Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile Robots - bet 75

|
Ultra fast cnn based Hardware Computing Platform Concepts for adas visual Sensors and Evolutionary Mobile RobotsBog'liq Alireza FasihSelective power-down for high performance CPU/system
. 1995, Google
Patents.
21.
Chhugani, J., et al.,
Efficient implementation of sorting on multi-core SIMD CPU
architecture.
Proceedings of the VLDB Endowment, 2008. 1(2): p. 1313-1324.
22.
White, H.,
Artificial neural networks: approximation and learning theory
. 1992:
Blackwell Publishers, Inc. Cambridge, MA, USA.
23.
Hagan, M.T., et al.,
Neural network design
. 1996: PWS Boston, MA.
24.
Agatonovic-Kustrin, S. and R. Beresford,
Basic concepts of artificial neural network
(ANN) modeling and its application in pharmaceutical research.
Journal of
pharmaceutical and biomedical analysis, 2000. 22(5): p. 717-727.
25.
Albó-Canals, J., et al.
An efficient FPGA implementation of a DT-CNN for small image
gray-scale pre-processing
. 2009: IEEE.
26.
Toledo, F.J., et al.,
Image processing with CNN in a FPGA-based augmented reality
system for visually impaired people.
Computational Intelligence and Bioinspired
Systems, 2005: p. 906-912.
27.
Ruaro, M.E., P. Bonifazi, and V. Torre,
Toward the neurocomputer: Image processing
and pattern recognition with neuronal cultures.
Biomedical Engineering, IEEE
Transactions on, 2005. 52(3): p. 371-383.
28.
Ham, F.M. and I. Kostanic,
Principles of neurocomputing for science and engineering
.
2000: McGraw-Hill Higher Education.
29.
Gonzalez, R.C., R.E. Woods, and S.L. Eddins,
Digital image processing using MATLAB
.
2004: Pearson Education India.
30.
Fasih, A., et al.
New computational modeling for solving higher order ODE based on
FPGA
: IEEE.
31.
Fasih, A., et al.,
An Ultra-fast and Adaptive Framework for FPGA-Based Real-Time
Machine Vision for Advanced Driver Assistance Systems: a CNN-Based Processing
Architec-ture.
32.
Aspray, W.,
Computing before computers
. 1990: Iowa State University Press.
33.
Williams, J.,
Analog circuit design: art, science and personalities
. 1991: Newnes.
34.
Hubner, M., K. Paulsson, and J. Becker.
Parallel and flexible multiprocessor system-
on-chip for adaptive automotive applications based on Xilinx microblaze soft-cores
:
IEEE.
35.
Nakamura, Y., et al.
A fast hardware/software co-verification method for system-on-
a-chip by using a C/C++ simulator and FPGA emulator with shared register
communication
. 2004: ACM.
36.
Schmitt, L.M.,
Theory of genetic algorithms.
Theoretical Computer Science, 2001.
259(1-2): p. 1-61.
37.
Roser, M. and F. Moosmann.
Classification of weather situations on single color
images
. 2008: IEEE.
38.
Zheng, N.N., et al.,
Toward intelligent driver-assistance and safety warning system.
Intelligent systems, IEEE, 2004. 19(2): p. 8-11.
39.
Langheim, J., et al.
CARSENSE-New environment sensing for advanced driver
assistance systems
. 2001.
40.
Donecker, S.M., T.A. Lasky, and B. Ravani,
A mechatronic sensing system for vehicle
guidance and control.
Mechatronics, IEEE/ASME Transactions on, 2003. 8(4): p.
500-510.
121
41.
Kim, S.Y., et al.,
An Intelligent and Integrated Driver Assistance System for Increased
|
| |