PATENT DOCUMENT

Publication Number: US-11611374-B2
Application Number: US-202117394336-A
Country: US
Kind Code: B2

Title: Low-frequency detection and ranging

Abstract:
Embodiments are disclosed for a low-frequency detection and ranging. In an embodiment, an apparatus comprises: an open electrode; an alternating current (AC) voltage source configured to supply an excitation voltage to the open electrode at an excitation frequency; a resonant circuit coupled to the open electrode, the resonant circuit configured to oscillate when an object is within a detection distance of the open electrode; one or more processors configured to: obtain time domain samples of an output voltage of the resonant circuit when the resonant circuit is oscillating; convert the time domain samples into frequency domain samples; for each frequency domain sample, determine an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; and determine a material class of the object based on the amplitude difference and the phase difference.

Claims:
What is claimed is: 
     
       1. An apparatus comprising:
 an open electrode; 
 an alternating current (AC) voltage source configured to supply an excitation voltage to the open electrode at an excitation frequency; 
 a resonant circuit coupled to the open electrode, the resonant circuit configured to oscillate when an object is within a detection distance of the open electrode; 
 one or more processors configured to:
 obtain time domain samples of an output voltage of the resonant circuit when the resonant circuit is oscillating; 
 convert the time domain samples into frequency domain samples; 
 for each frequency domain sample, determine an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; 
 determine a material class of the object based on the amplitude difference and the phase difference by:
 comparing the amplitude difference and the phase difference to a plurality of predetermined amplitude differences and phase differences for the excitation frequency; and 
 determining the material class of the object based on results of the comparing. 
 
 
 
     
     
       2. The apparatus of  claim 1 , wherein the resonant circuit is a series resistor-inductor-capacitor (RLC) resonance circuit. 
     
     
       3. The apparatus of  claim 2 , further comprising:
 a non-inverting amplifier having an inverting input terminal, a non-inverting input terminal and an output terminal; 
 a feedback resistor coupled between the non-inverting input terminal and the output terminal; 
 series resonant circuit coupled to the non-inverting input terminal; 
 an input voltage source coupled to the inverting input of the non-inverting amplifier; and 
 a load resistor coupled between the output terminal and ground. 
 
     
     
       4. The apparatus of  claim 1 , wherein the resonant circuit includes a resistor, an inductor and a capacitor, and the inductor is sized to allow the resonant circuit to self-resonate with a quality factor that provides sufficient resolution to distinguish between different object materials. 
     
     
       5. The apparatus of  claim 1 , wherein the resonant circuit is a parallel resistor-inductor-capacitor (RLC) resonance circuit. 
     
     
       6. The apparatus of  claim 1 , wherein the one or more processors are further configured to determine a range of the object from the open electrode. 
     
     
       7. The apparatus of  claim 1 , wherein the object is a human body part. 
     
     
       8. The apparatus of  claim 1 , wherein the apparatus is configured for differential input and output. 
     
     
       9. The apparatus of  claim 1 , wherein the minimum detection range of the apparatus is less than about 1 centimeter. 
     
     
       10. A method comprising:
 applying an excitation voltage to an open electrode of a low-frequency detecting and ranging (LFDAR) sensor embedded in an electronic device; 
 obtaining, using the LFDAR sensor, time domain samples of an output voltage of a resonant circuit of the LFDAR coupled to the open electrode; 
 converting, using one or more processors of the electronic device, the time domain samples into frequency domain samples; 
 for each frequency domain sample, determining, using the one or more processors, an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; and 
 determining, using the one or more processors, a material class of an object within a detection distance of the open electrode based on the amplitude difference and the phase difference by:
 comparing the amplitude difference and the phase difference to a plurality of predetermined amplitude differences and phase differences for an excitation frequency; and 
 determining the material class of the object based on results of the comparing. 
 
 
     
     
       11. The method of  claim 10 , further comprising:
 reducing electromagnetic radiation emission of the electronic device based on the material class and estimated range. 
 
