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Journal articles on the topic 'Embedded AI'

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

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1

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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9

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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Chatterjee, Sheshadri, Ranjan Chaudhuri, and Demetris Vrontis. "AI and digitalization in relationship management: Impact of adopting AI-embedded CRM system." Journal of Business Research 150 (November 2022): 437–50. http://dx.doi.org/10.1016/j.jbusres.2022.06.033.

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JÁVOR, ANDRÁS. "SIMULATION WITH EMBEDDED AI FOR TRANSDISCIPLINARY PROBLEM SOLVING." International Journal of Modeling, Simulation, and Scientific Computing 01, no. 01 (March 2010): 85–98. http://dx.doi.org/10.1142/s179396231000002x.

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In this article, a methodology and its application in various fields is dealt with. The theoretical research work has been undertaken at the McLeod Institute of Simulation Sciences Hungarian Center and its results have been implemented in the CASSANDRA (Cognizant Adaptive Simulation System for Applications in Numerous Different Relevant Areas) simulation system. The applications covered a wide range of areas where the tool system and the methodology have been applied with success. Most of these were in the framework of international projects of the European Union.
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Li, Lin, Shangxia Yin, and Kai Li. "Graphic programming design based on embedded AI processor." E3S Web of Conferences 213 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202021303007.

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This paper designs a system platform for graphical programming. The system is based on the open source software platform developed for the second time, which can realize the layer-by-layer conversion of diagrams and source codes, allowing users to deeply understand the logical relationship of programming, and programming and the host can be downloaded. The user can complete the drag-and-drop procedure, the icon shows the basic elements of the function, and the attribute box can modify each variable and automatically build it. After the user builds a graphics program with this system, click the run button to generate Python code and save the project.
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Zhang, Zhaoyun, and Jingpeng Li. "A Review of Artificial Intelligence in Embedded Systems." Micromachines 14, no. 5 (April 22, 2023): 897. http://dx.doi.org/10.3390/mi14050897.

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Advancements in artificial intelligence algorithms and models, along with embedded device support, have resulted in the issue of high energy consumption and poor compatibility when deploying artificial intelligence models and networks on embedded devices becoming solvable. In response to these problems, this paper introduces three aspects of methods and applications for deploying artificial intelligence technologies on embedded devices, including artificial intelligence algorithms and models on resource-constrained hardware, acceleration methods for embedded devices, neural network compression, and current application models of embedded AI. This paper compares relevant literature, highlights the strengths and weaknesses, and concludes with future directions for embedded AI and a summary of the article.
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15

Edwards, Chris. "AI: How Low can You Go?" New Electronics 54, no. 3 (February 23, 2021): 14–15. http://dx.doi.org/10.12968/s0047-9624(22)60328-6.

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Jang, Suyeon, Hyun Woo Oh, Young Hyun Yoon, Dong Hyun Hwang, Won Sik Jeong, and Seung Eun Lee. "A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition." Micromachines 12, no. 8 (July 21, 2021): 852. http://dx.doi.org/10.3390/mi12080852.

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Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.
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17

Campean, Felician, Unal Yildirim, Aleksandr Korsunovs, and Aleksandr Doikin. "Extending the function failure modes taxonomy for intelligent systems with embedded AI components." Proceedings of the Design Society 4 (May 2024): 1949–58. http://dx.doi.org/10.1017/pds.2024.197.

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AbstractEarly consideration of failure modes in the feature development process is essential to identify and trace risks across the physical and embedded AI components of intelligent systems, to enhance the robustness of the feature delivery as well as trust in the AI. This paper introduces an extension of the AIAG/VDA function failure modes taxonomy, to facilitate the integrated analysis of complex intelligent systems with embedded AI. A case study of an autonomous driving feature is discussed as validation of the proposed taxonomy.
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18

Krishna, Anirudh. "Will AI Slay the Poverty Dragon?" Current History 123, no. 849 (January 1, 2024): 37–39. http://dx.doi.org/10.1525/curh.2024.123.849.37.

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The rise of artificial intelligence and other new technologies has raised hopes that they might contribute to reducing poverty. History suggests that they need to be embedded in broader frameworks of institutions, regulations, and training.
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19

Han, Zhiyuan, Jian Shen, Dandan Luo, Bo Gao, HuaQi Chen, and Jin Xie. "The Optimization Of The Distributed AI Embedded Cluster System." Journal of Physics: Conference Series 2195, no. 1 (February 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2195/1/012010.

