Techniques for cover angle estimation

Abstract

Techniques for cover angle estimation are disclosed. In an illustrative embodiment, a computing device transmits a radio frequency (RF) signal on an antenna. The computing device may then detect RF signals on the same or different antennas. The computing device may use a machine learning-based algorithm to determine a lid angle based on the RF signal. The computing device may classify the cover angle into, for example, a closed configuration, a laptop configuration, a tablet configuration, a book configuration, a tent configuration, and the like.

Description

Background Art

[0001] Clamshell devices having a base portion and a lid portion connected by a hinge are typically able to sense the orientation of the base portion relative to the lid portion. This capability allows the device to respond to changes in the orientation of the lid portion, such as entering a low-power state when the lid is closed. A hinge sensor (e.g., a resistive or capacitive contact sensor or a magnetic-based sensor) can be used to sense the orientation of the lid portion relative to the base portion. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] The concepts described herein are illustrated in the accompanying drawings by way of example and not by way of limitation. For simplicity and clarity of illustration, the elements illustrated in the accompanying drawings are not necessarily drawn to scale. Where deemed appropriate, reference numerals are repeated between the drawings to indicate corresponding or similar elements.

[0003] Figure 1 is a simplified diagram of at least one embodiment of a computing device for determining an angle of a lid relative to a base.

[0004] Figure 2 yes Figure 1 A simplified block diagram of at least one embodiment of a computing device.

[0005] Figure 3 yes Figure 1 A simplified block diagram of at least one embodiment of an environment that a computing device may establish.

[0006] Figure 4 yes Figure 1 A simplified diagram of the signal flow of a computing device.

[0007] Figure 5 is in a closed configuration Figure 1 A simplified diagram of a computing device.

[0008] Figure 6 Is in laptop configuration Figure 1 A simplified diagram of a computing device.

[0009] Figure 7 Is in tablet configuration Figure 1 A simplified diagram of a computing device.

[0010] Figure 8 is in a flattened configuration Figure 1 A simplified diagram of a computing device.

[0011] Fig. 9 Is in tent configuration Figure 1 A simplified diagram of a computing device.

[0012] Fig.10 It can be Figure 1 A simplified flowchart of at least one embodiment of a method performed by a computing device of the invention for training a machine learning-based algorithm to determine an angle of a cover relative to a base.

[0013] Fig.11 It can be Figure 1 A simplified flow chart of at least one embodiment of a method for determining an angle of a cover relative to a base performed by a computing device of the present invention. DETAILED DESCRIPTION

[0014] refer to Figure 1 In an illustrative embodiment, computing device 100 has lid portion 102 connected to base portion 104 via hinge 106. In use, in one embodiment, transmit antennas 108A and / or 108B may generate wireless radio frequency (RF) signals 110A and / or 110B, respectively. Receive antennas 112A and / or 112B may receive a portion of RF signals 110A and / or 110B. As described in more detail below, computing device 100 may use RF signals reflected from transmit antennas and / or RF signals received at receive antennas to determine the angle of hinge 106 (i.e., the angle of lid portion 102 and base portion 104). In one embodiment, the position of a nearby object (e.g., lid portion 102) may change the impedance of transmit antennas 108A and / or 108B, causing some transmit signals to be reflected from antennas 108A and / or 108B.

[0015] Although the concept of the present disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that it is not intended to limit the concept of the present disclosure to the specific form disclosed, but on the contrary, it is intended to cover all modifications, equivalents and alternatives consistent with the present disclosure and the appended claims.

[0016] References in the specification to "one embodiment", "an embodiment", "an illustrative embodiment", etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but each embodiment may or may not include the particular feature, structure, or characteristic. In addition, such phrases do not necessarily refer to the same embodiment. In addition, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is considered to be within the knowledge of those skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments (whether or not explicitly described). In addition, it should be understood that items included in a list of the form "at least one of A, B, and C" may refer to (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of "at least one of A, B, or C" may refer to (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

[0017] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transient or non-transient machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure (e.g., volatile or non-volatile memory, a media disk, or other media device) for storing or transmitting information in a machine-readable form.

[0018] In the accompanying drawings, some structural or method features may be shown in a particular arrangement and / or order. However, it should be understood that such a particular arrangement and / or order may not be required. More specifically, in some embodiments, such features may be arranged in a manner and / or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or method feature in a particular drawing is not intended to imply that such feature is required in all embodiments, and in some embodiments, such feature may not be included or may be combined with other features. Terms modified by the word "substantially" include arrangements, directions, spacings, or positions that are slightly different from the meaning of the unmodified term. For example, a stereo camera with a field of view of substantially 180 degrees includes a camera with a field of view within a few degrees of 180 degrees.

[0019] The computing device 100 may be embodied as any type of computing device having a lid portion 102 or a similar housing and a base portion 104 or a similar housing that can rotate relative to each other. For example, the computing device 100 may be embodied as or otherwise included in, but not limited to, a laptop computer, a notebook computer, a cellular phone, a smart phone, an e-reader, a tablet computer, a dual-display device (e.g., with displays in the lid portion 102 and the base portion 104), a multi-processor system, a processor-based system, a consumer electronic device, a wearable computer, a cell phone, a messaging device, a camera device, and / or any other computing device.

[0020] The illustrative lid portion 102 includes a display 114, and the illustrative base portion 104 includes a keyboard 116. It should be appreciated that in some embodiments, both the lid portion 102 and the base portion 104 may have a different set of components. For example, in some embodiments, the lid portion 102 and the base portion 104 may each have a display 114, the base portion 104 may have a receiving antenna 112, and the lid portion may have a transmitting antenna 108, etc. In some embodiments, the computing device 100 may not have a preferred orientation, so that labeling one portion of the computing device 100 as the lid portion 102 and labeling another portion as the base portion 104 is arbitrary. The lid portion 102 may also be described as the housing 102, and the base portion 104 may also be described as the housing 104. It should be appreciated that the housing 102 and / or the housing 104 may have a transmitting antenna 108, a receiving antenna 112, a display 114, a keyboard 116, and / or any other suitable components.

[0021] In one embodiment, computing device 100 includes one or more transmit antennas 108 and one or more receive antennas 112. In other embodiments, some or all of antennas 108, 112 may be used for both transmission and reception. For example, in one embodiment, computing device 100 may include one antenna for a particular protocol that is used as both a receive antenna 108 and a transmit antenna 112.

