Literatura científica selecionada sobre o tema "Edge artificial intelligence"
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Artigos de revistas sobre o assunto "Edge artificial intelligence"
Deng, Shuiguang, Hailiang Zhao, Weijia Fang, Jianwei Yin, Schahram Dustdar e Albert Y. Zomaya. "Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence". IEEE Internet of Things Journal 7, n.º 8 (agosto de 2020): 7457–69. http://dx.doi.org/10.1109/jiot.2020.2984887.
Texto completo da fonteEdwards, Chris. "Shrinking artificial intelligence". Communications of the ACM 65, n.º 1 (janeiro de 2022): 12–14. http://dx.doi.org/10.1145/3495562.
Texto completo da fonteZhou, Zhi, Xu Chen, En Li, Liekang Zeng, Ke Luo e Junshan Zhang. "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing". Proceedings of the IEEE 107, n.º 8 (agosto de 2019): 1738–62. http://dx.doi.org/10.1109/jproc.2019.2918951.
Texto completo da fonteSonglin Chen, Songlin Chen, Hong Wen Songlin Chen e Jinsong Wu Hong Wen. "Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks". 網際網路技術學刊 23, n.º 5 (setembro de 2022): 989–96. http://dx.doi.org/10.53106/160792642022092305007.
Texto completo da fonteSathish. "Artificial Intelligence based Edge Computing Framework for Optimization of Mobile Communication". Journal of ISMAC 2, n.º 3 (9 de julho de 2020): 160–65. http://dx.doi.org/10.36548/jismac.2020.3.004.
Texto completo da fonteMichael, James Bret. "Security and Privacy for Edge Artificial Intelligence". IEEE Security & Privacy 19, n.º 4 (julho de 2021): 4–7. http://dx.doi.org/10.1109/msec.2021.3078304.
Texto completo da fonteYoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang e Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence". Electronics 9, n.º 7 (18 de julho de 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.
Texto completo da fonteHu, Gang, e Bo Yu. "Artificial Intelligence and Applications". Journal of Artificial Intelligence and Technology 2, n.º 2 (5 de abril de 2022): 39–41. http://dx.doi.org/10.37965/jait.2022.0102.
Texto completo da fonteFoukalas, Fotis, e Athanasios Tziouvaras. "Edge Artificial Intelligence for Industrial Internet of Things Applications: An Industrial Edge Intelligence Solution". IEEE Industrial Electronics Magazine 15, n.º 2 (junho de 2021): 28–36. http://dx.doi.org/10.1109/mie.2020.3026837.
Texto completo da fonteDebauche, Olivier, Meryem Elmoulat, Saïd Mahmoudi, Sidi Ahmed Mahmoudi, Adriano Guttadauria, Pierre Manneback e Frédéric Lebeau. "Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**". Journal of Ubiquitous Systems and Pervasive Networks 15, n.º 02 (1 de março de 2021): 11–17. http://dx.doi.org/10.5383/juspn.15.02.002.
Texto completo da fonteTeses / dissertações sobre o assunto "Edge artificial intelligence"
Antonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.
Texto completo da fonteAntonini, Mattia. "From Edge Computing to Edge Intelligence: exploring novel design approaches to intelligent IoT applications". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/308630.
Texto completo da fonteAbernot, Madeleine. "Digital oscillatory neural network implementation on FPGA for edge artificial intelligence applications and learning". Electronic Thesis or Diss., Université de Montpellier (2022-....), 2023. http://www.theses.fr/2023UMONS074.
