Introduction​

Technological innovation is a fundamental lever for improving the efficiency and sustainability of agricultural companies. Mative, a leading company in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services capable of revolutionizing agricultural practices. This report explores the energy and economic benefits derived from adopting these technologies in an agricultural company.

Internet of Things (IoT) in Agriculture​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In agriculture, IoT sensors can monitor various parameters such as soil moisture, temperature, air quality, and crop conditions.

Energy Benefits​

1. Optimization of Irrigation: Soil moisture sensors allow for irrigation only when necessary, reducing water and energy waste used for pumping.
2. Intelligent Management of Heating and Ventilation Systems: Temperature and humidity sensors help maintain optimal conditions for crops, minimizing unnecessary heating and ventilation use.
3. Reduction of Energy Consumption of Agricultural Machinery: Tractors and other machines equipped with IoT sensors can operate more efficiently, reducing fuel consumption through optimized routes and operations.

Economic Benefits​

1. Increased Productivity: Continuous and precise monitoring of environmental conditions allows for timely interventions, improving crop health and yield.
2. Reduction of Operating Costs: Optimization of resources such as water and energy leads to significant reductions in operating costs.
3. Predictive Maintenance: Data collected from IoT sensors allows for predicting equipment failures, reducing downtime and repair costs.

Machine Learning (ML) in Agriculture​

Definition and Operation​

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In agriculture, ML can be used to analyze data collected from IoT devices, identifying patterns and trends.

Energy Benefits​

1. Advanced Weather Forecasting: ML algorithms can analyze weather data to predict climatic conditions, helping farmers plan irrigation and other activities more efficiently.
2. Resource Optimization: ML can analyze historical data to suggest optimal use of resources such as fertilizers and pesticides, reducing environmental impact and energy consumption.

Economic Benefits​

1. Crop Management: Predictive data analysis allows for timely identification of diseases or infestations, reducing crop losses and improving product quality.
2. Production Planning: ML can help predict market demand, optimizing production and reducing waste.
3. Marketing and Sales: Market data analysis allows for optimizing sales and pricing strategies, improving the company's competitiveness.

IoT & ML: Implementation in an Agricultural Company​

Consider a medium-sized agricultural company that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

1. 30% Reduction in Water Consumption: Thanks to the optimization of irrigation based on real-time data.
2. 25% Decrease in Energy Costs: Through more efficient management of heating, ventilation, and agricultural machinery.
3. 20% Increase in Production: Due to better crop management and timely interventions against diseases and infestations.
4. 15% Savings in Operating Costs: Thanks to predictive maintenance and optimized resource use.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant turning point for agricultural companies seeking to improve their energy and economic efficiency. The benefits derived from implementing these technologies not only contribute to environmental sustainability but also increase the competitiveness and profitability of agricultural companies, positioning them well to face future industry challenges.

Introduction​

Technological innovation is a fundamental lever for improving the efficiency and sustainability of construction companies. Mative, a leading company in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services capable of revolutionizing practices in the construction sector. This report explores the energy and economic benefits derived from adopting these technologies in a construction company.

Internet of Things (IoT) in Construction​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In construction, IoT sensors can monitor various parameters such as the energy consumption of machinery, environmental conditions on construction sites, equipment maintenance, and worker safety.

Energy Benefits​

1. Optimization of Machinery Energy Consumption: IoT sensors can monitor the energy consumption of machinery, identifying inefficiencies and optimizing energy use.
2. Intelligent Climate Management on Construction Sites: Temperature and humidity sensors help maintain optimal environmental conditions on construction sites, minimizing unnecessary heating and cooling.
3. Monitoring of Construction Processes: IoT sensors can monitor construction processes in real-time, suggesting modifications to improve energy efficiency and reduce waste.

Economic Benefits​

1. Reduction of Operating Costs: Optimizing energy resources and production processes leads to significant reductions in operating costs.
2. Predictive Maintenance: Data collected from IoT sensors allows for predicting equipment failures, reducing downtime and repair costs.
3. Improvement of Project Quality: Real-time monitoring of construction conditions improves the quality of finished projects, reducing costs associated with defects and delays.

Machine Learning (ML) in Construction​

Definition and Operation​

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In construction, ML can be used to analyze data collected from IoT devices, identifying patterns and trends.

Energy Benefits​

1. Energy Consumption Forecasting: ML algorithms can analyze historical and current data to predict future energy consumption, helping to better plan resource use.
2. Optimization of Construction Processes: ML can suggest improvements in construction processes to reduce energy consumption based on operational efficiency data.

Economic Benefits​

1. Resource Management: Predictive data analysis allows for more efficient use of materials and labor, reducing waste and optimizing costs.
2. Project Planning: ML can help predict delays and issues in construction projects, improving planning and reducing unexpected costs.
3. Supply Chain Optimization: Analysis of procurement data allows for optimizing supply chain management, reducing logistics costs and improving material availability.

