Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J1/00—Photometry, e.g. photographic exposure meter
  • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
  • G01J1/44—Electric circuits
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J1/00—Photometry, e.g. photographic exposure meter
  • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
  • G01J1/4204—Photometry, e.g. photographic exposure meter using electric radiation detectors with determination of ambient light
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F9/00—Arrangements for program control, e.g. control units
  • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
  • G06F9/44—Arrangements for executing specific programs
  • G06F9/4401—Bootstrapping
  • G06F9/4405—Initialisation of multiprocessor systems
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04M—TELEPHONIC COMMUNICATION
  • H04M1/00—Substation equipment, e.g. for use by subscribers
  • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
  • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
  • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
  • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J1/00—Photometry, e.g. photographic exposure meter
  • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
  • G01J1/44—Electric circuits
  • G01J2001/444—Compensating; Calibrating, e.g. dark current, temperature drift, noise reduction or baseline correction; Adjusting
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04M—TELEPHONIC COMMUNICATION
  • H04M1/00—Substation equipment, e.g. for use by subscribers
  • H04M1/02—Constructional features of telephone sets
  • H04M1/22—Illumination; Arrangements for improving the visibility of characters on dials
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04M—TELEPHONIC COMMUNICATION
  • H04M2250/00—Details of telephonic subscriber devices
  • H04M2250/12—Details of telephonic subscriber devices including a sensor for measuring a physical value, e.g. temperature or motion

Definitions

• This disclosure relates generally to calibration of light sensors, and in particular, but not exclusively, relates to auto-calibrating light sensor data of a mobile device.
• a wide range of electronic devices including mobile wireless communication devices, personal digital assistants (PDAs), laptop computers, desktop computers, digital cameras, digital recording devices, and the like, include one or more embedded light sensors to enable applications to perform one or more light sensing operations.
• an indoor/outdoor detection application may utilize light sensor data to detect whether the electronic device is indoors or outdoors.
• the electronic device may include an electronic display (e.g., screen) where an automatic screen-brightness application adjusts the brightness of the screen based on the light sensor data.
• Further light-based applications may include sun intensity detection for a health-related application, or a white balancing application for adjusting the color of images captured by the electronic device.
• Light sensors are typically low power (e.g., around 0.1mA per hour) and may be implemented in a variety of ways depending on the make or model of the electronic device.
• the light sensor is implemented as an ambient light sensor (ALS), which may be comprised of one, or in some cases, a few photodetectors.
• the light sensor may be a Red Green Blue (RGB) sensor that detects intensities of red, green, and blue light.
• RGB Red Green Blue
• a light sensor may be implemented as an ultraviolet detector that detects the intensity of ultraviolet light.
• various devices may physically locate the light sensor on the electronic device in a variety of positions.
• a certain mobile device may embed a light sensor on a backside of the mobile device near a camera of the mobile device, while another mobile device may embed the light sensor on a front side of the mobile device, near the front-facing display.
• each light-based application typically requires some knowledge of the device that it is running on as well as a known calibration factor for calibrating light sensor data that is acquired from the specific light sensor that is included on that device. In some applications this means storing, or otherwise obtaining a sizeable list of calibration factors for each possible device that may be running the light-based application.
• the large number of different devices in the market jeopardizes applications based on device dependent light-sensors data.
• aspects of the present disclosure include a method, a mobile device, and computer- readable medium for auto-calibrating light sensor data of a mobile device.
• a method of auto-calibrating light sensor data of a mobile device includes, obtaining, by the mobile device, one or more reference parameters representative of light sensor data collected by a reference device. The method also includes obtaining, by the mobile device, light sensor data from a light sensor included in the mobile device, itself. One or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device are then determined. A calibration model is then determined for auto-calibrating the light sensor data of the light sensor included in the mobile device based on the one or more reference parameters and the one or more sample parameters.
• a mobile device for auto-calibrating light sensor data includes a light sensor, memory, and a processing unit.
• the light sensor is configured to generate the light sensor data
• the memory is adapted to store program code.
