Block truncation coding with color clumps: A novel feature extraction technique for content based image classification

The paper has explored principle of block truncation coding (BTC) as a means to perform feature extraction for content based image classification. A variation of block truncation coding, named BTC with color clumps has been implemented in this work to generate feature vectors. Classification performance with the proposed technique of feature extraction has been compared to existing techniques. Two widely used public dataset named Wang dataset and Caltech dataset have been used for analyses and comparisons of classification performances based on four different metrics. The study has established BTC with color clumps as an effective alternative for feature extraction compared to existing methods. The experiments were carried out in RGB color space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier were used to measure the classification performances. A paired t test was conducted to establish the statistical significance of the findings. Evaluation of classifier algorithms were done in receiver operating characteristic (ROC) space.     

Automated test data generation for branch testing using incremental genetic algorithm

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to accomplish the process. This paper focuses on test data generation for branch coverage. A major drawback in using meta-heuristic techniques is that the CFG paths have to be traversed from the starting node to end node for each automated test data. This kind of traversal could be improved by branch ordering, together with elitism. But still the population size and the number of iterations are maintained as the same to keep all the branches alive. In this paper, we present an incremental genetic algorithm (IGA) for branch coverage testing. Initially, a classical genetic algorithm (GA) is used to construct the population with the best parents for each branch node, and the IGA is started with these parents as the initial population. Hence, it is not necessary to maintain a huge population size and large number of iterations to cover all the branches. The performance is analyzed with five benchmark programs studied from the literature. The experimental results indicate that the proposed IGA search technique outperforms the other meta-heuristic search techniques in terms of memory usage and scalability.     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.     

Noncontact Sonic NDE and Defect Imaging Via Local Defect Resonance

A selective acoustic activation of defects based on the concept of local defect resonance enables to enhance considerably the intensity of defect vibrations and makes it possible to reduce the input acoustic powers to the levels permissible for noncontact nondestructive inspection. Since for cm-size defects in composite materials, the LDR frequencies lie in the low kHz-range, the resonant noncontact activation shifts to an audible frequency range and can be provided by conventional sonic equipment. In this paper, the feasibility of the resonant noncontact inspection is validated for the most “problematic” methodologies of nonlinear, thermosonic and shearosonic NDE that usually require an elevated acoustic power and, therefore, a reliable contact between the specimen and the transducer. In contrast, the noncontact versions developed employ commercial loudspeakers which can simultaneously insonify large areas and be applied for a contactless sonic inspection of different materials and various scale components.     

Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

A denoising procedure is proposed to remove both out-band and in-band noise for extraction of weak bursts in signal obtained from defective bearing. Energy of continuous wavelet scalogram is computed and the band having higher energy is selected to remove the out-band noise. Signals of selected band are brought together to form a high-dimensional waveform feature space. Further, low dimensional waveform manifold is formed using linear local tangent space alignment (LLTSA) algorithm to remove in-band noise. A criterion, entitled as frequency factor is also proposed to determine the optimum neighbour size of LLTSA. The two complicated conditions are chosen to demonstrate the effectiveness of the technique in the extraction of bursts in the noisy situations. A significant improvement in the signal to noise ratio is observed when in-band noise is removed using manifold learning by LLTSA algorithm. The experimental result reveals the success of the proposed denoising procedure in extraction of defect features, even in the case of noisy condition.     

Efficiency of surface modified Ti coated with copper nanoparticles to control marine bacterial adhesion under laboratory simulated conditions

Titanium (Ti) used as condenser material in nuclear power plants encounter severe biofouling in marine environment which in turn affects the efficiency of the metal. To reduce the biofouling by marine microorganisms, surface modification of the Ti was carried out by anodization process to obtain nanotubes (TiO₂-NTs). The electrolyte solution containing 1% of ammonium fluoride resulted in uniform growth of TiO₂-NTs. TiO₂-NTs were further coated with chemically synthesized copper nanoparticles (NT-CuNP) using 3-amino propyl triethoxy silane as a coupling agent. NT-CuNP was characterized by field-emission scanning electron microscopy (FE-SEM), energy-dispersive spectroscopy and X-ray diffraction. The stability of the coating was determined by the amount of Cu⁺ ions released into the surrounding using AAS. The microbial adhesion on the surface of Ti, TiO₂-NTs and NT-CuNP coupons were evaluated by sea water exposure studies using total viable count method and also characterized by FE-SEM for any morphological changes. The NT-CuNP coupons show a 60% reduction in microbial adhesion when compared to control Ti coupons.     

