Santa Clara, CA, United States

Intel Corporation

www.intel.com
Santa Clara, CA, United States

Intel Corporation is an American multinational corporation headquartered in Santa Clara, California. Intel is one of the world's largest and highest valued semiconductor chip makers, based on revenue. It is the inventor of the x86 series of microprocessors, the processors found in most personal computers.Intel Corporation, founded on July 18, 1968, is a portmanteau of Integrated Electronics . Intel also makes motherboard chipsets, network interface controllers and integrated circuits, flash memory, graphic chips, embedded processors and other devices related to communications and computing. Founded by semiconductor pioneers Robert Noyce and Gordon Moore and widely associated with the executive leadership and vision of Andrew Grove, Intel combines advanced chip design capability with a leading-edge manufacturing capability. Though Intel was originally known primarily to engineers and technologists, its "Intel Inside" advertising campaign of the 1990s made it a household name, along with its Pentium processors.Intel was an early developer of SRAM and DRAM memory chips, and this represented the majority of its business until 1981. Although Intel created the world's first commercial microprocessor chip in 1971, it was not until the success of the personal computer that this became its primary business. During the 1990s, Intel invested heavily in new microprocessor designs fostering the rapid growth of the computer industry. During this period Intel became the dominant supplier of microprocessors for PCs, and was known for aggressive and sometimes illegal tactics in defense of its market position, particularly against Advanced Micro Devices , as well as a struggle with Microsoft for control over the direction of the PC industry.The 2013 rankings of the world's 100 most valuable brands published by Millward Brown Optimor showed the company's brand value at number 61.Intel has also begun research into electrical transmission and generation. Intel has recently introduced a 3-D transistor that improves performance and energy efficiency. Intel has begun mass-producing this 3-D transistor, named the Tri-Gate transistor, with their 22 nm process, which is currently used in their 3rd generation core processors initially released on April 29, 2012. In 2011, SpectraWatt Inc., a solar cell spinoff of Intel, filed for bankruptcy under Chapter 11. In June 2013, Intel unveiled its fourth generation of Intel Core processors in an event named Computex in Taipei.The Open Source Technology Center at Intel hosts PowerTOP and LatencyTOP, and supports other open-source projects such as Wayland, Intel Array Building Blocks, Threading Building Blocks , and Xen. Wikipedia.

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Dublin, April 26, 2017 (GLOBE NEWSWIRE) -- Research and Markets has announced the addition of Wintergreen Research, Inc's new report "Internet of Things (IoT) Market Shares, Strategies, and Forecasts 2017 to 2023" to their offering. The study is designed to give a comprehensive overview of the Internet of Things (IoT): market segment. Research represents a selection from the mountains of data available of the most relevant and cogent market materials, with selections made by the most senior analysts. Worldwide Internet of Things (IoT) markets are poised to achieve significant growth with the use of sensors, cameras, and platforms that are used to help implement precision digital control and send alerts for all manner or management of devices and machinery. Visualization and digitization let people better control any device or mechanical thing. Providers of Industrial IoT aim to implement asset efficiency solutions. Designing the asset efficiency solution, developing the application, adapting advanced engineering knowledge for the use cases, and supplying the information platform is the composite task of the analytics engine. IBM is a premier supplier of an analytics engine with its Watson product. There is enormous variety in the Internet of things markets. Bosch supplies industrial IoT sensor technology, acquiring data from the edge, providing device management. Scalability is achieved by the Bosch IoT Suite and ProSyst IoT middleware. The Vorto code generator enables M2M modelling. PTC supplies the Thingworx Application Enablement Platform (AEP), used for creating dashboards, widgets and other user interface elements. Intel provides the Moon Island Gateway used for data aggregation at the edge, as well as horizontal infrastructure in collaboration with HP. Key Topics Covered: INTERNET OF THINGS (IOT) EXECUTIVE SUMMARY - Internet of Things (IoT) Market Driving Forces - IoT Technology Market Driving Forces - IoT Technology Market Challenges - Internet of Things (IoT) Market Shares - Internet of Things (IoT) Market Forecasts - IoT Market Opportunity Huge 1. INTERNET OF THINGS (IOT): MARKET DESCRIPTION AND MARKET DYNAMICS 1.1 IoT Sensor Types 1.2 Internet of Things (IoT) Based on Standards 1.3 With IoT, APIs Are Used for Everything 1.4 Internet of Things Revolution Dramatically Alters the Economy 2. INTERNET OF THINGS (IOT) MARKET SHARES AND FORECASTS 2.1 Internet of Things (IoT) Market Driving Forces 2.2 Internet of Things (IoT) Market Shares 2.3 Internet of Things (IoT) Market Forecasts 2.4 Internet of Things Market Segments: Security and Energy Management, Healthcare, Transportation and Self Driving Cars , Agriculture and Weather, Financial, Industrial and Manufacturing 2.5 Security and Energy Management Internet of Things Market 2.6 Healthcare 2.7 Self Driving Cars / Connected Cars / Transportation 2.8 Agricultural and Weather IoT 2.9 Industrial IoT 2.10 Financial Internet of Things Market Segment 2.11 IoT chipsets 2.12 IoT Data Use Forecasts 2.13 Mid IR Sensor Market Forecasts 2.14 Internet of Things (IoT) Regional Analysis 3. INTERNET OF THINGS IOT PRODUCT DESCRIPTION 3.1 IBM 3.2 Intel 3.3 Microsoft IoT 3.4 Hewlett HP IoT 3.5 Apple 3.6 Google 3.7 Cisco 3.8 Samsung 3.9 AutoDesk 3.10 Zebra 3.11 SAP 3.12 Siemens 3.13 Bosch Software Innovation 3.14 Huawei Technologies 3.15 Harman International Industries (ADITI Technologies) 3.16 Enevo Oy Technologies 3.17 Infineon Technologies 3.18 Symantec Corporation 3.19 Schneider Electric Software, Llc. 3.20 Apple IoT 3.21 AT&T 3.22 Softbank 3.23 Uber 3.24 oneM2M 3.25 Symantec / Norton Core Router 3.26 Kaptivo 3.27 Oracle 3.28 Schlage IoT Devices 3.29 AGCO 3.30 Alibaba Group in Shanghai 3.31 Essence 4. INTERNET OF THINGS (IOT) RESEARCH AND TECHNOLOGY 4.1 Internet of Things (IoT) Research and Technology 4.2 IoT Common Standards 4.3 Edge Computing 4.4 European Union Research & Innovation 4.5 Wearable Technology 4.6 Blockchain 4.7 Connected Home Camera Technology 4.8 IFTTT 4.9 Wireless Communication Standards 4.10 IBM and Texas Instruments Collaboratively Develop Lifecycle-Management for IoT Devices 5. INTERNET OF THINGS (IOT) COMPANY PROFILES 5.1 Aerialtronics 5.2 Adobe 5.3 Amazon 5.4 Apple 5.5 AutoDesk 5.6 Bosch 5.7 Cisco Systems 5.8 Digi International 5.9 Cybus 5.10 Enevo Oy Technologies 5.11 Essence 5.12 General Electric 5.13 Google 5.14 Health Slam - IoT Slam 5.15 Huawei 5.16 IBM Corporation 5.17 Infineon Technologies AG 5.18 Intel Corporation 5.19 Internet of Things Community 5.20 KT 5.21 Microsoft 5.22 Microsoft 5.23 MuleSoft 5.24 Nokia 5.25 oneM2M 5.26 Panoramic Power 5.27 Oracle 5.28 PTC 5.29 Qualcomm 5.30 Samsung 5.31 SAP 5.32 Schaeffler 5.33 Sierra Wireless Business and Innovation Development 5.34 Sigfox 5.35 Softbank 5.36 Spirent 5.37 STMicroelectronics 5.38 Symantec 5.39 Schneider Electric Software, Llc. 5.40 Uber 5.41 UIB 5.42 Zebra 5.43 ZTE 5.44 Appendix A: Selected IoT Market Participants For more information about this report visit http://www.researchandmarkets.com/research/35xb8h/internet_of


