Abstract: A package comprises a substrate including a first surface, and an upper conductive layer arranged on the first surface, a first light-emitting unit arranged on the upper conductive layer, and comprises a first semiconductor layer, a first substrate, a first light-emitting surface and a first side wall, a second light-emitting unit, which is arranged on the upper conductive layer, and comprises a second light-emitting surface and a second side wall, a light-transmitting layer arranged on the first surface and covers the upper conductive layer, the first light-emitting unit, and the second light-emitting unit, a light-absorbing layer, which is arranged between the substrate and the light-transmitting layer in a continuous configuration of separating the first light-emitting unit and the second light-emitting unit from each other, and a reflective wall arranged on the first side wall, wherein a height of the reflective wall is lower than that of the light-absorbing layer.

Abstract: Aspects of the subject disclosure may include, for example, assigning a first interest measure associated with a first input to a learning machine at a first cycle, determining a first intelligence level according to a first product of the first interest measure and a first performance level based on the first input, and responsive to receiving a subsequent input at a subsequent cycle, reducing the first interest measure associated with the first input at the first cycle of the learning machine according to a total number of cycles that have occurred since the first cycle, assigning a new interest measure to the subsequent input at the subsequent cycle, generating a subsequent performance level according to the subsequent input, and determining a subsequent intelligence level according to a second product of the subsequent interest level and a subsequent performance level. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, obtaining regression coefficients that quantify a relationship between premises feedback and first network and premises performance indicators, obtaining second network performance indicators for the network elements, obtaining second premises performance indicators for the customer premises equipment, and predicting customer complaints by applying the obtained regression coefficients to at least the second network performance indicators and the second premises performance indicators. Other embodiments are disclosed.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, obtaining regression coefficients that quantify a relationship between premises feedback and first network and premises performance indicators, obtaining second network performance indicators for the network elements, obtaining second premises performance indicators for the customer premises equipment, and predicting customer complaints by applying the obtained regression coefficients to at least the second network performance indicators and the second premises performance indicators. Other embodiments are disclosed.

Abstract: Methods and apparatus to characterize and predict network health status are presented. A particular method of characterizing and predicting a health status of a network node includes receiving a plurality of report values associated with the network node, where each report value is one of alert and normal, and where each report value is received at a corresponding time. The method includes calculating a current report weight associated with a current report value received at a current time. When a first previous report value was received at a first previous time before the current time, the current report weight is calculated based at least partially on the current report value and at least partially on a first previous report weight associated with the first previous value. The method includes storing the current report weight at a memory device.

Abstract: At least one exemplary embodiment of the present invention includes a method, comprising requesting real-time measurement data for a telephone network between a transit carrier and a predetermined terminating carrier; and automatically receiving the real-time measurement data via the Internet at an originating carrier associated with the transit carrier. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. This abstract is submitted with the understanding that it will not be used to interpret or limit the scope.

Abstract: Methods and apparatus to characterize and predict network health status are presented. A particular method of characterizing and predicting a health status of a network node includes receiving a plurality of report values associated with the network node, where each report value is one of alert and normal, and where each report value is received at a corresponding time. The method includes calculating a current report weight associated with a current report value received at a current time. When a first previous report value was received at a first previous time before the current time, the current report weight is calculated based at least partially on the current report value and at least partially on a first previous report weight associated with the first previous value. The method includes storing the current report weight at a memory device.

Abstract: At least one exemplary embodiment of the present invention includes a method, comprising automatically obtaining real-time measurement data for a telephone network between a transit carrier and a predetermined terminating carrier; and automatically providing the real-time measurement data via the Internet to an originating carrier associated with the transit carrier. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. This abstract is submitted with the understanding that it will not be used to interpret or limit the scope.