A new dynamic cellular learning automata-based skin detector

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.     

Bipartite isoperimetric graph partitioning for data co-clustering

Data co-clustering refers to the problem of simultaneous clustering of two data types. Typically, the data is stored in a contingency or co-occurrence matrix C where rows and columns of the matrix represent the data types to be co-clustered. An entry C _ij of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. In this paper, we present a novel graph theoretic approach to data co-clustering. The two data types are modeled as the two sets of vertices of a weighted bipartite graph. We then propose Isoperimetric Co-clustering Algorithm (ICA)—a new method for partitioning the bipartite graph. ICA requires a simple solution to a sparse system of linear equations instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our theoretical analysis and extensive experiments performed on publicly available datasets demonstrate the advantages of ICA over other approaches in terms of the quality, efficiency and stability in partitioning the bipartite graph.     

Electric vehicle energy consumption modelling and estimation—A case study

Electric vehicles (EVs) have a limited driving range compared to conventional vehicles. Accurate estimation of EV's range is therefore a significant need to eliminate “range anxiety” that refers to drivers' fear of running out of energy while driving. However, the range estimators used in the currently available EVs are not sufficiently accurate. To overcome this issue, more accurate range estimation techniques are investigated. Nonetheless, an accurate power‐based EV energy consumption model is crucial to obtain a precise range estimation. This paper describes a study on EV energy consumption modelling. For this purpose, EV modelling is carried out using MATLAB/Simulink software based on a real EV in the market, the BMW i3. The EV model includes vehicle powertrain system and longitudinal vehicle dynamics. The powertrain is modelled using efficiency maps of the electric motor and the power electronics' data available for BMW i3. It also includes a transmission and a battery model (ie, Thevenin equivalent circuit model). A driver model is developed as well to control the vehicle's speed and to represent human driver's behaviour. In addition, a regenerative braking strategy, based on a series brake system, is developed to model the behaviour of a real braking controller. Auxiliary devices are also included in the EV model to improve energy consumption estimation accuracy as they can have a significant impact on that. The vehicle model is validated against published energy consumption values that demonstrates a satisfactory level of accuracy with 2% to 6% error between simulation and experimental results for Environmental Protection Agency and NEDC tests.     

Structural analysis of pyrolytic carbon deposits on a planar cordierite substrate

Pyrolytic carbon deposits were produced by chemical vapor deposition on a planar substrate of cordierite using methane as a source gas. The structure of the deposits was characterized by light microscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) combined with electron-energy-loss spectroscopy (EELS). The surface morphology is characterized by a cell structure induced by grains elongated perpendicular to the substrate surface. Energetic shift and intensity fluctuation of plasmon peaks in EELS spectra taken from cell and interface regions between the cells correlate with an alteration of the SEM image contrast observed on freshly fractured surfaces. This correlation suggests the presence of a mixture of two materials exhibiting different crystallization degrees. The material located at the interface is more amorphous-like in comparison to the graphite-like material located within cells.     

Content-based image database system for epilepsy

We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy. The system consists of several modules including a database backbone, brain structure identification and localization, segmentation, registration, visual feature extraction, clustering/classification and query modules. Our newly developed anatomical landmark localization and brain structure identification method facilitates navigation through an image data and extracts useful information for segmentation, registration and query modules. The database stores T1-, T2-weighted and FLAIR MRI and ictal/interictal SPECT modalities with associated clinical data. We confine the visual feature extractors within anatomical structures to support semantically rich content-based procedures. The proposed system serves as a research tool to evaluate a vast number of hypotheses regarding the condition such as resection of the hippocampus with a relatively small volume and high average signal intensity on FLAIR. Once the database is populated, using data mining tools, partially invisible correlations between different modalities of data, modeled in database schema, can be discovered. The design and implementation aspects of the proposed system are the main focus of this paper.     

Causal end-effector inversion of a flexible link manipulator

A new method to obtain the required base torque for the rest-to-rest manoeuvre of a single flexible link manipulator as its end-effector moves along a desired trajectory is introduced. Contrary to available causal and non-causal end-effector inversion techniques, this new approach does not require pre- and/or post-actuation and works even in the presence of the purely imaginary zeros for the transfer function. In this approach, the desired end-effector trajectory is divided into a finite number of segments. In each segment, but the last one, the desired trajectory is redefined so that a bounded continuous torque can be obtained using causal dynamic inversion. For the last segment the desired trajectory is redefined so that not only the causal inversion is achieved but also the final conditions are satisfied; that is, the end-effector reaches its desired position and the manipulator comes to rest at a given time. The redefinition of the desired trajectory at each segment employs summation of the stable exponential functions, which leads to a family of possible answers for the redefined trajectory. To obtain the best member of this family which minimizes the difference between the desired trajectory and redefined trajectory, an efficient search algorithm is proposed and used. Simulation results show the effectiveness of this new method.     

Null controllability of degenerate/singular parabolic equations

The purpose of this paper is to provide a full analysis of the null controllability problem for the one dimensional degenerate/singular parabolic equation $ {u_t} - {\left( {a(x){u_x}} \right)_x} - \frac{\lambda }{{{x^\beta }}}u = 0 $ , (t, x) ∈ (0, T) × (0, 1), where the diffusion coefficient a(?) is degenerate at x = 0. Also the boundary conditions are considered to be Dirichlet or Neumann type related to the degeneracy rate of a(?). Under some conditions on the function a(?) and parameters β, λ, we prove global Carleman estimates. The proof is based on an improved Hardy-type inequality.     

Producing of AA5083/ZrO2 Nanocomposite by Friction Stir Processing (FSP)

In this study, friction stir processing (FSP) was used to produce AA5083/ZrO₂ nanocomposite layer. Optical microscopy and SEM were used to probe the microstructures formed in the composite layer. In addition, the mechanical properties of each sample are characterized using both tensile and hardness tests. Results showed that FSP is an effective process to fabricate AA5083/ZrO₂ nanocomposite layer with uniform distribution of ZrO₂ particles, good interfacial integrity, and significant grain refinement. On processing, in the proper combination of process parameters, the metal matrix composite layer was observed to have increased tensile and hardness properties.     

Optimal secondary storage access sequence for performing relationaljoin

Two graph models are developed to determine the minimum required buffer size for achieving the theoretical lower bound on the number of disk accesses for performing relational joins. Here, the lower bound implies only one disk access per joining block or page. The first graph model is based on the block connectivity of the joining relations. Using this model, the problem of determining an ordered list of joining blocks that requires the smallest buffer is considered. It is shown that this problem as well as the problem of computing the least upper bound on the buffer size is NP-hard. The second graph model represents the page connectivity of the joining relations. It is shown that the problem of computing the least upper bound on the buffer size for the page connectivity model is also NP-hard. Heuristic procedures are presented for the page connectivity model and it is shown that the sequence obtained using the heuristics requires a near-optimal buffer size The authors also show the performance improvement of the proposed heuristics over the hybrid-has join algorithm for a wide range of join factors