標(biāo)題: Titlebook: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing; Use Cases and Emergi Sudeep Pasricha,Muhammad Shafique Book 2024 The [打印本頁] 作者: 櫥柜 時間: 2025-3-21 17:29
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)學(xué)科排名
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書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次學(xué)科排名
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用學(xué)科排名
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋
書目名稱Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋學(xué)科排名
作者: MAG 時間: 2025-3-21 21:15
An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare App, by acting as clinical assistants, to analyzing electronic health records, deep learning models have proved to be beneficial in identifying health abnormalities and aiding diagnostics. This chapter discusses a framework that can be used to explore the design space of embedded neural network models 作者: URN 時間: 2025-3-22 02:50 作者: 紅腫 時間: 2025-3-22 08:27 作者: 典型 時間: 2025-3-22 09:37
Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applicationsnd data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute-intensive machine learning (ML) algorithms. Run-time variations with continuous stream of noisy input data, unreliable network conn作者: 階層 時間: 2025-3-22 14:56
A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applicationsly combines these concepts, leading to new application areas. Specifically, machine learning algorithms offer reliable decision-making, classification, and regression performance, while embedded devices allow these algorithms to run at the edge with limited computational power. New embedded devices 作者: 階層 時間: 2025-3-22 19:33
Reinforcement Learning for Energy-Efficient Cloud Offloading of Mobile Embedded Applicationson response time. Offloading refers to the act of transferring computations from a mobile device to servers in the cloud. We believe that the effect of different wireless network technologies such as 3G, 4G, and Wi-Fi on the performance of offloading is a major concern that needs to be addressed. Ne作者: Popcorn 時間: 2025-3-22 22:31
Context-Aware Adaptive Anomaly Detection in IoT Systemsability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, most works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is overlooked. I作者: 吸引人的花招 時間: 2025-3-23 03:22
Machine Learning Components for Autonomous Navigation Systemshuman supervision. Such systems have several intermediate tasks such as sensor data fusion, perception, planning, etc., that are critical for safe operation in the real world. Traditional CPS systems used model-based algorithms for such tasks that required domain knowledge, but in recent years machi作者: Adj異類的 時間: 2025-3-23 05:57
Machine Learning for Efficient Perception in Automotive Cyber-Physical Systemsand performance goals. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We pr作者: 蒼白 時間: 2025-3-23 11:11 作者: 深淵 時間: 2025-3-23 15:40 作者: 昏迷狀態(tài) 時間: 2025-3-23 20:24
On the Vulnerability of Deep Reinforcement Learning to Backdoor Attacks in Autonomous Vehicleslearning has shown advanced capabilities in complex tasks and has been applied to autonomous vehicles, e.g., deep neural networks for detection and classification of pedestrians and vehicles, deep reinforcement learning for steering and acceleration control, etc. However, security of autonomous vehi作者: 稀釋前 時間: 2025-3-23 23:43 作者: excrete 時間: 2025-3-24 03:33
Considering the Impact of Noise on Machine Learning Accuracy embedded machine learning (ML). Owing to the consistent improvement in the overall performance of artificial neural networks (ANNs), the reliance of these systems on ANNs as an integral component has seen a constant rise. However, ANNs are known to be considerably vulnerable to noise. This, along w作者: 的染料 時間: 2025-3-24 07:26 作者: 臭了生氣 時間: 2025-3-24 10:52
Book 2024f?machine learning in embedded, CPS, IoT, and edge computing;?.Offers comprehensive coverage of?hardware design, software design, and hardware/software co-design and co-optimization;?.Describes real applications to?demonstrate how embedded, CPS, IoT, and edge applications benefit from?machine learning..作者: 來就得意 時間: 2025-3-24 15:21
Melancholy and Literary Biography, 1640-1816rs due to an error or change in preferences. The ML models must adapt to these changes and provide accurate monitoring. To this end, this chapter reviews the current state of the art and challenges in robust machine learning algorithms for wearable devices. We also perform a case study with a human 作者: DAFT 時間: 2025-3-24 22:36 作者: Epidural-Space 時間: 2025-3-25 00:42 作者: Dri727 時間: 2025-3-25 06:14
Susana Ortiz-Urda,Wilson Ho,Albert Leeomputer Aided Design (ICCAD), pp. 1–9. IEEE, Piscataway (2020)) (Copyright ?2020 IEEE), we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which compri作者: chance 時間: 2025-3-25 07:59 作者: Anticoagulant 時間: 2025-3-25 15:37
Agomelatine in Depressive Disorders, accuracy. A wide range of illustrative examples with various types of triggers is considered, such as invisible triggers, triggers with real-world meaning, and dynamic triggers. The chapter ends with a discussion of potential benign applications of the backdoor phenomena and a discussion of potenti作者: ADJ 時間: 2025-3-25 17:25
