The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this Paper, we worked on the modeling of packet loss using very short segments of time, the model suggested in this paper is based on binary time series, it is represented by investigating the probability of losses occurrence and loss dependency using Markov models. A well known problems of time series modeling is achieving segment's stationarity, this obstacle dictates using long time segments...
In this paper, we used the lossless compression algorithm of Hamming Correction Code Compressor (HCDC) in compressing the parameters of the ITU-T G.729 CODEC, which is the Conjugate Structure Algebraic Code Exited Linear Prediction. This standard has two rates; the standard rate with 8 Kbps and Annex D rate with 6.4 Kbps. The CODEC Parameters were generated using the standard ITU source Codes. These...
In this Paper, we worked on the modeling of packet loss in EUMEDConnect Network (network connects 6 Arab countries) from the Palestinian side. This research exploited a data set of 72 hours. each country was expressed by a randomly selected 12-hour dataset, each dataset was divided into two-hour segments, each segment was modeled as a binary time series. From the 36 segments, 26 segments were found...
In this paper, we exploit the loss less Hamming Correction Code Compressor (HCDC) in compressing the parameters of the ITU-T G.729 Conjugate Structure Algebraic Code Exited Linear Prediction Annex D of 6.4 Kbps. The CODEC Parameters were generated using the standard ITU source Codes. These parameters includes Linear Prediction Coefficients represented as LSPs, Gain and Excitation Bits, forming 64...
In this paper, we exploit the Hamming Correction Code Compressor (HCDC) Code Excited Linear Prediction frame's Parameter. These parameters includes Linear Prediction Coefficients, Gain and Excitation Bits; which is DCT residual for the signal frame, consist of 40 coefficients, each is quantized using 4 bits. For the signals used in experiments; the total bits in frame were 261 bits with transmission...
In this Paper, we worked on the modeling of packet loss in EUMEDConnect Network (Mediterranean research network connects 6 Arab countries) from the Palestinian side. This research exploited a data set of 72 hours. each country was expressed by a randomly selected 12-hour dataset, each dataset was divided into two-hour segments, each segment was modeled as a binary time series. From the 36 segments,...
In this paper, we exploit the Hamming Correction Code Compressor (HCDC) to compress speech signals of different sampling rates and bit resolutions, the compression is carried out directly to samples in its Pulse Code Modulation (PCM) form, with no prior processing. The resulting compression rate was around 2.3 in average for the tested datasets. The results were compared against the Free Lossless...
In this paper, a replacement algorithm for Linear Prediction Coefficients (LPC) along with Hamming Correction Code based Compressor (HCDC) algorithms are investigated for speech compression. We started with an CELP system with order 12 and with Discrete Cosine Transform (DCT) based residual excitation. Forty coefficients with transmission rate of 5.14 kbps were first used. For each frame of the testing...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.