The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform

Amos Lapidoth - One of the best experts on this subject based on the ideXlab platform.

  • Conveying Data and state with feedback
    International Symposium on Information Theory, 2016
    Co-Authors: Shraga I Bross, Amos Lapidoth
    Abstract:

    The Rate-and-State capacity of a state-dependent channel with a state-cognizant encoder is the highest possible rate of communication over the channel when the decoder—in addition to reliably decoding the Data—must also reconstruct the state sequence with some required fidelity. Feedback from the channel output to the encoder is shown to increase this capacity even for channels that are memoryless with memoryless states. This capacity is calculated here for such channels with feedback when the state reconstruction fidelity is measured using a single-letter distortion function and the state sequence is revealed to the encoder in one of two different ways: strictly-causally or causally.

  • ISIT - Conveying Data and State with feedback
    2016 IEEE International Symposium on Information Theory (ISIT), 2016
    Co-Authors: Shraga I Bross, Amos Lapidoth
    Abstract:

    The Rate-and-State capacity of a state-dependent channel with a state-cognizant encoder is the highest possible rate of communication over the channel when the decoder—in addition to reliably decoding the Data—must also reconstruct the state sequence with some required fidelity. Feedback from the channel output to the encoder is shown to increase this capacity even for channels that are memoryless with memoryless states. This capacity is calculated here for such channels with feedback when the state reconstruction fidelity is measured using a single-letter distortion function and the state sequence is revealed to the encoder in one of two different ways: strictly-causally or causally.

David Mills - One of the best experts on this subject based on the ideXlab platform.

  • Pipeline Scaling Parameters
    Pneumatic Conveying Design Guide, 2016
    Co-Authors: David Mills
    Abstract:

    For the reliable design of a pneumatic Conveying system, actual Conveying Data for the material to be conveyed is required. If this is not available, it would be recommended that the material should be tested in order to obtain the Data. A test facility should be used, or possibly Data that has been obtained from another installation, but the actual pipeline configuration does not have to be the same or replicated. Scaling parameters are presented in this chapter that will allow such Data from one pipeline to be scaled to that of the required facility. Differences in pipeline bore, Conveying distance, number and geometry of bends, and pipeline orientation can all be taken into account. For a given plant pipeline, a number of different combinations of pipeline bore and air supply pressures will generally be capable of meeting the required duty. Such an analysis is included and it is shown how the choice of Conveying parameters can influence both the cost of operating the plant, in terms of power requirements, and the potential capital cost of the plant.

  • Chapter 17 – Design Procedures
    Pneumatic Conveying Design Guide, 2016
    Co-Authors: David Mills
    Abstract:

    Logic diagrams are presented for the design of pneumatic Conveying systems based on the use of both mathematical models and Conveying Data. Logic diagrams are also presented for checking the performance of an existing system, or for a potential change of duty, again based on the use of both models and Data. There is rarely a single solution to the specification of a pneumatic Conveying system for a given duty. As a consequence the logic diagrams include numerous checks so that optimum solutions are achieved in terms of either obtaining the minimum power requirement for a given duty, or achieving a maximum material flow rate for the given Conveying parameters. To help in this process several series of design curves are included to illustrate the potential influence of the major system variables such as Conveying distance, pipeline bore, and air supply pressure, as well as the problematical issue of material type.

  • Chapter 28 – Particle Degradation
    Pneumatic Conveying Design Guide, 2016
    Co-Authors: David Mills
    Abstract:

    A considerable number of bulk particulate materials that require conveyance are potentially degradable. By virtue of the nature of the Conveying process, requiring relatively high velocities, if friable materials are to be conveyed, then considerable damage can occur to any material being conveyed. As a consequence there is always some concern about using pneumatic Conveying systems for such materials. There are many variables in the process, related to both the material to be conveyed and the Conveying conditions, and a review of small-scale test facility Data are presented to illustrate the potential influence of such variables. Particulate material, particle size, velocity, impact angle, and surface material are all considered. Full-scale Conveying Data are also presented for a range of materials to show the order of magnitude of the problem. Consequent operational problems, such as the potential for an explosion, are also highlighted. The specific problem of particle melting, in the case of plastic-type particulate materials, is also considered.

  • chapter 12 Conveying capability
    Pneumatic Conveying Design Guide (Second Edition), 2004
    Co-Authors: David Mills
    Abstract:

    Publisher Summary The Conveying characteristics for different materials vary significantly. This is particularly so for the materials capable of being conveyed in dense phase. At low values of airflow rate, the lines of constant Conveying line pressure drop can have a wide variety of slopes. There is also the added complexity of different materials having different minimum Conveying limits. Thus, for a given air flow rate and Conveying line pressure drop, material flow rates for different materials vary considerably, and the air flow rate necessary to convey different materials can also vary considerably. If only low-pressure air is available for Conveying a material through a pipeline, such as that from a positive displacement blower or a vacuum system, and below about I bar gauge, a material will only be conveyed in dilute phase through a pipeline, unless the Conveying distance is very short. This chapter presents Conveying Data for four different materials.

