This paper presents a parametric audio compression scheme intended for scalable audio coding applications, and is particularly well suited for operation at low rates, in the vicinity of 5 to 32 Kbps. The model consists of two complementary components: Sines plus Noise (SN). The principal component of the system is an. overlap-add analysis-by-synthesis sinusoidal model based on conjugate matching pursuits. Perceptual information about human hearing is explicitly included into the model by psychoacoustically weighting the pursuit metric. Once analyzed, SN parameters are efficiently quantized and coded. Our informal listening tests demonstrated that our coder gave competitive performance to the-state-of-the- art HelixTM Producer Plus 9 from Real Networks®, and on the average our coder Offered a 20 percent lower bitrate for the some audio quality. The audio coder gives a much wider range of scalability than previous work of sinusoidal coders as well as existing commercial audio coders. Moreover, the audio coder a gracefully degrades in quality from hi-fidelity to a reasonable quality at a very low bitrate, 5 Kbps. The most obvious application for the SN coder is in scalable, high fidelity audio coding and signal modification.
Let R be a ring with identity and M is a unitary left R–module. M is called J–lifting module if for every submodule N of M, there exists a submodule K of N such that
Let R be associative ring with identity and M is a non- zero unitary left module over R. M is called M- hollow if every maximal submodule of M is small submodule of M. In this paper we study the properties of this kind of modules.
Throughout this work we introduce the notion of Annihilator-closed submodules, and we give some basic properties of this concept. We also introduce a generalization for the Extending modules, namely Annihilator-extending modules. Some fundamental properties are presented as well as we discuss the relation between this concept and some other related concepts.
In this paper, the concept of semi-?-open set will be used to define a new kind of strongly connectedness on a topological subspace namely "semi-?-connectedness". Moreover, we prove that semi-?-connectedness property is a topological property and give an example to show that semi-?-connectedness property is not a hereditary property. Also, we prove thate semi-?-irresolute image of a semi-?-connected space is a semi-?-connected space.
The purpose of this paper is to give some results theorems , propositions and corollaries concerning new algebraic systems flower , garden and farm with accustomed algebraic systems groupoid , group and ring.
A new class of generalized open sets in a topological space, called G-open sets, is introduced and studied. This class contains all semi-open, preopen, b-open and semi-preopen sets. It is proved that the topology generated by G-open sets contains the topology generated by preopen,b-open and semi-preopen sets respectively.
Weibull Distribution is one of most important distribution and it is mainly used in reliability and in distribution of life time. The study handled two parameter and three-parameter Weibull Distribution in addition to five –parameter Bi-Weibull distribution. The latter being very new and was not mentioned before in many of the previous references. This distribution depends on both the two parameter and the three –parameter Weibull distributions by using the scale parameter (α) and the shape parameter (b) in the first and adding the location parameter (g)to the second and then joining them together to produce a distribution with five parameters.
... Show MoreBackground: Inflammation of the brain parenchyma brought on by a virus is known as viral encephalitis. It coexists frequently with viral meningitis and is the most prevalent kind of encephalitis. Objectives: To throw light on viral encephalitis, its types, epidemiology, symptoms and complications. Results: Although it can affect people of all ages, viral infections are the most prevalent cause of viral encephalitis, which is typically seen in young children and old people. Arboviruses, rhabdoviruses, enteroviruses, herpesviruses, retroviruses, orthomyxoviruses, orthopneumoviruses, and coronaviruses are just a few of the viruses that have been known to cause encephalitis. Conclusion: As new viruses emerge, diagnostic techniques advan
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