scholarly journals Weighted Record Sample for Underwater Seismic Monitoring Application

10.29007/flck ◽  
2019 ◽  
Author(s):  
Hussain Albarakati ◽  
Reda Ammar ◽  
Raafat Elfouly
Keyword(s):  
Embedded System ◽  
Information Extraction ◽  
Real Time ◽  
New Technology ◽  
Seismic Monitoring ◽  
Advanced Technology ◽  
Oil Well ◽  
System Architectures ◽  
Research Activities ◽  
Acoustic Sensor Networks

Underwater acoustic sensor networks have been developed as a new technology for real-time underwater applications, including seismic monitoring, disaster prevention, and oil well inspection. Unfortunately, this new technology is constrained to data sensing, large-volume transmission, and forwarding. As a result, the transmission of large volumes of data is costly in terms of both time and power. We thus focused our research activities on the development of embedded underwater computing systems. In this advanced technology, information extraction is performed underwater using data mining techniques or compression algorithms. We previously presented a new set of real-time underwater embedded system architectures that can manage multiple network configurations. In this study, we extend our research to develop information extraction for seismic monitoring underwater application to meet real-time constraints. The system performance is measured in terms of the minimum end-to-end delay and power consumption. The simulation results are presented to measure the performance of our architecture based on the information extraction algorithm.

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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.

scholarly journals The Design of a 2D Graphics Accelerator for Embedded Systems

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 469
Author(s):  
Hyun Woo Oh ◽  
Ji Kwang Kim ◽  
Gwan Beom Hwang ◽  
Seung Eun Lee
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Line Drawing ◽  
Cmos Process ◽  
Embedded Processor ◽  
Processor Core ◽  
Field Programmable ◽  
The Embedded System ◽  
Graphics Processing

Recently, advances in technology have enabled embedded systems to be adopted for a variety of applications. Some of these applications require real-time 2D graphics processing running on limited design specifications such as low power consumption and a small area. In order to satisfy such conditions, including a specific 2D graphics accelerator in the embedded system is an effective method. This method reduces the workload of the processor in the embedded system by exploiting the accelerator. The accelerator assists the system to perform 2D graphics processing in real-time. Therefore, a variety of applications that require 2D graphics processing can be implemented with an embedded processor. In this paper, we present a 2D graphics accelerator for tiny embedded systems. The accelerator includes an optimized line-drawing operation based on Bresenham’s algorithm. The optimized operation enables the accelerator to deal with various kinds of 2D graphics processing and to perform the line-drawing instead of the system processor. Moreover, the accelerator also distributes the workload of the processor core by removing the need for the core to access the frame buffer memory. We measure the performance of the accelerator by implementing the processor, including the accelerator, on a field-programmable gate array (FPGA), and ascertaining the possibility of realization by synthesizing using the 180 nm CMOS process.

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