News Article | April 24, 2017
Site: marketersmedia.com

The Insight Partners: Increasing focus on security and privacy concerns will raise IoT Security Market by 2025 with a CAGR of 17.6%. April 24, 2017 /MarketersMedia/ — The “IoT Security Market to 2025 by Types (Network Security, End-point Security, Cloud Security, Application Security and Others), Solution (Threat Analysis, Identity Access Management, Data Loss Protection, Encryption, Dispatch & Incident Response, Distributed Denial of Service Protection and Others) and Application (Smart Home, Connected Car, Information & Communication Technology, Smart Factories, BFSI, Smart Retail, Smart Healthcare, Smart Transportation, Wearable and Others) – Global Analysis and Forecast” The scope of study involves understanding on the factors responsible for this growth of IoT Security market along with the estimates and forecasts in terms of revenue and market driving and restraining factors and also spots the significant IoT security players in the market and their key developments. Browse market data tables and in-depth TOC of the Global IoT Security Market to 2025 @ http://www.theinsightpartners.com/reports/iot-security-market Early buyers will receive 10% customization on reports. IoT Security Market to 2025 – Global Analysis and Forecast by type, solution and application, IoT security market is expected to reach US$ 30.9 billion by 2025 from US$ 7.28 billion in 2016. IoT security is the area concerned with protecting internet connected devices and data in both rest and motion. The IoT includes use of different semiconductor technologies, including power management devices, sensors and microprocessors. Performance and security necessities vary considerably from one application to another. Some of the remarkable partnership and collaboration in this industry are collaboration of Gemalto and Microsoft to provide seamless connectivity for Windows 10 devices. Also, In March 2017, IBM and Salesforce announced a global strategic partnership to deliver joint solutions designed to leverage artificial intelligence. Besides this in September 2016, Intel made two significant partnership announcement, first was Intel collaboration with Lenovo to bring secure online payments from the personal computers. Other participant in this partnership were PayPal, and Synaptics. And the second important collaboration was a definitive agreement with TPG under which the two parties will establish a newly formed, jointly-owned, independent cyber security company. The new company will be called McAfee following transaction close. Request Sample Copy @ http://www.theinsightpartners.com/sample/TIPTE100000288 The global IoT Security market by geography is segmented into six region including North America, Europe, Asia Pacific, Middle East, Africa and South America. North America being largest adopter of IoT enabled devices and network account for the largest share of the global IoT Security market in 2016, followed by Europe. Whereas, APAC are expected to have highest growth rate during the forecast period from 2017 to 2025. The report profiles key players such Cisco Systems, Inc., IBM Corp., Infineon Technologies, Intel Corporation, Symantec Corporation, ARM Holdings, NXP Semiconductor, INSIDE Secure, Gemalto NV, Trend Micro, Inc., Synopsys, ESCRYPT, Palo Alto Networks, Inc. and Microchip Technologies Inc. are among others. Inquire about discount on this report @ http://www.theinsightpartners.com/discount/TIPTE100000288 The report segments the global IoT Security market as follows: Global IoT Security Market – By Type • Network Security • End-point Security • Cloud Security • Application Security • Others Global IoT Security Market – By Solution • Threat Analysis • Identity Access Management • Data Loss Protection • Encryption • Dispatch and Incident Response • Distributed Denial of Service Protection • Others Global IoT Security Market – By Application • Smart Home • Connected Car • Information & Communication Technology • Smart Factories • BFSI • Smart Retail • Smart Healthcare • Smart Transportation • Wearable • Others Global IoT Security Market – By Geography • North America • Europe • Asia Pacific (APAC) • Middle East & Africa (MEA) • South America (SAM) Access Full Report @ http://www.theinsightpartners.com/buy/TIPTE100000288 About The Insight Partners: The Insight Partners is a one stop industry research provider of actionable intelligence. We help our clients in getting solutions to their research requirements through our syndicated and consulting research services. We are a specialist in Technology, Media, and Telecommunication industries. Contact Info:Name: Sameer JoshiEmail: sales@theinsightpartners.comOrganization: The Insight PartnersAddress: Pune, IndiaPhone: +1-646-491-9876Source URL: http://marketersmedia.com/global-iot-security-market-to-grow-at-a-cagr-of-17-6-by-2025-the-insight-partners/189389For more information, please visit http://www.theinsightpartners.com/Source: MarketersMediaRelease ID: 189389