[0022] The RF signals 110A, 110B transmitted and / or received by the computing device may be at any suitable frequency, for example, 100 megahertz to 100 gigahertz. In some embodiments, the RF signals 110A, 110B may be embodied as microwave or millimeter wave radiation.

[0023] Reference now Figure 2, in one embodiment, a block diagram of computing device 100 illustrates various components of computing device 100. Illustrative computing device 100 includes one or more processors 202, memory 204, input / output (I / O) subsystem 206, data storage 208, communication circuitry 210, display 114, and one or more peripherals 214. In some embodiments, one or more of the illustrative components of computing device 100 may be included in another component, or otherwise form a part of another component. For example, in some embodiments, memory 204 or portions thereof may be included in processor 202. In some embodiments, one or more of the illustrative components may be physically separate from another component.

[0024] The processor 202 may be embodied as any type of processor capable of performing the functions described herein. For example, the processor 202 may be embodied as (one or more) single-core or multi-core processors, single-socket or multi-socket processors, digital signal processors, graphics processors, neural network computing engines, image processors, microcontrollers, or other processors or processing / control circuits. Similarly, the memory 204 may be embodied as any type of volatile or non-volatile memory or data storage device capable of performing the functions described herein. In operation, the memory 204 may store various data and software used during the operation of the computing device 100, such as operating systems, applications, programs, libraries, and drivers. The memory 204 is communicatively coupled to the processor 202 via the I / O subsystem 206, which may be embodied as circuits and / or components for facilitating input / output operations with the processor 202, the memory 204, and other components of the computing device 100. For example, I / O subsystem 206 may be embodied as or otherwise include a memory controller hub, an input / output control hub, a firmware device, a communication link (i.e., a point-to-point link, a bus link, a wire, a cable, an optical guide, a printed circuit board trace, etc.), and / or other components and subsystems for facilitating input / output operations. I / O subsystem 206 may utilize any suitable connectors, interconnects, buses, protocols, etc. (e.g., SoC architecture, USB2, USB3, USB4, The I / O subsystem 206 may connect various internal and external components of the computing device 100 to each other. In some embodiments, the I / O subsystem 206 may form part of a system on a chip (SoC) and be included on a single integrated circuit chip with the processor 202, memory 204, and other components of the computing device 100.

[0025] Data storage device 208 may be embodied as any type of one or more devices configured for short-term or long-term storage of data. For example, data storage device 208 may include any one or more memory devices and circuits, memory cards, hard drives, solid-state drives, or other data storage devices.

[0026] The communication circuit 210 may be embodied as any type of interface capable of interfacing the computing device 100 with other computing devices, such as via one or more wired or wireless connections. In some embodiments, the communication circuit 210 may be capable of interfacing with any suitable cable type, such as an electrical or optical cable. The communication circuit 210 may be configured to communicate using any one or more communication technologies and associated protocols (e.g., Ethernet, The communication circuit 210 may be located on a separate silicon chip from the processor 202, or the communication circuit 210 may be included in a multi-chip package with the processor 202, or even on the same die as the processor 202. The communication circuit 210 may be embodied as one or more additional boards, daughter cards, network interface cards, controller chips, chipsets, dedicated components such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), or other devices that may be used by the computing device 100 to connect to another computing device. In some embodiments, the communication circuit 210 may be embodied as part of a system on chip (SoC) including one or more processors, or may be included on a multi-chip package that also includes one or more processors. In some embodiments, the communication circuit 210 may include a local processor (not shown) and / or a local memory (not shown) that are both local to the communication circuit 210. In such embodiments, the local processor of the communication circuit 210 may be capable of performing one or more functions of the processor 202 described herein. Additionally or alternatively, in such embodiments, the local memory of the communication circuit 210 may be integrated into one or more components of the computing device 100 at the board level, the socket level, the chip level, and / or other levels. The communication circuit 210 may include one or more antennas 212. The antennas may be embodied as, for example, the transmit antenna 108 and / or the receive antenna 112. In some embodiments, a single antenna 212 may be used as both the transmit antenna 108 and the receive antenna 112.

[0027] Display 114 may be embodied as any type of display that can be used to display information to a user of computing device 100, such as a touch screen display, a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a cathode ray tube (CRT) display, a plasma display, an image projector (e.g., 2D or 3D), a laser projector, a head-up display, and / or other display technologies.

[0028] In some embodiments, computing device 100 may include other or additional components, such as those commonly found in computing devices. For example, computing device 100 may also have peripherals 214, such as a keyboard, a mouse, external storage devices, and the like. In some embodiments, computing device 100 may be connected to a docking station that may interface with various devices, including peripherals 214. In some embodiments, peripherals 214 may include additional sensors, such as resistive or capacitive contact sensors or magnetic-based sensors, that computing device 100 may use to monitor the orientation of lid portion 102 relative to base portion 104.

[0029] Reference now Figure 3In an illustrative embodiment, computing device 100 establishes environment 300 during operation. Illustrative environment 300 includes wireless transmitter 302, wireless receiver 304, and lid angle determiner 306. Various modules of environment 300 may be embodied in hardware, software, firmware, or a combination thereof. For example, various modules, logic, and other components of environment 300 may form part of, or otherwise be established by, processor 202, memory 204, data storage 208, or other hardware components of computing device 100. Thus, in some embodiments, one or more modules of environment 300 may be embodied as a collection of circuits or electrical devices (e.g., wireless transmitter circuit 302, wireless receiver circuit 304, and lid angle determiner circuit 306, etc.). It should be appreciated that in such embodiments, one or more circuits (e.g., wireless transmitter circuit 302, wireless receiver circuit 304, and lid angle determiner circuit 306, etc.) may form part of one or more of processor 202, memory 204, I / O subsystem 206, data storage 208, and / or other components of computing device 100. For example, in some embodiments, some or all modules may be embodied as processor 202 and memory 204 and / or data storage 208 storing instructions to be executed by processor 202. Furthermore, in some embodiments, one or more of the illustrative modules may form part of another module, and / or one or more of the illustrative modules may be independent of one another. Additionally, in some embodiments, one or more modules of environment 300 may be embodied as virtualized hardware components or emulated architectures that may be established and maintained by processor 202 or other components of computing device 100. It should be appreciated that some functionality of one or more modules of environment 300 may require hardware implementation, in which case embodiments of modules implementing such functionality will be at least partially embodied as hardware.

