A Scheme of H.264 HD Encoding/Transcoding System Based on ASIC

2011 ◽  
Vol 58-60 ◽  
pp. 1354-1358 ◽  
Author(s):  
Ming Kui Zheng ◽  
Kai Xiong Su ◽  
Xiu Zhi Yang
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Digital Tv ◽  
High Definition ◽  
Control Module ◽  
Input Interface ◽  
Front End ◽  
Application Specific ◽  
Output Interface ◽  
Ethernet Interface

This paper proposes a scheme of H.264 high-definition encoder/transcoder . The system is based on ASIC. Application-specific high-definition encoding/transcoding chip, software control module, HDMI interface, analog video/audio input interface and ASI input/output interface ,etc. are discussed in detail in this paper. The system also provides the Ethernet interface, which is used for real-time monitoring of coding/transcoding system . The proposed scheme with the property of good real-time and low cost accord with the need of high-definition digital TV front-end system.

Promote the Ethernet Performance Based on Inline Assemble

2014 ◽  
Vol 926-930 ◽  
pp. 2690-2693
Author(s):  
Yao Cheng
Keyword(s):  
Control System ◽  
Real Time ◽  
Performance Improvement ◽  
Low Cost ◽  
Engineering Application ◽  
Real Time Control ◽  
Time Control ◽  
The Right ◽  
Ethernet Interface ◽  
Modern Industry

Ethernet has been broadly used in modern industry for its exoteric standard, flexibility and low cost. Ethernet is becoming the right choice for many engineering application. However, issues of performance must be considered when we apply it to timing sensitive field such as real time control system and so on. An Ethernet interface solution implemented based on s3c2410a and ax88796 was described in this article, and in order to improve the performance, inline assemble was introduced into this study. The effect of performance improvement through inline assemble was checked by ping experiments. It was proved that inline assemble can improve the performance of Ethernet interface distinctly.

The Research of the Real-Time High-Resolution Image Acquisition Based on Embedded System

2014 ◽  
Vol 631-632 ◽  
pp. 508-511
Author(s):  
Xi Ye Feng ◽  
Xiu Qing Huang
Keyword(s):  
High Resolution ◽  
Embedded System ◽  
Real Time ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Image Sensor ◽  
High Definition ◽  
The Real ◽  
Embedded Linux System

This paper presents the design of a real-time high-definition image acquisition. The hardware platform combines Intel Xscale PXA270 processor, high-resolution camera and SAA7114H. The system is based on the embedded Linux system. Beetween the image sensor and the system memory,there is a quick capture interface.The interface receives the data from the image sensor,and converts the raw image data to a suitable format, and sends H.264 stream to the memory via the DMA channel. The result shows that the design can realize the real-time and high-definition image acquisition in a complicated environment. The advantage of this system is small volume, low power consumption and low cost. It can be widely used in agricultural and hydrological monitoring, intelligent transportation, security monitoring and intelligent home.

Efficient Pipelined Circuits for Histogram-based Tone Mapping of Nonlinear CMOS Image Sensors

2021 ◽  
Author(s):  
Maikon Nascimento ◽  
Jing Li ◽  
Dileepan Joseph
Keyword(s):  
Real Time ◽  
Dynamic Range ◽  
Low Cost ◽  
Image Sensor ◽  
Frame Rate ◽  
Design Parameters ◽  
Tone Mapping ◽  
High Definition ◽  
Hard Real Time ◽  
Pipelined Circuits

Tone mapping is extensively researched to address the issue of displaying high dynamic range (DR) scenes on low DR displays. Even though several tone-mapping operators (TMOs) exist, not all are designed for hard real time. The operator has to be capable of scaling up the spatial resolution without compromising the frame rate. The implementation of a TMO should also be simple enough to embed in low-cost platforms for imaging systems. A computationally efficient, and well accepted, class of TMOs are global ones based on histograms. This work presents a method to implement TMOs that use histograms. This method is suitable for low-cost field-programmable gate arrays (FPGAs), using simple components such as adders, multipliers, and random access memories, and is particularly suited for a nonlinear CMOS image sensor (CIS) operating continuously in hard real time. The authors develop a fixed-point design, validated in bit-true fashion using Xilinx and Altera tools, from a background algorithm implemented using M ATLAB . Our generic design uses pipelined circuits and operates with low latency. The use of a hardware description language to model our circuits guarantees portability and modularity. Moreover, the complete TMO is generated from design parameters and a design template. The architecture is robust and scales well from kilopixel to megapixel formats. The circuits achieve 30 frames per second, at high definition resolutions, while occupying only a small fraction of the available logic elements in a low-cost FPGA device.

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.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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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