3D Model compression using Connectivity-Guided Adaptive Wavelet Transform built into 2D SPIHT

Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder.     

Donor-acceptor-donor type conjugated polymers for electrochromic applications: benzimidazole as the acceptor unit

Donor-acceptor-donor (DAD) type benzimidazole (BIm) and 3,4 ethylenedioxythiophene (EDOT) bearing monomers were synthesized and electrochemically polymerized. Pendant group at 2-C position of the imidazole ring was functionalized with phenyl (P1), EDOT (P2) and ferrocene (P3) in order to observe substituent effect on electrochemical and electrochromic properties of corresponding polymers. Spectroelectrochemical results showed that different pendant groups resulted in polymers with slightly different optical band gaps (1.75, 1.69 and 1.77 eV respectively) and different number of achievable colored states. Optoelectronic performance and comparison of results with other well known π-accepting benzazole bearing DAD type polymers were reported in detail.     

Prediction of the pH and the temperature-dependent swelling behavior of -alginate hydrogels by artificial neural networks

This paper considers the possibility of using artificial neural network models to identify model for swelling behavior as new techniques. Multi-layer feed-forward, radial basis function and generalized regression neural network models were employed to predict the swelling behaviors of Ca²⁺-alginate hydrogels under different environmental conditions of pH and temperature. The results show that an excellent correlation between the experimental and predicted swelling ratios was obtained by the artificial neural networks. Generalized regression neural network has a better performance than the other neural network models. The absolute mean error, the determination coefficient and the standard error of prediction were used as performance criteria. In addition, the performances of the neural network models are significantly superior compared with those of second-order swelling kinetics, quadratic and cubic models of response surface methodology.     

Investigation of Sn-Zn electrodeposition from acidic bath on EQCM

Tin-zinc (Sn-Zn) alloy with low tin content was deposited on gold electrode and steel substrate with use of chronoamperometric technique from an acidic bath. In order to evaluate coating efficiency of Sn-Zn alloy in 0.5 M NaCl solution, open circuit potential-time curve (E_OCP-t), polarization curves, mass change of the electrode (Δm-t) using quartz crystal microbalance (QCM) were compared to those of pure Sn and Zn coatings. Anodic stripping measurements were carried out simultaneously with the mass loss of the deposit. Scanning electron microscopy (SEM) and energy dispersive X-ray spectra (EDS) analysis were performed to characterize the surface morphology. Anodic stripping experiment and EDS analysis indicated that Sn, Zn, and SnO₂ formed on the electrode surface when Sn-Zn was coated from acidic bath. Furthermore, local mapping demonstrated homogeneous distribution of Sn and Zn atoms throughout the surface.     

From engineering diagrams to engineering models: Visual recognition and applications

We present a computational recognition approach to convert network-like, image-based engineering diagrams into engineering models with which computations of interests, such as CAD modeling, simulation, information retrieval and semantic-aware editing, are enabled. The proposed approach is designed to work on diagrams produced using computer-aided drawing tools or hand sketches, and does not rely on temporal information for recognition. Our approach leverages a Convolutional Neural Network (CNN) as a trainable engineering symbol recognizer. The CNN is capable of learning the visual features of the defined symbol categories from a few user-supplied prototypical diagrams and a set of synthetically generated training samples. When deployed, the trained CNN is applied either to the entire input diagram using a multi-scale sliding window or, where applicable, to each isolated pixel cluster obtained through Connected Component Analysis (CCA). Then the connectivity between the detected symbols are analyzed to obtain an attributed graph representing the engineering model conveyed by the diagram. We evaluate the performance of the approach with benchmark datasets and demonstrate its utility in different application scenarios, including the construction and simulation of control system or mechanical vibratory system models from hand-sketched or camera-captured images, content-based image retrieval for resonant circuits and sematic-aware image editing for floor plans.     

Sliding mode control of a bioreactor

In this paper, sliding mode control (SMC) of a bioreactor is considered and is compared with PID control. The magnitude of the error in SMC is found to be lower than that in PID control. Moreover, the magnitudes of cells and nutrients were very close to the selected reference values in SMC, whereas they were quite different in PID control. Overall, SMC was more robust against disturbances and had better performance than PID control.     