IoT & ML: Implementation in a Construction Company​

Consider a construction company that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

1. 20% Reduction in Energy Consumption: Thanks to the optimization of electrical and heating/cooling energy use on construction sites.
2. 15% Decrease in Operating Costs: Through more efficient resource management and predictive maintenance of equipment.
3. 10% Improvement in Project Quality: Due to continuous monitoring of construction conditions, reducing defects and delays.
4. 10% Savings in Maintenance Costs: Thanks to predictive maintenance and analysis of equipment data.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant turning point for construction companies seeking to improve their energy and economic efficiency. The benefits derived from implementing these technologies not only contribute to environmental sustainability but also increase the competitiveness and profitability of construction companies, positioning them well to face future industry challenges.

Introduction​

Technological innovation plays a key role in enhancing efficiency and sustainability in manufacturing companies. Mative, a leader in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services that can revolutionize operations in the window, door, and frame manufacturing sector. This report explores the energy and economic benefits derived from adopting these technologies in a manufacturing company.

Internet of Things (IoT) in Manufacturing​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In manufacturing, IoT sensors can monitor various parameters such as machinery performance, energy consumption, production quality, and supply chain conditions.

Energy Benefits​

1. Optimization of Machinery Energy Use: IoT sensors can monitor the energy consumption of machinery and equipment, identifying inefficiencies and suggesting improvements to reduce energy use.
2. Efficient Facility Management: Sensors can control heating, ventilation, and air conditioning (HVAC) systems based on real-time data, reducing unnecessary energy consumption.
3. Improved Production Processes: Monitoring equipment conditions in real-time helps optimize production processes, reducing energy waste associated with machinery downtime or suboptimal performance.

Economic Benefits​

1. Reduction of Operating Costs: Efficient management of energy consumption and machinery performance leads to significant reductions in operating costs.
2. Enhanced Production Efficiency: Real-time monitoring and optimization improve production efficiency, leading to higher output and reduced waste.
3. Predictive Maintenance: Data from IoT sensors enable predictive maintenance, reducing downtime and extending the lifespan of machinery, thereby saving on repair and replacement costs.

Machine Learning (ML) in Manufacturing​

Definition and Operation​

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In manufacturing, ML can analyze data from IoT devices to identify patterns, forecast trends, and optimize processes.

Energy Benefits​

1. Predictive Energy Management: ML algorithms can analyze historical and real-time data to forecast future energy needs, allowing for more precise energy management and reducing overall consumption.
2. Optimization of Production Schedules: ML can optimize production schedules based on data analysis, leading to better energy utilization and reduced peak demand.

Economic Benefits​

1. Process Optimization: ML helps in identifying the most efficient production processes, leading to cost savings and increased productivity.
2. Demand Forecasting: ML algorithms can predict market demand more accurately, allowing for better inventory and production planning, reducing excess inventory and associated costs.
3. Quality Improvement: ML can analyze data to identify quality issues early in the production process, reducing defects and waste, and improving overall product quality.

IoT & ML: Implementation in a Window, Door, and Frame Manufacturing Company​

Consider a manufacturing company specializing in windows, doors, and frames that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

1. 20% Reduction in Energy Consumption: Through optimized machinery use and efficient facility management based on real-time data.
2. 15% Decrease in Operating Costs: Due to enhanced production efficiency and reduced waste.
3. 25% Improvement in Production Efficiency: Resulting from optimized production processes and better use of resources.
4. 10% Savings in Maintenance Costs: Thanks to predictive maintenance and extended machinery lifespan.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant advancement for manufacturing companies specializing in windows, doors, and frames. The benefits from implementing these technologies not only contribute to energy and economic efficiency but also enhance competitiveness and profitability, positioning the company to effectively meet future industry challenges.

Introduction​

Technological innovation is crucial for improving the efficiency and sustainability of public transport companies. Mative, a leader in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services capable of revolutionizing operations in the public transport sector. This report explores the energy and economic benefits derived from adopting these technologies in a public transport company.

Internet of Things (IoT) in Public Transport​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In public transport, IoT sensors can monitor various parameters such as fuel consumption, vehicle conditions, traffic, and vehicle routes.

Energy Benefits​

1. Optimization of Fuel Consumption: IoT sensors can monitor real-time fuel consumption, suggesting more efficient routes and driving practices.
2. Preventive Maintenance: Data collected from sensors helps identify mechanical issues before they become serious, reducing energy consumption due to inefficiencies.
3. Intelligent Route Management: By monitoring traffic in real-time, IoT systems can suggest detours to avoid congestion, reducing travel time and energy consumption.

Economic Benefits​

1. Reduction of Operating Costs: Optimization of fuel consumption and preventive maintenance significantly reduces operating costs.
2. Increased Vehicle Efficiency: Intelligent route and vehicle condition management improves vehicle efficiency, lowering operating costs.
3. Improvement in Service Quality: Real-time monitoring of vehicle conditions and routes enables more reliable and punctual service, increasing customer satisfaction.

