This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact reconstruction is nearly optimal and much less then the sampling frequency and below the Nyquist frequency. (iii) It has very low complexity and fast computation. (iv) It is developed on the provable mathematical model from which we are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction of the audio signal. Compressed sensing CS is an attractive compression scheme due to its universality and lack of complexity on the sensor side. In this paper a study of applying compressed sensing on audio signals was presented. The performance of different bases and its reconstruction are investigated, as well as exploring its performance. Simulations results are present to show the efficient reconstruction of sparse audio signal. The results shows that compressed sensing can dramatically reduce the number of samples below the Nyquist rate keeping with a good PSNR.
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).
The physical substance at high energy level with specific circumstances; tend to behave harsh and complicated, meanwhile, sustaining equilibrium or non-equilibrium thermodynamic of the system. Measurement of the temperature by ordinary techniques in these cases is not applicable at all. Likewise, there is a need to apply mathematical models in numerous critical applications to measure the temperature accurately at an atomic level of the matter. Those mathematical models follow statistical rules with different distribution approaches of quantities energy of the system. However, these approaches have functional effects at microscopic and macroscopic levels of that system. Therefore, this research study represents an innovative of a wi
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The present study was conducted with a view to determine whether focal laser therapy result in visual recovery and regression of macular edema in patients with non proliferative diabetic retinopathy and maculopathy ,and whether combined focal and scatter laser therapy in patients with proliferative diabetic retinopathy and maculopathy results in visual recovery ,regression of macular edema and regression of the risk factors. In the present work, a frequency doubled Nd: YAG laser was used for the treatment of diabetic retinopathy. The study evaluates 41 eyes of 33 diabetic patients both with Insulin Dependent Diabetes Mellitus IDDM, (n=16) and Non Insulin Dependent Diabetes Mellitus NIDDM, (n=17) with diabetic retinopathy divided into two
... Show MoreBackground: 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 MoreFacial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f
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FG Mohammed, HM Al-Dabbas, Iraqi journal of science, 2018 - Cited by 6
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