Relevant bibliographies by topics / Edge AI Processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Edge AI Processing'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Edge AI Processing"

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Mani Sai Kamal Darla. "Edge AI: Revolutionizing IoT Data Processing." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 258–64. https://doi.org/10.32996/jcsts.2025.7.7.26.

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Edge AI represents a transformative computational paradigm that embeds artificial intelligence algorithms directly within Internet of effects bias, barring reliance on remote pall structure for real- time decision- making processes. The distributed intelligence frame unnaturally alters traditional IoT infrastructures by incorporating cognitive capabilities at network boundaries, enabling independent data processing and immediate response generation at the point of data origin. Edge AI infrastructures address critical challenges in wireless hindrance networks through deep learning optimization
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Seng, Jasmine Kah Phooi, Kenneth Li-minn Ang, Eno Peter, and Anthony Mmonyi. "Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing." Electronics 11, no. 14 (2022): 2239. http://dx.doi.org/10.3390/electronics11142239.

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The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to
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Sanghavi, Darshak. "Leveraging AI for Edge Computing: AI-Enabled SoCs in Consumer Devices." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem39241.

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Edge computing is rapidly becoming a pivotal element in the evolution of consumer electronics, particularly with the increasing use of System-on-Chip (SoC) architectures embedded with artificial intelligence (AI) capabilities. By enabling on-device processing, AI-powered SoCs allow consumer devices to process and analyze data locally, without relying on cloud infrastructure. This whitepaper explores the transformative role of AI in enabling edge computing, focusing on AI-embedded SoCs and their ability to deliver real-time data processing, autonomous decision-making, and improved privacy. The
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Jawalkar, Santosh Kumar. "Intelligent Edge Testing: Ensuring Performance and Reliability in AR/VR Devices with Edge AI." International Scientific Journal of Engineering and Management 03, no. 11 (2024): 1–7. https://doi.org/10.55041/isjem02158.

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AR and VR devices become more effective with Edge AI integration resulting in transformative experiences. This technology provides quick data processing combined with enhanced user interaction along with independent operations without needing cloud platforms. AR/VR platforms deliver unsustainable user experience since cloud-based systems produce slow processing times and network dependence delays the user experience. Fast response times are attainable through the direct processing of AI workloads by implementing Edge AI technology onto edge devices. Better first-person shooter performance toge
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Jain, Dr Prerna, Prof Nidhi Pateria, Prof Gulafsha Anjum, Ashwini Tiwari, and Ayush Tiwari. "Edge AI and On-Device Machine Learning For Real Time Processing." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (2023): 8137–46. http://dx.doi.org/10.15680/ijircce.2024.1205364.

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Edge Artificial Intelligence (Edge AI) and on-device machine learning represent significant advancements in computing paradigms, enabling real-time data processing directly at the edge of the network. This approach minimizes the need for centralized cloud-based processing, thereby reducing latency, enhancing privacy, and improving operational efficiency. This paper provides a comprehensive review of Edge AI and on-device machine learning, focusing on their applications, challenges, and future directions. Key applications include smart home devices, healthcare monitoring, autonomous vehicles, a
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Pamadi, Vishesh Narendra, and Pushpa Singh. "Edge AI vs Cloud AI: A Comparative Study of Performance Latency and Scalability." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 13–35. https://doi.org/10.63345/ijrmeet.org.v13.i3.2.

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The increasing need for real-time decision-making and low-latency processing in AI applications has created interest in the comparative study of Edge AI and Cloud AI. Cloud AI, with its high computational power and scalability, has been the choice for data-intensive applications. But with the introduction of IoT devices and the growing need for instant data processing, Edge AI, which processes data locally on devices close to the data source, has become a potential alternative. This shift in paradigm, however, introduces new challenges in balancing the computational power of edge devices with
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Shubhodip, Sasmal. "Edge Computing and AI in Modern Data Engineering." International Journal of Contemporary Research in Multidisciplinary 3, no. 1 (2024): 152–59. https://doi.org/10.5281/zenodo.10679950.

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The convergence of edge computing and artificial intelligence (AI) has emerged as a transformative paradigm in modern data engineering. This research paper explores the intricate interplay between edge computing and AI, unraveling their synergistic impact on data engineering workflows. With the proliferation of Internet of Things (IoT) devices generating vast amounts of data at the edge, coupled with the evolution of sophisticated AI algorithms, a new frontier in data processing and analytics has unfolded. The paper navigates through the fundamental principles of edge computing and AI, sheddin
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Ali, Khudiri Samuri. "Edge AI and IoT: Direct integration for on-the-device data processing." Advances in Engineering Innovation 5, no. 1 (2023): None. http://dx.doi.org/10.54254/2977-3903/5/2023040.

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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) devices has led to the emergence of Edge AI, a transformative solution that enables data processing directly on the IoT devices or "at the edge" of the network. This paper explores the benefits of Edge AI, emphasizing reduced latency, bandwidth conservation, enhanced privacy, and faster decision-making. Despite its advantages, challenges like resource constraints on IoT devices persist. By examining the practical implications of Edge AI in sectors like healthcare and urban development, this study underscores the
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Anushree Nagvekar. "Edge AI: Revolutionizing Embedded Systems through On-Device Processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2871–80. https://doi.org/10.32628/cseit251112289.