Machine Learning (ML) in Public Transport​

Definition and Operation​

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In public transport, ML can be used to analyze data collected from IoT devices, identifying patterns and trends.

Energy Benefits​

1. Fuel Consumption Forecasting: ML algorithms can analyze historical and current data to predict future fuel consumption, helping to better plan resource use.
2. Optimization of Routes: ML can suggest alternative routes based on traffic and energy consumption data, reducing travel time and fuel use.

Economic Benefits​

1. Resource Management: Predictive data analysis allows for more efficient use of resources, reducing waste and optimizing costs.
2. Service Planning: ML can help forecast service demand and optimize route planning and frequencies, improving operational efficiency.
3. Maintenance Optimization: Analyzing vehicle data allows for more efficient scheduling of maintenance, reducing downtime and repair costs.

IoT & ML: Implementation in a Public Transport Company​

Consider a public transport company that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

1. 15% Reduction in Fuel Consumption: Thanks to optimization of routes and driving practices based on real-time data.
2. 20% Decrease in Operating Costs: Through more efficient resource management and preventive vehicle maintenance.
3. 25% Increase in Service Punctuality: Due to intelligent route management and continuous monitoring of traffic conditions.
4. 10% Savings in Maintenance Costs: Thanks to predictive maintenance and vehicle data analysis.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant turning point for public transport companies seeking to improve their energy and economic efficiency. The benefits derived from implementing these technologies not only contribute to environmental sustainability but also increase the competitiveness and profitability of public transport companies, positioning them well to face future industry challenges.

What is the CAN BUS system?​

In an automotive CAN BUS system, Electronic Control Units (ECUs) can be, for example, the engine control, airbags, audio system, etc. A modern car can have up to 70 ECUs - and each of them can have information that needs to be shared with other parts of the network.

Here's where the CAN standard comes into play:​

The CAN BUS system allows each ECU to communicate with all the other ECUs - without complex dedicated wiring. Specifically, an ECU can prepare and transmit information (such as sensor data) via the CAN BUS (composed of two wires, CAN low and CAN high). The transmitted data is accepted by all the other ECUs on the CAN network - and each of them can then check the data and decide whether to receive or ignore it.

Physical layer and data link layer of the CAN BUS (OSI)​

In more technical terms, the area control network is described by a data link layer and a physical layer. In the case of high-speed CAN, ISO 11898-1 describes the data link layer, while ISO 11898-2 describes the physical layer. The role of CAN is often presented in the OSI 7-layer model as illustrated.

The physical layer of the CAN BUS defines things like cable types, electrical signal levels, node requirements, cable impedance, etc. For example, ISO 11898-2 standardizes a number of things, including the following:

• Baud rate: CAN nodes must be connected via a two-wire bus with baud rates up to 1 Mbit/s (Classical CAN) or 5 Mbit/s (CAN FD).
• Cable length: The maximum lengths of CAN cable should be between 500 meters (125 kbit/s) and 40 meters (1 Mbit/s).
• Termination: The CAN BUS must be properly terminated using a 120-ohm CAN bus termination resistor at each end of the bus.

High-speed CAN, Low-speed CAN, LIN BUS,...​

In the context of automotive vehicle networks, you are likely to encounter various types of networks. Below we provide a brief overview:

High-speed CAN BUS: The focus of this article is on high-speed CAN BUS (ISO 11898). It is by far the most popular CAN standard for the physical layer, supporting transmission speeds from 40 kbit/s to 1 Mbit/s (Classical CAN). It provides simple wiring and is used virtually in all modern automotive applications. It also serves as the basis for several higher-level protocols such as OBD2, J1939, NMEA 2000, CANopen, etc. The second generation of CAN is called CAN FD (Flexible Data Rate).

Low-speed CAN BUS: This standard allows transmission speeds from 40 kbit/s to 125 kbit/s and enables communication of the CAN BUS even if there is a fault on one of the two wires - hence it is also called 'fault-tolerant CAN BUS'. In this system, each CAN node has its CAN termination.

LIN BUS: The LIN BUS is a low-cost supplement to CAN BUS networks, with less wiring and cheaper nodes. LIN BUS clusters typically consist of a LIN master acting as a gateway and up to 16 slave nodes. Typical use cases include non-critical vehicle functions such as air conditioning, door functionality, etc. - for details see our introduction to LIN BUS or the LIN BUS data logger article.

Automotive Ethernet: This is increasingly being introduced in the automotive sector to support the broadband requirements of Advanced Driver Assistance Systems (ADAS), infotainment systems, cameras, etc. Automotive Ethernet offers much higher data transfer speeds than CAN BUS, but lacks some of the security/performance features of Classical CAN and CAN FD. Most likely, in the coming years, both Automotive Ethernet and CAN FD and CAN XL will be used in automotive and industrial development.

