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Startups of The Year 2023 Winners: Europe

Radio-frequency identification and near-field communication are two similar technologies used in many industries. Their fields of application include inventory tracking, location tracking, access control, contactless payments, logistics, manufacturing, marketing, retail, and more. In this article, we are going to talk about these systems from an electronics design perspective. We will also take a look at RFID and NFC device development cases. How do RFID and NFC Technologies Work? is a wireless communication technology that uses radio waves to uniquely identify tagged items. A radio-frequency identification system generally consists of three elements: a reader, a tag, and RFID software. The RFID tag carries unique ID information and other data associated with the item it is placed on. When activated, the reader sends radio waves. The waves power any tags within the interrogation zone. They send back the data to the reader. The software processes the raw data for the end user. Radio-frequency identification (RFID) Radio-frequency identification allows you to track and identify unique items and people. For this reason, RFID technology is used in many spheres: asset and location tracking, inventory management, access control, theft prevention, etc. is a short-range wireless communication technology that uses high-frequency radio waves to uniquely identify tagged items and exchange data with other NFC-enabled devices. While RFID uses a broad range of radio frequencies, NFC devices work at 13.56 MHz only. This technology evolved from RFID and can be regarded as its fine-tuned subset. Near field communication (NFC) NFC technology is commonly used in contactless payment terminals in retail stores. NFC systems consist of the same elements as RFID: a reader (called initiator), a tag (called target), and NFC software. They work the same way as RFID systems. However, they have a much shorter operating range (up to several centimeters). That is why near field communication is primarily used in contactless payment. Nevertheless, other applications similar to that of RFID systems are also possible. Unlike RFID, NFC devices are capable of two-way communication. So, they are also used where data exchange is required. These technologies are very similar, but suit different business tasks. NFC systems can work in . active or passive modes of communication , one active device (Initiator) generates a radio-frequency field. The passive device (Target) draws power from the alternating magnetic field and acts as a transponder. It uses load modulation for data transferring. In passive mode , both the Initiator and the Target are active and generate magnetic fields in turns. The field deactivates while the device is waiting for data and activates when the device is ready to respond. In active mode An active NFC device can work in any of these operating modes: The device acts as a contactless smart card. The radio-frequency field is generated by the reader. Typical application: smartphone as an instrument of payment. The device acts as a reader for NFC and RFID tags. When the tag is close enough, the device detects it with a collision avoidance mechanism. Then it can read the data from the tag or write new information to it. Typical application: smart posters. Both devices act as a reader and a tag to alternately transmit and receive data. It allows them to exchange information such as pictures, business cards, etc. Android Beam technology uses this mode. Card emulation mode. Reader/writer mode. Peer-to-peer mode. Here are the key differences between RFID and NFC systems. You can learn more about these and other differences and the applications of RFID and NFC systems in our previous article. RFID and NFC Tags RFID Tags RFID and NFC tags consist of three elements: a substrate, an antenna, and a microchip. Tags come in various shapes and sizes: plastic cards, key fobs, paper or plastic stickers, badges, etc. The microchip is responsible for the signal modulation and demodulation. It also stores and processes information. There are three types of RFID tags: tags do not have a power source. In order to function, they use the energy from the reader’s radio signal. tags have their own power source (usually a small battery). It allows them to transmit signals periodically. The battery boosts the signal. Active tags can be read from longer distances. (battery-assisted passive) tags also use batteries but do not transmit periodic signals. When the tag receives the signal from the reader, the battery only turns on the tag. So, all the energy from the reader’s signal is reflected back. They also have a longer range than passive tags. Passive Active Semi-passive RFID systems can operate at different frequencies. They commonly use the following radio frequency bands. Note that the bands are defined by national standards and regulations. , particularly 120-140 kHz. In ideal conditions, these radio waves can travel up to 50 cm. But in practice, their range does not exceed 10-20 cm. LF RFID tags do not have security standards. So, they are not recommended for applications requiring encrypted communication. They can also penetrate through metals and liquids. , particularly 13.56 MHz. The read range of these systems usually varies from 10 to 20 cm but can reach about 1 m. HF RFID tags cannot penetrate through liquids and dense metals but work fine with most other materials including thin metals. tags operate at frequencies of 865–868 MHz (Europe) and 902–928 MHz (North America). Their reading range usually does not exceed 12 m. Note that some UHF RFID systems use 433 MHz and can reach up to 100 m. Low frequency band High