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This comprehensive article explores the transformative impact of edge AI computing on embedded systems, highlighting the paradigm shift from cloud-dependent to on-device processing. The article examines the architectural foundations, performance benefits, security advantages, and implementation considerations of edge AI systems. The article demonstrates how edge computing addresses critical challenges in latency, cost efficiency, data privacy, and operational reliability across various applications, particularly in autonomous systems. The article encompasses detailed analyses of hardware accel
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DANG, Tuan Linh, Gia Tuyen Nguyen, and Thang Cao. "Real-Time Image Processing using Edge AI Devices." International Journal of Computer Applications 185, no. 43 (2023): 1–7. http://dx.doi.org/10.5120/ijca2023923236.

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Dissertations / Theses on the topic "Edge AI Processing"

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Ceccarelli, Andrea. "Infrastruttura per monitoraggio e controllo di macchine industriali basata su virtualizzazione in ambiente linux." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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Negli ultimi anni la crescita esponenziale che ha avuto l'Internet of Things, gli ha permesso di essere riconosciuto come vera e propria rivoluzione tecnologica. La flessibilità, la facoltà di adattarsi a qualsiasi tipo di ambiente ed esigenza, la possibilità di semplificare il controllo e la gestione dell'ambiente circostante sono le proprietà che hanno fatto di questo paradigma una della colonne portanti dello sviluppo tecnologico degli ultimi tempi. Il mondo industriale non è escluso da questa evoluzione, anzi, esso rappresenta uno dei principali attori in gioco, tanto da parlare di nuova r
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(10911822), Priyank Kalgaonkar. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021.

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Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irreleva
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Kalgaonkar, Priyank B. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021. http://dx.doi.org/10.7912/C2/64.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove)
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Books on the topic "Edge AI Processing"

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King, Katie. Using Artificial Intelligence in Marketing: How to Harness AI and Maintain the Competitive Edge. Kogan Page, Limited, 2019.

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King, Katie. Using Artificial Intelligence in Marketing: How to Harness AI and Maintain the Competitive Edge. Kogan Page, 2019.

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Using Artificial Intelligence in Marketing: How to Harness AI and Maintain the Competitive Edge. Kogan Page, 2019.

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Book chapters on the topic "Edge AI Processing"

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Karbowiak, Łukasz, and Mariusz Kubanek. "Using AI-based Edge Processing in Monitoring the Pedestrian Crossing." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30442-2_33.

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Tham, Mau-Luen, Xin Hao Ng, Rong-Chuan Leong, and Yasunori Owada. "Flood Forecasting Using Edge AI and LoRa Mesh Network." In 6th International Conference on Signal Processing and Information Communications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43781-6_7.

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Cardoso, Flávio Wellb, Mateus Coelho Silva, Natália F. de C. Meira, Ricardo Augusto Rabelo Oliveira, and Andrea G. Campos Bianchi. "Advancing Particle Size Detection in Mineral Processing: Exploring Edge AI Solutions." In Enterprise Information Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64748-2_5.

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Orsholits, Alex, and Manabu Tsukada. "Context-Rich Interactions in Mixed Reality Through Edge AI Co-processing." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87772-8_3.

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Zhang, Yan. "Edge Computing for Digital Twin." In Digital Twin. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51819-5_4.

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AbstractMobile edge computing is a promising solution for analysing and processing a portion of data using the computing, storage, and network resources distributed on the paths between data sources and a cloud computing centre. Mobile edge computing thus provides high efficiency, low latency, and privacy protection to sustain digital twin. In this chapter, we first introduce a hierarchical architecture of digital twin edge networks that consists of a virtual plane and a user/physical plane. We then introduce the key communication and computation technologies in the digital twin edge networks
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Vassilev, Vassil, Sylvia Ilieva, Iva Krasteva, Irena Pavlova, Dessisslava Petrova-Antonova, and Wiktor Sowinski-Mydlarz. "AI-Based Hybrid Data Platforms." In Data Spaces. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_8.

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AbstractThe current digital transformation of many businesses and the exponential growth of digital data are two of the key factors of digital revolution. For the successful meeting of high expectations, the data platforms need to employ the recent theoretical, technological, and methodological advances in contemporary computing and data science and engineering. This chapter presents an approach to address these challenges by combining logical methods for knowledge processing and machine learning methods for data analysis into a hybrid AI-based framework. It is applicable to a wide range of pr
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Dobariya, Vruti, Gone Neelakantam, and Hiren Kumar Thakkar. "Data Generation, Storage, and AI-Enabled Processing of IoMT Healthcare Data in Edge Computing." In Health 5.0. CRC Press, 2025. https://doi.org/10.1201/9781003393498-7.

