Disclosed is a broadcast receiver that includes an antenna connector configured to connect with an antenna for receiving a broadcast signal including broadcast content. The broadcast receiver also includes a signal processor configured to generate a reference signal having a strength corresponding to a signal input through the antenna connector and to process the generated reference signal to output an image signal for displaying the broadcast content, and a controller configured to determine whether the antenna is connected to the antenna connector based on an output value of the reference signal and to perform subsequent operations related to whether the antenna is connected based on the determination.

Dynamic uniform resource locator (URL) transmission may be provided for allowing a user to view a non-linear video content item along with additional content in order to provide a richer and deeper viewing experience giving more control to the user during video viewing. An indication may be provided to the user indicating the availability of additional interactive content associated with the non-linear video in a variety of different ways. Upon user request, the content associated with the URL may be updated or historic content presented to the user in order to present the non-linear video content combined with the additional content in a hybrid fashion. The transmitted URL may be static, or the URL may be dynamically updated based on one or more factors, such as time of day or action by the user or content provider.

A method for monitoring a monitored display monitors data to be output from a monitored display. The monitored data is analyzed to generate one or more content identifiers. The content identifiers are compared to a set of rules to determine if the monitored data should be blocked from being output or if an alert should be transmitted to a supervisor device. One or more supervisor devices may be used to respond to alerts and may also be used to control the output of the monitored display.

Methods and apparatus are disclosed to determine a power state of a device. An example apparatus includes a chart generator to determine respective counts for a plurality of measurements during a calibration period, the measurements indicative of an amount of power drawn by the device, a calculator to determine a first threshold and a second threshold based on at least one of the counts, the first threshold determined using most frequently logged measurement values, the most frequently logged measurement values based on counts performed after expiration of the calibration period, and a comparator to compare a measurement to the first threshold and to the second threshold.

Methods and apparatus are disclosed to determine a power state of a device. An example apparatus includes a chart generator to determine respective counts for a plurality of measurements during a calibration period, the measurements indicative of an amount of power drawn by the device, a calculator to determine a first threshold and a second threshold based on at least one of the counts, the first threshold determined using most frequently logged measurement values, the most frequently logged measurement values based on counts performed after expiration of the calibration period, and a comparator to compare a measurement to the first threshold and to the second threshold.

A method may comprise collecting data from a core network, an operator network and a customer premise network. KPI values corresponding to one or more KPIs may be determined, based on the collected data. KQI values corresponding to one or more KQIs may be determined, based on the one or more of the determined KPI values. The determined KQI values may be output to an operator. In an embodiment, data collected from the core network may include data collected from one or more analyzer devices and head end probes. Data collected from a customer premise network may include data collected from last mile equipment or customer premise probes.

A computer-implemented method of using video viewing activity data as input to an aggregation engine built on the Hadoop MapReduce framework which calculates second-by-second video viewing activity aggregated to the analyst's choice of (a) geographic area, (b) video server, (c) video content (channel call sign, video program, etc.), or (d) viewer demographic, or any combination of these fields, for each second of the day represented in the video viewing activity data. Also calculates overall viewing for use as a denominator in calculations. The source data may be extracted from a database defined according to the Cable Television Laboratories, Inc. Media Measurement Data Model defined in “Audience Data Measurement Specification” as “OpenCable™. Specifications, Audience Measurement, Audience Measurement Data Specification” document OC-SP-AMD-101-130502 or any similar format. These metrics provide detailed data needed to calculate information on customer viewing behavior that can drive business decisions for service providers, advertisers, and content producers.

A device may be configured to cause application based features corresponding to a service provided by a service provider to execute based on one or more determined user settings.

Methods and apparatus to perform an automated gain control protocol with an amplifier based on historical data corresponding to contextual data are disclosed. An example apparatus includes a controller to select an automatic gain control (AGC) parameter for an AGC protocol based on historical data corresponding to contextual data, the contextual data including at least one of a time during which the AGC protocol is performed, a panelist identified by a meter, demographics of an audience identified by the meter, a location of the meter, a station identified by the meter, a media type identified by the meter, or a sound pressure level identified by the meter; and a processor to perform the AGC protocol based on the selected AGC parameter.

A computer-implemented method of using video viewer interaction data as input to a measurement engine which then calculates: demographic level and aggregate level (a) household reach for advertisements and video content based on user defined reach periods, (b) One-minute, Thirty-minute, and Sixty-minute channel viewing metrics, and (c) One-minute, Thirty-minute, and Sixty-minute aggregate viewing metrics, all based on second-by-second household level viewing activity. Also shows creation of Commercial Schedule Cumulative Audience, Impressions for Reach Periods, Demographic Group Impressions, Average Audience, Average Audience Share, Average Rating, Households Using Television, and Households Using Television Percent using household level data. Works with IP and linear video delivery. Together these metrics provide detailed information on customer viewing behavior which can be used to drive business decisions for service providers, advertisers, and content producers.