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The demandforonlinemultimediaservicesiseverincreasing.Thereisaneedforband-width reduction(compression),whileatthesametimeforincreasedsecurity.Traditional cryptographicalgorithms/systemsfordatasecurityareoftennotfastenoughtoprocessthe vast amountsofdatageneratedbythemultimediaapplicationstomeetthereal-timecon-straints.Selectiveencryptionisaschemethatisoftenusedformultimediacontentpro-tection.Itinvolvesencryptingonlyaportionofthedatatoreducecomputationalcomplex-ity(the amountofdatatoencrypt)whilepreservingasucientlevelofsecurity.Atoolthat is sometimeusedinaselectiveencryptionschemeisWaveletTreeShuing.Wewillshow that WaveletTreeshuingwhenusedwithourmechanismscanconstructasecureselective encryption scheme(constructiveaspects),whereaswhenitisusedasasinglemechanism within aselectiveencryptionscheme,theresultcanbeinsecure(destructiveaspects).

Recognition of Arabic handwriting characters is a dicult task due to similar appearance of some dierent characters. However, the selection of the method for feature extraction remains the most important step for achieving high recognition accuracy. The purpose of this paper is to compare the eectiveness of Discrete Cosine Transform and Discrete Wavelet transform to capture discriminative features of Arabic handwritten characters. A new database containing 5600 characters covering all shapes of Arabic handwriting charac-ters has also developed for the purpose of the analysis. The coecients of both techniques have been used for classication based on a Articial Neural Network implementation. The results have been analysed and the nding have demonstrated that a Discrete Cosine Trans-form based feature extraction yields a superior recognition than its counterpart.

In this paper, we present a new form of Fourier series named Kernel Fourier series (KFS) which produces time-frequency coefficients similar to wavelet. Both continuous and discrete forms of KFS are presented together with inverse KFS formulated to perform signal approximation. As KFS coefficients are dependent upon selection of kernel function, it can be used in different applications. Results from KFS test on feature extraction, signal analysis and signal estimation are presented and discussed.

In the past decade rapid advances of microarray technologies have made it possible to monitor the expression profiles of thousands of genes under various experimental conditions. The requirements for methods to handle such amounts of data have arisen. These massive source of information extracted from the genome project contain the keys to address fundamental problems relating to the prevention and cure of diseases, biological evolution mechanisms and the understanding of particular functional elements in the human organism. The knowledge of the coding sequences of virtually every gene in an organism is an exciting opportunity to develop methods to study the role of a gene in a specific organism or biological function. One of such methods consists of the monitoring of the level of expression of a gene. It has been shown that specific patterns of gene expression occur during different biological states such as cell development and during normal physiological responses in tissues and cells. There are many data mining techniques which help to analyze the gene expression data. This paper discusses some of these methods adopted by different researchers.

We present in this paper a system of Amazigh handwriting recognition based on horizontal and vertical centerline of the character. After the image preprocessing, the text is segmented into lines and then into characters. The positions of the vertical and horizontal centerline of the character are used to obtain a set of independent and dependent features to those lines. These features are related to the densities of pixels and are extracted on binary images of characters using the sliding window technique. Finally, a multilayer perceptron is used for character classification. The system was tested on two bases of the Amazigh characters: on a printed database of Amazigh characters and on another one for handwritten characters created locally. The correct average recognition rate obtained using 10-cross-validation was 99.28 % for the 19437 Amazigh printed characters and 96.32% for the 20150 Amazigh handwritten characters.

In this present study, A356.2 Al/Rice hush ash (RHA) metal matrix composites (MMCs) were fabricated by vortex method. Different weight fractions of reinforcement were used to fabricate the composites. Scanning electron microscope equipped with energy dispersive X-ray analyzer is used for micro structural characterization. The properties like density, hardness, and ultimate tensile strength were investigated. The results of micro hardness revealed higher hardness of the matrix material in the immediate vicinity of rice husk ash particle. The addition of rice husk ash particles reduces the density of composite while increasing some of their mechanical properties.

The emerging need for high data rate and coding gain has raised considerable interest in coded modulation schemes. Trellis coded modulation (TCM) is a bandwidth efficient transmission scheme that can achieves high coding gain by integrating coding and modulation. Space-time trellis coding combines channel coding with multiple transmit and multiple receive antennas to achieve bandwidth and power efficient high data rate transmission over fading channels. This paper presents the design of hyper phase shift keying space-time trellis coded modulation (HPSK ST-TCM) for improving the bandwidth efficiency of wireless networks. HPSK offers an improvement in error performance comparable with other modulation schemes for the same energy per bit to noise power spectral density ratio.