The 4 main advantages of the CAN BUS​

The CAN BUS standard is used virtually in all vehicles and many machines thanks to the following key advantages:

The history of the CAN BUS in brief

Pre-CAN: Car ECUs relied on complex point-to-point wiring.

• 1986: Bosch developed the CAN protocol as a solution.
• 1991: Bosch released CAN 2.0 (CAN 2.0A: 11 bit, 2.0B: 29 bit).
• 1993: CAN is adopted as an international standard (ISO 11898).
• 2003: ISO 11898 becomes a standard series.
• 2012: Bosch released CAN FD 1.0 (Flexible Data Rate).
• 2015: The CAN FD protocol is standardized (ISO 11898-1).
• 2016: The CAN physical layer for data rates up to 5 Mbit/s is standardized in ISO 11898-2.

Today, CAN is a standard in cars (cars, trucks, buses, tractors, ...), ships, airplanes, EV batteries, machinery, and more.

The future of the CAN BUS​

Looking ahead, the CAN BUS protocol will remain relevant - although it will be influenced by significant trends:

A need for increasingly advanced vehicle functionalities. The rise of cloud computing. Growth of the Internet of Things (IoT) and connected vehicles. The impact of autonomous vehicles. The use of AI in data analysis (see e.g. our introduction to ChatGPT + CAN BUS).

In particular, the increase in connected vehicles (V2X) and the cloud will lead to rapid growth in vehicle telemetry and CAN BUS IoT data loggers.

In turn, bringing the CAN BUS network 'online' also exposes vehicles to security risks - and may require a transition to new CAN BUS protocols such as CAN FD.

The growth of CAN FD​

With the expansion of vehicle functionalities, the load on the CAN BUS also increases. To support this, CAN FD (Flexible Data Rate) has been designed as the 'next generation' of the CAN BUS.

In particular, CAN FD offers three advantages (compared to Classical CAN):

• Allows data rates up to 8 Mbit/s (compared to 1 Mbit/s).
• Allows data payloads up to 64 bytes (compared to 8 bytes).
• Provides improved security through authentication.

In short, CAN FD increases speed and efficiency - and is therefore being implemented in newer vehicles. This will also increase the need for CAN FD IoT data loggers.

"The first vehicles using CAN FD will appear in 2019/2020 and CAN FD will gradually replace Classical CAN."

CAN in Automation (CiA), "CAN 2020: The future of CAN technology" -Learn more-

What is a CAN frame?​

Communication via the CAN BUS occurs through CAN frames.

Below is a standard CAN frame with an 11-bit identifier (CAN 2.0A), which is the type used in most cars. The extended frame with a 29-bit identifier (CAN 2.0B) is identical except for the longer ID. It is for example used in the J1939 protocol for heavy vehicles.

Note that the CAN ID and the data are highlighted - these are important when logging CAN BUS data, as we'll see below.

The CAN Bus protocol message field​

• SOF: The start of the frame is a 'dominant 0' to inform other nodes that a CAN node intends to speak.
• ID: The ID is the frame identifier - lower values have higher priority.
• RTR: The remote transmission request indicates whether a node is sending data or requesting dedicated data from another node.
• Control: The Control contains the Identifier Extension bit (IDE) which is a 'dominant 0' for 11 bit. It also contains the data length code as a 4-bit value (DLC) specifying the length of the data bytes to be transmitted (from 0 to 8 bytes).
• Data: The data contains the data bytes aka payload, which includes the CAN signals that can be extracted and decoded for information.
• CRC: The Cyclic Redundancy Check is used to ensure data integrity.
• ACK: The ACK slot indicates whether the node has received and correctly acknowledged the data.
• EOF: EOF marks the end of the CAN frame.

CAN BUS errors​

The CAN frame must meet a series of properties to be valid. If an erroneous CAN frame is transmitted, CAN nodes will automatically detect this and take necessary measures. This is defined as CAN BUS error handling, where CAN nodes keep track of their own 'CAN error counters' and change states (active, passive, bus off) depending on their counters. The ability of problematic CAN nodes to transmit data is thus delicately reduced to avoid further CAN BUS errors and bus jams. For details, see our introduction to CAN BUS error handling.

CAN data logging - illustrative use cases​

There are several common use cases for logging CAN BUS data:

How to log CAN BUS data

As mentioned, two CAN fields are important for CAN logging: The CAN ID and the Data.

To log CAN data, a CAN logger is required. This allows logging CAN data with timestamps on an SD card. In some cases, a CAN interface is needed to transmit the data to a PC - e.g. for car hacking.

