RC Thool | S.G.G.S.I.E&T Nanded (original) (raw)

Papers by RC Thool

... Page 3. Holambe AN, Thool RC, Shinde UB and Holambe SN ... [18] Wakabayashi T., Tsuruoka S., ... more ... Page 3. Holambe AN, Thool RC, Shinde UB and Holambe SN ... [18] Wakabayashi T., Tsuruoka S., Kimura F. and Miyake Y. (1995) System and Computers in Japan, 26 (8), 35-44. [19] Kimura F., Miyake Y., Shridhar M. (1994) Proc. Of 4Th IWFHR. ...

IJCA Proceedings on National Conference on Advancement in Electronics & Telecommunication Engineering, Dec 5, 2012

The accelerated progress in wireless technologies and the increasing growth of the Internet, wire... more The accelerated progress in wireless technologies and the increasing growth of the Internet, wireless networks, especially Wireless Mesh Networks (WMNs) are going through an important evolution .In a WMN, designing efficient and scalable multicast protocol still a major task for researchers. In this work, we propose a protocol named MESHSPT (Shortest Path Tree algorithm for wireless Mesh network) for efficient and scalable multicast routing inside the mesh backbone of a WMN. The MESHSPT protocol builds source-based trees based on the network topology. It prevents flooding and employs an effective mechanism to prevent the implosion and exposure problems when a tree is constructed and when nodes join and leave. Our simulation results show that the MESHSPT protocol outperforms existing protocols such as ODMRP (On-Demand Multicast Routing Protocol), MNT (Minimum Number of Transmissions) in terms of throughput, and end-to-end delay.

In recent years, object detection and tracking has been a dynamic research area. Rapid developmen... more In recent years, object detection and tracking has been a dynamic research area. Rapid
development of the multimedia and the associated technologies urge the processing of a huge
database of video clips. The processing efficiency lies on the search methodologies utilised in the
video processing system. Usage of unsuitable search methodologies may make the processing
system ineffective. Hence, effective object detection and tracking system is an essential criterion
for searching relevant videos from a huge collection of videos. This paper proposes a unique
object detection and tracking system where video segmentation, feature extraction, object
detection and tracking are combined perfectly using various features. Initially, the database video
clips are segmented into different shots before performing the feature extraction process. The
proposed system consists of two stages, namely, feature extraction and tracking of object in the
video clips. In the feature extraction stage, firstly, colour feature is extracted based on colour
quantisation. Next, edge density feature is extracted for the objects present in the query video.
Then, the texture feature is extracted based on LGXP technique. Finally, based on these feature
extracted, the object will be detected and the detected objects will be tracked by utilising both
forward and backward tracking technique. The proposed methodology proved to be more
effective and accurate in object detection and tracking.

System in this paper is designed and implemented using Visual C++ software with Intel's OpenCV vi... more System in this paper is designed and implemented using Visual C++ software with Intel's OpenCV video stream processing system to realize the real-time automatic vehicle detection and vehicle counting. Expressways, highways and roads are getting overcrowded due to increase in number of vehicles. Vehicle detection, tracking, classification and counting is very important for military, civilian and government applications, such as highway monitoring, traffic planning, toll collection and traffic flow. For the traffic management, vehicles detection is the critical step. Computer Vision based techniques are more suitable because these systems do not disturb traffic while installation and they are easy to modify. In this paper we present inexpensive, portable and Computer Vision based system for moving vehicle detection and counting. Image from video sequence are taken to detect moving vehicles, so that background is extracted from the images. The extracted background is used in subsequent analysis to detect and classify moving vehicles as light vehicles, heavy vehicles and motorcycle. The system is implemented using OpenCV image development kits and experimental results are demonstrated from real-time video taken from single camera. We tested this system on a laptop powered by an Intel Core Duo (1.83 GHZ) CPU and 2GB RAM. This highway traffic counting process has been developed by background subtraction; image filtering and segmentation methods are used. This system is also capable of counting moving vehicles from prerecorded videos.

IJCA Proceedings on National Conference on Advancement in Electronics & Telecommunication Engineering, Dec 5, 2012