• the processing unit is coupled to the memory to access and execute instructions included in the program code to direct the mobile device to perform the auto- calibrating of the light sensor data.
• the program code may include instructions to direct the mobile device to obtain one or more reference parameters representative of light sensor data collected by a reference device; obtain the light sensor data from the light sensor; determine one or more sample parameters of the light sensor data obtained from the light sensor; and determine a calibration model for auto-calibrating the light sensor data of the light sensor based on the one or more reference parameters and the one or more sample parameters.
• a mobile device for auto-calibrating light sensor data includes a light sensor configured to generate the light sensor data and means for obtaining one or more reference parameters representative of light sensor data collected by a reference device.
• the mobile device also includes means for obtaining the light sensor data from the light sensor, means for determining one or more sample parameters of the light sensor data obtained from the light sensor, and means for determining a calibration model for auto-calibrating the light sensor data of the light sensor based on the one or more reference parameters and the one or more sample parameters.
• a non-transitory computer-readable medium includes program code stored thereon for auto-calibrating light sensor data, where the program code includes instructions to direct a mobile device to obtain one or more reference parameters representative of light sensor data collected by a reference device and to obtain the light sensor data from a light sensor included in the mobile device.
• the program code also includes instructions to direct the mobile device to determine one or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device, and to determine a calibration model for auto- calibrating the light sensor data of the light sensor included in the mobile device based on the one or more reference parameters and the one or more sample parameters.
• FIG. 1 is a wireless communications system for auto-calibrating light sensor data of a mobile device.
• FIG. 2 is a flowchart illustrating a process of auto-calibrating light sensor data of a mobile device.
• FIG. 3 is a flowchart illustrating a process of obtaining light sensor data from a light sensor included in a mobile device.
• FIG. 4 is a functional block diagram illustrating an apparatus capable of performing the processes discussed herein.
• FIG. 5 is a simplified block diagram illustrating several sample aspects of components that may be employed in a mobile device configured to support the auto- calibrating of light sensor data as taught herein.
• Embodiments discussed herein are based on a recognition that certain statistical values of light sensor data depend mainly on the device that generated the light sensor data and are approximately constant among different users. Accordingly, a method of auto-calibrating light sensor data is disclosed that includes determining a relationship among light sensor data for different devices in order to generate a calibration model. Embodiments discussed herein enable device agnostic light-based applications where Applications developed for one device can be seamlessly used in other devices.
• FIG. 1 illustrates a mobile device 102 operating in a wireless communications system 100, where the mobile device 102 includes a light sensor (not shown in current view) and is capable of auto-calibrating light sensor data that is generated by the light sensor.
• the mobile device 102 includes a light sensor (not shown in current view) and is capable of auto-calibrating light sensor data that is generated by the light sensor.
• a “mobile device” refers to a device such as a cellular or other wireless communication device, personal communication system (PCS) device, personal navigation device (P D), Personal Information Manager (PHVI), Personal Digital Assistant (PDA), laptop, wearable computer (e.g., a watch), or other suitable mobile device which is capable of receiving wireless communication signals.
• PCS personal communication system
• P D personal navigation device
• PHVI Personal Information Manager
• PDA Personal Digital Assistant
• laptop wearable computer
• wearable computer e.g., a watch
• the term “mobile device” is also intended to include all devices, including wireless communication devices, computers, laptops, etc. which are capable of communication with a server, such as via the Internet, WiFi, or other network, and regardless of whether signal reception and/or light sensor auto-calibrating-related processing occurs at the mobile device, at a server, or at another device associated with the network.
• a “mobile device” may also include all electronic devices which are capable of performing a light sensing operation and which may include one or more light-based applications running locally on (or remotely through) the device. Any operable combination of the above are also considered a “mobile device.”
• the mobile device 102 may include one or more light-based applications that perform one or more light sensing operations, such as indoor/outdoor detection, auto-screen brightness adjustment, sun intensity detection, and/or white balancing of one or more pictures.
