In low light conditions, mechanical sensors (accelerometer g-sensors) may sense that a vehicle entered a curve and may adjust the direction of lighting of headlights of the vehicle to point to a direction that is not straight ahead of the vehicle.
In the low light condition, the mechanical sensors may sense that the vehicle climbs a hill and may lower the direction of lighting.
The mechanical sensors may sense that the vehicle enters a curve and/or climbs a hill at a certain delay after actually entering the curve and after climbing the hill. The delay may result from the response period of the mechanical sensors as well from the need to distinguish between a curve and small turns of the wheel and/or minor maneuvers of the vehicle.
In some cases the certain delay may cause the driver to miss various obstacles that appear immediately after the beginning of the curve. Additionally or alternatively, the certain delay may reduce the response period of the driver in a manner that does not enable the driver to response to obstacles that appear after the beginning of the curve.
There is a growing need to provide an efficient method for adjusting adjustable lights.
There may be provided a method for operating adjustable headlights of a vehicle, the method may include sensing, by a vehicle night-vision sensor, an environment of the vehicle to provide sensed information; searching, in the sensed information, for a headlight adjustment event identifier, the headlight adjustment event identifier identifies a future occurrence of a headlight adjustment event, the headlight adjustment event requires an adjustment of a lighting pattern formed by at least one of the adjustable highlights of the vehicle; and when finding the headlight adjustment event identifier then adjusting the lighting pattern according to the headlight adjustment event, wherein the adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.
The method may include receiving or generating different headlight adjustment event identifiers for identifying different headlight adjustment events.
The method may include receiving or generating a mapping between different headlight adjustment event identifiers and lighting patterns associated with the different headlight adjustment events.
The headlight adjustment event may be a curve.
The headlight adjustment event may be a change in an inclination of the road.
The headlight adjustment event may be a presence of an obstacle, a presence of an entity that may be expected to cross a path of the vehicle, a presence of an entity located at the side of the road (such as an animal that may cross the road).
The method may include sensing, by a visible light sensor, the environment of the vehicle to provide additional sensed information; searching, in the additional sensed information, for a headlight adjustment event identifier, the headlight adjustment event identifier identifies a future occurrence of a headlight adjustment event, the headlight adjustment event requires an adjustment of the lighting pattern formed by at least one of the adjustable highlights of the vehicle; when finding the headlight adjustment event identifier then adjusting the lighting pattern according to the headlight adjustment event, wherein the adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.
The adjusting of the lighting pattern may be also based on information sensed by another sensor of the vehicle.
The vehicle night-vision sensor may be a radar.
The vehicle night-vision sensor may be a LIDAR.
The vehicle night-vision sensor may be an infrared sensor.
A non-transitory computer readable medium for operating adjustable headlights of a vehicle, the non-transitory computer readable medium that may store instructions for sensing, by a vehicle night-vision sensor, an environment of the vehicle to provide sensed information; searching, in the sensed information, for a headlight adjustment event identifier, the headlight adjustment event identifier identifies a future occurrence of a headlight adjustment event, the headlight adjustment event requires an adjustment of a lighting pattern formed by at least one of the adjustable highlights of the vehicle; and when finding the headlight adjustment event identifier then adjusting the lighting pattern according to the headlight adjustment event, wherein the adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.
The non-transitory computer readable medium may store instructions for receiving or generating different headlight adjustment event identifiers for identifying different headlight adjustment events.
The non-transitory computer readable medium may store instructions for receiving or generating a mapping between different headlight adjustment event identifiers and lighting patterns associated with the different headlight adjustment events.
The headlight adjustment event may be a curve.
The headlight adjustment event may be a change in an inclination of the road.
The headlight adjustment event may be an entity that may be expected to cross a path of the vehicle.
The non-transitory computer readable medium may store instructions for sensing, by a visible light sensor, the environment of the vehicle to provide additional sensed information; searching, in the additional sensed information, for a headlight adjustment event identifier, the headlight adjustment event identifier identifies a future occurrence of a headlight adjustment event, the headlight adjustment event requires an adjustment of the lighting pattern formed by at least one of the adjustable highlights of the vehicle; when finding the headlight adjustment event identifier then adjusting the lighting pattern according to the headlight adjustment event, wherein the adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.
The adjusting of the lighting pattern may be also based on information sensed by another sensor of the vehicle.
The embodiments of the disclosure will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which:
Any reference to a system or system should be applied, mutatis mutandis to a method that is executed by a system or system and/or to a non-transitory computer readable medium that stores instructions that once executed by the system or system will cause the system or system to execute the method.
Any reference to method should be applied, mutatis mutandis to a system or system that is configured to execute the method and/or to a non-transitory computer readable medium that stores instructions that once executed by the system or system will cause the system or system to execute the method.
Any reference to a non-transitory computer readable medium should be applied, mutatis mutandis to a method that is executed by a system or system and/or a system or system that is configured to execute the instructions stored in the non-transitory computer readable medium.
The term “and/or” is additionally or alternatively.
The term “night-vision sensor” is a sensor that is configured to sense scenes even at very low ambient conditions—and even at totally dark environments. Non-limiting examples of night-vision sensors include radars, LIDARs, infrared sensors, thermal sensor, and the like.
The term “visible light sensor” is sensor capable of sensing visible light that differs from the night-vision sensor.
The term “vehicle night-vision sensor” is a night-vision sensor that is installed in a vehicle and/or is connected to the vehicle.
The term “headlight adjustment event” is an event that requires an adjustment of a lighting pattern formed by at least one of the adjustable highlights of a vehicle.
The term “headlight adjustment event identifier” is an identifier that identifies a future occurrence of a headlight adjustment event. Accordingly—the identifier enable the vehicle to identify the headlight adjustment event before the headlight adjustment event occurs—thereby increasing the response period of the driver to any obstacle or other development that may be related to the occurrence of the event—for example any obstacle that is located after the beginning of the headlight adjustment event, any entity that may not be seen (initially) by the driver—but may cross the path of the vehicle, and the like.
Method 10 may start by step 12 of sensing, by a vehicle night-vision sensor, an environment of the vehicle to provide sensed information.
The vehicle night-vision sensor may be operated continuously, non-continuously, at certain time windows (for example—during the night), be activated based on ambient light conditions (for example—when the ambient light is below a threshold), under the control of a human driver, under the control of an automatic driving system, and the like.
The vehicle may include one or more vehicle night-vision sensors and may include additional sensors such as one or more visible light sensors, accelerometers, velocity sensors, shock sensors, telemetric sensors, and the like.
Step 12 may be followed by step 14 of searching, in the sensed information, for a headlight adjustment event identifier.
The vehicle may receive or generate different headlight adjustment event identifiers for identifying different headlight adjustment events such as a future curve, a future change in an inclination of a road, and the like.
The headlight adjustment event identifiers may include any combination of pixels, patches of image or any other signatures that once sensed by the night-vision sensor indicate of the future occurrence of the headlight adjustment event.
The vehicle may receive or generate a mapping between different headlight adjustment event identifiers and lighting patterns associated with the different headlight adjustment events.
The different headlight adjustment event identifiers may be learnt using object recognition, machine learning or any other image processing technique. It should be noted that sensed information from one or more other vehicle sensors nay be sued to verify the occurrence of the headlight adjustment event. For example—an accelerator may verify that the vehicle drove over a curve.
