Relevant bibliographies by topics / Embedded AI

Contents

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

Academic literature on the topic 'Embedded AI'

Author: Grafiati
Published: 18 May 2024
Last updated: 7 July 2024

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Journal articles on the topic "Embedded AI"

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Hammer, Jürgen. "Berührungsängste mit "Embedded AI"?" MTZ - Motortechnische Zeitschrift 82, no. 4 (March 12, 2021): 70. http://dx.doi.org/10.1007/s35146-021-0653-1.

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Ashfaq, Zarlish, Rafia Mumtaz, Abdur Rafay, Syed Mohammad Hassan Zaidi, Hadia Saleem, Sadaf Mumtaz, Adnan Shahid, Eli De Poorter, and Ingrid Moerman. "Embedded AI-Based Digi-Healthcare." Applied Sciences 12, no. 1 (January 5, 2022): 519. http://dx.doi.org/10.3390/app12010519.

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Healthcare is an indispensable part of human life and chronic illnesses like cardiovascular diseases (CVD) have a deeply negative impact on the healthcare sector. Since the ever-growing population of chronic patients cannot be managed at hospitals, therefore, there is an urgent need for periodic monitoring of vital parameters and apposite treatment of these patients. In this paper, an Internet of Medical Things (IoMT) -based remote patient monitoring system is proposed which is based on Artificial Intelligence (AI) and edge computing. The primary focus of this paper is to develop an embedded prototype that can be used for remote monitoring of cardiovascular patients. The system will continuously monitor physiological parameters like body temperature, heart rate, and blood oxygen saturation, and then report the health status to the authenticated users. The system employs edge computing to perform multiple functionalities including health status inference using a Machine Learning (ML) model which makes predictions on real-time data, alert notifications in case of an emergency, and transferring data between the sensor network and the cloud. A web-based application is developed for the depiction of raw data and ML results and to provide a direct communication channel between the patient and the doctor. The ML module achieved an accuracy of 96.26% on the test set using the K-Nearest Neighbors (KNNs) algorithm. This solution aims to address the sense of emergency due to the alarming statistics that highlight the mortality rate of cardiovascular patients. The project will enable a smart option based on IoT and ML to improve standards of living and prove crucial in saving human lives.
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Ortmeyer, Cliff. "AI Options for Embedded Systems." New Electronics 52, no. 3 (February 12, 2019): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)60909-x.

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Yoon, Young Hyun, Dong Hyun Hwang, Jun Hyeok Yang, and Seung Eun Lee. "Intellino: Processor for Embedded Artificial Intelligence." Electronics 9, no. 7 (July 18, 2020): 1169. http://dx.doi.org/10.3390/electronics9071169.

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The development of computation technology and artificial intelligence (AI) field brings about AI to be applied to various system. In addition, the research on hardware-based AI processors leads to the minimization of AI devices. By adapting the AI device to the edge of internet of things (IoT), the system can perform AI operation promptly on the edge and reduce the workload of the system core. As the edge is influenced by the characteristics of the embedded system, implementing hardware which operates with low power in restricted resources on a processor is necessary. In this paper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures low power operation based on optimized AI algorithms and reduces the workload of the system core through the hardware implementation of a neural network. In addition, intellino’s dedicated protocol helps the embedded system to enhance the performance. We measure intellino performance, achieving over 95% accuracy, and verify our proposal with an field programmable gate array (FPGA) prototyping.
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Hammer, Jürgen. "A Reluctance to Use Embedded AI?" MTZ worldwide 82, no. 4 (March 12, 2021): 68. http://dx.doi.org/10.1007/s38313-021-0636-0.

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Bastani, F. B., and I. R. Chen. "The reliability of embedded AI systems." IEEE Expert 8, no. 2 (April 1993): 72–78. http://dx.doi.org/10.1109/64.207431.

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Tyler, Neil. "DSPs Target Embedded Vision and AI." New Electronics 54, no. 7 (April 27, 2021): 6. http://dx.doi.org/10.12968/s0047-9624(22)60260-8.

