في هذه الصفحة يمكنك الحصول على تحليل مفصل لكلمة أو عبارة باستخدام أفضل تقنيات الذكاء الاصطناعي المتوفرة اليوم:
ألاسم
تَلْوِيح ; تَمَوُّج ; مَوْجَة
الفعل
أَلاحَ ( بِسَيْفِهِ , بِسِلاحِهِ إلخ ) ; تَرَقْرَقَ ; تَمَوَّجَ ; تَمَوَّرَ ; خَفَقَ العَلَمُ ; رَفْرَفَ العَلَمُ ; لَوَّحَ ( إِلَى أو لِـ ) ; مَوَّجَ ; ناسَ
ألاسم
تَلْوِيح ; تَمَوُّج ; مَوْجَة
الفعل
أَلاحَ ( بِسَيْفِهِ , بِسِلاحِهِ إلخ ) ; تَرَقْرَقَ ; تَمَوَّجَ ; تَمَوَّرَ ; خَفَقَ العَلَمُ ; رَفْرَفَ العَلَمُ ; لَوَّحَ ( إِلَى أو لِـ ) ; مَوَّجَ ; ناسَ
A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases or decreases, and then returns to zero one or more times. Wavelets are termed a "brief oscillation". A taxonomy of wavelets has been established, based on the number and direction of its pulses. Wavelets are imbued with specific properties that make them useful for signal processing.
For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly one tenth of a second. If this wavelet were to be convolved with a signal created from the recording of a melody, then the resulting signal would be useful for determining when the Middle C note appeared in the song. Mathematically, a wavelet correlates with a signal if a portion of the signal is similar. Correlation is at the core of many practical wavelet applications.
As a mathematical tool, wavelets can be used to extract information from many different kinds of data, including – but not limited to – audio signals and images. Sets of wavelets are needed to analyze data fully. "Complementary" wavelets decompose a signal without gaps or overlaps so that the decomposition process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet-based compression/decompression algorithms, where it is desirable to recover the original information with minimal loss.
In formal terms, this representation is a wavelet series representation of a square-integrable function with respect to either a complete, orthonormal set of basis functions, or an overcomplete set or frame of a vector space, for the Hilbert space of square integrable functions. This is accomplished through coherent states.
In classical physics, the diffraction phenomenon is described by the Huygens–Fresnel principle that treats each point in a propagating wavefront as a collection of individual spherical wavelets. The characteristic bending pattern is most pronounced when a wave from a coherent source (such as a laser) encounters a slit/aperture that is comparable in size to its wavelength. This is due to the addition, or interference, of different points on the wavefront (or, equivalently, each wavelet) that travel by paths of different lengths to the registering surface. Multiple, closely spaced openings (e.g., a diffraction grating), can result in a complex pattern of varying intensity.