News Article | May 5, 2017
Site: cleantechnica.com

Intel’s new Advanced Vehicle Lab in Silicon Valley, a “cutting edge” facility dedicated to the development of self-driving vehicle tech (amongst other things), was recently unveiled by the company. Intel’’s Kathy Winter (from left), Doug Davis, and Patti Robb cut the entrance ribbon, officially opening Intel’’s Silicon Valley Center for Autonomous Driving in San Jose, California, to the public on Wednesday, May 3, 2017. (Credit: Intel Corporation) The new facility will join Intel’s other related facilities located in Arizona, Oregon, and Germany. These facilities were created specifically to aid the company in its further entrance into the rapidly growing “next gen transportation” sector — which encompasses self-driving vehicles, vehicle-to-vehicle communication, AI, connectivity, sensor technology and processing, etc. The press release provides more: “With the slew of information captured by cameras, LIDAR, RADAR, and other sensors, autonomous cars are expected to generate approximately 4 terabytes of data every 90 minutes of operation. Most of this data will be processed, filtered, and analyzed in the car, while the most valuable data will be moved to the data center to update maps, enhance data models and more. “Intel’s Autonomous Garage Labs work with customers and partners to come up with new ways of addressing the data challenge inside the vehicle, across the network and in the data center. Engineers at the labs use a variety of tools to advance and test in these areas, including vehicles equipped with Intel-based computing systems and different kinds of sensors that help gather data; autonomous test vehicles that practice real-world driving; partner vehicles and teams that are collaborating with Intel’s research efforts; and dedicated autonomous driving data centers.” The news follows earlier reports about Intel acquiring the Israel-based self-driving vehicle tech pioneer Mobileye for $15 billion, and it seemingly represents another sign of the company’s expectation that the self-driving vehicle sector is approaching a boom period. Keep up to date with all the hottest cleantech news by subscribing to our (free) cleantech daily newsletter or weekly newsletter, or keep an eye on sector-specific news by getting our (also free) solar energy newsletter, electric vehicle newsletter, or wind energy newsletter. James Ayre 's background is predominantly in geopolitics and history, but he has an obsessive interest in pretty much everything. After an early life spent in the Imperial Free City of Dortmund, James followed the river Ruhr to Cofbuokheim, where he attended the University of Astnide. And where he also briefly considered entering the coal mining business. He currently writes for a living, on a broad variety of subjects, ranging from science, to politics, to military history, to renewable energy. You can follow his work on Google+.


Patent
Intel Corporation | Date: 2017-01-24

Technologies for assisting vehicles with changing road conditions includes vehicle assistance data based on crowd-sourced road data received from a plurality of vehicles and/or infrastructure sensors. The crowd-sourced road data may be associated with a particular section of roadway and may be used to various characteristics of the roadway such as grade, surface, hazardous conditions, and so forth. The vehicle assistance data may be provided to an in-vehicle computing device to assist or facilitate traversal of the roadway.

Claims which contain your search:

1. One or more machine readable storage media comprising a plurality of instructions stored thereon that, when executed, cause an in-vehicle computing system of a first vehicle to: transmit vehicle profile information indicative of at least one characteristic of the first vehicle to a vehicle assistance server while the first vehicle is located on a first road segment; receive vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment; determine at least one vehicle control command based on the received vehicle assistance data; and adjust a vehicle parameter of the first vehicle based on the vehicle control command.

2. The one or more machine readable storage media of claim 1, wherein the plurality of instructions, when executed, further cause the in-vehicle computing system to generate, in response to receipt of the vehicle assistance data, a notification for a driver of the first vehicle that includes information related to the vehicle assistance data.

3. The one or more machine readable storage media of claim 1, wherein to transmit vehicle profile information comprises to: sense vehicle operational data indicative of an operation of the first vehicle while the first vehicle traverses the first road segment; and transmit the vehicle operational data and vehicle identification data indicative of at least one permanent characteristic of the first vehicle.

4. The one or more machine readable storage media of claim 1, wherein to: receive vehicle assistance data comprises to receive cruise control data, and adjust the vehicle parameter comprises to adjust a throttle of an engine of the first vehicle based on the cruise control data.

5. The one or more machine readable storage media of claim 1, wherein to receive vehicle assistance data comprises to: receive, from the vehicle assistance server, refueling prediction data that estimates a distance the first vehicle can travel before a refueling is required, and wherein the plurality of instructions, when executed, further cause the in-vehicle computing system to adjust a refueling estimate for the first vehicle based on the refueling prediction data.