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McLennan, Stuart, Amelia Fiske, Leo Anthony Celi, Ruth Müller, Jan Harder, Konstantin Ritt, Sami Haddadin, and Alena Buyx. "An embedded ethics approach for AI development." Nature Machine Intelligence 2, no. 9 (July 31, 2020): 488–90. http://dx.doi.org/10.1038/s42256-020-0214-1.

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Cho, Sungjae, Yoonsu Kim, Jaewoong Jang, and Inseok Hwang. "AI-to-Human Actuation." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (March 27, 2022): 1–32. http://dx.doi.org/10.1145/3580812.

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Imagine a near-future smart home. Home-embedded visual AI sensors continuously monitor the resident, inferring her activities and internal states that enable higher-level services. Here, as home-embedded sensors passively monitor a free person, good inferences happen randomly. The inferences' confidence highly depends on how congruent her momentary conditions are to the conditions favored by the AI models, e.g., front-facing or unobstructed. We envision new strategies of AI-to-Human Actuation (AHA) that empower the sensory AIs with proactive actuation so that they induce the person's conditions to be more favorable to the AIs. In this light, we explore the initial feasibility and efficacy of AHA in the context of home-embedded visual AIs. We build a taxonomy of actuations that could be issued to home residents to benefit visual AIs. We deploy AHA in an actual home rich in sensors and interactive devices. With 20 participants, we comprehensively study their experiences with proactive actuation blended with their usual home routines. We also demonstrate the substantially improved inferences of the actuation-empowered AIs over the passive sensing baseline. This paper sets forth an initial step towards interweaving human-targeted AIs and proactive actuation to yield more chances for high-confidence inferences without sophisticating the model, in order to improve robustness against unfavorable conditions.
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Dini, Pierpaolo, Lorenzo Diana, Abdussalam Elhanashi, and Sergio Saponara. "Overview of AI-Models and Tools in Embedded IIoT Applications." Electronics 13, no. 12 (June 13, 2024): 2322. http://dx.doi.org/10.3390/electronics13122322.

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The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.
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Dissertations / Theses on the topic "Embedded AI"

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Chollet, Nicolas. "Embedded-AI-enabled semantic IoT platform for agroecology." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG078.