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Abstract This paper improves the core functions of the real-time video analysis system for edge computing which we proposed before. The improvement of this paper including distributed cluster status management and two-level horizontal expansion. The three-level task scheduling mechanism is realized, and the complete data link processing is realized. At the same time, RK3399pro is proposed to replace RK3399 as the hardware computing unit of the AI business. On the premise of satisfying real-time processing, the computing unit resources are saved. Compared with our previous system, the processing capacity of the new system is improved by 6-8 times.
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20

Codeluppi, Gaia, Luca Davoli, and Gianluigi Ferrari. "Forecasting Air Temperature on Edge Devices with Embedded AI." Sensors 21, no. 12 (June 9, 2021): 3973. http://dx.doi.org/10.3390/s21123973.

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With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402∘C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.
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21

Hooman, J. "Top-Down Design of Embedded Real-Time AI Systems." IFAC Proceedings Volumes 25, no. 10 (June 1992): 453–58. http://dx.doi.org/10.1016/s1474-6670(17)50862-9.

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Hooman, J. "Top-down design of embedded real-time AI systems." Annual Review in Automatic Programming 17 (January 1992): 453–58. http://dx.doi.org/10.1016/s0066-4138(09)91074-6.

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23

Pudner, A. "DLM — a powerful ai computer for embedded expert systems." Future Generation Computer Systems 3, no. 4 (December 1987): 299–306. http://dx.doi.org/10.1016/0167-739x(87)90034-3.

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24

Mahtani, Ankur, Eddy Doba, and Nadia Ammad. "The Autonomous Train vision: Embedded AI for pedestrians monitoring." Transportation Research Procedia 72 (2023): 949–56. http://dx.doi.org/10.1016/j.trpro.2023.11.522.

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25

Papaioannou, Alexios, Asimina Dimara, Charalampos S. Kouzinopoulos, Stelios Krinidis, Christos-Nikolaos Anagnostopoulos, Dimosthenis Ioannidis, and Dimitrios Tzovaras. "LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems." Sensors 24, no. 7 (March 26, 2024): 2125. http://dx.doi.org/10.3390/s24072125.

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Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.
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Brinkley, Alex. "Cards Carry the Full Weight of AI Innovation." New Electronics 56, no. 6 (June 2023): 34–35. http://dx.doi.org/10.12968/s0047-9624(23)60813-2.

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27

Jin, Shitao, Huijun Tu, Jiangfeng Li, Yuwei Fang, Zhang Qu, Fan Xu, Kun Liu, and Yiquan Lin. "Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course." Buildings 14, no. 6 (June 1, 2024): 1613. http://dx.doi.org/10.3390/buildings14061613.

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This study addresses the current lack of research on the effectiveness assessment of Artificial Intelligence (AI) technology in architectural education. Our aim is to evaluate the impact of AI-assisted architectural teaching on student learning. To achieve this, we developed an AI-embedded teaching model. A total of 24 students from different countries participated in this 9-week course, completing a comprehensive analysis of architectural programming and design using AI technologies. This study conducted questionnaire surveys with students at both midterm and final stages of the course, followed by structured interviews after the course completion, to explore the effectiveness and application status of the teaching model. The results indicate that the AI-embedded teaching model positively and effectively influenced student learning. The “innovative capability” and “work efficiency” of AI technologies were identified as key factors affecting the effectiveness of the teaching model. Furthermore, the study revealed a close integration of AI technologies with architectural programming but identified challenges in the uncontrollable expression of architectural design outcomes. Student utilization of AI technologies appeared fragmented, lacking a systematic approach. Lastly, the study provides targeted optimization suggestions based on the current application status of AI technologies among students. This research offers theoretical and practical support for the further integration of AI technologies in architectural education.
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Gusti, Wahyu Ramadhani. "Embedded System Training Kit for Artificial Intelligence." International Journal of Information and Education Technology 14, no. 1 (2024): 72–80. http://dx.doi.org/10.18178/ijiet.2024.14.1.2026.