When finding the headlight adjustment event identifier then step 14 may be followed by step 16 of adjusting the lighting pattern according to the headlight adjustment event.
The adjusting may begin before the future occurrence of the headlight adjustment event.
The adjusting may begin immediately at a beginning of the headlight adjustment event. Immediately may include within a microsecond or millisecond range. Immediately may include within a period that is shorter than the certain delay introduced by detecting the occurrence of the events using physical sensors such as accelerometers.
The adjustment of the lighting pattern may include adjusting at least one out of (a) a direction of illumination, (b) an intensity of the illumination and (c) a shape of the lighting pattern.
It should be noted that the sensing of the future occurrence of the headlight adjustment event may also be based on the input from one or more other sensor of the vehicle—such as a visible light sensor.
Thus—method 10 may also include step 22 of sensing, by a visible light sensor, the environment of the vehicle to provide additional sensed information.
Step 22 may be followed by step 24 of searching, in the additional sensed information, for a headlight adjustment event identifier.
When finding the headlight adjustment event identifier then step 24 may be followed by step 26 of adjusting the lighting pattern according to the headlight adjustment event. The adjusting begins before the future occurrence of the headlight adjustment event or immediately at a beginning of the headlight adjustment event.
For example—the headlight adjustment event may be an object that may be still or may move towards the vehicle or may perform any movement that may place the object within an area that may affect the vehicle. For example—the event may be a vehicle that illuminates its vicinity with its own headlights.
Yet for another example—the night vision sensor and/or the visible light sensor may sense the lights of another vehicle that drives towards the vehicle—which may be defined as a headlight adjustment.
If, for example a curve is properly illuminated by night and/or road signs indicative of the curve are properly illuminated by night then the curve may be identifies by both night vision sensor and visible light sensor—and may be regarded as a headlight adjustment event.
It should be noted that events that should be recognized by both night vision sensor and visible light sensor may be used to verify each other.
Furthermore, the method may include storing information about the location of expected headlight adjustment events—(for example the location of the curve). Detecting the headlight adjustment event at the expected location further verifies the detection. A lack of detection may trigger an alert or otherwise require to check the sensor that did not detect the expected event.
The adjustable headlights module 202 may include adjustable headlights 204, mechanics 205 (for example motors, gears) for mechanically moving the adjustable headlights, and adjustment headlight controller 206. Any other optical and/or electrical component for adjusting the lighting pattern of the adjustable headlights may be provided. For example, the adjustment may include activating one headlight and deactivating another headlight.
The database 270 may store headlight adjustable event metadata such as (a) headlight adjustable event identifiers, and (b) comprising receiving or generating a mapping between different headlight adjustment event identifiers and lighting patterns associated with the different headlight adjustment events. The mapping may include any form of instructions, tables, pseudo-code and the like that links an identified headlight adjustment event to the required adjustment of the lighting pattern.
It should be noted that the vehicle may include (a) other systems or modules or units and/or (b) additional systems or modules or units, (c) and/or fewer systems or modules or units. For example—vehicle 105 may include only one out of autonomous driving manager 260 and ADAS manager 280.
Autonomous driving manager 260 may be instantiated in a suitable memory for storing software such as, for example, an optical storage medium, a magnetic storage medium, an electronic storage medium, and/or a combination thereof. It will be appreciated that system 200 may be implemented as an integrated component of an onboard computer system in a vehicle. Alternatively, system 200 may be implemented and a separate component in communication with the onboard computer system. It will also be appreciated that in the interests of clarity, while system 200 may comprise additional components and/or functionality e.g., for autonomous driving of vehicle 105, such additional components and/or functionality are not depicted in
Processing circuitry 210 may be operative to execute instructions stored in memory (not shown). For example, processing circuitry 210 may be operative to execute autonomous driving manager 260 and/or may be operative to execute headlight adjustment event metadata generator 290 and/or may be operative to execute ADAS manager 280 and/or may operable to execute the controller of the adjustable headlights module 202 and/or may be operable to execute the headlight adjustable event identification module 295.
It will be appreciated that processing circuitry 210 may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. It will similarly be appreciated that system 200 may comprise more than one instance of processing circuitry 210. For example, one such instance of processing circuitry 210 may be a special purpose processor operative to execute autonomous driving manager 260 to perform some, or all, of the functionality of system 200 as described herein.
I/O module 220 may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with other elements, such as, for example, system 400, night-vision sensor 230, visible light sensor 232, speed sensor 235, telemetry ECU 240, and/or accelerometer 250. As such, I/O module 220 may be operative to use a wired or wireless connection to connect to system 400 via a communications network such as a local area network, a backbone network and/or the Internet, etc. I/O module 220 may also be operative to use a wired or wireless connection to connect to other components of system 200, e.g., night-vision sensor 230, telemetry ECU 240, any other sensor, and/or accelerometer 250, adjustable headlights module 202, and the like. It will be appreciated that in operation I/O module 220 may be implemented as a multiplicity of modules, where different modules may be operative to use different communication technologies. For example, a module providing mobile network connectivity may be used to connect to system 400, whereas a local area wired connection may be used to connect to night-vision sensor 230, visible light sensor 232, telemetry ECU 240, and/or accelerometer 250.
In accordance with embodiments described herein, night-vision sensor 230, telemetry ECU 240, speed sensor 235, visible light sensor 232, and accelerometer 250 represent implementations of sensor(s). It will be appreciated that night-vision sensor 230, telemetry ECU 240, visible light sensor 232, and/or accelerometer 250 may be implemented as integrated components of vehicle 105 and may provide other functionality that is the interests of clarity is not explicitly described herein. As described hereinbelow, system 200 may use information about a current driving environment as received from night-vision sensor 230, visible light sensor 232, telemetry ECU 240, and/or accelerometer 250 to determine an appropriate driving policy for vehicle 105.
Autonomous driving manager 260 may be an application implemented in hardware, firmware, or software that may be executed by processing circuitry 210 to provide driving instructions to vehicle 105. For example, autonomous driving manager 260 may use images received from night-vision sensor 230, and/or from visible light sensor 232, and/or telemetry data received from telemetry ECU 240 to determine an appropriate driving policy for arriving at a given destination and provide driving instructions to vehicle 105 accordingly. It will be appreciated that autonomous driving manager 260 may also be operative to use other data sources when determining a driving policy, e.g., maps of potential routes, traffic congestion reports, etc. The autonomous driving manager 260 may use headlight adjustable event identifiers stored in database 270 to search for the future occurrence of headlight adjustment events. The autonomous driving manager 260 may use the mapping to determine how to adjust the lighting pattern—when detecting a future occurrence of an identified headlight adjustment event.
ADAS manager 280 may be an application implemented in hardware, firmware, or software that may be executed by processing circuitry 210 to assist a driver in driving the vehicle 105. The ADAS manager may assist the driver in any manner known in the art—for example—plan a suggested driving path, provide collision alerts, obstacle alerts, cross lane alerts, and the like. The ADAS manager 280 may provide indication to a driver (either upon request or else) that the headlights should be adjusted—before the occurrence of the headlight adjustment event—and ask the human driver to approve the adjustment from the adjustable vehicle module. The ADAS manager 280 may use the mapping to determine how to adjust the lighting pattern—when detecting a future occurrence of an identified headlight adjustment event.