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L'agriculture moderne nécessite une profonde transformation pour répondre aux défis du développement durable tout en nourrissant qualitativement et quantitativement la population mondiale croissante. Dans cette optique, les agriculteurs adoptent le "Smart Farming" ou agriculture intelligente. C'est une méthode agricole qui utilise la technologie pour améliorer l'efficacité, la productivité et la durabilité de la production agricole. Elle englobe l'usage de capteurs, l'internet des objets (IoT), l'Intelligence Artificielle (IA), l'analyse de données, la robotique et divers autres outils numériques optimisant des aspects tels que la gestion des sols, l'irrigation, la lutte antiparasitaire ou encore la gestion de l'élevage. L'objectif est d'augmenter la production tout en réduisant la consommation de ressources, minimisant les déchets et améliorant la qualité des produits. Toutefois, malgré ses avantages et son déploiement réussi dans divers projets, l'agriculture intelligente rencontre des limites notamment dans le cadre de l'IoT. Premièrement, les plateformes doivent être capables de percevoir des données dans l'environnement, de les interpréter et de prendre des décisions pour aider à la gestion des fermes. Le volume, la variété et la vélocité de ces données, conjuguées à la grande diversité d'objets ainsi qu'à l'avènement de l'IA embarquée dans les capteurs, rendent difficile les communications sur les réseaux agricoles sans fils. Deuxièmement, les recherches tendent à se focaliser sur des projets répondant aux problématiques de l'agriculture conventionnelle non durable et les projets concernant les petites exploitations axées sur l'agroécologie sont rares. Dans ce contexte, cette thèse explore la création d'une plateforme IoT composée d'un réseau de capteurs intelligents sémantiques, visant à guider les agriculteurs dans la transition et la gestion de leur ferme en agriculture durable tout en minimisant l'intervention humaine
Modern agriculture requires a profound transformation to address the challenges of sustainable development while qualitatively and quantitatively feeding the growing global population. In this light, farmers are adopting "Smart Farming" also called precision agriculture. It is an agricultural method that leverages technology to enhance the efficiency, productivity, and sustainability of agricultural production. This approach encompasses the use of sensors, the Internet of Things (IoT), Artificial Intelligence (AI), data analysis, robotics, and various other digital tools optimizing aspects such as soil management, irrigation, pest control, and livestock management. The goal is to increase production while reducing resource consumption, minimizing waste, and improving product quality. However, despite its benefits and successful deployment in various projects, smart agriculture encounters limitations, especially within the context of IoT. Firstly, platforms must be capable of perceiving data in the environment, interpreting it, and making decisions to assist in farm management. The volume, variety, and velocity of those data, combined with a wide diversity of objects and the advent of AI embedded in sensors, make communication challenging on wireless agricultural networks. Secondly, research tends to focus on projects addressing the issues of non-sustainable conventional agriculture, and projects related to small-scale farms focused on agroecology are rare. In this context, this thesis explores the creation of an IoT platform comprised of a network of semantic smart sensors, aiming to guide farmers in transitioning and managing their farm sustainably while minimizing human intervention
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Biswas, Avishek Ph D. Massachusetts Institute of Technology. "Energy-efficient smart embedded memory design for IoT and AI." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117831.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted student-submitted PDF version of thesis.
Includes bibliographical references (pages 137-146).
Static Random Access Memory (SRAM) continues to be the embedded memory of choice for modern System-on-a-Chip (SoC) applications, thanks to aggressive CMOS scaling, which keeps on providing higher storage density per unit silicon area. As memory sizes continue to grow, increased bit-cell variation limits the supply voltage (Vdd) scaling of the memory. Furthermore, larger memories lead to data transfer over longer distances on chip, which leads to increased power dissipation. In the era of the Internet-of-Things (IoT) and Artificial Intelligence (AI), memory bandwidth and power consumption are often the main bottlenecks for SoC solutions. Therefore, in addition to Vdd scaling, this thesis also explores leveraging data properties and application-specfic features to design more tailored and "smarter" memories. First, a 128Kb 6T bit-cell based SRAM is designed in a modern 28nm FDSOI process. Dynamic forward body-biasing (DFBB) is used to improve the write operation, and reduce the minimum Vdd to 0.34V, even with 6T bit-cells. A new layout technique is proposed for the array, to reduce the energy overhead of DFBB and decrease the unwanted bit-line switching for un-selected columns in the SRAM, providing dynamic energy savings. The 6T SRAM also uses data prediction in its read path, to provide upto 36% further dynamic energy savings, with correct predictions. The second part of this thesis, explores in-memory computation for reducing data movement and increasing memory bandwidth, in data-intensive machine learning applications. A 16Kb SRAM with embedded dot-product computation capability, is designed for binary-weight neural networks. Highly parallel analog processing in- side the memory array, provided better energy-efficiency than conventional digital implementations. With our variation-tolerant architecture and support of multi-bit resolutions for inputs/outputs, > 98% classication accuracy was demonstrated on the MNIST dataset, for the handwritten digit recognition application. In the last part of the thesis, variation-tolerant read-sensing architectures are explored for future non-volatile resistive memories, e.g. STT-RAM.
by Avishek Biswas.
Ph. D.
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Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an intelligent camera equipped with a graphic board to optimize Computer Vision algorithms. Then, we test the performances of CORe50, a dataset for continuous object recognition, on embedded systems. The techniques developed in these chapters will be finally used to solve a challenge within the Audi Autonomous Driving Cup 2018, where a mobile car equipped with a camera, sensors and a graphic board must recognize pedestrians and stop before hitting them.
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Royles, Christopher Andrew. "Intelligent presentation and tailoring of online legal information." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343616.

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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.

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MOCERINO, LUCA. "Hardware-Aware Cross-Layer Optimizations of Deep Neural Networks for Embedded Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972558.

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Fredriksson, Tomas, and Rickard Svensson. "Analysis of machine learning for human motion pattern recognition on embedded devices." Thesis, KTH, Mekatronik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246087.

