iosr-JECE   Volume-4 ~ Issue-3

Abstract: This paper presents a method to reduce the computation and memory access for variable block size motion estimation (ME) using pixel truncation. Previous work has focused on implementing pixel truncation using a fixed-block size(16×16 pixels) ME. However, pixel truncation fails to give satisfactory results for smaller block partitions. In this paper, we analyze the effect of truncating pixels for smaller block partitions and propose a method to improve the frame prediction. Our method is able to reduce the total computation and memory access compared to conventional full-search method without significantly degrading picture quality. With unique data arrangement, the proposed architectures are able to saveup to 53% energy compared to the conventional full-search architecture. This makes such architectures attractive for H.264application in future mobile devices.
Keywords:Low-power design, motion estimation (ME), video coding, VLSI architecture.

[1] Advanced Video Coding for Generic Audiovisual Services, ITU-T Recommendation H.264 & ISO/IEC 14496-10 (MPEG-4) AVC,
2005.
[2] C.-Y. Chen, S.-Y. Chien, Y.-W. Huang, T.-C. Chen, T.-C. Wang, and L.-G. Chen, "Analysis and architecture design of variable block-size motion estimationfor H.264/AVC," IEEE Trans. Circuits Syst. I: Regular Papers, vol. 53, no. 3, pp. 578–593, Mar.
2006.
[3] Z.-L. He, C.-Y. Tsui, K.-K. Chan, and M. L. Liou, "Low-power VLSI design for motion estimation using adaptive pixel
truncation," IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 5, pp. 669–678, Aug. 2000.
[4] A. Bahari, T. Arslan, and A. T. Erdogan, "Low computation and memory access for variable block size motion estimation using
pixel truncation," in Proc. IEEE Workshop Signal Process. Syst. 2007 (SiPS '07), pp. 681–685.
[5] A. Bahari, T. Arslan, and A. Erdogan, "Low-power hardware architecture for vbsme using pixel truncation," in Proc. 21st Int. Conf.
VLSI Design, Hyderabad, Andhra Pradesh, 2008, pp. 389–394.
[6] B. Natarajan, V. Bhaskaran, and K. Konstantinides, "Low-complexity block-based motion estimation via one-bit transforms," IEEE
Trans. Circuits Syst. Video Technol., vol. 7, no. 4, pp. 702–706, Aug. 1997.
[7] A. Erturk and S. Erturk, "Two-bit transform for binary block motion estimation," IEEE Trans. Circuits Syst. Video Technol., vol.
15, no. 7,pp. 938–946, Jul. 2005.
[8] S. Lee, J.-M. Kim, and S.-I. Chae, "New motion estimation algorithm using adaptively quantized low bit-resolutionimage and its
VLSI architecture for MPEG2 video encoding," IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 6, pp. 734–744, Oct. 1998.
[9] Y. Chan and S. Kung, "Multi-level pixel difference classification methods," in Proc. IEEE Int. Conf. Image Process., vol. 3.
Washington D.C., 1995, pp. 252–255.
[10] V. G. Moshnyaga, "Msb truncation scheme for low-power video processors," in Proc. IEEE Int. Symp. Circuits Syst., vol. 4.
Orlando, FL,1999, pp. 291–294.

Abstract:Skilled navigation in mobile robotics usually re-quires solving two problems pertaining to the knowledge of the position of the robot, and to a motion control strategy. When no prior knowledge of the environment is available, the problem becomes even more challenging, since the robot has to build a map of its surroundings as it moves. These three tasks ought to be solved in conjunction due to their interdependency. The present manuscript proposes a novel mobile robot navigation technique using a customized RFID reader with two receiving antennas mounted on the robot and a number of standard RFID tags attached in the robot's environment to define its path. In here, we show that using the RF signal from the RFID tags as an analog feedback signals can be a promising strategy to navigate a mobile robot within an unknown or uncertain indoor environment. This method is computationally simpler and more cost-effective than many of its counterparts in the state of the art. It is also modular and easy to implement since it is independent of the robot's architecture and its workspace. A set of numerical computer simulations are provided to illustrate the effectiveness of the proposed scheme.
Keywords: RFID, Phase Difference, Fuzzy Logic Con-troller, Robot Sensing and Perception.

[1] W. Gueaieb and M. S. Miah, "Experiments on a novel modular cost-effective RFID-based mobile robot naviga- tion system," in
Proceedings of the IEEE InternationalConference on Systems, Man, and Cybernetics, Montral,Canada, October 7–10 2007, pp.
1658–1663.
[2] J. Borenstein, H. R. Everett, L. Feng, and D. Wehe, "Mo-bile robot positioning: Sensors and techniques," Journal ofRobotic Systems, vol. 14, no. 4, pp. 231–249, April 1997.
[3] L. R. Ojeda, G. D. Cruz, and J. Borenstein, "Current-based slippage detection and odometry correction for mo-bile robots and
planetary rovers," IEEE Transactions onRobotics, vol. 22, no. 2, pp. 366–378, April 2006.
[4] H. Makela and K. Koskinen, "Navigation of outdoor mobile robots using dead reckoning and visually detected landmarks," in Fifth
International Conference on AdvancedRobotics, 1991.
[5] T. D'Orazio, M. Ianigro, E. Stella, F. P. Lovergine, and
[6] J. Borenstein, H. R. Everett, L. Feng, and D. Wehe, "Mo-bile robot positioning: Sensors and techniques," Journal ofRobotic Systems, vol. 14, no. 4, pp. 231–249, April 1997.
[7] L. R. Ojeda, G. D. Cruz, and J. Borenstein, "Current-based slippage detection and odometry correction for mo-bile robots and
planetary rovers," IEEE Transactions onRobotics, vol. 22, no. 2, pp. 366–378, April 2006. Canada, October 7–10 2007, pp. 1658–
1663.

Abstract:A Brain computer interface acts as communication channel between human brain and the computer system. Many challenging disorders and diseases is been faced by the brain in the past, but still that may not be the case in the mere future. Among all the challenges, paralysis stands the most. An advanced brain computer interface called as Nano brain computer interface that could provide a two – way communication between human brain and external system. The major goal of BCI research is to develop a system that allows disabled people to communicate with other persons and help in replacing the destroyed neurons. This paper provides an insight into the future look BCI, its application recent developments and an open discussion in the measures and evaluation of brain activity.
Keywords: Nano BCI, BCI, Invasive and Non – Invasive, EEG, MEG, MRI and FRMI.

[1]" Edlinger, G., Holzner, C., Groenegress, C., Guger, C. and Slater, M. Goal -orientated control with braincomputer interface.
Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, Springer (2009), 732-740.
[2] Pfurtscheller G et al.: Human Brain-Computer Interface, In: Riehle A., Vaadia E., MotorCortex in Voluntary Movements, pp. 367-
401, (2005), CRC press
[3] Dornhege, G, Millán, J., Hinterberger, T., McFarland, D.J., Müller, K.-R. Toward Brain-Computer Interfacing, MIT Press,
Cambridge, MA, 2007.
[4] Allison, B.Z. and Pineda, J.A. Effects of SOA and flash pattern manipulations on ERPs, performance and preference: Implications
for a BCI system. Int. J. of Psychophysiology, 59, 2 (2006), 127-140.
[5] Pfurtscheller et al.: 15 years of BCI research at Graz University of Technology:Current projects, IEEE TNSRE (2006), 14(2):205-
210.