6. The one or more machine readable storage media of claim 1, wherein to receive vehicle assistance data comprises to receive, from the vehicle assistance server, road condition data indicative of one or more road conditions of the first road segment, and wherein the plurality of instructions, when executed, further cause the in-vehicle computing system to generate a notification to inform a driver of the first vehicle about the one or more road conditions.

7. The one or more machine readable storage media of claim 6, wherein to receive road condition data comprises to receive, from the vehicle assistance server, road grade data indicative of a vertical change in elevation along the first road segment.

8. A method for assisting a driver of a vehicle, the method comprising: transmitting, by an in-vehicle computing system of a first vehicle, vehicle profile information indicative of at least one characteristic of the first vehicle to a vehicle assistance server while the first vehicle is located on a first road segment; receiving, by the in-vehicle computing system, vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment; determining, by the in-vehicle computing system, at least one vehicle control command based on the received vehicle assistance data; and adjusting, by the in-vehicle computing system, a vehicle parameter of the first vehicle based on the vehicle control command.

9. The method of claim 8, further comprising generating, by the in-vehicle computing system and in response to receiving the vehicle assistance data, a notification for a driver of the first vehicle that includes information related to the vehicle assistance data.

10. The method of claim 8, wherein transmitting vehicle profile information comprises: sensing, by the in-vehicle computing system, vehicle operational data indicative of an operation of the first vehicle while the first vehicle traverses the first road segment; and transmitting, by the in-vehicle computing system, the vehicle operational data and vehicle identification data indicative of at least one permanent characteristic of the first vehicle.

11. The method of claim 8, wherein: receiving vehicle assistance data comprises receiving, by the in-vehicle computing system, cruise control data, and adjusting the vehicle parameter comprises adjusting, by the in-vehicle computing system, a throttle of an engine of the first vehicle based on the cruise control data.

12. The method of claim 8, wherein receiving vehicle assistance data comprises: receiving, by the in-vehicle computing system and from the vehicle assistance server, refueling prediction data that estimates a distance the first vehicle can travel before a refueling is required, and further comprising adjusting, by the in-vehicle computing system, a refueling estimate for the first vehicle based on the refueling prediction data.

13. The method of claim 8, wherein receiving vehicle assistance data comprises receiving, by the in-vehicle computing system and from the vehicle assistance server, road condition data indicative of one or more road conditions of the first road segment, and further comprising generating, by the in-vehicle computing system, a notification to inform a driver of the first vehicle about the one or more road conditions.

14. The method of claim 13, wherein receiving road condition data comprises receiving, by the in-vehicle computing system and from the vehicle assistance server, road grade data indicative of a vertical change in elevation along the first road segment.

15. An in-vehicle computing system for assisting a driver of a first vehicle, the in-vehicle computing system comprising: a vehicle profile module to (i) sense vehicle operational data indicative of an operation of the first vehicle while the first vehicle traverses a first road segment, (ii) acquire vehicle identification data indicative of at least one permanent characteristic of the first vehicle, and (ii) transmit the vehicle operational data and the vehicle identification data to a vehicle assistance server while the first vehicle is located on the first road segment; a vehicle output module to (i) receive vehicle assistance data from the vehicle assistance server, wherein the vehicle assistance data is generated based on the vehicle profile information and crowd-sourced road data associated with the first road segment, (ii) determine at least one vehicle control command based on the received vehicle assistance data, and (iii) adjust a vehicle parameter of the first vehicle based on the vehicle control command.

16. The in-vehicle computing system of claim 15, further comprising a notification module to generate, in response to receiving the vehicle assistance data, a notification for a driver of the first vehicle that includes information related to the vehicle assistance data.

17. The in-vehicle computing system of claim 15, wherein the vehicle output module is to: receive cruise control data, and adjust the vehicle parameter comprises adjusting, by the in-vehicle computing system, a throttle of an engine of the first vehicle based on the cruise control data.

18. The in-vehicle computing system of claim 15, wherein the vehicle output module is to: receive refueling prediction data that estimates a distance the first vehicle can travel before a refueling is required, and adjust a throttle of an engine of the first vehicle based on the cruise control data.

19. The in-vehicle computing system of claim 15, wherein the vehicle output module is to: receive road condition data, from the vehicle assistance server, indicative of one or more road conditions of the first road segment; and generate a notification to inform a driver of the first vehicle about the one or more road conditions.

20. The in-vehicle computing system of claim 19, wherein the vehicle output module is to receive road grade data indicative of a vertical change in elevation along the first road segment from the vehicle assistance server.


Methods, systems, and storage media are described for assisting the operation of a first vehicle. In embodiments, a computing device of the first vehicle may obtain first sensor data from a first sensor of the first vehicle. The first sensor data may be representative of a second vehicle proximate to the first vehicle. The computing device may determine a first position of the second vehicle relative to the first vehicle; initiate a vehicle-to-vehicle ( V2V ) communications session with the second vehicle; receive second sensor data from the second vehicle during the V2V communications session; and determine a second position based on the second sensor data. The second position may be a position of the second vehicle relative to a third vehicle. The computing device may display an image of the third vehicle on a display device. Other embodiments may be described and/or claimed.

Claims which contain your search:

1. A computing device for assisting a first vehicle, the computing device comprising: device interface circuitry to obtain first sensor data from a sensor in or on the first vehicle, wherein the first sensor data is representative of a second vehicle proximate to the first vehicle; and a detection module to be operated by at least one processor, the detection module to obtain the first sensor data from the device interface circuitry; determine based on the first sensor data, a position of the second vehicle relative to the first vehicle; in response to a determination of the position, initiate a vehicle-to-vehicle (V2V) communications session with the second vehicle; and receive second sensor data from the second vehicle during the V2V communications session.