[0030] The wireless transmitter 302, which may be embodied as hardware, firmware, software, virtualized hardware, emulation architecture, and / or a combination thereof as described above, is configured to control the transmission of wireless signals on antennas 108A, 108B. As described above, in some embodiments, one or more antennas 108 used to transmit wireless signals may also be used to receive wireless signals. In an illustrative embodiment, the wireless transmitter 302 transmits wireless signals as part of general wireless communications, such as WiFi, Bluetooth, 4G, 5G, etc. Additionally or alternatively, in some embodiments, a wireless beacon transmitter 308 of the wireless transmitter 302 may transmit wireless beacons on antennas 108A, 108B. The wireless beacon may be any suitable signal that can be used to determine the angle of the lid. For example, the wireless beacon may be a pulse of a specific frequency, a pulse of a certain frequency range, or any other suitable RF signal. In some embodiments, any suitable wireless signal (including a wireless signal transmitted as part of, for example, WiFi communication) may be used to determine the angle of the lid. In such an embodiment, the wireless beacon transmitter 308 does not need to transmit a wireless beacon as long as there is a wireless signal that can be used to determine the angle of the lid recently. If no wireless signal suitable for determining the angle of the cover has been transmitted in the past (eg, 0.1-10 seconds), the wireless beacon transmitter 308 may transmit a wireless beacon.

[0031] exist Figure 4 A representation of the signal flow is shown in . The transmitter 404 generates a signal to be transmitted on the antenna 212 (which may be, for example, the antenna 108). When appropriate, the beacon signal trigger 402 may trigger the transmitter 404 to generate a signal. The signal from the transmitter 404 is sent to the power amplifier (PA) 406, which amplifies the signal. The signal is sent to the front end 408 and then to the antenna 212. In the illustrative embodiment, the bidirectional coupler 410 couples the signals sent to the antenna 212 and received (or reflected) from the antenna 212 onto the signal lines 412, 414, which will be described in more detail below with respect to the wireless receiver 304. In the illustrative embodiment, the wireless transmitter 302 may include Figure 4 Any suitable components of the signal flow are shown, such as beacon signal trigger 402, transmitter 404, power amplifier 406, and front end 408.

[0032] The wireless receiver 304, which may be embodied as hardware, firmware, software, virtualized hardware, emulation architecture, and / or a combination thereof as described above, is configured to receive signals from one or more antennas 108A, 108B, including transmitted signals reflected from one or more antennas 108A, 108B. As described above, in some embodiments, one or more antennas 112 used to receive wireless signals may also be used to transmit wireless signals. The wireless receiver 304 may receive signals transmitted by the computing device 100 on the same or different antennas 112, or the wireless receiver 304 may receive signals transmitted by a remote device. The wireless receiver 304 may receive signals as part of, for example, WiFi communications, or the wireless receiver 304 may receive wireless beacons transmitted by a wireless beacon transmitter 308. The wireless receiver 304 may pre-process the signal, for example, by demodulating the signal and determining parameters such as the I / Q in-phase / quadrature components of the signal.

[0033] Return to Figure 4 4. The signal flow shown in FIG. 4 shows a bidirectional coupler 410 that couples the signal sent to the antenna 212 to the line 412 and couples the signal received (or reflected) by the antenna 212 to the line 414. It should be understood that in some embodiments, the wireless signal is affected by the position and properties of objects near the antenna. The angle of the cover changes the relative position of the cover portion 102 and / or the base portion 104 relative to the antenna 212, which can affect the wireless signal from both the transmission and reception perspectives. For example, the signal sent by the front end 408 to the antenna 212 may be partially reflected by the antenna 212 (e.g., due to impedance mismatch) to the line 414. Similarly, the signal sent by the antenna 212 may be reflected back to the antenna 212 and the line 414 by objects in the environment. The signals on the lines 412 and 414 are sampled by the sampler 416 and demodulated by the demodulator 418. In some embodiments, the demodulator 418 can demodulate the analog signals on the lines 412 and 414 using analog signal processing techniques. The demodulator 418 may determine parameters such as the I / Q in-phase / quadrature components of each signal on lines 412, 414. The averager 420 may determine an average value of parameters such as the I / Q in-phase / quadrature components of each signal on lines 412, 414. The parameters, ratios of parameters, and / or average values ​​of parameters (and / or ratios of parameters) may be sent to the lid angle detector 422. It should be appreciated that Figure 4 The data flow shown in is only one possible embodiment, and other embodiments may include fewer, more, or different components. For example, in one embodiment, there may be one or more filters or amplifiers in lines 412, 414.

[0034] The lid angle determiner 306, which may be embodied as hardware, firmware, software, virtualized hardware, simulation architecture, and / or a combination thereof as described above, may be configured to determine the angle of the lid portion 102 relative to the base portion 104. To this end, the lid angle determiner 306 uses parameters such as I / Q in-phase / quadrature components (and / or averages, ratios, etc. of the I / Q in-phase / quadrature components) of the signal transmitted and / or received on the antenna 212. The lid angle determiner 306 may use any appropriate technique in determining the lid angle, such as a machine learning-based algorithm 310. The machine learning-based algorithm 310 may employ any appropriate machine learning-based algorithm, such as a neural network. The machine learning-based algorithm 310 may employ, for example, scalar regression, multi-class classification, binary classification, and the like. For example, in one embodiment, the machine learning-based algorithm 310 may determine the lid angle as a continuous angle value between 0 and 360 degrees. The computing device 100 may then classify the lid angle into one of several configurations, such as Figure 5-Figure 9 The configuration shown in . Figure 5 Computing device 100 is shown in a closed configuration. Figure 6 Computing device 100 is shown in a laptop configuration. Figure 7 Computing device 100 is shown in a tablet configuration. Figure 8 Computing device 100 is shown in a flat configuration. Fig. 9 The computing device 100 is shown in a tent configuration. The computing device 100 may classify angles of 0-30° as being in a closed configuration, angles of 30-150° as being in a laptop configuration, angles of 150-200° as being in a flat configuration, angles of 200-330° as being in a tent configuration, and angles of 330-360° as being in a tablet configuration. In another embodiment, the machine learning-based algorithm 310 may use the machine learning-based algorithm 310 to directly classify the lid angles as corresponding to the laptop configuration. Figure 5-Figure 9 In yet another embodiment, the machine learning-based algorithm 310 may classify the lid angle as corresponding to one of two configurations, such as open or closed.