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Ever-developing science and technology require us always to be ready to adapt. The current challenging era is Society 5.0, which places a strong emphasis on harnessing human potential to overcome diverse challenges, including the development of Artificial Intelligence (AI) technology. Therefore, to improve the quality of human resources, this paper proposes the development of an artificial intelligence training kit based on embedded systems according to industry needs. The development of a training kit utilizing the RnD method was accomplished through the use of the ADDIE (analysis design, development, implementation, and evaluation) model. This model encompasses analysis, design, development, implementation, and evaluation. The technology of the training kit combines fuzzy logic, Artificial Neural Network (ANN), and image processing, consisting of hardware, software, and job sheets. The controller used to process embedded systems is the ESP32 board. Arduino UNO is used to execute the training results of the artificial intelligence system. The training kit performance test results show that all AI programs run optimally, and each component can function according to performance indicators. A group of subject matter and media experts evaluated the feasibility of the project and determined it to be very feasible, with a score of 83.64% and 86.67%. In addition, a feasibility test was conducted with 38 respondents, resulting in a score of 83.35%, and it was categorized as a very feasible tool. The effectiveness of the training kit applied to the experimental class resulted in a post-test mean score of 89.58, while the control class mean score was 76.39, so the AI training kit showed more effectiveness.
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Agiollo, Andrea, and Andrea Omicini. "Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices." Applied Sciences 11, no. 24 (December 15, 2021): 11957. http://dx.doi.org/10.3390/app112411957.

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The application of Artificial Intelligence to the industrial world and its appliances has recently grown in popularity. Indeed, AI techniques are now becoming the de-facto technology for the resolution of complex tasks concerning computer vision, natural language processing and many other areas. In the last years, most of the the research community efforts have focused on increasing the performance of most common AI techniques—e.g., Neural Networks, etc.—at the expenses of their complexity. Indeed, many works in the AI field identify and propose hyper-efficient techniques, targeting high-end devices. However, the application of such AI techniques to devices and appliances which are characterised by limited computational capabilities, remains an open research issue. In the industrial world, this problem heavily targets low-end appliances, which are developed focusing on saving costs and relying on—computationally—constrained components. While some efforts have been made in this area through the proposal of AI-simplification and AI-compression techniques, it is still relevant to study which available AI techniques can be used in modern constrained devices. Therefore, in this paper we propose a load classification task as a case study to analyse which state-of-the-art NN solutions can be embedded successfully into constrained industrial devices. The presented case study is tested on a simple microcontroller, characterised by very poor computational performances—i.e., FLOPS –, to mirror faithfully the design process of low-end appliances. A handful of NN models are tested, showing positive outcomes and possible limitations, and highlighting the complexity of AI embedding.
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Mallesham, Goli. "Leveraging AI in Embedded and Extended Warehouse Management for Enhanced Efficiency." International Journal of Scientific Research and Management (IJSRM) 10, no. 06 (June 9, 2022): 907–17. http://dx.doi.org/10.18535/ijsrm/v10i6.ec04.

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Speed and accuracy of decision-making at the operational and tactical levels are critical in warehouse management. This paper conceptually presents decision support systems (DSS) powered by artificial intelligence (AI) at two levels – embedded warehouse management at the operational level and extended warehouse management at the tactical level. For enhanced efficiency, suggestions are categorized at the tactical level into system-front-end/back-end-heavy lifting, other back-end system suggestions, and system extensions. Several AI technologies such as expert system rule engines, machine learning models, and natural language understanding models can be applied at both levels. Efforts required for data preparation and model training are highlighted. Warehouse management takes place in a dynamic environment. New inventory arrives, and orders for shipping out inventory are constantly issued. There is a large number of decisions to be made regularly to coordinate the flow of materials in and out of a warehouse. Speed and accuracy of operational and tactical decision-making are important in warehouse management. This paper begins by discussing decision support systems (DSS) enabled by artificial intelligence (AI) for efficient decision-making at both the operational and tactical levels. Subsequently, several AI technologies that can be applied to offer intelligence at both levels are discussed. Throughout the paper, suggestions are made about how to apply these technologies to enhance efficiency. Furthermore, the effort required in terms of data preparation and model training is discussed. The pathways presented are only feasible with a supporting, intelligent IT infrastructure. Intelligence needs to be built not only within the warehouse system but also extended out to the surrounding ecosystem. The paper wraps up by either highlighting or reiterating the suggestions and insights to help stakeholders make the most of the possibilities AI offers for decision-making in warehouse management. With the rapid growth of e-commerce, flexible, adaptable AI-driven DSS could be the solution needed to help warehouse management keep up with the ever-increasing pace and dynamism of the industry.
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Plancher, Brian, Sebastian Buttrich, Jeremy Ellis, Neena Goveas, Laila Kazimierski, Jesus Lopez Sotelo, Milan Lukic, et al. "TinyML4D: Scaling Embedded Machine Learning Education in the Developing World." Proceedings of the AAAI Symposium Series 3, no. 1 (May 20, 2024): 508–15. http://dx.doi.org/10.1609/aaaiss.v3i1.31265.

