Low frequency detection and ranging

Abstract

Disclosed herein are implementations for low frequency detection and ranging. In one implementation, 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 while the resonant circuit is oscillating; convert the time domain samples to frequency domain samples; for each frequency domain sample, determine an amplitude difference and a phase difference compared to an amplitude and a phase of the excitation voltage; and determine a material class of the object based on the amplitude difference and the phase difference.

Description

Technical Field

[0001] This disclosure relates in general to sensors for proximity detection and ranging. Background Technology

[0002] Mobile devices are known to emit electromagnetic radiation that may be harmful to humans. To ensure public safety, the U.S. Federal Communications Commission (FCC) has limited the power density in the 6 GHz to 100 GHz frequency band to 1 mW / cm². 2 Therefore, the goal is to detect when a human approaches a mobile device so that the power density can be reduced to comply with FCC regulations when the mobile device is near a human.

[0003] Many mobile devices include proximity detectors to detect when the device approaches a body part. However, these proximity detectors suffer from numerous false positives, consume significant power, have a fixed minimum detection range, require specific antenna shapes and orientations, and / or are not easily adaptable to other products. Furthermore, many mobile devices with small form factors (e.g., smartphones, smartwatches) typically lack sufficient physical space to add additional circuitry solely for ranging and detection. Summary of the Invention

[0004] This article discloses an implementation scheme for low-frequency detection and ranging (LFDAR).

[0005] In one embodiment, an apparatus includes: 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 and configured to oscillate when an object is within a detection distance of the open electrode; and one or more processors configured to: obtain a time-domain sample of the output voltage of the resonant circuit while the resonant circuit is oscillating; convert the time-domain sample to a frequency-domain sample; for each frequency-domain sample, determine an amplitude difference and a phase difference compared to the amplitude and phase of the excitation voltage; and determine the material class of the object based on the amplitude difference and the phase difference.

[0006] In one embodiment, a method includes: applying an excitation voltage to an open electrode of a low-frequency detection and ranging (LFDAR) sensor embedded in an electronic device; obtaining a time-domain sample of the output voltage of a resonant circuit of the LFDAR coupled to the open electrode using the LFDAR sensor; converting the time-domain sample into a frequency-domain sample using one or more processors of the electronic device; for each frequency-domain sample, determining an amplitude difference and a phase difference compared to the amplitude and phase of the excitation voltage using the one or more processors; and determining, based on the amplitude difference and the phase difference, the material class of an object within a detection distance of the open electrode using the one or more processors.

[0007] In one embodiment, a method includes: applying an excitation voltage at a specific excitation frequency to an open electrode of a low-frequency detection and ranging (LFDAR) sensor; (a) obtaining a time-domain sample of the output voltage of a resonant circuit of the LFDAR coupled to the open electrode using the LFDAR sensor; (b) converting the time-domain sample to a frequency-domain sample using one or more processors; (c) for each frequency-domain sample, determining an amplitude difference and a phase difference compared to the amplitude and phase of the excitation voltage using one or more processors; (d) forming clusters of amplitude and phase differences using one or more processors; (e) classifying and estimating the range of the clusters using one or more processors; (f) storing the classified and estimated ranges using one or more processors; (g) adjusting the excitation frequency using one or more processors; and (h) repeating steps (a) to (g) until a specified number of excitation frequencies are exhausted.

[0008] In one embodiment, an apparatus includes: one or more motion sensors; one or more radio frequency (RF) transmitters; a low-frequency detection and ranging (LFDAR) sensor including: 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; and one or more processors configured to: obtain a time-domain sample of the output voltage of the resonant circuit; convert the time-domain sample to a frequency-domain sample; for each frequency-domain sample, determine an amplitude difference and a phase difference compared to the amplitude and phase of the excitation voltage; determine a material category of an object by comparing the amplitude difference and phase difference of the frequency-domain sample with a plurality of previously generated material categories; and reduce electromagnetic radiation emitted by the one or more RF transmitters based on the material type and estimated range.

[0009] Other implementations may include apparatus, computing devices, and nontransitory computer-readable storage media.

