Abstract: The present invention relates to a vehicle communication system application that enables users or drivers of vehicles to communicate with other drivers of nearby vehicles in a safe and effective manner. The application can be downloaded and installed on the vehicle dashboard or any other user device, such as a mobile phone or the like, and can be used to send and receive messages from one vehicle owner to another nearby vehicle owner, or another nearby business, while driving to a destination. Additionally, the application can be used for local advertising by nearby business owners to promote businesses and can be used to seek help or assistance in case of any emergency situations.

Abstract: A method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems. The automatic tuning method provides for time-varying tuning parameters &lgr;k and &mgr;k that are functions of the instantaneous measured acoustic noise signal, weight vector length, and measurement noise variance. The method addresses situations in which signal-to-noise ratio varies substantially due to nonstationary noise fields, affecting stability, convergence, and steady-state noise cancellation performance of LMS algorithms. The method has been embodied in the particular context of active noise cancellation in communication headsets. However, the method is generic, in that it is applicable to a wide range of systems subject to nonstationary, i.e., time-varying, noise fields, including sonar, radar, echo cancellation, and telephony.

Abstract: A method to automatically and adaptively tune a leaky, normalized least-mean-square (LNLMS) algorithm so as to maximize the stability and noise reduction performance in feedforward adaptive noise cancellation systems. The automatic tuning method provides for time-varying tuning parameters &lgr;k and &mgr;k that are functions of the instantaneous measured acoustic noise signal, weight vector length, and measurement noise variance. The method addresses situations in which signal-to-noise ratio varies substantially due to nonstationary noise fields, affecting stability, convergence, and steady-state noise cancellation performance of LMS algorithms. The method has been embodied in the particular context of active noise cancellation in communication headsets. However, the method is generic, in that it is applicable to a wide range of systems subject to nonstationary, i.e., time-varying, noise fields, including sonar, radar, echo cancellation, and telephony.