Over the last decade or so, wavelets have had a growing impact on signal processing theory and practice, both because of their unifying role and their successes in applications. Filter banks, which lie at the heart of wavelet-based algorithms, have become standard signal processing operators, used routinely in applications ranging from compression to modems. The contributions of wavelets have often been in the subtle interplay between discrete-time and continuous-time signal processing. The purpose of this article is to look at recent wavelet advances from a signal processing perspective. In particular, approximation results are reviewed, and the implication on compression algorithms is discussed. New constructions and open problems are also addressed. Finding a good basis to solve aproblem dates at least back to Fourier and his investigation of the heat equation. The series proposed by Fourier has several distinguishing features: The series is able to represent any finite energy function on an interval. The basis functions are eigenfunctions of linear shift invariant systems or, in other words, Fourier series diagonalize linear, shift invariant operators.

Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures. The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution.This essay based on modified PSO methodology. The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes, and using updated data during 2008- 2012 for final forecasting, to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

Background: Breast engorgement (BE) is a physiologic condition in the postpartum period characterized by painful swelling of the breasts. BE‑ associated pain is the second main cause of exclusive breastfeeding failure in the early weeks of childbirth. Objectives: This study aimed to compare the effects of hot compress and hot ginger compress on BE‑ associated pain. Methods: This randomized clinical trial was conducted in 2018 on 76 breastfeeding women with BE conveniently recruited from Imam Reza hospital, Mashhad, Iran. Participants were randomly allocated to a control group to receive hot compress (n = 38) and an intervention group to receive hot ginger compress (n = 38). Study intervention was implemented in both groups thrice daily for 2 consecutive days. A Visual Analog scale was used for the assessment of BE‑ associated pain before and after the study intervention. Data were analyzed using the Mann– Whitney U, the independent‑ samples t, the Wilcoxon, and the Chi‑ square tests as well as the analysis of covariance. Results: The mean score of BE‑ associated pain in the right and the left breasts significantly reduced by, respectively, 6. 25 ± 1. 76 and 6. 06 ± 1. 76 points in the intervention group (P < 0. 05) and by 3. 21 ± 1. 02 and 3. 48 ± 1. 21 points in the control group (P > 0. 05). The decreases in the mean scores of the right and the left BE‑ associated pain in the intervention group were significantly greater than the control group (P < 0. 05). Conclusion: Hot ginger compress is more effective than hot compress in reducing BE‑ associated pain among breastfeeding women.

The recent development in renewable energy systems and the high demand for having clean and low cost energy sources encourage people to use distributed generator (DG) systems. Proper addition and placement of DG units can increase reliability and reduce the loss and production cost. In this paper using Ant Colony method, we developed an optimum placing scheme for DGs. The proposed method is tested on an IEEE 34-shinhe system. Results show that if DGs are able to generate active power, their effectiveness will increase.

Population growth, urbanization and immigration from rural areas to cities results in increasing expansion of the cities. The adequate urban development requires proper and accurate planning, such those facilities for the new areas that are provided and environmental impacts are lessened. Computer based simulation and prediction of urban growth can be assumed as a good start for urban planning. In this research, a new agent base simulation of urban growth is developed, in which the communication and decision of agents are imitated from the Particle Swarm Optimization (PSO) algorithm. The model is tested on the urban growth of Zanjan city- Iran between 2005 and 2015. In this model, land developers are classified into three groups of agents according to their income level. These agents search the environment and find proper lands for development according to their priorities and conditions. The output of the model is 74% similar to the reality according to the Kappa index. This and other results show that the model can predict the expansion of the city adequately.Moreover, the comparison made shows that modeling of the relations and communications between agents similar to PSO can slightly improve the quality of the model. The results showed the adequacy of the proposed agent-based modelling for the simulation of urban growth. To have a more accurate model, it is recommended to model the behavior of the agents with more details and also to consider the competition between agents.

Multicarrier phase coded signals have been recently introduced to achieve high range resolution in radar systems. As single carrier phase coded radars, the common method for compression of these signals, is using matched filter or computing the auto correlation function directly. In this paper we propose a new method based on fast Fourier transform (FFT) with lower computational load with respect to traditional approach. Furthermore, based on this new approach, a method for estimation of communication channel is introduced that can be used for improving detection performance and target position estimation in tracking mode.

The use of distributed generation (DG) resources is seen as a key important factor in improving technical and economic aspects of distribution systems. However, little research exists concerning the optimal location and operation of DGs. This paper proposes a novel method to find simultaneously the optimal location and operation of DGs. The method is developed in two phases. Phase 1 deals with the optimal location of DGs using a multiobjective optimization problem in which active power loss reduction, voltage profile improvement, and voltage regulation are the objectives. A MPSO algorithm is developed to solve the multiobjective problem. Phase 2 copes with the optimal income of the DGs’ owners along with the optimal total payment of the distribution company by adopting a bi-level optimization method with two agents. Game theory is employed to assist in finding optimal contract prices. The performance of the proposed method is evaluated by using two IEEE, 33-bus, standard distribution network. This paper concludes that the proposed method can serve as an accurate tool for practitioners in finding the optimal location and operation strategy of DGs.

The existence of a proper road network is one of the factors of economical growth and sustainable development. Traditional routing methods are time-consuming and costly. In addition, the horizontal and vertical components of the route are taken into consideration separately. Since 1970, efforts have been made to automate routing optimization. The Genetic Algorithm is a heuristic method that is used for solving different optimization problems. This research uses the genetic algorithm for path optimization. This algorithm takes both horizontal and vertical dimensions into consideration simultaneously. Chromosomes are defined as a vector array of station points. The suggested method was implemented for the route of Damghan to Dibaj. At first this research explores the importance of the objective functions of the existing route by using an innovative method with an inverse modeling approach. The results show that the share of the length factor is only 10%, so the low degree of the importance of the path length function imposes a lot of cost on the path users, and as a result, it is the main factor of the instability of the existing path. In order to improve the performance, the algorithm parameters were tuned on a simulation region before the final implementation. The objective functions are: route length, technical and engineering, economical, geological and environmental principles. In the final implementation, the algorithm specifies a corridor for the path at the level of the detailed routing. Then in semi-detailed level, the best paths in this corridor will be found. At the end, the optimal alignment is determined at the executive level. Finally the circular arches were implemented automatically based on the Policy and Geometric Design of Highways. By comparing the proposed alignment with the existing road, it shows a reduction in the length of the road by 9. 1 km (%18), and 20% less passing than high-cost landuses. The present study shows the high ability of the genetic algorithm in path optimization.