[0010] The specific embodiments disclosed herein offer one or more of the following advantages over other detection and ranging systems (e.g., RADAR). Mobile devices employing LFDAR sensors exhibit extremely high sensitivity at close range (e.g., between 10 cm and 20 cm) with no minimum detection range. This range can be determined by defining the size of the electrodes and grounding and their relative positions to each other. For tethered products (e.g., smart speakers, desktop computers), LFDAR sensors can have a range of several meters because the form factor is distributed. LFDAR sensors have low power consumption. LFDAR sensors are unaffected by frequency (tens of kHz to tens of MHz), unlike the fixed and strictly regulated frequencies of RADAR. LFDAR sensors are also unaffected by the shape and orientation of the antenna, and multiple existing antennas can be used as electrodes. LFDAR sensors use low-cost, general-purpose electronic components and / or existing components in mobile devices (e.g., ADCs, processors) can be reused for detection and ranging applications, and LFDAR sensors can be easily adapted for differentiated designs for small form factor products (such as smartwatches or TV remotes).

[0011] Details of one or more specific embodiments of the subject matter are set forth in the following figures and description. Other features, aspects, and advantages of the subject matter will become apparent from the specification, figures, and claims. Attached Figure Description

[0012] Figure 1A This is a circuit diagram of an open electrode LFDAR sensor according to one implementation scheme.

[0013] Figure 1B This is based on an embodiment shown for testing several different materials. Figure 1A The graph shown is a curve of the output voltage of the open electrode LFDAR sensor.

[0014] Figure 2A This is a circuit diagram of an LFDAR sensor according to one embodiment, which uses a smaller inductor and provides a higher efficiency than... Figure 1A The open electrode LFDAR sensor offers higher resolution (higher Q).

[0015] Figure 2B This is based on an embodiment shown for testing several different materials. Figure 2A The graph shown is a curve of the output voltage of the open electrode LFDAR sensor.

[0016] Figure 3 This is a flowchart of an exemplary LFDAR sensor process according to one implementation.

[0017] Figure 4 It is used according to the illustration of an implementation scheme. Figure 2A The output of the LFDAR sensor shown is a graph of clustering for two-dimensional (2D) material classification.

[0018] Figure 5 It includes Figure 2A The LFDAR sensor shown is used and performs as described in the reference. Figure 3 An exemplary device architecture for the LFDAR process described herein. Detailed Implementation

[0019] Exemplary circuit

[0020] Figure 1A This is a circuit diagram of an open-electrode LFDAR sensor 100 according to one embodiment. The sensor 100 includes an AC input voltage source 101 (VIN), an inductor 102 (L1), a resistor 103 (R1), and a capacitor 104 (C1). The input voltage source 101 is coupled to ground 106. When the capacitor 104 is capacitively coupled to ground 106 through a human hand 105, the sensor 100 generates a resonant frequency f at node 107. r The oscillating output voltage operates using a series resistor-inductor-capacitor (RLC) resonant circuit. The series RLC resonant circuit resonates based on the values ​​of inductor 102 and resistor 103, the combined capacitance of capacitor 104, the capacitance of hand 105, and the values ​​of parasitic capacitance.

[0021] Figure 1B This is based on an embodiment shown for testing several different materials. Figure 1A The graph shows the output voltage of the open-electrode LFDAR sensor. As described above, the presence of the hand 105 within the detection range of the LFDAR sensor 100 grounds the series RLC resonant circuit, causing the amplitude of the output voltage VF to increase relative to the input excitation voltage (e.g., a single-frequency modulated sine wave) generated by the input voltage source 101. Furthermore, the phase of the output voltage VF is shifted relative to the phase of the input excitation voltage. More specifically, Figure 1B This illustrates how the resonant frequency of sensor 100 changes when hand 105 is within the detection range of sensor 100, resulting in amplitude and phase differences in the output voltage VF that can be sampled at output node 107. These amplitude and phase changes can be used to distinguish hand 105 from other materials (e.g., plastic, wood, water), as referenced below. Figure 4 The aforementioned measures aim to reduce false negatives and false positives in human proximity testing.