For example—the mapping may be a representation of use cases and the expected adjustments—such as but not limited to
Headlight adjustment event identifier 290 may receive information from one or more sensors (for example from night-vision sensor 230, visible light sensor 232) and search for headlight adjustable event identifiers.
Reference is now made to
Headlight adjustable event metadata generator 460 may be instantiated in a suitable memory for storing software such as, for example, an optical storage medium, a magnetic storage medium, an electronic storage medium, and/or a combination thereof.
Processing circuitry 410 may be operative to execute instructions stored in memory (not shown). For example, processing circuitry 410 may be operative to execute headlight adjustable event metadata generator 460. It will be appreciated that processing circuitry 410 may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits. It will similarly be appreciated that server 400 may comprise more than one instance of processing circuitry 410. For example, one such instance of processing circuitry 410 may be a special purpose processor operative to execute headlight adjustable event metadata generator 460 to perform some, or all, of the functionality of server 400 as described herein.
I/O module 420 may be any suitable communications component such as a network interface card, universal serial bus (USB) port, disk reader, modem or transceiver that may be operative to use protocols such as are known in the art to communicate either directly, or indirectly, with system 200 (
Headlight adjustable event metadata generator 460 may be an application implemented in hardware, firmware, or software that may be executed by processing circuitry 410 to generate Headlight adjustable event metadata. For example, headlight adjustable event metadata generator 460 may use any sensed information from any sensor of any vehicle to determine headlight adjustable event identifier and/or may also determine, based on information sensed during, before or after the occurrence of a headlight adjustment event whether the current respond (the applied adjusted lighting pattern) is appropriate (if for example it properly captures the field of interest)—or should be modified.
It will be appreciated that headlight adjustable event metadata generator 460 may also be operative to use other data sources.
The terms “including”, “comprising”, “having”, “consisting” and “consisting essentially of” are used in an interchangeable manner. For example—any method may include at least the steps included in the figures and/or in the specification, only the steps included in the figures and/or the specification. The same applies to the system.
It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.
In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims.
Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.
Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative and that alternative embodiments may merge logic blocks or circuit elements or impose an alternate decomposition of functionality upon various logic blocks or circuit elements. Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality.
Any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality.
Furthermore, those skilled in the art will recognize that boundaries between the above described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments.
Also for example, in one embodiment, the illustrated examples may be implemented as circuitry located on a single integrated circuit or within a same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.
Also for example, the examples, or portions thereof, may implemented as soft or code representations of physical circuitry or of logical representations convertible into physical circuitry, such as in a hardware description language of any appropriate type.
Also, the invention is not limited to physical devices or units implemented in non-programmable hardware but can also be applied in programmable devices or units able to perform the desired device functions by operating in accordance with suitable program code, such as mainframes, minicomputers, servers, workstations, personal computers, notepads, personal digital assistants, electronic games, automotive and other embedded systems, cell phones and various other wireless devices, commonly denoted in this application as ‘computer systems’.
However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms “a” or “an,” as used herein, are defined as one as or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements the mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.
Any system, apparatus or device referred to this patent application includes at least one hardware component.
While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.
Any combination of any component of any component and/or unit of system or module that is illustrated in any of the figures and/or specification and/or the claims may be provided.
Any combination of any system or module illustrated in any of the figures and/or specification and/or the claims may be provided.
Any combination of steps, operations and/or methods illustrated in any of the figures and/or specification and/or the claims may be provided.
Any combination of operations illustrated in any of the figures and/or specification and/or the claims may be provided.
Any combination of methods illustrated in any of the figures and/or specification and/or the claims may be provided.
This application claims priority from U.S. provisional patent 62/811,134 filing date Feb. 27, 2019 and from US provisional patent 62/827,112 filing date Mar. 31, 2019 both incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
4733353 | Jaswa | Mar 1988 | A |
