標(biāo)題: Titlebook: Embedded Machine Learning with Microcontrollers; Applications on STM3 Cem ünsalan,Berkan H?ke,Eren Atmaca Textbook 2025 The Editor(s) (if a [打印本頁(yè)] 作者: Jaundice 時(shí)間: 2025-3-21 19:56
書目名稱Embedded Machine Learning with Microcontrollers影響因子(影響力)
書目名稱Embedded Machine Learning with Microcontrollers影響因子(影響力)學(xué)科排名
書目名稱Embedded Machine Learning with Microcontrollers網(wǎng)絡(luò)公開(kāi)度
書目名稱Embedded Machine Learning with Microcontrollers網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Embedded Machine Learning with Microcontrollers被引頻次
書目名稱Embedded Machine Learning with Microcontrollers被引頻次學(xué)科排名
書目名稱Embedded Machine Learning with Microcontrollers年度引用
書目名稱Embedded Machine Learning with Microcontrollers年度引用學(xué)科排名
書目名稱Embedded Machine Learning with Microcontrollers讀者反饋
書目名稱Embedded Machine Learning with Microcontrollers讀者反饋學(xué)科排名
作者: 錢財(cái) 時(shí)間: 2025-3-21 22:01
Methodology of Uniform Contract Law explaining what machine learning is. Then, we will cover different embedded system types. Since these have their specific structures, we pick the STM32F746NG microcontroller based on Arm. Cortex?-M architecture as our embedded system in this book. Afterward, we will discuss how machine learning can作者: Simulate 時(shí)間: 2025-3-22 02:40 作者: Dawdle 時(shí)間: 2025-3-22 08:31
Robert S. Cohen,Marx W. Wartofskyt. In all these operations, we follow the same strategy as hardware setup, data acquisition and transfer at the microcontroller side, and data transfer at the PC side. Hence, the reader can form a complete setup to acquire and transfer data between the microcontroller and PC. Moreover, we cover all 作者: GRIN 時(shí)間: 2025-3-22 11:36 作者: 擁護(hù) 時(shí)間: 2025-3-22 14:44 作者: 擁護(hù) 時(shí)間: 2025-3-22 20:47
Introduction, explaining what machine learning is. Then, we will cover different embedded system types. Since these have their specific structures, we pick the STM32F746NG microcontroller based on Arm. Cortex?-M architecture as our embedded system in this book. Afterward, we will discuss how machine learning can作者: 來(lái)這真柔軟 時(shí)間: 2025-3-22 23:39
Software to Be Used in the Book,d Studio, and Keil Studio Cloud platforms for this purpose. To be more specific, we will benefit from STM32CubeIDE for C and C++ language-based programming. We will use Mbed Studio and Keil Studio Cloud platforms for C++ language-based programming. As the end-of-chapter application, we will provide 作者: 貞潔 時(shí)間: 2025-3-23 03:41
Data Acquisition from Sensors,t. In all these operations, we follow the same strategy as hardware setup, data acquisition and transfer at the microcontroller side, and data transfer at the PC side. Hence, the reader can form a complete setup to acquire and transfer data between the microcontroller and PC. Moreover, we cover all 作者: 正式通知 時(shí)間: 2025-3-23 08:27 作者: 尋找 時(shí)間: 2025-3-23 12:01 作者: temperate 時(shí)間: 2025-3-23 15:47 作者: 使熄滅 時(shí)間: 2025-3-23 21:25
Physical Nature of Thermography,owing chapters. Then, we will evaluate sensor data normalization as the third concept. Finally, we will consider feature extraction from accelerometer data, audio signals, and digital images as end of chapter applications. These will show us how the topics considered in this chapter can be applied to real-life machine learning problems.作者: 腐爛 時(shí)間: 2025-3-24 01:32
R. A. Olsson,R. D. Thompson,S. Kusachiwe will explore its formation in Python language on PC. Afterward, we will show methods to deploy the formed regressor to the STM32 microcontroller. We will form a regressor to estimate future temperature values as end of chapter application.作者: 諷刺滑稽戲劇 時(shí)間: 2025-3-24 05:56 作者: dissolution 時(shí)間: 2025-3-24 07:41 作者: 閃光東本 時(shí)間: 2025-3-24 10:46 作者: 表示問(wèn) 時(shí)間: 2025-3-24 17:55 作者: faucet 時(shí)間: 2025-3-24 19:23
Lecture Notes in Earth Sciences we will explain the mechanism in training, loss function, and optimizers used in training. We will cover training both in TensorFlow and Keras. Next, we will form a classifier and regressor by the single neuron. Finally, we will consider real-life applications introduced in the previous chapters now from the single neuron perspective.作者: 開(kāi)始沒(méi)有 時(shí)間: 2025-3-25 01:40 作者: ligature 時(shí)間: 2025-3-25 05:56
Clustering, Afterward, we will introduce methods to deploy the formed clustering algorithm to the STM32 microcontroller. As end of chapter applications, we will provide solution to two real-life problems via clustering as fall detection and image quantization.作者: resuscitation 時(shí)間: 2025-3-25 08:23 作者: aspersion 時(shí)間: 2025-3-25 14:41 作者: Criteria 時(shí)間: 2025-3-25 16:31 作者: Interdict 時(shí)間: 2025-3-25 23:11
https://doi.org/10.1007/978-981-10-5436-5ller. As end of the chapter applications, we will form classifiers for the accelerometer data, audio signals, and digital images. We will benefit from the extracted features in Chap. 5 for this purpose.作者: Obstruction 時(shí)間: 2025-3-26 01:14