• one or more light sensing operations such as indoor/outdoor detection, auto-screen brightness adjustment, sun intensity detection, and/or white balancing of one or more pictures.
• Wireless communications system 100 may be a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an Orthogonal Frequency Division Multiple Access (OFDMA) network, a Single-Carrier Frequency Division Multiple Access (SC-FDMA) network, Long Term Evolution (LTE), and so on.
• CDMA Code Division Multiple Access
• TDMA Time Division Multiple Access
• FDMA Frequency Division Multiple Access
• OFDMA Orthogonal Frequency Division Multiple Access
• SC-FDMA Single-Carrier Frequency Division Multiple Access
• a CDMA network may implement one or more radio access technologies (RATs) such as cdma2000, Wideband-CDMA (W-CDMA), and so on.
• Cdma2000 includes IS-95, IS-2000, and IS-856 standards.
• a TDMA network may implement Global System for Mobile Communications (GSM), Digital Advanced Mobile Phone System (D-AMPS), or some other RAT.
• GSM and W-CDMA are described in documents from a consortium named "3rd Generation Partnership Project" (3GPP).
• Cdma2000 is described in documents from a consortium named "3rd Generation Partnership Project 2" (3GPP2).
• 3GPP and 3GPP2 documents are publicly available.
• Wireless communications system 100 may be an IEEE 802.1 lx network, a Bluetooth network, an IEEE 802.15x, or some other type of network. The techniques may also be implemented in conjunction with any combination of WW AN, WLAN and/or WPAN.
• wireless communications system 100 includes mobile device 102 receiving light 120 (e.g., at a light sensor embedded in mobile device 102) while mobile device 102 is physically located in a first environment 116.
• the first environment 116 refers to an environment associated with a user of mobile device 102.
• the first environment 116 may include an indoors location, such as, a home, an office, a room, or may include an outdoors location, such as a yard, a park, or even an automobile.
• mobile device 102 may be configured to perform an auto-calibrating process of light sensor data that is generated by a light sensor of mobile device 102 in response to the light 120.
• the auto- calibrating processes described herein may be performed by mobile device 102 to generate a calibration model that is stored at the mobile device 102.
• the calibration model may then be used during light sensing operations to adjust light sensor data that is collected during such light-sensing operations.
• the auto- calibration processes described herein may be run periodically to update or adjust the stored calibration model.
• mobile device 102 collects light sensor data while the mobile device 102 is located in the first environment 116.
• the light sensor data collected by mobile device 102 is collected over an extended period of time, such as an overnight period of time.
• the auto-calibrating process is autonomous. That is, the mobile device 102 may perform the auto- calibrating of light sensor data independent of any user interaction with mobile device 102.
• mobile device 102 may be configured to perform the auto- calibrating of light sensor data as a background process or during a period of inactivity of a user of mobile device 102, without the need for a user to initiate the auto-calibrating process.
• the mobile device 102 may calculate one or more sample parameters of the light sensor data.
• the sample parameters are statistical values, such as a mean or standard deviation of the light sensor data.
• the sample parameters and/or light sensor data, itself may be expressed in terms of illuminance and may be represented as units of lux.
• the mobile device 102 may then determine a calibration model for auto-calibrating the light sensor data based on the sample parameters and also based on one or more reference parameters.
• the reference parameters are representative of light sensor data that is collected by another device that is separate and distinct from mobile device 102. In the illustrated example of FIG.
• reference device 114 exchanges one or more messages with server 110 in order to obtain the reference parameters 124 via network 112.
• the reference parameters 124 are representative of light sensor data 126 that is collected by reference device 114.
• reference device 114 is a light sensor device, such as an ambient light sensor (ALS), a red-green-blue (RGB) sensor, and/or an ultra-violet (UV) sensor, without being embedded into a system such as a mobile device.
• reference device 114 is a mobile device including its own light sensor.
• reference device 114 is a mobile device that is used as a reference for auto-calibrating a variety of types/models of mobile devices 102.
• the light sensor utilized by reference device 114 may be of a different type, sensitivity, comprised of differing circuitry, and/or embedded within a different physical location on reference device 114 when compared to the light sensor of the mobile device 102.
