scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 521 ◽  
Author(s):  
Alejandro Baldominos ◽  
Alejandro Cervantes ◽  
Yago Saez ◽  
Pedro Isasi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Leisure Activities ◽  
Machine Learning Techniques ◽  
Learning Networks ◽  
Extremely Randomized Trees ◽  
Learning Techniques ◽  
Improved Accuracy ◽  
Learning Architectures

We have compared the performance of different machine learning techniques for human activity recognition. Experiments were made using a benchmark dataset where each subject wore a device in the pocket and another on the wrist. The dataset comprises thirteen activities, including physical activities, common postures, working activities and leisure activities. We apply a methodology known as the activity recognition chain, a sequence of steps involving preprocessing, segmentation, feature extraction and classification for traditional machine learning methods; we also tested convolutional deep learning networks that operate on raw data instead of using computed features. Results show that combination of two sensors does not necessarily result in an improved accuracy. We have determined that best results are obtained by the extremely randomized trees approach, operating on precomputed features and on data obtained from the wrist sensor. Deep learning architectures did not produce competitive results with the tested architecture.

Domain-Specific Programming Environment for Heterogeneous Multicore Embedded Systems

2014 ◽  
Vol 5 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Alexey Syschikov ◽  
Yuriy Sheynin ◽  
Boris Sedov ◽  
Vera Ivanova
Keyword(s):  
Embedded Systems ◽  
Software Development ◽  
Embedded System ◽  
Multicore Processors ◽  
General Purpose ◽  
Domain Experts ◽  
Wide Range ◽  
Portable Software ◽  
The Embedded System ◽  
Computing Platforms

Nowadays embedded systems are used in a broad range of domains such as avionics, space, automotive, mobile, domestic appliances etc. Sophisticated software determines the quality of embedded systems and requires high-qualified experts for software development. Software becomes the main assert of embedded systems that is valuable to retain in changing computing platforms in embedded systems evolution. Computing platforms for embedded systems became multicore processors and SoC, they can change in the embedded system lifetime that could be long (dozen of years for an automobile and airplane). It requires software porting to new platforms as a regular process. Many tools and approaches allow developing of software for domain area experts, but mainly for general-purpose computing systems. In this paper the authors present the complex technology and tools that allows involving domain experts in software development for embedded systems. The proposed technology has various aspects and abilities that can be used to build verifiable and portable software for a wide range of embedded platforms.

Compiler-Aided Run-Time Performance Speed-Up in Super-Scalar Processor

10.28945/3391 ◽  
2009 ◽  
Author(s):  
Moshe Pelleh
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Experimental Results ◽  
Medium Size ◽  
Organic System ◽  
Software Application ◽  
Organic Systems ◽  
Special Cases ◽  
Run Time

In our world, where most systems become embedded systems, the approach of designing embedded systems is still frequently similar to the approach of designing organic systems (or not embedded systems). An organic system, like a personal computer or a work station, must be able to run any task submitted to it at any time (with certain constrains depending on the machine). Consequently, it must have a sophisticated general purpose Operating System (OS) to schedule, dispatch, maintain and monitor the tasks and assist them in special cases (particularly communication and synchronization between them and with external devices). These OSs require an overhead on the memory, on the cache and on the run time. Moreover, generally they are task oriented rather than machine oriented; therefore the processor's throughput is penalized. On the other hand, an embedded system, like an Anti-lock Braking System (ABS), executes always the same software application. Frequently it is a small or medium size system, or made up of several such systems. Many small or medium size embedded systems, with limited number of tasks, can be scheduled by our proposed hardware architecture, based on the Motorola 500MHz MPC7410 processor, enhancing its throughput and avoiding the software OS overhead, complexity, maintenance and price. Encouraged by our experimental results, we shall develop a compiler to assist our method. In the meantime we will present here our proposal and the experimental results.

Detection of Pavement Maintenance Treatments using Deep-Learning Network

2021 ◽  
pp. 036119812110078
Author(s):  
Lu Gao ◽  
Yao Yu ◽  
Yi Hao Ren ◽  
Pan Lu
Keyword(s):  
Deep Learning ◽  
Measurement Errors ◽  
Short Term Memory ◽  
Learning Networks ◽  
Pavement Maintenance ◽  
Time Period ◽  
Learning Techniques ◽  
Maintenance And Rehabilitation ◽  
History Of ◽  
Deep Learning Network

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

Anomaly Detection in Drone-Captured Images Using Machine Learning Techniques and Deep Learning Architectures

2021 ◽  
pp. 783-791
Author(s):  
Kartik Joshi ◽  
G. Vidya ◽  
Soumya Shaw ◽  
Abitha K. Thyagarajan ◽  
Akhil Pathak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Learning Architectures

scholarly journals Deep-learning networks and the functional architecture of executive control

2017 ◽  
Vol 40 ◽  
Author(s):  
Richard P. Cooper
Keyword(s):  
Deep Learning ◽  
Executive Control ◽  
Infants And Children ◽  
Human Intervention ◽  
Learning Networks ◽  
Functional Architecture ◽  
Learning Techniques ◽  
Multisensory Training

AbstractLake et al. underrate both the promise and the limitations of contemporary deep learning techniques. The promise lies in combining those techniques with broad multisensory training as experienced by infants and children. The limitations lie in the need for such systems to possess functional subsystems that generate, monitor, and switch goals and strategies in the absence of human intervention.

scholarly journals Deep Learning-Based Embedded System for Carabao Mango (Mangifera Indica L.) Sorting

2019 ◽  
Vol 8 (2) ◽  
pp. 5456-5462
Keyword(s):  
Deep Learning ◽  
Embedded System ◽  
Mangifera Indica ◽  
Raspberry Pi ◽  
Single Shot ◽  
Embedded Computer ◽  
Quality Classification ◽  
Learning Techniques ◽  
Multiple Classification ◽  
Basic Quality

This paper presents the design and development of an embedded system for ‘Carabao’ or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques.

scholarly journals Open-Source FPGA Coprocessor for the Doppler Emulation of Moving Fluids

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1549
Author(s):  
Stefano Ricci
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Open Source ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Synthetic Signal ◽  
Doppler System ◽  
Wide Range ◽  
Field Programmable ◽  
Moving Fluid

Embedded systems are nowadays employed in a wide range of application, and their capability to implement calculation-intensive algorithms is growing quickly and constantly. This result is obtained by the exploitation of powerful embedded processors that are often connected to coprocessors optimized for a particular application. This work presents an open-source coprocessor dedicated to the real-time generation of a synthetic signal that mimics the echoes produced by a moving fluid when investigated by ultrasounds. The coprocessor is implemented in a Field Programmable Gate Array (FPGA) device and integrated in an embedded system. The system can replace the complex and inaccurate flow-rigs employed in laboratorial tests of Doppler ultrasound systems and methods. This paper details the coprocessor and its standard interfaces, and shows how it can be integrated in the wider architecture of an embedded system. Experiments showed its capability to emulate a fluid flowing in a pipe when investigated by an echographic Doppler system.

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