frequency band Ultra-high frequency Some RFID tags operate at 2.45-5.8 GHz, 3.1-10 GHz, and 24.125 GHz. However, frequency bands higher than 928 MHz are not common for commercial RFID tags. Most civil-grade systems use LF, HF, and UHF tags. NFC Tags Near field communication utilizes the same frequency as HF RFID tags – 13.56 MHz. NFC-Forum defines the following types of NFC tags. Note that custom RFID and NFC systems usually do not require custom tags. However, one may require to design a non-standard device with a reader, which includes custom PCB design and embedded software development. RFID and NFC Readers NFC devices can work as readers and tags. RFID readers can only scan tags. Readers consist of an oscillator, transceiver, receiver, control unit (processor), and an external antenna. They come in three form factors: - Mobile (handheld) readers that often have the shape of a pistol; - Fixed readers that can be attached to walls or near doors; - Vehicle-mounted readers that are often integrated into material handling devices. The reading range of RFID and NFC readers depends on their transmitting power. This, in turn, is defined by the type of antenna, the operating frequencies, and the power source. Environmental conditions and local regulations can cap the reader’s maximum range as well. Many RFID-enabled devices have an integrated NFC module, which enables them to read both RFID and NFC tags. They can also have a few additional modules: a 1D/2D scanner, a camera, GPS, or a cellular connection (in case Wi-Fi is unavailable). RFID readers can be either active or passive. Depending the type of readers and tags, RFID systems can be classified as follows: - (Passive Reader Active Tag) systems. The reader has no power source and can only receive data from active tags. PRAT - (Active Reader Passive Tag) systems. The reader sends interrogation signals to passive tags and powers them. ARPT - (Active Reader Active Tag) systems. Both elements have power sources. Semi-passive tags can also be used here. ARAT Unlike NFC devices, RFID readers can scan multiple tags simultaneously with one of these anti-collision algorithms: either probabilistic or deterministic detection. To transmit the data from the reader to a host computer, several network connection types can be used. Fixed readers usually utilize USB, serial interface, Ethernet, Wi-Fi, and POE. Mobile models usually use Wi-Fi, Bluetooth, and USB. These network connections can also be used to charge the device. Custom RFID and NFC Device Development Usually, one has a number of options here. If it is possible to create the system with off-the-shelf (OTS) readers and tags, then all you need is to develop software. In some cases, one can even purchase OTS software packages for RFID and NFC systems. However, there are relatively few of them. Due to complicated or specific tasks, businesses may also require custom-design readers and, thus, custom NFC or RFID hardware development. Non-standard applications and requirements will probably need a research and development stage. As for tags, engineers usually use OTS models even for custom solutions. Manufacturers offer so many models that there is no need to reinvent the wheel. Designing a printed circuit board for an RFID or NFC-enabled device starts with choosing a suitable reader. The choice is defined by various factors: the required read range, frequency band, environmental conditions, etc. The PCB will also require a microcontroller or a microprocessor, as well as various passive components. Another obligatory component is the antenna, but it is placed outside the board. Depending on what additional functions the device must perform, the team may need to mount additional modules and components. Custom NFC and RFID device development also involves software design. Such systems typically require three types of software. 1. that manages the work of the hardware. Tags come with firmware developed by the manufacturer and don’t require any changes. But designing a custom reader also includes RFID or NFC firmware development. Simpler solutions can be written in C, while more complex devices will most likely be written in C++. Firmware 2. , i.e. the highest-level software that manages the processed data, collected with the reader, to address particular business needs. Application 3. , i.e. the software between the firmware and the high-level application. As a rule, NFC systems do not require middleware, unless an NFC-enabled device serves as an intermediate link between the tag and an enterprise information system, which is uncommon. Middleware is more common for RFID systems. connects the readers and the data they collect with the application or enterprise information system. It processes the raw data coming from the readers before passing it on as aggregated data. It also provides an application-level interface for managing readers and querying data. Middleware Middleware The middleware and the application level software can be written in C++, C#, Python, Java, and other languages. The choice is defined by the system’s size, the complexity of its tasks, and hardware requirements. When there’s a need for cross-platform solutions, Qt development is a better option. The most important requirements for the language for middleware is the ability to reach the hardware on the one hand and to interact with the high-level software on the other hand. The following cases demonstrate two projects from our portfolio that involved custom RFID and NFC device development. Case #1: Custom RFID Device Development Our customer wanted to develop an RFID system for livestock enterprises to identify animals, track their movements, and collect other data for timely disease diagnostics. Our job was to design