2. The computing device of claim 1, wherein the detection module is to poll, on a periodic basis, the sensor via the device interface circuitry to obtain the first sensor data.

3. The computing device of claim 1, wherein the detection module is to determine, based on the position, whether the second vehicle is within a threshold distance from the first vehicle.

4. The computing device of claim 3, wherein the detection module is to dynamically adjust the threshold distance based on a size of the second vehicle and a field of view of the sensor.

5. The computing device of claim 3, wherein the detection module is to determine whether the second vehicle is within the threshold distance from the first vehicle for a desired period of time, and detection module is to initiate the V2V communications session when the detection module determines that the second vehicle is within the threshold distance from the first vehicle for the desired period of time.

6. The computing device of claim 1, wherein the sensor is a first sensor, and the second sensor data is to be captured by a second sensor on or in the second vehicle, and wherein the second sensor data are raw data captured by the second sensor.

7. The computing device of claim 1, wherein the sensor is a first sensor and the position is a first position, and wherein the detection module is to obtain the second sensor data from a second sensor on or in the second vehicle or obtain position data based on a second position of a third vehicle relative to the second vehicle derived based on the second sensor data, and the computing device further comprises: an image generator, to be operated by the at least one processor, the image generator to obtain the second sensor data or the position data associated with the second position of the third vehicle, and generate one or more images based on the second sensor data or the position data associated with the second position of the third vehicle; and a display module, to be operated by the at least one processor, the display module to provide the one or more images to be displayed in a display device of the first vehicle.

8. The computing device of claim 7, wherein the display device is a head-up display (HUD) integrated into a windshield of the first vehicle.

9. The computing device of claim 7, wherein the first sensor is arranged in the first vehicle such that a field of view of the first sensor is in front of the first vehicle, and the second sensor is arranged in the second vehicle such that a field of view of the second sensor is in front of the second vehicle.

10. The computing device of claim 9, wherein the device interface module is to obtain the first sensor data from the first sensor during operation of the first vehicle.

11. The computing device of claim 9, wherein the second sensor data is representative of at least the third vehicle to be detected by the second sensor, and wherein on receipt of the second sensor data, the detection module is to determine, based on the second sensor data, the second position of the third vehicle relative to the second vehicle, and the detection module is to further determine, based on the second position, a third position wherein the third position is a position of the third vehicle relative to the first vehicle.

12. The computing device of claim 11, wherein to determine the third position the detection module is to combine the first position with the second position.

13. The computing device of claim 11, wherein to generate the one or more images, the image generator is to generate an image representative of the third vehicle, and the display module is to map the third position to a corresponding region of the display device such that the image representative of the third vehicle is to be displayed in the corresponding region.

14. The computing device of claim 1, wherein the first vehicle includes the computing device, the first sensor, radio frequency (RF) circuitry, and baseband circuitry, wherein the baseband circuitry is coupled with the RF circuitry to initiate the V2V communications session with the second vehicle, and receive the second sensor data from the second vehicle during the V2V communications session.

15. At least one computer-readable medium including instructions that, when executed by one or more processors of a computing device associated with a first vehicle, cause the computing device to: obtain first sensor data from a sensor on or in the first vehicle, wherein the first sensor data is representative of a second vehicle proximate to the first vehicle; determine based on the first sensor data, a first position of the second vehicle relative to the first vehicle; initiate a vehicle-to-vehicle (V2V) communications session with the second vehicle; receive second sensor data from the second vehicle during the V2V communications session; determine a second position based on the second sensor data, wherein the second position is a position of the second vehicle relative to a third vehicle detected by a second sensor of the second vehicle; and display an image representative of the third vehicle on a display device associated with the computing device.

16. The at least one computer-readable medium of claim 15, wherein the instructions, when executed by the one or more processors, cause the computing device to poll the sensor on a periodic basis to obtain the first sensor data.

17. The at least one computer-readable medium of claim 15, wherein the instructions, when executed by the one or more processors, cause the computing device to: determine, based on the first position, whether the second vehicle is within a threshold distance from the first vehicle.

18. The at least one computer-readable medium of claim 17, wherein the instructions, when executed by the one or more processors, cause the computing device to dynamically adjust the threshold distance based on a size of the second vehicle and a field of view of the sensor.

19. The at least one computer-readable medium of claim 17, wherein the instructions, when executed by the one or more processors, cause the computing device to: determine whether the second vehicle is within the threshold distance from the first vehicle for a desired period of time, and wherein the instructions, when executed by the one or more processors, cause the computing device to initiate the V2V communications session when the second vehicle is determined to be within the threshold distance from the first vehicle for the desired period of time.

20. The at least one computer-readable medium of claim 15, wherein the second sensor data is raw sensor data captured by a second sensor on or in the second vehicle.

21. The at least one computer-readable medium of claim 15, wherein the second sensor data is representative of at least a third vehicle to be detected by the second sensor, and the instructions, when executed by the one or more processors, cause the computing device to: determine, based on the second position, a third position wherein the third position is a position of the third vehicle relative to the first vehicle, and wherein to determine the third position, the instructions, when executed by the one or more processors, cause the computing device to combine the first position with the second position.

22. The at least one computer-readable medium of claim 21, wherein to display the image, the instructions, when executed by the one or more processors, cause the computing device to map the third position to a corresponding region of a display device such that the image representative of the third vehicle is to be displayed in the corresponding region.