     
     
       12. A method comprising:
 applying an excitation voltage at a particular excitation frequency to an open electrode of a low-frequency detecting and ranging (LFDAR) sensor; 
 (a) obtaining, using the LFDAR sensor, time domain samples of an output voltage of a resonant circuit of the LFDAR coupled to the open electrode; 
 (b) converting, using one or more processors, the time domain samples into frequency domains samples; 
 (c) for each frequency domain sample, determining, using the one or more processors, an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; 
 (d) forming, using the one or more processors, a cluster of the amplitude differences and phase differences; 
 (e) classifying and estimating, using the one or more processors, a range for the cluster; 
 (f) storing, using the one or more processors, the classification and estimated range; 
 (g) adjusting, using the one or more processors, the excitation frequency; and 
 (h) repeating steps (a)-(g) until a specified number of excitation frequencies is exhausted. 
 
     
     
       13. An apparatus comprising:
 one or more motion sensors; 
 one or more radio frequency (RF) transmitters; 
 a low-frequency detection and ranging (LFDAR) sensor comprising:
 an open electrode; 
 an alternating current (AC) voltage source configured to supply an excitation voltage to the open electrode at an excitation frequency; 
 a resonant circuit coupled to the open electrode; 
 
 one or more processors configured to:
 obtain time domain samples of an output voltage of the resonant circuit; 
 convert the time domain samples into a frequency domain samples; 
 for each frequency domain sample, determine an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; 
 determine a material class for an object by comparing the amplitude differences and phase differences of the frequency domain samples to a plurality of previously generated material classes; and 
 reduce electromagnetic radiation emitted by the one or more RF transmitters based on the material type and estimated range. 
 
 
     
     
       14. The apparatus of  claim 13 , wherein the one or more processors are configured to stabilize the output voltage of the resonant circuit based on motion data output by the one or more motion sensors. 
     
     
       15. The apparatus of  claim 13 , wherein the open electrode is an antenna coupled to the one or more RF transmitters. 
     
     
       16. The apparatus of  claim 13 , wherein the excitation frequency is randomized to prevent interference with other LFDARs. 
     
     
       17. The apparatus of  claim 13 , wherein the maximum or minimum detection range of the LFDAR is determined by a size of the open electrode and a ground, and their relative placement with respect to each other. 
     
     
       18. The apparatus of  claim 13 , wherein the object is a human body part. 
     
     
       19. The apparatus of  claim 13 , wherein the resonant circuit includes a resistor, an inductor and a capacitor, and the inductor is sized to allow the resonant circuit to self-resonate with a quality factor that provides sufficient resolution to distinguish between different object materials. 
     
