Intel Corporation

Apple Inc.

Lei Yang

Xue Yang

Shao-Wen Yang

Mi Sun Park

Shahrokh Shahidzadeh

Myung Hwangbo

Xingang Guo

Vijay Sarathi Kesavan

Jung Woo

Jonathan J Huang

Anand P Rangarajan

Praveen Gopalakrishnan

Lama Nachman

Rahul C Shah

Chieh-Yih Wan

Anthony G Lamarca

Xiaoyong Pan

Hong Lu

Jing Zhu

Arvind A Kumar

Richard D Roberts

Victor B Lortz

Hsin-Yuo Liu

Justin Lipman

Ke Ding

Addicam V Sanjay

Robert Colby

Mathys C Walma

Daniel Gurdan

Tzahi Weisman

Hongtao Xu

Alexander Essaian

Xiaochao Yang

Donnie H Kim

Martin Junkar

Scott Thomas

Jun Li

Aveek Purohit

Yi Lin Chen

Jared B Counts

Sharon Yang

Martin Moerth

An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Vision and radio fusion based precise indoor localization

A particular device is provided with a communications module to receive signals of a plurality of devices within range of the particular device and further provisioned with grouping logic. The grouping logic is executable by one or more processors to determine from each of the signals a respective identifier for each of the plurality of devices, determine, based at least in part on the identifiers, that a particular subset of the plurality of devices are also included with the particular device in a particular one of a plurality of defined groups, and converge data received from the particular subset of devices based on the particular group.

Group-based data transfer in machine-to-machine systems

A method for localizing a device. The method is performed by a communication device. The method includes measuring a phase of a signal that the communication device has received from the device, the signal having a first frequency; measuring a phase of at least one further signal that the communication device has received from the device; the at least one further signal having a second frequency, determining a phase pattern of a measured phase versus frequency, pattern matching the phase pattern with each reference phase pattern of a plurality of pre-determined reference phase patterns. Each reference phase pattern is associated with a distance between the communication device and the further communication device. The method further includes determining the distance between the further communication device and the communication device based on the pattern matching.

Communication device and a method for localization

A controller comprises a communication interface to receive an anchor localization dataset comprising a plurality of anchor range measurements and a processing circuitry to identify a qualified subset of anchor range measurements from the anchor localization dataset, wherein the anchor range measurements in the qualified subset are consistent, select a first anchor range measurement in the anchor localization dataset from outside the qualified subset of anchor range measurements, and add the first anchor range measurement to the qualified subset of anchor range measurements when the first anchor range measurement is consistent with the anchor range measurements in the qualified subset of anchor range measurements.

Drone localization

Sensors provisioned on a first device detect a movement of the first device corresponding to the first device changing position within an environment. Location information for the first device is updated based on the position change. A plurality of signals are detected, at the first device, from a second device in the environment, determine, and a distance between the first and second devices is determined based on each of the signals. From the signals, another change in position of the first device within the environment is determined and the location information updated for the first device. The movement is detected at the first device at an instance between two of the plurality of signals, and location information for the first device based on the first position change is updated prior to detection of the later of the two signals in the plurality of signals.

Two-dimensional encounter location detection

Various embodiments are generally directed to techniques to merge a virtual map derived from sensors of computing devices moved about an interior of a structure with a corresponding physical map. An apparatus to merge maps includes a processor component; and a merged map generator for execution by the processor component to merge a virtual map and a physical map to generate a merged map, the virtual map comprising indications of virtual pathways through an interior of a structure based on sensors, and the physical map comprising indications of physical pathways of the interior. Other embodiments are described and claimed.

Techniques for merging virtual and physical floor maps

Some demonstrative embodiments include apparatuses, systems and/or methods of estimating an orientation of a mobile device. For example, an apparatus may include an orientation estimator to receive an indication of first and second consecutive steps of a user carrying a mobile device, to determine an angular rotation of an orientation parameter between the first and second steps, and to determine a value of the orientation parameter based on a comparison between the angular rotation and at least one predefined angular rotation threshold.

Apparatus, system and method of estimating an orientation of a mobile device

A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located. The portable electronic device may then generate a proposed semantic label for the physical subdivision.

Autonomous semantic labeling of physical locations

Embodiments herein relate to determining the location of a device using hybrid localization techniques. For example, a first technique such as trilateration may be used to determine an approximate location of the device. An error associated with the approximate location may also be implemented to increase the likelihood of locating the device upon applying a second localization technique, such as fingerprinting. Fingerprinting, when applied to the approximate location determined from trilateration, may determine the location of the device, or a more precise location than that determined from trilateration, such that reduced power consumption by the device may be achieved without sacrificing location accuracy.

Efficient location determination of wireless communication devices using hybrid localization techniques

Embodiments of the present disclosure provide techniques and configurations for a system to manage a home environment. In one instance, an apparatus for managing a home environment may be configured to detect variations in parameters of signals provided by RFID tags disposed in the home environment to monitor the home environment. The apparatus may identify one or more conditions of an area of the home environment in which the RFID tags are disposed, based at least in part on the variations in the one or more parameters and respective locations of the RFID tags in the area of the home environment. The apparatus may select one or more actions, from a plurality of actions associated with the home environment, based at least in part on the identified conditions. The selected actions may be performed to respond to the identified conditions. Other embodiments may be described and/or claimed.

Home environment management method and apparatus

Embodiments of a mobile station and method for Wi-Fi scan scheduling and power adaption for low-power indoor location are generally described herein. In some embodiments, the mobile station may identify channels, beacon timing and rough signal strength levels of nearby access points (APs) from at least one of a previous full-channel scan or a Wi-Fi fingerprint database and may configure receiver sensitivity based on the rough signal strength levels for receipt of subsequent beacons. The mobile station may wake-up from a low-power state to receive beacons for the nearby access points on the identified channels at times based on the identified beacon timing. The received signal strength indicators (RSSIs) levels of the received beacons may be used for location determination.

Wi-Fi scan scheduling and power adaptation for low-power indoor location

A system for locating a mobile device includes an input that accesses a plurality of scans of wireless network access signaling, where the scans indicate received signal measurement results. A similarity measure module executes comparisons between the data of different scans in order to assess the similarity between those scans. The comparisons produce multi-dimensional comparison results. A dimension reduction module reduces dimensionality of the multi-dimensional comparison results to produce a dimension-reduced set of comparison results. A clustering module identifies groupings of similar scans based on the dimension-reduced set of comparison results.

Self-learning locator for mobile device

A system for early warning of non-compliance with an established workflow in a work area includes a radio frequency (RF) sensor configured to detect each of a plurality of RF signals located within the work area, and a monitoring system operably connected to the RF sensor. The monitoring system is configured to compare a temporal sequence of detected RF signals to a predetermined temporal sequence of the RF signals associated with the established workflow, and to determine whether a warning condition exists based on the comparison. The RF signals can be emitted by RFID tags within the work area, such as a patient room of a healthcare provider, and the RFID tags may be associated with a hand sanitizing station, an intravenous solution dispenser, and a healthcare provider staff member, each of which play a role in reducing the incidence of hospital-acquired infections through compliance with corresponding patient safety protocols.

Early warning of non-compliance with an established workflow in a work area

Growing community of inventors

Hillsboro, OR, United States of America

Lei Yang