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With an increased amount of connected devices and the recent surge of artificial intelligence, the two technologies need more attention to fully bloom as a useful tool for creating new and exciting products. As machine learning traditionally is implemented on computers and online servers this thesis explores the possibility to extend machine learning to an embedded environment. This evaluation of existing machine learning in embedded systems with limited processing capa-bilities has been carried out in the specific context of an application involving classification of basic human movements. Previous research and implementations indicate that it is possible with some limitations, this thesis aims to answer which hardware limitation is affecting clas-sification and what classification accuracy the system can reach on an embedded device. The tests included human motion data from an existing dataset and included four different machine learning algorithms on three devices. Support Vector Machine (SVM) are found to be performing best com-pared to CART, Random Forest and AdaBoost. It reached a classification accuracy of 84,69% between six different included motions with a clas-sification time of 16,88 ms per classification on a Cortex M4 processor. This is the same classification accuracy as the one obtained on the host computer with more computational capabilities. Other hardware and machine learning algorithm combinations had a slight decrease in clas-sification accuracy and an increase in classification time. Conclusions could be drawn that memory on the embedded device affect which al-gorithms could be run and the complexity of data that can be extracted in form of features. Processing speed is mostly affecting classification time. Additionally the performance of the machine learning system is connected to the type of data that is to be observed, which means that the performance of different setups differ depending on the use case.
Antalet uppkopplade enheter ökar och det senaste uppsvinget av ar-tificiell intelligens driver forskningen framåt till att kombinera de två teknologierna för att både förbättra existerande produkter och utveckla nya. Maskininlärning är traditionellt sett implementerat på kraftfulla system så därför undersöker den här masteruppsatsen potentialen i att utvidga maskininlärning till att köras på inbyggda system. Den här undersökningen av existerande maskinlärningsalgoritmer, implemen-terade på begränsad hårdvara, har utförts med fokus på att klassificera grundläggande mänskliga rörelser. Tidigare forskning och implemen-tation visar på att det ska vara möjligt med vissa begränsningar. Den här uppsatsen vill svara på vilken hårvarubegränsning som påverkar klassificering mest samt vilken klassificeringsgrad systemet kan nå på den begränsande hårdvaran. Testerna inkluderade mänsklig rörelsedata från ett existerande dataset och inkluderade fyra olika maskininlärningsalgoritmer på tre olika system. SVM presterade bäst i jämförelse med CART, Random Forest och AdaBoost. Den nådde en klassifikationsgrad på 84,69% på de sex inkluderade rörelsetyperna med en klassifikationstid på 16,88 ms per klassificering på en Cortex M processor. Detta är samma klassifikations-grad som en vanlig persondator når med betydligt mer beräknings-resurserresurser. Andra hårdvaru- och algoritm-kombinationer visar en liten minskning i klassificeringsgrad och ökning i klassificeringstid. Slutsatser kan dras att minnet på det inbyggda systemet påverkar vilka algoritmer som kunde köras samt komplexiteten i datan som kunde extraheras i form av attribut (features). Processeringshastighet påverkar mest klassificeringstid. Slutligen är prestandan för maskininlärningsy-stemet bunden till typen av data som ska klassificeras, vilket betyder att olika uppsättningar av algoritmer och hårdvara påverkar prestandan olika beroende på användningsområde.
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Hasanzadeh, Mujtaba, and Alexandra Hengl. "Real-Time Pupillary Analysis By An Intelligent Embedded System." Thesis, Mälardalens högskola, Inbyggda system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44352.

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With no online pupillary analysis methods today, both the medical and the research fields are left to carry out a lengthy, manual and often faulty examination. A real-time, intelligent, embedded systems solution to pupillary analysis would help reduce faulty diagnosis, speed-up the analysis procedure by eliminating the human expert operator and in general, provide a versatile and highly adaptable research tool. Therefore, this thesis has sought to investigate, develop and test possible system designs for pupillary analysis, with the aim for caffeine detection. A pair of LED manipulator glasses have been designed to standardize the illumination method across testing. A data analysis method of the raw pupillary data has been established offline and then adapted to a real-time platform. ANN was chosen as classification algorithm. The accuracy of the ANN from the offline analysis was 94% while for the online classification the obtained accuracy was 17%. A realtime data communication and synchronization method has been developed. The resulting system showed reliable and fast execution times. Data analysis and classification took no longer than 2ms, faulty data detection showed consistent results. Data communication suffered no message loss. In conclusion, it is reported that a real-time, intelligent, embedded solution is feasible for pupillary analysis.
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TUVERI, GIUSEPPE. "Integrated support for Adaptivity and Fault-tolerance in MPSoCs." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266097.

