Abstract: Embodiments described herein introduce systems and methods of configurable robots for network based processes. An application server may provide a graphical user interface (GUI) including different modular components of virtual robots. The application server may provide quantitative models for virtual robots with a variety of trading styles. A user may be able to create his/her own virtual robot by dragging and dropping different modular components according to the user's requirements. The virtual robots may automatically conduct transactions and manage wealth for the user. The virtual robot may automatically and dynamically perform quantitative analysis, risk management, trading activity, and portfolio management. In addition to creating a customized virtual robot, a user may lease his/her virtual robot and collect payments from the renters. A user may also offer trading signals generated by his/her robots for other users to purchase.

Abstract: Embodiments described herein introduce systems and methods of configurable robots for network based processes. An application server may provide a graphical user interface (GUI) including different modular components of virtual robots. The application server may provide quantitative models for virtual robots with a variety of trading styles. A user may be able to create his/her own virtual robot by dragging and dropping different modular components according to the user's requirements. The virtual robots may automatically conduct transactions and manage wealth for the user. The virtual robot may automatically and dynamically perform quantitative analysis, risk management, trading activity, and portfolio management. In addition to creating a customized virtual robot, a user may lease his/her virtual robot and collect payments from the renters. A user may also offer trading signals generated by his/her robots for other users to purchase.

Abstract: Data from a communications channel is decoded by receiving data bits corresponding to encoded data, determining a set of data representations from the data bits, distributing the set of data representations into bins across a dynamic range to generate a distribution of the data representations, assigning a respective intermediate scale factor to each bin, deriving a set of moments from the intermediate scale factors, combining the moments into a scaling factor, scaling the data representations by the scaling factor, and sending the scaled data representations to a decoder. The data representations may be a histogram or cumulative distribution function of log-likelihood ratios (LLRs) or values based on channel estimates. In an iterative implementation performed until a stopping condition is met, the data representations may be scaled down on later iterations to avoid saturation. A correction factor may be applied to update the scaling factor for later data bits.

Abstract: Data from a communications channel is decoded by receiving data bits corresponding to encoded data, determining a set of data representations from the data bits, distributing the set of data representations into bins across a dynamic range to generate a distribution of the data representations, assigning a respective intermediate scale factor to each bin, deriving a set of moments from the intermediate scale factors, combining the moments into a scaling factor, scaling the data representations by the scaling factor, and sending the scaled data representations to a decoder. The data representations may be a histogram or cumulative distribution function of log-likelihood ratios (LLRs) or values based on channel estimates. In an iterative implementation performed until a stopping condition is met, the data representations may be scaled down on later iterations to avoid saturation. A correction factor may be applied to update the scaling factor for later data bits.