     
       20. The apparatus of  claim 13 , wherein the one or more processors are further configured to estimate a range of the object from the open electrode by comparing the amplitude differences and phase differences of the frequency domain samples to a plurality of previously generated range estimations.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to U.S. Provisional Patent Application No. 63/061,712, filed Aug. 5, 2020, the entire contents of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to sensors for proximity detection and ranging. 
     BACKGROUND 
     Mobile devices are known to emit electromagnetic radiation that may be harmful to humans. To ensure public safety, the U.S. Federal Communication Commission (FCC) limits power density to 1 mW/cm 2  in the 6-100 GHz band. Accordingly, it is desirable to detect when a human is in proximity to the mobile device, so that power density can be reduced when the mobile device is proximate to a human to comply with the FCC regulations. 
     Many mobile devices include proximity detectors for detecting when the mobile device is near a human body part. These proximity detectors, however, have many false positive detections, consume a lot of power, have a fixed minimum detection range, require specific antenna shapes and orientations and/or are not easily adapted to other products. Moreover, many mobile devices with small form factors (e.g., smartphone, smartwatch) often do not have enough physical space available to add additional circuitry to be used solely for ranging and detection. 
     SUMMARY 
     Embodiments are disclosed for a low-frequency detection and ranging (LFDAR). 
     In an embodiment, an apparatus comprises: an open electrode; an alternating current (AC) voltage source configured to supply an excitation voltage to the open electrode at an excitation frequency; a resonant circuit coupled to the open electrode, the resonant circuit configured to oscillate when an object is within a detection distance of the open electrode; one or more processors configured to: obtain time domain samples of an output voltage of the resonant circuit when the resonant circuit is oscillating; convert the time domain samples into frequency domain samples; for each frequency domain sample, determine an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; and determine a material class of the object based on the amplitude difference and the phase difference. 
     In an embodiment, a method comprises: applying an excitation voltage to an open electrode of a low-frequency detecting and ranging (LFDAR) sensor embedded in an electronic device; obtaining, using the LFDAR sensor, time domain samples of an output voltage of a resonant circuit of the LFDAR coupled to the open electrode; converting, using one or more processors of the electronic device, the time domain samples into frequency domain samples; for each frequency domain sample, determining, using the one or more processors, an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; and determining, using the one or more processors, a material class of an object within a detection distance of the open electrode based on the amplitude difference and the phase difference. 
     In an embodiment, a method comprises: applying an excitation voltage at a particular excitation frequency to an open electrode of a low-frequency detecting and ranging (LFDAR) sensor; (a) obtaining, using the LFDAR sensor, time domain samples of an output voltage of a resonant circuit of the LFDAR coupled to the open electrode; (b) converting, using one or more processors, the time domain samples into frequency domains samples; (c) for each frequency domain sample, determining, using the one or more processors, an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; (d) forming, using the one or more processors, a cluster of the amplitude differences and phase differences; (e) classifying and estimating, using the one or more processors, a range for the cluster; (f) storing, using the one or more processors, the classification and estimated range; (g) adjusting, using the one or more processors, the excitation frequency; and (h) repeating steps (a)-(g) until a specified number of excitation frequencies is exhausted. 
     In an embodiment, an apparatus comprises: one or more motion sensors; one or more radio frequency (RF) transmitters; a low-frequency detection and ranging (LFDAR) sensor comprising: an open electrode; an alternating current (AC) voltage source configured to supply an excitation voltage to the open electrode at an excitation frequency; a resonant circuit coupled to the open electrode; one or more processors configured to: obtain time domain samples of an output voltage of the resonant circuit; convert the time domain samples into a frequency domain samples; for each frequency domain sample, determine an amplitude difference and a phase difference as compared to an amplitude and phase of the excitation voltage; determine a material class for the object by comparing the amplitude differences and phase differences of the frequency domain samples to a plurality of previously generated material classes; and reduce electromagnetic radiation emitted by the one or more RF transmitters based on the material type and estimated range. 
     Other embodiments can include an apparatus, computing device and non-transitory, computer-readable storage medium. 
     Particular embodiments disclosed herein provide one or more of the following advantages over other detection and ranging systems (e.g., RADAR). A mobile device employing a LFDAR sensor has extreme sensitivity at close range (e.g., between 10 cm and 20 cm) with no minimum detection range. The range can be determined by defining the size of the electrode and ground and their relative placement with respect to each other. For tethered products (e.g., a smart speaker, desktop computer), the LFDAR sensor can have meters of range since the form factor is distributed. The LFDAR sensor has low power consumption. The LFDAR sensor is agnostic to frequency (10s of KHz to 10s of MHz), as opposed to RADAR frequencies which are fixed and tightly regulated. The LFDAR sensor is agnostic to antenna shapes and orientations and multiple existing antennas can be used as electrodes. The LFDAR sensor uses low-cost, generic electronic components and/or can repurpose existing components (e.g., an ADC, processor) in the mobile device for detection and ranging applications, and the LFDAR sensor can be easily adapted for differential design for use with small form factor products, such as a smartwatch or television remote. 
     The details of one or more implementations of the subject matter are set forth in the accompanying drawings and the description below. Other features, aspects and advantages of the subject matter will become apparent from the description, the drawings and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1 A  is a circuit schematic diagram of an open electrode LFDAR sensor, according to an embodiment. 
         FIG.  1 B  is a graph illustrating an output voltage of the open electrode LFDAR sensor shown in  FIG.  1 A  for several different material detections, in accordance with an embodiment. 
         FIG.  2 A  is a circuit schematic diagram of a LFDAR sensor that uses a smaller inductor and provides higher resolution (higher Q) than the open electrode LFDAR sensor of  FIG.  1 A , according to an embodiment. 
         FIG.  2 B  is a graph illustrating an output voltage of the open electrode LFDAR sensor shown in  FIG.  2 A  for several different material detections, in accordance with an embodiment. 
         FIG.  3    is a flow diagram of an example LFDAR sensor process, according to an embodiment. 
         FIG.  4    is a graph illustrating the use of clusters for two-dimensional (2D) material classification using the output of the LFDAR sensor shown in  FIG.  2 A , according to an embodiment. 
         FIG.  5    is example device architecture that includes the LFDAR sensor shown in  FIG.  2 A  and performs the LFDAR process as described in reference to  FIG.  3   . 
     