[0022] Figure 1BThe output voltage VF shown is for test cases where the hand 105 is outside the detection range of the sensor 100 (e.g., 10 cm), within the detection range of the sensor 100, and the hand 105 is pointing away from the sensor 100. As can be observed, the presence of the hand 105 within the detection range of the sensor 100 increases the amplitude and causes a phase shift compared to the input excitation voltage.

[0023] While sensor 100 provides a high quality factor Q due to its physically very large inductor 102, such a large inductor is impractical for low-frequency detection and ranging in mobile devices, where it is desirable to use existing components without incurring additional material costs. Therefore, an alternative LFDAR sensor 200 is described below, which uses a non-inverting operational amplifier and a series RLC circuit that uses a physically smaller inductor and still provides a higher quality factor Q.

[0024] Figure 2A This is a schematic diagram of an LFDAR sensor 200 according to one embodiment, which uses a small inductor and provides a higher voltage ratio than... Figure 1A The open-electrode LFDAR sensor provides higher resolution (higher Q). In one embodiment, sensor 200 includes a non-inverting operational amplifier 209 coupled to a supply voltage 208. The inverting input terminal (2) of amplifier 209 is coupled to a series RLC resonant circuit 201, which includes a capacitor 202 (C), an inductor 203 (L), and a 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 in this example is configured to generate a single-frequency modulated sine wave as the excitation signal for circuit 200. The series RLC resonant circuit 201, input AC voltage source 211, and power supply voltage are coupled to ground 205. Feedback resistor 207 (R F It is coupled between the inverting input terminal (2) and the output terminal (6) of amplifier 209. The output terminal (6) of amplifier 209 is used as the output node (VF1 / V) of sensor 200. O The load resistor 210 (R1) is coupled between the output terminal (6) and the ground 205.

[0025] The output voltage (VF1 / V) captured at the output node O The following is given by equations [1] and [2]:

[0026]

[0027] The LFDAR sensor 200 uses physically small inductors (e.g., 1 mH) to provide a higher quality factor Q, improving the sensor 200's ability to distinguish different materials. The sensor 200 also features low power consumption and is frequency-independent, unlike the fixed and strictly regulated RADAR frequencies. The sensor 200 is unaffected by the shape and orientation of the antenna, and multiple existing antennas can be used as electrodes. The sensor 200 uses low-cost, general-purpose electronic components and / or existing components from mobile devices (e.g., ADCs, processors) can be reused for detection and ranging applications, and the LFDAR circuitry can be easily adapted for differentiated designs for small form factor products such as smartwatches or TV remotes. In one embodiment, the sensor 200 is included in an integrated circuit (IC) chip or a system-on-a-chip (SoC).

[0028] Figure 2B It is based on the implementation plan shown for testing several different materials. Figure 2A The graph shown depicts the output voltage of the LFDAR sensor 200. The AC input voltage is plotted for comparison with different sensing materials having varying capacitances. As can be observed, different materials produce different amplitude and phase difference clusters, which can be utilized using the LFDAR. It should be noted that the amplitude of the output voltage varies depending on the type of material and the range. For example, a capacitor value of 25.33 pF provides a peak-to-peak voltage swing exceeding 1 volt.

[0029] Exemplary process

[0030] Figure 3 This is a flowchart of an exemplary LFDAR process 300 according to one implementation. Process 300 may, for example, use references... Figure 5 The described device architecture is implemented using 500.

[0031] Process 300 begins by exciting the material proximity sensor (301) with an input excitation signal of a specific frequency. For example, a frequency-adjustable single-frequency modulated sine wave can be used to generate excitation signals with different excitation frequencies. Other types of periodic signals, such as square waves or triangular waves, can also be used.

[0032] Process 300 continues (302) by capturing the output voltage of the material proximity sensor. For example, the output voltage of the output node of the LFDAR sensor 200 can be sampled using an analog-to-digital converter (ADC) to generate a digital value representing the captured output voltage. In one embodiment, the ADC may be an ADC pre-existing in the host device (e.g., an ADC in a smartphone, smartwatch, TV remote control, or smart speaker).