[0035] After determining the lid angle, lid angle determiner 306 may take appropriate actions, such as transitioning to sleep mode when computing device 100 is changed to a closed configuration, waking up when computing device 100 is changed to an out of closed configuration, changing to tablet mode when computing device 100 is changed to a tablet configuration, and so on. Computing device 100 may also provide lid angle information as context for various applications or usage scenarios. For example, computing device 100 may adjust display lighting based on lid angle, may use lid angle to help facial recognition determine facial angle for fast login / logout authentication, may help camera applications activate / deactivate user sensing, and so on.

[0036] In the illustrative embodiment, the lid angle determiner 306 trains the machine-learning-based algorithm 310. The lid angle determiner 306 uses the labeled training data to train the machine-learning-based algorithm 310 and evaluates the machine-learning-based algorithm 310. The lid angle determiner 306 can then store the parameters of the machine-learning-based algorithm 310 for later use. It should be understood that, in general, the computing device 100 does not need to train the machine-learning-based algorithm 310 itself. Rather, the computing device 100 can store the parameters of the machine-learning-based algorithm 310 in the computing device 100, and the machine-learning-based algorithm 310 can access these parameters when necessary.

[0037] Reference now Fig.10 In use, the computing device 100 may perform a method 1000 for training a machine learning-based algorithm to determine an angle between a lid portion 102 and a base portion 104 of the computing device 100. The method 1000 begins in block 1002, where the computing device 100 accesses an RF signal. The computing device 100 may sense the RF signal itself in block 1004. Additionally or alternatively, in some embodiments, the computing device 100 may access an RF signal stored in the computing device 100 in block 1006 and / or may receive an RF signal from another device in block 1008.

[0038] In block 1010, the computing device 100 trains the machine-learning based algorithm. The computing device 100 may train for, for example, scalar regression, multi-class classification, binary classification, etc. The computing device 100 may verify the machine-learning based algorithm in block 1012, for example, by using some training data samples that were not used to train the machine-learning based algorithm.

[0039] Reference now Fig.11In use, the computing device 100 may perform a method 1100 for determining an angle between a lid portion 102 and a base portion 104 of the computing device 100. The method 1100 begins in block 1102, where the computing device 100 generates an RF signal on one or more antennas 108. The computing device 100 may generate any suitable RF signal, such as an RF signal having any suitable combination of one or more frequencies that may have a particular phase or timing relationship, linear frequency modulated RF pulses, and the like. The computing device 100 may generate the RF signal as part of wireless communications (e.g., WiFi or Bluetooth communications) that are already occurring independently of determining the angle of the lid. In some embodiments, if there is not sufficient wireless communication to determine the angle of the lid, the computing device 100 may transmit a wireless beacon for determining the angle of the lid.

[0040] In block 1106, computing device 100 senses the RF signal generated in block 1102 at antenna 112 or 108. In some embodiments, computing device 100 may sense the RF signal using the same antenna used to transmit the RF signal. Computing device 100 may sense the RF signal when the RF signal is transmitted (e.g., reflected from an antenna) or received. In block 1108, in some embodiments, computing device 100 senses the RF signal at multiple antennas 112 or 108.

[0041] In block 1110, computing device 100 pre-processes the RF signal. For example, computing device 100 may apply one or more filters, amplifiers, samplers, digital or analog demodulators, etc. In an illustrative embodiment, computing device 100 determines I / Q in-phase / quadrature values ​​of the RF signal. Computing device 100 may determine the I / Q in-phase / quadrature values ​​for both signals transmitted on the antenna and signals received on the antenna. Computing device 100 may also determine, for example, an average I / Q value or a ratio of I / Q values ​​(e.g., a ratio of a signal transmitted on the antenna to a signal received or reflected on the antenna).

[0042] In block 1112, computing device 100 determines the lid angle using the sensed RF signal. In block 1114, computing device 100 determines the lid angle using a machine learning based algorithm such as a neural network. In block 1116, computing device 100 may determine the lid angle using scalar regression. In block 1118, computing device 100 may determine the lid angle using multi-class classification. In block 1120, computing device 100 may determine the lid angle using binary classification. In some embodiments, computing device 100 may determine an indication of the lid angle, e.g., the result of the multi-class classification, without explicitly determining the lid angle as a numerical value.

[0043] In block 1122, computing device 100 may take an action based on the determined lid angle. For example, when computing device 100 is changed to a closed configuration, computing device 100 may transition to a sleep mode; when computing device 100 is changed out of a closed configuration, computing device 100 may wake up; when computing device 100 is changed to a tablet configuration, computing device 100 may change to a tablet mode, and so on.

[0044] Example

[0045] Illustrative examples of the techniques disclosed herein are provided below. Implementations of these techniques may include any one or more of the examples described below, as well as any combination thereof.

[0046] Example 1 includes a computing device comprising: a lid portion; a base portion; a hinge, wherein the lid portion and the base portion are rotatably coupled by the hinge; an antenna; a wireless receiver circuit for receiving a radio frequency (RF) signal from the antenna; and a lid angle determiner circuit for determining one or more parameters based on the RF signal; and determining an indication of an angle of the lid portion relative to the base portion based on the one or more parameters.

[0047] Example 2 includes the subject matter of Example 1, and further includes a wireless transmitter circuit to transmit the RF signal prior to receiving the RF signal.

[0048] Example 3 includes the subject matter of any of Examples 1 and 2, and wherein transmitting the RF signal comprises transmitting the RF signal on the antenna.

[0049] Example 4 includes the subject matter of any of Examples 1-3, and wherein transmitting the RF signal comprises transmitting the RF signal on a second antenna different from the antenna.

[0050] Example 5 includes the subject matter of any of Examples 1-4, and wherein sending the RF signal comprises sending the RF signal as part of wireless communication with a remote device.

