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trendingreportz · 5 months ago

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Indoor Positioning and Navigation Market - Forecast(2024 - 2030)

Indoor Positioning and Navigation Market Overview:

The global Indoor Positioning and Navigation market was valued at $6.92 billion in 2017 and is projected to grow to $23.6 billion in 2023 at a CAGR of 27.9%. Developed countries in North America and Europe remain the largest market for Indoor Positioning and Navigation Systems sales which accounts for a major share in the global market. The USA is a major market in for Indoor Positioning and Navigation in Americas accounting around 40% share in the global market. The market in the USA was valued at $2.78 billion in 2017 and is anticipated to grow to $9.58 billion in 2023 at a CAGR of 28.1%. In the Asia Pacific, China and Japan are the major contributors in this market. The market in the Asia Pacific is rapidly growing owing to the increased penetration and growing applications of indoor positioning and navigation in aviation, healthcare, logistics etc., at a CAGR of 31.8% and it is expected to reach $7.52 billion by 2023. The European market is expected to reach $8.75 billion in 2023 growing at a CAGR of 25.6%. The growing applications of indoor positioning and navigation systems and consumer demands are driving the market in Europe. UK holds the largest share in the European market for indoor positioning and navigation which accounts around 25% share in its market and Germany comes next to the UK with a share around 21% in the European Indoor Positioning and Navigation Market.

Indoor Positioning and Navigation Market Outlook:

Indoor positioning and navigation systems are based on technologies like Magnetic Positioning, Simultaneous Localization and Mapping (SLAM), Global Navigation Satellite System (GNSS), Global Positioning System (GPS), Global Navigation Satellite Systems (GLONASS), Galileo, A-GPS, Cellular Sensors (Gyroscope, Acceleration, Barometer, Antenna and Near Field Communication (NFC)), Wi-Fi, Ultra-Wide Band (UWB), Radio-frequency identification (RFID) and Bluetooth Low Energy (BLE) Beacons. The major applications of these technologies include positioning & navigation (contextual information, modelling, surveying and mapping of infrastructures), location-based promotion, asset tracking (personnel and object tracking in an infrastructure escalating real-time monitoring and management) and emergency services (navigation to locate people in case of emergencies during disaster management or law enforcement situations).

In the verticals of this Indoor Positioning and Navigation Market, aviation industry uses these technologies for passenger navigation to designated lounges, baggage tracking, emergency and other airport-related services. Advertising industry includes geo-fencing, location-based promotions and other applications related to E-commerce and digital retail. Healthcare uses RFID, Wi-Fi in hospitals to navigate patients to desired wards. Logistics uses these technologies for automating inventory control and asset tracking in warehouses. The transportation industry has many applications of these technologies in railway stations navigating to ticket counters, inquiry counters, lounges, ATMs etc. Tourism Industry uses these technologies in Hotels for navigating tourists to rooms, lounges, receptions, parking lots and many other places. The automotive industry uses these technologies for vehicle identification and tracking, quality control in production or asset management.

Indoor Positioning and Navigation Market report comprises of pricing analysis for different types of Indoor Positioning and Navigation. The average pricing of indoor positioning and navigation tags which use RFID technology is estimated at $3 and that of tags using Wi-Fi technology is $40. Pricing of indoor positioning and navigation systems also depends on the range covered. For covering an area of 50,000 sqft is estimated to be costing around $5300 and that of covering 100,000 sqft is $10,200.

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Indoor Positioning and Navigation Market Growth Drivers: One of the main drivers in the global Indoor Positioning and Navigation Market is the growing adaption of these technologies many industries like healthcare, logistics etc. There is a high return on investment using these technologies as using these indoor positioning and navigation technologies will result in low operational costs thereby maximizing efficiency and profit.

Indoor Positioning and Navigation Market Challenges: One of the major challenges in the global Indoor Positioning and Navigation Market is security and privacy concerns of using these technologies. The major privacy concern is the collection and sharing of data with third party companies.

Indoor Positioning and Navigation Market Research Scope:

The base year of the study is 2017, with forecast done up to 2023. The study presents a thorough analysis of the competitive landscape, taking into account the market shares of the leading companies. It also provides information on unit shipments. These provide the key market participants with the necessary business intelligence and help them understand the future of the Indoor Positioning and Navigation market. The assessment includes the forecast, an overview of the competitive structure, the market shares of the competitors, as well as the market trends, market demands, market drivers, market challenges, and product analysis. The market drivers and restraints have been assessed to fathom their impact over the forecast period. This report further identifies the key opportunities for growth while also detailing the key challenges and possible threats. The key areas of focus include the types of Indoor Positioning and Navigation market and their specific applications in different types of vehicles.

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Indoor Positioning and Navigation Market Report: Industry Coverage

Indoor Positioning and Navigation Market Technologies: Magnetic Positioning, Simultaneous Localization and Mapping (SLAM), Global Navigation Satellite System (GNSS), Global Positioning System (GPS), Global Navigation Satellite Systems (GLONASS), Galileo, A-GPS, Cellular Sensors (Gyroscope, Acceleration, Barometer, Antenna and Near Field Communication (NFC)), Wi-Fi, Ultra-Wide Band (UWB), Radio-frequency identification (RFID) and Bluetooth Low Energy (BLE) Beacons.

Applications of Indoor Positioning and Navigation Technologies: Navigation & Positioning, Location Based Promotion, Geo-Fencing, Asset Tracking, Emergency Services and Others

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Indoor Positioning and Navigation Market Platforms: Android, iOS and others.

Verticals in Indoor Positioning and Navigation Market: Aviation, Healthcare, Education, Transportation, Logistics, Automotive, Advertising, Tourism and others.

Indoor Positioning and Navigation market report also analyzes the major geographic regions for the market as well as the major countries for the market in these regions. The regions and countries covered in the study include:

North America: The U.S., Canada, Mexico

South America: Brazil, Venezuela, Argentina, Ecuador, Peru, Colombia, Costa Rica

Europe: The U.K., Germany, Italy, France, The Netherlands, Belgium, Spain, Denmark

APAC: China, Japan, Australia, South Korea, India, Taiwan, Malaysia, Hong Kong

The Middle East and Africa: Israel, South Africa, Saudi Arabia

Indoor Positioning and Navigation Market Key Players Perspective:

Some of the key players mentioned in this report are Google, Qualcomm, Microsoft, Samsung, Cisco, Apple, Here Holdings, Hewlett Packard, INSITEO, AeroScout Industrial among others.

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Indoor Positioning and Navigation Market Trends:

Google is developing an indoor location positioning technology based on its Tango Augmented Reality (AR) system. This new technology is called Visual Positioning Service (VPS). It will allow a mobile device to swiftly and accurately understand its position indoors. Using visual clues from distinct features in the room, a Tango-enabled phone would be able to recognize where it is in space by comparing new points captured by its camera with previously observed ones.

January 24, 2018: Here Technologies, a mapping company valued at around $3 billion has acquired Micello, a company based in California that operates a platform for creating, editing and publishing indoor maps. This acquisition will support HERE’s strategy to provide world-class mapping and advanced location services both indoors and outdoors. In combination with HERE’s unique tracking technologies, indoor maps will enable new and innovative market solutions such as the tracking of parts on a factory floor, the optimization of workspace usage, indoor wayfinding at complex transit interchanges, and last mile guidance in vehicles.

Fantasmo, a notable technology start-up, has camera positioning standard (CPS) which captures spatial data through your device’s camera. With this data stored in a server, AR apps can readily and accurately determine position and location relative to recognized landmarks in the immediate vicinity. Fantasmo’s approach also mirrors another startup, 6D.ai, whose platform can capture a dense 3D mesh with smartphone cameras while running in the background.

Sydney airport introduces Apple Maps. Getting around Sydney Airport just got easier due to the gateway’s adoption of Apple Maps (web mapping service), which shows users how to navigate their way through T1 International, T2/T3 Domestic terminals and its car parks. Customers can now easily find facilities and services inside the terminal, including restrooms, parent’s rooms, elevators and shops, and see current location with indoor positioning.

Infsoft 360° Antennas’ Angle of Arrival technology has spurred a new way about how to calculate location. These receiver antennas detect the radio signals emitted by any mobile 2.4 GHz transmitter (e.g. beacon) that is in range. Based on the angle and distance determined, it is possible to calculate the position of an object equipped with such transmitter with an accuracy of 1 to 3 meters. This makes server-based BLE solutions a favorable alternative to cost intensive Ultra-wideband (UWB) applications as long as no centimeter accuracy is required. In comparison to using UWB, a beacon based tracking system utilizing AoA technology can be set up at much lesser costs and also has an advantage of a longer battery lifetime. Practical applications of this technology can include tracking systems in logistics, for example, which can make a decisive contribution to maximizing warehouse operations.