• the light sensor data 126 generated by reference device 114 may vary with respect to the light sensor data generated by mobile device 102, even if the reference device 114 and mobile device 102 were subject to identical lighting conditions.
• the light sensor data 126 collected by reference device 114 is collected over an extended period of time while reference device 114 is physically located in a second environment 118.
• Second environment 118 is separate and distinct from first environment 116 and thus, reference device 114 is subject to differing lighting conditions than are experienced by the mobile device 102.
• certain statistical values of light sensor data depend mainly on the device that generated the light sensor data and are approximately constant among different users or even differing lighting conditions.
• second environment 118 is a controlled environment (e.g., a laboratory), such that while in the second environment 118, the reference device 114 is subjected to a variety of controlled and known lighting conditions.
• the server 110 is configured to provide one or more reference parameters 124 to the mobile device 102.
• the reference parameters 124 may include a mean and/or standard deviation of the light sensor data 126 that is collected by the reference device 114. Also, the time period during which the light sensor data 126 is collected by reference device 114 need not occur at the same time that mobile device 102 obtains its light sensor data, nor does it need to be of the same duration. In one implementation, the reference device 114 generates light sensor data 126 prior to (e.g., well in advance of) the generation of light sensor data by mobile device 102. [0033] While FIG. 1 shows one server 110, it should be understood that multiple servers may be used.
• mobile device 102 is configured to determine the calibration model by itself based on the received reference parameter(s) 124 and based on the sample parameter determined locally at mobile device 102.
• the sample parameter is determined locally at mobile device 102 based on light sensor data generated at mobile device 102.
• the determination of the calibration model may be performed by the server 110 (or other server), where either the light sensor data generated by mobile device 102, or the determined sample parameter is provided to the server 110 by the mobile device 102.
• the mobile device 102 may be configured to apply the calibration model to further light sensor data obtained from the light sensor included in the mobile device 102 during one or more light sensing operations, such as indoor/outdoor detection, auto-screen brightness adjustment, sun intensity detection, and/or white balancing of one or more pictures, etc.
• the determining of a calibration model in accordance with the embodiments discussed herein allow for device agnostic light-based applications where applications developed for one device can be seamlessly used in other devices without the need for maintaining a list/table of known calibration factors specific to each of a variety of devices.
• FIG. 2 is a flowchart illustrating a process 200 of auto-calibrating light sensor data of a mobile device.
• Process 200 is one possible implementation of an auto-calibrating process performed by mobile device 102 of FIG. 1.
• Process 200 will be described with reference to FIGS. 1-3.
• process block 202 includes the mobile device 102 obtaining one or more reference parameters 124 that are representative of light sensor data 126 that is collected by reference device 114.
• mobile device 102 obtains the one or more reference parameters 124 by communicating with server 110 over network 112.
• the mobile device 102 may store the received reference parameters 124 for future use in future auto-calibrating of the light sensor data.
• the mobile device 102 may be configured to obtain the one or more reference parameters 124 from server 110, dynamically, during each instance of the auto-calibrating process 200.
• Reference parameters 124 may include one or more statistical values such as a mean and/or a standard deviation of the light sensor data 126.
• the mobile device 102 is configured to collect light sensor data from a light sensor that is included in the mobile device 102.
• the mobile device 102 is configured to collect a plurality of light sensor data values over an extend period of time.
• the mobile device 102 may collect a plurality of light sensor data values overnight (e.g., beginning at 8PM, 9PM, 10PM, or 11PM and ending at 6AM, 7AM, 8AM, or 9AM, local time to mobile device 102).
• the mobile device 102 may collect a plurality of light sensor data values over a period of several days, weeks, etc.
• FIG. 3 is a flowchart illustrating a process 300 of collecting light sensor data values from a light sensor included in mobile device 102.
• Process 300 is one possible implementation of process block 204, of FIG. 2.
• the mobile device 102 determines whether the beginning of a sampling period should begin.