[0051] Example 6 includes the subject matter of any of Examples 1-5, and wherein the wireless receiver circuit also measures the transmitted RF signal, wherein the lid angle determiner circuit also determines one or more additional parameters based on the measured transmitted RF signal, and wherein determining the indication of the angle of the lid portion relative to the base portion includes: determining the indication of the angle of the lid portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

[0052] Example 7 includes the subject matter of any of Examples 1-6, and wherein the computing device includes a bidirectional coupler coupled to the antenna, wherein receiving the RF signal from the antenna includes: receiving a reflection of the transmitted RF signal from the antenna by the bidirectional coupler, and wherein measuring the transmitted RF signal includes: coupling the transmitted RF signal to the bidirectional coupler.

[0053] Example 8 includes the subject matter of any of Examples 1-7, and further comprising wireless transmitter circuitry to transmit a wireless beacon, wherein receiving the RF signal from the antenna comprises receiving the wireless beacon.

[0054] Example 9 includes the subject matter of any of Examples 1-8, and wherein sending the wireless beacon comprises determining that a wireless signal suitable for determining the indication of the angle of the cover portion has not been sent in a previous threshold amount of time.

[0055] Example 10 includes the subject matter of any of Examples 1-9, and wherein determining the one or more parameters based on the RF signal comprises demodulating the RF signal, wherein the one or more parameters include in-phase and quadrature components of the demodulated RF signal.

[0056] Example 11 includes the subject matter of any of Examples 1-10, and further includes a wireless transmitter circuit for transmitting the RF signal before receiving the RF signal, wherein the wireless receiver circuit measures the transmitted RF signal, wherein the lid angle determiner circuit determines one or more additional parameters based on the measured transmitted RF signal, wherein determining an indication of the angle of the lid portion relative to the base portion comprises: determining the indication of the angle of the lid portion relative to the base portion based on the one or more parameters and the one or more additional parameters, wherein determining the one or more additional parameters based on the measured transmitted RF signal comprises demodulating the measured transmitted RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated measured transmitted RF signal.

[0057] Example 12 includes the subject matter of any of Examples 1-11, and wherein the one or more parameters include a ratio of an amplitude of the demodulated RF signal to an amplitude of the demodulated measured transmitted RF signal.

[0058] Example 13 includes the subject matter of any of Examples 1-12, and wherein the one or more parameters include an average value of an in-phase component of the demodulated RF signal and an average value of a quadrature component of the demodulated RF signal.

[0059] Example 14 includes the subject matter of any of Examples 1-13, and wherein determining an indication of the angle of the cover portion relative to the base portion comprises utilizing a machine learning based algorithm to determine the indication of the angle of the cover portion relative to the base portion.

[0060] Example 15 includes the subject matter of any of Examples 1-14, and wherein the machine learning based algorithm is a neural network.

[0061] Example 16 includes the subject matter of any of Examples 1-15, and wherein the indication of the angle of the cover portion relative to the base portion is an estimate of the angle of the cover portion relative to the base portion.

[0062] Example 17 includes the subject matter of any of Examples 1-16, and wherein the lid angle determiner circuit further classifies the computing device into one of a plurality of categories based on an estimate of an angle of the lid portion relative to the base portion.

[0063] Example 18 includes the subject matter of any of Examples 1-17, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0064] Example 19 includes the subject matter of any of Examples 1-18, and wherein utilizing the machine learning-based algorithm to determine the indication of the angle of the cover portion relative to the base portion includes classifying the angle of the cover portion relative to the base portion into one of a plurality of categories.

[0065] Example 20 includes the subject matter of any of Examples 1-19, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0066] Example 21 includes the subject matter of any of Examples 1-20, and wherein determining the indication of the angle of the lid portion relative to the base portion using the machine learning-based algorithm includes classifying the angle of the lid portion relative to the base portion into one of two categories, wherein the two categories include an open configuration and a closed configuration.

[0067] Example 22 includes the subject matter of any of Examples 1-21, and wherein the machine learning-based algorithm requires less than 10 kilobytes of storage space.

[0068] Example 23 includes the subject matter of any of Examples 1-22, and wherein the machine learning-based algorithm requires fewer than 2000 floating point operations to determine the indication of the angle of the lid portion relative to the base portion.

[0069] Example 24 includes the subject matter of any of Examples 1-23, and wherein the RF signal has a frequency between 100 megahertz and 60 gigahertz.

[0070] Example 25 includes the subject matter of any of Examples 1-24, and wherein the cover portion is rotatable substantially 360° relative to the base portion.

[0071] Example 26 includes a method comprising receiving, by a computing device, a radio frequency (RF) signal from an antenna of the computing device, wherein the computing device includes a cover portion and a base portion; determining, by the computing device, one or more parameters based on the RF signal; and determining, by the computing device, an indication of an angle of the cover portion relative to the base portion based on the one or more parameters.

[0072] Example 27 includes the subject matter of Example 26, and further includes sending, by the computing device, the RF signal prior to receiving the RF signal.

[0073] Example 28 includes the subject matter of any of Examples 26 and 27, and wherein transmitting the RF signal comprises transmitting the RF signal on the antenna.

[0074] Example 29 includes the subject matter of any of Examples 26-28, and wherein transmitting the RF signal comprises transmitting the RF signal on a second antenna different from the antenna.

[0075] Example 30 includes the subject matter of any of Examples 26-29, and wherein sending the RF signal by the computing device comprises sending the RF signal as part of wireless communication with a remote device.

[0076] Example 31 includes the subject matter of any of Examples 26-30, and also includes measuring, by the computing device, a transmitted RF signal; and determining, by the computing device, one or more additional parameters based on the measured transmitted RF signal, wherein determining an indication of the angle of the cover portion relative to the base portion includes determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

[0077] Example 32 includes the subject matter of any of Examples 26-31, and wherein the computing device includes a bidirectional coupler coupled to the antenna, wherein receiving the RF signal from the antenna includes: receiving a reflection of the transmitted RF signal from the antenna by the bidirectional coupler, and wherein measuring the transmitted RF signal includes: coupling the transmitted RF signal to the bidirectional coupler.

[0078] Example 33 includes the subject matter of any of Examples 26-32, and further comprising transmitting, by the computing device, a wireless beacon, wherein receiving the RF signal from the antenna comprises receiving the wireless beacon.

[0079] Example 34 includes the subject matter of any of Examples 26-33, and wherein sending the wireless beacon comprises determining that a wireless signal suitable for determining the indication of the angle of the cover portion has not been sent in a previous threshold amount of time.