#indoor positioning market #indoor navigation market #location based promotions #geo-fencing #asset tracking #emergency services #navigation market #indoor positioning market size #Localization #Mapping

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zitabujugula · 3 years ago

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RG174 2m GPS GLONASS Dual Antenna, Vehicle Navigation Active Antenna,GPS Positioning Antenna SMA Connector GNSS Antenna TOPGNSS

RG174 2m GPS GLONASS Dual Antenna, Vehicle Navigation Active Antenna,GPS Positioning Antenna SMA Connector GNSS Antenna TOPGNSS

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t2mip · 2 years ago

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Ultra-low power 5G Sub-6GHz RF Transceiver IP Cores at 22nm

T2MIP, the global independent semiconductor IP Cores provider & Technology experts, is pleased to announce the availability of a 5G Sub 6GHz RF Transceiver IP cores in 22nm LP RF CMOS process for integration to your SoC targeted for low power Cellular and Industrial IoT applications and fixed wireless access.

Fully compliant to 3GPP standards the 5G Sub 6GHz RF Transceiver IP cores is a 2x2 configuration with integrated frequency synthesizer and Analog-Mixed signal functions, suitable for low-cost low-power IoT applications. The RF IP cores supports Cellular 3GPP 5G/4G/3G applications and is also ideal for Fixed Wireless Access with a high level of programmability. Supporting both FDD and TDD mode. The RF Transceiver IP cores come integrated with the GPS receiver and frequency synthesizer with a small die-size area. The RF IP cores support up to 200MHz RF channel Bandwidth with ultra-low power consumption which is ideal for IoT applications.

The initial deployments of 5G are focused on the FR1 band (7.125GHz band) as compared to the mm-Wave FR2, frequency bands above 24 GHz. MIMO further enhances the throughput performance for users. MIMO is deployed throughout the legacy of 4G / LTE networks and is used as a 2x2 and 4x4 configuration. Massive MIMO (mMIMO) is an extension of MIMO, increasing the number of antennas to a 64-transmit/64-receive (64T64R MIMO) configuration. Furthermore, Beamforming comes into play which is a signal processing technique to create a focussed signal between the antenna and specific UE.

Availability: The 5G sub 6GHz RF Transceiver IP cores are available for immediate licensing. For further information on licensing options and pricing please drop a request at contact.

T2M’s broad Wireless and Cellular IP cores also include matching Digital and SW IPs for these RF Transceiver IP cores including, 5G UE and g-node B L1-L2-L3 Stack IPs, NB-IoT Protocol Stack SW + Digital, NB-IoT ultra-low power RF Transceiver IP core, GNSS multi-constellation RF Receiver IP cores, GNSS Multi-constellation Digital IP with ultra-low power and high performance. The wide portfolio also includes Interface and high-speed Analog data converters. These IPs are available for immediate licensing

About T2M: T2M-IP is the global independent semiconductor technology expert, supplying complex semiconductor IP Cores, Software, KGD, and disruptive technologies enabling accelerated development of your Wearables, IOT, Communications, Storage, Servers, Networking, TV, STB and Satellite SoCs. For more information, please visit: www.t-2-m.com

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miotsolutions · 4 years ago

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Miot Wireless Solutions GNSS/GPS antenna solutions are designed for applications such as time synchronization, commercial tracking, and precision navigation covering all GNSS frequency bands. GPS antennas provide precise global location and timing services to radios or other electronic systems based on signals received from orbiting satellites. Active GPS antennas contain additional electronic components and filters which improve antenna performance. Miot Solutions Ltd., manufacturing GPS antennas to meet the market demand and win great reputation from the customers around the globe. Our strict QC procedure, rapid shipment service and lowest cost structure can always support your projects to be successful. We keep investing in instruments and developing new products endlessly to pursue the best in the areas of quality, innovation, service, speed and cost-efficiency. We continue to provide the best product with competitive prices, and any custom-made products are welcome within our range of products. To learn more about Industrial GPS antenna manufacturer in Miot Solutions or making a deals regarding the same visit the website www.miotsolutions.com

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gisforlife · 4 years ago

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Garmin GPSMAP 66st GPS Review

Our Verdict

The Garmin GPSMAP 66st is really a suped-up rendition of different models offered inside the generally very good quality GPSMAP arrangement — effectively acquiring our Editors’ Choice honor. The GPSMAP 66st uses a high-powered, quad-helix antenna, and includes an astounding 16 GB of memory (not counting the additional microSD cards you’ll increase quadruple the storage.) This unit can map thousands of waypoints, provide satellite imagery, and update you on the weather, either on the device or linked to your phone. While the essential functions were intuitive, a number of the more advanced features — just like the Bluetooth connectivity — were complicated to setup. But if you recognize your way around GPS units, and are trying to find a highly capable device for logging tons of knowledge, this could be your top choice.

Garmin GPSMAP 66st GPS

GPS Rugged Multi-satellite Handheld with Sensors and Topo Maps, 3″ Color Display

Premium GPS handheld with Birdseye Satellite Imagery membership and TOPO maps

Enormous, 3″ daylight intelligible shading show for simple review. Show size – 1.5 W x 2.5 H inch (3.8 x 6.3 cm); 3 inch diag (7.6 cm)

Numerous Global Navigation Satellite Systems (GNSS) backing to follow your movements in more testing conditions than GPS alone in addition to a three-pivot compass and barometric altimeter

Admittance to Birdseye Satellite Imagery with direct-to-gadget downloads and no yearly membership and preloaded TOPO U.S. also, Canada maps

Extended remote availability underpins Active Weather for modern estimates and energized climate radar and Geocaching Live for versatile matching up and refreshes

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Performance Comparison Products Table

In the field, we found the Garmin GPSMAP 66st to measure up to its reputation. When mapping old mountain biking trails in Southwestern Utah, we were ready to leave and simply devour the trail again, instead of being forever lost among the ocean of junipers.

ImageProductPrice

9.7

MasTrack Hardwired Real Time GPS Vehicle TrackerAmazon Choice

GPS Vehicle Tracker Includes 12 Months of Premium Service

Price

9.6

TKSTAR GPS Tracker,GPS Tracker for VehiclesBest Offer

GPS Tracker,GPS Tracker for Vehicles 120 Days Long Standby Time Waterproof Real Time Car GPS Tracker Strong Magnet Tracking Device For Motorcycle Trucks Anti Theft Alarm TK915

Price

9.6

AES RGT90 GPS Tracker GPRS Mini Portable VehicleBest Deal

GPS Tracker GPRS Mini Portable Vehicle Locating Tracking Device. PRE-Activated SIM Card with 3 Months Service Included

Price

Ease of Use

The GPSMAP 66st was one of the simplest units to use straight from the get-go that we tested. All the buttons are large and have easy to read labels on all. Unlike a number of the opposite units, the 66st buttons are pretty straightforward and there are enough of them for every to possess a definite function during a particular setting.

While some folks may prefer a touchscreen, we actually liked the large buttons on this unit, especially in the cold. The Garmin GPSMAP 66st functionality works great no matter the conditions, whether it’s freezing or wet. The buttons are large enough to push with gloves on, and there is no touch screen to urge wet and inoperable. Like most of Garmin‘s handheld units, it’s rated to IPX7 water resistance, which suggests it’s good to travel for rainstorms or snow, just not full immersion.

As the functions get more in-depth, the controls do get more complicated, but that’s to be expected. some of the blending with different gadgets and applications took huge loads of to and fro enlistment and matching, which was an issue, however just a one-time bother.

Display Quality

With the transflective TFT display on a three-inch screen, the Garmin 66st model is basically readable, even in sunlight, which is basically nice on snow or on a bright, sunny day.

The 240 x 400-pixel display is good when viewing photos or satellite imagery on the germander speedwell function, but otherwise, we’d be hard-pressed to inform much of a difference otherwise. Unlike the touchscreens, the Garmin GPSMAP 66st doesn’t rotate from a portrait view, but it wouldn’t really add up to on this unit.

Speed

Like most of the button-operated GPS units we tested, the Garmin 66st isn’t quite as responsive in function as a number of the touch-screen units we tested, but it wasn’t slow enough to be noticeable or create any issues from the slight lag.

It did actually draw maps quite fast, fast enough for the maps to be redrawn almost as fast as a pan across the map, counting on the size . It can also undergo tons of knowledge quickly, and it doesn’t take much time loaded new big route files or germander speedwell satellite images.

Weight and Size

At eight ounces, the 66st is one of the heaviest units within the review. On one hand, eight ounces isn’t that much. On the opposite hand, it’s half a pound, which looks like tons when considering something that’s the dimensions of your cellphone.

It fits into most jacket pockets, but it’s large enough that hiking with it during a pants pocket is annoying. this is often definitely not the unit for the ultra lighters out there, but rather a significant tool for tons of data gathering.

Versatility

The Garmin GPSMAP 66st may be a highly versatile and accurate GPS unit. It’s exact, simple to utilize, and appears to be very bombproof. The battery pack is only two AA’s, which, although not rechargeable, are easily found around the world. Lithium batteries make this thing cold-resistant too, and with the large buttons, it works great for ski touring or other wet and cold activities. we expect this thing might be utilized in any given situation where the simplest thing about the Garmin 66st Model is that the way Garmin paired accuracy, ease-of-use, and a heaping helping of memory. For professionals who got to use tons of knowledge on a GPS unit, but do not have the allow a Trimble, this is often an honest option. With the 64GB microSD cards now available, the Garmin 66st GPS model could carry up to 80GB, which is much quiet we expect we could ever use within the lifetime of a unit, without deleting anything. handheld GPS is acceptable.

