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Hari Krishnan, P.
- An Efficient Clustering Algorithm for MRI Brain Image Segmentation
Abstract Views :159 |
PDF Views:2
Authors
Affiliations
1 Anna University of Technology, Coimbatore, IN
2 Sri Sakthi Institute of Engineering and Technology, Coimbatore, IN
1 Anna University of Technology, Coimbatore, IN
2 Sri Sakthi Institute of Engineering and Technology, Coimbatore, IN
Source
Digital Image Processing, Vol 3, No 19 (2011), Pagination: 1212-1216Abstract
Image processing plays an important role in medical field because of its capability. Particularly, image segmentation offer several guides in medical field for analyzing the captured image. Usually, the medical images are captured via different medical image acquisition techniques. The captured image may be affected by noise because of some faults in the capturing devise; this will leads to false diagnosis. This paper focuses on enhancing the captured brain image by using image segmentation technique. Usually, brain image is captured using Magnetic Resonance Imaging (MRI) technique. The captured brain image will have high amount of noise or distortion, this noise must be removed before it is used for diagnosis purpose. Brain segmentation is widely applied for removing those noises to produce the clear image. The segmentation can be achieved with the help of clustering techniques. The widely used clustering technique for brain image segmentation technique is Fuzzy C-Means (FCM) clustering. But FCM will result in poor segmentation when more edge regions are involved. To overcome this problem, Fuzzy Possibilistic C-Means Algorithm (FPCM) is introduces. Even FPCM will result in poor segmentation when more noise are involved. To overcome all these problems, a Modified Fuzzy Possibilistic C-Means Algorithm (MFPCM) is proposed in this paper. The experimental result show that the segmentation resulted for the proposed technique is better when compared to the existing methods.Keywords
Brain Image Segmentation, Fuzzy C-Means, Fuzzy Possibilistic C-Means, Modified Fuzzy Possibilistic C-Means.
- Smart Collision Recorder with Life Saver System in Car Using LabVIEW
Abstract Views :164 |
PDF Views:4
Authors
Affiliations
1 Dept. of Electrical and Electronics Engineering, Anna University of Technology, Coimbatore, IN
1 Dept. of Electrical and Electronics Engineering, Anna University of Technology, Coimbatore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 3 (2010), Pagination:Abstract
Collision recorder in the car that makes the concern authority to review the status and condition of the car before collision. Various sensors are used to measure internal and external parameters in the car. The output of various sensors is digitized and its values are stored in temporary memory. Dedicated Short Range Communication in adapted to provide highly interoperable communication to transmit data. During collision, collision switch opens, the data stored in temporary memory of the cars transmitted between two cars through Dedicated Short Range Communication, the information’s are stored in permanent memory. Recorded data can be used for future reference.If Occupant in conscious, occupant reset the user tag to reset the life saver system. If user tag is set then information regarding place of collision is sent to nearby emergency station is send through GSM/GPS modem.Keywords
Sensors, DSRC, Collision Switch, Permanent Memory, GSM, GPS.- Effects of Adaption Gain in Direct Model Reference Adaptive Control for a Single Conical Tank System
Abstract Views :201 |
PDF Views:3
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore, Tamil Nadu, IN
Source
Digital Signal Processing, Vol 10, No 5 (2018), Pagination: 77-82Abstract
This paper implement and analysis the effects of direct Model Reference Adaptive Control (MRAC) for a single conical tank system. Classical control strategies fail due to its high nonlinearity, continuously varying area, and loading effects of a single conical tank system. To overcome the shortfalls, this article proposed the MRAC scheme for controlling the level of a single conical tank process. The MRAC can be regarded as an adaptive servo system, in which the desired performance is expressed in terms of a reference model that provides the anticipated response to a command signal. The selection of adaption gain is the most important part in the design of MRAC, particularly in real-time applications. This article analyses the effect of adaption gain from the lower value to upper value. The simulation result shows the higher values of adaption gain improves the system performances and reduces the error when compared with the lower values of adaption gain. To improving the stability and reliability of adaptive control, improved MRAC scheme is proposed with PID controller. This improved version has two loops such as inner loop and outer loop. Inner loop act as a normal feedback with PID controller and outer loop act as an adaptive control for parameter adjustment. This proposed approach shows, better performance than normal MRAC. The entire analyses are simulated in MATLAB R2013a environment.
Keywords
Model Reference Adaptive Control, Adaption Gain, PID Controller, Single Conical Tank System, Reference Model, MIT Rule.References
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