[0033] Process 300 continues to calculate the frequency transformation of the captured output voltage (303). For example, the digital sample output by the ADC can be input to a digital signal processor (DSP) or a central processing unit (CPU) (see...). Figure 5 (Component 504 in the original text). The DSP / CPU uses, for example, a Fast Fourier Transform (FFT) or any other suitable transform (e.g., a Discrete Cosine Transform) to transform the digital values ​​to the frequency domain. The output of the FFT provides the amplitude and phase at the frequency of the excitation signal. In one implementation, the DSP / CPU may be pre-existing in a host device (e.g., a smartphone, smartwatch, TV remote control, smart speaker).

[0034] Process 300 measures the amplitude difference ΔA and phase difference of the captured output voltage. These values ​​are calculated by comparing the amplitude and phase of the input excitation signal with the excitation frequency to proceed (304). For example, the DSP can calculate the difference between the amplitude and phase of the captured output voltage and the known amplitude and phase of the excitation signal.

[0035] Process 300 uses amplitude difference ΔA and phase difference To classify the materials and estimate the distance to the sensor, we continue (305). For example, the amplitude difference ΔA and the phase difference can be used. The data is compared with multiple predetermined clusters of amplitude and phase differences of the excitation frequencies, where each cluster is associated with a specific material at a specific frequency. Clusters can be determined empirically using any suitable clustering algorithm. In one implementation, the amplitude and phase differences can be used to index a lookup table stored in the host device's memory to classify the materials.

[0036] Process 300 continues by determining whether all desired excitation frequencies have been excited (306). If none of the desired frequencies have been excited, the excitation frequencies are changed (308) and previous steps 301 to 306 are repeated. If all the desired frequencies have been excited, the knowledge of the amplitude and phase differences of all the excitation frequencies of interest is used to improve material classification and distance estimation (307). For example, the average of these pairs can be used to correlate the captured voltage signals with specific clusters to classify materials and ranges.

[0037] In one implementation, data can be captured offline for different materials and different ranges from each material from the sensor. For example, amplitude and phase differences in the frequency domain can be clustered using a suitable clustering algorithm. The characteristics of the clusters (e.g., centroids) can then be included in a database stored in the memory of the mobile device. In one implementation, regression analysis can be used to fit a function (e.g., a line) to a cluster of points, and the resulting coefficients (e.g., the slope and intercept of the line) can be stored in a database. In one implementation, principal component analysis (PCA) can be used to characterize the clusters, which can be stored in a database. At a specific excitation frequency, multiple clusters can exist for the same material at different ranges from the sensor and in different orientations relative to the sensor.

[0038] During online operation, amplitude and phase are calculated in the frequency domain and compared with a database to find the closest matching cluster. The closest match can be determined by using comparative clustering properties such as distance metrics (e.g., Euclidean distance) or least squares methods. In one implementation, the output voltage of the resonant circuit is stabilized based on motion data output from one or more motion sensors (e.g., 3-axis MEM accelerometers, 3-axis MEM gyroscopes).

[0039] In one embodiment, the open electrode is an antenna coupled to an RF transmitter used for transmitting and / or receiving RF signals. That is, the antenna is used for both RF communications, as long as the RF communications are at a higher frequency than LFDAR. In one embodiment, the antenna may be time-division multiplexed, so that the antenna is not used for both communication and LFDAR simultaneously.

[0040] In one implementation, the excitation frequency is randomized to prevent interference with other nearby LFDARs. In one implementation, frequency hopping can be used. In one implementation, the device can announce its frequencies to other devices via wireless broadcast.

[0041] In one implementation, the minimum / maximum detection range of the LFDAR is determined by the size of the open electrode and the ground and their relative placement to each other.

[0042] In one implementation, the minimum detection range is less than 1 cm.

[0043] Figure 4 It is a phase versus amplitude curve for material classification based on an implementation scheme. For example... Figure 4 As shown, for each excitation frequency of interest, there exist amplitude and phase. Different clusters for the difference pair. In the example shown, there are different clusters for remote control 401, plastic 402, wood 403, water 404, laptop computer 405, finger 406, and hand 407. The finger and hand clusters 406 and 407 can be combined into a single cluster 406 as the human hand category.