Provided you’re willing to distribute for a high-end GPS unit, this thing will go anywhere. Skiing, backpacking, fieldwork, you name it. It is often put in expedition mode to save lots of battery life on long trips; and maybe soaked, knocked, dropped, and therefore the thing keeps kicking. With the military-built specifications, this thing is rugged, and if you add a lithium battery pack the GPSMAP 66st will work in nearly any environment.

Value

The GPSMAP 66st is one of the foremost expensive units in our review. the sole feature that might have made this costlier may be a touch screen. If you are doing need the functionality of the Garmin 66st Model, then it’s worthwhile needless to say. If you propose downloading your own maps anyway, you’ll knock off a number of the value and just get the 66s model, for what looks like a reasonable bargain considering the potential of this line.

Conclusion

Garmin GPSMAP 66st GPS Model

For a very capable, really durable GPS unit, the GPSMAP 66st GPS model is a superb choice. It holds more data than anyone but a very dedicated field researcher on an extended trip could use, can hook up with smartphones and wifi, and may view the planet via maps or satellite imaging. The GPSMAP 66st isn’t cheap, but you get what you pay for: a burly, accurate, featured unit; ok to earn the Editors’ Choice Award.

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The post Garmin GPSMAP 66st GPS Review appeared first on GIS for LIfe.

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emorphistechno · 5 years ago

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Smart Farming Techniques — Spotlight on site-specific and sensing methods

Sustainable and so additionally economical farming needs precise adaptation to the natural and economic conditions. The irradiation of the sun, the natural water system, the soil properties and the demand of the market simply are not uniform in any respect.

Thus, sustainable and economical farming needs precise adaptation to the varying soil and plant properties within fields. Consequently, farming operations must be adjusted to this in a site-specific way.

Before jumping to the smart farming techniques, let’s read What is Smart Farming — Everything you want to know about it.

1. Electromagnetic Radiation Sensing

Electromagnetic radiation lends itself to wireless sensing of varied soil and crop properties. The premise is often in theory — as well as constituents of soils and crops — can be known by an electromagnetic field index that is derived from its radiation. This electromagnetic index will act as an optical fingerprint of the matter or constituent.

Sensing from satellites or from aerial platforms permits getting maps that gives an outline at intervals regarding roughly a similar time about soil or crop properties from field or wide areas for tactical science inspections. Sensors that are located on farm machines never will do that, not to mention owing to the time it takes to hide a large space.

Yet once it involves the management of site-specific field operations, sensors on farm machines will offer the simplest spatial and temporal exactitude that is attainable. Their glorious spatial exactitude results from the low distance to soils or crops. The high temporal exactitude is feasible since the signals square measure recorded simply in time. this can be vital for those soil- and crop properties that change quick in time.

2. Farm machinery precision

Georeferencing by Global Navigation Satellite System (GNSS) has opened new possibilities for precise steering of farm machinery on virtual lines within the fields. The guidance takes place either manually with the help of lightbar indications or an automatic approach.

In earlier pass direction, each keeps running over the field pursues the individual earlier way balanced by the working width of the machine. In opposition to the present, in fixed-line direction, the courses over the field are not characterized by the individual earlier way but rather exclusively by the principal pass. Earlier pass direction is essential in unpredictably formed fields, while in rectangular fields fixed-line direction ought to be liked.

On declines and uneven territories, utilizing more than one GNSS receiving antenna permits to adjust for roll, pitch or yaw of the tractor. Descending drifting on side slants can be checked by latent or dynamic direction rectifications.

3. Soil properties sensing

Site-specific sensing and detecting of varying soil properties is an essential control for many field tasks. Geology can be mapped rather effectively as a result of other cultivating activities by methods for RTK-GPS (Real Time Kinematic-Global Positioning System). Important data regarding dampness and salinity of soils in a combined mode can be obtained by means of electric conductivity sensing. In humid and damp areas usually, salinity can be left out. So here the electric conductivity is characterized mainly by a mix of clay and water content of the soil. The consolidated impact of these factors is very much identified with the yield capability of soils. Henceforth in humid regions, electric conductivity sensing can supply data that is required for the control of ranch activities as indicated by yield desires.

Electric conductivity detecting depends on soil volumes that may incorporate the topsoil just as the subsoil. In contrast, infrared light reflectance senses only soil surfaces and thus may be less illustrative. However, reflectance detecting may send flag at the same time around a few soil properties, for example, surface, carbon content, cation-trade limit and water content.

4. Crop properties sensing

Detecting of yield by visible and infrared reflectance permits evaluating the chlorophyll concentration within leaves as well as the leaf-area-index. The result of the chlorophyll focus inside leaves and leaf-area-index provides the chlorophyll content per unit field area. Recording this paradigm over and over amid the season gives dependable assessments of the site-explicit yield potential as dependent on past developing conditions.

Fluorescent light too can detect the chlorophyll absorption within leaves or the working of the photosynthetic contraption of yields. Infrared reflectance just as warm radiation can be utilized to get data about the site-explicit water supply of harvests. From the backscatter of radar waves, data about the biomass, the leaf-area-index and particularly about the yield species for vegetation arrangement inside expansive farming regions can be obtained.

Proximal detection and sensing from farm machines permit direct site-specific control of ranch activities continuously. Then again, remote sensing from satellites offers itself for repetitive recording of fields or bigger areas amid the growing season. However remote sensing and detecting needs radiation that can infiltrate the atmosphere.

5. Site-Specific Soil Cultivation

Site-specific soil cultivation and development has numerous goals. In primary cultivation, the primary goal is the control of the working profundity. Signals for this control can be consequent from the clay content, the natural and organic matter content, the hydromorphic properties and the slope of the soil. An algorithm cum calculation can combine these signals to control the working profundity. The soil resistance to penetration is a reasonable control signal for detecting hardpans underneath the topsoil, but not for the working profundity inside the topsoil, since it depends generally on the water content.

In secondary cultivation, clod size reduction is a significant goal. The site-specific control signals for this can be obtained from the forces acting on a sensing spike of a cultivator. The standard deviations of the forces can make available for appropriate control parameters.

In stubble or neglected cultivations either quick or moderate decay of the remains should be aimed at depending on alternations, weather and peril of soil erosion.

With the inception of controlled traffic farming or of unmanned farm machinery will promote crop production and yield.

6. Site-Specific Sowing

Site-specific control and management of the seed-density will depend on maps of soil texture. The seed-density ought to rise from sand to silt and soil and fall once more towards clay. In this fashion either the yields can be augmented, or seeds can be hoarded.

For the sowing depth, the site-specific control and management ought to be primarily based either on texture or on water content of the soil. In regions with maritime climate and consequently frequent rain, the control and management via soil texture appears rational. This could be understood by means of texture maps and adjusting the depth of openers on-the-go by means that of ultrasonic distance sensing.

In areas with continental climate and therefore longer dry spells, a control built on the water content of the soil is a decent and optimal. Here a soil dampness seeking control system that adjusts the sowing depth on-the-go to the drying front within the soil via infrared reflectance or resistivity appears reasonable.

A unique challenge is that the increasing conflict between no-till with crop residues on the surface and sowing techniques. The trend to smaller row widths to understand yield will increase adds to the current conflict. However, there are ideas on the market which will address this conflict. These are addressed.

7. Site-Specific Fertilizing

Fertilization aims at providing soils with nutrients for towering crop yields while not adversely distressing the environment. Since in most cases the properties of soils as well as of crops vary amongst individual fields, site-specific fertilization is required. The challenge is to seek out sensing ways that give appropriate signals for the site-specific control and management of fertilizer application. Possible approaches to satisfy this challenge is predicted on:

· Recording the yield of previous crops and therefore the nutrient removal derived from it

· Chemical science (electrochemical) indication of nutrients in soils by ion-selective electrodes

· Sensing the nutrients either in soils or in crops via optical reflectivity.

The optimal selection of choice depends on a range of aspects such as e.g. nutrient kind, properties of soils, properties of crops and climate. The last listed methodology — sensing via optical reflectivity — is utilized in a proximal mode from farm machines or additionally in a very remote mode from satellites provided clouds do not impede the radiation. Its use for in-season nitrogen application with proximal sensing from farm machines is turning into a number one technology.

8. Site-Specific Weed Control

Spatial and temporal variations in weed seedling distributions in fertile fields are analyzed. It is pronounced how weed distributions can be weighed by manual grid sampling and by using sensor technologies from the near array. The probable for herbicide savings using site-specific weed management in different crops is premeditated.

Two completely different approaches for site-specific weed control and management are conferred. First, an offline approach built on georeferenced weed distribution maps and next a real-time method combining sensor and patch spraying technologies.

The decision rules for patch spraying ought to think about density, coverage and yield loss effects by weed species, its growth stages and the price of weed control. Herbicide reserves using precision weed control varied from 20 to 70 %. Real-time patch spraying is the utmost economical treatment trailed by map-based site-specific weed control and management. Uniform herbicide applications and uncontrolled treatments gave all time low economic return. Many studies showed that weed species distribution remained stable over time once site-specific herbicide applications were realized basis economic weed thresholds.