    
    
     DETAILED DESCRIPTION 
     Example Circuits 
       FIG.  1 A  is a circuit schematic diagram of an open electrode LFDAR sensor  100 , according to an embodiment. Sensor  100  includes alternating current (AC) input voltage source  101  (VIN), inductor  102  (L 1 ), resistor  103  (R 1 ) and capacitor  104  (C 1 ). Input voltage source  101  is coupled to ground  106 . When capacitor  104  is capacitively coupled through human hand  105  to ground  106 , sensor  100  operates as a series Resistor-Inductor-Capacitor (RLC) resonant circuit that generates an output voltage at node  107  that oscillates at a resonant frequency, f r . The series RLC resonant circuit resonates based on the values of inductor  102  and resistor  103 , and the combined capacitance of capacitor  104 , the capacitance of hand  105  and parasitic capacitance. 
       FIG.  1 B  is a graph illustrating an output voltage of the open electrode LFDAR sensor shown in  FIG.  1 A  for several different material detections, in accordance with an embodiment. As stated above, the presence of hand  105  in the detection range of LFDAR sensor  100  grounds the series RLC resonant circuit, causing the output voltage VF to increase in amplitude relative to the input excitation voltage (e.g., a single tone sine wave) generated by input voltage source  101 . Also, the phase of the output voltage VF shifts relative to the phase of the input excitation voltage. More particularly,  FIG.  1 B  illustrates how the resonant frequency of sensor  100  changes when hand  105  is within detection range of sensor  100 , resulting in an amplitude difference and phase difference of output voltage VF that can be sampled at the output node  107 . The change in amplitude and phase can be used to distinguish hand  105  from other materials (e.g., plastic, wood, water) as described below in reference to  FIG.  4   , to reduce false negative and false positive human proximity detections. 
     The output voltages VF shown in  FIG.  1 B  are for the test cases when hand  105  is outside the detection range of sensor  100  (e.g., 10 cm), within the detection range of sensor  100  and with hand  105  pointing away from sensor  100 . As can be observed, the presence of hand  105  within the detection range of sensor  100  increases in amplitude and is phase shifted compared to the input excitation voltage. 
     Although sensor  100  provides a high quality factor Q due to a physically very large inductor  102 , such a large inductor is not practical for low-frequency detection and ranging in a mobile device, where it is desired to use existing components and to not incur additional material costs. Accordingly, an alternative LFDAR sensor  200  is described below that uses a non-inverting operational amplifier and series RLC circuit that uses a physically smaller inductor and still provides a higher quality factor Q. 
       FIG.  2 A  is a schematic diagram of LFDAR sensor  200  that uses a small inductor and provides a higher resolution (higher Q) than provided by the open electrode LFDAR sensor of  FIG.  1 A , according to an embodiment. In an embodiment, sensor  200  includes non-inverting operational amplifier  209  coupled to supply voltage  208 . The inverting input terminal ( 2 ) of amplifier  209  is coupled to series RLC resonant circuit  201 , which includes capacitor  202  (C), inductor  203  (L) and lumped resistor  204  (R S ). Resistor  204  represents the lumped resistance of RLC circuit  201 . The non-inverting input terminal ( 3 ) of amplifier  209  is coupled to input AC voltage source  211 , which is configured in this example to generate a single tone sine wave as an excitation signal for circuit  200 . Series RLC resonant circuit  201 , input AC voltage source  211  and supply voltage are coupled to ground  205 . Feedback resistor  207  (R F ) is coupled between the inverting input terminal ( 2 ) of amplifier  209  and output terminal ( 6 ) of amplifier  209 . The output terminal ( 6 ) of amplifier  209  serves as an output node (VF 1 /V O ) for sensor  200 . Load resistor  210  (R 1 ) is coupled between output terminal ( 6 ) and ground  205 . 
     The output voltage captured at output node (VF 1 /V O ) is given by Equations [1] and [2]: 
     
       
         
           
             
               
                 
                   
                     