[0044] If possible Figure 4 Observedly, cluster 406 can be distinguished from clusters associated with other materials, thus allowing for the detection of human hands with reduced false negatives and false positives. Clusters can be empirically determined 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, and density-based (e.g., DBSCAN). For example, the centroid and range of each cluster can be associated with its material type / range and stored in a lookup table in the memory of the sensor or host device. When a new detection occurs, the captured amplitude and phase differences are used to classify the material type / range using the clusters.

[0045] Exemplary wearable computer architecture

[0046] Figure 5 An exemplary device architecture 500 is shown, which is implemented with reference to Figures 1 to 2000. Figure 5 The features and operations described. Architecture 500 may include a memory interface 502, one or more data processors, a digital signal processor (DSP), a graphics processor, and / or a central processing unit (CPU) 504, and a peripheral device interface 506. The memory interface 502, one or more processors 504, and / or the peripheral device interface 506 may be standalone components or may be integrated into one or more integrated circuits.

[0047] Sensors, devices, and subsystems can be coupled to the peripheral interface 506 to provide multiple functions. For example, one or more motion sensors 510, light sensors 512, and proximity sensors 514 can be coupled to the peripheral interface 506 to facilitate motion sensing (e.g., acceleration, rotation rate), illumination, and proximity functions of the wearable computer. A location processor 515 can be connected to the peripheral interface 506 to provide geolocation. In some implementations, the location processor 515 may be a GNSS receiver, such as a Global Positioning System (GPS) receiver. An electronic magnetometer 516 (e.g., an integrated circuit chip) can also be connected to the peripheral interface 506 to provide data that can be used to determine magnetic north. The electronic magnetometer 516 can provide data to an electronic compass application. The motion sensor 510 may include one or more accelerometers and / or gyroscopes configured to determine changes in the wearable computer's speed and direction of motion. A barometer 517 may be configured to measure atmospheric pressure around the mobile device.

[0048] The material proximity sensor 520 performs low-frequency detection and ranging, as shown in Figures 1 to 12. Figure 4 For example, sensor 502 may include the LFDAR circuit 200 described with reference to FIG2, and perform the reference... Figure 3 The LFDAR process described above.

[0049] Communication functions can be facilitated by wireless communication subsystems 524, which may include radio frequency (RF) receivers and transmitters (or transceivers) and / or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of communication subsystem 524 may depend on one or more communication networks intended to be used by the mobile device to perform operations. For example, architecture 500 may include designs for communication via GSM networks, GPRS networks, EDGE networks, Wi-Fi... TM Network and Bluetooth TM A network-operated communication subsystem 524. Specifically, the wireless communication subsystem 524 may include a host protocol that enables mobile devices to be configured as base stations for other wireless devices.

[0050] The audio subsystem 526 can be coupled to the speaker 528 and microphone 530 to facilitate the activation of voice functions such as voice recognition, voice copying, digital recording, and telephone functions. The audio subsystem 526 can be configured to receive voice commands from the user.

[0051] I / O subsystem 540 may include touch surface controller 542 and / or other input controller 544. Touch surface controller 542 may be coupled to touch surface 546. Touch surface 546 and touch surface controller 542 may, for example, use any of a variety of touch-sensitive technologies to detect contact and movement or their interruption, 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 may include, for example, a touchscreen or a digital crown of a smartwatch. I / O subsystem 540 may include a haptic engine or device for providing haptic feedback (e.g., vibration) in response to commands from processor 504. In one embodiment, touch surface 546 may be a pressure-sensitive surface.

[0052] Other input controllers 544 may be coupled to other input / control devices 548, such as one or more buttons, rocker switches, thumbwheels, infrared ports, and USB ports. One or more buttons (not shown) may include up / down buttons for volume control of the speaker 528 and / or microphone 530. Touch surfaces 546 or other controllers 544 (e.g., buttons) may include or be coupled to fingerprint identification circuitry for use with fingerprint authentication applications to authenticate the user based on their fingerprint.

[0053] In one implementation, pressing the button for a first duration unlocks the touch surface 546, and pressing the button for a second duration longer than the first duration turns the mobile device on or off. Users can customize the function of one or more buttons. For example, the touch surface 546 can also be used to implement virtual or soft buttons.