[0080] Example 35 includes the subject matter of any of Examples 26-34, and wherein determining the one or more parameters based on the RF signal comprises demodulating the RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated RF signal.

[0081] Example 36 includes the subject matter of any of Examples 26-35, and also includes sending, by the computing device, the RF signal before receiving the RF signal; measuring, by the computing device, the transmitted RF signal; and determining, by the computing device, one or more additional parameters based on the measured transmitted RF signal, wherein determining an indication of the angle of the cover portion relative to the base portion comprises: determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters, wherein determining the one or more additional parameters based on the measured transmitted RF signal comprises demodulating the measured transmitted RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated measured transmitted RF signal.

[0082] Example 37 includes the subject matter of any of Examples 26-36, and wherein the one or more parameters include a ratio of an amplitude of the demodulated RF signal to an amplitude of the demodulated measured transmitted RF signal.

[0083] Example 38 includes the subject matter of any of Examples 26-37, and wherein the one or more parameters include an average value of an in-phase component of the demodulated RF signal and an average value of a quadrature component of the demodulated RF signal.

[0084] Example 39 includes the subject matter of any of Examples 26-38, and wherein determining an indication of the angle of the cover portion relative to the base portion comprises utilizing a machine learning based algorithm to determine the indication of the angle of the cover portion relative to the base portion.

[0085] Example 40 includes the subject matter of any of Examples 26-39, and wherein the machine learning based algorithm is a neural network.

[0086] Example 41 includes the subject matter of any of Examples 26-40, and wherein the indication of the angle of the cover portion relative to the base portion is an estimate of the angle of the cover portion relative to the base portion.

[0087] Example 42 includes the subject matter of any of Examples 26-41, and further includes classifying the computing device into one of a plurality of categories based on an estimate of an angle of the lid portion relative to the base portion.

[0088] Example 43 includes the subject matter of any of Examples 26-42, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0089] Example 44 includes the subject matter of any of Examples 26-43, and wherein utilizing the machine learning-based algorithm to determine the indication of the angle of the cover portion relative to the base portion includes classifying the angle of the cover portion relative to the base portion into one of a plurality of categories.

[0090] Example 45 includes the subject matter of any of Examples 26-44, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0091] Example 46 includes the subject matter of any of Examples 26-45, and wherein utilizing the machine learning-based algorithm to determine the indication of the angle of the lid portion relative to the base portion comprises classifying the angle of the lid portion relative to the base portion into one of two categories, wherein the two categories comprise an open configuration and a closed configuration.

[0092] Example 47 includes the subject matter of any of Examples 26-46, and wherein the machine learning-based algorithm requires less than 10 kilobytes of storage space.

[0093] Example 48 includes the subject matter of any of Examples 26-47, and wherein the machine learning-based algorithm requires fewer than 2000 floating point operations to determine the indication of the angle of the lid portion relative to the base portion.

[0094] Example 49 includes the subject matter of any of Examples 26-48, and wherein the RF signal has a frequency between 100 megahertz and 60 gigahertz.

[0095] Example 50 includes the subject matter of any of Examples 26-49, and wherein the cover portion is capable of rotating substantially 360° relative to the base portion.

[0096] Example 51 includes a computing device comprising means for receiving a radio frequency (RF) signal from an antenna of the computing device, wherein the computing device includes a cover portion and a base portion; means for determining one or more parameters based on the RF signal; and means for determining an indication of an angle of the cover portion relative to the base portion based on the one or more parameters.

[0097] Example 52 includes the subject matter of Example 51, and further includes means for transmitting the RF signal prior to receiving the RF signal.

[0098] Example 53 includes the subject matter of any of Examples 51 and 52, and further includes a device for measuring a transmitted RF signal; and a device for determining one or more additional parameters based on the measured transmitted RF signal, wherein the device for determining an indication of the angle of the cover portion relative to the base portion includes: a device for determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

[0099] Example 54 includes the subject matter of any of Examples 51-53, and wherein the means for determining the one or more parameters based on the RF signal comprises means for demodulating the RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated RF signal.

[0100] Example 55 includes the subject matter of any of Examples 51-54, and further includes a device for sending the RF signal before receiving the RF signal; a device for measuring the transmitted RF signal; and a device for determining one or more additional parameters based on the measured transmitted RF signal, wherein the device for determining an indication of the angle of the cover portion relative to the base portion includes: a device for determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters, wherein the device for determining the one or more additional parameters based on the measured transmitted RF signal includes a device for demodulating the measured transmitted RF signal, wherein the one or more parameters include in-phase and quadrature components of the demodulated measured transmitted RF signal.

[0101] Example 56 includes the subject matter of any of Examples 51-55, and wherein the one or more parameters include a ratio of an amplitude of the demodulated RF signal to an amplitude of the demodulated measured transmitted RF signal.

[0102] Example 57 includes the subject matter of any of Examples 51-56, and wherein the one or more parameters include an average value of an in-phase component of the demodulated RF signal and an average value of a quadrature component of the demodulated RF signal.

[0103] Example 58 includes the subject matter of any of Examples 51-57, and wherein the means for determining an indication of the angle of the cover portion relative to the base portion comprises: means for determining the indication of the angle of the cover portion relative to the base portion utilizing a machine learning based algorithm.

[0104] Example 59 includes the subject matter of any of Examples 51-58, and wherein the machine learning based algorithm is a neural network.

[0105] Example 60 includes the subject matter of any of Examples 51-59, and wherein the indication of the angle of the cover portion relative to the base portion is an estimate of the angle of the cover portion relative to the base portion.

[0106] Example 61 includes the subject matter of any of Examples 51-60, and further includes means for classifying the computing device into one of a plurality of categories based on an estimate of an angle of the lid portion relative to the base portion.

[0107] Example 62 includes the subject matter of any of Examples 51-61, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0108] Example 63 includes the subject matter of any of Examples 51-62, and wherein the means for determining an indication of the angle of the cover portion relative to the base portion using the machine learning-based algorithm includes: means for classifying the angle of the cover portion relative to the base portion into one of a plurality of categories.