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The technology improvement and the adoption of more and more complex applications in consumer electronics are forcing a rapid increase in the complexity of multiprocessor systems on chip (MPSoCs). Following this trend, MPSoCs are becoming increasingly dynamic and adaptive, for several reasons. One of these is that applications are getting intrinsically dynamic. Another reason is that the workload on emerging MPSoCs cannot be predicted because modern systems are open to new incoming applications at run-time. A third reason which calls for adaptivity is the decreasing component reliability associated with technology scaling. Components below the 32-nm node are more inclined to temporal or even permanent faults. In case of a malfunctioning system component, the rest of the system is supposed to take over its tasks. Thus, the system adaptivity goal shall influence several de- sign decisions, that have been listed below: 1) The applications should be specified such that system adaptivity can be easily supported. To this end, we consider Polyhedral Process Networks (PPNs) as model of computation to specify applications. PPNs are composed by concurrent and autonomous processes that communicate between each other using bounded FIFO channels. Moreover, in PPNs the control is completely distributed, as well as the memories. This represents a good match with the emerging MPSoC architectures, in which processing elements and memories are usually distributed. Most importantly, the simple operational semantics of PPNs allows for an easy adoption of system adaptivity mechanisms. 2) The hardware platform should guarantee the flexibility that adaptivity mechanisms require. Networks-on-Chip (NoCs) are emerging communication infrastructures for MPSoCs that, among many other advantages, allow for system adaptivity. This is because NoCs are generic, since the same platformcan be used to run different applications, or to run the same application with different mapping of processes. However, there is a mismatch between the generic structure of the NoCs and the semantics of the PPN model. Therefore, in this thesis we investigate and propose several communication approaches to overcome this mismatch. 3) The system must be able to change the process mapping at run-time, using process migration. To this end, a process migration mechanism has been proposed and evaluated. This mechanism takes into account specific requirements of the embedded domain such as predictability and efficiency. To face the problem of graceful degradation of the system, we enriched the MADNESS NoC platform by adding fault tolerance support at both software and hardware level. The proposed process migration mechanism can be exploited to cope with permanent faults by migrating the processes running on the faulty processing element. A fast heuristic is used to determine the new mapping of the processes to tiles. The experimental results prove that the overhead in terms of execution time, due to the execution time of the remapping heuristic, together with the actual process migration, is almost negligible compared to the execution time of the whole application. This means that the proposed approach allows the system to change its performance metrics and to react to faults without a substantial impact on the user experience.
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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.

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The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.
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Books on the topic "Embedded AI"

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Wang, Cliff, S. S. Iyengar, and Kun Sun, eds. AI Embedded Assurance for Cyber Systems. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7.

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Ni de ai qing, wo zai li mian: Embedded in your love. Taibei Shi: Chun tian chu ban guo ji wen hua you xian gong si, 2008.

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AI at the Edge: Solving Real World Problems with Embedded Machine Learning. O'Reilly Media, Incorporated, 2023.

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Drage, Eleanor, and Kerry McInerney, eds. The Good Robot. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350399990.

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What is good technology? Is ‘good’ technology even possible? And how can feminism help us work towards it? The Good Robot addresses these crucial questions through the voices of leading feminist thinkers, activists and technologists. Each thinker provides a snapshot of key challenges, questions and provocations in the field of feminism and technology. While the question of whether various AI and technological advances can be ethical is not new, the embedded nature of feminist perspectives pulls out whether this perceived ‘goodness’ or ‘wrongness’ might actually impact our lives in the 21st century. This book explores both the radical possibilities of technology to disrupt practices of patriarchy, colonialism, racism and beyond but also provides a significant critique of how we can contain the ethical possibilities of entities we cannot predict. In exploring unjust technological practices and engaging critical voices in the tech industry, the existing moral issues are brought to light as well as the possible ethical quagmires. This book opens a new space of discussion on digital technologies – one that insists that the future of AI is an urgent feminist issue.
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Gouzouasis, Peter, and Danny Bakan. Arts-Based Educational Research in Community Music. Edited by Brydie-Leigh Bartleet and Lee Higgins. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190219505.013.17.

