seminars: cse ieee projects

IEEE Computer Science Projects

RESEQUENCING ANALYSIS OF STOP-AND-WAIT ARQ FOR PARALLEL MULTICHANNEL COMMUNICATIONS:--DOTNET--2009

Abstract—In this paper, we consider a multichannel data communication system in which the stop-and-wait automatic-repeat request protocol for parallel channels with an in-sequence delivery guarantee (MSW-ARQ-inS) is used for error control. We evaluate the resequencing delay and the resequencing buffer occupancy, respectively. Under the assumption that all channels have the same transmission rate but possibly different time-invariant error rates, we derive the probability generating function of the resequencing buffer occupancy and the probability mass function of the resequencing delay. Then, by assuming the Gilbert–Elliott model for each channel, we extend our analysis to time-varying channels. Through examples, we compute the probability mass functions of the resequencing buffer occupancy and the resequencing delay for time-invariant channels. From numerical and simulation results, we analyze trends in the mean resequencing buffer occupancy and the mean resequencing delay as functions of system parameters. We expect that the modeling technique and analytical approach used in this paper can be applied to the performance evaluation of other ARQ protocols (e.g., the selective-repeat ARQ) over multiple time-varying channels. Index Terms—In-sequence delivery, modeling and performance, multichannel data communications, resequencing buffer occupancy, resequencing delay, SW-ARQ.

COLLUSIVE PIRACY PREVENTION IN P2P CONTENT DELIVERY NETWORKS:--J2EE--2009

Collusive piracy is the main source of intellectual property violations within the boundary of a P2P network. Paid clients (colluders) may illegally share copyrighted content files with unpaid clients (pirates). Such online piracy has hindered the use of open P2P networks for commercial content delivery. We propose a proactive content poisoning scheme to stop colluders and pirates from alleged copyright infringements in P2P file sharing. The basic idea is to detect pirates timely with identity-based signatures and time stamped tokens. The scheme stops collusive piracy without hurting legitimate P2P clients by targeting poisoning on detected violators, exclusively. We developed a new peer authorization protocol (PAP) to distinguish pirates from legitimate clients. Detected pirates will receive poisoned chunks in their repeated attempts. Pirates are thus severely penalized with no chance to download successfully in tolerable time. Based on simulation results, we find 99.9 percent prevention rate in Gnutella, KaZaA, and Freenet. We achieved 85-98 percent prevention rate on eMule, eDonkey, Morpheus, etc. The scheme is shown less effective in protecting some poison-resilient networks like BitTorrent and Azureus. Our work opens up the low-cost P2P technology for copyrighted content delivery. The advantage lies mainly in minimum delivery cost, higher content availability, and copyright compliance in exploring P2P network resources.

NOISE REDUCTION BY FUZZY IMAGE FILTERING:--JAVA--2006

A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.

PATTERN ANALYSIS AND MACHINE INTELLIGENCE

FACE RECOGNITION USING LAPLACIAN FACES:--JAVA--2005

Abstract: The face recognition is a fairly controversial subject right now. A system such as this can recognize and track dangerous criminals and terrorists in a crowd, but some contend that it is an extreme invasion of privacy. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be put in place to allow computer access or access to a specific room using face recognition. Another possible application would be to integrate this technology into an artificial intelligence system for more realistic interaction with humans. We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacian faces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Principal Component Analysis (PCA) is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables. The jobs which PCA can do are prediction, redundancy removal, feature extraction, data compression, etc. Because PCA is a known powerful technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D face image into the compact principal components of the feature space. This is called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of fingerprint images (vectors).

INFORMATION TECHNOLOGY IN BIOMEDICINE

ENHANCING PRIVACY AND AUTHORIZATION CONTROL SCALABILITY IN THE GRID THROUGH ONTOLOGIES:--JAVA--2009

The use of data Grids for sharing relevant data has proven to be successful in many research disciplines. However, the use of these environments when personal data are involved (such as in health) is reduced due to its lack of trust. There are many approaches that provide encrypted storages and key shares to prevent the access from unauthorized users. However, these approaches are additional layers that should be managed along with the authorization policies. We present in this paper a privacy-enhancing technique that uses encryption and relates to the structure of the data and their organizations, providing a natural way to propagate authorization and also a framework that fits with many use cases. The paper describes the architecture and processes, and also shows results obtained in a medical imaging platform.