Adaptive differential pulse-code modulation(ADPCM) is a variant of differential pulse-code modulation (DPCM) that varies the size of the quantization step, to allow further reduction of the required bandwidth for a given signal-to-noise ratio.
Typically, the adaptation to signal statistics in ADPCM consists simply of an adaptive scale factor before quantizing the difference in the DPCM encoder.[1]
ADPCM was developed in the early 1970s at Bell Labs for voice coding, by P. Cummiskey, N. S. Jayant, and James L. Flanagan.[2]
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An adaptive program is one that changes its behavior base on the current state of its environment. This notion of adaptivity is formalized, and a logic for reasoning about adaptive programs is presented. The logic includes several composition operators that can be used to define an adaptive program in terms of given constituent programs; programs resulting from these compositions retain the adaptive properties of their constituent programs. The authors begin by discussing adaptive sequential programs, then extend the discussion to adaptive distributed programs. The relationship between adaptivity and self-stabilization is discussed. A case study for constructing an adaptive distributed program where a token is circulated in a ring of processes is presented.
Adaptive delta modulation is used in audio communication systems to avoid the two drawback of delta modulation.
1. A healing agent 2. A help-seeker 3. A healing relationship 4. An ultimate objective of removing distress and enhancing adaptive competence of the help-seeking for his or her own sake
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There are 2 main methods of software modeling · Predictive(e.g.waterfall) - Good when requirements are well understood and have low technical risk. · Adaptive(e.g.spiral) - Good when requirements and needs are uncertain. High technical risk In a failure - In a predictive LC it will be expensive because we get to know the failure after finishing everything but in adaptive LC only an iteration is wasted. There several phases in the waterfall model a. Planning b. Requirements c. Specification (b and c together we call analysis) d. Design e. Implementation f. Maintenance This is called a Functional Decomposition and also a top down approach (Where we see from above and identify the big picture first and then go in to higher details) In the waterfall model, before going to the next phase we have to freeze the previous step (no turning back). This is good when we have a good understanding about the project at the beginning. But iterative models gives us more flexibility through giving chance to revisit early phases. Spiral model is a adaptive model. This is an adaptive SDLC that cycles over and over again through development activities until project is complete. Here we go through some major steps, 1. Plan 2. Analyze and Design 3. Construct prototype 4. Test and integrate These steps iterate till completion of the project. This is also can be viewed as a divide and conquer approach. In first few iterations complex or risky parts of the system are handled.
there is no difference there is no difference
in pcm entie sample is sent.. where as in dpcm the difference between the predicted value and the original sample is sent which will be smaller when compared rto the original sample..
Let (M) and (D) be the peak amplitudes of signals m(t) and d(t) respectively. If the same quantization level (L), is used to sample both signals. The quantization step (Dv) in DPCM is reduced by a factor of (D/M). The quantization noise power is [(Dv)^2]/2. So we can say, that in DPCM the quatization noise reduces by the factor (M/D)^2. As the SNR is inversely proportional to Noise, this parameter (SNR) increases by the same factor. In Practice the SNR improvement may be as high as 25 dB in such cases as short-term voiced speech spectra. Alternatively for the same SNR, the bit rate for DPCM could be lower than for PCM by 3 to 4 bits per sample. Thus, telephone systems using DPCM can often operate at 32 kbits/s (4 bits * 8000 saples/s) or even 24 kbit/s (3 bits * 8000 saples/s). So DPCM could improve the SNR of the system, using less bandwidth.
An adaptive zone is an environment which allows the development of adaptive radiation.
Adaptive Radiation :)
Adaptive systems are ones that are continually changing to meet the demands of the environment. Non-adaptive systems do not change.
why are adaptive expectations inefficient
An adaptive enzyme is an enzyme which is present in a cell only under conditions where it is clear of adaptive value.
Adaptive Planning was created in 2003.
Non adaptive algorithm requires any changes to be made manually. Adaptive algorithms are able to make any changes automatically.
Robert K. Tyson has written: 'Principles of adaptive optics' -- subject(s): Adaptive Optics, Optics, Adaptive 'Astronomical adaptive optics systems and applications III' -- subject(s): Congresses, Adaptive Optics, Astronomical instruments, Imaging systems in astronomy, Design and construction 'Lighter side of adaptive optics' -- subject(s): Adaptive Optics, Humor, Imaging systems in astronomy, Optics, Adaptive
The Adaptive Ultimate was created in 1935-11.