Papers by Dr. Nemi Chand Barwar
2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)
2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)
IEEE Access
Small file processing in Hadoop is one of the challenging task. The performance of the Hadoop is ... more Small file processing in Hadoop is one of the challenging task. The performance of the Hadoop is quite good when dealing with large files because they require lesser metadata and consume less memory. But while dealing with enormous amount of small files, metadata grows linearly and Name Node memory gets overloaded hence overall performance of the Hadoop degrades. This paper presents a dual merge technique HB-EHA (Hash Based-Extended Hadoop Archive), that will resolve the small file issue of Hadoop and provide an excellent solution for massive small files that are generated in the health care management applications. The proposed technique merges the small files using two-level compaction, therefore, the size of metadata at the name node gets reduced and less memory will be used. The indexing will be carried out over the archives and files can be accessed after merging in real-time. Index files in the proposed approach can read partially that improves the name node memory usage and also offers the file appending capability in the existing archive. The proposed technique first creates Hadoop archive from the small files and then uses two special hash functions i.e. SSHF (Scalable-Splittable Hash Function) and HT-MMPHF (Hollow Trie Monotone Minimal Perfect Hash Function), SSHF is used to dynamically distribute the archives meta-data to the associated slave index files, and these slave index files will be further written to the final index files, the order of the meta-data in final index file will be preserved by the HT-MMPHF. The evaluation outcome exhibit that the proposed technique is 13% & 17% faster than HDFS with caching enabled and disabled respectively, and 38% & 47% faster than the HAR with caching and without caching, respectively. While comparing with the map file, the proposed technique is 28 & 35 times faster with caching and without caching, respectively. HB-EHA is a maximum of 40% & 28% faster than the HBAF with and without caching, respectively.
Proceedings of the International Congress on Information and Communication Technology, 2016
This paper explores mesh-based clustering for different start video streaming in P2P systems and ... more This paper explores mesh-based clustering for different start video streaming in P2P systems and estimates the performance of noncluster and clustered models. These models are based on mesh-based topology of P2P streaming consisting of peer join/leave. A new approach by way of “clustering” peers is proposed to tackle P2P VOD streaming. The proposed models were simulated and verified using OMNET++ V.4. A clustered model for video streaming is proposed and simulated to consider the performance of network under startup buffering for frame loss, startup delay, and end-to-end delay parameters. The results obtained from simulations are compared for both noncluster versus cluster models. The results show the impact of startup buffering on both models is also bounded due to time limits of release buffer and playing buffer under the proposed models, which causes reduction in wait time to view video improving the overall VOD system performance. The proposed model is also able to provide missing parts (of video) to late viewers, which gives the facilities of both live and stored streaming from user’s point of view, therefore it serves to be functionally hybrid and is most useful.
Volume 4, Issue 4, July – August 2015 Page 183 Abstract This paper explores mesh based clustering... more Volume 4, Issue 4, July – August 2015 Page 183 Abstract This paper explores mesh based clustering for different start video streaming in P2P systems and estimates the performance of non cluster and clustered models. These models are based on mesh based topology of P2P streaming consisting of peer join/leave. A new approach by way of “clustering” peers has been proposed to tackle P2P VOD streaming. The proposed models were simulated and verified using OMNET++ V.4. A mathematical model has been formulated to evaluate the performance metrics such as buffer size, buffer exchange period over frame loss ratio, startup delay and end to end delay. The results show that(i) unpunctual viewers can be enabled view missing part of video from beginning during live streaming, (ii)the associated buffer in conjunction with join/release time provides constant buffer size and thus ability to provide better service to viewers,(iii)Starting and play out delay gets bounded as the number of peers predefin...
Now a days the driver drowsiness is leading cause for major accidents. The regular monitoring of ... more Now a days the driver drowsiness is leading cause for major accidents. The regular monitoring of drivers drowsiness is one of the best solution in order to reduce the accidents caused by drowsiness. In order to detect and remove this cause of road accident many driver fatigue detection methods have been proposed.Consequently, it is very necessary to design a road accidents prevention system by detecting driver’s drowsiness, which determines the level of driver inattentiveness and give a warning when an impending danger exists. In this paper, a simulation and analysis of fusion method has done. This method of eye blinking and yawning detection is based on the changes in the mouth geometric features. The programming for this is done in OpenCV using the Haarcascade library for the detection of facial features and Active Contour Method for the activity of lips . Keywords— Driver Face Detection, Driver Eye Blink Detection, Driver Yawning Detection, Driver Drowsiness, Real time system, RO...
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Papers by Dr. Nemi Chand Barwar