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Relevant bibliographies by topics / Edge devices

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Academic literature on the topic 'Edge devices'

Author: Grafiati

Published: 29 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Edge devices"

1

Wong, Yuki, Nurul Ezaila Alias, Tian Swee Tan, and Michael Loong Peng Tan. "Investigating Transmission Coefficients of AB-Stacked Bilayer Graphene Nanoribbons with Varied Edge Configurations." ELEKTRIKA- Journal of Electrical Engineering 23, no. 2 (2024): 92–98. http://dx.doi.org/10.11113/elektrika.v23n2.575.

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In this study, the transmission coefficients of AB-stacked bilayer graphene nanoribbons (AB-BGNRs) with different edge configurations, specifically zigzag and armchair edges, are comprehensively investigated. These coefficients are modeled and simulated using the tight-binding (TB) model and non-equilibrium Green’s function (NEGF) formalism. The impact of edge structures on the electronic properties of AB-BGNRs is highlighted, providing insights into their potential applications in nanoelectronics devices. Significant variations between zigzag and armchair-edged bilayer graphene nanoribbons (B

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Sang, Yongxuan, Junqiang Cheng, Bo Wang, and Ming Chen. "A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing." PeerJ Computer Science 8 (January 18, 2022): e851. http://dx.doi.org/10.7717/peerj-cs.851.

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Device-edge-cloud cooperative computing is increasingly popular as it can effectively address the problem of the resource scarcity of user devices. It is one of the most challenging issues to improve the resource efficiency by task scheduling in such computing environments. Existing works used limited resources of devices and edge servers in preference, which can lead to not full use of the abundance of cloud resources. This article studies the task scheduling problem to optimize the service level agreement satisfaction in terms of the number of tasks whose hard-deadlines are met for device-ed

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Jin, Wenquan, Yinan Xu, Yilin Dai, and Yihu Xu. "Blockchain-Based Continuous Knowledge Transfer in Decentralized Edge Computing Architecture." Electronics 12, no. 5 (2023): 1154. http://dx.doi.org/10.3390/electronics12051154.

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Edge computing brings computational ability to network edges to enable low latency based on deploying devices close to the environment where the data is generated. Nevertheless, the limitation of size and energy consumption constrain the scalability and performance of edge device applications such as deep learning, although, cloud computing can be adopted to support high-performance tasks with centralized data collection. However, frequently communicating with a central cloud server brings potential risks to security and privacy issues by exposing data on the Internet. In this paper, we propos

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Zendebudi, Ahmad, and Salimur Choudhury. "Designing a Deep Q-Learning Model with Edge-Level Training for Multi-Level Task Offloading in Edge Computing Networks." Applied Sciences 12, no. 20 (2022): 10664. http://dx.doi.org/10.3390/app122010664.

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Even though small portable devices are becoming increasingly more powerful in terms of processing power and power efficiency, there are still workloads that require more computational capacity than these devices offer. Examples of such workloads are real-time sensory input processing, video game streaming, and workloads relating to IoT devices. Some of these workloads such as virtual reality, however, require very small latency; hence, the workload cannot be offloaded to a cloud service. To tackle this issue, edge devices, which are closer to the user, are used instead of cloud servers. In thi

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Mahmod, Md Jubayer al, and Ujjwal Guin. "A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications." Cryptography 4, no. 1 (2020): 8. http://dx.doi.org/10.3390/cryptography4010008.

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The edge devices connected to the Internet of Things (IoT) infrastructures are increasingly susceptible to piracy. These pirated edge devices pose a serious threat to security, as an adversary can get access to the private network through these non-authentic devices. It is necessary to authenticate an edge device over an unsecured channel to safeguard the network from being infiltrated through these fake devices. The implementation of security features demands extensive computational power and a large hardware/software overhead, both of which are difficult to satisfy because of inherent resour

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Lee, Dongkyu, Hyeongyun Moon, Sejong Oh, and Daejin Park. "mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications." Sensors 20, no. 12 (2020): 3337. http://dx.doi.org/10.3390/s20123337.

