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Overview of Video Transport
Published in Wes Simpson, Video Over IP, 2013
The original digital telecom standards were organized by means of a Plesiochronous Digital Hierarchy (PDH), which means “nearly synchronized.” More technically, two circuits are considered plesiochronous if their bit rates fall within a strictly limited tolerance range. By far, the most common PDH signal in North America is a T1 (also known as a DS1), which operates at a rate of 1.544 Mbps. This is enough capacity to carry 24 voice channels, each operating at 64 kbps, plus overhead. In Europe, the corresponding signal is commonly called an E1, which operates at 2.048 Mbps and has the capacity to carry 30 voice channels (also at 64 kbps each) plus overhead. Moving up in speed in North America is a DS3 (or T3), which operates at 44.736 Mbps and carries 28 DS1's, equivalent to 672 voice channels. In Europe, the E3 rate is used, which operates at a speed of 34.368 Mbps and carries 16 E1s, equivalent to 480 voice channels. Higher-speed PDH interfaces are no longer used; they have been replaced by SONET/SDH standards.2
Telephony
Published in J. Dunlop, D. G. Smith, Telecommunications Engineering, 2017
In the late 1980s, the need to circumvent these restrictions for future communications was addressed. The result was a proposal by AT&T of a system called SONET which, after some modification, was adopted by CCITT under the name synchronous digital hierarchy, or SDH for short. At first the title seems strange, but it refers to the main feature of the technique which is to provide a simple method of multiplexing and demultiplexing information streams of various bit rates; in contrast to the existing system which is now referred to as the plesiochronous network, or PDH.
Telephony and associated systems
Published in Geoff Lewis, Communications Technology Handbook, 2013
This system was the precursor of SDH and while the two are currently in operation, PDH will progressively become obsolete. The rather slower bit rates of 2, 8, 34 and 140 Mbit/s are standard and the system allows varying clock rates across the multiplex. Unlike SDH, the network management of accounting, configuration, performance monitoring and fault location are not embedded within the system.
Optimised ensemble learning-based IoT-enabled heart disease monitoring system: an optimal fuzzy ranking concept
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
N.V.L.M Krishna Munagala, Lakshmi Rajeswara Rao Langoju, A. Daisy Rani, D.V.rama Koti Reddy
Sarmah (2020) has implemented a new ‘IoT-centred deep learning modified neural network (DLMNN)’ to monitor patients, especially heart patients, to diagnose and offer medication regularly. The following three steps have been used in the recommended model: data authentication, data encryption and classification. Wearable ‘IoT sensor devices’ were deployed in the patient’s body, concurrent to the cloud utilising ‘Patient id, Doctor Id, Hospital Id-Advanced Encryption Standard (PDH-AES) Algorithm’. Then, the decryption is carried out on the encrypted data through the DLMNN technique for the classification, and the outcomes are obtained and classified as abnormal or normal. This indicates the heart condition of patients, and when the outcomes were achieved as abnormal, the alert message was sent to the medical professional to treat the patient. The analysis of the designed PDH-AES has supported achieving the lowest time for encryption and decryption.
An extended solvation theory for electrolyte osmotic and activity coefficients. Application to 1:1, 1:2, 1:3, 1:4, 2:1, 2:2, 3:1, 3:2 and 4:1 aqueous electrolyte solutions
Published in Chemical Engineering Communications, 2022
Javier Temoltzi-Avila, Gustavo A. Iglesias-Silva, Mariana Ramos-Estrada, Indira Yarely López-Cortés, Kenneth R. Hall
We have correlated simultaneously the mean activity coefficient and the osmotic coefficient of electrolyte aqueous solutions. Table 2 shows the AAPD, SD, bias for each system together with the source of the experimental data used in this work to obtain the parameters in the Archer, DH-MS, and PDH-MS models. Also, Table 3 shows a summary of these values for each type of electrolyte together with the total value for electrolyte solutions with and without univalent ions. Calculated parameters together with its asymptotic standard error (in parenthesis) are shown in Tables 4 and 5. In the case of the Archer equation, we have included statistically invalid parameters because without them the error in the correlation of the model increases considerably. The parameters are highly correlated.
Pd–H and Ni–H phase diagrams using cluster variation method and Monte Carlo simulation
Published in Philosophical Magazine, 2019
Natacha Bourgeois, Pierre Cenedese, Jean-Claude Crivello, Jean-Marc Joubert
The previously described method allows to sample at low temperature, however, it cannot reach 0 K, for which a ground state analysis was performed by minimising, in matrix form:To solve the LP (linear programming) problem defined by Equation (6), we used the interior point algorithm from Ref. [38] and to solve the LP problem defined by Equation (7), we used the simplex based code LA04D adding upper bounds on probabilities [39]. In the case of PdH in addition to pure palladium and the hydride PdH , we identified two other ground states occurring at that is a PdH structure and at or PdH through which low temperature phase diagram can be computed if required.