CLASSIFICATION OF EEG SIGNALS FOR LIMB MOVEMENTS AND IMAGINARY TASKS USING SOFT COMPUTING TECHNIQUES

R.Kottaimalai ¹, J.Goldwyn Sudhakar ², T.ElizabethRani^3
1 Assistant Professor. Dept of ECE ,Sree Sowdambika College of Engineering, Aruppukottai, India
² Assistant. Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
³ Asstistant Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.

     The people who have lost movement or language function due to traffic accidents or neuromuscular disease, necessitating large numbers of care assistants to support severely disable patients who have almost no voluntary control of body movements. Brain-Computer interface (BCI) is a direct communication pathway between a human brain and an external device. Such systems permit people to communicate through direct measurements of brain activity, without requiring any movement. The task of the BCI is to identify and predict behaviorally induced changes or cognitive states in a user‘s brain signals. Brain signals are recorded from electrodes placed on the scalp. In this report the Soft Computing technique like ANFIS was applied on the EEG signals which are taken during six limb movement tasks and also during six imaginary tasks, and the data are taken for classification. From the test results it is observed that the probability of correct classification has been increased for the limb movements by using ANFIS and for feature extraction we use wavelet transform.

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PERFORMANCE OF LOW CALCIUM FLY ASH BASED GEOPOLYMER CONCRETE STRUCTURAL ELEMENTS WITH M-SAND, METAKOLIN & POLYPROPYLENE FIBRE

R.Don isaac¹, Dr.C. Selvamony², A. Maria rajesh³,M.Shaju pragash^4
1 Research Scholar,Anna University-Chennai, Tamilnadu, India
² Professor, Department of Civil Engineering, Sun College of Engineering & Technology Tamilnadu,
³ Professor, Department of Civil Engineering, Arunachala College of Engineering, Tamilnadu,
⁴ Assistant Professor, Ponjesly Engineering College, Nagercoil,Tamilnadu

     Concrete is the fundamental material in civil engineering industry. This project deals with the effect of geopolymer concrete by replacing cement. Cement is the important ingredient in the conventional concrete is the Portland cement. The production of cement in the factory emits enormous amount of carbon-dioxide which pollutes the environment, to prevent this low calcium fly ash based geopolymer concrete is used. Here geopolymers cement is used which is obtained from the reaction of low calcium fly ash with the alkaline solution (i.e.) sodium hydroxide and sodium silicate. It does not pollute the environment and so it is eco-friendly. Metakolin is obtained by Thermal treatment of China clay. It increases the long term strength, durability and resistance to attack in peaty/acidic environments. Fiber reinforced concrete (FRC) may be defined as a composite materials made with Portland cement, aggregate, and incorporating discrete discontinuous fibers. Polypropylene fibers increases the durability of geopolymer concrete. To enhance the curing Metakolin is added. It is proposed to determine and compare the differences in properties of geopolymer concrete with Metakolin and Polypropylene fiber. The investigation are to be carried out using several tests which include workability test, impact value test, sieve analysis, specific gravity test, compression test, split tensional strength and flexural strength.

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CONTENT BASED IMAGE RETRIEVAL FOR MRI BRAIN IMAGES USING SVM CLASSIFIER

E. Sandhiya¹, Mr. G. Raghuraman^2
1Master of Computer Science and Engineering Department of Computer Science and Engineering SSN College of Engineering,Chennai, India.
²Assistant Professor Department of Computer Science and Engineering SSN College Engineering,Chennai, India.

     Content-based image retrieval might help the radiologists throughout medical diagnosis involving human brain tumor by simply searching and retrieving the similar images through a medical image repository. It makes use of image features, such as color, shape and texture, to index images with minimal human intervention. Among many retrieval features associated with CBIR, texture retrieval is one of the most powerful. As a way to tackle this concern , proposed a new method for medical image retrieval using a supervised classifier which concentrates on extracted features. We have obtained the texture based features such as GLCM (Gray Level Co-occurrence Matrix) of MRI images that contains information about the position of pixels having similar gray level values. SVM classifier is performed to classify the affected images into two categories such as normal and abnormal. The query image is classified by the classifier to a particular class and the relevant images are retrieved from the database. This will help the physician or radiologist to perform the diagnosis in a faster and non invasive way and help to increase the response time and also gives the accuracy of retrieval results.

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SVM BASED INTELLIGENT SYSTEM FOR CLASSIFICATION OF MRI BRAIN IMAGES

S.Sakkaravarthi 1, Dr.K.G.Srinivasagan 2, S.MuthuKumar 3
1 Assistant Professor, Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.
2 Professor & Head - Department of CSE-PG, National Engineering College, Aruppukottai, Tamilnadu, India.
3 Professor & Head - Department of CSE, Sree Sowdambika College of Engineering, Aruppukottai, Tamilnadu, India.