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Embedded machine learning (ML) on low-power devices, also known as "TinyML," enables intelligent applications on accessible hardware and fosters collaboration across disciplines to solve real-world problems. Its interdisciplinary and practical nature makes embedded ML education appealing, but barriers remain that limit its accessibility, especially in developing countries. Challenges include limited open-source software, courseware, models, and datasets that can be used with globally accessible heterogeneous hardware. Our vision is that with concerted effort and partnerships between industry and academia, we can overcome such challenges and enable embedded ML education to empower developers and researchers worldwide to build locally relevant AI solutions on low-cost hardware, increasing diversity and sustainability in the field. Towards this aim, we document efforts made by the TinyML4D community to scale embedded ML education globally through open-source curricula and introductory workshops co-created by international educators. We conclude with calls to action to further develop modular and inclusive resources and transform embedded ML into a truly global gateway to embedded AI skills development.
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32

Ismail Mousa, Sayed M., and Bassem Alhawamda. "Translating Dialectal Expressions and Terms Embedded in Saudi Modern Novels with a particular Emphasis on Ragaa Alsanea’s The Girls of Riyadh." Revista Amazonia Investiga 9, no. 31 (August 7, 2020): 104–15. http://dx.doi.org/10.34069/ai/2020.31.07.10.

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Translating dialectal terms and idiomatic expressions embedded in Saudi contemporary fiction is an underresearched topic, and the assessment of translating dialectal terms and expressions has not been examined adequately as there is a scarcity in the studies addressing such a translation issue. Therefore, the current study is mainly interested in assessing how far the translators of the Girls of Riyadh could succeed in translating the embedded dialectal expressions in the novel and whether their translation could transfer the overall effect, aesthetic values, cultural atmosphere, style and pragmatic effect. To achieve this end, the study has classified dialectal elements under the rubric of cultural markers and assessed the rendition of these cultural markers in connection with Dickins’ degrees of cultural transposition and House’s concept of covert translation and its criteria. Following the assessment of samples from the novel, the study has found that the translators neither follow domestication nor foreignization and that they rely heavily on the communicative translation strategy, and in most cases dialectal expressions are omitted or rendered into formal English.
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Buarque, Bernardo S., Ronald B. Davies, Ryan M. Hynes, and Dieter F. Kogler. "OK Computer: the creation and integration of AI in Europe." Cambridge Journal of Regions, Economy and Society 13, no. 1 (February 1, 2020): 175–92. http://dx.doi.org/10.1093/cjres/rsz023.

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Abstract This article investigates the creation and integration of artificial intelligence (AI) patents in Europe. We create a panel of AI patents over time, mapping them into regions at the NUTS2 level. We then proceed by examining how AI is integrated into the knowledge space of each region. In particular, we find that those regions where AI is most embedded into the innovation landscape are also those where the number of AI patents is largest. This suggests that, to increase AI innovation, it may be necessary to integrate it with industrial development, a feature central to many recent AI-promoting policies.
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Kim, Seong-Gon, and Yong-Gi Kim. "A Learning AI Algorithm for Poker with Embedded Opponent Modeling." International Journal of Fuzzy Logic and Intelligent Systems 10, no. 3 (September 1, 2010): 170–77. http://dx.doi.org/10.5391/ijfis.2010.10.3.170.