9. Site-Specific Sensing for Fungicide Spraying

Especially in wet and humid moderate climates, high yields need the applying of fungicides. Its site-specific application supported the biomass of crops is state of the art. Nonetheless, this method does not contemplate that plant infections in most cases begin and displayed from tiny, initial spots at intervals within a field. So, a sensing technique to notice these ab initio tiny infected spots would be of nice importance for saving fungicides, for reducing harm to crops yet on the surroundings and

for permitting higher driving speeds in uninfected areas. Reflecting index of evident and near-infrared light yet as indices of fluorescent light are candidates for detecting these spots.

Detecting the fungi in early stages of infection (= latency stage) are often vital for an efficacious treatment, since discontinuing the infection after this time grows more problematic. In this latency phase, the diseases may not be nonetheless visible by human eyes. Plant fungal diseases often change the biological state of plants either by means of the photosynthesis or by the establishment of subordinate metabolites like phenols. These changes are often detected within the smartest approach by optical sensing. Herewith fluorescence (visible light) could be a sensitive methodology with the potential of detecting changes before infections are visible by human eyes.

10. Site-Specific Recording of Yields

Site-specific recording of yields, conjointly referred to as geo-referenced yield observance and monitoring, contributes to site-specific precision farming ideas and concepts by delivering elementary / fundamental information concerning the variety of the yield potential. It is feeding back the results of site-specific management on yields and permits to calculate the exports of nutrients.

In the early 1990s it started with mix of harvesters and incessantly operating systems for recording yields together with the position of the machine. Attempts were made to see site-specific yield of manually harvested cultures like oranges, apples or coffee. Testing procedures to see the accuracy of material flow sensors and yield measuring systems in the lab and in the field are now standardized.

Systems for the site-specific recording of yields in combine-harvesters, forage choppers, and cotton pickers are accessible at the market. The adoption of skilled farming is concentrating on combinational crops. Efforts are required to form systems accessible for all necessary crops, to integrate the sensing of essential crop ingredients and to standardize knowledge formats also as algorithms for data filtering and data analysis.

Originally published at - http://bit.ly/2TVYzEe

#smart farming

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sunilrathod2504-blog · 6 years ago

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Geographic Information System (GIS) Market Overview, Research & Analysis To 2022

The global Geographic Information System (GIS) market is valued at XX million USD in 2016 and is expected to reach XX million USD by the end of 2017, growing at a CAGR of XX% between 2016 and 2022.

Japan plays an important role in global market, with market size of xx million USD in 2016 and will be xx million USD in 2017, with a CAGR of xx%.

This report, with focus on top players in the global and Japan, studies the Geographic Information System (GIS) market's development status and future trend in the global and Japan. It splits Geographic Information System (GIS) market by type and by applications to fully and deeply research and reveal market profile and prospect.

Download Free Sample Report @ https://www.radiantinsights.com/research/2017-2022-global-and-japan-geographic-information-system-gis-market-analysis-report/request-sample

The major players include • Autodesk Inc. (US) • Bentley System, Incorporated (US) • Environmental Systems Research Institute, Inc. (Esri) (US) • General Electric Co. (US) • Hexagon AB (Sweden) • Pitney Bowes Inc. (US) • Topcon Corporation (Japan) • Trimble Inc. (US) • Blue Marble Geographics (US) • Caliper Corporation (US) • Computer Aided Development Corporation Limited (Cadcorp) (UK) • Geosoft Inc. (Canada) • Handheld Group (Sweden) • Harris Corporation (US) • Hi-Target Surveying Instrument Co. Ltd. (China) • MacDonald, Dettwiler and Associates Ltd. (Canada) • PASCO CORPORATION (Japan) • Beijing Unistrong Science & Technology Co., Ltd. (China) • Champion Instruments, LLC (US) • Golden Software LLC (US) • SuperMap Software Co., Ltd. (China)

Geographically, this report splits the Global market into the following regions: • Asia Pacific • Japan • Tokyo • Yokohama • Osaka • Nagoya • Others • China • Korea • India • Others • Europe • France • Germany • United Kingdom • Italy • Russia • Others • North America • United States • Canada • Latin America • Brazil • Mexico • Others • Southeast Asia • Singapore • Malaysia • Vietnam • Myanmar • Thailand • Indonesia • Philippines • Others • Middle East & Africa • Saudi Arabia • Iran • UAE • Turkey • Israel • Egypt • South Africa • Others

On the basis of product, the Geographic Information System (GIS) market is primarily split into By Hardware • GIS Collectors • Total Stations • LIDAR • GNSS/GPS Antennas • Imaging Sensors By Function • Mapping • Surveying • Telematics and Navigation • Location-Based Services

Key Applications • Agriculture • Construction • Transportation • Utilities • Mining • Oil & Gas

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Table of Contents

2017-2022 Global and Japan Geographic Information System (GIS) Market Analysis Report 1 Geographic Information System (GIS) Overview 1.1 Product Overview and Scope of Geographic Information System (GIS) 1.2 Global and Japan Geographic Information System (GIS) Market by Applications/End Users 1.2.1 Japan Geographic Information System (GIS) Sales (K Units) and Market Share Comparison by Applications (2012-2017) 1.2.1.1 Japan Geographic Information System (GIS) Sales (K Units) and Market Share by Applications (2012-2017) 1.2.1.2 Japan Geographic Information System (GIS) Sales Growth Rate (%) by Applications (2012-2017) 1.2.1.3 Market Drivers and Opportunities 1.2.2 Japan Geographic Information System (GIS) Sales (K Units) and Market Share Comparison by Applications (2012-2017) 1.2.3 Global Geographic Information System (GIS) Sales by Application (2012-2017) 1.2.3.1 Global Geographic Information System (GIS) Sales (K Units) and Market Share by Applications (2012-2017) 1.2.3.2 Global Geographic Information System (GIS) Sales Growth Rate (%) by Applications (2012-2017) 1.2.3.3 Market Drivers and Opportunities 1.2.4 Global Geographic Information System (GIS) Sales (K Units) and Market Share Comparison by Applications (2012-2017) 1.2.5 Agriculture 1.2.6 Construction 1.2.7 Transportation 1.2.8 Utilities 1.2.9 Mining 1.2.10 Oil & Gas 1.3 Global and Japan Market Size (Sales and Revenue) of Geographic Information System (GIS) (2012-2017) 1.3.1 Japan Market Size (Sales and Revenue) of Geographic Information System (GIS) (2012-2017) 1.3.1.1 Japan Geographic Information System (GIS) Sales (K Units) and Growth Rate (%) (2012-2017) 1.3.1.2 Japan Geographic Information System (GIS) Revenue (Million USD) and Growth Rate (2012-2017) 1.3.2 Global Market Size (Sales and Revenue) of Geographic Information System (GIS) (2012-2017) 1.3.2.1 Global Geographic Information System (GIS) Sales (K Units) and Growth Rate (%) (2012-2017) 1.3.2.2 Global Geographic Information System (GIS) Revenue (Million USD) and Growth Rate (2012-2017) 1.4 Global and Japan Classification of Geographic Information System (GIS) by Hardware 1.4.1 Japan Geographic Information System (GIS) Sales (K Units) and Market Share by Hardware (2012-2017) 1.4.2 Global Geographic Information System (GIS) Sales (K Units) and Market Share by Hardware (2012-2017) 1.4.3 Japan Geographic Information System (GIS) Revenue (Million USD) and Market Share by Hardware (2012-2017) 1.4.4 Global Geographic Information System (GIS) Revenue (Million USD) and Market Share by Hardware (2012-2017) 1.4.5 Japan Geographic Information System (GIS) Price (USD/Unit) by Hardware (2012-2017) 1.4.6 Global Geographic Information System (GIS) Price (USD/Unit) by Hardware (2012-2017) 1.4.7 Japan Geographic Information System (GIS) Sales Growth Rate (%) by Hardware (2012-2017) 1.4.8 Global Geographic Information System (GIS) Sales Growth Rate (%) by Hardware (2012-2017) 1.4.9 GIS Collectors 1.4.10 Total Stations 1.4.11 LIDAR 1.4.12 GNSS/GPS Antennas 1.4.13 Imaging Sensors 1.5 Global and Japan Classification of Geographic Information System (GIS) by Function 1.5.1 Japan Geographic Information System (GIS) Sales (K Units) and Market Share by Function (2012-2017) 1.5.2 Global Geographic Information System (GIS) Sales (K Units) and Market Share by Function (2012-2017) 1.5.3 Japan Geographic Information System (GIS) Revenue (Million USD) and Market Share by Function (2012-2017) 1.5.4 Global Geographic Information System (GIS) Revenue (Million USD) and Market Share by Function (2012-2017) 1.5.5 Japan Geographic Information System (GIS) Price (USD/Unit) by Function (2012-2017) 1.5.6 Global Geographic Information System (GIS) Price (USD/Unit) by Function (2012-2017) 1.5.7 Japan Geographic Information System (GIS) Sales Growth Rate (%) by Function (2012-2017) 1.5.8 Global Geographic Information System (GIS) Sales Growth Rate (%) by Function (2012-2017) 1.5.9 Mapping 1.5.10 Surveying 1.5.11 Telematics and Navigation 1.5.12 Location-Based Services