4932645 | Schorey et al. | Jun 1990 | A |
4972363 | Nguyen et al. | Nov 1990 | A |
5078501 | Hekker et al. | Jan 1992 | A |
5214746 | Fogel et al. | May 1993 | A |
5307451 | Clark | Apr 1994 | A |
5412564 | Ecer | May 1995 | A |
5436653 | Ellis et al. | Jul 1995 | A |
5568181 | Greenwood et al. | Oct 1996 | A |
5638425 | Meador, I et al. | Jun 1997 | A |
5745678 | Herzberg et al. | Apr 1998 | A |
5754938 | Herz et al. | May 1998 | A |
5763069 | Jordan | Jun 1998 | A |
5806061 | Chaudhuri et al. | Sep 1998 | A |
5835087 | Herz et al. | Nov 1998 | A |
5835901 | Duvoisin et al. | Nov 1998 | A |
5852435 | Vigneaux et al. | Dec 1998 | A |
5870754 | Dimitrova et al. | Feb 1999 | A |
5873080 | Coden et al. | Feb 1999 | A |
5887193 | Takahashi et al. | Mar 1999 | A |
5926812 | Hilsenrath et al. | Jul 1999 | A |
5978754 | Kumano | Nov 1999 | A |
5991306 | Burns et al. | Nov 1999 | A |
6052481 | Grajski et al. | Apr 2000 | A |
6070167 | Qian et al. | May 2000 | A |
6076088 | Paik et al. | Jun 2000 | A |
6122628 | Castelli et al. | Sep 2000 | A |
6128651 | Cezar | Oct 2000 | A |
6137911 | Zhilyaev | Oct 2000 | A |
6144767 | Bottou et al. | Nov 2000 | A |
6147636 | Gershenson | Nov 2000 | A |
6163510 | Lee et al. | Dec 2000 | A |
6243375 | Speicher | Jun 2001 | B1 |
6243713 | Nelson et al. | Jun 2001 | B1 |
6275599 | Adler et al. | Aug 2001 | B1 |
6314419 | Faisal | Nov 2001 | B1 |
6329986 | Cheng | Dec 2001 | B1 |
6381656 | Shankman | Apr 2002 | B1 |
6411229 | Kobayashi | Jun 2002 | B2 |
6422617 | Fukumoto et al. | Jul 2002 | B1 |
6507672 | Watkins et al. | Jan 2003 | B1 |
6523046 | Liu et al. | Feb 2003 | B2 |
6524861 | Anderson | Feb 2003 | B1 |
6546405 | Gupta et al. | Apr 2003 | B2 |
6550018 | Abonamah et al. | Apr 2003 | B1 |
6557042 | He et al. | Apr 2003 | B1 |
6594699 | Sahai et al. | Jul 2003 | B1 |
6601026 | Appelt et al. | Jul 2003 | B2 |
6611628 | Sekiguchi et al. | Aug 2003 | B1 |
6618711 | Ananth | Sep 2003 | B1 |
6640015 | Lafruit | Oct 2003 | B1 |
6643620 | Contolini et al. | Nov 2003 | B1 |
6643643 | Lee et al. | Nov 2003 | B1 |
6665657 | Dibachi | Dec 2003 | B1 |
6681032 | Bortolussi et al. | Jan 2004 | B2 |
6704725 | Lee | Mar 2004 | B1 |
6732149 | Kephart | May 2004 | B1 |
6742094 | Igari | May 2004 | B2 |
6751363 | Natsev et al. | Jun 2004 | B1 |
6751613 | Lee et al. | Jun 2004 | B1 |
6754435 | Kim | Jun 2004 | B2 |
6763069 | Divakaran et al. | Jul 2004 | B1 |
6763519 | McColl et al. | Jul 2004 | B1 |
6774917 | Foote et al. | Aug 2004 | B1 |
6795818 | Lee | Sep 2004 | B1 |
6804356 | Krishnamachari | Oct 2004 | B1 |
6813395 | Kinjo | Nov 2004 | B1 |
6819797 | Smith et al. | Nov 2004 | B1 |
6877134 | Fuller et al. | Apr 2005 | B1 |
6901207 | Watkins | May 2005 | B1 |
6938025 | Lulich et al. | Aug 2005 | B1 |
6985172 | Rigney et al. | Jan 2006 | B1 |
7013051 | Sekiguchi et al. | Mar 2006 | B2 |
7020654 | Najmi | Mar 2006 | B1 |
7023979 | Wu et al. | Apr 2006 | B1 |
7043473 | Rassool et al. | May 2006 | B1 |
7158681 | Persiantsev | Jan 2007 | B2 |
7215828 | Luo | May 2007 | B2 |
7260564 | Lynn et al. | Aug 2007 | B1 |
7289643 | Brunk et al. | Oct 2007 | B2 |
7299261 | Oliver et al. | Nov 2007 | B1 |
7302089 | Smits | Nov 2007 | B1 |
7302117 | Sekiguchi et al. | Nov 2007 | B2 |
7313805 | Rosin et al. | Dec 2007 | B1 |
7340358 | Yoneyama | Mar 2008 | B2 |
7346629 | Kapur et al. | Mar 2008 | B2 |
7353224 | Chen et al. | Apr 2008 | B2 |
7376672 | Weare | May 2008 | B2 |
7383179 | Alves et al. | Jun 2008 | B2 |
7433895 | Li et al. | Oct 2008 | B2 |
7464086 | Black et al. | Dec 2008 | B2 |
7529659 | Wold | May 2009 | B2 |
7657100 | Gokturk et al. | Feb 2010 | B2 |
7660468 | Gokturk et al. | Feb 2010 | B2 |
7801893 | Gulli | Sep 2010 | B2 |
7805446 | Potok et al. | Sep 2010 | B2 |
7860895 | Scofield et al. | Dec 2010 | B1 |
7872669 | Darrell et al. | Jan 2011 | B2 |
7921288 | Hildebrand | Apr 2011 | B1 |
7933407 | Keidar et al. | Apr 2011 | B2 |
8023739 | Hohimer et al. | Sep 2011 | B2 |
8266185 | Raichelgauz et al. | Sep 2012 | B2 |
8275764 | Jeon | Sep 2012 | B2 |
8285718 | Ong et al. | Oct 2012 | B1 |
8312031 | Raichelgauz et al. | Nov 2012 | B2 |
8315442 | Gokturk et al. | Nov 2012 | B2 |
8345982 | Gokturk et al. | Jan 2013 | B2 |
8386400 | Raichelgauz et al. | Feb 2013 | B2 |
8396876 | Kennedy et al. | Mar 2013 | B2 |
8418206 | Bryant et al. | Apr 2013 | B2 |
RE44225 | Aviv | May 2013 | E |
8442321 | Chang et al. | May 2013 | B1 |
8457827 | Ferguson et al. | Jun 2013 | B1 |
8495489 | Everingham | Jul 2013 | B1 |
8527978 | Sallam | Sep 2013 | B1 |
8634980 | Urmson | Jan 2014 | B1 |
8635531 | Graham et al. | Jan 2014 | B2 |
8655801 | Raichelgauz et al. | Feb 2014 | B2 |
8655878 | Kulkarni et al. | Feb 2014 | B1 |
8781152 | Momeyer | Jul 2014 | B2 |
8782077 | Rowley | Jul 2014 | B1 |
8799195 | Raichelgauz et al. | Aug 2014 | B2 |
8799196 | Raichelquaz et al. | Aug 2014 | B2 |
8818916 | Raichelgauz et al. | Aug 2014 | B2 |
8868861 | Shimizu et al. | Oct 2014 | B2 |
8886648 | Procopio et al. | Nov 2014 | B1 |
8954887 | Tseng et al. | Feb 2015 | B1 |
8990199 | Ramesh et al. | Mar 2015 | B1 |
9009086 | Raichelgauz et al. | Apr 2015 | B2 |
9104747 | Raichelgauz et al. | Aug 2015 | B2 |
9165406 | Gray et al. | Oct 2015 | B1 |
9298763 | Zack | Mar 2016 | B1 |
9311308 | Sankarasubramaniam et al. | Apr 2016 | B2 |
9323754 | Ramanathan et al. | Apr 2016 | B2 |
9440647 | Sucan | Sep 2016 | B1 |
9466068 | Raichelgauz et al. | Oct 2016 | B2 |
9646006 | Raichelgauz et al. | May 2017 | B2 |
9679062 | Schillings et al. | Jun 2017 | B2 |
9734533 | Givot | Aug 2017 | B1 |
9807442 | Bhatia et al. | Oct 2017 | B2 |
9875445 | Amer et al. | Jan 2018 | B2 |
9984369 | Li et al. | May 2018 | B2 |
10133947 | Yang | Nov 2018 | B2 |
10347122 | Takenaka | Jul 2019 | B2 |
10491885 | Hicks | Nov 2019 | B1 |
