FPGA Based Implementation of Semi-Fragile Watermarking Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1419-1423
Author(s):  
Fang Ming Liu ◽  
Xiao Zhong Pan
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Digital Camera ◽  
Fpga Implementation ◽  
Fragile Watermarking ◽  
Invariant Property ◽  
Verilog Hdl ◽  
Time Performance ◽  
Dct Coefficients

This paper presents an FPGA implementation of a semi-fragile watermarking-based algorithm for a digital camera. The architecture of digital authentication camera is discussed and a semi-fragile watermarking algorithm based the invariant property of DCT coefficients quantization is designed which can survive a certain amount of compression. The components of a digital camera and the watermarking algorithm are described in Verilog HDL and implemented on the DE2-70 board. The results shown that the hardware implementation can provide real time performance and resist off-line attacks compare with the software-assisted solution.

A Strapdown Measurement of Vehicle Posture

2010 ◽  
Vol 455 ◽  
pp. 643-647
Author(s):  
L.M. Wu ◽  
L.K. Zhang ◽  
Y.F. Li ◽  
X. Xiao
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
High Reliability ◽  
Wheel Speed ◽  
Noise Filtering ◽  
Triaxial Accelerometer ◽  
Accuracy Requirement ◽  
Time Performance ◽  
Assurance System ◽  
Posture Measurement

Vehicle bodywork posture measurement has been a gordian technique in automotive．This paper presented a non-gyro bodywork posture measurement program．Using only one triaxial acceleration transducer with wheel speed encoders it provided a solution of calculating real-time bodywork posture．A lot of streamlining had been made within this program in order to assurance system for real-time performance．Concretely it gave an example of hardware implementation circuit through using LPC1758 as MCU and MMA7260 as triaxial accelerometer, as well as the noise filtering solusion for the circuit．This strapdown measurement has the advantages of high reliability，adaptability and good real-time performance，its precision meets the necessary accuracy requirement of normal application.

scholarly journals Real-Time FPGA Implementation of Parallel Connected Component Labelling for a 4K Video Stream

2021 ◽  
Author(s):  
Marcin Kowalczyk ◽  
Piotr Ciarach ◽  
Dominika Przewlocka-Rus ◽  
Hubert Szolc ◽  
Tomasz Kryjak
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Video Stream ◽  
Fpga Implementation ◽  
Connected Component ◽  
Reconfigurable Logic ◽  
Real Time Processing ◽  
Time Processing ◽  
Single Pass ◽  
4K Uhd

AbstractIn this paper, a hardware implementation in reconfigurable logic of a single-pass connected component labelling (CCL) and connected component analysis (CCA) module is presented. The main novelty of the design is the support of a video stream in 2 and 4 pixel per clock format (2 and 4 ppc) and real-time processing of 4K/UHD video stream (3840 x 2160 pixels) at 60 frames per second. We discuss several approaches to the issue and present in detail the selected ones. The proposed module was verified in an exemplary application – skin colour areas segmentation – on the ZCU 102 and ZCU 104 evaluation boards equipped with Xilinx Zynq UltraScale+ MPSoC devices.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

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