2 Global and Japan Geographic Information System (GIS) Market Competition by Players/Manufacturers 2.1 Japan Geographic Information System (GIS) Market Competition by Players/Manufacturers 2.1.1 Japan Geographic Information System (GIS) Sales and Market Share of Key Players/Manufacturers (2012-2017) 2.1.2 Japan Geographic Information System (GIS) Revenue and Share by Players/Manufacturers (2012-2017) 2.1.3 Japan Geographic Information System (GIS) Average Price (USD/Unit) by Players/Manufacturers (2012-2017) 2.1.4 Japan Geographic Information System (GIS) Market Competitive Situation and Trends 2.1.4.1 Japan Geographic Information System (GIS) Market Concentration Rate 2.1.4.2 Japan Geographic Information System (GIS) Market Share of Top 3 and Top 5 Players/Manufacturers 2.1.4.3 Mergers & Acquisitions, Expansion in Japan Market 2.1.5 Japan Players/Manufacturers Geographic Information System (GIS) Manufacturing Base Distribution, Sales Area, Product Types 2.2 Global Geographic Information System (GIS) Market Competition by Players/Manufacturers 2.2.1 Global Geographic Information System (GIS) Sales and Market Share of Key Players/Manufacturers (2012-2017) 2.2.2 Global Geographic Information System (GIS) Revenue and Share by Players/Manufacturers (2012-2017) 2.2.3 Global Geographic Information System (GIS) Average Price (USD/Unit) by Players/Manufacturers (2012-2017) 2.2.4 Global Geographic Information System (GIS) Market Competitive Situation and Trends 2.2.4.1 Global Geographic Information System (GIS) Market Concentration Rate 2.2.4.2 Global Geographic Information System (GIS) Market Share of Top 3 and Top 5 Players/Manufacturers 2.2.4.3 Mergers & Acquisitions, Expansion in Global Market 2.2.5 Global Players/Manufacturers Geographic Information System (GIS) Manufacturing Base Distribution, Sales Area, Product Types

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#GeographicInformationSystem(GIS)Market #gis market

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rfantennaindia · 1 year ago

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ETEILY 5G Screw Mount Antenna Manufacturers in India

A 5G screw mount antenna is a type of antenna designed for use in 5G wireless communication systems. These antennas are typically compact and designed to be mounted onto a surface using a screw or bolt attachment mechanism. They are commonly used in various applications where 5G connectivity is required, such as IoT (Internet of Things) devices, routers, modems, and other wireless communication equipment.

Here are some key features and considerations related to 5G screw mount antennas:

Frequency Bands: 5G operates in a range of frequency bands, including sub-6 GHz and mmWave (millimeter wave). 5G screw mount antennas are designed to operate in specific frequency bands or across a wide range of 5G frequencies, depending on the application.

Form Factors: These antennas come in various shapes and sizes, including omni-directional and directional designs. The choice of form factor depends on the specific application and coverage requirements.

Gain: Antenna gain determines the directionality and strength of the antenna's signal. High-gain antennas provide greater signal coverage in a specific direction, while omni-directional antennas offer 360-degree coverage.

Mounting: Screw mount antennas are designed for easy installation. They typically have a threaded base or connector that can be securely attached to a mounting surface, such as a device enclosure or a bracket.

Materials: Antennas are often constructed from materials that are durable and weather-resistant, making them suitable for both indoor and outdoor use.

Connector Type: The antenna may have different types of connectors, such as SMA, N-type, or others, depending on the equipment it is intended to connect to.

Radiation Pattern: The radiation pattern of the antenna describes how it emits or receives signals in different directions. Some antennas have a narrow radiation pattern for long-range directional communication, while others have a broader pattern for more widespread coverage.

Compatibility: Ensure that the 5G screw mount antenna you choose is compatible with your specific 5G device or equipment.

When selecting a 5G screw mount antenna, it's essential to consider factors such as the desired coverage area, frequency band compatibility, and the specific requirements of your application. Proper antenna selection and installation are critical to achieving optimal performance in a 5G communication system.

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bobsweepmop · 7 years ago

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IOT temperature logger with GSM transmission and 1wire sensor

Learn how to build your own IoT temperature logger with data uploaded to the cloud. This project uses itbrainpower.net dual SIM full-size GSM shield [a-gsmII] for data transmission (or itbrainpower.net dual SIM full-size GSM GNSS shield [b-gsmgnss]) and Dallas/Maxim 18B20 1wire temperature sensor.

Required time:

30-45 minutes – hardware side

30-60 minutes for software and cloud setup.

Difficulty: intermediate.

IoT hardware – Bill of Materials

Arduino UNO,

a-gsmII v2.105 – Arduino GSM Shield [dual SIM, integrated antenna + uFL, quad-band GSM/GPRS/DTMF/SMS], or

b-gsmgnss v2.105 – Arduino GSM GPS Shield [dual SIM, integrated antenna + uFL, quad-band GSM/GPRS/DTMF/SMS, Bluetooth 3.0, GNSS (GPS+GLONASS)],

A 1WIRE temperature sensor – I use DALLAS/MAXIM 18B20

8.2kb resistor

2G SIM card [with data plan enabled]

some connecting wires

7.5-16V [12V recommended] 1A power source having UNO compatible power jack

Components being assembled

About GSM shields used in this project

We’re using the new a-gsmII v2.105 (Arduino full size GSM shield), together with b-gsmgnss v2.105 (Arduino full size GSM + GNSS (GPS + GLONASS) shield). They’re both Arduino, Teensy, BeagleBone & Raspberry Pi compatible shields. They belong to the next generation of the successfully a-gsm v2.064 and offers to best market performances in their product class, and are offered at a reasonable price.

In this how-to a-gsmII v2.105 shield is used for representation. The very same hardware settings are applied for b-gsmgnss shield [jumpers and connectors are placed in the same position].

The GSM shields documentation can be found here:

a-gsmII documentation

b-gsmgnss documentation

Hardware – Step 1 – 18B20 sensor connection details and references

Solder the 8.2kb resistor between “18B20 Vdd” (pin3) and “18B20 DQ/DATA” (pin2).

DALLAS/MAXIM 18B20 reference here.

18B20 sensor wiring details

Hardware – Step 2 – How to assemble the components together

Below, you can see all the IoT components bound together.

Arduino + GSM temperature IOT sensor wired

Here are the wiring steps to follow:

Solder the Arduino headers to a-gsmII shield

Plug-in the a-gsmII shield into Arduino UNO

Set the a-gsmII “Voltage selector” jumper in “Vin” position

Insert the SIM card in primary SIM socket [a-gsmII/b-gsmgnss – the SIM socket being in the proximity of the PCB]. The SIM must have disabled the PIN-checking procedure (See here for removing PIN check procedure).

Solder the 8k2 resistor to bw. 18B20 Vdd (pin3)and 18B20 DQ/DATA (pin2).

Solder the wires to the 18B20 terminals.

Wire 18B20 Vdd (pin3) to Arduino 5V,

Wire 18B20 DQ/DATA (pin2) to Arduino D8

Wire 18B20 GND (pin1) to Arduino GND.

Connect Arduino UNO USB to your PC

The IOT temperature sensor will be powered via Arduino UNO power barrel connector.

HInt: Multiple 1WIRE temperature sensors are required by your application? Just connect the temperature sensors in parallel mode (single pull up resistor is required, check for proper value), hack the software (use 1,2.. sensorIndex in “sensors.getTempCByIndex(sensorIndex)” and multiple upload sessions). Also, define multiple token sensors in Cloud interface.

Software – Part 1 – Download packages and set IOT variables

Download

either the itbpGSMclass library (beta) “a-gsmII series software IoT REST support for Arduino” from a-gsmII download section

or “b-gsmgnss series software IoT REST support for ARDUINO” from the b-gsmgnss download section. In order to download “itbpGSMclass”, some information is required: the modem’s IMEI (marked to the modem, or can be read using AT+CGSN at command) and your email address.

Expand the archive and install the class and restart the Arduino environment. More details about manual library installing, read Arduino library manual installation.

Create a folder named agsmII_temperature_Robofun_cloud_logger.

Download the main code from agsmII_temperature_Robofun_cloud_logger.ino – main code and save it with that name in the folder created in the previous step.

You’ll need to set up some variables in agsmII_temperature_Robofun_cloud_logger.ino: – tempToken (line 19) – replace with the sensor token generated on Robofun Cloud (see below) – samplingPeriod (line 29)

You’ll need to set up some variables in itbpGSMclass library:

Set SERVER_ADDRESS and SERVER_PORT in itbpGPRSIPdefinition.h for Robofun Cloud #define SERVER_ADDRESS “iot.robofun.ro” #define SERVER_PORT “80”

Set APN, USERNAME and PASSWORD for your SIM card in itbpGPRSIPdefinition.h

Choose itbrainpower modem used in itbpGSMdefinition.h, in this example: #define __itbpModem__ agsmII

Software – Part 2 – Cloud setup tasks. Define new sensor. Copy TOKEN data

I’ve chosen Robofun Cloud because it is a very easy-to-use cloud service and the usage it’s free of charge. Next, I will guide your steps to set up the cloud service account and sensor.

Create a new account at https://iot.robofun.ro

Account creation screen

Create a new sensor

Add sensor

Chose a name for your sensor

Add a name to your sensor

Scroll down the page to the section named Token

Sensor Token Data

Copy your sensor Token ID. Use this value for the tempToken variable at line 19 in agsmII_temperature_Robofun_cloud_logger.ino.