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35

Zhang, D. L., and B. Cantor. "Heterogeneous nucleation of In particles embedded in an AI matrix." Philosophical Magazine A 62, no. 5 (November 1990): 557–72. http://dx.doi.org/10.1080/01418619008244919.

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36

Yuan, Tian, and Feng Shan. "An AI-Embedded Object-Oriented Approach to Problem-Solving Automation." IFAC Proceedings Volumes 28, no. 7 (July 1995): 111–16. http://dx.doi.org/10.1016/s1474-6670(17)47099-6.

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Garcia-Perez, Asier, Raúl Miñón, Ana I. Torre-Bastida, and Ekaitz Zulueta-Guerrero. "Analysing Edge Computing Devices for the Deployment of Embedded AI." Sensors 23, no. 23 (November 29, 2023): 9495. http://dx.doi.org/10.3390/s23239495.

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In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing within the context of our study. This integral relationship not only addresses the challenges posed by data proliferation but also propels a transformative wave of innovation, shaping a new era of data processing capabilities at the network’s edge. Edge devices can perform real-time data analysis and make autonomous decisions without relying on constant connectivity to the cloud. This article aims at analysing and comparing Edge Computing devices when artificial intelligence algorithms are deployed on them. To this end, a detailed experiment involving various edge devices, models and metrics is conducted. In addition, we will observe how artificial intelligence accelerators such as Tensor Processing Unit behave. This analysis seeks to respond to the choice of a device that best suits the necessary AI requirements. As a summary, in general terms, the Jetson Nano provides the best performance when only CPU is used. Nevertheless the utilisation of a TPU drastically enhances the results.
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Wang, Tun, and Yu Tian. "Design of Embedded Ai Engine Based on the Microkernel Operating System." Wireless Communications and Mobile Computing 2022 (April 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/9304019.

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At present, the application of the embedded microkernel operating system in military and civil fields has begun to take shape, but it has not yet formed a unified method and standard. Due to its high performance, low frequency, and high reliability, dual-core embedded processors are getting the attention of many chip manufacturers. Compatibility has been favored by many telecom equipment manufacturers and embedded high-end application integrators, but the dual-core embedded processor needs a new real-time operating system to support it, so that it can give full play to the high performance of the dual-core. It paves the way for the application of its processor in the embedded field, but the design of embedded AI engine is not transparent to it; so, it needs the support of operating system. The user code is used to be in the operating system processor environment; so, it can be used on dual core processor first. In the real-time operating system that supports dual-core processors, the part that needs to be modified is mainly concentrated in the kernel part; so, the core design is the key point to support dual-core processors. This article is to seize this key point to carry out in-depth research. The difference and influence of hardware architecture between dual-core processor and single-core processor are the primary content of the study. Through the research of the dual-core processor architecture, the general abstraction of the dual-core processor architecture is obtained, which is the starting point of the follow-up research. This paper mainly studies the design of embedded AI engine based on the microkernel operating system, extracts the security requirements of the operating system, designs and implements the operating system from the perspective of formal verification, and considers the verification problem under the background of RTOS development, so as to avoid using too many complex data structures and algorithms in the system design and reduce the difficulty of experimental verification. In this paper, we use the spatiotemporal data model, data sharing security in the cloud environment, symmetric encryption scheme, and Paillier homomorphic encryption method to study the design of embedded AI engine based on the microkernel operating system. According to the idea of microkernel architecture, the kernel is divided into four main modules: task processing, semaphore, message queue, interrupt, and exception processing, and the lock mechanism to prevent reentrancy in software is analyzed separately. Three core function modules, initialization, process grinding, and interrupt processing, are extracted from the microkernel operating system to form the formal verification area of the operating system. At the same time, the system syntax and semantics of related rights are separated, and the main rationalization rules are described. The results show that the core of the microkernel operating system in five states adopts the microkernel architecture in the dual core environment. The microkernel architecture is a compact system kernel with good adjustability. Based on the analysis of the microkernel operating system, the internal structure of each module in the kernel is summarized, and the modules are modified according to the machine characteristics of the dual core processor; it corresponds to adding modules to meet the characteristics of dual core architecture. Kernel design is a systematic theoretical research process. This paper only uncovers the tip of the iceberg of real-time operating system kernel design on dual-core embedded processors. It is necessary to understand the kernel more deeply and master the kernel in the future work and study.
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Li, Wei, Zhiyuan Han, Jian Shen, Dandan Luo, Bo Gao, and Jin Xie. "Distributed AI embedded cluster for real-time video analysis systems with edge computing." MATEC Web of Conferences 355 (2022): 03036. http://dx.doi.org/10.1051/matecconf/202235503036.

