Qualcomm Incorporated

Qualcomm Technologies, Inc.

Brilliant Labs Limited

Other

Tata America International Corporation

Venkat Rangan

Alok Govil

Nelson Rasquinha

Evgeni Petrovich Gousev

Jeffrey Alexander Levin

Jacek Maitan

Edwin Chongwoo Park

Bobak Tavangar

Benjamin Raymond Heald

Manu Rastogi

William Howard Constable

Austin Wilson

Erik Christopher Malone

Eugene Izhikevich

Filip Lukasz Piekniewski

Jayram Moorkanikara Nageswaran

Xin Wang

Seung Hyuk Kang

Xia Li

Rashid Ahmed Akbar Attar

Raghuraman Krishnamoorthi

Russell Wayne Gruhlke

Vladimir Aparin

Michael-David Nakayoshi Canoy

Somdeb Majumdar

Hae-Jong Seo

Ravishankar Sivalingam

Kebin Li

Jan Krzys Wegrzyn

Subramaniam Venkatraman

Yi Tang

Soo Youn Kim

Sanjay Deshpande

Robert J Vachon

Rodolfo Beraha

Kamal Bhadada

William Richard Bell, Ii

Praful Naphade

C V K Rao

Nicholas Ka Ming Stevens-Yu

John Daniels

Ramakrishna Kintada

Jeffery Henckels

Computer-implemented methods of operating an augmented reality device can involve capturing camera images, processing the camera images, and displaying virtual display images. The camera images can be captured automatically using a camera disposed within an augmented reality device worn by a user. The camera images can be processed automatically using a processor located within the augmented reality device. The virtual display images can be displayed automatically to the user within the augmented reality device while the user is looking through the augmented reality device and simultaneously viewing real objects through the augmented reality device. The virtual display images can be based on the processed camera images. Additional steps can include accepting a first user input, storing camera image(s) on a memory located within the augmented reality device based on the first input, accepting a second user input, and displaying stored image(s) to the user based on the second input.

Augmented reality device and methods of use

An augmented reality device can include an outer housing, an attachment component, a camera, and a display arrangement. The outer housing can have a transparent front and back arranged to allow a user to see therethrough. The attachment component can removably attach the augmented reality device to user eyewear. The camera can capture images through the outer housing front. The display arrangement can provide a virtual display at an angle to the user looking through the augmented reality device. The display arrangement can include an image emitting component, a prism component, a projecting optic component, a combining optic component, and a unibody optics enclosure. The augmented reality device can also include a processor and a memory configured to store and retrieve buffer clips of captured images, and a wireless communication component configured for communications with outside devices.

Apparatuses, methods, and systems are presented for sensing scene-based occurrences. Such an apparatus may comprise a vision sensor system comprising a first processing unit and dedicated computer vision (CV) computation hardware configured to receive sensor data from at least one sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels. The vision sensor system may be configured to send an event to be received by a second processing unit in response to processing of the one or more computed CV features by the first processing unit. The event may indicate possible presence of one or more irises within a scene.

Low-power iris scan initialization

Certain aspects of the present disclosure relate to a system. In some aspects, the system may include an emitter configured to emit a light. The system may include a light guide configured to direct the light toward a pupil of an eye. The light guide may include a directing portion configured to propagate the light. The light guide may include a turning portion configured to receive light from the directing portion and direct the light toward the pupil of the eye and configured to receive reflected light directed from the pupil of the eye and direct the reflected light toward the directing portion. The system may include a receiver configured to receive the reflected light from the light guide. The system may include a processor configured to determine a gaze direction of the pupil of the eye based at least in part on the reflected light received by the receiver.

System for eye tracking

Apparatuses, methods, and systems are presented for reacting to scene-based occurrences. Such an apparatus may comprise dedicated computer vision (CV) computation hardware configured to receive sensor data from a sensor array comprising a plurality of sensor pixels and capable of computing one or more CV features using readings from neighboring sensor pixels of the sensor array. The apparatus may further comprise a first processing unit configured to control operation of the dedicated CV computation hardware. The first processing unit may be further configured to execute one or more application programs and, in conjunction with execution of the one or more application programs, communicate with at least one input/output (I/O) device controller, to effectuate an I/O operation in reaction to an event generated based on operations performed on the one or more computed CV features.

