Text-to-picture tools,systems, and approaches: a survey

Multimedia Tools and Applications - Text-to-picture systems attempt to facilitate high-level, user-friendly communication between humans and computers while promoting understanding of natural...     

Radiolabeling,Preparation, and Bioevaluation of 99mTc-Azathioprine as a Potential Targeting Agent for Solid Tumor Imaging

Radiochemistry - Azathioprine, an antitumor agent, was labeled with 99mTc using stannous chloride dihydrate as a reducing agent. Factors such as the amounts of the reducing agent and substrate, pH,...     

Structural properties of phosphorous-doped polycrystalline silicon

The structural properties and hydrogen bonding of undoped and phosphorous doped polycrystalline silicon produced by step-by-step laser dehydrogenation and crystallization technique were investigated using Raman spectroscopy and hydrogen effusion measurements. At low laser fluences, E_L, a two-layer system is created. This is accompanied by the change in hydrogen bonding. The intensity of the Si–H vibration mode at 2000 decreases faster than the one at 2100 cm⁻¹. This is even more pronounced in phosphorous-doped specimens. The laser crystallization results in an increase of the hydrogen binding energy by approximately 0.2–0.3 eV compared to the amorphous starting materials.     

Breakdown probabilities for thin heterostructure avalanche photodiodes

The recurrence theory for the breakdown probability in avalanche photodiodes (APDs) is generalized to heterostructure APDs that may have multiple multiplication layers. The generalization addresses layer-boundary effects such as the initial energy of injected carriers as well as the layer-dependent profile of the dead space in the multiplication region. Reducing the width of the multiplication layer serves to both downshift and sharpen the breakdown probability curve as a function of the applied reverse-bias voltage. In structures where the injected carriers have an initial energy that is comparable to the ionization threshold energy, the transition from linear mode to Geiger-mode is more abrupt than in structures in which such initial energy is negligible. The theory is applied to two recently fabricated Al/sub 0.6/Ga/sub 0.4/As-GaAs heterostructure APDs and to other homostructure thin GaAs APDs and the predictions of the breakdown-voltage thresholds are verified.     

Effect of electricity tariff on the optimum insulation-thickness in building walls as determined by a dynamic heat-transfer model

Thermal insulation is one of the most effective energy-conservation measures in buildings. Despite the widespread use of insulation materials in recent years, little is known regarding their optimum thickness under dynamic thermal conditions. Insulated concrete blocks are among the units most commonly used in the construction of building walls in Saudi Arabia. Typically, the insulation layer thickness is fixed at a value in the range 2.5–7.5 cm, regardless of the climatic conditions, type and cost of insulation material, and other economic parameters. In the present study, a numerical model based on a finite-volume, time-dependent implicit procedure, which has been previously validated, is used to compute the yearly cooling and heating transmission loads under steady periodic conditions through a typical building wall, for different insulation thicknesses. The transmission loads, calculated by using the climatic conditions of Riyadh for a west-facing wall, are fed into an economic model in order to determine the optimum thickness of insulation (L_opt). The latter corresponds to the minimum total cost, which includes the cost of insulation material and its installation plus the present value of energy consumption cost over the lifetime of the building. The optimum insulation thickness depends on the electricity tariff as well as the cost of insulation material, lifetime of the building, inflation and discount rates, and coefficient of performance of the air-conditioning equipment. In the present study, the effect of electricity tariff on the computed optimum insulation thickness is investigated. Different average electricity tariffs are considered; namely, 0.05, 0.1, 0.2, 0.3 and 0.4 SR/kWh (designated as Cases 1–5, respectively; 1 US$ = 3.75 Saudi Riyals). Results using moulded polystyrene as an insulating material show that the values of L_opt are: 4.8, 7.2, 10.9, 13.7 and 16.0 cm for Cases 1–5. Under the conditions of optimal insulation thickness for each electricity tariff, Case 1 gives the lowest total cost of 17.4 SR/m², while Case 5 gives the highest total cost of 53.1 SR/m². Corresponding thermal performance characteristics in terms of yearly total and peak transmission loads, R-value, time lag and decrement factor are presented.     

Structures in superconducting YBa2Cu3O7−δ thin films investigated by magneto-optic technique

Using a reflection magneto-optic technique we have investigated natural inhomogeneities and artificial structures in YBCO thin films exposed to an external magnetic field. The artificial structures were mechanically scratched by scanning a diamond tip with different loading over the film surface. Alternatively planar structures with reduced oxygen content could be patterned by heating the YBCO film with a focused laser beam in nitrogen atmosphere. Depending on the laser annealing parameters different screening properties concerning the applied magnetic field could be achieved.As a magneto-optically active layer we used EuS films evaporated on glass as well as bismuth- and gallium-doped lutetium-iron-garnet films grown onto (111) oriented gadolinium-gal lium-gar net substrates by liquid phase epitaxy. In contrast to measurements with EuS films that show only weak faraday rotation for temperatures higher than 20 K the magneto-optic studies have been expanded to about 60 K by using the garnet films.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Au/ZnO and Au/Fe2O3 catalysts for CO oxidation at ambient temperature: comments on the effect of synthesis conditions on the preparation of high activity catalysts prepared by coprecipitation

The preparation of Au/ZnO and Au/Fe₂O₃ catalysts using two coprecipitation methods is investigated to determine the important factors that control the synthesis of high activity catalysts for the oxidation of carbon monoxide at ambient temperature. In particular, the factors involved in the preparation of catalysts that are active without the need for a calcination step are evaluated. The two preparation methods differ in the manner in which the pH is controlled during the precipitation, either constant pH throughout or variable pH in which the pH is raised from an initial low value to a defined end point. Non-calcined Au/ZnO catalysts prepared using both methods are very sensitive to pH and ageing time, and catalysts prepared at a maximum pH = 5 with a short ageing time (ca. 0–3 h) exhibit high activity. Catalysts prepared at higher pH give lower activity. However, all catalysts require a short operation period during which the oxidation activity increases. In contrast, the calcined catalysts are not particularly sensitive to the preparation conditions. Non-calcined Au/Fe₂O₃ catalysts exhibit high activity when prepared at pH ≥ 5. Calcined Au/Fe₂O₃ prepared using the controlled pH method retain high activity, whereas calcined catalysts prepared using the variable pH method are inactive. The study shows the immense sensitivity of the catalyst performance to the preparation methods. It is therefore not surprising that marked differences in the performance of supported Au catalysts for CO oxidation that are apparent in the extensive literature on this subject, particularly the effect of calcination, can be expected if the preparation parameters are not carefully controlled and reported.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.