scholarly journals Lightweight Neural Networks for Context Aware Embedded System

2021 ◽  
Author(s):  
Abdi Dera
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Resource Limitation ◽  
Learning System ◽  
Context Aware ◽  
Specific Task ◽  
Sensor Reading ◽  
Smart Systems ◽  
Main Challenge ◽  
Machine Speed

<p>An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This paper resents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on Arduino board for sensor reading.</p>

scholarly journals Lightweight Neural Networks for Context Aware Embedded System

2021 ◽  
Author(s):  
Abdi Dera
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Resource Limitation ◽  
Learning System ◽  
Context Aware ◽  
Specific Task ◽  
Sensor Reading ◽  
Smart Systems ◽  
Main Challenge ◽  
Machine Speed

<p>An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This paper resents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on Arduino board for sensor reading.</p>

scholarly journals Hybrid Laminate for Haptic Input Device with Integrated Signal Processing

2018 ◽  
Vol 8 (8) ◽  
pp. 1261 ◽  
Author(s):  
René Schmidt ◽  
Alexander Graf ◽  
Ricardo Decker ◽  
Verena Kräusel ◽  
Wolfram Hardt ◽  
...  
Keyword(s):  
Embedded System ◽  
Learning System ◽  
Input Device ◽  
Full Potential ◽  
Action Execution ◽  
Additional Function ◽  
Human Interface ◽  
Hybrid Laminates ◽  
Function Integration ◽  
Materials Used

Achieving lightweight construction through only material substitution does not realize the full potential of producing a lightweight material, hence, it is no longer sufficient. Weight-saving goals are best achieved through additional function integration. In order to implement this premise for mass production, a manufacturing process for joining and forming hybrid laminates using a new tool concept is presented. All materials used are widely producible and processable. The manufactured cover of an automotive center console serves to demonstrate a human interface device with impact detection and action execution. This is only possible through a machine learning system, which is implemented on a small—and thus space- and energy-saving—embedded system. The measurement results confirm the objective and show that localization was sufficiently accurate.

scholarly journals 2204 Noise Ratio Learning System for Telemeter Data of Aircraft by Neural Networks

2009 ◽  
Vol 2009.19 (0) ◽  
pp. 346-347
Author(s):  
Toshinori Hori ◽  
Hidehiko Yamamoto ◽  
Takayoshi Yamada
Keyword(s):  
Neural Networks ◽  
Learning System ◽  
Noise Ratio

Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

2022 ◽  
Vol 192 ◽  
pp. 106586
Author(s):  
Yanchao Zhang ◽  
Jiya Yu ◽  
Yang Chen ◽  
Wen Yang ◽  
Wenbo Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Real Time ◽  
Deep Neural Networks

Literature Survey for Applications of Artificial Neural Networks

2022 ◽  
pp. 669-682
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

scholarly journals Implementation a Context-Aware Plant Ecology Mobile Learning System

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402092070
Author(s):  
Chih-Chiang Wang ◽  
Chia-Lun Lo ◽  
Ming-Ching Hsu ◽  
Chih-Yung Tsai ◽  
Chun-Ming Tsai
Keyword(s):  
Outdoor Education ◽  
Plant Ecology ◽  
Learning System ◽  
Identification Algorithm ◽  
Context Aware ◽  
Shape Identification ◽  
Growth Environment ◽  
Learning And Teaching ◽  
Leaf Vein ◽  
Mobile Learning System

Mobile devices are becoming ubiquitous methodologies and tools, providing application for learning and teaching field. On the basis of the widespread use of wireless devices and mobile computing technology, this study proposes a context-aware plant ecology learning system (CAPELS) based on context-aware technology; adapting deep neural networks (DNN) and leaf vein and shape identification algorithm which can identify plant leaves, this system automatically provides relevant botanical and growth environment knowledge to the learners. Therefore, during outdoor education, it can assist learners in accurately obtaining the required relevant botanical and growth environment knowledge. The experimental results indicate that students who used CAPELS performed better learning about plant ecology than those who did not. We also delivered questionnaires to those who used CAPELS and analyzed the results by using the partial least squares (PLS) method. The results have shown that CAPELS can encourage student’s learning motivation and thus improve their learning effectiveness. Thus, CAPELS provides a new educational platform for promoting ecology learning approach and effectively improves student learning efficiency and motivation.

Towards a Flipped Classroom Based on a Context-Aware Mobile Learning System (FC-CAMLS)

2020 ◽  
pp. 43-56
Author(s):  
Mahnane Lamia ◽  
Hafidi Mohamed
Keyword(s):  
Mobile Learning ◽  
Structural Equation ◽  
Flipped Classroom ◽  
Learning System ◽  
Equation Modeling ◽  
Context Aware ◽  
Course Content ◽  
Positive Effects ◽  
Mobile Learning System ◽  
Students Satisfaction

The approach proposed in this chapter called flipped classroom based on context-aware mobile learning system (FC-CAMLS) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The contribution was applied in the context of the flipped learning in order to manage the heterogeneity of context imposed by this approach. Firstly, the authors present a quantitative analysis by means of structural equation modeling to analyze the causal relationships of knowledge, skills, and motivation with students' satisfaction. Secondly, they confirm that the proposed flipped classroom has positive effects on students' knowledge, skills, and motivation. Finally, the research provides useful results that the use of the context dimensions and learner feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

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