23. A method for assisting a first vehicle, the method comprising: obtaining, by a computing device, sensor data from a sensor associated with a second vehicle, wherein the sensor data is representative of a third vehicle that is to approach the second vehicle in an opposite direction of travel; and providing, by the computing device, the sensor data or position data associated with the position of the third vehicle to the first vehicle during a vehicle-to-vehicle (V2V) communications session with the first vehicle.

24. The method of claim 23, further comprising: receiving, by the computing device, a message that is to initiate the V2V communications session between the second vehicle and the first vehicle; and providing, by the computing device, the sensor data to the first vehicle in response to receipt of the message.

25. The method of claim 23, wherein the first vehicle is to travel in a same direction of travel as the second vehicle and the second vehicle is to travel in front of the first vehicle.


Grant
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-16-2015 | Award Amount: 4.60M | Year: 2016

Cloud-LSVA will create Big Data Technologies to address the open problem of a lack of software tools, and hardware platforms, to annotate petabyte scale video datasets. The problem is of particular importance to the automotive industry. CMOS Image Sensors for Vehicles are the primary area of innovation for camera manufactures at present. They are the sensor that offers the most functionality for the price in a cost sensitive industry. By 2020 the typical mid-range car will have 10 cameras, be connected, and generate 10TB per day, without considering other sensors. Customer demand is for Advanced Driver Assistance Systems (ADAS) which are a step on the path to Autonomous Vehicles. The European automotive industry is the world leader and dominant in the market for ADAS. The technologies depend upon the analysis of video and other vehicle sensor data. Annotations of road traffic objects, events and scenes are critical for training and testing computer vision techniques that are the heart of modern ADAS and Navigation systems. Thus, building ADAS algorithms using machine learning techniques require annotated data sets. Human annotation is an expensive and error-prone task that has only been tackled on small scale to date. Currently no commercial tool exists that addresses the need for semi-automated annotation or that leverages the elasticity of Cloud computing in order to reduce the cost of the task. Providing this capability will establish a sustainable basis to drive forward automotive Big Data Technologies. Furthermore, the computer is set to become the central hub of a connected car and this provides the opportunity to investigate how these Big Data Technologies can be scaled to perform lightweight analysis on board, with results sent back to a Cloud Crowdsourcing platform, further reducing the complexity of the challenge faced by the Industry. Car manufacturers can then in turn cyclically update the ADAS and Mapping software on the vehicle benefiting the consumer.


Grant
Agency: GTR | Branch: EPSRC | Program: | Phase: Research Grant | Award Amount: 4.56M | Year: 2016

Today we use many objects not normally associated with computers or the internet. These include gas meters and lights in our homes, healthcare devices, water distribution systems and cars. Increasingly, such objects are digitally connected and some are transitioning from cellular network connections (M2M) to using the internet: e.g. smart meters and cars - ultimately self-driving cars may revolutionise transport. This trend is driven by numerous forces. The connection of objects and use of their data can cut costs (e.g. allowing remote control of processes) creates new business opportunities (e.g. tailored consumer offerings), and can lead to new services (e.g. keeping older people safe in their homes). This vision of interconnected physical objects is commonly referred to as the Internet of Things. The examples above not only illustrate the vast potential of such technology for economic and societal benefit, they also hint that such a vision comes with serious challenges and threats. For example, information from a smart meter can be used to infer when people are at home, and an autonomous car must make quick decisions of moral dimensions when faced with a child running across on a busy road. This means the Internet of Things needs to evolve in a trustworthy manner that individuals can understand and be comfortable with. It also suggests that the Internet of Things needs to be resilient against active attacks from organised crime, terror organisations or state-sponsored aggressors. Therefore, this project creates a Hub for research, development, and translation for the Internet of Things, focussing on privacy, ethics, trust, reliability, acceptability, and security/safety: PETRAS, (also suggesting rock-solid foundations) for the Internet of Things. The Hub will be designed and run as a social and technological platform. It will bring together UK academic institutions that are recognised international research leaders in this area, with users and partners from various industrial sectors, government agencies, and NGOs such as charities, to get a thorough understanding of these issues in terms of the potentially conflicting interests of private individuals, companies, and political institutions; and to become a world-leading centre for research, development, and innovation in this problem space. Central to the Hub approach is the flexibility during the research programme to create projects that explore issues through impactful co-design with technical and social science experts and stakeholders, and to engage more widely with centres of excellence in the UK and overseas. Research themes will cut across all projects: Privacy and Trust; Safety and Security; Adoption and Acceptability; Standards, Governance, and Policy; and Harnessing Economic Value. Properly understanding the interaction of these themes is vital, and a great social, moral, and economic responsibility of the Hub in influencing tomorrows Internet of Things. For example, a secure system that does not adequately respect privacy, or where there is the mere hint of such inadequacy, is unlikely to prove acceptable. Demonstrators, like wearable sensors in health care, will be used to explore and evaluate these research themes and their tension. New solutions are expected to come out of the majority of projects and demonstrators, many solutions will be generalisable to problems in other sectors, and all projects will produce valuable insights. A robust governance and management structure will ensure good management of the research portfolio, excellent user engagement and focussed coordination of impact from deliverables. The Hub will further draw on the expertise, networks, and on-going projects of its members to create a cross-disciplinary language for sharing problems and solutions across research domains, industrial sectors, and government departments. This common language will enhance the outreach, development, and training activities of the Hub.