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As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the

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Mahmudah, Haniah, Aulia Siti Aisjah, Syamsul Arifin, and Catur Arif Prastyanto. "Detecting road damage utilizing retinanet and mobilenet models on edge devices." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1430. https://doi.org/10.11591/ijai.v14.i2.pp1430-1440.

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A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devi

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Haniah, Mahmudah, Siti Aisjah Aulia, Arifin Syamsul, and Arif Prastyanto Catur. "Detecting road damage utilizing retinanet and mobilenet models on edge devices." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1430–40. https://doi.org/10.11591/ijai.v14.i2.pp1430-1440.

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A particular form of road digitalization produces a system that detects road damage automatically and in real time, employing the device to detect road damage as an edge device. The application of RetinaNet152 and MobileNetV2 models for road damage detection on edge devices necessitates a trade-off between high system performance and efficiency. Currently, edge devices have limited storage. In this paper, we explore how tuning hyperparameters with batch size and several optimizers improves system performance on RetinaNet152 and MobileNet models, as well as how they are implemented on edge devi

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van Dijke, Koen, Gert Veldhuis, Karin Schroën, and Remko Boom. "Parallelized edge-based droplet generation (EDGE) devices." Lab on a Chip 9, no. 19 (2009): 2824. http://dx.doi.org/10.1039/b906098g.

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Richter, K., and H. Rosemann. "Experimental investigation of trailing-edge devices at transonic speeds." Aeronautical Journal 106, no. 1058 (2002): 185–93. http://dx.doi.org/10.1017/s0001924000012987.

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AbstractThe influence of trailing-edge devices such as Gurney flaps and divergent trailing edges of different height on the aerodynamic performance of an aerofoil at transonic speeds has been investigated experimentally. The investigation has been carried out in the Transonic Wind Tunnel Göttingen (TWG) using the two-dimensional aerofoil model VC-Opt at freestream Mach numbers of M ε [0.755, 0.775, 0.790] and a Reynolds number of Re = 5.0 x 106.The results have shown that the trailing-edge devices increase the circulation of the aerofoil leading to a lift enhancement and pitching-moment decrea

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Dissertations / Theses on the topic "Edge devices"

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Choi, Myung Cheon. "Traffic flow management for RSVP/ATM edge devices." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15520.

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Khizar, Sadia. "Metrology for 5G edge networks (MEC). Leveraging mobile devices beyond the edge toward task offloading." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS069.

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L'omniprésence des dispositifs mobiles équipés d'une connectivité Internet et de systèmes de positionnement, nous pousse à les considérer comme une ressource précieuse à exploiter. Dans cette thèse, nous abordons l'utilisation des dispositifs mobiles sous un nouvel angle. Nous considérons l'extension de la capacité du MEC en utilisant les ressources disponibles des dispositifs mobiles au-delà de la bordure du réseau d'infrastructure. L'objectif est de tirer parti de leurs ressources inexploitées pour traiter les tâches de calculs au profit du MEC de manière distribuée. Pour pouvoir s'appuyer s

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Barnes, Brian E. "Real-time resource management for RSVP/ATM edge devices." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15395.

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Pinkney, Thomas David. "Wound-edge protection devices to reduce surgical site infection." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7588/.

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This thesis provides an overview of the design, conduct and analysis of a multicentre phase III trial exploring the clinical effectiveness of a novel in-theatre intervention to try to reduce rates of post-operative surgical site infection (SSI). The pitfalls inherent in the conduct of research into SSI are discussed, along with measures to try and overcome these. The wound-edge protection device (WEPD) intervention is defined and the published evidence assessing its clinical effectiveness systematically appraised. Clinical surgical research and its difficulties are described, and the paradigm-

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Kaarlela, H. (Heikki). "Edge adaptive filtering of depth maps for mobile devices." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201910313045.

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Abstract. Mobile phone cameras have an almost unlimited depth of field, and therefore the images captured with them have wide areas in focus. When the depth of field is digitally manipulated through image processing, accurate perception of depth in a captured scene is important. Capturing depth data requires advanced imaging methods. In case a stereo lens system is used, depth information is calculated from the disparities between stereo frames. The resulting depth map is often noisy or doesn’t have information for every pixel. Therefore it has to be filtered before it is used for emphasizi

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Ciaccio, Giovanni. "Flow and transport in the edge of fusion devices." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423733.