Texto completo da fonteIn the last decades, the multiplication of edge devices in many industry domains drastically increased the amount of data to treat and the complexity of tasks to solve, motivating the emergence of probabilistic machine learning algorithms with artificial intelligence (AI) and artificial neural networks (ANNs). However, classical edge hardware systems based on von Neuman architecture cannot efficiently handle this large amount of data. Thus, novel neuromorphic computing paradigms with distributed memory are explored, mimicking the structure and data representation of biological neural networks. Lately, most of the neuromorphic paradigm research has focused on Spiking neural networks (SNNs), taking inspiration from signal transmission through spikes in biological networks. In SNNs, information is transmitted through spikes using the time domain to provide a natural and low-energy continuous data computation. Recently, oscillatory neural networks (ONNs) appeared as an alternative neuromorphic paradigm for low-power, fast, and efficient time-domain computation. ONNs are networks of coupled oscillators emulating the collective computational properties of brain areas through oscillations. The recent ONN implementations combined with the emergence of low-power compact devices for ONN encourage novel attention over ONN for edge computing. State-of-the-art ONN is configured as an oscillatory Hopfield network (OHN) with fully coupled recurrent connections to perform pattern recognition with limited accuracy. However, the large number of OHN synapses limits the scalability of ONN implementation and the ONN application scope. The focus of this thesis is to study if and how ONN can solve meaningful AI edge applications using a proof-of-concept of the ONN paradigm with a digital implementation on FPGA. First, it explores novel learning algorithms for OHN, unsupervised and supervised, to improve accuracy performances and to provide continual on-chip learning. Then, it studies novel ONN architectures, taking inspiration from state-of-the-art layered ANN models, to create cascaded OHNs and multi-layer ONNs. Novel learning algorithms and architectures are demonstrated with the digital design performing edge AI applications, from image processing with pattern recognition, image edge detection, feature extraction, or image classification, to robotics applications with obstacle avoidance
Hasanaj, Enis, Albert Aveler e William Söder. "Cooperative edge deepfake detection". Thesis, Jönköping University, JTH, Avdelningen för datateknik och informatik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-53790.
Texto completo da fonteWoldeMichael, Helina Getachew. "Deployment of AI Model inside Docker on ARM-Cortex-based Single-Board Computer : Technologies, Capabilities, and Performance". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17267.
Texto completo da fontePELUSO, VALENTINO. "Optimization Tools for ConvNets on the Edge". Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2845792.
Texto completo da fonteLaroui, Mohammed. "Distributed edge computing for enhanced IoT devices and new generation network efficiency". Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7078.
Texto completo da fonteTraditional cloud infrastructure will face a series of challenges due to the centralization of computing, storage, and networking in a small number of data centers, and the long-distance between connected devices and remote data centers. To meet this challenge, edge computing seems to be a promising possibility that provides resources closer to IoT devices. In the cloud computing model, compute resources and services are often centralized in large data centers that end-users access from the network. This model has an important economic value and more efficient resource-sharing capabilities. New forms of end-user experience such as the Internet of Things require computing resources near to the end-user devices at the network edge. To meet this need, edge computing relies on a model in which computing resources are distributed to the edge of a network as needed, while decentralizing the data processing from the cloud to the edge as possible. Thus, it is possible to quickly have actionable information based on data that varies over time. In this thesis, we propose novel optimization models to optimize the resource utilization at the network edge for two edge computing research directions, service offloading and vehicular edge computing. We study different use cases in each research direction. For the optimal solutions, First, for service offloading we propose optimal algorithms for services placement at the network edge (Tasks, Virtual Network Functions (VNF), Service Function Chain (SFC)) by taking into account the computing resources constraints. Moreover, for vehicular edge computing, we propose exact models related to maximizing the coverage of vehicles by both Taxis and Unmanned Aerial Vehicle (UAV) for online video streaming applications. In addition, we propose optimal edge-autopilot VNFs offloading at the network edge for autonomous driving. The evaluation results show the efficiency of the proposed algorithms in small-scale networks in terms of time, cost, and resource utilization. To deal with dense networks with a high number of devices and scalability issues, we propose large-scale algorithms that support a huge amount of devices, data, and users requests. Heuristic algorithms are proposed for SFC orchestration, maximum coverage of mobile edge servers (vehicles). Moreover, The artificial intelligence algorithms (machine learning, deep learning, and deep reinforcement learning) are used for 5G VNF slices placement, edge-autopilot VNF placement, and autonomous UAV navigation. The numerical results give good results compared with exact algorithms with high efficiency in terms of time
MAZZIA, VITTORIO. "Machine Learning Algorithms and their Embedded Implementation for Service Robotics Applications". Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968456.
Texto completo da fonteLabouré, Iooss Marie-José. "Faisabilité d'une carte électronique d'opérateurs de seuillage : déformation d'objets plans lors de transformations de type morphologique". Saint-Etienne, 1987. http://www.theses.fr/1987STET4014.
Texto completo da fonteBusacca, Fabio Antonino. "AI for Resource Allocation and Resource Allocation for AI: a two-fold paradigm at the network edge". Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/573371.