20010019633 | Tenze et al. | Sep 2001 | A1 |
20010034219 | Hewitt et al. | Oct 2001 | A1 |
20010038876 | Anderson | Nov 2001 | A1 |
20020004743 | Kutaragi et al. | Jan 2002 | A1 |
20020010682 | Johnson | Jan 2002 | A1 |
20020010715 | Chinn et al. | Jan 2002 | A1 |
20020019881 | Bokhari et al. | Feb 2002 | A1 |
20020032677 | Morgenthaler et al. | Mar 2002 | A1 |
20020038299 | Zernik et al. | Mar 2002 | A1 |
20020042914 | Walker et al. | Apr 2002 | A1 |
20020072935 | Rowse et al. | Jun 2002 | A1 |
20020087530 | Smith et al. | Jul 2002 | A1 |
20020087828 | Arimilli et al. | Jul 2002 | A1 |
20020091947 | Nakamura | Jul 2002 | A1 |
20020107827 | Benitez-Jimenez et al. | Aug 2002 | A1 |
20020113812 | Walker et al. | Aug 2002 | A1 |
20020126002 | Patchell | Sep 2002 | A1 |
20020126872 | Brunk et al. | Sep 2002 | A1 |
20020129140 | Peled et al. | Sep 2002 | A1 |
20020147637 | Kraft et al. | Oct 2002 | A1 |
20020157116 | Jasinschi | Oct 2002 | A1 |
20020163532 | Thomas et al. | Nov 2002 | A1 |
20020174095 | Lulich et al. | Nov 2002 | A1 |
20020184505 | Mihcak et al. | Dec 2002 | A1 |
20030004966 | Bolle et al. | Jan 2003 | A1 |
20030005432 | Ellis et al. | Jan 2003 | A1 |
20030037010 | Schmelzer | Feb 2003 | A1 |
20030041047 | Chang et al. | Feb 2003 | A1 |
20030089216 | Birmingham et al. | May 2003 | A1 |
20030093790 | Logan et al. | May 2003 | A1 |
20030101150 | Agnihotri et al. | May 2003 | A1 |
20030105739 | Essafi et al. | Jun 2003 | A1 |
20030110236 | Yang et al. | Jun 2003 | A1 |
20030115191 | Copperman et al. | Jun 2003 | A1 |
20030122704 | Dubrovin | Jul 2003 | A1 |
20030126147 | Essafi et al. | Jul 2003 | A1 |
20030140257 | Peterka et al. | Jul 2003 | A1 |
20030165269 | Fedorovskaya et al. | Sep 2003 | A1 |
20030174859 | Kim | Sep 2003 | A1 |
20030184598 | Graham | Oct 2003 | A1 |
20030200217 | Ackerman | Oct 2003 | A1 |
20030217335 | Chung et al. | Nov 2003 | A1 |
20030229531 | Heckerman et al. | Dec 2003 | A1 |
20040059736 | Willse | Mar 2004 | A1 |
20040091111 | Levy et al. | May 2004 | A1 |
20040095376 | Graham et al. | May 2004 | A1 |
20040098671 | Graham et al. | May 2004 | A1 |
20040111432 | Adams et al. | Jun 2004 | A1 |
20040117638 | Monroe | Jun 2004 | A1 |
20040128511 | Sun et al. | Jul 2004 | A1 |
20040153426 | Nugent | Aug 2004 | A1 |
20040162820 | James et al. | Aug 2004 | A1 |
20040230572 | Omoigui | Nov 2004 | A1 |
20040267774 | Lin et al. | Dec 2004 | A1 |
20050021394 | Miedema et al. | Jan 2005 | A1 |
20050080788 | Murata | Apr 2005 | A1 |
20050114198 | Koningstein et al. | May 2005 | A1 |
20050131884 | Gross et al. | Jun 2005 | A1 |
20050163375 | Grady | Jul 2005 | A1 |
20050172130 | Roberts | Aug 2005 | A1 |
20050177372 | Wang et al. | Aug 2005 | A1 |
20050193015 | Logston | Sep 2005 | A1 |
20050226511 | Short | Oct 2005 | A1 |
20050238198 | Brown et al. | Oct 2005 | A1 |
20050238238 | Xu et al. | Oct 2005 | A1 |
20050249398 | Khamene et al. | Nov 2005 | A1 |
20050256820 | Dugan et al. | Nov 2005 | A1 |
20050262428 | Little et al. | Nov 2005 | A1 |
20050281439 | Lange | Dec 2005 | A1 |
20050289163 | Gordon et al. | Dec 2005 | A1 |
20050289590 | Cheok et al. | Dec 2005 | A1 |
20060004745 | Kuhn et al. | Jan 2006 | A1 |
20060015580 | Gabriel et al. | Jan 2006 | A1 |
20060020958 | Allamanche et al. | Jan 2006 | A1 |
20060033163 | Chen | Feb 2006 | A1 |
20060050993 | Stentiford | Mar 2006 | A1 |
20060069668 | Braddy et al. | Mar 2006 | A1 |
20060080311 | Potok et al. | Apr 2006 | A1 |
20060100987 | Leurs | May 2006 | A1 |
20060112035 | Cecchi et al. | May 2006 | A1 |
20060120626 | Perlmutter | Jun 2006 | A1 |
20060129822 | Snijder et al. | Jun 2006 | A1 |
20060217818 | Fujiwara | Sep 2006 | A1 |
20060217828 | Hicken | Sep 2006 | A1 |
20060218191 | Gopalakrishnan | Sep 2006 | A1 |
20060224529 | Kermani | Oct 2006 | A1 |
20060236343 | Chang | Oct 2006 | A1 |
20060242130 | Sadri et al. | Oct 2006 | A1 |
20060248558 | Barton et al. | Nov 2006 | A1 |
20060251338 | Gokturk et al. | Nov 2006 | A1 |
20060251339 | Gokturk | Nov 2006 | A1 |
20060253423 | McLane et al. | Nov 2006 | A1 |
20060288002 | Epstein et al. | Dec 2006 | A1 |
20070022374 | Huang et al. | Jan 2007 | A1 |
20070033170 | Sull et al. | Feb 2007 | A1 |
20070038614 | Guha | Feb 2007 | A1 |
20070042757 | Jung et al. | Feb 2007 | A1 |
20070061302 | Ramer et al. | Mar 2007 | A1 |
20070067304 | Ives | Mar 2007 | A1 |
20070074147 | Wold | Mar 2007 | A1 |
20070083611 | Farago et al. | Apr 2007 | A1 |
20070091106 | Moroney | Apr 2007 | A1 |
20070130159 | Gulli et al. | Jun 2007 | A1 |
20070136782 | Ramaswamy et al. | Jun 2007 | A1 |
20070156720 | Maren | Jul 2007 | A1 |
20070196013 | Li | Aug 2007 | A1 |
20070244902 | Seide et al. | Oct 2007 | A1 |
20070253594 | Lu et al. | Nov 2007 | A1 |
20070298152 | Baets | Dec 2007 | A1 |
20080049789 | Vedantham et al. | Feb 2008 | A1 |
20080072256 | Boicey et al. | Mar 2008 | A1 |
20080079729 | Brailovsky | Apr 2008 | A1 |
20080109433 | Rose | May 2008 | A1 |
20080152231 | Gokturk | Jun 2008 | A1 |
20080159622 | Agnihotri et al. | Jul 2008 | A1 |
20080165861 | Wen et al. | Jul 2008 | A1 |
20080166020 | Kosaka | Jul 2008 | A1 |
20080201299 | Lehikoinen et al. | Aug 2008 | A1 |
20080201314 | Smith et al. | Aug 2008 | A1 |
20080201361 | Castro et al. | Aug 2008 | A1 |
20080228995 | Tan et al. | Sep 2008 | A1 |
20080237359 | Silverbrook et al. | Oct 2008 | A1 |
20080247543 | Mick et al. | Oct 2008 | A1 |
20080253737 | Kimura et al. | Oct 2008 | A1 |
20080263579 | Mears et al. | Oct 2008 | A1 |
20080270373 | Oostveen et al. | Oct 2008 | A1 |
20080270569 | McBride | Oct 2008 | A1 |
20080294278 | Borgeson et al. | Nov 2008 | A1 |
20080307454 | Ahanger et al. | Dec 2008 | A1 |
20080313140 | Pereira et al. | Dec 2008 | A1 |
20090022472 | Bronstein | Jan 2009 | A1 |
20090024641 | Quigley et al. | Jan 2009 | A1 |