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Gawande, Manvi Vijay, Sanika Shrikant Hange, Mukesh Mukundraj Tandale, and Archana Kollu. "Edge Computing." In Advances in Computer and Electrical Engineering. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3940-4.ch005.

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Data processing near the network edge solves decentralized cloud infrastructures. Without time-consuming transfers to centralized data centers, edge computing processes and analyzes data locally. Network processing at the edge minimizes latency when transferring or sharing data with remote servers. Reduced data center processing improves real-time application speed, latency, and energy savings. Edge computing makes AI and IoT devices work without cloud load. Energy is saved by offloading computation to edge devices. Edge computing selectively processes and consolidates data at the network edge
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Chandrasekaran, Saravanan, S. Athinarayanan, M. Masthan, Anmol Kakkar, Pranav Bhatnagar, and Abdul Samad. "Edge Intelligence Paradigm Shift on Optimizing the Edge Intelligence Using Artificial Intelligence State-of-the-Art Models." In Advances in Computer and Electrical Engineering. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3739-4.ch001.

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Edge AI changes data processing (EI) strategy incorporating powerful AI capabilities directly at the network's edge, where devices and sensors gather data. This convergence solves cloud-centric AI's latency and bandwidth challenges by enabling real-time analysis and decision-making. Three paradigm-shifting effects of edge intelligence include localized processing allowing real-time data analysis and quick responses. Second, minimizing large-scale data transfers to centralized cloud services greatly minimizes latency. Third, edge intelligence's distributed architecture reduces centralized syste
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Krishna Pasupuleti, Murali. "Quantum AI, edge AI for IoT, blockchain for privacy-preserving analytics, AGI, and quantum cryptography for finance and communications." In Beyond Intelligence: AGI, Quantum AI, Edge Computing, and Blockchain in the Age of Digital Transformation. National Education Services, 2024. http://dx.doi.org/10.62311/nesx/92472.

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Abstract: This chapter explores the convergence of cutting-edge technologies—Quantum AI, Edge AI for IoT, Blockchain for Privacy-Preserving Analytics, Artificial General Intelligence (AGI), and Quantum Cryptography—and their transformative potential in finance and communications. Quantum AI accelerates machine learning and decision-making through quantum computing, while Edge AI enables real-time data processing at the network edge for IoT systems. Blockchain ensures secure and decentralized privacy-preserving analytics, and AGI aims to develop AI systems capable of human-like general intellig
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Conference papers on the topic "Edge AI Processing"

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Kiruba G, Sweety Prasanna, Esakki Madura E, Bura Vijay Kumar, M. Kavitha, T. Srinivas Reddy, and A. Athiraja. "Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915254.

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Orsholits, Alex, and Manabu Tsukada. "Edge Vision AI Co-Processing for Dynamic Context Awareness in Mixed Reality." In 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2025. https://doi.org/10.1109/vrw66409.2025.00293.

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Li, Kongyang, Long Chen, Tianwen Peng, and Jigang Wu. "Energy Balanced Cooperative Edge-AI Services for Service Quality Guarantee." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00112.

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Koç, Yunus, Ahmet Ayberk Tarçın, and Doğukan Köse. "Evaluation of Voice Recognition Platforms and Methods for Edge AI Devices." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10711025.

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G, Gokila Deepa, Gomathi S, Aadhitya S, Sujitha R, Sundarrajan M, and Mani Deepak Choudhry. "Optimizing Real-Time Image Processing in Augmented Reality with Low-Latency Edge AI." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990829.

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Song, Zhifan, Yuan Zhang, Xinrui Liao, and Abd Al Rahman M. Abu Ebayyeh. "Optimizing Transmission Line Insulator Defect Detection: Lightweight Edge AI on UAVs." In 2025 IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2025. https://doi.org/10.1109/ipas63548.2025.10924536.

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Li, Lanpei, Jack Bell, Massimo Coppola, and Vincenzo Lomonaco. "Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum." In 2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2025. https://doi.org/10.1109/pdp66500.2025.00053.

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Swati, Swati, Shubh Kawa, Rinkesh Patel, Amin Meet Amrish, Mitul Sudhirkumar Nagar, and Pinalkumar Engineer. "Exploring Quantization Approaches for Optimized Training and Inference for Edge AI Applications." In 2025 11th International Conference on Communication and Signal Processing (ICCSP). IEEE, 2025. https://doi.org/10.1109/iccsp64183.2025.11088699.

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Armoush, Rakan, Muhammad Nadeem Khan, Alireza Esfahani, Nasser Matoorianpour, Mohammad Shojafar, and Shidrokh Goudarzi. "Optimizing UAV Task Processing in Disaster Response with Lyapunov-Based Edge Computing." In 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET). IEEE, 2024. https://doi.org/10.1109/honet63146.2024.10822891.

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Cho, Li, Chien-Chiao Huang, and Chi-Yu Huang. "Slimming Edge AI: A Case Study of Gesture Recognition for Air Conditioner Control." In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10868520.

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Reports on the topic "Edge AI Processing"

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Liu, Tairan. Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2322.

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In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tool
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