Low-power design has recently become very important especially in nanoelectronic VLSI circuits and systems. Functioning of circuits at ultra-low voltages leads to lower power consumption per operation. An efficient method is to separate the logic blocks based on their performance requirement and applying a specific supply voltage for each block. In order to prevent an enormous static current in these multi-VDD circuits, voltage level converters are essential. This study presents an energy-efficient and robust single-supply level converter (SSLC) based on multi-threshold carbon nanotube FETs (CNTFETs). Unique characteristics of the CNTFET device and transistor stacking are utilized suitably to reduce the power and energy consumption of the proposed LC. The results of the extensive simulations, conducted using 32nm CNTFET technology of Stanford University indicate the superiority of the proposed design in terms energy-efficiency and robustness to process, voltage and temperature variations, as compared to the other conventional and state-of-the-art LC circuits, previously presented in the literature. The results demonstrate almost on average 35%, 55%, 90% and 68% improvements in terms of delay, total power, static power and energy consumption, respectively.

Multimodal biometric systems have proven more efficient in personal verification or identification than single biometric ones, so it is also a focus of this paper. Particularly, in the paper, the authors present a multimodal biometric system in which features from face and fingerprint images are extracted using Zernike Moment (ZM), the personal authentication is done using Relevance Vector Machine (RVM) and feature-level fusion technique. The proposed system has proven its remarkable ability to overcome the limitations of uni-modal biometric systems and to tolerate local variations in the face or fingerprint image of an individual. Also, the achieved experimental results have demonstrated that using RVM can assure a higher level of forge resistance and enables faster authentication than the state-of-the-art technique , namely the support vector machine (SVM).

Professional use of cloud health storage around the world implies Information-Retrieval extensions. These developments should help users find what they need among thousands or billions of enterprise documents and reports. However, extensions must offer protection against existing threats, for instance, hackers, server administrators and service providers who use people’s personal data for their own purposes. Indeed, cloud servers maintain traces of user activities and queries, which compromise user security against network hackers. Even cloud servers can use those traces to adapt or personalize their platforms without users’ agreements. For this purpose, we suggest implementing Private Information Retrieval (PIR) protocols to ease the retrieval task and secure it from both servers and hackers. We study the effectiveness of this solution through an evaluation of information retrieval time, recall and precision. The experimental results show that our framework ensures a reasonable and acceptable level of confidentiality for retrieval of data through cloud services.

The main aim of this paper is to avoid hot-spot problem in wireless sensor network with uniform energy dissipation among cluster heads in the network. It proposes an energy efficient unequal clustering mechanism to form limited and equivalent number of clusters across different levels of wireless sensor network to enable invariable energy consumption among them. Concentrated cluster formation near base station ensures minimum relay burden on cluster heads to avoid hot-spot problem in multi-hop data forwarding model. Equivalent number of clusters at each level ensures in-common network load on each cluster head among different data forwarding routes. In addition, a simple disjoint multi-hop routing technique is proposed for smooth data forwarding process. Simulation results evidence that the proposed unequal clustering algorithm overcomes hot-spot problem with invariable energy dissipation among cluster heads across the network and elevates sensor network lifetime.

In this paper, we propose a graphics processing unit (GPU) based matching technique to perform fast feature matching between different images. Lowe proposed a scale invariant feature transform algorithm that has been successfully used in various feature matching applications such as stereo vision, object recognition, and many others, but this algorithm is computationally intensive. In order to solve this problem, we propose a matching technique optimized for graphics processing units to perform computation with less time. We have applied GPU optimization for the fast computation of keypoints to make our system fast and efficient. The proposed method used self-organizing map feature matching technique to perform efficient matching between different images. The experiments are performed on various images to examine the performance of the system in diverse conditions such as image rotation, scaling, and blurring conditions. The experimental results reveal that the proposed algorithm outperforms the existing feature matching methods resulting into fast feature matching with the optimization of graphics processing unit.

The Outlier detection is very active area of research in data mining where outlier is a mismatched data in dataset with respect to the other available data. In existing approaches the outlier detection done only on numeric dataset. For outlier detection if we use clustering method , then they mainly focus on those elements as outliers which are lying outside the clusters but it may possible that some of the unknown elements with any possible reasons became the part of the cluster so we have to concentrate on that also. The Proposed method uses hybrid approach to reduce the number of outliers. The number of outlier can only reduce by improving the cluster formulation method. The proposed method uses two data mining techniques for cluster formulation i.e. weighted k-means and neural network where weighted k-means is the clustering technique that can apply on text and date data set as well as numeric data set. Weighted k-means assign the weights to each element in dataset. The output of weighted k-means becomes the input for neural network where the neural network is the classification and clustering technique of data mining. Training is provided to the neural network and according to that neurons performed the testing. The neural network test the cluster formulated by weighted k-means to ensure that the clusters formulated by weighted k-means are group accordingly. There is lots of outlier detection methods present in data mining. The proposed method use Integrating Semantic Knowledge (SOF) for outlier detection. This method detects the semantic outlier where the semantic outlier is a data point that behaves differently with other data points in the same class or cluster. The main motive of this research work is to reduce the number of outliers by improving the cluster formulation methods so that outlier rate reduces and also to decrease the mean square error and improve the accuracy. The simulation result clearly shows that proposed method works pretty well as it significantly reduces the outlier.

The application of Information and Communication Technology which is shaping and changing the world socially, educationally and economically cannot be over emphasized. In view of that, this paper looks at the factors militating against successful implementation of computer studies in 9-year Universal Basic Education (UBE) programmes. Five UBE schools were randomly selected for the research. Two research questions were postulated to guide the conduct of the research and t-test analysis was used for testing the hypotheses. The findings showed that there is positive perception by the students on factors militating against successful implementation of 9-year UBE programme. Based on the findings, some recommendations were made: provision of qualified teachers, instructional materials, provision of laboratories and so on in order to improve and ensure effective and efficient implementation of the 9-year Universal Basic (UBE) programme.

Multi view face recognition using multiple camera networks is an active research area. The main aim of this paper is to handle different pose variations in multi camera network and recognizing face from those videos. The traditional approaches handle the pose estimation explicitly ,the proposed work will handle the multiple views of the poses .For a given set of multi view video sequences we use particle filter to track the 3D location of the head. The texture map is generated by back projecting the multi view video. The proposed work is developed using the Spherical Harmonic (SH) representation of the face from the texture mapped on to the sphere. A robust feature is constructed based on the properties of SH projection.