20090034791 | Doretto | Feb 2009 | A1 |
20090037088 | Taguchi | Feb 2009 | A1 |
20090043637 | Eder | Feb 2009 | A1 |
20090043818 | Raichelgauz | Feb 2009 | A1 |
20090080759 | Bhaskar | Mar 2009 | A1 |
20090096634 | Emam et al. | Apr 2009 | A1 |
20090125544 | Brindley | May 2009 | A1 |
20090157575 | Schobben et al. | Jun 2009 | A1 |
20090165031 | Li et al. | Jun 2009 | A1 |
20090172030 | Schiff et al. | Jul 2009 | A1 |
20090208106 | Dunlop et al. | Aug 2009 | A1 |
20090208118 | Csurka | Aug 2009 | A1 |
20090216761 | Raichelgauz | Aug 2009 | A1 |
20090220138 | Zhang et al. | Sep 2009 | A1 |
20090245573 | Saptharishi et al. | Oct 2009 | A1 |
20090254572 | Redlich et al. | Oct 2009 | A1 |
20090278934 | Ecker | Nov 2009 | A1 |
20090282218 | Raichelgauz et al. | Nov 2009 | A1 |
20090297048 | Slotine et al. | Dec 2009 | A1 |
20100042646 | Raichelqauz | Feb 2010 | A1 |
20100082684 | Churchill | Apr 2010 | A1 |
20100104184 | Bronstein et al. | Apr 2010 | A1 |
20100111408 | Matsuhira | May 2010 | A1 |
20100125569 | Nair et al. | May 2010 | A1 |
20100162405 | Cook et al. | Jun 2010 | A1 |
20100191391 | Zeng | Jul 2010 | A1 |
20100198626 | Cho et al. | Aug 2010 | A1 |
20100212015 | Jin et al. | Aug 2010 | A1 |
20100284604 | Chrysanthakopoulos | Nov 2010 | A1 |
20100293057 | Haveliwala et al. | Nov 2010 | A1 |
20100306193 | Pereira | Dec 2010 | A1 |
20100312736 | Kello | Dec 2010 | A1 |
20100318493 | Wessling | Dec 2010 | A1 |
20100325138 | Lee et al. | Dec 2010 | A1 |
20100325581 | Finkelstein et al. | Dec 2010 | A1 |
20110029620 | Bonforte | Feb 2011 | A1 |
20110035373 | Berg et al. | Feb 2011 | A1 |
20110038545 | Bober | Feb 2011 | A1 |
20110055585 | Lee | Mar 2011 | A1 |
20110164180 | Lee | Jul 2011 | A1 |
20110164810 | Zang et al. | Jul 2011 | A1 |
20110216209 | Fredlund et al. | Sep 2011 | A1 |
20110218946 | Stern et al. | Sep 2011 | A1 |
20110246566 | Kashef | Oct 2011 | A1 |
20110276680 | Rimon | Nov 2011 | A1 |
20110296315 | Lin et al. | Dec 2011 | A1 |
20120131454 | Shah | May 2012 | A1 |
20120133497 | Sasaki | May 2012 | A1 |
20120136853 | Kennedy et al. | May 2012 | A1 |
20120167133 | Carroll et al. | Jun 2012 | A1 |
20120179642 | Sweeney et al. | Jul 2012 | A1 |
20120179751 | Ahn | Jul 2012 | A1 |
20120185445 | Borden et al. | Jul 2012 | A1 |
20120207346 | Kohli et al. | Aug 2012 | A1 |
20120221470 | Lyon | Aug 2012 | A1 |
20120227074 | Hill et al. | Sep 2012 | A1 |
20120239690 | Asikainen et al. | Sep 2012 | A1 |
20120239694 | Avner et al. | Sep 2012 | A1 |
20120265735 | McMillan et al. | Oct 2012 | A1 |
20120294514 | Saunders et al. | Nov 2012 | A1 |
20120299961 | Ramkumar et al. | Nov 2012 | A1 |
20120301105 | Rehg et al. | Nov 2012 | A1 |
20120331011 | Raichelgauz et al. | Dec 2012 | A1 |
20130043990 | Al-Jafar | Feb 2013 | A1 |
20130066856 | Ong et al. | Mar 2013 | A1 |
20130067364 | Berntson et al. | Mar 2013 | A1 |
20130086499 | Dyor et al. | Apr 2013 | A1 |
20130089248 | Remiszewski et al. | Apr 2013 | A1 |
20130103814 | Carrasco | Apr 2013 | A1 |
20130151522 | Aggarwal et al. | Jun 2013 | A1 |
20130159298 | Mason et al. | Jun 2013 | A1 |
20130212493 | Krishnamurthy | Aug 2013 | A1 |
20130226820 | Sedota, Jr. | Aug 2013 | A1 |
20130226930 | Amgren et al. | Aug 2013 | A1 |
20130227023 | Raichelgauz et al. | Aug 2013 | A1 |
20130283401 | Pabla et al. | Oct 2013 | A1 |
20130346412 | Raichelgauz et al. | Dec 2013 | A1 |
20140019264 | Wachman et al. | Jan 2014 | A1 |
20140025692 | Pappas | Jan 2014 | A1 |
20140059443 | Tabe | Feb 2014 | A1 |
20140095425 | Sipple | Apr 2014 | A1 |
20140111647 | Atsmon | Apr 2014 | A1 |
20140125703 | Roveta et al. | May 2014 | A1 |
20140147829 | Jerauld | May 2014 | A1 |
20140149918 | Asokan et al. | May 2014 | A1 |
20140152698 | Kim et al. | Jun 2014 | A1 |
20140156691 | Conwell | Jun 2014 | A1 |
20140169681 | Drake | Jun 2014 | A1 |
20140176604 | Venkitaraman et al. | Jun 2014 | A1 |
20140193077 | Shiiyama et al. | Jul 2014 | A1 |
20140198986 | Marchesotti | Jul 2014 | A1 |
20140201330 | Lozano Lopez | Jul 2014 | A1 |
20140250032 | Huang et al. | Sep 2014 | A1 |
20140282655 | Roberts | Sep 2014 | A1 |
20140300722 | Garcia | Oct 2014 | A1 |
20140330830 | Raichelgauz et al. | Nov 2014 | A1 |
20140341476 | Kulick et al. | Nov 2014 | A1 |
20140363044 | Williams et al. | Dec 2014 | A1 |
20140379477 | Sheinfeld | Dec 2014 | A1 |
20150033150 | Lee | Jan 2015 | A1 |
20150052089 | Kozloski et al. | Feb 2015 | A1 |
20150100562 | Kohlmeier et al. | Apr 2015 | A1 |
20150117784 | Lin | Apr 2015 | A1 |
20150120627 | Hunzinger et al. | Apr 2015 | A1 |
20150127516 | Studnitzer et al. | May 2015 | A1 |
20150134688 | Jing | May 2015 | A1 |
20150248586 | Gaidon et al. | Sep 2015 | A1 |
20150254344 | Kulkarni et al. | Sep 2015 | A1 |
20150286742 | Zhang et al. | Oct 2015 | A1 |
20150286872 | Medioni et al. | Oct 2015 | A1 |
20150324356 | Gutierrez et al. | Nov 2015 | A1 |
20150332588 | Bulan et al. | Nov 2015 | A1 |
20150363644 | Wnuk | Dec 2015 | A1 |
20160007083 | Gurha | Jan 2016 | A1 |
20160026707 | Ong et al. | Jan 2016 | A1 |
20160132194 | Grue et al. | May 2016 | A1 |
20160210525 | Yang | Jul 2016 | A1 |
20160221592 | Puttagunta | Aug 2016 | A1 |
20160275766 | Venetianer et al. | Sep 2016 | A1 |
20160306798 | Guo et al. | Oct 2016 | A1 |
20160342683 | Kwon | Nov 2016 | A1 |
20160357188 | Ansari | Dec 2016 | A1 |
20160377251 | Kim | Dec 2016 | A1 |
20170017638 | Satyavarta et al. | Jan 2017 | A1 |
20170032257 | Sharifi | Feb 2017 | A1 |
20170041254 | Agara Venkatesha Rao | Feb 2017 | A1 |
20170109602 | Kim | Apr 2017 | A1 |
20170154241 | Shambik et al. | Jun 2017 | A1 |
20170255620 | Raichelgauz | Sep 2017 | A1 |
20170262437 | Raichelgauz | Sep 2017 | A1 |
20170323568 | Inoue | Nov 2017 | A1 |
20180081368 | Watanabe | Mar 2018 | A1 |
20180101177 | Cohen | Apr 2018 | A1 |