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Herein, on the basis of a distributed AI cluster, a real-time video analysis system is proposed for edge computing. With ARM cluster server as the hardware platform, a distributed software platform is constructed. The system is characterized by flexible expansion, flexible deployment, data security, and network bandwidth efficiency, which makes it suited to edge computing scenarios. According to the measurement data, the system is effective in increasing the speed of AI calculation by over 20 times in comparison with the embedded single board and achieving the calculation effect that matches GPU. Therefore, it is considered suited to the application in heavy computing power such as real-time AI computing.
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Gnacy-Gajdzik, Anna, and Piotr Przystałka. "Automating the Analysis of Negative Test Verdicts: A Future-Forward Approach Supported by Augmented Intelligence Algorithms." Applied Sciences 14, no. 6 (March 9, 2024): 2304. http://dx.doi.org/10.3390/app14062304.

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In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing.
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Harras, Mohamed Salim, and Shadi Saleh. "Applied AI Solutions on Edge Devices." Embedded Selforganising Systems 10, no. 5 (January 11, 2023): 1–2. http://dx.doi.org/10.14464/ess.v10i5.588.

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Call for submission. This editorial introduces the first issue of 2023 for Embedded Selforganising Systems (ESS) journal. The focus of this issue is the deployment of AI solutions in robotics and embedded systems. Our journal uses electronic publication, which provides a flexible way to submit and review contributions of authors from all countries. The advantages of such an e-journal are multifarious. In comparison to traditional paper journals, we replace the classic review and creation process with a new sliding issue model. Each issue starts with an initial editorial and an official call for papers. The submitted articles will be reviewed and, if accepted, published as soon as the final version is received by the committee. Based on this process, each sliding issue can be filled successively until the maximum number of articles is reached. During this period, all accepted papers can, already be read by other researchers while other papers are still in the reviewing process. Accordingly, the time to publish shrinks to a minimum. In addition, multiple issues with different focus can co-exist at the same time, which provides completely new possibilities to react on latest research topics. The journal also allows the integration of discussions and other reactions on published articles in the same journal issue. We are welcoming fresh ideas, on-going research technical reports and novel scientific works. We also intend to create a promising platform for creative and constructive discussions.
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Chao, Heng-Sheng, Chiao-Yun Tsai, Chung-Wei Chou, Tsu-Hui Shiao, Hsu-Chih Huang, Kun-Chieh Chen, Hao-Hung Tsai, Chin-Yu Lin, and Yuh-Min Chen. "Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial." Biomedicines 11, no. 1 (January 6, 2023): 147. http://dx.doi.org/10.3390/biomedicines11010147.

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Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4–5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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Choudhury, Avishek, and Onur Asan. "Human factors: bridging artificial intelligence and patient safety." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (September 2020): 211–15. http://dx.doi.org/10.1177/2327857920091007.

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The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, we discuss the interactions of providers with AI and how HFE can influence these interacting components to patient safety.
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Akhtar, Awais, Rehan Ahmed, Muhammad Haroon Yousaf, and Sergio A. Velastin. "Real-Time Motorbike Detection: AI on the Edge Perspective." Mathematics 12, no. 7 (April 7, 2024): 1103. http://dx.doi.org/10.3390/math12071103.

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Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation.
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Yeo, K. K. "Artificial intelligence in cardiology: did it take off?" Russian Journal for Personalized Medicine 2, no. 6 (January 21, 2023): 16–22. http://dx.doi.org/10.18705/2782-3806-2022-2-6-16-22.