  • Chapter 12 – Conveying capability
    Pneumatic Conveying Design Guide, 2004
    Co-Authors: David Mills
    Abstract:

    Publisher Summary The Conveying characteristics for different materials vary significantly. This is particularly so for the materials capable of being conveyed in dense phase. At low values of airflow rate, the lines of constant Conveying line pressure drop can have a wide variety of slopes. There is also the added complexity of different materials having different minimum Conveying limits. Thus, for a given air flow rate and Conveying line pressure drop, material flow rates for different materials vary considerably, and the air flow rate necessary to convey different materials can also vary considerably. If only low-pressure air is available for Conveying a material through a pipeline, such as that from a positive displacement blower or a vacuum system, and below about I bar gauge, a material will only be conveyed in dilute phase through a pipeline, unless the Conveying distance is very short. This chapter presents Conveying Data for four different materials.

Shraga I Bross - One of the best experts on this subject based on the ideXlab platform.

  • Conveying Data and state with feedback
    International Symposium on Information Theory, 2016
    Co-Authors: Shraga I Bross, Amos Lapidoth
    Abstract:

    The Rate-and-State capacity of a state-dependent channel with a state-cognizant encoder is the highest possible rate of communication over the channel when the decoder—in addition to reliably decoding the Data—must also reconstruct the state sequence with some required fidelity. Feedback from the channel output to the encoder is shown to increase this capacity even for channels that are memoryless with memoryless states. This capacity is calculated here for such channels with feedback when the state reconstruction fidelity is measured using a single-letter distortion function and the state sequence is revealed to the encoder in one of two different ways: strictly-causally or causally.

  • ISIT - Conveying Data and State with feedback
    2016 IEEE International Symposium on Information Theory (ISIT), 2016
    Co-Authors: Shraga I Bross, Amos Lapidoth
    Abstract:

    The Rate-and-State capacity of a state-dependent channel with a state-cognizant encoder is the highest possible rate of communication over the channel when the decoder—in addition to reliably decoding the Data—must also reconstruct the state sequence with some required fidelity. Feedback from the channel output to the encoder is shown to increase this capacity even for channels that are memoryless with memoryless states. This capacity is calculated here for such channels with feedback when the state reconstruction fidelity is measured using a single-letter distortion function and the state sequence is revealed to the encoder in one of two different ways: strictly-causally or causally.

Timo Koskela - One of the best experts on this subject based on the ideXlab platform.

  • Device-to-device (D2D) communication in cellular network - Performance analysis of optimum and practical communication mode selection
    IEEE Wireless Communications and Networking Conference WCNC, 2010
    Co-Authors: Sami Hakola, Janne Lehtoma, Tao Chen, Timo Koskela
    Abstract:

    In a cellular network system one way to increase its capacity is to allow direct communication between closely located user devices when they are communicating with each other instead of Conveying Data from one device to the other via the radio and core network. The problem is then when the network shall assign direct communication mode and when not. In previous works the decision has been done individually per communicating device pair not taking into account other devices and the current state of the network. We derive means for getting optimal communication mode for all devices in the system in terms of system equations. The system equations capture information of the network such as link gains, noise levels, signal-to-interference-and-noise-ratios, etc., as well as communication mode selection for the devices. Using the derived equations performance bounds for the cellular system where D2D communication is an additional communication mode are illustrated via simulations. Further, practical communication mode selection algorithms are used to evaluate their system performance against the achievable bounds. Analysis show the usability of the system equations and the potential of having D2D operation integrated into a cellular system when there is enough local communication occurring.

Sami Hakola - One of the best experts on this subject based on the ideXlab platform.

  • Device-to-device (D2D) communication in cellular network - Performance analysis of optimum and practical communication mode selection
    IEEE Wireless Communications and Networking Conference WCNC, 2010
    Co-Authors: Sami Hakola, Janne Lehtoma, Tao Chen, Timo Koskela
    Abstract:

    In a cellular network system one way to increase its capacity is to allow direct communication between closely located user devices when they are communicating with each other instead of Conveying Data from one device to the other via the radio and core network. The problem is then when the network shall assign direct communication mode and when not. In previous works the decision has been done individually per communicating device pair not taking into account other devices and the current state of the network. We derive means for getting optimal communication mode for all devices in the system in terms of system equations. The system equations capture information of the network such as link gains, noise levels, signal-to-interference-and-noise-ratios, etc., as well as communication mode selection for the devices. Using the derived equations performance bounds for the cellular system where D2D communication is an additional communication mode are illustrated via simulations. Further, practical communication mode selection algorithms are used to evaluate their system performance against the achievable bounds. Analysis show the usability of the system equations and the potential of having D2D operation integrated into a cellular system when there is enough local communication occurring.