[0054] In some implementations, the computing device may display recorded audio and / or video files, such as MP3, AAC, and MPEG files. In some implementations, the mobile device may include MP3 player functionality. Other input / output and control devices may also be used.

[0055] Memory interface 502 may be coupled to memory 550. Memory 550 may include high-speed random access memory and / or non-volatile memory, such as one or more disk storage devices, one or more optical storage devices, and / or flash memory (e.g., NAND, NOR). Memory 550 may store operating system 552, such as the iOS operating system developed by Apple Inc. in Cupertino, California. Operating system 552 may include instructions for handling basic system services and for performing hardware-related tasks. In some implementations, operating system 552 may include a kernel (e.g., a UNIX kernel).

[0056] The memory 550 may also store communication instructions 554 that facilitate communication with one or more additional devices, one or more computers, and / or one or more servers, such as instructions for implementing wired or wireless communication with other devices. The memory 550 may include: graphical user interface instructions 556 to facilitate graphical user interface processing; sensor processing instructions 558 to facilitate sensor-related processing and functions; telephone instructions 560 to facilitate telephone-related processes and functions; electronic message processing instructions 562 to facilitate electronic message processing 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 general GNSS and location-related processes and instructions; and instructions for performing reference... Figure 3 The LFDAR process is described in instruction 570. Memory 550 also includes application instructions 572 for executing various applications, including applications that can utilize material proximity detection and ranging, such as power management units for RF transmitters or material analyzer applications.

[0057] Each of the instructions and applications identified above may correspond to a set of instructions for performing one or more of the functions described above. These instructions do not need to be implemented as a separate software program, process, or module. Memory 550 may include additional instructions or fewer instructions. Furthermore, various functions of the mobile device may be performed in hardware and / or software, including in one or more signal processing and / or application-specific integrated circuits.

[0058] The described features can be advantageously implemented in one or more computer programs that can be executed on a programmable system, the programmable system including at least one input device, at least one output device, and at least one programmable processor coupled to receive data and instructions from and transmit data and instructions to the data storage system. A computer program is a set of instructions that can be used directly or indirectly in a computer to perform an activity or produce a result. Computer programs can be written in any form of programming language (e.g., SWIFT, Objective-C, C#, Java), including compiled and interpreted languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, browser-based web application, or other unit suitable for use in a computing environment.

[0059] While this specification contains numerous specific implementation details, these details should not be construed as limiting the scope of any invention or potentially claimed content, but rather as descriptions of features specific to particular embodiments of a particular invention. Certain features described in the context of different embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may be implemented individually or in any suitable sub-combination in multiple embodiments. Furthermore, while certain features may be described above as functioning in certain combinations and even initially claimed in this manner, one or more features of a claimed combination may be removed from that combination in certain circumstances, and the claimed combination may involve sub-combinations or variations thereof.

[0060] Similarly, although operations are shown in a specific order in the accompanying drawings, this should not be construed as requiring such operations to be performed in a sequential order or the specific order shown, or requiring all shown operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the division of various system components in the above embodiments should not be construed as requiring such division in all embodiments, and it should be understood that the program components and the system may generally be integrated together in a single software product or packaged into multiple software products.

Claims

1. An apparatus, the apparatus comprising: Open electrode; An alternating current (AC) voltage source is 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 being configured to oscillate when an object is within the detection distance of the open electrode; One or more processors, said one or more processors being configured to: When the resonant circuit is oscillating, obtain a time-domain sample of the output voltage of the resonant circuit; Convert the time-domain samples into frequency-domain samples; For each frequency domain sample, determine the amplitude difference and phase difference compared to the amplitude and phase of the excitation voltage; as well as The material type of the object is determined based on the amplitude difference and the phase difference as follows: The amplitude difference and the phase difference are compared with a plurality of predetermined amplitude differences and phase differences of the excitation frequency; and The material category of the object is determined based on the results of the comparison.

2. The device according to claim 1, wherein the resonant circuit is a series resistor-inductor-capacitor (RLC) resonant circuit.