[0109] Example 64 includes the subject matter of any of Examples 51-63, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0110] Example 65 includes the subject matter of any of Examples 51-64, and wherein the means for determining an indication of the angle of the lid portion relative to the base portion using the machine learning-based algorithm comprises: means for classifying the angle of the lid portion relative to the base portion into one of two categories, wherein the two categories include an open configuration and a closed configuration.

[0111] Example 66 includes the subject matter of any of Examples 51-65, and wherein the machine learning-based algorithm requires less than 10 kilobytes of storage space.

[0112] Example 67 includes the subject matter of any of Examples 51-66, and wherein the machine learning-based algorithm requires fewer than 2000 floating point operations to determine the indication of the angle of the lid portion relative to the base portion.

[0113] Example 68 includes the subject matter of any of Examples 51-67, and wherein the RF signal has a frequency between 100 megahertz and 60 gigahertz.

[0114] Example 69 includes the subject matter of any of Examples 51-68, and wherein the cover portion is capable of rotating substantially 360° relative to the base portion.

[0115] Example 70 includes one or more computer-readable media including a plurality of instructions stored thereon that, when executed, cause a computing device to: receive a radio frequency (RF) signal from an antenna of the computing device; determine one or more parameters based on the RF signal; and determine, based on the one or more parameters, an indication of an angle of a lid portion of the computing device relative to a base portion of the computing device.

[0116] Example 71 includes the subject matter of Example 70, and wherein the plurality of instructions further cause the computing device to transmit the RF signal prior to receiving the RF signal.

[0117] Example 72 includes the subject matter of any of Examples 70 and 71, and wherein transmitting the RF signal comprises transmitting the RF signal on the antenna.

[0118] Example 73 includes the subject matter of any of Examples 70-72, and wherein transmitting the RF signal comprises transmitting the RF signal on a second antenna different from the antenna.

[0119] Example 74 includes the subject matter of any of Examples 70-73, and wherein sending the RF signal comprises sending the RF signal as part of wireless communication with a remote device.

[0120] Example 75 includes the subject matter of any of Examples 70-74, and wherein the plurality of instructions further cause the computing device to measure the transmitted RF signal, wherein the plurality of instructions further cause the computing device to determine one or more additional parameters based on the measured transmitted RF signal, wherein determining an indication of the angle of the cover portion relative to the base portion comprises determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

[0121] Example 76 includes the subject matter of any of Examples 70-75, and wherein the computing device includes a bidirectional coupler coupled to the antenna, wherein receiving the RF signal from the antenna includes: receiving a reflection of the transmitted RF signal from the antenna by the bidirectional coupler, and wherein measuring the transmitted RF signal includes: coupling the transmitted RF signal to the bidirectional coupler.

[0122] Example 77 includes the subject matter of any of Examples 70-76, and wherein the plurality of instructions further cause the computing device to transmit a wireless beacon, wherein receiving the RF signal from the antenna comprises receiving the wireless beacon.

[0123] Example 78 includes the subject matter of any of Examples 70-77, and wherein sending the wireless beacon comprises determining that a wireless signal suitable for determining the indication of the angle of the cover portion has not been sent in a previous threshold amount of time.

[0124] Example 79 includes the subject matter of any of Examples 70-78, and wherein determining the one or more parameters based on the RF signal comprises demodulating the RF signal, wherein the one or more parameters include in-phase and quadrature components of the demodulated RF signal.

[0125] Example 80 includes the subject matter of any of Examples 70-79, and wherein the plurality of instructions further cause the computing device to transmit the RF signal before receiving the RF signal; measure the transmitted RF signal; and determine one or more additional parameters based on the measured transmitted RF signal, wherein determining an indication of the angle of the cover portion relative to the base portion comprises: determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters, wherein determining the one or more additional parameters based on the measured transmitted RF signal comprises demodulating the measured transmitted RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated measured transmitted RF signal.

[0126] Example 81 includes the subject matter of any of Examples 70-80, and wherein the one or more parameters include a ratio of an amplitude of the demodulated RF signal to an amplitude of the demodulated measured transmitted RF signal.

[0127] Example 82 includes the subject matter of any of Examples 70-81, and wherein the one or more parameters include an average value of an in-phase component of the demodulated RF signal and an average value of a quadrature component of the demodulated RF signal.

[0128] Example 83 includes the subject matter of any of Examples 70-82, and wherein determining an indication of the angle of the cover portion relative to the base portion includes: utilizing a machine learning based algorithm to determine the indication of the angle of the cover portion relative to the base portion.

[0129] Example 84 includes the subject matter of any of Examples 70-83, and wherein the machine learning based algorithm is a neural network.

[0130] Example 85 includes the subject matter of any of Examples 70-84, and wherein the indication of the angle of the cover portion relative to the base portion is an estimate of the angle of the cover portion relative to the base portion.

[0131] Example 86 includes the subject matter of any of Examples 70-85, and wherein the plurality of instructions further cause the computing device to classify the computing device into one of a plurality of categories based on an estimate of an angle of the lid portion relative to the base portion.

[0132] Example 87 includes the subject matter of any of Examples 70-86, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0133] Example 88 includes the subject matter of any of Examples 70-87, and wherein utilizing the machine learning-based algorithm to determine the indication of the angle of the cover portion relative to the base portion includes classifying the angle of the cover portion relative to the base portion into one of a plurality of categories.

[0134] Example 89 includes the subject matter of any of Examples 70-88, and wherein the plurality of categories includes a closed configuration, a laptop configuration, a tablet configuration, a flat configuration, and a tent configuration.

[0135] Example 90 includes the subject matter of any of Examples 70-89, and wherein utilizing the machine learning-based algorithm to determine the indication of the angle of the lid portion relative to the base portion includes classifying the angle of the lid portion relative to the base portion into one of two categories, wherein the two categories include an open configuration and a closed configuration.

[0136] Example 91 includes the subject matter of any of Examples 70-90, and wherein the machine learning-based algorithm requires less than 10 kilobytes of storage space.

[0137] Example 92 includes the subject matter of any of Examples 70-91, and wherein the machine learning-based algorithm requires fewer than 2000 floating point operations to determine the indication of the angle of the lid portion relative to the base portion.

[0138] Example 93 includes the subject matter of any of Examples 70-92, and wherein the RF signal has a frequency between 100 megahertz and 60 gigahertz.

