Baud

Abstract: An orthogonally multiplexed QAM (O-QAM) system is a multichannel system with a baud rate spacing between adjacent carrier frequencies; this property is desirable to digitally implement the system using the discrete Fourier transformation (DFT). This paper provides a novel digital signal processing method based on an N /2-point DFT processing in the O-QAM system. A complexity comparison between a digital O-QAM system and a digital singlechannel QAM system shows that the digital O-QAM system using the new method is more economical than the digitally implemented conventional single-channel data transmission system.

Abstract: A method and system for reading code symbols using a code symbol reading system having a programmable decode time-window filter mode of operation During this mode of operation, only decoded code symbols that have been scanned within a selected (eg central) portion of the laser scan line field are processed according to a special decode time-window filtering function In particular, if the decoded bar code symbol is a programming-type bar code symbol, then the system controller applies the function represented by the programming-type bar code symbol; and if the decoded bar code symbol is a non-programming-type bar code symbol, then the system controller either transmits symbol character data associated therewith to the host system or stores the symbol character data within memory aboard the bar code symbol reading system

Abstract: A system and method are presented for improving the performance of full range code scanners. A distance detection module determines the distance of the code symbol from the code symbol reader. In response to the detected distance, the sweep angle of the scanning element is changed to ensure that the code symbol is within the code symbol reader's field of view. The sweep angle is larger when the code symbol is in the near range, and smaller when the code symbol is in the far range.

Abstract: Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for light-path QoT belong to two categories: “exact” analytical models estimating physical-layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas, which are computationally faster but typically introduce high link margins that lead to underutilization of network resources. In this paper, we explore a third option, i.e., machine learning (ML), as ML techniques have already been successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/ or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.