Image selective restoration using multi-scale variational decomposition

In this paper we propose a multi-scale variational decomposition model for image selective restoration. Firstly, we introduce a single-parameter (BV, G, L²) variational decomposition functional and theoretically analyze the relationship between the parameter and the scale of image features. And then, by replacing the fixed scale parameter with a varying sequence in the single-parameter decomposition functional, we obtain the multi-scale variational decomposition which can decompose the input image into a series of image slices of different scales. Furthermore, we show some properties and prove the convergence of the multi-scale decomposition. Finally, we introduce an alternating and iterative method based on Chambolle’s projection algorithm to numerically solve the multi-scale variational decomposition model. Experiments are conducted on both synthetic and real images to demonstrate the effectiveness of the proposed multi-scale variational decomposition. In addition, we use the multi-scale variational decomposition to achieve image selective restoration, and compare it with several state-of-the-art models in denoising application. The numerical results show that our model has better performance in terms of PSNR and SSIM indexes.     

Characterization of vanadyl phthalocyanine based surface-type capacitive humidity sensors

This study presents the fabrication and characterization of novel surface-type capacitive humidity sensors using vanadyl phthalocyanine (VOPc) as the active material. The devices, which comprise three different thicknesses,have been fabricated using the thermal evaporation technique. A thin film of VOPc is deposited on thoroughly cleaned glass substrates with pre-pattemed Ag electrodes. The capacitive effect of the samples under humidity has been in-vestigated. Comparison of the samples with different thicknesses shows that the thinnest device seems more sensitive towards humidity. The humidity dependent capacitance properties of the sensor make it beneficial for use in commercial hygrometers.     

Non-Intrusive Monitoring of Everyday Behavioral Activities in an Indoor Environment

A non-intrusive design for monitoring everyday activities of an elderly person is presented. The proposed system is intended to be used in the bedroom, allowing the elders to stay at home in a safe environment. The required hardware design is simple in structure and cost effective. The sensor design is implemented by using capacitive sensors and an Arduino microcontroller unit. And a real time graphical user interface is implemented to monitor the elderly person. The performance analysis shows that the sensor design is able to differentiate between a human body and a house pet.     

A new design approach for dual‐band patch antenna serving Ku/K band satellite communications

A printed planar antenna with simple and intelligent geometrical structure has been proposed for Ku/K band satellite communication systems. The radiating patch of the antenna is formed by cutting rectangular slots and extending the radiating element to some extent. The final design of the antenna with optimized parameters is fabricated on ceramic–polytetrafluoroethylene substrate materials of dielectric constant ε_r = 10.2. The antenna is excited through a microstrip feed line and has reduced ground plane that covers only the non‐radiating portion of the antenna. The reduced complexity of the antenna is easy to fabricate and has overall dimension of 40 × 35 × 1.905 mm³. The results from experimental analysis show that the proposed antenna can guarantee a wide bandwidth of 12.0 to 16.4 GHz at lower band, and the upper band covers the frequency in the range of 17.53 to 19.5 GHz. The antenna has achieved appreciable gain in the range of 3.14 to 4.68 dBi for lower band and 2.03 to 3.65 dBi for upper band. The proposed antenna has offered almost symmetrical and directional radiation pattern that is essentially suitable for serving Ku/K band satellite applications. Copyright © 2015 John Wiley & Sons, Ltd.     

Silicon‐Modified Rare‐Earth Transitions—A New Route to Near‐ and Mid‐IR Photonics

Silicon underpins microelectronics but lacks the photonic capability needed for next‐generation systems and currently relies on a highly undesirable hybridization of separate discrete devices using direct band gap semiconductors. Rare‐earth (RE) implantation is a promising approach to bestow photonic capability to silicon but is limited to internal RE transition wavelengths. Reported here is the first observation of direct optical transitions from the silicon band edge to internal f‐levels of implanted REs (Ce, Eu, and Yb); this overturns previously held assumptions about the alignment of RE levels to the silicon band gap. The photoluminescence lines are massively redshifted to several technologically useful wavelengths and modeling of their splitting indicates that they must originate from the REs. Eu‐implanted silicon devices display a greatly enhanced electroluminescence efficiency of 8%. Also observed is the first crystal field splitting in Ce luminescence. Mid‐IR silicon photodetectors with specific detectivities comparable to existing state‐of‐the‐art mid‐IR detectors are demonstrated.     