Compile the Arduino project and upload it to your Arduino UNO. Now, your temperature sensor is ready to upload temperature data samples to the Robofun Cloud service.

If you need to debug, use Arduino serial monitor (speed was chosen to 115200bps).

How to visualize temperature log records

Visualize temperature log records links

Cloud stored temperature data can be visualized in your cloud sensor page, or in public page.If you want to enable the public page, scroll up the page until “Share sensor” chapter, and follow the link.

Congratulations! Your 1 wire temperature data logger is ready!

TUTORIAL PROVIDED WITHOUT ANY WARRANTY – USE IT AT YOUR OWN RISK. Originally written by Dragos Iosub & itbrainpower.net team and published on itbrainpower.net

directory

1) Uber accident cause analysis

2) automatic driving first day - Safety

3) automatic driving vehicle hardware system

4) automatic driving vehicle sensor sensor

5) automatic driving vehicle sensor

6) automatic driving calculation unit

7) automatic Driving by wire system

< 8) Apollo hardware development platform

Original: Apollo developer community? A week ago

last week, Apollo introduced the basic situation of Baidu Apollo open platform. In this issue, a Bojun will introduce the Apollo hardware development platform.

If you don't say much, you are welcome to enter the fifth advanced course.

The accident happened on the night of March 18, 2018, when a Uber car in driverless test collided with a pedestrian in Tempe, Arizona. The pedestrian was sent to hospital and later declared invalid.

According to the preliminary investigation results, Uber vehicle was in the state of automatic driving when it hit the pedestrian, which is the first case in history that the automatic driving vehicle injured the pedestrian on the open road. This incident sounded the alarm about safety for driverless.

Issued by the U.S. highway Commission on June 22, 2018Accident report:

in the first 6 seconds of the accident, the sensors of the system have found pedestrians; in the first 1 second of the accident, the emergency brake AEB of the original car has been started, but the car has not implemented the brake, because when Uber refits Volvo CX90, the brake system of the original car is cut off, and the subsequent refitted computer will send control instructions to brake.

the system does not have a fully closed-loop state (main cause);

the driver lowers his head to play with his mobile phone, and the system does not give a warning after detecting a pedestrian;

automatic driving and infrastructure are related, and the lack of illumination on the road in the four seconds before the accident results in no pedestrian to be seen from the photos.

There have been other traffic problems before

Uber, such as vehicle scratch, direct rollover, etc.

The reason is that the vehicle sensor (velodyne 64 line) installed later by Uber is heavier, and the SUV center point is higher. After the sensor is installed, the center of gravity moves up, and it is easy to roll when turning a sharp corner.

From the perspective of the R & D process of automatic driving, it can be roughly divided into the following four steps:

software in the loop software in the loop is based on Simulation and simulation software simulation, similar to racing games. That is to simulate the real road environment such as light, weather and other natural environment in the software system. After the development of the automatic driving code, the developer can run it in the simulation system to test whether the goal can be achieved.

Hardware in the loop hardware in the loop is based on the necessaryHardware platform. After the first step of software simulation, all the simulation results, sensors and computing units are assembled and tested in the hardware environment.

Vehicle in loop vehicle in loop is based on vehicle execution. The third step is to test the functions developed by developers in a closed environment. There will be no interference of traffic flow in the closed environment.

Driver in the loop driver in the loop is based on the actual road. After the third step is successful, the driver in the loop is mainly to study the interaction between people, vehicle, road and traffic. It not only tests the program code of automatic driving, but also obtains the judgment of professional drivers.

The above four steps are the whole process of automatic driving R & D. according to the above process R & D can ensure sufficient safety of automatic driving.

Automatic driving is divided into three systems: perception, decision-making and control. Each system has corresponding hardware system. ?

perception system is divided into three parts: vehicle movement, environment perception and driver monitoring.

Vehicle motion is mainly divided into inertial navigation, speed sensor, angle sensor and global positioning system.

Environmental perception mainly includes lidar, ultrasonic, camera, millimeter wave radar and v2x.

Driver monitoring is mainly divided into camera and bioelectric sensor.

The decision system is divided into three parts: calculation unit, T-box and black box.

In the computing unit is the algorithm of autopilot perception decision control. Current autoDriving with X86 structure of the server or industrial computer.

T-box, or telematics box, is the communication gateway of the Internet of vehicles. It is connected to the Internet and connected to the CAN bus. For example, the door opening and closing commands sent by the app on the mobile phone are controlled by sending the operation commands to the CAN bus through the T-box gateway.

The black box is used to record all information and status in the process of unmanned driving.

The control system is divided into vehicle control and warning system.

Vehicle control mainly includes braking, steering, engine and gearbox. Warning system is mainly divided into sound, image and vibration?.

The above is the framework of the whole autopilot hardware system.

introduces the sensor category of automatic driving vehicle.

The camera is mainly used for lane line, traffic sign, traffic light, vehicle and pedestrian detection. It has the advantages of comprehensive detection information and low price, but its performance is greatly affected by weather.

The camera is mainly composed of lens, lens module, filter, CMOS / CCD, ISP and data transmission. The cameras are divided into monocular and binocular.

The basic working principle of the camera: light is focused on the sensor of the CMOS behind through the lens and filter in front of the camera.

the sensor will expose the light signal and convert it into an electrical signal, then convert it into a standard RGB or YUV data grid through the ISP image processor, and finally transmit it to the back-end computerLine processing.

The core principle of lidar is TOF (time of flight), that is, after a beam of light hits an obstacle, the light will generate an echo, and receive and calculate the light turn back distance on APD.

According to its scanning principle, lidar can be divided into coaxial rotation, prism rotation, MEMS, OPA phased array and flash. ? lidar is not only used for perception, but also for mapping and positioning.

Millimeter wave radar is mainly used for the detection of traffic vehicles. Millimeter wave radar is mainly composed of RF antenna, chip and algorithm. The basic principle is to transmit a beam of electromagnetic wave, and then observe the intake difference of electromagnetic wave echo to calculate the distance and speed.

It has the advantages of fast and accurate detection speed, no interference from weather conditions, and the disadvantage is that it can not identify and detect lane lines.

Integrated navigation is to receive all visible GPS satellite signals through GNSS board and calculate them, so as to get the spatial position of the object in the geodetic coordinate system.

When vehicles pass through tunnels, buildings and shaded roads, GPS signal can not provide a good settlement and real-time navigation, so it is necessary to integrate the information of inertial navigation.

Inertial navigation is a completely closed system, which can directly give the position, speed and attitude of the car body without any external influence.

automatic driving vehicle sensor installation location is generally: the

lidar is rotated at 360 degrees.As it is installed on the roof;

millimeter wave radar has strong aluminium sliding window lock manufacturer directivity, so it is generally installed on the front and rear bumpers;

considering the interference of the vehicle body's pitch and attitude on the Road, the integrated navigation system is generally installed on the central axis of the two rear wheels;

cameras are installed on 360 ° of the vehicle body.

The figure above summarizes the sensors used for autonomous driving.

Automatic Sliding window lock manufacturer cruise, emergency braking and pedestrian detection are all L1 and L2 functions.

The European standard is for vehicles with mandatory AEB function in 2017; the American standard is for all vehicles to have auxiliary driving, lane departure and AEB function by 2020;

the Chinese standard is to list the automatic driving L1 function AEB as mandatory standard by 2018, which are mainly used for commercial vehicles, trucks and buses.

The following is a brief introduction of the differences between the current mass production plans of L1 and L2 and the plans above L3 + developed by Baidu and many AI companies.

? grade L1 and grade L2 are most afraid of sensor error detection, for example, sensor error detection while driving, and then sudden braking will make driving feel bad. L1 and L2 algorithm can avoid certain aluminium alloy sliding door lock false detection rate.

Above L3, we pay attention to the missing detection of sensors. The main body of driving is a system. We must not allow the missing detection of the sensor section of the system.

This is the current traditional car companies and some AI companies in the automatic driving sensor for two major differencesDifferent.

At present, L4 adapts to some automatic driving on urban roads and expressways. The speed limit of expressways in China is 120km / h. according to the road friction coefficient, the braking distance at different speeds can be calculated. Add the reaction time of the whole system, and calculate the following table according to the mathematical formula.

At present, the reaction time of the whole system of automatic driving will be within 500ms. It takes 0.3-0.5s for the vehicle to brake hydraulically, and 0.8s for the truck to brake pneumatically.

At present, most of the cars on sale are lower than this technical index, which shows that the performance of the cars on sale is very good. At present, it is enough to measure 150 meters for the requirements of sensors.

This is a trigonometric function arctangent function, but this formula will be divided by one more 2 to avoid omission.

When there is an object between the angles of two beam lines of lidar, which is just at the detection edge, it will generate a certain amount of missed detection. Dividing by 2 is to ensure that there will be no missed detection at each angle. .

At the resolution of 0.1 °, we can actually detect a person, a car or a rider 400 meters away.

, however, can detect that it does not mean that the automatic driving system can be identified. Only one line of imaging or low resolution imaging can automatically identify the driver.

At present, on the baidu Apollo platform, we can use four or five lines of lidar on the same vehicle to classify the obstacles well.

Now, like velodyne's 64 line lidar, his object perception distance at 0.4 ° resolution is 50 meters.

