Umer Javed

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An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel.(More)
A technique for magnetic resonance brain image classification using perceptual texture features, fuzzy weighting and support vector machine is proposed. In contrast to existing literature which generally classifies the magnetic resonance brain images into normal and abnormal classes, classification with in the abnormal brain which is relatively hard and(More)
In orthogonal frequency division multiplexing (OFDM) based communication systems multiple carriers having different frequencies are used to transmit different data at the same time. Complex values that describe attenuation on different subcarriers are called channel state information (CSI). This paper describes a novel method of two dimensional (2D)(More)
This paper presents an improved region scalable fitting model that uses fuzzy weighted local features and active contour model for medical image segmentation. Local variance is used with local entropy to extract the regional information from the image which is then processed with the Takagi-Sugeno fuzzy system to compute weights. The use of regional(More)
A fuzzy logic based active contour model for medical image segmentation is proposed. Image local features are incorporated in active contour model. Fuzzy logic is used to assign weights to pixels. Higher weights are assigned to pixels having less entropy and local variance whereas Lower weights are assigned to pixels having high entropy and local variance.(More)
of the Master’s thesis Author: Umer Javed Name of the thesis: Frequency hopping in wireless sensor networks Date: March 2009 Number of pages: 58 Faculty: Faculty of Electronics, Communications and Automation Professorship: S-72 Communications Engineering Supervisor: Professor Riku Jäntti Instructor: M.Sc. Aamir Mahmood Wireless sensor networks (WSNs) are(More)