In this section is to give a brief description of dynamic programming algorithm that produces an optimal encoding under on very reasonable assumption. A more detailed description can be found in Motta al.. Although this is not likely to be precisely true in practice, it is likely to be a very good approximation to what happens. That is, two encodings of a frame at the same quality are likely to be equivalent in their ability to predict a subsequent frame.

The dynamic sub window scheme is summarized as follows: If 1st frame eq kth then Skip the transcoding of the k+1th sub-window else Transcode the frame no=2

Hyper spectral images contain a wealth of data, but hyper spectral images are interpreting them requires an understanding of exactly what properties of ground materials we are references is provided on trying to measure, and how they relate to the measurements actually made by the hyper spectral sensor.

In multi spectral imaging, a series of images acquired at many wavelength producing an “image cube” in Figure 1.
Fig1A Novel Hyper Spectral_decrypted
Figure 1 : Image cube


For the purposes of this paper, the planes of decreasing spectral energy were numbered beginning with the lowest level sub-band plane. Then, the multiplier for each of the data points in each plane was determined as follows

To assume that at a time t=n a new video frame should be transmitted through the variable network and at this time t the available bandwidth, say B(n), is less than the minimum required for transmitting a frame in one frame period, In case that the current bandwidth is greater than the requested one, all multimedia information can be delivered. A better solution is proposed here that performs a content-based sampling. In this way, among the current and the candidate frames for skipping (i.e., k, k+1, k+2, …,k+K-1), the most representative is selected to be delivered whereas the remaining frames are discarded.


Fig2A Novel Hyper Spectral_decrypted

Figure 2 : Video frame skipping

We calculate the average feature vector f over all K frames F=1/K XVif

To select as most representative frame for the one whose feature vector is closer in the sense of the L2 norm. Figure 3 presents a graphical representation of the proposed scheme. In this example, at time t=n, the algorithm estimates that the bandwidth is four times lower than the minimum required, i.e., K=4. Then, the algorithm selects as representative among the four successive frames the one whose feature vector is the closest to the min vector of the four frames. In this example, we assume that this frame is the second, i.e., J=2. We have also assumed that this frame completes its transmission within three frames instead of four, due to the fact the network bandwidth has slightly increase during the frame transmission. Thus, at time n=4 the current bandwidth is again evaluated resulting in K=5.

Representative APR 391%

Let's say you want to borrow $100 for two week. Lender can charge you $15 for borrowing $100 for two weeks. You will need to return $115 to the lender at the end of 2 weeks. The cost of the $100 loan is a $15 finance charge and an annual percentage rate of 391 percent. If you decide to roll over the loan for another two weeks, lender can charge you another $15. If you roll-over the loan three times, the finance charge would climb to $60 to borrow the $100.

Implications of Non-payment: Some lenders in our network may automatically roll over your existing loan for another two weeks if you don't pay back the loan on time. Fees for renewing the loan range from lender to lender. Most of the time these fees equal the fees you paid to get the initial payday loan. We ask lenders in our network to follow legal and ethical collection practices set by industry associations and government agencies. Non-payment of a payday loan might negatively effect your credit history.

Calculate APR