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The main topic of the Thesis is the study of the electrostatic, plasma response (Er and flows) to magnetic islands embedded in the chaotic edge of a tokamak, when resonant magnetic perturbations (RMPs) are applied. Results are compared with the known phenomenology and theory in the chaotic edge of a reversed-field pinch (RFP). Proxies of the two configurations are the tokamak TEXTOR, with the application of the dynamic ergodic divertor (DED); and the RFX-mod RFP. The main tool used for simulations of islands, two-fluid transport (electrons and ions), and ambipolar Er field, is the Hami

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Brada, Matej. "Graphene electronic devices in magnetic field." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21800.

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This thesis discusses the two dimensional allotrope of carbon known as graphene in presence of magnetic field, with special focus on edge states. The structure of graphene is described in detail and from the structure, two models are formed. The Dirac equation is a good description of graphene for large samples, far away from edges, where the boundaries can be ignored. However, it causes problems with most types of edge and hard wall approximation has to be implemented. The Dirac equation is described in detail and used to obtain an energy spectrum, wavefunction and density of states for graph

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Sankin, Igor. "Edge termination and RESURF technology in power silicon carbide devices." Diss., Mississippi State : Mississippi State University, 2006. http://library.msstate.edu/etd/show.asp?etd=etd-12162005-141206.

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Wood, Michael G. "Active Silicon Photonic Devices Based on Degenerate Band Edge Resonances." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480432902683812.

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Hansson, Gustav. "Computation offloading of 5G devices at the Edge using WebAssembly." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85898.

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With an ever-increasing percentage of the human population connected to the internet, the amount of data produced and processed is at an all-time high. Edge Computing has emerged as a paradigm to handle this growth and, combined with 5G, enables complex time-sensitive applications running on resource-restricted devices. This master thesis investigates the use of WebAssembly in the context of computa¬tional offloading at the Edge. The focus is on utilizing WebAssembly to move computa¬tional heavy parts of a system from an end device to an Edge Server. An objective is to improve program performa

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Books on the topic "Edge devices"

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Krasheninnikov, Sergei, Andrei Smolyakov, and Andrei Kukushkin. On the Edge of Magnetic Fusion Devices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49594-7.

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Hirose, Akira. Collisionless edge instability in tokamaks. Plasma Physics Laboratory, University of Saskatchewan, 1995.

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Etkin, Grisha. Cove-Edge Graphene Nanoribbon Semiconductors: From Molecules to Devices. [publisher not identified], 2018.

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Zhang, Wei. Improved confinement and edge plasma fluctuations in the STOR-M tokamak. University of Saskatchewan, Plasma Physics Laboratory, 1991.

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Springett, Alan. Do colour measuring devices maximise a printing company's competitive edge?. LCP, 2001.

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Scott, Samuel J. Effects of leading-edge devices on the low-speed aerodynamic characteristics of a highly-swept arrow-wing. National Aeronautics and Space Administration, Langley Research Center, 1985.

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W, Nicks Oran, Imbrie P. K, and Langley Research Center, eds. Effects of leading-edge devices on the low-speed aerodynamic characteristics of a highly-swept arrow-wing. National Aeronautics and Space Administration, Langley Research Center, 1985.

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8

Nakano, Hiroshi. DC-SQUIDs fabricated by shapes of sputtering deposition at step edge and anodic oxidization. [Kyushu University Research Institute of Fundamental Information Science, 1986.

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Abich, Geancarlo, Luciano Ost, and Ricardo Reis. Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Constrained IoT Edge Devices. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-18599-1.

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Pitcher, Charles Spencer. Tokamak plasma interaction with limiters. Institute for Aerospace Studies, 1988.

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Book chapters on the topic "Edge devices"

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Fordham, Stuart. "Edge Devices." In Learning SD-WAN with Cisco. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7347-0_7.

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Chhabra, Adil, Florian Kurpicz, Christian Schulz, Dominik Schweisgut, and Daniel Seemaier. "Partitioning Trillion Edge Graphs on Edge Devices." In 2025 Proceedings of the Conference on Applied and Computational Discrete Algorithms (ACDA). Society for Industrial and Applied Mathematics, 2025. https://doi.org/10.1137/1.9781611978759.6.