20180108258 | Dilger | Apr 2018 | A1 |
20180157903 | Tu et al. | Jun 2018 | A1 |
20180157916 | Doumbouya | Jun 2018 | A1 |
20180158323 | Takenaka | Jun 2018 | A1 |
20180189613 | Wolf et al. | Jul 2018 | A1 |
20180204111 | Zadeh | Jul 2018 | A1 |
20180373929 | Ye | Dec 2018 | A1 |
20190005726 | Nakano | Jan 2019 | A1 |
20190039627 | Yamamoto | Feb 2019 | A1 |
20190043274 | Hayakawa | Feb 2019 | A1 |
20190045244 | Balakrishnan | Feb 2019 | A1 |
20190056718 | Satou | Feb 2019 | A1 |
20190065951 | Luo | Feb 2019 | A1 |
20190096135 | Mutto et al. | Mar 2019 | A1 |
20190171912 | Vallespi-Gonzalez et al. | Jun 2019 | A1 |
20190188501 | Ryu | Jun 2019 | A1 |
20190220011 | Della Penna | Jul 2019 | A1 |
20190279046 | Han et al. | Sep 2019 | A1 |
20190304102 | Chen et al. | Oct 2019 | A1 |
20190317513 | Zhang | Oct 2019 | A1 |
20190364492 | Azizi | Nov 2019 | A1 |
20190384303 | Muller | Dec 2019 | A1 |
20190384312 | Herbach | Dec 2019 | A1 |
20190385460 | Magzimof | Dec 2019 | A1 |
20190389459 | Berntorp | Dec 2019 | A1 |
20200004248 | Healey | Jan 2020 | A1 |
20200004251 | Zhu | Jan 2020 | A1 |
20200004265 | Zhu | Jan 2020 | A1 |
20200005631 | Visintainer et al. | Jan 2020 | A1 |
20200018606 | Wolcott | Jan 2020 | A1 |
20200018618 | Ozog | Jan 2020 | A1 |
20200020212 | Song | Jan 2020 | A1 |
20200050973 | Stenneth | Feb 2020 | A1 |
20200073977 | Montemerlo | Mar 2020 | A1 |
20200090484 | Chen | Mar 2020 | A1 |
20200097756 | Hashimoto | Mar 2020 | A1 |
20200133307 | Kelkar | Apr 2020 | A1 |
20200043326 | Tao | Jun 2020 | A1 |
Number | Date | Country |
---|---|---|
1085464 | Jan 2007 | EP |
0231764 | Apr 2002 | WO |
2003067467 | Aug 2003 | WO |
2005027457 | Mar 2005 | WO |
2007049282 | May 2007 | WO |
2014076002 | May 2014 | WO |
2014137337 | Sep 2014 | WO |
2016040376 | Mar 2016 | WO |
2016070193 | May 2016 | WO |
Entry |
---|
Jasinschi et al., A Probabilistic Layered Framework for Integrating Multimedia Content and Context Information, 2002, IEEE, p. 2057-2060. (Year: 2002). |
Jones et al., “Contextual Dynamics of Group-Based Sharing Decisions”, 2011, University of Bath, p. 1777-1786. (Year: 2011). |
Iwamoto, “Image Signature Robust to Caption Superimpostion for Video Sequence Identification”, IEEE, pp. 3185-3188 (Year: 2006). |
Cooperative Multi-Scale Convolutional Neural, Networks for Person Detection, Markus Eisenbach, Daniel Seichter, Tim Wengefeld, and Horst-Michael Gross Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab (Year; 2016). |
Chen, Yixin, James Ze Wang, and Robert Krovetz. “CLUE: cluster-based retrieval of images by unsupervised learning.” IEEE transactions on Image Processing 14.8 (2005); 1187-1201. (Year: 2005). |
Wusk et al (Non-Invasive detection of Respiration and Heart Rate with a Vehicle Seat Sensor; www.mdpi.com/journal/sensors; Published: May 8, 2018). (Year: 2018). |
Chen, Tiffany Yu-Han, et al. “Glimpse: Continuous, real-time object recognition on mobile devices.” Proceedings of the 13th ACM Confrecene on Embedded Networked Sensor Systems. 2015. (Year: 2015). |
“Computer Vision Demonstration Website”, Electronics and Computer Science, University of Southampton, 2005, USA. |
Big Bang Theory Series 04 Episode 12, aired Jan. 6, 2011; [retrieved from Internet: ]. |
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995, pp. 1-14. |
Burgsteiner et al., “Movement Prediction from Real-World Images Using a Liquid State machine”, Innovations in Applied Artificial Intelligence Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, LNCS, Springer-Verlag, BE, vol. 3533, Jun. 2005, pp. 121-130. |
Cernansky et al, “Feed-forward Echo State Networks”, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, Jul. 31-Aug. 4, 2005, pp. 1-4. |
Chinchor, Nancy A. et al.; Multimedia Analysis + Visual Analytics = Multimedia Analytics; IEEE Computer Society; 2010; pp. 52-60. (Year: 2010). |
Fathy et al, “A Parallel Design and Implementation for Backpropagation Neural Network Using MIMD Architecture”, 8th Mediterranean Electrotechnical Conference, 19'96. MELECON '96, Date of Conference: May 13-16, 1996, vol. 3 pp. 1472-1475, vol. 3. |
Freisleben et al, “Recognition of Fractal Images Using a Neural Network”, Lecture Notes in Computer Science, 1993, vol. 6861,1993, pp. 631-637. |
Garcia, “Solving the Weighted Region Least Cost Path Problem Using Transputers”, Naval Postgraduate School, Monterey, California, Dec. 1989. |
Guo et al, AdOn: An Intelligent Overiay Video Advertising System (Year: 2009). |
Hogue, “Tree Pattern Inference and Matching for Wrapper Induction on the World Wide Web”, Master's Thesis, Massachusetts Institute of Technology, Jun. 2004, pp. 1-106. |
Howlett et al, “A Multi-Computer Neural Network Architecture in a Virtual Sensor System Application”, International joumal of knowledge-based intelligent engineering systems, 4 (2). pp. 86-93, 133N 1327-2314. |
Hua et al., “Robust Video Signature Based on Ordinal Measure”, Image Processing, 2004, 2004 International Conference on Image Processing (ICIP), vol. 1, IEEE, pp. 685-688, 2004. |
International Search Report and Written Opinion for PCT/US2016/050471, ISA/RU, Moscow, RU, dated May 4, 2017. |
International Search Report and Written Opinion for PCT/US2016/054634, ISA/RU, Moscow, RU, dated Mar. 16, 2017. |
International Search Report and Written Opinion for PCT/US2017/015831, ISA/RU, Moscow, RU, dated Apr. 20, 2017. |
Johnson et al, “Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance for Images”, Applied Optics, vol. 33, No. 26,1994, pp. 6239-6253. |
Lau et al., “Semantic Web Service Adaptation Model for a Pervasive Learning Scenario”, 2008 IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, 2008, pp. 98-103. |
Li et al (“Matching Commercial Clips from TV Streams Using a Unique, Robust and Compact Signature” 2005) (Year: 2005). |