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     LFDAR sensor  200  uses a physically small inductor (e.g., 1 mH) that provides a higher quality factor Q that improves the ability of sensor  200  to distinguish between different materials. Sensor  200  also has low power consumption and is agnostic to frequency, as opposed to RADAR frequencies which are fixed and tightly regulated. Sensor  200  is also agnostic to antenna shapes and orientations and multiple existing antennas can be used as electrodes. Sensor  200  uses low-cost, generic electronic components and/or can repurpose existing components (e.g., an ADC, processor) in the mobile device for detection and ranging applications, and the LFDAR circuit can be easily adapted for differential design for use with small form factor products, such as a smartwatch or television remote. In an embodiment, sensor  200  is included in an integrated circuit (IC) chip or system on chip (SoC). 
       FIG.  2 B  is a graph illustrating an output voltage of LFDAR sensor  200  shown in  FIG.  2 A  for several different material detections, in accordance with an embodiment. The AC input voltage is plotted for comparison with different detected materials having different capacitances. As can be observed, different materials produce different amplitude and phase difference clusters, which can be exploited using LFDAR. Note that the amplitude of the output voltage changes for different types of material and range. For example, a capacitor value of 25.33 pF provides a peak-to-peak voltage swing that exceeds 1 volt. 
     Example Process 
       FIG.  3    is a flow diagram of an example LFDAR process  300 , according to an embodiment. Process  300  can be implemented using, for example, the device architecture  500  described in reference to  FIG.  5   . 
     Process  300  begins by exciting the material proximity sensor with an input excitation signal of frequency ( 301 ). For example, a frequency adjustable single tone sine wave can be used to generate excitation signals at different excitation frequencies. Other types of period signals could also be used, such as a square wave or triangle wave. 
     Process  300  continues by capturing an output voltage of the material proximity sensor ( 302 ). For example, the output voltage of an output node of the LFDAR sensor  200  can be sampled using an analog-to-digital converter (ADC) to generate digital values representing the captured output voltage. In an embodiment, the ADC can be an ADC that is preexisting in the host device (e.g., an ADC in a smartphone, smart watch, TV remote, smart speaker). 
     Process  300  continues by calculating a frequency transform of the captured output voltage ( 303 ). For example, the digital samples output by the ADC can be input into a digital signal processor (DSP) or central processing unit (CPU) (See element  504  in  FIG.  5   ). The DSP/CPU transforms the digital values into the frequency domain using, for example, a Fast Fourier Transform (FFT) or any other suitable transform (e.g., Discrete Cosine Transform). The output of the FFT provides an amplitude and phase at the frequency of the excitation signal. In an embodiment, the DSP/CPU can be preexisting in the host device (e.g., a smartphone, smartwatch, TV remote, smart speaker). 
     Process  300  continues by computing an amplitude difference ΔA and phase difference Δφ of the captured output voltage by comparing these values to the amplitude and phase of the input excitation signal at the excitation frequency ( 304 ). For example, the DSP can compute the difference between the amplitude and phase of the captured output voltage and the known amplitude and phase of the excitation signal. 
     Process  300  continues by using the amplitude difference ΔA and phase difference Δφ to classify the material and estimate the distance from the sensor ( 305 ). For example, the amplitude difference ΔA and phase difference Δφ can be compared to a plurality of predetermined clusters of amplitude differences and phase differences for the excitation frequency, where each cluster is associated with a particular material at a particular frequency. The clusters can be determined empirically using any suitable clustering algorithm. In an embodiment, the amplitude and phase difference can be used to index a look-up table stored in memory of the host device to classify the material. 
     Process  300  continues by determining if all desired excitation frequencies have been excited ( 306 ). In accordance with all desired frequencies not being excited, changing the excitation frequency ( 308 ) and repeating the previous steps  301 - 306 . In accordance with all desired frequencies being excited, refine the material classification and distance estimation using knowledge of the amplitude difference and phase difference at all the excitation frequencies of interest ( 307 ). For example, the average of these pairs can be used to associate the captured voltage signal with a particular cluster to classify the material and range. 
     In an embodiment, data can be captured offline for different materials and different ranges for each material from the sensor. For example, amplitudes and phase differences in the frequency domain can be clustered using a suitable clustering algorithm. Characteristics of the cluster (e.g., a centroid) can then be included in a database which is stored in memory of the mobile device. In an embodiment, regression analysis can be used to fit a function (e.g., a line) to the cluster of points and the resulting coefficients (e.g., slope and intercept of the line) can be stored in the database. In an embodiment, principal component analysis (PCA) can be used to characterize the cluster, which can be stored in the database. There can be multiple clusters for the same material for different ranges from the sensor and different orientations relative to the sensor at a particular excitation frequency. 
