Low-power embedded system for real-time correction of fish-eye automotive cameras

2012 ◽  
Author(s):  
M. Turturici ◽  
S. Saponara ◽  
L. Fanucci ◽  
E. Franchi
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Real Time ◽  
Time Correction ◽  
Fish Eye

Low-power DSP system for real-time correction of fish-eye cameras in automotive driver assistance applications

2013 ◽  
Vol 9 (3) ◽  
pp. 463-478 ◽  
Author(s):  
Mauro Turturici ◽  
Sergio Saponara ◽  
Luca Fanucci ◽  
Emilio Franchi
Keyword(s):  
Low Power ◽  
Real Time ◽  
Driver Assistance ◽  
Dsp System ◽  
Time Correction ◽  
Fish Eye

DEVICE-CENTRIC LOW-POWER SCHEDULING FOR REAL-TIME EMBEDDED SYSTEMS

2005 ◽  
Vol 15 (02) ◽  
pp. 461-466 ◽  
Author(s):  
PAO-ANN HSIUNG ◽  
HSIN-CHIEH KAO
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Real Time ◽  
Dynamic Scheduling ◽  
Process Scheduling ◽  
Energy Usage ◽  
Feasible Schedule ◽  
Power Scheduling ◽  
Earliest Deadline ◽  
Fixed System

Existing low power schedules mainly try to decrease overall system energy usage by shutting down unused devices after process scheduling. This often leads to suboptimal energy usage due to the lack of process slack time to shut down unused devices in a fixed system schedule such as that generated by a rate-monotonic or an earliest deadline first scheduler in a real-time embedded system. In this work, we try to integrate process scheduling with low power scheduling such that a low-power real-time feasible schedule is obtained. The proposed method called Low-Power Quasi-Dynamic Scheduling (LQS) was implemented and applied to some examples, including a sensor network node and Bluetooth devices, to prove its benefits.

High speed interferometric device for real-time correction of aero-optic effects

1995 ◽  
Author(s):  
Rod Clark ◽  
John Karpinsky ◽  
Gregg Borek ◽  
Eric Johnson
Keyword(s):  
Real Time ◽  
High Speed ◽  
Time Correction

scholarly journals A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Learning Outcomes ◽  
Real World ◽  
Time Constraints ◽  
Network Simulator ◽  
Practical Applications ◽  
Rail Network ◽  
Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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