Single-processor computer vision hardware control and application execution

Multi-level cell (MLC) static random access memory (SRAM) (MLC SRAM) cells configured to perform multiplication operations are disclosed. In one aspect, an MLC SRAM cell includes SRAM bit cells, wherein data values stored in SRAM bit cells correspond to a multiple-bit value stored in the MLC SRAM cell that serves as first operand in multiplication operation. Voltage applied to read bit line is applied to each SRAM bit cell, wherein the voltage is an analog representation of a multiple-bit value that serves as a second operand in the multiplication operation. For each SRAM bit cell, if a particular binary data value is stored, a current correlating to the voltage of the read bit line is added to a current sum line. A magnitude of current on the current sum line is an analog representation of a multiple-bit product of the first operand multiplied by the second operand.

Multi-level cell (MLC) static random access memory (SRAM) (MLC SRAM) cells configured to perform multiplication operations

Methods, systems, computer-readable media, and apparatuses for image processing and utilization are presented. In some embodiments, an image containing at a face of a user may be obtained using a mobile device. An orientation of the face of the user within the image may be determined using the mobile device. The orientation of the face of the user may be determined using multiple stages: (a) a rotation stage for controlling a rotation applied to a portion of the image, to generate a portion of rotated image, and (b) an orientation stage for controlling an orientation applied to orientation-specific feature detection performed on the portion of rotated image. The determined orientation of the face of the user may be utilized as a control input to modify a display rotation of the mobile device.

Multiple orientation detection

Aspects of the present disclosure provide methods and apparatus for allocating memory in an artificial nervous system simulator implemented in hardware. According to certain aspects, memory resource requirements for one or more components of an artificial nervous system being simulated may be determined and portions of a shared memory pool (which may include on-chip and/or off-chip RAM) may be allocated to the components based on the determination.

Shared memory architecture for a neural simulator

A method of interfacing an event based processing system with a frame based processing system is presented. The method includes converting multiple events into a frame. The events may be generated from an event sensor. The method also includes inputting the frame into the frame based processing system.

Interfacing an event based system with a frame based processing system

Techniques disclosed herein utilize a vision sensor that integrates a special-purpose camera with dedicated computer vision (CV) computation hardware and a dedicated low-power microprocessor for the purposes of detecting, tracking, recognizing, and/or analyzing subjects, objects, and scenes in the view of the camera. The vision sensor processes the information retrieved from the camera using the included low-power microprocessor and sends 'events' (or indications that one or more reference occurrences have occurred, and, possibly, associated data) for the main processor only when needed or as defined and configured by the application. This allows the general-purpose microprocessor (which is typically relatively high-speed and high-power to support a variety of applications) to stay in a low-power (e.g., sleep mode) most of the time as conventional, while becoming active only when events are received from the vision sensor.

Low-power always-on face detection, tracking, recognition and/or analysis using events-based vision sensor

An apparatus includes a hardware sensor array including a plurality of pixels arranged along at least a first dimension and a second dimension of the array, each of the pixels capable of generating a sensor reading. A hardware scanning window array includes a plurality of storage elements arranged along at least a first dimension and a second dimension of the hardware scanning window array, each of the storage elements capable of storing a pixel value based on one or more sensor readings. Peripheral circuitry for systematically transfers pixel values, based on sensor readings, into the hardware scanning window array, to cause different windows of pixel values to be stored in the hardware scanning window array at different times. Control logic coupled to the hardware sensor array, the hardware scanning window array, and the peripheral circuitry, provides control signals to the peripheral circuitry to control the transfer of pixel values.

Apparatus and method for low-power object-detection in images using computer vision feature computation hardware

Scanning window for isolating pixel values in hardware for computer vision operations

A method of event-based down sampling includes receiving multiple sensor events corresponding to addresses and time stamps. The method further includes spatially down sampling the addresses based on the time stamps and the addresses. The method may also include updating a pixel value for each of the multiple sensor events based on the down sampling.

Event-based down sampling

Growing community of inventors

San Diego, CA, United States of America

Venkat Rangan