News Article | February 21, 2017
Site: www.businesswire.com

HILLSBORO, Ore.--(BUSINESS WIRE)-- To maximize 5G mobile service opportunities across China Unicom’s network, services acceleration company Radisys® Corporation (NASDAQ:RSYS) and China Unicom, one of the world’s largest mobile service providers, are partnering to build and integrate Mobile CORD (M-CORD) development PODs that use open source software. M-CORD, built on top of ONOS and the CORD (Central Office Re-architected as a Datacenter) open source project, combines data center economics and the agility of the cloud with the benefits of a virtualized and disaggregated mobile core, as well as access infrastructure and mobile edge computing for innovation and deployment of 5G services. The companies will work together to develop deployment scenarios for this solution in China Unicom’s network. “As China Unicom evolves its network from 4G to 5G, it requires a fully virtualized and disaggregated infrastructure to better serve its residential, enterprise and mobile customers,” said Joseph Sulistyo, senior director of open networking solutions, Radisys. “We’re confident that together we can build and integrate an M-CORD POD to facilitate a robust 5G mobility infrastructure, thereby enabling China Unicom to rapidly deploy new 5G services on its network.” As a CORD systems integrator, Radisys will leverage CORD’s open reference implementation to bring China Unicom’s network cloud agility and improved economics. M-CORD is an emerging platform for 5G services with three key components: mobile edge services, virtualized Radio Access Network (RAN), and virtualized Evolved Packet Core (EPC). M-CORD makes mobility more cost-efficient, more software-programmable and more cloud-elastic. “China Unicom is collaborating with Radisys to collectively develop technology enablers and use cases for 5G services,” said Dr. Tang Xiongyan, CTO, Network Technology Research Institute, China Unicom. “Radisys will also provide us with its recommended deployment scenarios and feasibility analysis as well as an effective prototype demonstration of the solution in our mobile network. We’re encouraged that its efforts will yield great results for our subscribers.” Additionally, Radisys and China Unicom will develop together an open reference implementation of a virtualized RAN (vRAN) and an open reference implementation of next-generation mobile core architecture with observability and analytics. The companies will also explore opportunities for projects surrounding mobile edge computing (MEC), virtualization technology for connected cars and service development and demonstration, and connectionless architecture. See the M-CORD Demonstration at Mobile World Congress Radisys and ON.Lab will demonstrate M-CORD use cases running on commodity hardware and on DCEngine™ at Mobile World Congress, February 27-March 2 in its Booth 5I61 in Hall 5. To see the demonstration and meet with Radisys’ CORD and open source experts, contact info@radisys.com. CORD™ (Central Office Re-architected as a Datacenter) brings datacenter economics and cloud flexibility to the telco Central Office and to the entire access network. CORD is an open source service delivery platform that combines SDN, NFV, and elastic cloud services to network operators and service providers. It integrates ONOS, OpenStack, Docker, and XOS—all running on merchant silicon, white-box switches, commodity servers, and disaggregated access devices. The CORD reference implementation serves as a platform for multiple domains of use, with open source communities building innovative services for residential, mobile, and enterprise network customers. The CORD ecosystem comprises ON.Lab and organizations that are funding and contributing to the CORD initiative. These organizations include AT&T, China Unicom, Google, NTT Communications Corp., SK Telecom Co. Ltd., Verizon, Ciena Corporation, Cisco Systems, Inc., Fujitsu Ltd., Intel Corporation, NEC Corporation, Nokia, Radisys, and Samsung Electronics Co. See the full list of members, including CORD’s collaborators, and learn how you can get involved with CORD at www.opencord.org. CORD is an independently funded software project hosted by The Linux Foundation, the nonprofit advancing professional open source management for mass collaboration to fuel innovation across industries and ecosystems. China Unicom was officially established on 15 October 2008, and is committed to forging itself into a world-leading innovative ICT service provider. As of 2015, China Unicom is ranked 207 among the Fortune Global 500. As a leading operator providing both fixed-line and mobile services in China, the subscriber number of fixed service and mobile service is more than 300 million, and its service covers 31 provincial branches and 7 overseas operations. It has about 400,000 employees. For more information about China Unicom, please visit http://chinaunicom.com.cn/. Radisys helps communications and content providers, and their strategic partners, create new revenue streams and drive cost out of their services delivery infrastructure. Radisys’ hyperscale software defined infrastructure, service aware traffic distribution platforms, real-time media processing engines and wireless access technologies enable its customers to maximize, virtualize and monetize their networks. For more information about Radisys please visit www.radisys.com. Radisys® is a registered trademark of Radisys. All other trademarks are the property of their respective owners.