3. The apparatus according to 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 is coupled between the in-phase input terminal and the output terminal; A series resonant circuit, wherein the series resonant circuit is coupled to the non-inverting input terminal; An input voltage source, the input voltage source being coupled to the inverting input terminal of the non-inverting amplifier; as well as A load resistor is coupled between the output terminal and ground.

4. The apparatus of claim 1, wherein the resonant circuit comprises a resistor, an inductor, and a capacitor, and the inductor is sized to allow the resonant circuit to provide sufficient resolution to distinguish the quality factor self-resonance of different object materials.

5. The apparatus of claim 1, wherein the resonant circuit is a parallel resistor-inductor-capacitor (RLC) resonant circuit.

6. The apparatus of claim 1, wherein the one or more processors are further configured to determine the range of the object from the open electrode.

7. The apparatus according to 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 device according to claim 1, wherein the minimum detection range of the device is less than about 1 cm.

10. A method, the method comprising: An excitation voltage is applied to the open electrodes of a low-frequency detection and ranging sensor embedded in an electronic device; The time-domain sample of the output voltage of the resonant circuit of the low-frequency detection and ranging coupled to the open electrode is obtained using the low-frequency detection and ranging sensor. The time-domain samples are converted into frequency-domain samples using one or more processors of the electronic device. For each frequency domain sample, the one or more processors are used to determine the amplitude difference and phase difference compared to the amplitude and phase of the excitation voltage; as well as The material class of the object within the detection distance of the open electrode is determined by the one or more processors based on the amplitude difference and the phase difference, as follows: The amplitude difference and the phase difference are compared with a plurality of predetermined amplitude differences and phase differences of the excitation frequency; and The material category of the object is determined based on the results of the comparison.

11. The method of claim 10, further comprising: The electromagnetic radiation emissions of the electronic device are reduced based on the material type and the estimated range.

12. A method, the method comprising: An excitation voltage of a specific excitation frequency is applied to the open electrode of a low-frequency detection and ranging sensor; (a) Using the low-frequency detection and ranging sensor, obtain a time-domain sample of the output voltage of the resonant circuit of the low-frequency detection and ranging coupled to the open electrode; (b) Use one or more processors to convert the time-domain samples into frequency-domain samples; (c) For each frequency domain sample, the one or more processors are used to determine the amplitude difference and phase difference compared to the amplitude and phase of the excitation voltage; (d) Using the one or more processors to form clusters of the amplitude difference and the phase difference; (e) Use the one or more processors to classify and estimate the extent of the clusters; (f) Use the one or more processors to store the classification and estimated range; (g) Using one or more of the processors to adjust the excitation frequency; and (h) Repeat steps (a) to (g) until the specified number of excitation frequencies are exhausted.

13. An apparatus comprising: One or more motion sensors; One or more radio frequency (RF) transmitters; Low-frequency detection and ranging sensor, the low-frequency detection and ranging sensor comprising: Open electrode; An alternating current (AC) voltage source is configured to supply an excitation voltage to the open electrode at an excitation frequency; A resonant circuit, wherein the resonant circuit is coupled to the open electrode; One or more processors, said one or more processors being configured to: Obtain time-domain samples of the output voltage of the resonant circuit; Convert the time-domain samples into frequency-domain samples; For each frequency domain sample, determine the amplitude difference and phase difference compared to the amplitude and phase of the excitation voltage; The material category of the object is determined by comparing the amplitude difference and phase difference of the frequency domain samples with multiple previously generated material categories; and The electromagnetic radiation emitted by the one or more radio frequency (RF) transmitters is reduced based on the material type and the 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 radio frequency (RF) transmitters.

16. The apparatus of claim 13, wherein the excitation frequency is randomized to prevent interference with other low-frequency detection and ranging.

17. The apparatus of claim 13, wherein the maximum or minimum detection range of the low-frequency detection and ranging is determined by the size of the open electrode and the ground and their relative arrangement 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 comprises a resistor, an inductor, and a capacitor, and the inductor is sized to allow the resonant circuit to provide sufficient resolution to distinguish the quality factor self-resonance of different object materials.

20. The apparatus of claim 13, wherein the one or more processors are further configured to estimate the range of the object from the open electrode by comparing the amplitude difference and the phase difference of the frequency domain samples with a plurality of previously generated range estimates.

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