The future trend of autopilot sensors: autopilot sensors can not do without multi-sensor fusion. Lidar and camera belong to optical sensors, and their core parts and processing circuits are very similar,

in the future, it is possible to integrate lidar and camera front-end, directly output RGB and XYZ fusion color plus point cloud information, and then transmit it to the back-end calculation for processing.

The iradar system developed by aeye, an American start-up company, can not only truly reflect the color information of two-dimensional world, but also overlay the information of point cloud. Each pixel has not only color information but also spatial coordinate information.

above is the calculation unit structure of the automatic driving vehicle. In the calculation unit of automatic driving vehicle,

needs to consider the overall vehicle gauge, electromagnetic interference and vibration design as well as the requirements of ISO-26262 standard.

All CPU, GPU, FPGA, MCU and bus should be designed redundantly to prevent single point failure.

At present, the computing unit is a centralized architecture, that is, all the work is put into an industrial computer.

The disadvantages of this architecture are large size, high power consumption and discomfortIt is convenient for fast code iteration, and the design of industrial computer card slot is convenient for hardware update and expansion?.

Because of the disadvantage of centralization, the embedded scheme will be considered in the future. First, the original data of each sensor is fused into a sensor box, in which data fusion is completed, and then the fused data is sent to the back-end computing platform for processing.

Function of sensor box: how to judge whether the original data given by the current sensor is the same target after the fusion is completed? A time stamp synchronization is required to ensure that each sensor detects the same coordinate system under the time stamp. The synchronization of time stamps is completed in the sensor box. ?

this scheme breaks down the original centralized computing function, which can reduce the power consumption of the whole system, but it is not enough for more mass production.

The self driving algorithm we are developing now can be made into a special chip when the sensing algorithm solidifies.

ASIC chip is a special customized chip based on specific requirements. Its advantages are smaller volume, lower power consumption, stable performance and mass production compared with general GPU and FPGA.

Now the semiconductor industry is very mature. sinceDynamic driving algorithm companies only need to do a good job in the front-end design of the chip, such as solidifying the algorithm, then choosing the appropriate IP core, and finally carrying out EDA (electronic automation design), and then handing the circuit diagram of the chip design to the back-end, chip manufacturing enterprises such as TSMC carry out the production of chips.

The whole chip design process is divided into three parts: chip design, chip manufacturing and chip packaging. Now the whole semiconductor industry is developing from deep ultraviolet (Dov) to extreme ultraviolet (EUV).

Semiconductor is stepping into the 7 nanometer era, and the new technology has brought great improvement to the performance. Compared with 16 nm process, 7 nm process can improve 40% performance and save 60% energy consumption.

Control by wire refers to that the control of the car is completed by some simple commands, not by physical operation.

The wire control part is equivalent to the human hand and foot, and the upper command is executed in the online control system. It is mainly divided into three parts: deceleration control, steering control and acceleration control.

these control systems of traditional cars are assisted by hydraulic system and vacuum booster pump. The automatic control of automobile must be controlled by electronically controlled components, such as electronic hydraulic braking system (EHB).

Solution. Its MK C1 integrates hydraulic and braking modules, uses compact and light weight design to save braking unit, and the braking signal sent by electrical signal also makes the braking distance shorter.

ESC (body electronic stability system) used by mk100 can back up with MK C1. When MK C1 system fails, mk100 will take over.

From the perspective of schematic diagram, all power supply, execution, circuit and pipeline diagrams of continental braking are double backup, which greatly improves the safety, but the system is only applicable to passenger vehicles. Commercial vehicles such as trucks and buses are braked by air brake system.

At present, many autonomous vehicles use EPS (electronic power steering system). The EPS directly uses the steering column in combination with the lower rack, without electrical control.

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? The automatic driving vehicle's wire control system

, such as Infiniti Q50's steering system, is driven by a clutch to turn the steering column off. When the vehicle is started, the clutch is released. All the automatic driving commands transmit control commands through the ECU (electronic control element) to the two steering motors on the lower part for steering control.

The accelerator by wire is used to control the acceleration of the automatic driving vehicle. There is a position sensor on the deceleration brake pedal that can detect the brake depth. The sensor transmitsWhen EMS (engine brake system) is reached, the more air intake the valve, the faster acceleration.

?????????????????????????????????? - automatic driving vehicle's line control system

automatic driving vehicle at present mostly is the new energy source vehicle, the new energy vehicle completes the acceleration control by the drive motor torque control, from the entire line control, divides into three stages:

1.0 ? refit the steering wheel pedal of the original vehicle. After cutting off some steering columns, install a steering motor to conduct steering by controlling the motor. The disadvantage is that the system of the original vehicle has not been tested and verified, and there is a potential safety hazard.

2.0 based on the auxiliary driving system of the original vehicle, the CAN bus protocol is cracked, and the steering and braking of the vehicle are controlled by the command of the original vehicle bus.

3.0 a set of system developed from the vehicle chassis, the steering by wire control is completely customized according to the requirements of automatic driving, and the difference with 2.0 is that the redundancy and backup requirements are considered.

On July 4, 2018, the Apollo hardware development platform was officially released, with 15 new hardware manufacturers selecting models and also Apollo sensor units.

After adding the underlying abstraction layer (upgrading the original hardware reference design to a hardware development platform), the content of the hardware development platform is more abundant.

Some developers put forward the hardware reference design provided by BaiduAt present, it can't be bought or the supply cycle is very long.

Based on these demands, we have enriched the selection of hardware, and will release the sensors, industrial computers and control units that have passed our test and verification, so as to facilitate the selection of developers.

In the current reference design provided by Baidu, we divide it into Apollo platform certification and Apollo hardware development platform certification.

Apollo platform certification means that the sensors currently in use by Baidu are published after certification. For example, velodyne 64 line lidar is an Apollo platform certified product. We will provide data sets based on the sensors in use.

The authentication of Apollo hardware development platform is carried out at the level of Apollo code, such as the additional work of data acquisition and annotation of perception module and model training, which also needs to be completed by the developers themselves.

In the future, Apollo will continue to enrich the ecosystem, continue to provide chip and sensor support and selection.

The sensor box integrates all the sensor information into the sensor unit, completes the alignment of the whole time stamp, and transmits the pre-processing data to the back-end industrial computer computing unit for processing.

this is developed based on Baidu's use of sensors, not necessarily applicable to all developers. In the future, Apollo will introduce aXu expansion unit, and the unit with PCI card slot will be more flexible.

In the abstraction layer of Apollo, there areHardware interface, such as kernel driver, USP library, etc.

USP library is mainly used in CAN bus protocol. Because the CAN bus protocol of each vehicle factory / model / batch is different, the control instruction information is written in USP library for operation.

In the Apollo development platform, there is also Hal hardware abstraction layer, which is the middle development layer to prevent the whole system hardware kernel from crashing due to single hardware short circuit. Different hardware manufacturers can choose to open all the source code or publish the compiled code on the Apollo platform.

After Apollo completes the code entry, it will be published on GitHub. Developers do not need to develop different drivers for different hardware options.

Finally, it is the layout of the auto driving industry chain released by VSI.

The auto driving industry is a large-scale aggregation industry of 100000 scale industries, such as new energy, it, traffic communication, semiconductor artificial intelligence, mobile Internet, etc.

autopilot is the aggregate of material flow, energy flow and information flow. It is necessary to deeply integrate and cooperate with the hardware and software industry to ensure the success of the automatic driving industry.

A Bojun said:

in the learning process, developers can not only ask questions about the course content in the Applo developer community applet, but also exchange learning experience in the developer wechat group, and learn autonomous driving skills togetherOperation. The

developer community will also push the curriculum related technical dry cargo regularly on the official account, and will accompany you all the way to make progress. I wish the Apollo developers to successfully complete the advanced course of auto driving, and go further and further on the road of auto driving!

You are also welcome to post your learning notes in the Q & a section of the app. You can also join the developer communication community to exchange and interact with you, and have a good gift!

After watching / reading the course, upload the screenshot of your course notes in the developer community and the "community Q & a" section of the app. You can contact the community assistant to get a souvenir of Baidu's surroundings by hand written notes / computer documents. Let's hurry up and have a lift ~ Bixin ~

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miotsolutions · 4 years ago

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ketan-wagh121-blog · 4 years ago

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Impact of COVID-19 Outbreak on Geographic Information System (GIS) Market 2020

Global Geographic Information System (GIS) Industry

New Study On “2018-2022 Geographic Information System (GIS) Market Global Key Player, Demand, Growth, Opportunities and Analysis Forecast” Added to Wise Guy Reports Database

This report mainly introduces volume and value market share by players, by regions, by product type, by consumers and also their price change details. As a Detailed Analysis report, it covers all details inside analysis and opinion in Geographic Information System (GIS) industry.

This report splits Geographic Information System (GIS) market By Hardware, By Function, which covers the history data information from 2012 to 2016 and forecast from 2017 to 2022.