Connecting to the CAN BUS​

The first step is to connect the CAN logger to the CAN BUS. Typically, this involves using an adapter cable:

• Auto: In most cars, simply using an OBD2 adapter is sufficient for connection. In most cars, this will allow you to log raw CAN data, as well as perform requests to log OBD2 or UDS (Unified Diagnostic Services) data.
• Heavy vehicles: To log J1939 data from trucks, excavators, tractors, etc., you can connect to the J1939 CAN BUS via a standard J1939 connector cable (9-pin deutsch).
• Marine: Most ships/boats use the NMEA 2000 protocol and enable connection via an M12 adapter to log marine data.
• CANopen: For logging CANopen, it is often possible to directly use the CiA 303-1 DB9 connector (i.e. the default connector for our CAN data loggers), optionally with a CAN BUS extension.
• Contactless: If no connector is available, a typical solution is to use a contactless CAN reader - e.g. the CANCrocodile. This allows you to log data directly from the raw CAN twisted pair wiring, without direct connection to the CAN BUS (often useful for warranty purposes).
• Other: In practice, countless other connectors are used and often a custom CAN BUS adapter needs to be created - in this case, a generic open-wire adapter is helpful.

Once the correct connector has been identified and the pin-out verified, you can connect the CAN logger to start logging data. For the CANedge/CLX000, the CAN baud rate is automatically detected and the device will immediately start logging raw CAN data.

Example: raw CAN data sample (J1939)

You can optionally download OBD2 and J1939 data samples from the CANedge2 in our introductory documents. You can upload for example this data into free CAN BUS decoding software tools.

CANedge data is logged in popular binary format MF4 and can be converted to any file format using our simple MF4 converters (e.g. to CSV, ASC, TRC, ...).

Below is an example CSV of raw CAN frames logged from a heavy truck using the J1939 protocol. Note that the CAN IDs and data bytes are in hexadecimal format:

Example: CAN BUS logger CANedge

The CANedge1 allows you to easily log data from any CAN BUS on an 8-32 GB SD card. Simply connect it to e.g. a car or a truck to start logging - and decode the data using free software/API.

Additionally, the CANedge2 (WiFi) and CANedge3 (3G/4G) allow you to send the data to your own server - and update the devices over-the-air. -Learn more about the CANedge-

How to decode raw CAN data into 'physical values'​

If you examine the raw CAN data sample above, you will likely notice something:

Raw CAN data is not readable by humans.

To interpret it, you need to decode the CAN frames into scaled engineering values aka physical values (km/h, °C, ...).

Below we show step by step how it works:

Extracting CAN signals from raw CAN frames

Each CAN frame on the bus contains a number of CAN signals (parameters) within the CAN data bytes. For example, a CAN frame with a specific CAN ID may carry data for e.g. 2 CAN signals.

To extract the physical value of a CAN signal, the following information is required:

• Start bit: At which bit the signal starts.
• Length in bits: The length of the signal in bits.
• Offset: Value to offset the signal value.
• Scale: Value to multiply the signal value.

To extract a CAN signal, you 'clip' the relevant bits, take the decimal value, and perform a linear scaling:

physical_value = offset + scale * raw_decimal_value

The challenge of proprietary CAN data

Very often, the 'CAN decoding rules' are proprietary and not easily available (except for the OEM, i.e., the original manufacturer). There are several solutions to this when you are not the OEM:

• Log J1939 data: If you are logging raw data from heavy vehicles (trucks, tractors, ...), you are probably logging J1939 data. This is standardized across brands - and you can use our J1939 DBC file to decode the data. Also, see our introduction to J1939 data logger.
• Log OBD2/UDS data: If you need to log data from cars, you can request OBD2/UDS data, which is a standardized protocol across cars. For details see our introduction to OBD2 data logger and our free OBD2 DBC file.
• Use public DBC files: For cars, there are online databases where others have decoded some of the proprietary CAN data. We maintain a list of such databases in our introduction to DBC file.
• Reverse engineer the data: You can also try to reverse engineer the data yourself using a CAN BUS sniffer, although it can be laborious and challenging.
• Use sensor modules: In some cases, you may need sensor data that is not available on the CAN BUS (or is difficult to reverse engineer). Here, CAN sensor modules like the CANmod series can be used. You can integrate such modules with your CAN BUS, or use them as add-ons to your CAN logger to add data such as GNSS/IMU or temperature data.
• Collaborate with the OEM: In some cases, you can also collaborate with the OEM to get proprietary decoding rules. This may be necessary for optimizing vehicle control parameters or for debugging/diagnostics.

Real-time CAN decoding​

Our site supports decoding of CAN frames in real-time for diagnosis/troubleshooting or real-time vehicle monitoring. We specialize in decoding:

• OBD2: Including support for OBD2 PID and the entire SAE J1979 standard PIDs.
• J1939: Support for standard J1939 parameters including J1939 PGNs, SPNs, etc.
• NMEA 2000: Support for standard NMEA 2000 data, including NMEA 2000 PGN messages.