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Artificial intelligence (AI) has been touted as a paradigm shifting, game-changing development in medicine. Did AI in cardiology take off? In this paper, we discuss some areas within cardiology in which there has some been progress in the implementation of AI technologies. Despite the promise of AI, challenges remain including cybersecurity, implementation and change management difficulties. This paper discusses the use of AI embedded as a ‘black box’ technology in existing diagnostic and interventional tools, AI as an adjunct to diagnostic tools such as echo or CT or MRI scans, AI in commercially available wearables, and AI in chatbots and other patient-fronting technologies. Lastly, while there has been some progress, the legal, regulatory, financial and ethical framework remains a work in evolution at national and international levels.
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Schwabe, Nils, Yexu Zhou, Leon Hielscher, Tobias Röddiger, Till Riedel, and Sebastian Reiter. "Tools and methods for Edge-AI-systems." at - Automatisierungstechnik 70, no. 9 (September 1, 2022): 767–76. http://dx.doi.org/10.1515/auto-2022-0023.

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Abstract The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embedded devices. This article provides a detailed summary of today’s challenges and gives a deeper insight into existing solutions that enable neural network performance with modern HW/SW co-design techniques.
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Almusaed, Amjad, Ibrahim Yitmen, and Asaad Almssad. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review." Energies 16, no. 6 (March 10, 2023): 2636. http://dx.doi.org/10.3390/en16062636.

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The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era of architectural concepts. AI simulation models can improve home functions and users’ comfort and significantly cut energy consumption through better control, increased reliability, and automation. This article highlights the potential of using artificial intelligence (AI) models to improve the design and functionality of smart houses, especially in implementing living spaces. This case study provides examples of how artificial intelligence can be embedded in smart homes to improve user experience and optimize energy efficiency. Next, the article will explore and thoroughly analyze the thorough analysis of current research on the use of artificial intelligence (AI) technology in smart homes using a variety of innovative ideas, including smart interior design and a Smart Building System Framework based on digital twins (DT). Finally, the article explores the advantages of using AI models in smart homes, emphasizing living spaces. Through the case study, the theme seeks to provide ideas on how AI can be effectively embedded in smart homes to improve functionality, convenience, and energy efficiency. The overarching goal is to harness the potential of artificial intelligence by transforming how we live in our homes and improving our quality of life. The article concludes by discussing the unresolved issues and potential future research areas on the usage of AI in smart houses. Incorporating AI technology into smart homes benefits homeowners, providing excellent safety and convenience and increased energy efficiency.
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Yue, Xuebin, Hengyi Li, and Lin Meng. "AI-based Prevention Embedded System Against COVID-19 in Daily Life." Procedia Computer Science 202 (2022): 152–57. http://dx.doi.org/10.1016/j.procs.2022.04.021.

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Boutekkouk, Fateh. "AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–44. http://dx.doi.org/10.4018/ijcini.290308.

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Artificial Intelligence is becoming more attractive to resolve nontrivial problems including the well known real time scheduling (RTS) problem for Embedded Systems (ES). The latter is considered as a hard multi-objective optimization problem because it must optimize at the same time three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction and reliability enhancement. In this paper, we firstly present the necessary background to well understand the problematic of RTS in the context of ES, then we present our enriched taxonomies for real time, energy and faults tolerance aware scheduling algorithms for ES. After that, we survey the most pertinent existing works of literature targeting the application of AI methods to resolve the RTS problem for ES notably Constraint Programming, Game theory, Machine learning, Fuzzy logic, Artificial Immune Systems, Cellular Automata, Evolutionary algorithms, Multi-agent Systems and Swarm Intelligence. We end this survey by a discussion putting the light on the main challenges and the future directions.
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Labayen, Mikel, Laura Medina, Fernando Eizaguirre, José Flich, and Naiara Aginako. "HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence." Applied Sciences 13, no. 15 (August 7, 2023): 9017. http://dx.doi.org/10.3390/app13159017.

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The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance and safety-critical applications on embedded systems and in real-time is a challenge. There are not many commercially available solutions, since High-Performance Computing (HPC) platforms are typically seen as being beyond the business of safety-critical systems. This work proposes a novel safety-critical and high-performance computing platform for CV- and AI-enhanced technology execution used for automatic accurate stopping and safe passenger transfer railway functionalities. The resulting computing platform is compatible with the majority of widely-used AI inference methodologies, AI model architectures, and AI model formats thanks to its design, which enables process separation, redundant execution, and HW acceleration in a transparent manner. The proposed technology increases the portability of railway applications into embedded systems, isolates crucial operations, and effectively and securely maintains system resources.
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