Intelligent Human — Machine Systemsg and storing neural network models under TensorFlow as the second end of chapter application. Finally, we will evaluate converting neural network models from other platforms to TensorFlow format as the third end of chapter application.作者: Kinetic 時(shí)間: 2025-3-26 06:08
Francesco Amigoni,Viola Schiaffonati the usage examples of recurrence models on PC. Afterward, we will consider implementing recurrence models on the STM32 microcontroller. Finally, we will provide examples on the usage of recurrence based models to solve real-life problems.作者: 向下 時(shí)間: 2025-3-26 08:37
Hardware to Be Used in the Book,ard and its processing unit (as microcontroller) in this chapter. Afterward, we will introduce the sensors to be used throughout the book. As all the hardware is introduced, we will be ready to use them in practical machine learning applications in solving real-life problems.作者: acetylcholine 時(shí)間: 2025-3-26 16:07 作者: Limousine 時(shí)間: 2025-3-26 19:23 作者: 離開(kāi)就切除 時(shí)間: 2025-3-26 23:24 作者: 帶傷害 時(shí)間: 2025-3-27 02:52 作者: 內(nèi)閣 時(shí)間: 2025-3-27 09:09 作者: transdermal 時(shí)間: 2025-3-27 13:17 作者: intimate 時(shí)間: 2025-3-27 16:16
https://doi.org/10.1007/978-3-7091-2606-6hon running on PC. Python will allow the reader to implement and test the desired machine learning algorithm offline. Besides, it will help the reader to visualize data obtained from sensors. Hence, it can be analyzed easily. Our second perspective will be from the microcontroller side. The microcon作者: INCUR 時(shí)間: 2025-3-27 18:42 作者: 全面 時(shí)間: 2025-3-27 22:29
Physical Nature of Thermography,udosensor data. This will help us while testing machine learning methods when there is no active sensor around. Then, we will consider feature extraction as a way of summarizing the acquired sensor data as the second concept. This will act as a bridge between Chap. 4 on data acquisition and the foll作者: 歡笑 時(shí)間: 2025-3-28 03:59 作者: irreducible 時(shí)間: 2025-3-28 06:36
R. A. Olsson,R. D. Thompson,S. Kusachiusing the information on other variables. To fully explain the regression concept, we will start with its definition in this chapter. Then, we will introduce linear, polynomial, kNN, and decision tree regression. While handling each regression method, we will cover its theoretical background. Then, 作者: Assemble 時(shí)間: 2025-3-28 11:23
Validation and Generalization of CTM in classification and regression. In order to fully explain the clustering concept, we will start with its definition. Then, we will introduce the k-means and DBSCAN clustering algorithms. These algorithms have different working principles. Hence, they provide different clusters for the same data a作者: 主動(dòng)脈 時(shí)間: 2025-3-28 17:10
Intelligent Human — Machine SystemssorFlow provides a platform to perform all these operations. The Keras API will simplify life for use while benefiting from TensorFlow. Therefore, we will consider both in this chapter. To do so, we will start with their installation. Then, we will explore constant and variable declarations in Tenso作者: languid 時(shí)間: 2025-3-28 22:50 作者: 引水渠 時(shí)間: 2025-3-29 00:09
Springer Series in Pharmaceutical Statisticsis not sufficient enough to solve complex machine learning problems. Therefore, we should form a more powerful structure called multilayer neural networks, generally called neural networks. We will consider this structure in this chapter. To do so, we will start with the background information on ne作者: arthroscopy 時(shí)間: 2025-3-29 04:47
Norio Matsumoto,Genshiro Kitagawato the microcontroller. To do so, we will benefit from TensorFlow Lite as the specialized version of TensorFlow for embedded systems (including microcontrollers). Hence, we will start with its properties. Then, we will show ways of converting TensorFlow and Keras models to TensorFlow Lite format. Mo作者: 毗鄰 時(shí)間: 2025-3-29 10:46 作者: peritonitis 時(shí)間: 2025-3-29 12:57
Francesco Amigoni,Viola Schiaffonaticurrence. This will help us in forming neural network structures with memory capability. Hence, they will be more suitable for sequential data processing. To explain these concepts better, we will first associate recurrence with memory in this chapter. Afterward, we will introduce three popular neur作者: faultfinder 時(shí)間: 2025-3-29 15:50
978-3-031-70914-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 頌揚(yáng)國(guó)家 時(shí)間: 2025-3-29 23:27 作者: 天氣 時(shí)間: 2025-3-30 03:45 作者: 簡(jiǎn)潔 時(shí)間: 2025-3-30 05:58 作者: 極力證明 時(shí)間: 2025-3-30 08:13
Introduction,machine learning adds intelligence to the system. Hence, the system can make decisions, predict future value of a variable, or apply data grouping. On the other hand, machine learning methods (especially neural network-based ones) depend on high computation power and memory requirements. Fortunately作者: 紋章 時(shí)間: 2025-3-30 15:29
Hardware to Be Used in the Book,ms. Therefore, the reader should become familiar with the hardware to be used. This chapter aims to introduce these concepts. To do so, we will assume a novice user as our target. Besides, we will cover all hardware topics as abstract as possible. Hence, they can give insight on similar platforms. A
