Adaptive Variable Degree-k Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet-Packet Transformed Audio and High Quality Speech

A fast, efficient and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations, and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising: 1- Embedded Zero-tree Wavelet (EZW) and 2- The set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.Comment: 30 pages (Double space), 15 figures, 5 tables, ISRN Signal Processing (in Press

A numerical scheme for a class of nonlinear Fredholm integral equations of the second kind

AbstractIn this paper an iterative approach for obtaining approximate solutions for a class of nonlinear Fredholm integral equations of the second kind is proposed. The approach contains two steps: at the first one, we define a discretized form of the integral equation and prove that by considering some conditions on the kernel of the integral equation, solution of the discretized form converges to the exact solution of the problem. Following that, in the next step, solution of the discretized form is approximated by an iterative approach. We finally on some examples show the efficiency of the proposed approach

Forecasting Oxygen Demand in Treatment Plant Using Artificial Neural Networks

Modeling the wastewater treatment plant is difficult due to nonlinear properties of most of its different processes. Due to the increasing concerns over environmental effects of treatment plants considering the poor operation, fluctuations in process variables and problems of linear analyses, algorithms developed using artificial intelligence methods such as artificial neural networks have attracted a great deal of attention. In this research, first using regression analysis, the parameters of biological oxygen demand, chemical oxygen demand, and pH of the input wastewater were chosen as input parameter among other different parameters. Next, using error analysis, the best topology of neural networks was chosen for prediction. The results revealed that multilayer perception network with the sigmoid tangent training function, with one hidden layer in the input and output as well as 10 training nodes with regression coefficient of 0.92 is the best choice. The regression coefficients obtained from the predictions indicate that neural networked are well able to predict the performance of the wastewater treatment plant in Yazd

DESIGN AND DEVELOPMENT OF SPECIAL CUTTING SYSTEM FOR SWEET SORGHUM HARVESTER

Sweet Sorghum is similar to racemose maize with about 3m height and 0.5-3cm thickness of stalk. Sweet Sorghum has sweet flavor stalk, which is used for sugar production. Developed cutting mechanism in this research has a rotary disk with 50 cm diameter and four cutting blades that spin clockwise. The stalks are cut with the impact and inertia forces at the linear velocity of 27 m/s, by cutting blades. This system has a simple bar mechanism guiding the whole-stalk to one side. The cutting quality tests were achieved by two series of blades with 30°and 45° blade angles on the stalk. The results showed that the stalk cutting surface with 30° blade angle was smooth and without fracture on filaments and vasculums, compared to that of 45° blade angle. Blade penetration was accomplished very well with 30° blade angle

Robust optimization based on analytical evaluation of uncertainty propagation

Optimization under uncertainty requires proper handling of those input parameters that contain scatter. Scatter in input parameters propagates through the process and causes scatter in the output. Stochastic methods (e.g. Monte Carlo) are very popular for assessing uncertainty propagation using black-box function metamodels. However, they are expensive. Therefore, in this article a direct method of calculating uncertainty propagation has been employed based on the analytical integration of a metamodel of a process. Analytical handling of noise variables not only improves the accuracy of the results but also provides the gradients of the output with respect to input variables. This is advantageous in the case of gradient-based optimization. Additionally, it is shown that the analytical approach can be applied during sequential improvement of the metamodel to obtain a more accurate representative model of the black-box function and to enhance the search for the robust optimum

Cost benefit analysis of various California renewable portfolio standard targets: is a 33% RPS optimal?

Renewable Portfolio Standards (RPSs') require that a certain fraction of the electricity generated for a given region be produced from renewable resources. California's RPS mandates that by 2020, 33% of the electricity sold in the state must be generated from renewables. Such mandates have important implications for the electricity sector as well as for the whole society. In this paper, we estimate the costs and benefits of varying 2020 California RPS targets on electricity prices, greenhouse gas (GHG) emissions, criteria pollutant emissions, the electricity generation mix, the labor market, renewable investment decisions, and social welfare. We have extended the RPS Calculator model, developed by Energy and Environmental Economics (E3) Inc., to account for distributions of fuel and generation costs, to incorporate demand functions, and to estimate the effects of RPS targets on GHG emissions, criteria pollutant emissions, and employment. The results of our modeling provide the following policy insights: (1) the average 2020 electricity price increases as the RPS target rises, with values ranging between $0.152 and$0.175/kWh (2008 dollars) for the 20% RPS to 50% RPS, respectively; (2) the 33% and 50% RPS targets decrease the GHG emissions by about 17.6 and 35.8 million metric tons of carbon dioxide equivalent (MMTCO2e) relative to the 20% RPS; (3) the GHG emission reduction costs of the RPS options are high ($71 to$94 per ton) relative to results from policy options other than RPS or prices that are common in the carbon markets; and (4) a lower target (e.g., a 27% RPS) provides higher social welfare than the 33% RPS (mandate) under low and moderate CO2 social costs (lower than $35/ton); while a higher RPS target (e.g., 50%) is more beneficial when using high CO2 social costs or with rapid renewable technology diffusion. However, under all studied scenarios, the mandated 33% RPS for California would not provide the best cost/benefit values among the possible targets and would not maximize the net social benefit objective

Effect of Combined Subsurface Structures and Steps on Hyporheic Exchange

The deployment of artificial structures in streambeds has been proposed as a way to enhance hyporheic exchange, and numerical models can be used to quantify their effects. In this study, combinations of different structures—that is, boxes, steps and a new type of subsurface structure (L-shaped structure)—were considered to evaluate their potential applicability on river restoration. Flow-3D and COMSOL were applied to simulate surface and subsurface flow, respectively. The performance of the structures was evaluated on the basis of hyporheic flow and residence time distributions. For the structure sizes here considered, results showed for steps (single step, combination of two steps) and L-shaped structures (single L-shaped structure, combination of two L-shaped structures) most hyporheic flowpaths return to the stream after 5 and 2.5 hr, respectively. Instead, shorter residence times (<0.25 hr) were found for boxes (single box, combination of two boxes). For combinations of steps and permeable boxes, the values of hyporheic flow per unit width are higher (0.35 and 0.3 m2/hr, respectively) than for the combination of L-shaped (0.06 m 2/hr). As a result, the combinations of steps and boxes are more effective in increasing hyporheic flow. However, when subsurface structures are combined with steps the resulting hyporheic exchange is dominated by the steps. Therefore, the combined use of in-stream and subsurface structures separately may increase their benefits for hyporheic exchange, but when steps are the other subsurface structures provide minor advantages