     

Medical imaging plays a vital role in diagnosing the diseases as well as the study of human anatomy and physiology. MRI is an advanced medical imaging technique especially used for capturing the human brain. The manual interpretation of brain tumor slices based on visual examination by physician may lead to missing diagnosis and time consuming when a large number of MRI brain images are analyzed. To avoid human based diagnostic error, automated brain tumor classification is preferred. For automated brain tumor classification, various techniques are available. Those techniques suffer due to misclassification which is unfavoured by physicians. Still the problem is open and research is going to promote better result in very fast manner. Still the problem is open and research is going to promote better result in very fast manner. Automated MRI Brain image classification using Support Vector Machine classifier is proposed to classify brain image into normal or abnormal. Brain abnormality can be further classified into benign or malignant. In this paper, MRI brain image is pre-processed using median filter. Then, statistical based texture features are extracted from Gray Level Co-occurrence Matrix (GLCM) of an input brain image. After feature extraction, relevant features are obtained using forward feature selection technique to reduce the feature space. The selected features are given as input to Support Vector Machine classifier. Finally, Support vector machine classifier is utilized to perform two functions. The first is to differentiate between normal and abnormal. The second function is to classify the type of abnormality in benign or malignant tumor. 

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A MODIFIED ACO USING ROULETTE WHEEL SELECTION FOR SOLVING COMBINATORIAL OPTIMIZATION PROBLEM

Sathya V
PG Scholar, Dept of CSE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India .

  

   In Cloud computing, private cloud is a branch where resource sharing is becoming more popular now days. The importance of resource sharing has lead to the development of many algorithms. The existing works in the field of resource sharing used many optimization techniques. The two techniques namely Service Composition Optimal Selection (SCOS) and Optimal Allocation of Computing Resources (OACR) are combined and named as Dual Scheduling of Cloud Services and Computing Resources (DS-CSCR). This dual scheduling is not suitable for large scale problems and hence, a new Ranking Chaos Optimization (RCO) was introduced. RCO executes with low time consumption. But the design of heuristic function is complex and hence, a Modified Adaptive Chaos Optimization (MACO) technique with Roulette Wheel Selection (RWS), is used for allocating tasks to the resources. Finally the comparison of the proposed work with the existing RCO proves that, the resource allocation can be made with low time consumption and the stability of the virtual machine increases. 

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LOW POWER AND HIGH PERFORMANCE ADDRESS GENERATOR FOR WIMAX DEINTERLEAVER

D.Poornima Devi
PG Scholar, Department of Electronics and Communication(VLSI), SriVidya college of Engineering and technology, Virudhunagar, Tamilnadu, India.

     The aim is to generate the address generation circuitry of Deinterleaver used in the WiMAX transreceiver using the Xilinx Field Programmable Gate Array (FPGA). The floor function associated with the implementation of FPGA is very difficult in IEEE 802.16e standard. So we eliminate the requirement of floor function by using a simple mathematical algorithm. Some modulations like QPSK, 16-QAM and 64-QAM along with its code rates make our approach to be novel and high efficient. By using the majority logic circuit and biorthogonal decoding it can be used to reduce power consumption and latency compared to the previous paper. Power consumption can be reduced up to 34mw and latency can be reduced up to 1.206ns.

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FREE ENERGY ELECTRIC BI-CYCLE

Jothilingam S1, Pavunraj P2
1 UG scholar, Department of Electrical and Electronics, Vickram College of Engineering, Madurai, Tamil Nadu, India.
2 Faculty of Electrical and Electronics, Vickram College of Engineering, Madurai, Tamil Nadu, India.

     The main work deals with free energy concept. The objective is to produce energy and also utilize it without any external energy source or external charging. As we are using two different modes to power as a hybrid model. The next source is a dc generator (24 volt) which is arranged in such a way that it produces energy when the front wheel of the cycle starts rotating. Finally we have used a dc motor (12 volt) to drive the cycle. There is lead acid battery (12 volt) used for storing the power produced by the generator and the stored energy is utilized by the motor to drive the system simultaneously. This paper describes the process of planning, designing, and testing a hybrid electric bicycle. It provides a lot of detail into the challenges of modifying an existing mechanical system to one that is based on both human propulsion as well as a set of electro-mechanical interfaces that provide assists. Through designing an electro-mechanical system, with various non-human inputs and feedback channels, a major challenge was centralizing the control of the system. After establishing criteria for speed, control, efficiency, and weight, we began a process of selecting parts and developing models for how the overall system including the rider could be integrated in a way that is both safe, and easy to use. 

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