ge of?hardware design, software design, and hardware/software co-design and co-optimization;?.Describes real applications to?demonstrate how embedded, CPS, IoT, and edge applications benefit from?machine learning..978-3-031-40679-9978-3-031-40677-5作者: Medicaid 時間: 2025-3-25 21:45
Robust Machine Learning for Low-Power Wearable Devices: Challenges and Opportunitiesrs due to an error or change in preferences. The ML models must adapt to these changes and provide accurate monitoring. To this end, this chapter reviews the current state of the art and challenges in robust machine learning algorithms for wearable devices. We also perform a case study with a human 作者: 言外之意 時間: 2025-3-26 00:33 作者: nonchalance 時間: 2025-3-26 07:05 作者: 后退 時間: 2025-3-26 10:02 作者: insurrection 時間: 2025-3-26 15:52 作者: Water-Brash 時間: 2025-3-26 19:11 作者: Ganglion-Cyst 時間: 2025-3-26 21:41 作者: nonplus 時間: 2025-3-27 02:51 作者: 心神不寧 時間: 2025-3-27 08:43
Book 2024covering different application domains. The?focus is on?presenting interesting and new use cases of?applying machine learning to?innovative application domains, exploring the?efficient hardware design of?efficient machine learning accelerators, memory optimization techniques, illustrating model comp作者: 自愛 時間: 2025-3-27 11:55
https://doi.org/10.1007/978-3-476-04894-3s (CNNs). Our proposed CNN-based indoor localization framework (.) is validated across several indoor locales and shows improvements over the best-known prior works, with an average localization error of <2?m.作者: Locale 時間: 2025-3-27 15:08
Cannabinoids, Sleep, and the MCH System,offs, and cross-layered sense–compute co-optimization for ML-driven eHealth applications. We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor–edge–cloud framework for an objective pain assessment case study.作者: 傳染 時間: 2025-3-27 19:17 作者: Host142 時間: 2025-3-27 22:24
Bilal Fawaz,Gordana Rasic,Teviah E. Sachsapter, we first introduce related work on deep learning in autonomous vehicles and discuss respective applications. Afterward, we present the backdoor attack literature, focusing on autonomous vehicle controllers employing deep reinforcement learning models. Finally, we introduce backdoor defenses and analyze their effectiveness.作者: 罐里有戒指 時間: 2025-3-28 03:58 作者: 事物的方面 時間: 2025-3-28 08:17 作者: chronology 時間: 2025-3-28 11:34
Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applicationsoffs, and cross-layered sense–compute co-optimization for ML-driven eHealth applications. We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor–edge–cloud framework for an objective pain assessment case study.作者: covert 時間: 2025-3-28 16:44
A Survey of Embedded Machine Learning for Smart and Sustainable Healthcare Applicationslows performing machine learning directly on devices used in the field, thus leading to numerous novel applications. Promising target applications include health-related applications such as health monitoring, human activity recognition, human pose estimation, and service applications such as energy management in mobile devices.作者: OPINE 時間: 2025-3-28 20:01 作者: 蕨類 時間: 2025-3-28 23:17 作者: Phagocytes 時間: 2025-3-29 04:36
https://doi.org/10.1007/978-3-319-78310-9ne learning algorithms have been proposed that are more accurate as they automatically extract pertinent information from large volumes of data. In this chapter, we explore the recent machine learning algorithms that have been proposed to perform the various tasks within an autonomous system.作者: expeditious 時間: 2025-3-29 08:50 作者: 玷污 時間: 2025-3-29 12:58
Melanoma Antigens and Antibodiesizes a gated recurrent unit (GRU)-based recurrent autoencoder network to detect cyber-attacks in automotive cyber-physical systems. Our proposed INDRA framework is evaluated under different attacks and compared against various state-of-the-art anomaly detection works using a commercially available vehicular network dataset.作者: Fillet,Filet 時間: 2025-3-29 18:45 作者: countenance 時間: 2025-3-29 20:12
Machine Learning for Efficient Perception in Automotive Cyber-Physical Systemsesent PASTA, a novel framework for global co-optimization of deep learning and sensing for ADAS-based vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions.作者: 符合你規(guī)定 時間: 2025-3-30 03:57 作者: 切碎 時間: 2025-3-30 07:10 作者: Nostalgia 時間: 2025-3-30 09:17 作者: 淘氣 時間: 2025-3-30 16:26
An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare Appexecution platform. The models explored by the framework are successful in reducing the hardware overhead of network by a factor of 53?× while achieving a quality loss of <0.2% compared to state of the art.作者: habile 時間: 2025-3-30 17:08
MELETI: A Machine-Learning-Based Embedded System Architecture for Infrastructure Inspection with UAVmous infrastructure inspection through innovative intelligent systems and machine learning algorithms. Finally, several infrastructure inspection challenges that implement MELETI are introduced on both power and telecommunication infrastructure inspection.作者: 你不公正 時間: 2025-3-30 22:17 作者: excrete 時間: 2025-3-31 04:50 作者: Haphazard 時間: 2025-3-31 08:56
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