     During online operation, the amplitudes and phases are calculated in the frequency domain and compared to the database to find the closest matching cluster. Closest matching can be determined by comparing characteristics of the clusters using a distance metric (e.g., Euclidian distance) or method of least squares, etc. In an embodiment, the output voltage of the resonant circuit is stabilized based on motion data output by one or more motion sensors (e.g., 3-axis MEMs accelerometer, 3-axis MEMs gyro). 
     In an embodiment, the open electrode is an antenna coupled to an R F  transmitter used for transmission and/or reception of R F  signals. I.e., the antenna is used for both R F  communications, provided the R F  communications are at a higher frequency than the LFDAR. In an embodiment, the antenna usage can be time multiplexed so that the antenna is not used for communication and LFDAR at the same time. 
     In an embodiment, the excitation frequency is randomized to prevent interference with other nearby LFDARs. In an embodiment, frequency hopping can be used. In an embodiment, devices can advertise their frequency to other devices through a wireless broadcast. 
     In an embodiment, a minimum/maximum detection range of the LFDAR is determined by a size of the open electrode and a ground, and their relative placement with respect to each other. 
     In an embodiment, a minimum detection range is less than 1 centimeter. 
       FIG.  4    is a graph of phase versus amplitude for use in material classification, according to an embodiment. As shown in  FIG.  4   , there are distinct clusters of amplitude and phase (A, φ) difference pairs for each excitation frequency of interest. In the example shown, there are distinct clusters for a remote  401 , plastic  402 , wood  403 , water  404 , laptop  405 , finger  406  and hand  407 . The finger and hand clusters  406 ,  407  can be joined into a single cluster  406  as a human hand class. 
     As can be observed from  FIG.  4   , cluster  406  can be distinguished from clusters associated with other materials, allowing for detection of a human hand with reduced false negatives and false positives. The clusters can be determined empirically for different types of materials and ranges. Any suitable clustering algorithm can be used, including but not limited to: hierarchical, centroid-based (e.g., k-means clustering), distribution-based, density-based (e.g., DBSCAN). For example, the centroid and extent of each cluster can be associated with its material type/range and stored in a look-up table in memory of the sensor or host device. When a new detection is occurs, the captured amplitude and phase differences are used to classify the material type/range using the clusters. 
     Exemplary Wearable Computer Architecture 
       FIG.  5    illustrates example device architecture  500  implementing the features and operations described in reference to  FIGS.  1 - 5   . Architecture  500  can include memory interface  502 , one or more data processors, digital signal processors (DSPs), image processors and/or central processing units (CPUs)  504  and peripherals interface  506 . Memory interface  502 , one or more processors  504  and/or peripherals interface  506  can be separate components or can be integrated in one or more integrated circuits. 
     Sensors, devices and subsystems can be coupled to peripherals interface  506  to provide multiple functionalities. For example, one or more motion sensors  510 , light sensor  512  and proximity sensor  514  can be coupled to peripherals interface  506  to facilitate motion sensing (e.g., acceleration, rotation rates), lighting and proximity functions of the wearable computer. Location processor  515  can be connected to peripherals interface  506  to provide geo-positioning. In some implementations, location processor  515  can be a GNSS receiver, such as the Global Positioning System (GPS) receiver. Electronic magnetometer  516  (e.g., an integrated circuit chip) can also be connected to peripherals interface  506  to provide data that can be used to determine the direction of magnetic North. Electronic magnetometer  516  can provide data to an electronic compass application. Motion sensor(s)  510  can include one or more accelerometers and/or gyros configured to determine change of speed and direction of movement of the wearable computer. Barometer  517  can be configured to measure atmospheric pressure around the mobile device. 
     Material proximity sensor  520  performs low-frequency detection and ranging, as described in reference to  FIGS.  1 - 4   . For example, sensor  502  can include LFDAR circuit  200 , described in reference to  FIG.  2    and perform the LFDAR process described in reference to  FIG.  3   . 
     Communication functions can be facilitated through wireless communication subsystems  524 , which can include radio frequency (RF) receivers and transmitters (or transceivers) and/or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of the communication subsystem  524  can depend on the communication network(s) over which a mobile device is intended to operate. For example, architecture  500  can include communication subsystems  524  designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi™ network and a Bluetooth™ network. In particular, the wireless communication subsystems  524  can include hosting protocols, such that the mobile device can be configured as a base station for other wireless devices. 