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Lin, Sen, Zhi Zhou, Zhaofeng Zhang, Xu Chen, and Junshan Zhang. "Cooperative Edge Model Inference over Heterogeneous Edge Devices." In Synthesis Lectures on Learning, Networks, and Algorithms. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84563-5_8.

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Stackowiak, Robert. "IoT Edge Devices and Microsoft." In Azure Internet of Things Revealed. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5470-7_3.

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Kwatra, Shikhar, Utpal Mangla, and Mudhakar Srivatsa. "Model Operationalization at Edge Devices." In Artificial Intelligence for Edge Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40787-1_12.

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Shin, Changhwan. "Line Edge Roughness (LER)." In Variation-Aware Advanced CMOS Devices and SRAM. Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-017-7597-7_2.

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Akuthota, Uday Chandra, Abhishek, and Lava Bhargava. "Plant Disease Detection on Edge Devices." In Data Science and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7817-5_25.

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Vijouyeh, Lyla Naghipour, Rodrigo Bruno, and Paulo Ferreira. "EdgeEmu - Emulator for Android Edge Devices." In Distributed Applications and Interoperable Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35260-7_7.

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Riegler, Michael, Johannes Sametinger, and Christoph Schönegger. "Mode Switching for Secure Edge Devices." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14343-4_32.

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Van Vaerenbergh, Kevin, and Tom Tourwé. "Distributed Data Compression for Edge Devices." In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79157-5_24.

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Conference papers on the topic "Edge devices"

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Rachmanto, Rakandhiya D., Zaki Sukma, Ahmad N. L. Nabhaan, Arief Setyanto, Ting Jiang, and In Kee Kim. "Characterizing Deep Learning Model Compression with Post-Training Quantization on Accelerated Edge Devices." In 2024 IEEE International Conference on Edge Computing and Communications (EDGE). IEEE, 2024. http://dx.doi.org/10.1109/edge62653.2024.00023.

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Saha, Rappy, Jude Haris, and José Cano. "Accelerating PoT Quantization on Edge Devices." In 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2024. https://doi.org/10.1109/icecs61496.2024.10849093.

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Taruh, Elvis, Marko Raičević, Ivan Jovović, Dejan Babić, and Tomo Popović. "Detection of Livestock using Edge Devices." In 2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES). IEEE, 2024. https://doi.org/10.1109/ciees62939.2024.10811348.

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Swati, Vasikarla Nikhil Kumar, Shubh Dinesh Kawa, and Pinalkumar Engineer. "An Efficient Object Tracking on Edge Devices with Quantized Siamese Networks." In 2025 Devices for Integrated Circuit (DevIC). IEEE, 2025. https://doi.org/10.1109/devic63749.2025.11012629.

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Telsang, Vinayak A., Mahabaleshwar S. Kakkasageri, and Anil D. Devangavi. "Edge Computing Devices Authentication using Quantum Computing." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725671.

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Sheetal, Attar Mahay, Jenolin Rex M, Rohit Ravi, Madakari Nayaka HM, Tejas S, and Venkatesh M. Bhat. "Intelligent Monitoring and Managing of Edge Devices." In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2024. http://dx.doi.org/10.1109/icesc60852.2024.10689881.

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Nakabayashi, Takuya, and Hideo Saito. "Multimodal Human Activity Recognition on Edge Devices." In 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). IEEE, 2024. https://doi.org/10.1109/ismar-adjunct64951.2024.00037.

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Ypsilantis, Pantelis, Theodoros Toliopoulos, and Anastasios Gounaris. "Storage fabric for autonomous collaborative edge devices." In 2024 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2024. http://dx.doi.org/10.1109/ic2e61754.2024.00036.

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Röhm, André, Rie Sai, Takatomo Mihana, Ryoichi Horisaki, Kazutaka Kanno, and Atsushi Uchida. "Digital twins for neuromorphic photonic edge devices." In AI and Optical Data Sciences VI, edited by Masaya Notomi and Tingyi Zhou. SPIE, 2025. https://doi.org/10.1117/12.3041645.