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This chapter, written creatively as a scripted conversation between a professor and a doctoral student, asks how researchers might study music-making in a plethora of community music settings using arts-based methods. On the surface, arts-based educational research (ABER), art-based research (ABR), creative analytical practices (CAP), and arts inquiry (AI), may seem one and the same, but there are distinctive historical and theoretical nuances between them. We crafted this composition in a reflexive manner with theory and research embedded in the scripted conversation to explore these nuances. We point towards the conclusion that music communities, where participants are actively engaged, are well suited to inquiry through methods that include creative ways of representing and understanding both music and learning. In a conversational way, we explore distinctions, contexts, possibilities, problems, and the power of engaging arts-based research in the study of community music-making.
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Blömer, Michael, Stefan Riedel, Miguel John Versluys, and Engelbert Winter, eds. Common Dwelling Place of all the Gods. Commagene in its Local, Regional and Global Hellenistic Context. Franz Steiner Verlag, 2021. http://dx.doi.org/10.25162/9783515129268.

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The history and archaeology of Hellenistic Commagene is a rich field of study, not in the least because of the remarkable monuments and inscriptions of king Antiochos I (c. 70–36 BC). Over the last decades important new work has been done on Commagene proper, providing novel interpretations of the epigraphical and historical record or the archaeological data and individual sites, like Nemrud Dağ, Samosata or Arsameia. Simultaneously scholars have tried to better understand Hellenistic Commagene by situating the region and its history in a wider Mediterranean and Near Eastern context. This long-awaited e-book provides a critical evaluation of all these new data and ideas on the basis of a theoretically embedded, state-of-the-art overview for the history and archaeology of Hellenistic Commagene. From this volume a new picture emerges in which Hellenistic Commagene is no longer understood as peripheral and out-of-the-ordinary, but as an important node in a global Hellenistic network, from Ai-Khanoum to Pompeii and from Alexandria to Armawir.
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Book chapters on the topic "Embedded AI"

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Bräunl, Thomas. "AI Concepts." In Embedded Robotics, 403–19. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-0804-9_18.

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Feyock, Stefan, and James L. Rogers. "Embedded AI for Structural Optimization." In Computational Mechanics ’88, 1281–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61381-4_340.

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Vermesan, Ovidiu, and Marcello Coppola. "Edge AI Platforms for Predictive Maintenance in Industrial Applications." In Embedded Artificial Intelligence, 89–104. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-9.

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Yoo, Hoi-Jun. "Mobile Embedded DNN and AI SoCs." In Low Power Circuit Design Using Advanced CMOS Technology, 287–361. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338772-4.

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Mateu, Loreto, Johannes Leugering, Roland Müller, Yogesh Patil, Maen Mallah, Marco Breiling, and Ferdinand Pscheidl. "Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators." In Embedded Artificial Intelligence, 25–34. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-3.

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Miro-Panades, Ivan, Inna Kucher, Vincent Lorrain, and Alexandre Valentian. "Meeting the Latency and Energy Constraints on Timing-critical Edge-AI Systems." In Embedded Artificial Intelligence, 61–67. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-6.

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Gu, Yichi. "AI Embedded Transparent Health and Medicine System." In Advances in Intelligent Systems and Computing, 18–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32520-6_2.

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Qu, Zhe, Rui Duan, Yao Liu, and Zhuo Lu. "Federated Learning for IoT Applications, Attacks and Defense Methods." In AI Embedded Assurance for Cyber Systems, 161–81. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_9.

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Losavio, Michael. "Forensic Proof and Criminal Liability for Development, Distribution and Use of Artificial Intelligence." In AI Embedded Assurance for Cyber Systems, 37–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_3.

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Kumar, K. J. Latesh, Yashas Hariprasad, K. S. Ramesh, and Naveen Kumar Chaudhary. "AI Powered Correlation Technique to Detect Virtual Machine Attacks in Private Cloud Environment." In AI Embedded Assurance for Cyber Systems, 183–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42637-7_10.