a handheld RFID data terminal for this system. Our RFID device development resulted in a data terminal operating at 860-960 MHz with multiple modules. The terminal can read multiple tags simultaneously from a distance of several meters. For this project, our team developed a custom single-board computer  based on the STM32MP151AAD3 microprocessor with one Arm® Cortex®-A7 core for high-level operations and one Cortex®-M4 core for low-level operations. This microprocessor features a low price and allows us to curb the number of components to make the device smaller but more energy efficient. Pared with the microprocessor, the device also uses energy-saving 1GB MT41K512M16HA-125A SDRAM-DDR3L memory and 4GB EMMC04G-MK27-A01C FLASH-NAND (MLC) memory with eMMC interface. The RFID reading function is implemented with the ST25RU3980 single chip reader. It’s a highly-integrated solution compatible with ISO 18000-6C/EPC Class1 Gen2 transponders. The reader is battery-powered and features very low power consumption. Pared with a custom patch antenna, it provides a very high sensitivity and resistance to reflection and self-clutter effects. The ESP32-C3-MINI-1-N4 module with an integrated PCB antenna is responsible for the WiFi 802.11b/g/n and Bluetooth v5.0 interfaces. They allow the device to connect to local data networks. The Telit ME310G1W103T040100 module is responsible for cellular connection and data transmission in the field. The device is also equipped with a Newland EM2096 module for scanning 1D and 2D barcodes. The team used a USB Type C Amphenol 124019322112A connector for data transmission over USB 2.0 and battery charging. It has IPX4 level of protection, which makes it suitable for outdoor applications. The terminal is also equipped with an MC6470 inertial measurement unit. It allows businesses to track the location of the device, which can be important for keeping record of its work. The device is powered with a 3.7V 12Ah single-cell Li-ion battery, which should be enough for a 12-hour shift. The scope of work also included firmware and software development. For the microprocessor, the team used the Android distribution package provided by the manufacturer. We developed the firmware in C for the ESP32-C3-MINI-1-N4 module with programming libraries. It is controlled by the microprocessor with the help of AT commands. We also wrote a custom driver in C for the RFID reader, as it uses an SPI interface. Other modules use standard drivers. Case #2: Custom NFC Device Development This case demonstrates how NFC technology can be adapted for non-standard use. The project involved both custom NFC hardware and software development. Our job was to design an NFC-enabled device for orienteering events and location-based games. With the device we created, event participants can scan NFC tags placed on control points. The reader then transmits the data to a host computer used by the administration to let them know which team reached the point. The device can also perform a number of other tasks. The device is based on an NXP PN7362BNHN microcontroller. It has an integrated radio interface with NFC support and low energy consumption for autonomous work. The microcontroller has all the necessary NFC components inside. The team only had to add impedance-matching circuits. It also has enough memory capacity (160 KB) for complex algorithms. As the microcontroller has an integrated radio interface, it is also responsible for reading NFC tags. The microcontroller is paired with a passive, small-size flexible antenna – Pulse/Larsen W7001. The system supports all protocols and modes defined by NFC-Forum. It features high sensitivity and stability. Its read range equals 40 mm. Data transmission in field conditions is implemented with a Quectel MC60ECB-04-BLE module. It has an integrated navigation receiver (GPS/GLONASS/Galileo/BeiDou/QZSS) and supports 2G cellular communication. The module supports navigation, based on GSM signals, and multiple data transmission protocols (TCP/UDP/FTP/PPP/HTTP/NITZ/NTP/PING/HTTPS/TCP/SSL/MQTT). It allows the device to send data to the administration as well as receive various messages: confirmations, warnings, hints, statistics, etc. The information is displayed on a monochrome graphic display (Orient Display JAZZ-BC-W). The device is equipped with an emergency button. When it is pressed, the device sends an emergency signal to the host computer along with location data. The device’s location is tracked with an MC6470 inertial measurement unit. The device is powered by a built-in single-cell 3.7V 1.2Ah Li-ion battery. It can work over 8 hours on a single charge. Using the energy-saving mode of the microcontroller can extend this period. The device is fitted with a USB Type C Amphenol 124019322112A connector, so it is well protected against moisture and dust. The software development team wrote firmware for the microcontroller in C using an AT command library to control the 2G module. Conclusion In most cases, one can use off-the-shelf RFID and NFC systems. Different types of readers and tags suit different purposes. Technical characteristics of the devices, such as operating frequency, power source, or memory capacity, define the system’s read range, penetration capability, etc. With off-the-shelf elements, it is only a matter of software design. But sometimes, complicated or non-standard tasks can require a custom solution. RFID and NFC device development involves designing a custom PCB, as well as embedded firmware and software development. Depending on what tasks the system must perform, the team can also install additional components, so that the device could work in field conditions, or scan 1D/2D codes, and so on. This story was first published here.