BARCELONA, Spain--(BUSINESS WIRE)--HARMAN (NYSE: HAR), the premier connected technologies company for the automotive, consumer and enterprise markets, and Intel Corporation today announced HARMAN® Quick Predict, an end-to-end industrial Internet of Things (IoT) solution that provides early detection of problems with rotating equipment in industrial settings. HARMAN’s leading engineering services team developed Quick Predict based on a vibration analytical algorithm developed by Intel. Unlike other systems, HARMAN® Quick Predict generates predictions based on real-time information instead of historical data collected over time. Using advanced machine learning algorithms, the solution enables prediction of potential failure based on capture and analysis of abnormal vibration patterns at the gateway, which are detected and flagged. The collaboration uses a vibration analytical algorithm developed by Intel and HARMAN Connected Services’ Mobile and Communications Services’ team to deliver an operational product. With HARMAN® Quick Predict, enterprises are able to increase equipment uptime, decrease spare parts costs, and optimize the use of their workforce by reducing the number of emergency repairs. The solution collects the high-resolution vibration data needed to detect problems early and provides learning analytics that help map abnormal vibration and rotation speed patterns to associated failure mechanisms. Rotating equipment maintenance is expensive and resource intensive. Even with full spare parts and machinery in place, a pump failure on a line can cause costly production delays leading to emergency work orders and hurried scheduling of maintenance crews. Spot manual vibration readings collected under preventative maintenance programs on a weekly or monthly basis by technicians simply do not provide the data needed to identify all problems early enough to allow for planned repair. According to Sandip Ranjhan, senior vice president, HARMAN Connected Services, “HARMAN is committed to partnering with the best-of-the-best to help grow the IoT ecosystem ensuring enterprise customers have well integrated products and solutions for their next generation technology deployments. With the help of Intel® technology, we have developed HARMAN® Quick Predict, a product needed by the thousands of companies looking for sophisticated predictive maintenance solutions. “We originally designed the vibration analytical algorithm for our own fabrication plants to improve maintenance and uptime. Working with HARMAN Connected Services, the Intel® technology is now commercially available to the growing number of enterprises that need to enhance their IoT deployments with a solid solution that helps increase productivity and save costs by preventing full failure,” said Chet Hullum, general manager, Industrial Solutions Division at Intel. HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and connected services With leading brands including AKG®, Harman Kardon®, Infinity®, JBL®, Lexicon®, Mark Levinson® and Revel®, HARMAN is admired by audiophiles, musicians and the entertainment venues where they perform around the world. More than 25 million automobiles on the road today are equipped with HARMAN audio and connected car systems. The Company’s software services power billions of mobile devices and systems that are connected, integrated and secure across all platforms, from work and home to car and mobile. HARMAN has a workforce of approximately 30,000 people across the Americas, Europe, and Asia and reported sales of $7.2 billion during the 12 months ended December 31, 2016. The Company’s shares are traded on the New York Stock Exchange under the symbol NYSE:HAR. Intel and the Intel logo are trademarks of Intel Corporation in the United States and other countries.


News Article | February 16, 2017
Site: www.prnewswire.co.uk

Internet of Things (IoT) enables physical and virtual objects to connect with each other via cloud technology and exchange data and information. With rapid technological advancements and increasing dependence on technology, it is evident that the IoT concept has a promising future. The increased research and development (R&D) in the field of IoT in terms of new and improved technologies and the increasing need for improved lifestyle are the two crucial factors driving the IoT chip market. Several companies and organizations across the globe, especially the large firms operating in the technology sector, are now working on IoT and plan to expand in this market space. "According to Mr. Sachin Garg - Associate Director at MarketsandMarkets who tracks the global semiconductor market, the global IoT chip market is expected to grow at a CAGR of 13.2% during the forecast period, to reach USD 14.81 Billion by 2022 from USD 5.75 Billion in 2015". Given the rate of proliferation of the network of wireless sensors, increasing adoption of emerging technologies, and mainstreaming of many smart consumer applications, IoT has gained popularity across domains. IoT has opened up a huge opportunity for the growth of semiconductor companies. Billions of devices are expected to be connected to the Internet by 2020, and the growing IoT market would drive the IoT chip market. Early buyers will receive 10% customization on this report. Retail is likely to be the fastest adopter IoT chips, while Transportation and Automotive held largest market share in the IoT chip market in 2015 The core application areas of IoT are wearable devices, healthcare, building automation, retail, agriculture, industrial, BFSI, oil and gas, and automotive and transportation, among others. It is expected that the IoT chip market for the retail end-use application would grow from USD 0.5 million in 2015 to USD 795.2 million by 2022, at the highest CAGR of 172.9% between 2016 and 2022. For instance, the Amazon Go concept by Amazon will make use of computer vision, sensor fusion, and deep learning algorithms. It will use Just Walk Out technology, which adds items to the virtual cart of the customer and charges from the Amazon account. After charging, the technology sends the receipt to the app. Amazon Go would help provide smooth shopping experience to the customers. The introduction of such technologies would help the customers reduce checkout times, facilitate easier payment procedures, allow a comparative cost analysis, and make overall shopping experience easier. Further, the growth of IoT in the automotive and transportation application would be driven by significant business opportunities for connected cars. Automotive companies believe that the use of IoT would help to evaluate the performance of the vehicle. Increased connectivity will also provide additional ways for automotive companies to cross-sell their products to customers. The automotive and transportation sector is expected to considerably drive the demand for connectivity ICs in the near future. In 2015, connectivity ICs held the largest share of 46.3% of the IoT chip market. The increasing use of IoT in the automotive and transportation sector requires better wireless connectivity technologies to support new segments such as connected cars and intelligent transportation systems (ITS), which would result in increasing demand for connectivity ICs.. Integration of connectivity capabilities in large number of devices and applications and development of IPv6 have considerably driven the IoT chip market. Further, increasing investments in the IoT industry space to develop new IoT-based products would create demand for more IoT chips. As of 2016, companies such as Intel Corporation (U.S.), Qualcomm Incorporated (U.S.), STMicroelectronics (Switzerland), Texas Instruments Incorporated (U.S.), and MediaTek Inc. (Taiwan) were the leaders in the IoT chip market evaluated on the basis of their R&D investment and growth strategies such as new product launches, acquisitions, collaborations, and partnerships in the IoT industry space. For instance, Intel believes that the automotive market is a critical space, and it plans to invest about USD 250 million for autonomous driving. These kinds of investments would drive the technological development in terms of connectivity, communications, and security and further fuel the growth of the IoT chip market. Thus, the growing penetration of IoT and development of new IoT-based products would significantly generate demand for the development of more IoT chips in the near future. INTERNET OF THINGS TECHNOLOGY MARKET by Hardware (Processor, Sensor, Connectivity Technology), Platform (Device Management Platform, Application Management Platform, Network Management Platform), Software Solutions and Services, Application, and Geography-Forecast to 2022 MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M's flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical info graphics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository.

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