• the mobile device 102 may include one or more onboard sensors to determine whether to begin the sampling period.
• mobile device 102 may include a clock, where the mobile device 102 monitors the time (e.g., 9PM) in order to begin an overnight sampling of light sensor data values.
• the mobile device 102 includes a timer, where the mobile device 102 is configured to perform periodic sampling periods (e.g., every 12 hours).
• the mobile device 102 may be configured to utilize one or more other sensors included in the mobile device 102 to determine a period of inactivity by the user, during which the sampling period may occur.
• process block 304 the mobile device 102 collects one or more light sensor data values by sampling data generated by the light sensor included in the mobile device 102.
• decision block 306 the mobile device 102 determines whether the sampling period has ended, again by utilizing, e.g., a clock, a timer, etc. If the sampling period has ended, the collection of light sensor data values has ended and process 300 ceases. If not, process 300 returns to process block 304 to collect further light sensor data values.
• process 300 includes collecting light sensor data values at a sampling rate of 5 Hz. Of course, other sampling rates of the light sensor data values are possible in accordance with the teachings herein. [0040] Returning now to FIG.
• process block 206 includes calculating one or more sample parameters for the light sensor data obtained from the light sensor included in the mobile device 102 (i.e., the light sensor data collected in process block 204).
• the determined sample parameters may include one or more statistical values such as a mean and/or a standard deviation of the light sensor data generated by the light sensor of the mobile device 102.
• the mobile device 102 determines a calibration model for use in the auto-calibrating of the light sensor data generated by the light sensor of mobile device 102.
• the determination of the calibration model is based on both the one or more reference parameters 124 and on the one or more sample parameters calculated in process block 206.
• determining the calibration model includes determining a calibration factor, where the calibration factor may be used to calibrate or otherwise adjust subsequently acquired light sensor data at mobile device 102.
• the calibration factor can be determined from both the mean and standard deviations of the reference and sample parameters using a maximum likelihood estimator.
• mobile device 102 may simply store the determined calibration model for future light-sensing operations, or may begin one of the light-sensing operations itself.
• the mobile device 102 may be configured to apply the calibration model to further light sensor data obtained from the light sensor included in the mobile device 102 during one or more light sensing operations, such as indoor/outdoor detection, auto-screen brightness adjustment, sun intensity detection, and/or white balancing of one or more pictures, etc.
• processes 200 and 300 are performed once upon an initial use of the light sensor in the mobile device 102.
• processes 200 and 300 may be performed periodically (e.g., nightly) so as to improve and/or update the calibration model stored at mobile device 102.
• FIG. 4 is a functional block diagram illustrating an apparatus 400 capable of performing the processes discussed herein.
• apparatus 400 is a computer capable performing the auto-calibrating of light sensor data, such as process 200, described above.
• Apparatus 400 is one possible implementation of mobile device 102 of FIG. 1.
• Apparatus 400 may optionally include a camera 402 as well as an optional user interface 406 that includes the display 422 capable of displaying images captured by the camera 402.
• User interface 406 may also include a keypad 424 or other input device through which the user can input information into the apparatus 400. If desired, the keypad 424 may be obviated by integrating a virtual keypad into the display 422 with a touch sensor.
• User interface 406 may also include a microphone 426 and speaker 428.
• Apparatus 400 also includes a control unit 404 that is connected to and communicates with the camera 402 and user interface 406, if present.
• the control unit 404 accepts and processes images received from the camera 402 and/or from network adapter 416.
• Control unit 404 may be provided by a processing unit 408 and associated memory 414, hardware 410, firmware 412, software 415, and graphics engine 420.
• Control unit 404 may further include a light sensor 417 and an auto-calibration unit 418.
• Light sensor 417 may be configured to generate one or more light sensor data values in response to light 419 received at apparatus 400 and incident upon light sensor 417.
• light sensor 417 includes an ambient light sensor (ALS).
• ALS ambient light sensor
• RGB red-green-blue
• UV ultra-violet
• Auto-calibration unit 418 may be configured to perform one or more auto-calibrating processes of the light sensor data generated by light sensor 417, such as described above with reference to process 200 of FIG. 2.