     Audio subsystem  526  can be coupled to a speaker  528  and a microphone  530  to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording and telephony functions. Audio subsystem  526  can be configured to receive voice commands from the user. 
     I/O subsystem  540  can include touch surface controller  542  and/or other input controller(s)  544 . Touch surface controller  542  can be coupled to a touch surface  546 . Touch surface  546  and touch surface controller  542  can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch surface  546 . Touch surface  546  can include, for example, a touch screen or the digital crown of a smart watch. I/O subsystem  540  can include a haptic engine or device for providing haptic feedback (e.g., vibration) in response to commands from processor  504 . In an embodiment, touch surface  546  can be a pressure-sensitive surface. 
     Other input controller(s)  544  can be coupled to other input/control devices  548 , such as one or more buttons, rocker switches, thumb-wheel, infrared port and USB port The one or more buttons (not shown) can include an up/down button for volume control of speaker  528  and/or microphone  530 . Touch surface  546  or other controllers  544  (e.g., a button) can include, or be coupled to, fingerprint identification circuitry for use with a fingerprint authentication application to authenticate a user based on their fingerprint(s). 
     In one implementation, a pressing of the button for a first duration may disengage a lock of the touch surface  546 ; and a pressing of the button for a second duration that is longer than the first duration may turn power to the mobile device on or off. The user may be able to customize a functionality of one or more of the buttons. The touch surface  546  can, for example, also be used to implement virtual or soft buttons. 
     In some implementations, the mobile device can present recorded audio and/or video files, such as MP3, AAC and MPEG files. In some implementations, the mobile device can include the functionality of an MP3 player. Other input/output and control devices can also be used. 
     Memory interface  502  can be coupled to memory  550 . Memory  550  can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices and/or flash memory (e.g., NAND, NOR). Memory  550  can store operating system  552 , such as the iOS operating system developed by Apple Inc. of Cupertino, Calif. Operating system  552  may include instructions for handling basic system services and for performing hardware dependent tasks. In some implementations, operating system  552  can include a kernel (e.g., UNIX kernel). 
     Memory  550  may also store communication instructions  554  to facilitate communicating with one or more additional devices, one or more computers and/or one or more servers, such as, for example, instructions for implementing a software stack for wired or wireless communications with other devices. Memory  550  may include graphical user interface instructions  556  to facilitate graphic user interface processing; sensor processing instructions  558  to facilitate sensor-related processing and functions; phone instructions  560  to facilitate phone-related processes and functions; electronic messaging instructions  562  to facilitate electronic-messaging related processes and functions; web browsing instructions  564  to facilitate web browsing-related processes and functions; media processing instructions  566  to facilitate media processing-related processes and functions; GNSS/Location instructions  568  to facilitate generic GNSS and location-related processes and instructions; and instructions  570  for performing the LFDAR process described in reference to  FIG.  3   . Memory  550  further includes application instructions  572  for performing various applications, including applications that can utilize material proximity detection and ranging, such as a power management unit for an R F  transmitter or material analyzer application. 
     Each of the above identified instructions and applications can correspond to a set of instructions for performing one or more functions described above. These instructions need not be implemented as separate software programs, procedures, or modules. Memory  550  can include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be implemented in hardware and/or in software, including in one or more signal processing and/or application specific integrated circuits. 
     The described features can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language (e.g., SWIFT, Objective-C, C#, Java), including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, a browser-based web application, or other unit suitable for use in a computing environment. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub combination or variation of a sub combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Metadata:
Filing Date: 20210804
Publication Date: 20230321
Grant Date: 20230321
Priority Date: 20200805
Inventors: JADIDIAN, Jouya
PATOLE, SUJEET MILIND
Assignee: APPLE INC
CPC Classifications: [{"code": "H03K17/955", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B5/0081", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04B5/0043", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B5/73", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04B5/26", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 77821978