High-resolution 3D optical sensing and real-time 3D video data streaming

2018 ◽  
Author(s):  
Tyler Be ◽  
Song Zhang
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Optical Sensing ◽  
3D Video ◽  
Video Data ◽  
Data Streaming

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

Research on Video Encryption and Privacy Protection in Cloud Monitoring System Based on H.264

2019 ◽  
Vol 12 ◽  
Author(s):  
Qingtao Wu ◽  
Zaihui Cao
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Video Data ◽  
Encryption Algorithm ◽  
Management Tool ◽  
Video Encryption ◽  
Wireless Multimedia ◽  
Monitoring Technology ◽  
Security Issues ◽  
Cloud Monitoring

: Cloud monitoring technology is an important maintenance and management tool for cloud platforms.Cloud monitoring system is a kind of network monitoring service, monitoring technology and monitoring platform based on Internet. At present, the monitoring system is changed from the local monitoring to cloud monitoring, with the flexibility and convenience improved, but also exposed more security issues. Cloud video may be intercepted or changed in the transmission process. Most of the existing encryption algorithms have defects in real-time and security. Aiming at the current security problems of cloud video surveillance, this paper proposes a new video encryption algorithm based on H.264 standard. By using the advanced FMO mechanism, the related macro blocks can be driven into different Slice. The encryption algorithm proposed in this paper can encrypt the whole video content by encrypting the FMO sub images. The method has high real-time performance, and the encryption process can be executed in parallel with the coding process. The algorithm can also be combined with traditional scrambling algorithm, further improve the video encryption effect. The algorithm selects the encrypted part of the video data, which reducing the amount of data to be encrypted. Thus reducing the computational complexity of the encryption system, with faster encryption speed, improve real-time and security, suitable for transfer through mobile multimedia and wireless multimedia network.

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 Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

Accelerating real‐time object detection in high‐resolution video surveillance

2021 ◽  
Author(s):  
Yuefeng Wang ◽  
Kuang Mao ◽  
Tong Chen ◽  
Yanglong Yin ◽  
Shuibing He ◽  
...  
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Video Surveillance

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

scholarly journals Tailoring exceptional points in a hybrid PT-symmetric and anti-PT-symmetric scattering system

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zhicheng Xiao ◽  
Andrea Alù
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Quantum Systems ◽  
Symmetric System ◽  
Microwave Circuit ◽  
High Resolution Imaging ◽  
Fano Resonances ◽  
Real Time Control ◽  
Time Control ◽  
Resolution Imaging

Abstract Fano resonances feature an asymmetric lineshape with controllable linewidth, stemming from the interplay between bright and dark resonances. They provide efficient opportunities to shape the scattering lineshape, but they usually lack flexibility and tunability and are hindered by loss in passive systems. Here, we explore a hybrid parity-time (PT) and anti-parity-time (APT) symmetric system supporting unitary scattering features with highly tunable Fano resonances. The PT-APT-symmetric system can be envisioned in nanophotonic and microwave circuit implementations, allowing for real-time control of the scattering lineshape and its underlying singularities. Our study shows the opportunities enabled by non-Hermitian platforms to control scattering lineshapes for a plethora of photonic, electronic, and quantum systems, with potential for high-resolution imaging, switching, sensing, and multiplexing.

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