Our fleet monitoring software is designed to support real-time CAN BUS data analysis across a wide range of industries and use cases, such as:

• Remote diagnostics: Monitor real-time CAN BUS data to identify vehicle issues - e.g. in the field of a broken car.
• Vehicle safety: Monitor real-time CAN BUS data to identify hazardous driving situations (e.g. driver behavior) or malfunctioning vehicles.
• Autonomous deployment: Monitor real-time CAN BUS data to monitor autonomous vehicles (e.g. drones, robots) and ensure they are functioning properly.
• Fleet: Monitor real-time CAN BUS data for predictive fleet maintenance and optimize vehicle downtime.
• Cargo tracking: Monitor real-time CAN BUS data to track the position and condition of cargo in transit.

Additionally, we support a wide range of hardware for capturing real-time CAN BUS data, such as:

• OBD2 interfaces: Support for standard and advanced OBD2 interfaces to capture real-time data directly from the ECU.
• OBD2 gateways: Support for OBD2 gateways to capture and transmit real-time CAN BUS data to a remote monitoring platform.
• J1939 interfaces: Support for J1939 interfaces to capture real-time data directly from the ECU.
• J1939 gateways: Support for J1939 gateways to capture and transmit real-time CAN BUS data to a remote monitoring platform.
• NMEA 2000 interfaces: Support for NMEA 2000 interfaces to capture real-time data directly from the NMEA 2000 BUS.
• NMEA 2000 gateways: Support for NMEA 2000 gateways to capture and transmit real-time CAN BUS data.

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Mative employs these predictive algorithms on its time series data. Below is a brief description of how they work and a hint on how they can be used.

Linear Regression​

Linear regression is a statistical method that models the relationship between a dependent variable (target) and one or more independent variables (predictors) using a straight line. The formula is:

y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \ldots + \beta_nx_n + \epsilon

Where:

• y is the dependent variable
• x_i are the independent variables
• \beta_i are the coefficients
• \epsilon is the residual error

OLS Linear Regression​

OLS (Ordinary Least Squares) linear regression is a specific form of linear regression that minimizes the sum of the squares of the differences between the observed values and the predicted values. Essentially, it finds the best-fit line that minimizes the mean squared error:

 \text{Minimize} \sum (y_i - \hat{y}_i)^2 

Where:

• y_i are the observed values.
• \hat{y}_i are the predicted values.

ARIMA​

ARIMA (AutoRegressive Integrated Moving Average) is a model used for time series analysis that combines three components:

1. AutoRegressive (AR): the model uses past values to predict future values.
2. Integrated (I): it differentiates the data to make it stationary.
3. Moving Average (MA): it uses past errors in predicting future values.

The model is often written as ARIMA(p, d, q), where:

• p is the order of the autoregressive term.
• d is the number of differences needed to make the series stationary.
• q is the order of the moving average term.

Fourier Transformation​

The Fourier transformation is a mathematical method for transforming a function from the time domain to the frequency domain. It is used to analyze the frequency components of signals. In the context of time series, it can be used to identify cycles or periodic patterns:

 F(k) = \sum_{n=0}^{N-1} x(n) e^{-2\pi i \frac{kn}{N}} 

Where:

• x(n) is the signal in the time domain.
• F(k) is the representation in the frequency domain.
• N is the number of samples.
• i is the imaginary unit.

If you have any questions about these algorithms, feel free to ask!

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Warning and monitoring systems in Agriculture​

Warning systems in agriculture play a fundamental role in monitoring environmental conditions, plant diseases, pests and adverse weather conditions. These systems provide farmers with timely and relevant information to make informed decisions regarding agricultural practices, improving productivity and reducing losses. In this paper, we will explore the software used to develop warning systems in agriculture and provide some examples of such systems.

Software for Warning Systems in Agriculture​

1. OpenWeatherMap API​

Description: OpenWeatherMap provides global weather data through an API that can be integrated into agricultural warning systems.

Main features:

• Provides real-time weather data and long-term forecasts.
• Includes detailed information such as temperature, humidity, wind speed, precipitation and more.
  Use: Integration with warning systems to alert farmers in case of adverse weather conditions.

2. Crop Disease Prediction using Machine Learning​

Description: This software uses machine learning algorithms to predict the spread of plant diseases based on historical and current data.
Main features:

• Analyze data relating to environmental conditions, crop type and previous disease attacks.
• Identify patterns and correlations to predict the spread of plant diseases.
  Use: Alerts farmers in advance of potential plant disease outbreaks, allowing them to take preventative measures.

3. Agricultural Decision Support Systems (ADSS)​

Description: ADSS are systems that integrate data from various sources to support agricultural decisions.
Main features:

• They collect and analyze weather data, soil data, crop data and more.
• They provide personalized recommendations to farmers based on local conditions.
  Use: Helps farmers plan agricultural operations efficiently and mitigate climate-related and plant disease risks.

Examples of Warning Systems in Agriculture​

1. Early Warning System for Plant Diseases​

Description: This system integrates weather data, crop data and predictive models to warn farmers in advance of the possible spread of plant diseases.
Functionality:

• Constantly monitor environmental conditions and crop health.
• It uses machine learning algorithms to predict the spread of specific diseases.
• Send alerts to farmers via SMS or mobile app.
  Benefits: Reduces losses caused by plant diseases and optimizes the use of pesticides.