Custom RFID and NFC Device Development: Theory and Practice

The aim of this is to read air quality sensor data and show it on an LCD display that is connected to STM32 board. A web browser will read the sensor data and pass it to STM32 board using BleuIO. Bluetooth LE project Introduction The project is based on which controls using BleuIO. STM32 Nucleo-144, LCD display We will need for this project, one connected to the Nucleo board and the other to a computer running the web script and a . When the BleuIO Dongle is connected to the Nucleo board’s USB port, the STM32 will recognize it and directly start advertising. This allows the Dongle on the computer port to connect with the web script. two BleuIO USB dongles HibouAir – Air quality monitoring device With the web script on the computer, we can scan and get air quality sensor data from HibouAir. Then we send these data to an LCD screen connected to STM32 using Bluetooth. We have used a STM32 Nucleo-144 development board with STM32H743ZI MCU (STM32H743ZI micro mbed-Enabled Development Nucleo-144 series ARM® Cortex®-M7 MCU 32-Bit Embedded Evaluation Board) for this example. This development board has a USB host where we connect the BleuIO dongle. If you want to use another setup, you will have to make sure it supports a USB Host and beware that the GPIO setup might be different and may need to be reconfigured in the .ioc file. About the Code The project source code is available at . Github Either clone the project or download it as a zip file and unzip it into your STM32CubeIDE workspace. If you download the project as a zip file, you will need to rename the project folder from ‘stm32_bleuio_lcd-master’ to ‘stm32_bleuio_lcd’. Connect the SDA to PF0 on the Nucleo board and SCL to PF1. Then setup I2C2 in the STM32Cube ioc file as follows. (Make sure to change the I2C speed frequency to 50 kHz as per LCD display requirements.) In the USBH_CDC_ReceiveCallback function in USB_HOST\usb_host.c, we copy the CDC_RX_Buffer into an external variable called dongle_response that is accessible from the main.c file. void USBH_CDC_ReceiveCallback(USBH_HandleTypeDef *phost)
{
  	if(phost == &hUsbHostFS)
  	{
  		// Handles the data recived from the USB CDC host, here just printing it out to UART
  		rx_size = USBH_CDC_GetLastReceivedDataSize(phost);
		HAL_UART_Transmit(&huart3, CDC_RX_Buffer, rx_size, HAL_MAX_DELAY);

		// Copy buffer to external dongle_response buffer
		strcpy((char *)dongle_response, (char *)CDC_RX_Buffer);

		// Reset buffer and restart the callback function to receive more data
		memset(CDC_RX_Buffer,0,RX_BUFF_SIZE);
		USBH_CDC_Receive(phost, CDC_RX_Buffer, RX_BUFF_SIZE);
  	}

  	return;
} In main.c we create a simple interpreter so we can react to the data we are receiving from the dongle. void dongle_interpreter(uint8_t * input)
{

	if(strlen((char *)input) != 0)
	{
		if(strstr((char *)input, "\r\nADVERTISING...") != NULL)
		{
			isAdvertising = true;
		}
		if(strstr((char *)input, "\r\nADVERTISING STOPPED") != NULL)
		{
			isAdvertising = false;
		}
		if(strstr((char *)input, "\r\nCONNECTED") != NULL)
		{
			isConnected = true;
			HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_SET);
		}
		if(strstr((char *)input, "\r\nDISCONNECTED") != NULL)
		{
			isConnected = false;
			HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_RESET);
		}


		if(strstr((char *)input, "L=0") != NULL)
		{

			isLightBulbOn = false;
			//HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_RESET);
			lcd_clear();

			writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_OFF);

			uart_buf_len = sprintf(uart_tx_buf, "\r\nClear screen\r\n");
			HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
		}

		if(strstr((char *)input, "L=1") != NULL)
		{
				isLightBulbOn = true;
				writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_ON);


				lcd_clear();

				lcd_write(input);

		}

	}
	memset(&dongle_response, 0, RSP_SIZE);
} We put the interpreter function inside the main loop. /* Infinite loop */
  /* USER CODE BEGIN WHILE */
  while (1)
  {
    /* USER CODE END WHILE */
    MX_USB_HOST_Process();

    /* USER CODE BEGIN 3 */
    // Simple handler for uart input
    handleUartInput(uartStatus);

    // Inteprets the dongle data
    dongle_interpreter(dongle_response);