Texto completo da fonteLivros sobre o assunto "Edge artificial intelligence"
Vermesan, Ovidiu, e Dave Marples. Advancing Edge Artificial Intelligence. New York: River Publishers, 2024. http://dx.doi.org/10.1201/9781003478713.
Texto completo da fonteSrivatsa, Mudhakar, Tarek Abdelzaher e Ting He, eds. Artificial Intelligence for Edge Computing. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40787-1.
Texto completo da fonteMisra, Sanjay, Amit Kumar Tyagi, Vincenzo Piuri e Lalit Garg, eds. Artificial Intelligence for Cloud and Edge Computing. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-80821-1.
Texto completo da fonteBlondie24: Playing at the edge of AI. San Francisco, Calif: Morgan Kaufmann, 2002.
Encontre o texto completo da fonteCurtis, Anthony R. Space almanac: Facts, figures, names, dates, places, lists, charts, tables, maps covering space from earth to the edge of the universe. Woodsboro, Md: Arcsoft, 1989.
Encontre o texto completo da fonteShi, Yong. Cutting-Edge Research Topics on Multiple Criteria Decision Making: 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, China, June 21-26, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Encontre o texto completo da fonteEdges of reality: Mind vs. computer. New York: Insight Books, 1996.
Encontre o texto completo da fonteMobile Edge Artificial Intelligence. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-00624-9.
Texto completo da fonteCutting-Edge Artificial Intelligence. Lerner Publishing Group, 2018.
Encontre o texto completo da fonteCutting-Edge Artificial Intelligence. Lerner Publishing Group, 2018.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Edge artificial intelligence"
Wang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Fundamentals of Artificial Intelligence". In Edge AI, 33–47. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_3.
Texto completo da fonteWang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Artificial Intelligence Applications on Edge". In Edge AI, 51–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_4.
Texto completo da fonteWang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Artificial Intelligence Inference in Edge". In Edge AI, 65–76. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_5.
Texto completo da fonteWang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Artificial Intelligence Training at Edge". In Edge AI, 77–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_6.
Texto completo da fonteWang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Edge Computing for Artificial Intelligence". In Edge AI, 97–115. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_7.
Texto completo da fonteWang, Xiaofei, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan e Xu Chen. "Artificial Intelligence for Optimizing Edge". In Edge AI, 117–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6186-3_8.
Texto completo da fontePurice, Dinu, Francesco Barchi, Thorsten Röder e Claus Lenz. "Edge AI Lifecycle Management". In Advancing Edge Artificial Intelligence, 43–63. New York: River Publishers, 2024. http://dx.doi.org/10.1201/9781003478713-2.
Texto completo da fonteXu, Lamei. "Designing Blended Learning Activities in the Era of Artificial Intelligence". In Edge Computing – EDGE 2023, 37–45. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51826-3_4.
Texto completo da fonteMeng, Fanrong, Wei Lin e Zhixiao Wang. "Space Edge Detection Based SVM Algorithm". In Artificial Intelligence and Computational Intelligence, 656–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_81.
Texto completo da fonteTsolkas, Dimitris, Harilaos Koumaras, Anastasios-Stavros Charismiadis e Andreas Foteas. "Artificial Intelligence in 5G and Beyond Networks". In Applied Edge AI, 73–103. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003145158-4.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Edge artificial intelligence"
Goswami, Siddharth, e Sachin Sharma. "DNA Sequencing using Artificial Intelligence". In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936101.
Texto completo da fonteWang, Dong, Daniel Zhang, Yang Zhang, Md Tahmid Rashid, Lanyu Shang e Na Wei. "Social Edge Intelligence: Integrating Human and Artificial Intelligence at the Edge". In 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2019. http://dx.doi.org/10.1109/cogmi48466.2019.00036.
Texto completo da fonteGlavan, Alina Florina, e Constantin Viorel Marian. "Cognitive edge computing through artificial intelligence". In 2020 13th International Conference on Communications (COMM). IEEE, 2020. http://dx.doi.org/10.1109/comm48946.2020.9142010.
Texto completo da fonteKum, Seungwoo, Youngkee Kim, Domenico Siracusa e Jaewon Moon. "Artificial Intelligence Service Architecture for Edge Device". In 2020 IEEE 10th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 2020. http://dx.doi.org/10.1109/icce-berlin50680.2020.9352184.