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Sinha, Soumik, Sayandeep Saha, Ayantika Chatterjee, and Debdeep Mukhopadhyay. "FHEDGE: Encrypted Inference on Lightweight Edge Devices." In 2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2024. https://doi.org/10.1109/apccas62602.2024.10808548.

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Reports on the topic "Edge devices"

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Pirkelbauer, P., and C. Liao. Trustworthy Computing with Edge Devices. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1825848.

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Miller, L. S., S. Huang, and G. A. Quandt. Atmospheric tests of trailing-edge aerodynamic devices. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/565655.

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Leathers, Emily, Clayton Thurmer, and Kendall Niles. Encryption for edge computing applications. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48596.

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As smart sensors and the Internet of Things (IoT) exponentially expand, there is an increased need for effective processing solutions for sensor node data located in the operational arena where it can be leveraged for immediate decision support. Current developments reveal that edge computing, where processing and storage are performed close to data generation locations, can meet this need (Ahmed and Ahmed 2016). Edge computing imparts greater flexibility than that experienced in cloud computing architectures (Khan et al. 2019). Despite these benefits, the literature highlights open security i

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Dickens, John B., and Dean R. Dukes. Innovative Decentralized Decision-Making Enabling Capability on Mobile Edge Devices. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1008918.

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Hupp, William, Danish Saleem, Jordan Peterson, and Kenneth Boyce. Cybersecurity Certification Recommendations for Interconnected Grid Edge Devices and Inverter Based Resources. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1832209.

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Perr-Sauer, Jordan, Kristin Munch, and Robert White. Computer Vision on Edge Devices for the Short Term Prediction of Cloud Cover. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480502.

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Hill, C. Summary Report of the First Research Coordination Meeting on the Formation and Properties of Molecules in Edge Plasmas. IAEA Nuclear Data Section, 2023. http://dx.doi.org/10.61092/iaea.4w1d-eec2.

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11 experts in the field of atomic collisional physics and edge plasma modelling for magnetic confinement fusion devices, together with IAEA Staff met from 6 – 8 December 2023 for the First Research Coordination Meeting of the IAEA Coordinated Research Project (CRP) F43027: The Formation and Properties of Molecules in Edge Plasmas. This report summarizes the CRP participants’ workplans for the duration of the project and for its first cycle (12 – 18 months). Collaborative sub-projects were initiated in the specific areas of data needed for molecular hydrogen, boron-containing species, water-der

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Ruby, Jeffrey, Robert Fischer, Richard Massaro, et al. Optimization strategies for geospatial data on end-user devices. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49359.

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The ability to quickly disseminate geospatial data across all echelons, particularly those at the tactical edge, is critical to meeting threats described by the Multi-Domain Operations doctrine. The US Army Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL), is researching the optimization of the formats, data models, file sizes, and quality of geospatial products to be exploited by end-user devices (EUDs). This report describes a processing methodology comprising custom software and open-source tools to optimize Army Geospatial Enterprise Standard Sharable Geo

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Ruby, Jeffrey, Robert Fischer, Richard Massaro, et al. Optimization strategies for geospatial data on end-user devices. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49366.

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The ability to quickly disseminate geospatial data across all echelons, particularly those at the tactical edge, is critical to meeting threats described by the Multi-Domain Operations doctrine. The US Army Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL), is researching the optimization of the formats, data models, file sizes, and quality of geospatial products to be exploited by end-user devices (EUDs). This report describes a processing methodology comprising custom software and open-source tools to optimize Army Geospatial Enterprise Standard Sharable Geo

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Hill, Christian. Consultancy Meeting on Evaluation of Fundamental Data on Beryllium-containing Species for Edge Plasma Modelling. IAEA Nuclear Data Section, 2020. http://dx.doi.org/10.61092/iaea.t5at-c64q.

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Beryllium is a major plasma-facing material in the ITER fusion energy research reactor, where 440 beryllium-coated panels form the first wall (FW) of the vacuum reactor vessel. It is expected that plasma–wall interactions will result in the creation of a complex mixture of atomic, ionic and molecular species containing He, Be and isotopes of H. The aim of this meeting was to advise the IAEA Atomic and Molecular Data Unit on the data required for modelling edge plasma processes in fusion devices and to recommend state-resolved data sets for electron-collision excitation, de-excitation and disso

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