Signal denoising is directly related to sample estimation of received signals, either by estimating the equation parameters for the target reflections or the surrounding noise and clutter accompanying the data of interest. Radar signals recorded using analogue or digital devices are not immune to noise. Random or white noise with no coherency is mainly produced in the form of random electrons, and caused by heat, environment, and stray circuitry loses. These factors influence the output signal voltage, thus creating detectable noise. Differential Evolution (DE) is an effectual, competent, and robust optimisation method used to solve different problems in the engineering and scientific domains, such as in signal processing. This paper looks at the feasibility of using the differential evolution algorithm to estimate the linear frequency modulation received signal parameters for radar signal denoising. The results gave high target recognition and showed feasibility to denoise received signals.
Abstract
The use of modern scientific methods and techniques, is considered important topics to solve many of the problems which face some sector, including industrial, service and health. The researcher always intends to use modern methods characterized by accuracy, clarity and speed to reach the optimal solution and be easy at the same time in terms of understanding and application.
the research presented this comparison between the two methods of solution for linear fractional programming models which are linear transformation for Charnas & Cooper , and denominator function restriction method through applied on the oil heaters and gas cookers plant , where the show after reac
... Show MoreIn this paper, a random transistor-transistor logic signal generator and a synchronization circuit are designed and implemented in lab-scale measurement device independent–quantum key distribution systems. The random operation of the weak coherent sources and the system’s synchronization signals were tested by a time to digital convertor.
Fifty one patients with serologically confirmed brucellosis and 70 healthy controls were phenotyped for HLA-A, -B, -DR and -DQ antigens by using standard microlympho-cytotoxicity method, and lymphocytes defined by their CD markers (CD3, CD4, CD8 and CD19). The results revealed a significant (Pc = 0.001) increased frequency of HLA-DR8 (41.18 vs. 10.0%) in the patients . A significant increased percentage of CD8+ lymphocytes was also increased in the patients (25.15 vs. 22.0%; P = 0.006), while CD3+ lymphocytes were significantly decreased (75.1 vs. 79.4%; P = 0.02).
Background: Neonatal macrosomia is defined as a birth weight of more than 4000 g. Significant maternal and neonatal complications can result from the birth of macrosomic infants like hypoglycemia and birth injuries.Objectives: To determine the frequency of hypoglycemia in neonates with macrosomia in Amarah, IraqMethods: The study involved 146 macrosomic newborn neonates delivered in 2 maternity hospitals in Amarah, Iraq during a period from June 2011 to June 2014.Results: Hypoglycemia was observed in 16% of neonates affected by macrosomia. Maternal diabetes was the most common cause of fetal macrosomia (28%).Our results were compared with those from other parts of the world.Conclusion Macrosomia is associated with increase rate ofneonata
... Show MoreKE Sharquie, AA Noaimi, SY Mohsin, 2011 - Cited by 4
One of the most opportunistic mycosis globally is the Candida ssp., which is considered as the most agent that cause nosocomial urinary tract infections (UTIs), oral candidiasis and genitourinary candidiasis. This study included 100 samples of Iraqi subjects suffering from urinary tract infections. Identification of Candida have been done by different methods such as; characteristic of colony on culture, gram stain, and microscopically. This study aimed to isolation and identification of Candida spp from urine sample of UTI patients and find the relevance of ages and blood group of patients with the infections rate, also determine the effect of age on ESR and CRP levels in the patients. The results showed the higher frequency of
... Show MoreIn this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.