• Processing unit 408 and auto- calibration unit 418 are illustrated separately for clarity, but may be a single unit and/or implemented in the processing unit 408 based on instructions in the software 415 which is run in the processing unit 408.
• Processing unit 408, as well as the auto- calibration unit 418 can, but need not necessarily include, one or more microprocessors, embedded processors, controllers, application specific integrated circuits (ASICs), digital signal processors (DSPs), and the like.
• ASICs application specific integrated circuits
• DSPs digital signal processors
• memory refers to any type of computer storage medium, including long term, short term, or other memory associated with apparatus 400, and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.
• the processes described herein may be implemented by various means depending upon the application. For example, these processes may be implemented in hardware 410, firmware 412, software 415, or any combination thereof.
• the processing units may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
• ASICs application specific integrated circuits
• DSPs digital signal processors
• DSPDs digital signal processing devices
• PLDs programmable logic devices
• FPGAs field programmable gate arrays
• processors controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
• the processes may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein.
• modules e.g., procedures, functions, and so on
• Any non-transitory computer-readable medium tangibly embodying instructions may be used in implementing the processes described herein.
• program code may be stored in memory 414 and executed by the processing unit 408.
• Memory may be implemented within or external to the processing unit 408.
• firmware 412 and/or software 415 the functions may be stored as one or more instructions or code on a computer-readable medium. Examples include non-transitory computer-readable media encoded with a data structure and computer- readable media encoded with a computer program.
• Computer-readable media includes physical computer storage media. A storage medium may be any available medium that can be accessed by a computer.
• such computer-readable media can comprise RAM, ROM, Flash Memory, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer; disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer- readable media.
• FIG. 5 is a simplified block diagram illustrating several sample aspects of components that may be employed in a mobile device 500 configured to support the auto-calibrating of light sensor data as taught herein.
• Mobile device 500 is one possible implementation of mobile device 102 of FIG. 1 represented as a series of interrelated functional modules.
• a module 502 for obtaining one or more reference parameters representative of light sensor data collected by a reference device may correspond at least in some aspects to, for example, a network adapter 416 of FIG. 4.
• a module 504 for collecting light sensor data from a light sensor included in a mobile device may correspond at least in some aspects to, for example, an auto-calibration unit 418 and/or light sensor 417 of FIG. 4.
• a module 506 for calculating one or more sample parameters of the light sensor data obtained from the light sensor included in the mobile device 500 may correspond at in some aspects to, for example, auto-calibration unit 418 and/or processing unit 408, of FIG. 4.
• a module 508 for determining a calibration model based on the reference parameters and sample parameters may correspond at in some aspects to, for example, auto-calibration unit 418 and/or processing unit 408, of FIG. 4.
• a module 510 for applying the calibration model to light sensor data during one or more light sensing operations may correspond at in some aspects to, for example, auto-calibration unit 418 and/or processing unit 408, of FIG. 4.
• the functionality of the modules 502-510 of FIG. 5 may be implemented in various ways consistent with the teachings herein.
• the functionality of these modules 502-510 may be implemented as one or more electrical components.
• the functionality of these modules 502-510 may be implemented as a processing system including one or more processor components.
• the functionality of these modules 502-510 may be implemented using, for example, at least a portion of one or more integrated circuits (e.g., an ASIC).
• an integrated circuit may include a processor, software, other related components, or some combination thereof.
• the functionality of different modules may be implemented, for example, as different subsets of an integrated circuit, as different subsets of a set of software modules, or a combination thereof.
• a given subset e.g., of an integrated circuit and/or of a set of software modules
• FIG. 5 may be implemented using any suitable means. Such means also may be implemented, at least in part, using corresponding structure as taught herein.
• the components described above in conjunction with the "module for" components of FIG. 5 also may correspond to similarly designated “means for” functionality.
• one or more of such means may be implemented using one or more of processor components, integrated circuits, or other suitable structure as taught herein.

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