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Conference papers on the topic "Embedded AI"

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Brandalero, Marcelo, Muhammad Ali, Laurens Le Jeune, Hector Gerardo Munoz Hernandez, Mitko Veleski, Bruno da Silva, Jan Lemeire, et al. "AITIA: Embedded AI Techniques for Embedded Industrial Applications." In 2020 International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2020. http://dx.doi.org/10.1109/coins49042.2020.9191672.

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Metwaly, Aly, Jorge Peña Queralta, Victor Kathan Sarker, Tuan Nguyen Gia, Omar Nasir, and Tomi Westerlund. "Edge Computing with Embedded AI." In INTESA2019: INTelligent Embedded Systems Architectures and Applications Workshop 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3372394.3372397.

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Costa, Bárbara, Octavian Postolache, and John Araujo. "From cloud AI to embedded AI in cardiac healthcare." In 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2023. http://dx.doi.org/10.1109/i2mtc53148.2023.10176077.

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Ghajargar, Maliheh, Jeffrey Bardzell, Alison Smith Renner, Peter Gall Krogh, Kristina Höök, David Cuartielles, Laurens Boer, and Mikael Wiberg. "From ”Explainable AI” to ”Graspable AI”." In TEI '21: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3430524.3442704.

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Yoo, Hoi-Jun. "Mobile/embedded DNN and AI SoCs." In 2018 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2018. http://dx.doi.org/10.1109/vlsi-dat.2018.8373285.

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Yoo, Hoi-Jun. "Mobile/embedded DNN and AI SoCs." In 2018 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA). IEEE, 2018. http://dx.doi.org/10.1109/vlsi-tsa.2018.8403807.

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Ghajargar, Maliheh, Jeffrey Bardzell, Alison Marie Smith-Renner, Kristina Höök, and Peter Gall Krogh. "Graspable AI: Physical Forms as Explanation Modality for Explainable AI." In TEI '22: Sixteenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490149.3503666.

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Blazevic, Romana, Omar Veledar, and Georg Macher. "Insides to Trustworthy AI-Based Embedded Systems." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2014.

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<div class="section abstract"><div class="htmlview paragraph">In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution. On the other hand, the positive outcome of this dialogue about the dependability of AI in embedded systems is that experts foster a shared understanding across diverse domains of expertise. We enhance the outcomes by considering the entirety of the PESTEL analysis framework encompassing political, environmental, social, technological, economic and legal dimensions. Therefore, this work synthesizes insights aiming to provide a comprehensive view informed by a multitude of perspectives and factors.</div></div>
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Kum, Seungwoo, Miseon Yu, Youngkee Kim, Jaewon Moon, and Silvio Cretti. "AI Management Platform with Embedded Edge Cluster." In 2021 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2021. http://dx.doi.org/10.1109/icce50685.2021.9427731.

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Brandalero, Marcelo, Mitko Veleski, Hector Gerardo Munoz Hernandez, Muhammad Ali, Laurens Le Jeune, Toon Goedeme, Nele Mentens, et al. "AITIA: Embedded AI Techniques for Industrial Applications." In 2021 31st International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 2021. http://dx.doi.org/10.1109/fpl53798.2021.00071.

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Reports on the topic "Embedded AI"

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Volz, Richard A. Report on the Embedded AI Languages Workshop Held in Ann Arbor, Michigan on 16-18 November 1988. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada218531.

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Dafflon, Baptiste, S. Wielandt, S. Uhlemann, Haruko Wainwright, K. Bennett, Jitendra Kumar, Sebastien Biraud, Susan Hubbard, and Stan Wullschleger. Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769774.

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Beiker, Sven. Next-generation Sensors for Automated Road Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, February 2023. http://dx.doi.org/10.4271/epr2023003.

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<div class="section abstract"><div class="htmlview paragraph">This follow-up report to the inaugural SAE EDGE Research Report on “Unsettled Topics Concerning Sensors for Automated Road Vehicles” reviews the progress made in automated vehicle (AV) sensors over the past four to five years. Additionally, it addresses persistent disagreement and confusion regarding certain terms for describing sensors, the different strengths and shortcomings of particular sensors, and procedures regarding how to specify and evaluate them.</div><div class="htmlview paragraph"><b>Next-gen Automated Road Vehicle Sensors</b> summarizes current trends and debates (e.g., sensor fusion, embedded AI, simulation) as well as future directions and needs.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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