FFT-based algorithm for scattering from a circular, cylindrical shell in the presence of an inhomogeneous cylinder

An FFT-based algorithm is proposed to solve the problem of scattering from a homogeneous, thin, circular, dielectric cylindrical shell in the presence of an inhomogenous, cylindrical scatterer. The incident wave is taken to be transverse magnetic (TM); the problem considered is one of two-dimensional, scalar type. Results of computer simulations are provided illustrating the method proposed.<>     

Non-iterative estimation of heat transfer coefficients using artificial neural network models

The Inverse Heat Conduction Problem (IHCP) dealing with the estimation of the heat transfer coefficient for a solid /fluid assembly from the knowledge of inside temperature was accomplished using an artificial neural network (ANN). Two cases were considered: (a) a cube with constant thermophysical properties and (b) a semi-infinite plate with temperature dependent thermal conductivity resulting in linear and nonlinear problem, respectively. The Direct Heat Conduction Problems (DHCP) of transient heat conduction in a cube and in a semi-infinite plate with a convective boundary condition were solved. The dimensionless temperature-time history at a known location was then correlated with the corresponding dimensionless heat transfer coefficient/Biot number using appropriate ANN models. Two different models were developed for each case i.e. for a cube and a semi-infinite plate. In the first one, the ANN model was trained to predict Biot number from the slope of the dimensionless temperature ratio versus Fourier number. In the second, an ANN model was developed to predict the dimensionless heat transfer coefficient from non-dimensional temperature. In addition, the training data sets were transformed using a trigonometric function to improve the prediction performance of the ANN model. The developed models may offer significant advantages when dealing with repetitive estimation of heat transfer coefficient. The proposed approach was tested for transient experiments. A ‘parameter estimation’ approach was used to obtain Biot number from experimental data.     

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.     

Strategies for improvement of biohydrogen production from organic-rich wastewater: A review

Biohydrogen can be produced from organic wastewater but the process is limited by low production yields. The aim of this review is to summarize the production strategies which are recently researched for enhancing biohydrogen yield and productivity from organic wastewater. The survey of published work indicates that the dark hydrogen fermentation is the most promising production mode. Current strategies geared towards improving biohydrogen production include: microbial culture immobilization, bioreactor modifications, the optimization of process conditions (temperature, pH, OLR and HRT), culture selection and enrichments, substrate choice, and the metabolic engineering of biohydrogen specialists. Comparative analysis of energy recovery from anaerobic digestion using vinasse-related substrates indicates that the production of methane has a higher energy yield than production of hydrogen. A sequential combination of biohydrogen and biomethanation production phases has the potential for even higher bioenergy recovery from organic wastewater.     

Relationships among Jatropha curcas seed yield and vegetative plant components under different management and cropping systems in Indonesia

An understanding of how Jatropha curcas seed yield relates to vegetative plant components under different management regimes is lacking. Such information is necessary to predict yields and design management strategies. This study investigated yield and vegetative plant component interactions, and the effects of management practices in monoculture, intercropping, and hedge cropping systems in Indonesia. Monoculture and intercropping experiments in Gunungkidul Regency used jatropha IP-1M material; hedge experiments in Sumbawa Regency used the Sumbawa provenance. In two-year-old monoculture, pruning significantly decreased yield from 109 kg ha⁻¹ to 28 kg ha⁻¹ due to a 40% decrease in canopy volume and LAI. In four-year-old jatropha intercropping, root barriers reduced yields 80% by limiting jatropha root access to soil moisture and nutrients in the maize plantings. Intercropping without root barrier and with leaf mulch produced the largest yields of 25 kg ha⁻¹. In hedge plantings, plant height influenced yield. Single rows of one-year-old monoculture produced 0.97 g m⁻¹ at 10 cm spacing, 1.69 g m⁻¹ at 30 cm, and 0.14 g m⁻¹ for 20 cm of mixed jatropha–gliricidia. Pruning significantly decreased LAI with 20 cm spacing indicating a higher proportion of above-ground biomass allocated for wood growth. Results indicate that seed yield across the three cropping systems can be determined by plant height and numbers of productive twig/branch, although number of inflorescences cluster per productive twig may be more important. Future research should focus on the transition of branches to reproductive phases, and on increasing numbers of productive twigs/branches and inflorescence clusters.