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.
Communication has seen a big advancement through ages; concepts, procedures and technologies, it has also seen a similar advancement of language. What unites language and media is the fact that each one of them guides and contributes to the other; media exists and results from language and from the other sign systems, and what strengthens this connection is the symbolic language system, as media helps it by providing knowledge and information. The change that occurred through time must leave a significant trace in the media, for example Diction, which has changed concerning development and growth, also the ways and mediums of media have become manifold and widespread. This change affected the recipient whether it was a reader, listener o
... Show MoreAnti-Neutrophil Cytoplasmic Antibodies (ANCA) are a heterogeneous group of autoantibodies with a broad spectrum of clinically associated diseases. The diagnostic value is established for Proteinase 3 (PR3)-ANCA as well as Myeloperoxidase (MPO)-ANCA. To estimate the frequency of anti-neutrophile cytoplasmic antibodies (ANCA) in sera from a group of Iraqi patients with some autoimmune diseases compared with a healthy control group. Serum samples were collected from one hundred patient, 47 males and 53 females; with age range of 16-70 years; 20 specimens from patients with systemic lupus erythematosus (SLE), 30 from patients with ulcerative colitis (UC), and 50 from patients with rheumatoid arthritis (RA). A group of 40 apparently healthy b
... Show Morethe behavior of the first-order black and gray solitons propagtedin optical fiber in the presence of frequency chirp is studied analytically and numerically results show that phase profile of black solitons changes abruptly
This research describes the design & implementation of frequency synthesizer using single loop Phase lock loop with the following specifications: Frequency range (1.5 – 2.75) GHz,Step size (1 MHz), Switching time 36.4 µs, & phase noise @10 kHz = -92dBc & spurious -100 dBc
The development in I.C. technology provide the simplicity in the design of frequency synthesizer because it implements the phase frequency detector(PFD) , prescalar & reference divider in single chip. Therefore our system consists of a single chip contains (low phase noise PFD, charge pump, prescalar & reference divider), voltage controlled oscillator , loop filter & reference oscillator. The single chip
... Show MoreThe problem of frequency estimation of a single sinusoid observed in colored noise is addressed. Our estimator is based on the operation of the sinusoidal digital phase-locked loop (SDPLL) which carries the frequency information in its phase error after the noisy sinusoid has been acquired by the SDPLL. We show by computer simulations that this frequency estimator beats the Cramer-Rao bound (CRB) on the frequency error variance for moderate and high SNRs when the colored noise has a general low-pass filtered (LPF) characteristic, thereby outperforming, in terms of frequency error variance, several existing techniques some of which are, in addition, computationally demanding. Moreover, the present approach generalizes on existing work tha
... Show MoreData Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need
... Show MoreMost of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
... Show MoreRecently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results
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