Effects of chromium (VI) on the activities of ureolytic mixed culture

BACKGROUND: Ureolytic microbial carbonate precipitation (MCP) is a novel process for removing excess calcium from industrial effluents. This process is based on the hydrolization of urea to provide a suitable medium for the precipitation of Ca as CaCO₃. RESULT: A toxicity identification evaluation was conducted on synthetic wastewater simulating wastewater from paper recycling to determine the inhibition or toxicity of chromium(VI) ions on the activities of a ureolytic mixed culture (UMC) with respect to the removal rate of COD and removal of Ca²⁺ in batch reactors. The 50% inhibiting concentration (IC₅₀) and the 25% inhibiting concentration (IC₂₅) values of Cr(VI) on UMC were determined as 40 and 18 mg L^?1, respectively, for an exposure time of 1 day. The inhibitory effects of Cr(VI) were lower on UMC after longer exposure times of 3, 4 and 5 days. COD removal rate, ammonium production and Ca removal of samples with 512 mg Cr(VI) L^?1 were significantly inhibited. CONCLUSION The present study has shown that the effects of different metallic species, organic toxicants and other environmental factors should be taken into account when removing problematic Ca and other possible elements from the environment using UMC. Copyright © 2008 Society of Chemical Industry     

A Tutorial on Multiplierless Design of FIR Filters: Algorithms and Architectures

Finite impulse response (FIR) filtering is a ubiquitous operation in digital signal processing systems and is generally implemented in full custom circuits due to high-speed and low-power design requirements. The complexity of an FIR filter is dominated by the multiplication of a large number of filter coefficients by the filter input or its time-shifted versions. Over the years, many high-level synthesis algorithms and filter architectures have been introduced in order to design FIR filters efficiently. This article reviews how constant multiplications can be designed using shifts and adders/subtractors that are maximally shared through a high-level synthesis algorithm based on some optimization criteria. It also presents different forms of FIR filters, namely, direct, transposed, and hybrid and shows how constant multiplications in each filter form can be realized under a shift-adds architecture. More importantly, it explores the impact of the multiplierless realization of each filter form on area, delay, and power dissipation of both custom (ASIC) and reconfigurable (FPGA) circuits by carrying out experiments with different bitwidths of filter input, design libraries, reconfigurable target devices, and optimization criteria in high-level synthesis algorithms.     

Characterization of products evolved from supercritical water gasification of xylose (principal sugar in hemicellulose)

The catalyst decomposition of xylose (the principal sugar in hemicellulose) was examined in supercritical water for temperature from 400 to 600°C. Experiments were performed in the absence and presence of three main types of catalysts [alkali catalysts (K₂CO₃ and KOH) and metal impregnated activated carbons (Ni/AC) and (Ru/AC)] with a reaction time of 1h. Gasification yield reaches maximum values by using K₂CO₃ and KOH at the highest temperature of 600°C. The highest H₂ yield and the highest CH₄ yield were obtained by using Ru/AC and Ni/AC, respectively. Acetic acid and 5-methyl furfural were determined as the main aqueous products and reached maximum value by using Ru/AC.     

The effect of various factors on the hydrogen generation by hydrolysis reaction of potassium borohydride

Potassium borohydride (KBH₄) reacted very slowly with water to liberate 4 mol of hydrogen/mol of compound at room temperature. The hydrolysis and stability conditions of KBH₄ investigated depend on KBH₄ concentrations, concentration of alkaline solutions, temperatures and electrical field intensity. Yield of produced hydrogen by self-hydrolysis of KBH₄ increases as the temperature increased and it produced 53.9% yield at the end of 300 min at 60 °C. The reaction rate order of hydrolysis of KBH₄ in aqueous solution is found at about 0.7–0.8 and the activation energy for hydrolysis is calculated as 14,700 kJ/mol. Potassium borohydride is stable both in room temperature and in aqueous alkaline solution. In this study, the electrical field that is not needed for catalytic activity was used for the hydrolysis of KBH₄ aqueous solution. It was found that 6 mol of hydrogen/mol of potassium borohydride was liberated in the presence of electrical field, whereas 4 mol of hydrogen was produced in the absence of electrical field per mol of potassium borohydride.