Abstract: For example, an apparatus may include a processor, which may be configured to identify a particular Transmit (Tx) configuration selected from a plurality of Tx configurations. For example, the plurality of Tx configurations may have a plurality of different Pulse Repetition Intervals (PRIs), respectively. For example, the particular Tx configuration may have a particular PRI from the plurality of different PRIs. For example, the processor may be configured to generate Tx configuration information to configure transmission of a plurality of radar Tx pulses from a radar device according to the particular PRI of the particular Tx configuration. For example, the apparatus may include an output to provide the Tx configuration information.

Abstract: For example, a radar Radio Head (RH) may be configured to determine Range-Doppler (RD) information corresponding to a plurality of RD bins based on digital radar Receive (Rx) signals representing radar Radio Frequency (RF) Rx signals received by one or more Rx antennas; to detect one or more detected RD bins based on the RD information; to provide filtered RD information including RD information corresponding to the one or more detected RD bins and excluding RD information of one or more excluded RD bins, which are not included in the one or more detected RD bins; and to send the filtered RD information to another processor via a communication interconnect.

Abstract: For example, a radar processor may include an input to receive radar Receive (Rx) information based on radar Rx signals received by a plurality of Radio Heads (RHs); and one or more Baseband (BB) Processing Units (BPUs) including a plurality of processing resources configured to generate radar information by processing the radar Rx information according to a plurality of BB-processing tasks. The one or more BPUs may be configured to allocate the plurality of processing resources to the plurality of RHs based on an RH to resource (RH-resource) allocation scheme. The RH-resource allocation scheme may be configured to define a plurality of RH-specific resource allocations for the plurality of RHs, respectively. For example, an RH-specific resource allocation for an RH may define a plurality of RH-allocated processing resources to perform the plurality of BB-processing tasks based on radar Rx information from the RH.

Abstract: A crystal oscillator is provided. The crystal oscillator includes a crystal resonator including a pair of terminals and being capable of oscillating at a fundamental resonance frequency and at least one overtone resonance frequency. Further, the crystal oscillator includes an inverter circuit coupled between the pair of terminals. The crystal oscillator additionally includes a suppression circuit configured to suppress oscillation of the crystal resonator at the fundamental resonance frequency. Further, the crystal oscillator includes a control circuit configured to control a switch circuit for selectively coupling the suppression circuit to the crystal resonator.

Abstract: A crystal oscillator is provided. The crystal oscillator includes a crystal resonator including a pair of terminals and being capable of oscillating at a fundamental resonance frequency and at least one overtone resonance frequency. Further, the crystal oscillator includes an inverter circuit coupled between the pair of terminals. The crystal oscillator additionally includes a suppression circuit configured to suppress oscillation of the crystal resonator at the fundamental resonance frequency. Further, the crystal oscillator includes a control circuit configured to control a switch circuit for selectively coupling the suppression circuit to the crystal resonator.

Abstract: Technologies for privately processing voice data include a compute device configured to continually or periodically capture voice data of a user by the compute device. The captured voice data is processed to remove or reduce the user's privacy-sensitive information. For example, the compute device fragments the captured voice data to generate a set of voice data fragments and further scrambles the voice data fragments to generate scrambled voice data fragments having a sequential order different from the plurality of voice data fragments. To scramble the voice data fragments, the compute device may reorder the voice data fragments such that each fragment is repositioned from its corresponding original sequential position in the voice data by a particular number of words, syllables, or phrases.

Abstract: A mechanism is described for facilitating dynamic and intelligent conversion of text into real user speech according to one embodiment. A method of embodiments, as described herein, includes receiving a textual message from a first user, and accessing a voice profile associated with the first user, where the voice profile includes a real voice of the first user and at least one of emotional patterns relating to the first user, context distinctions relating to the first user, and speech characteristics relating to the first user, where accessing further includes extracting the real voice and at least one of an emotional pattern, a context distinction, and a speech characteristic based on subject matter of the textual message. The method may further include converting the textual message into a real speech of the first user based on the voice profile including the real voice and at least one of the emotional pattern, the context distinction, and the speech characteristic.

Abstract: Currently, transmission power of a STA in multi-user uplink environment is determined by the AP without addressing STA considerations that relate to voltage, power use, power saving, overhead, etc., and in general relate to the STA's power consumption (All of which can have secondary effects such as interference, etc.). By adopting the protocol and/or the restriction on assigned transmission power per STA, better power consumption can be achieved for the STA. In accordance with an exemplary embodiment, techniques are disclosed that employ the concept of such a restriction and a protocol, as well as a technique and device to implement the concept.

Abstract: Technologies for privately processing voice data include a compute device configured to continually or periodically capture voice data of a user by the compute device. The captured voice data is processed to remove or reduce the user's privacy-sensitive information. For example, the compute device fragments the captured voice data to generate a set of voice data fragments and further scrambles the voice data fragments to generate scrambled voice data fragments having a sequential order different from the plurality of voice data fragments.

Abstract: A mechanism is described for facilitating dynamic and intelligent conversion of text into real user speech according to one embodiment. A method of embodiments, as described herein, includes receiving a textual message from a first user, and accessing a voice profile associated with the first user, where the voice profile includes a real voice of the first user and at least one of emotional patterns relating to the first user, context distinctions relating to the first user, and speech characteristics relating to the first user, where accessing further includes extracting the real voice and at least one of an emotional pattern, a context distinction, and a speech characteristic based on subject matter of the textual message. The method may further include converting the textual message into a real speech of the first user based on the voice profile including the real voice and at least one of the emotional pattern, the context distinction, and the speech characteristic.