In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

/pdf/situations-and-attitudes-57f065zg2d.pdf

Situations and Attitudes.

Hawaii International Conference on System Sciences

http://www4.comp.polyu.edu.hk/~cslzhang/paper/CBM-MF.pdf

Retinal vessel extraction by matched filter with first-order derivative of Gaussian

Wireless networks are passing through a transition phase for the past few years now and this transition is giving a way towards the convergence of all IP-based networks to form the Next Generation Networks (NGNs). With the proliferation of these networks in daily life, users' needs are also increasing and service operators are offering different services to satisfy their customers for a better grade of service and an elevated quality of experience (QoE). However, a single operator cannot fulfill the huge demands of the users especially, if a user is nomadic. In nomadism, a user traverses number of available networks that might contain cellular or wireless data networks, usually known as heterogeneous wireless networks. These networks offer various services from email to live video streaming depending upon their capacity and nature. During this traversing procedure, a user switches among different networks to satisfy his/her needs in terms of quality of service. This process is commonly known as a vertical handover or handoff (VHO) due to the involvement of heterogeneous wireless networks in it. An extensive work has been carried out in this field in order to fulfill user demands for better QoS and QoE. In this paper, we give a detailed state-of-the-art of these existing vertical handover decision mechanisms that aim at providing ubiquitous connectivity to the mobile users. We have categorized these vertical handover measurement and decision schemes on the basis of their employed techniques and parameters. Also, we present a comprehensive summary of their advantages and drawbacks. This paper gives its readers an overview of the active research initiatives in the area of handover decision making process in heterogeneous wireless networks and identifies the challenges behind the seamless services provisioning during mobility.

Enabling Vertical Handover Decisions in Heterogeneous Wireless Networks: A State-of-the-Art and A Classification

Review: An overview of vertical handover techniques: Algorithms, protocols and tools

This paper deals with the implementation of a new compression methodology, which uses curvelet coefficients with SPIHT (Set Partitioning In Hierarchical Trees) encoding scheme. The first phase deals with the transformation of the stimulus image into the curvelet coefficients. The curvelet transform is a new family of multi-scale representation containing the information about the scale and location parameters. Unlike wavelets, it also contains the directional parameters. The orientation selectivity behavior and anisotropic nature of the curvelet transform helps to represent suitably the objects with curves and handles other two-dimensional singularities better than wavelets, which makes it a more proficient transformation for image compression application. During the second phase, a threshold-based selection mechanism has been developed to get prominent coefficients out of different scales. Final phase deals with the application of lossy SPIHT encoding technique on selected significant coefficients. SPIHT exploits the multi-scale nature of curvelet transform and removes the statistical and subjective redundancies. The empirical results on standard test images provide higher PSNR than some of the previous approaches, which strengthen the idea of using curvelet transform instead of wavelet transform in order to get lesser bits to represent more prominent features.

Curvelet-Based Image Compression with SPIHT

In order to sustain connection and service in heterogeneous wireless environment, vertical handover is necessary. Now-a-days research focus is to optimize vertical handover so that optimized and application specific (QoS based) handover can be made possible. In this paper, an intelligent approach to vertical handover is proposed. The fuzzy logic based decision making is used to select among the available networks. Triggers and events are used to generate the input which is sent to fuzzy expert system. In this way, the intelligent selection of networks can optimize vertical handover and improve QoS of real-time application running on mobile device which is roaming around heterogeneous wireless networks.

Intelligent vertical handover decision model to improve QoS

Open source projects for example Eclipse and Fire fox have open source bug repositories. User reports bugs to these repositories. Users of these repositories are usually non-technical and cannot assign correct class to these bugs. Triaging of bugs, to developer, to fix them is a tedious and time consuming task. Developers are usually expert in particular areas. For example, few developers are expert in GUI and others are in java functionality. Assigning a particular bug to relevant developer could save time and would help to maintain the interest level of developers by assigning bugs according to their interest. However, assigning right bug to right developer is quite difficult for triager without knowing the actual class, the bug belongs to. In this research, we have classified the bugs in different labels on the basis of summary of the bug. Multinomial Naive Bayes text classifier is used for classification purpose. For feature selection, Chi-Square and TFIDF algorithms were used. Using Naive Bayes and Chi-square, we get average of 83 % accuracy.

An Automated Approach for Software Bug Classification

This research paper deals with the implementation of face recognition using neural network (recognition classifier) on lowresolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses backpropagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image. Keywords—Average filtering, Bicubic Interpolation, Neurons, vectorization.

Low Resolution Single Neural Network Based Face Recognition

The categorization of retinal vessels morphological features and the investigation of their branching patterns are used in the process for automated diagnosis and screening of ophthalmologic diseases. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. In this paper, the performance comparison of two retinal vessel segmentation approaches based on combination of multi scale morphological reconstruction and morphological bit plane slicing with the vessel centerlines is presented. The segmentation accuracy and the processing time are taken as the performance criteria. These approaches are tested on two publicly available databases and results demonstrate that morphological bit plane slicing outperforms other approach in respect of processing time without a significant degradation of sensitivity and specificity.

Muhammad Younus Javed

Papers

Curvelet-Based Image Compression with SPIHT

Intelligent vertical handover decision model to improve QoS

An Automated Approach for Software Bug Classification

Low Resolution Single Neural Network Based Face Recognition

Evaluation of retinal vessel segmentation methodologies based on combination of vessel centerlines and morphological processing