2. Weather Alert System for Precision Agriculture​

Description: This system uses real-time weather data and forecast models to alert farmers

Smart Farming Revolution: Software, Examples and New Ideas​

Smart farming, or intelligent agriculture, is revolutionizing the agricultural sector through the innovative use of technology. This modern approach to farming uses advanced software, IoT (Internet of Things) sensors, data analytics and other technologies to optimize farming practices, improve productivity, reduce costs and mitigate environmental impacts. In this article, we will explore the key role of software in smart farming, provide examples of successful implementations, and discuss new ideas to further improve this agricultural revolution.

The Role of Software in Smart Farming​

Software plays a vital role in smart farming, allowing farmers to collect, analyze and use real-time data to make informed decisions. The main software features in smart farming include:

1. Agricultural Data Management​

• Collection and storage of data from sensors, agricultural machinery and other sources
• Efficiently organize and manage agricultural data to enable in-depth analysis

2. Monitoring of Crop Conditions​

• Using IoT sensors to monitor plant growth, soil quality, humidity and other key parameters
• Data analysis to identify trends and anomalies in crop conditions

3. Resource Optimization​

• Optimize the use of water, fertilizers and pesticides through data-driven dosing systems
• Monitoring energy consumption and optimizing the use of agricultural machinery

4. Forecasting and Planning​

• Using predictive models based on historical and current data to predict crop yields, plant diseases and other factors
• Planning agricultural activities based on forecasts to maximize yields and reduce risks

Examples of Software-based Smart Farming​

1. Precision Agriculture​

Precision agriculture uses detailed data on soil and crop conditions to tailor agricultural practices in a targeted manner. Precision agriculture software integrates data from sensors, drones and satellite images to optimize irrigation, fertilization and disease management.

2. Livestock Management​

Livestock management software allows farmers to monitor animal welfare, milk and meat production and health parameters in real time. These systems improve productivity and animal welfare through continuous monitoring and proactive management.

3. Monitoring of Plant Diseases​

Plant disease monitoring systems use weather data, satellite imagery and image analysis to identify early signs of disease and pests in crops. This allows farmers to intervene quickly to prevent the spread of diseases and limit damage to crops.

New Ideas to Improve Smart Farming​

1. Artificial Intelligence for the Diagnosis of Plant Diseases​

Using artificial intelligence algorithms to analyze plant images could enable faster and more accurate diagnosis of diseases. This would help farmers make timely decisions on the use of plant protection treatments and crop management.

2. Blockchain for Food Traceability​

Integrating blockchain technology into smart farming could ensure greater transparency and traceability throughout the entire food supply chain. This would allow consumers to access detailed information on the provenance and quality of food.

3. Vertical and Indoor Agriculture​

The adoption of advanced software for vertical and indoor farming could revolutionize food production in urban areas. The use of automated environmental control systems and data analytics could enable the efficient cultivation of crops in limited spaces, reducing dependence on food imports.

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Car Rental and Software Solutions​

Car rental services have long been a staple in the travel industry, providing individuals and businesses with access to vehicles for short-term use. With the advancement of technology, many car rental companies have adopted software solutions to streamline operations, enhance customer experience, and improve efficiency. Below are examples of car rental services and their associated software platforms:

1. Enterprise Rent-A-Car​

Enterprise Rent-A-Car is one of the largest car rental companies globally, offering a wide range of vehicles for various purposes, including leisure travel, business trips, and insurance replacements. Their software solutions enable customers to book vehicles online or through mobile apps and streamline the rental process. Key features include:

Online Booking: Customers can browse available vehicles, compare rates, and make reservations through the Enterprise website or mobile app. Fleet Management: Enterprise's software platform enables efficient management of their extensive vehicle fleet, including inventory tracking, maintenance scheduling, and vehicle rotation.

Customer Relationship Management (CRM): Enterprise utilizes CRM software to manage customer interactions, preferences, and loyalty programs, ensuring personalized service and customer satisfaction.

Payment Processing: Seamless payment processing and invoicing systems integrated into the booking platform, allowing customers to complete transactions securely and conveniently.

Mobile Check-in and Check-out: Mobile apps facilitate the check-in and check-out process, enabling customers to skip the counter and go directly to their rental vehicle upon arrival.

Example: A business traveler books a rental car for a week-long trip through the Enterprise mobile app, selects a vehicle category, and completes the reservation with a few taps on their smartphone.