[0139] Example 94 includes the subject matter of any of Examples 70-93, and wherein the cover portion is capable of rotating substantially 360° relative to the base portion.

Claims

1. A computing device, comprising: Cover part; Base part; a hinge by which the lid portion and the base portion are rotatably coupled; antenna; a wireless receiver circuit for receiving a radio frequency (RF) signal from the antenna; as well as A lid angle determiner circuit is provided to: determining one or more parameters based on the RF signal; and Based on the one or more parameters, an indication of an angle of the cover portion relative to the base portion is determined.

2. The computing device of claim 1, further comprising a wireless transmitter circuit to transmit the RF signal prior to receiving the RF signal.

3. The computing device of claim 2, wherein: Transmitting the RF signal includes transmitting the RF signal over the antenna.

4. The computing device of claim 2, wherein: Transmitting the RF signal includes transmitting the RF signal on a second antenna different from the antenna.

5. The computing device of any one of claims 2 to 4, wherein: Transmitting the RF signal includes transmitting the RF signal as part of wireless communication with a remote device.

6. The computing device of claim 2, wherein: The computing device includes a bidirectional coupler coupled to the antenna, The receiving of the RF signal from the antenna comprises: receiving, by the bidirectional coupler, a reflection of the transmitted RF signal from the antenna; The measuring of the transmitted RF signal includes: coupling the transmitted RF signal to the bidirectional coupler.

7. The computing device of claim 1, further comprising a wireless transmitter circuit for transmitting a wireless beacon, wherein: Receiving the RF signal from the antenna includes receiving the wireless beacon.

8. The computing device of claim 7, wherein: Transmitting the wireless beacon includes determining that a wireless signal suitable for determining the indication of the angle of the cover portion has not been transmitted in a previous threshold amount of time.

9. The computing device of any one of claims 1 to 8, wherein: Determining the one or more parameters based on the RF signal includes demodulating the RF signal, wherein the one or more parameters include in-phase and quadrature components of the demodulated RF signal.

10. The computing device of claim 9, further comprising a wireless transmitter circuit for transmitting the RF signal prior to receiving the RF signal, in, The wireless receiver circuit measures the transmitted RF signal, wherein the lid angle determiner circuit determines one or more additional parameters based on the measured transmitted RF signal, wherein determining the indication of the angle of the cover portion relative to the base portion comprises: determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters, Wherein determining the one or more additional parameters based on the measured transmitted RF signal comprises demodulating the measured transmitted RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated measured transmitted RF signal.

11. The computing device of claim 9, wherein: Determining the indication of the angle of the cover portion relative to the base portion includes determining the indication of the angle of the cover portion relative to the base portion using a machine learning based algorithm.

12. The computing device of claim 11, wherein: The indication of the angle of the cover portion relative to the base portion is an estimate of the angle of the cover portion relative to the base portion.

13. The computing device of claim 11, wherein: Utilizing the machine learning based algorithm to determine the indication of the angle of the cover portion relative to the base portion includes classifying the angle of the cover portion relative to the base portion into one of a plurality of categories.

14. The computing device of claim 13, wherein: Utilizing the machine learning-based algorithm to determine the indication of the angle of the lid portion relative to the base portion includes classifying the angle of the lid portion relative to the base portion into one of two categories, wherein the two categories include an open configuration and a closed configuration.

15. A computing device comprising: means for receiving a radio frequency (RF) signal from an antenna of the computing device, wherein the computing device comprises a lid portion and a base portion; means for determining one or more parameters based on the RF signal; as well as Means for determining an indication of an angle of the cover portion relative to the base portion based on the one or more parameters.

16. The computing device of claim 15, further comprising means for transmitting the RF signal prior to receiving the RF signal.

17. The computing device of claim 16, further comprising: means for measuring the transmitted RF signal; as well as means for determining one or more additional parameters based on the measured transmitted RF signal, Wherein, the means for determining an indication of the angle of the cover portion relative to the base portion comprises means for determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

18. The computing device of any one of claims 15 to 17, wherein: The means for determining the one or more parameters based on the RF signal comprises means for demodulating the RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated RF signal.

19. One or more computer-readable media comprising a plurality of instructions stored thereon, which instructions, when executed, cause a computing device to: receiving a radio frequency (RF) signal from an antenna of the computing device, determining one or more parameters based on the RF signal; and Based on the one or more parameters, an indication of an angle of a lid portion of the computing device relative to a base portion of the computing device is determined.

20. The one or more computer-readable media of claim 19, wherein: The plurality of instructions further cause the computing device to transmit the RF signal prior to receiving the RF signal.

21. One or more computer-readable media as recited in claim 20, wherein: The plurality of instructions further cause the computing device to measure the transmitted RF signal, wherein the plurality of instructions further cause the computing device to determine one or more additional parameters based on the measured transmitted RF signal, Wherein determining the indication of the angle of the cover portion relative to the base portion comprises determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters.

22. One or more computer-readable media as claimed in claim 21, wherein: The computing device includes a bidirectional coupler coupled to the antenna, The receiving of the RF signal from the antenna comprises: receiving, by the bidirectional coupler, a reflection of the transmitted RF signal from the antenna; The measuring of the transmitted RF signal includes: coupling the transmitted RF signal to the bidirectional coupler.

23. One or more computer-readable media as described in any of claims 19-22, wherein: Determining the one or more parameters based on the RF signal includes demodulating the RF signal, wherein the one or more parameters include in-phase and quadrature components of the demodulated RF signal.

24. One or more computer-readable media as claimed in claim 23, wherein: The plurality of instructions further cause the computing device to: sending the RF signal before receiving the RF signal; measuring the transmitted RF signal; and determining one or more additional parameters based on the measured transmitted RF signal, wherein determining the indication of the angle of the cover portion relative to the base portion comprises: determining the indication of the angle of the cover portion relative to the base portion based on the one or more parameters and the one or more additional parameters, Wherein determining the one or more additional parameters based on the measured transmitted RF signal comprises demodulating the measured transmitted RF signal, wherein the one or more parameters comprise in-phase and quadrature components of the demodulated measured transmitted RF signal.

25. The one or more computer-readable media of claim 23, wherein: Determining the indication of the angle of the cover portion relative to the base portion includes determining the indication of the angle of the cover portion relative to the base portion using a machine learning based algorithm.

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