A study on the damping capacity of BaTiO<Subscript>3</Subscript>-reinforced Al-matrix composites

To study the damping capacity of BaTiO₃/Al composites, Al composites reinforced with BaTiO₃ powder (average grain sizes: 100 and 1000 nm) were fabricated by the hot-pressing sintering method. The damping properties of pure Al and BaTiO₃/Al composites were investigated and compared based on the dynamic mechanical analysis over a wide range of temperatures (50–285^°C). Compared with pure Al matrix, 1000 nm BaTiO₃/Al composites with 5 and 10% mass fractions of BaTiO₃ exhibited better damping capacity. For 100 nm BaTiO₃/Al composite, its damping capacity is slightly higher than that of pure Al below 145^°C, while it becomes lower above this degree. The damping capacity enhancement of BaTiO₃/Al composites can be explained by the ferroelastic domain damping. Furthermore, 5 and 10% BaTiO₃/Al composites have higher bending strength and hardness than pure Al sample.     

Co-based soft magnetic bulk glassy alloys optimized for glass-forming ability and plasticity

Co-based bulk glassy alloys (BGAs) have become more and more important because of their nearly zero magnetostriction and high giant magneto-impedance effect. Here, we report the improvement of glass-forming ability (GFA), soft-magnetic properties and plasticity by a small addition of Mo atoms in CoFeBSiNbMo BGAs. (Co_0.6Fe_0.4)₆₉ B _20.8Si_5.2Nb_5?x Mo _x ferromagnetic BGA cylindrical glassy rods were fabricated successfully with a diameter of 5 mm by conventional copper mould casting method. It reveals that the substitution of a small amount of Mo for Nb makes the composition to approach a eutectic point and effectively enhances the GFA of alloy. In addition to high GFA and superhigh strength, the compressive test shows that the Mo addition can improve the plasticity for the obtained BGAs. The combination of high GFA, excellent soft-magnetic properties and good plasticity demonstrated in our alloys is promising for the future applications as functional materials.     

Synthesis of 1-D ZnO nanorods and polypyrrole/1-D ZnO nanocomposites for photocatalysis and gas sensor applications

1-D ZnO nanorods and PPy/1-D ZnO nanocomposites were prepared by the surfactant-assisted precipitation and in situ polymerization method, respectively. The synthesized nanorods and nanocomposites were characterized by UV–Vis spectrophotometer, Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD) and field emission scanning electron microscope (FE-SEM), which gave the evidence of 1-D ZnO nanorods, polymerization of pyrrole monomer and strong interaction between PPy and 1-D ZnO nanorods, respectively. Photocatalytic activity of 1-D ZnO nanorods was conducted by 3³ level full-factorial design to evaluate the effect of three independent process variables viz., dye concentration (crystal violet), catalyst concentration (1-D ZnO nanorods) and the reaction time on the preferred response: photodegradation efficiency (%). The PPy/1-D ZnO nanocomposites were used for the sensing of NH₃, LPG, CO₂ and H₂S gases, respectively, at room temperature. It was observed that PPy/1-D ZnO nanocomposites with different 1-D ZnO nanorod weight ratios (15 and 25%) had better selectivity and sensitivity towards NH₃ at room temperature.     

Exploring damping characteristics of composite tower of cable-stayed bridges

The damping characterization is important in making accurate predictions of the seismic response of the hybrid structures dominated by different damping mechanisms. Different damping characteristics arise from the construction of the tower with different materials: steel for the upper part; reinforced concrete for the lower main part and interaction with supporting soil. The process of modeling damping matrices and experimental verification is challenging because damping cannot be determined via static tests as mass and stiffness can be. The assumption of classical damping is not appropriate if the system to be analyzed consists of two or more parts with significantly different levels of damping. The dynamic response of structures is critically determined by the damping mechanisms, and its value is very important for the design and analysis of vibrating structures. An analytical approach that is capable of evaluating the equivalent modal damping ratio from structural components is desirable for improving seismic design. Two approaches are considered to define and investigate dynamic characteristics of a composite tower of cable-stayed bridges: The first approach makes use of a simplified approximation of two lumped masses to investigate the structure irregularity effects including damping of different material, mass ratio, frequency ratio on dynamic characteristics and modal damping. The second approach employs a detailed numerical step-by-step integration procedure.