Lin et al., “Generating robust digital signature for image/video authentication”, Multimedia and Security Workshop at ACM Multimedia '98, Bristol, U.K., Sep. 1998, pp. 245-251. |
Lu et al, “Structural Digital Signature for Image Authentication: An Incidental Distortion Resistant Scheme”, IEEE Transactions on Multimedia, vol. 5, No. 2, Jun. 2003, pp. 161-173. |
Lyon, “Computational Models of Neural Auditory Processing”, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '84, Date of Conference: Mar. 1984, vol. 9, pp. 41-44. |
Ma Et El “Semantics modeling based image retrieval system using neural networks”, 2005. |
Marian Stewart B et al., “Independent component representations for face recognition”, Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging III, San Jose, California, Jan. 1998, pp. 1-12. |
May et al, “The Transputer”, Springer-Verlag Berlin Heidelberg 1989, vol. 41. |
McNamara et al., “Diversity Decay in opportunistic Content Sharing Systems”, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 1-3. |
Morad et al., “Performance, Power Efficiency and Scalability of Asymmetric Cluster Chip Multiprocessors”, Computer Architecture Letters, vol. 4, Jul. 4, 2005, pp. 1-4, XP002466254. |
Nagy et al, “A Transputer, Based, Flexible, Real-Time Control System for Robotic Manipulators”, UKACC International Conference on Control '96, Sep. 2-5, 1996, Conference Publication No. 427, IEE 1996. |
Natschlager et al., “The “Liquid Computer”: A novel strategy for real-time computing on time series”, Special Issue on Foundations of Information Processing of telematik, vol. 8, No. 1, 2002, pp. 39-43, XP002466253. |
Odinaev et al, “Cliques in Neural Ensembles as Perception Carriers”, Technion—Institute of Technology, 2006 International Joint Conference on neural Networks, Canada, 2006, pp. 285-292. |
Ortiz-Boyer et al, “CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features”, Journal of Artificial Intelligence Research 24 (2005) Submitted Nov. 2004; published Jul. 2005, pp. 1-48. |
Pandya etal. A Survey on QR Codes: in context of Research and Application. International Journal of Emerging Technology and U Advanced Engineering. ISSN 2250-2459, ISO 9001:2008 Certified Journal, vol. 4, Issue 3, Mar. 2014 (Year: 2014). |
Queluz, “Content-Based Integrity Protection of Digital Images”, SPIE Conf. on Security and Watermarking of Multimedia Contents, San Jose, Jan. 1999, pp. 85-93. |
Rui, Yong et al. “Relevance feedback: a power tool for interactive content-based image retrieval.” IEEE Transactions on circuits and systems for video technology 8 5 (1998): 644-655. |
Santos et al., “SCORM-MPEG: an Ontology of Interoperable Metadata for multimediaand E-Learning”, 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCom), 2015, pp. 224-228. |
Scheper et al, “Nonlinear dynamics in neural computation”, ESANN'2006 proceedings—European Symposium on Artificial Neural Networks, Bruges (Belgium), Apr. 26-28, 2006, d-side publication, ISBN 2-930307-06-4, pp. 1-12. |
Schneider et al, “A Robust Content based Digital Signature for Image Authentication”, Proc. ICIP 1996, Lausane, Switzerland, Oct. 1996, pp. 227-230. |
Srihari et al., “Intelligent Indexing and Semantic Retrieval of Multimodal Documents”, Kluwer Academic Publishers, May 2000, vol. 2, Issue 2-3, pp. 245-275. |
Srihari, Rohini K. “Automatic indexing and content-based retrieval of captioned images” Computer 0 (1995): 49-56. |
Stolberg et al (“HIBRID-SOC: A Multi-Core SOC Architecture for Multimedia Signal Processing” 2003). |
Stolberg et al, “HIBRID-SOC: A Mul Ti-Core SOC Architecture for Mul Timedia Signal Processing”, 2003 IEEE, pp. 189-194. |
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop on Parallel and Distributed Processing, 1996. PDP '96, pp. 274-281. |
Vallet et al (“Personalized Content Retrieval in Context Using Ontological Knowledge” Mar. 2007) (Year: 2007). |
Verstraeten et al, “Isolated word recognition with the Liquid State Machine: a case study”, Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium, Available onlline Jul. 14, 2005, pp. 521-528. |
Wang et al., “Classifying Objectionable Websites Based onImage Content”, Stanford University, pp. 1-12. |
Ware et al, “Locating and Identifying Components in a Robot's Workspace using a Hybrid Computer Architecture” Proceedings of the 1995 IEEE International Symposium on Intelligent Control, Aug. 27-29, 1995, pp. 139-144. |
Whitby-Strevens, “The transputer”, 1985 IEEE, pp. 292-300. |
Wilk et al., “The Potential of Social-Aware Multimedia Prefetching on Mobile Devices”, International Conference and Workshops on networked Systems (NetSys), 2015, pp. 1-5. |
Yanagawa et al, “Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts”, Columbia University ADVENT Technical Report # 222-2006-8, Mar. 20, 2007, pp. 1-17. |
Yanagawa et al., “Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts”, Columbia University ADVENT Technical Report #222, 2007, pp. 2006-2008. |
Zhou et al, “Ensembling neural networks: Many could be better than all”, National Laboratory for Novel Software Technology, Nanjing University, Hankou Road 22, Nanjing 210093, PR China, Available online Mar. 12, 2002, pp. 239-263. |
Zhou et al, “Medical Diagnosis With C4.5 Rule Preceded by Artificial Neural Network Ensemble”, IEEE Transactions on Information Technology in Biomedicine, vol. 7, Issue: 1, Mar. 2003, pp. 37-42. |
Zhu et al., “Technology-Assisted Dietary Assesment”, Proc SPIE. Mar. 20, 2008, pp. 1-15. |
Zou et al., “A Content-Based Image Authentication System with Lossless Data Hiding”, ICME 2003, pp. 213-216. |
Number | Date | Country | |
---|---|---|---|
20200269746 A1 | Aug 2020 | US |
Number | Date | Country | |
---|---|---|---|
62827112 | Mar 2019 | US | |
62811134 | Feb 2019 | US |