighted the enhanced outcomes of the EAMcRRTDM algorithm over the existing approaches.

关键词： Internet of things disaster management wireless sensor networks real time applications routing clustering

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Automatic Liver Tumor Segmentation in cT Modalities Using MAT-AcM

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computer Systems Science & Engineering 2022年第12期43卷 1057-1068页

作者： S.Priyadarsini carlos Andrés tavera Romero Abolfazl Mehbodniya P.Vidya Sagar Sudhakar Sengan Department of computer Science and Engineering P.S.R.Engineering CollegeSivakasiTamil Nadu626123India cOMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Electronics and communications Engineering Kuwait College of Science and TechnologyKuwait Department of computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra Pradesh522502India Department of computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamil Nadu627152India

In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active contour Model (MAT-AcM) method, the input computed Tomography (cT) image is preprocessed by contrast Enhancement (cE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with canny Edge Detection(cED) technique, which segments the Liver Region (LR) from the given cTimages. An AEM with a cED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-AcM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-AcM method.

关键词： computed tomography contrast enhancement adaptive edge modeling multi-angle texture active contour liver tumor segmentation

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