Try Sample Report @ https://www.wiseguyreports.com/sample-request/1870706-global-geographic-information-system-gis-detailed-analysis-report-2017-2022

This report focuses Global market, it covers details as following:

Major Companies Autodesk Inc. (US) Bentley System, Incorporated (US) Environmental Systems Research Institute, Inc. (Esri) (US) General Electric Co. (US) Hexagon AB (Sweden) Pitney Bowes Inc. (US) Topcon Corporation (Japan) Trimble Inc. (US) Blue Marble Geographics (US) Caliper Corporation (US) Computer Aided Development Corporation Limited (Cadcorp) (UK) Geosoft Inc. (Canada) Handheld Group (Sweden) Harris Corporation (US) Hi-Target Surveying Instrument Co. Ltd. (China) MacDonald, Dettwiler and Associates Ltd. (Canada) PASCO CORPORATION (Japan) Beijing Unistrong Science & Technology Co., Ltd. (China) Champion Instruments, LLC (US) Golden Software LLC (US) SuperMap Software Co., Ltd. ?(China)

Main Regions North America United States Canada Latin America Mexico Brazil Argentina Others Europe Germany United Kingdom France Italy Spain Russia Netherland Others Asia & Pacific China Japan India Korea Australia Southeast Asia Indonesia Thailand Philippines Vietnam Singapore Malaysia Others Africa & Middle East South Africa Egypt Turkey Saudi Arabia Iran Others

Main Product Type Geographic Information System (GIS) Market, by Hardware GIS Collectors Total Stations LIDAR GNSS/GPS Antennas Imaging Sensors Geographic Information System (GIS) Market, by Function Mapping Surveying Telematics and Navigation Location-Based Services

Main Applications Agriculture Construction Transportation Utilities Mining Oil & Gas

Also Read: http://heraldkeeper.com/tech/global-geographic-information-system-gis-market-2018-share-trend-segmentation-forecast-2022-98336.html

Some Major Points from Table of content:

Global Geographic Information System (GIS) Detailed Analysis Report 2017-2022 Chapter One Geographic Information System (GIS) Market Overview 1.1 Global Geographic Information System (GIS) Market Sales Volume Revenue and Price 2012-2022 1.2 Geographic Information System (GIS), By Hardware 2012-2022 1.2.1 Global Geographic Information System (GIS) Sales Market Share by Hardware 2012-2022 1.2.2 Global Geographic Information System (GIS) Revenue Market Share by Hardware 2012-2022 1.2.3 Global Geographic Information System (GIS) Price by Hardware 2012-2022 1.2.4 GIS Collectors 1.2.5 Total Stations 1.2.6 LIDAR 1.2.7 GNSS/GPS Antennas 1.2.8 Imaging Sensors 1.3 Geographic Information System (GIS), by Function 2012-2022 1.3.1 Global Geographic Information System (GIS) Sales Market Share by Function 2012-2022 1.3.2 Global Geographic Information System (GIS) Revenue Market Share by Function 2012-2022 1.3.3 Global Geographic Information System (GIS) Price by Function 2012-2022 1.3.4 Mapping 1.3.5 Surveying 1.3.6 Telematics and Navigation 1.3.7 Location-Based Services

Chapter Two Geographic Information System (GIS) by Regions 2012-2017 2.1 Global Geographic Information System (GIS) Sales Market Share by Regions 2012-2017 2.2 Global Geographic Information System (GIS) Revenue Market Share by Regions 2012-2017 2.3 Global Geographic Information System (GIS) Price by Regions 2012-2017 2.4 North America 2.4.1 United States 2.4.2 Canada 2.5 Latin America 2.5.1 Mexico 2.5.2 Brazil 2.5.3 Argentina 2.5.4 Others in Latin America 2.6 Europe 2.6.1 Germany 2.6.2 United Kingdom 2.6.3 France 2.6.4 Italy 2.6.5 Spain 2.6.6 Russia 2.6.7 Netherland 2.6.8 Others in Europe 2.7 Asia & Pacific 2.7.1 China 2.7.2 Japan 2.7.3 India 2.7.4 Korea 2.7.5 Australia 2.7.6 Southeast Asia 2.7.6.1 Indonesia 2.7.6.2 Thailand 2.7.6.3 Philippines 2.7.6.4 Vietnam 2.7.6.5 Singapore 2.7.6.6 Malaysia 2.7.6.7 Others in Southeast Asia 2.8 Africa & Middle East 2.8.1 South Africa 2.8.2 Egypt 2.8.3 Turkey 2.8.4 Saudi Arabia 2.8.5 Iran 2.8.6 Others in Africa & Middle East

Chapter Three Geographic Information System (GIS) by Players 2012-2017 3.1 Global Geographic Information System (GIS) Sales Volume Market Share by Players 2012-2017 3.2 Global Geographic Information System (GIS) Revenue Share by Players 2012-2017 3.3 Global Top Players Geographic Information System (GIS) Key Product Model and Market Performance 3.4 Global Top Players Geographic Information System (GIS) Key Target Consumers and Market Performance

Chapter Four Geographic Information System (GIS) by Consumer 2012-2017 4.1 Global Geographic Information System (GIS) Sales Market Share by Consumer 2012-2017 4.2 Agriculture 4.3 Construction 4.4 Transportation 4.5 Utilities 4.6 Mining 4.7 Oil & Gas 4.8 Consuming Habit and Preference

Chapter Five Global Top Players Profile 5.1 Autodesk Inc. (US) 5.1.1 Autodesk Inc. (US) Company Details and Competitors 5.1.2 Autodesk Inc. (US) Key Geographic Information System (GIS) Models and Performance 5.1.3 Autodesk Inc. (US) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.1.4 Autodesk Inc. (US) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross Margin 5.2 Bentley System, Incorporated (US) 5.2.1 Bentley System, Incorporated (US) Company Details and Competitors 5.2.2 Bentley System, Incorporated (US) Key Geographic Information System (GIS) Models and Performance 5.2.3 Bentley System, Incorporated (US) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.2.4 Bentley System, Incorporated (US) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross Margin 5.3 Environmental Systems Research Institute, Inc. (Esri) (US) 5.3.1 Environmental Systems Research Institute, Inc. (Esri) (US) Company Details and Competitors 5.3.2 Environmental Systems Research Institute, Inc. (Esri) (US) Key Geographic Information System (GIS) Models and Performance 5.3.3 Environmental Systems Research Institute, Inc. (Esri) (US) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.3.4 Environmental Systems Research Institute, Inc. (Esri) (US) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross Margin 5.4 General Electric Co. (US) 5.4.1 General Electric Co. (US) Company Details and Competitors 5.4.2 General Electric Co. (US) Key Geographic Information System (GIS) Models and Performance 5.4.3 General Electric Co. (US) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.4.4 General Electric Co. (US) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross Margin 5.5 Hexagon AB (Sweden) 5.5.1 Hexagon AB (Sweden) Company Details and Competitors 5.5.2 Hexagon AB (Sweden) Key Geographic Information System (GIS) Models and Performance 5.5.3 Hexagon AB (Sweden) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.5.4 Hexagon AB (Sweden) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross Margin 5.6 Pitney Bowes Inc. (US) 5.6.1 Pitney Bowes Inc. (US) Company Details and Competitors 5.6.2 Pitney Bowes Inc. (US) Key Geographic Information System (GIS) Models and Performance 5.6.3 Pitney Bowes Inc. (US) Geographic Information System (GIS) Business SWOT Analysis and Forecast 5.6.4 Pitney Bowes Inc. (US) Geographic Information System (GIS) Sales Volume Revenue Price Cost and Gross

Continued…..

For more information or any query mail at [email protected]

About Us Wise Guy Reports is part of the Wise Guy Consultants Pvt. Ltd. and offers premium progressive statistical surveying, market research reports, analysis & forecast data for industries and governments around the globe. Wise Guy Reports understand how essential statistical surveying information is for your organization or association. Therefore, we have associated with the top publishers and research firms all specialized in specific domains, ensuring you will receive the most reliable and up to date research data available.

Contact Us: Norah Trent +1 646 845 9349 / +44 208 133 9349 Follow on LinkedIn: https://www.linkedin.com/company/wise-guy-research-consultants-pvt-ltd-?trk=biz-companies-cym

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gisforlife · 4 years ago

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Garmin GPSMAP 66st GPS Review

Our Verdict

Garmin GPSMAP 66st GPS

GPS Rugged Multi-satellite Handheld with Sensors and Topo Maps, 3″ Color Display

Premium GPS handheld with Birdseye Satellite Imagery membership and TOPO maps

Enormous, 3″ daylight intelligible shading show for simple review. Show size – 1.5 W x 2.5 H inch (3.8 x 6.3 cm); 3 inch diag (7.6 cm)

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Admittance to Birdseye Satellite Imagery with direct-to-gadget downloads and no yearly membership and preloaded TOPO U.S. also, Canada maps

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Performance Comparison Products Table

ImageProductPrice

9.7

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GPS Vehicle Tracker Includes 12 Months of Premium Service

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9.6

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9.6

AES RGT90 GPS Tracker GPRS Mini Portable VehicleBest Deal

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Price

Ease of Use

Display Quality

With the transflective TFT display on a three-inch screen, the Garmin 66st model is basically readable, even in sunlight, which is basically nice on snow or on a bright, sunny day.

Speed

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Versatility

Value

Conclusion

Garmin GPSMAP 66st GPS Model

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The post Garmin GPSMAP 66st GPS Review appeared first on GIS for LIfe.

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