Texto completo da fonteRawat, Yash, Yash Gupta, Garima Khothari, Amit Mittal e Devendra Rautela. "The Role of Artificial Intelligence in Biometrics". In 2023 2nd International Conference on Edge Computing and Applications (ICECAA). IEEE, 2023. http://dx.doi.org/10.1109/icecaa58104.2023.10212224.
Texto completo da fonteYang, Bo. "Spatial Intelligence in Edge Cognitive Computing". In 2023 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2023. http://dx.doi.org/10.1109/cai54212.2023.00024.
Texto completo da fonteManmatha, R. "Edge Detection To Subpixel Accuracy". In Applications of Artificial Intelligence V, editado por John F. Gilmore. SPIE, 1987. http://dx.doi.org/10.1117/12.940627.
Texto completo da fonteQu, Jingwei, Haibin Ling, Chenrui Zhang, Xiaoqing Lyu e Zhi Tang. "Adaptive Edge Attention for Graph Matching with Outliers". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/134.
Texto completo da fonteBanjanović-Mehmedović, Lejla, e Anel Husaković. "Edge AI: Reshaping the Future of Edge Computing with Artificial Intelligence". In BASIC TECHNOLOGIES AND MODELS FOR IMPLEMENTATION OF INDUSTRY 4.0. Academy of Sciences and Arts of Bosnia and Herzegovina, 2023. http://dx.doi.org/10.5644/pi2023.209.07.
Texto completo da fonteKhare, Aryan, Ujjwal Kumar Singh, Samta Kathuria, Shaik Vaseem Akram, Manish Gupta e Navjot Rathor. "Artificial Intelligence and Blockchain for Copyright Infringement Detection". In 2023 2nd International Conference on Edge Computing and Applications (ICECAA). IEEE, 2023. http://dx.doi.org/10.1109/icecaa58104.2023.10212277.
Texto completo da fonteRelatórios de organizações sobre o assunto "Edge artificial intelligence"
Hwang, Tim, e Emily Weinstein. Decoupling in Strategic Technologies: From Satellites to Artificial Intelligence. Center for Security and Emerging Technology, julho de 2022. http://dx.doi.org/10.51593/20200085.
Texto completo da fontePerdigão, Rui A. P. Course on Nonlinear Frontiers: From Dynamical Systems, Information and Complexity to Cutting-Edge Physically Cognitive Artificial Intelligence. Meteoceanics, fevereiro de 2021. http://dx.doi.org/10.46337/uc.210211.
Texto completo da fonteHunt, Will, e Owen Daniels. Sustaining and Growing the U.S. Semiconductor Advantage: A Primer. Center for Security and Emerging Technology, junho de 2022. http://dx.doi.org/10.51593/20220006.
Texto completo da fonteCary, Dakota. China’s CyberAI Talent Pipeline. Center for Security and Emerging Technology, julho de 2021. http://dx.doi.org/10.51593/2020ca017.
Texto completo da fonteChahal, Husanjot, Helen Toner e Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, setembro de 2021. http://dx.doi.org/10.51593/20200075.
Texto completo da fonteLuong, Ngor, Rebecca Gelles e Melissa Flagg. Mapping the AI Investment Activities of Top Global Defense Companies. Center for Security and Emerging Technology, outubro de 2021. http://dx.doi.org/10.51593/20210015.
Texto completo da fonteGehlhaus, Diana, e Santiago Mutis. The U.S. AI Workforce: Understanding the Supply of AI Talent. Center for Security and Emerging Technology, janeiro de 2021. http://dx.doi.org/10.51593/20200068.
Texto completo da fonteDavid, Aharon. Controlling Aircraft—From Humans to Autonomous Systems: The Fading Humans. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, julho de 2023. http://dx.doi.org/10.4271/epr2023014.
Texto completo da fonteRuvinsky, Alicia, Timothy Garton, Daniel Chausse, Rajeev Agrawal, Harland Yu e Ernest Miller. Accelerating the tactical decision process with High-Performance Computing (HPC) on the edge : motivation, framework, and use cases. Engineer Research and Development Center (U.S.), setembro de 2021. http://dx.doi.org/10.21079/11681/42169.
Texto completo da fonteKhan, Samir. Towards MRO 4.0: Challenges for Digitalization and Mapping Emerging Technologies. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, abril de 2023. http://dx.doi.org/10.4271/epr2023007.
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