2. Hertz​

Hertz is another leading car rental company known for its extensive global presence and diverse vehicle fleet. Their software solutions focus on enhancing customer experience, optimizing fleet management, and streamlining rental operations. Key features include:

Online Check-in: Customers can check-in online before arriving at the rental location, reducing wait times and expediting the rental process. GPS Navigation Integration: Hertz's software platform integrates with GPS navigation systems to provide customers with real-time directions and traffic updates during their rental period. Fleet Optimization: Hertz utilizes predictive analytics and demand forecasting tools to optimize their vehicle fleet, ensuring adequate inventory availability and minimizing idle time. Mobile Concierge Services: Hertz offers mobile concierge services through their app, providing customers with personalized recommendations, local attractions, and exclusive deals during their rental experience. Feedback and Reviews: Customers can provide feedback and reviews directly through the Hertz app, enabling continuous improvement and enhancing service quality.

Example: A family on vacation rents a minivan from Hertz for a road trip, checking in online and receiving personalized recommendations for family-friendly attractions along their route through the Hertz app.

3. Avis Budget Group​

Avis Budget Group operates under two primary brands, Avis and Budget, offering a range of rental vehicles at various price points to meet customer needs. Their software solutions focus on digital innovation, customer convenience, and operational efficiency. Key features include:

Self-Service Kiosks: Avis Budget Group provides self-service kiosks at select rental locations, allowing customers to complete the check-in process, select their vehicle, and obtain their rental agreement without assistance from staff. Dynamic Pricing: Utilizing dynamic pricing algorithms and real-time data analysis, Avis Budget Group adjusts rental rates based on demand, availability, and other factors to maximize revenue and optimize fleet utilization. Mobile Wallet Integration: Avis Budget Group integrates with mobile wallet platforms, allowing customers to store payment information securely and complete transactions quickly and conveniently through the rental app. Digital Key Access: Avis Budget Group is exploring digital key technologies that enable customers to unlock and start their rental vehicles using their smartphones, eliminating the need for physical keys. Predictive Maintenance: Leveraging data analytics and telematics, Avis Budget Group proactively schedules vehicle maintenance and repairs to minimize downtime and ensure the reliability of their fleet.

Example: A business traveler uses a self-service kiosk at the airport to pick up their rental car from Avis Budget Group, completing the check-in process in minutes and proceeding directly to their vehicle without waiting in line.

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Car Sharing and Software Solutions​

Car sharing has emerged as a popular transportation option in urban areas, offering users the convenience of on-demand access to vehicles without the need for ownership. Car sharing services typically utilize mobile apps and software platforms to facilitate reservations, vehicle access, and payments. Below are examples of car sharing services and their software solutions:

1. Zipcar​

Zipcar is one of the largest car sharing services globally, providing access to vehicles on an hourly or daily basis. Their software platform offers a seamless user experience for booking, unlocking, and driving shared vehicles. Key features include:

Mobile App: Users can search for available vehicles, make reservations, and unlock cars using the Zipcar mobile app, providing convenience and flexibility. Membership Management: Streamlined membership signup and management process through the app, allowing users to easily sign up and access vehicles. Real-time Availability: Real-time vehicle availability updates to ensure users can find and book a car when needed, enhancing the overall user experience. Billing and Payments: Automated billing and payment processing for reservations, with transparent pricing and invoicing through the app. Vehicle Tracking: GPS tracking and vehicle monitoring for enhanced security and fleet management.

Example: A commuter uses Zipcar to access a vehicle for a weekend getaway, making a reservation through the mobile app and unlocking the car with their smartphone upon arrival.

2. Car2Go​

Car2Go offers a flexible car sharing service with a focus on one-way trips and short-term rentals. Their software platform provides users with access to a fleet of compact vehicles for spontaneous trips around the city. Key features include:

One-way Trips: Users can pick up a car from one location and drop it off at another, making it ideal for short trips and last-minute errands. Instant Access: Instant vehicle access through the Car2Go mobile app, allowing users to locate and unlock nearby vehicles on the go. Flexible Pricing: Pay-per-minute or hourly pricing options, with transparent rates displayed in the app for easy cost estimation. Parking Finder: Integration with parking apps and navigation tools to help users find available parking spots near their destination. Customer Support: In-app customer support chat and assistance for resolving issues or inquiries during the rental period.

Example: A city resident uses Car2Go to run errands, locating a nearby vehicle through the mobile app and dropping it off at their destination without worrying about parking.

3. Turo​

Turo offers a peer-to-peer car sharing marketplace, connecting vehicle owners with renters for short-term rentals. Their software platform facilitates the entire rental process, from vehicle listing to payment processing. Key features include:

Vehicle Listings: Owners can list their vehicles on the Turo platform, providing details, photos, and availability for potential renters to browse. Booking Management: Renters can search for available vehicles, request bookings, and communicate with owners through the Turo mobile app or website. Insurance Coverage: Comprehensive insurance coverage for both owners and renters, including liability, collision, and theft protection for added peace of mind. Payment Processing: Secure payment processing and transaction handling through the Turo platform, with options for direct deposit or PayPal payouts for owners. Rating and Reviews: Users can rate and review both owners and renters after each transaction, building trust and transparency within the Turo community.

Example: A traveler uses Turo to rent a car from a local owner for a weekend trip, browsing available listings and completing the booking process through the Turo platform.

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