Associative classification is a supervised classification approach, integrating association mining and classification. Several studies in data mining have shown that associative classification achieves higher classification accuracy than do traditional classification techniques. However, the associative classification suffers from a major drawback: The huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we have proposed an associative classification method that reduces associative classification rules without jeopardizing the classification accuracy. Moreover, we will introduce in this paper two different strategies to classify new instances based on some interestingness measures that arise from data mining literature in order to select the best rules during classification. A detailed description of this method is presented in this paper, as well as the experimentation study on 12 benchmark data sets proving that our approach is highly competitive in terms of accuracy in comparison with popular classification approaches.

Wireless communication is known as key to global development and has become main stream of our daily business. Protecting information and communication speed is an issue which we need to focus on the wireless networks. Universal mobile telecommunication system (UMTS) is evolution of third generation mobile communication system. It was built on the success of global system for mobile communication (GSM), UMTS security is also built upon GSM, so there is lot of security challenges such as the user identity, confidentiality. The international mobile subscriber identity travels through radio interface in free text format without any encryption. Due to this, the user identity and location can be theft which is serious problem. In this paper, we have proposed and emulated a model which protects user identity through IMSI hashing, which will provide anonymity to user. We have compared different hashing techniques and the results are given at the end.

As a data management technique, indexing aims to judicious organization of data allowing efficient query processing. In the context of location based services (LBSs), indexing techniques are, substantially, affected by location dependency and modes of data access. This paper focuses on the processing of location dependent queries (nearest neighbors and range queries) based on indexing structures with respect to the context of LBSs. In fact, traditional indexes (R-trees, kd-trees …) adopt either overlapped partitioning schemes or backtracked search algorithms. These features increase response times for an in-memory index within an on-demand data access mode. They, also, make such indexes impractical for a broadcast data access mode. As a solution, we propose a novel index called VNR-tree (Voronoi-Neighboring Regions tree), based on the Delaunay Triangulation. VNR-tree adopts a non-overlapping partitioning scheme, supports backtracking-free search algorithms and generates a special clustering of the Voronoi-neighboring regions. This clustering is adequate to the processing of several types of location dependent queries based on the exploration of the mobile client’s Voronoi-neighborhoods. We conduct various experiments to compare VNR-tree with R*-tree as in-memory indexes in the on-demand data access mode. Main results show that VNR-tree outperforms R*-tree significantly.

The energy resources being scarce, their efficient utilization and over use remained a heated topic on International forums. The average energy consumption per person per annum is expected to cross the value of 2000 kilograms of energy/fuel resources figure. The gauged estimations are limited. There is no mechanism for regular checks on idle losses. The estimation/ predication of consumptions have been neglected for years, resulting in huge financial losses. In view of the arising problems, the paper proposes a model design based on Image Processing, Wireless Sensors Networks and Artificial Neural Networks. The model has low design cost and is ideal for miscellaneous gauged analog estimations in close-spaced industrial environments.

The face overshadowing effect (FOE) has been noted in cases where recognition of voices is impaired if they are presented simultaneous to a face at encoding. The current study investigated the effect of facial concealment (with and without wearing a balaclava) and emotionality of vocal tone on the face overshadowing effect in voice identification. It was predicted that the FOE would be reduced in the case of presentation of a concealed face along with voices, as the lack of facial feature information would result in greater attention being paid to the voice. It was further anticipated that angry voices would attract more attention and result in better voice recognition and reductions in the FOE than neutral voices, as hostile voices represent a level of threat that captures attention analogous to the weapon focus effect in eyewitness memory. Results replicated the FOE in a voice plus face video presentation but, contrary to expectations; a concealed face presentation also demonstrated a FOE, with highest accuracy of voice identification in the voice only condition. Angry vocal tone had a slight tendency to result in better recognition of voices across groups and somewhat improve performance in the visual conditions. It was concluded that voice identification is as fallible and prone to error as eyewitness identification but that conditions where the voice is made salient and visual information is absent result in higher accuracy. Implications for the criminal justice system are discussed.