	// Starts advertising as soon as the Dongle is ready.
	if(!isAdvertising && !isConnected && isBleuIOReady)
	{
		HAL_Delay(200);
		writeToDongle((uint8_t*)DONGLE_CMD_AT_ADVSTART);
		isAdvertising = true;
	}
  }
  /* USER CODE END 3 */ Using the example project What we will need Two BleuIO dongles A board with a STM32 Microcontroller with a USB port. (A Nucleo-144 development board: NUCLEO-H743ZI2, was used to develop this example. To connect the dongle to the Nucleo board, a “USB A to Micro USB B”-cable with a USB A female-to-female adapter can be used.) STM32CubeIDE Liquid Crystal Display Module – NHD-0420D3Z-NSW-BBW-V3 – Air quality monitoring device HibouAir Importing as an Existing Project From STM32CubeIDE choose File>Import… Then choose General>Existing Projects into Workspace, then click ‘Next >’ Make sure you’ve chosen your workspace in ‘Select root directory:’ You should see the project “stm32_bleuio_SHT85_example”, check it, and click ‘Finish’. Running the example Upload the code to STM32 and run the example. The USB dongle connected to STM32 will start advertising automatically. Send Sensor Data to LCD screen from a web browser Connect the BleuIO dongle to the computer. Run the web script to connect to the other BleuIO dongle on the STM32. Now you can send sensor data to the LCD screen. For this script to work, we need connected to the computer. BleuIO USB dongle BleuIO javascript library Chrome 78 or later, and you need to enable the flag in #enable-experimental-web-platform-features chrome://flags A web bundler Create a simple Html file called index.html which will serve as the frontend of the script. This Html file contains some buttons that help connect, read advertised data from the HibouAir to get air quality sensor data and send this data to the LCD screen, which is connected to stm32. <!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <link
      href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css"
      rel="stylesheet"
      integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3"
      crossorigin="anonymous"
    />
    <title>Bluetooth LE Air quality sensor data to LCD screen</title>
  </head>
  <body class="mt-5">
    <div class="container mt-5">
      <img
        src="https://www.bleuio.com/blog/wp-content/themes/bleuio/images/logo.png"
      />
      <h1 class="mb-5">Bluetooth LE Air quality sensor data to LCD screen</h1>

      <div class="row">
        <div class="col-md-4 pt-5">
          <button class="btn btn-success mb-2" id="connect">Connect</button>
          <form method="post" id="sendDataForm" name="sendMsgForm" hidden>
            <div class="mb-3">
              <label for="sensorID" class="form-label">Sensor ID</label>
              <input
                type="text"
                class="form-control"
                name="sensorID"
                id="sensorID"
                required
                maxlength="60"
                value="0578E0"
              />
            </div>

            <button type="submit" class="btn btn-primary">Get Data</button>
          </form>
          <br />
          <button class="btn btn-danger" id="clearScreen" disabled>
            Clear screen
          </button>
        </div>
        <div class="col-md-8">
          <img src="air_quality_lcd.jpg" class="img-fluid" />
        </div>
      </div>
    </div>

    <script src="script.js"></script>
  </body>
</html> Create a js file called script.js and include it at the bottom of the Html file. This js file uses the BleuIO js library to write AT commands and communicate with the other dongle. import * as my_dongle from 'bleuio'
import 'regenerator-runtime/runtime'

const dongleToConnect='[0]40:48:FD:E5:2F:17'
//const sensorID = '0578E0'
document.getElementById('connect').addEventListener('click', function(){
  my_dongle.at_connect()
  document.getElementById("clearScreen").disabled=false;
  document.getElementById("connect").disabled=true;
  document.getElementById("sendDataForm").hidden=false;
})

document.getElementById("sendDataForm").addEventListener("submit", function(event){
    event.preventDefault()
    const sensorID = document.getElementById('sensorID').value
    getSensorData(sensorID)
    setInterval(function () {getSensorData(sensorID)}, 10000);

   
  });

  const getSensorData =((sensorID)=>{
    my_dongle.ati().then((data)=>{
        //make central if not
        if(JSON.stringify(data).includes("Peripheral")){
            console.log('peripheral')
            my_dongle.at_dual().then((x)=>{
                console.log('central now')
            })
        }        
    })
    .then(()=>{
        // connect to dongle
        my_dongle.at_getconn().then((y)=>{
            if(JSON.stringify(y).includes(dongleToConnect)){
                console.log('already connected')
            }else{
                my_dongle.at_gapconnect(dongleToConnect).then(()=>{
                    console.log('connected successfully')
                })
            }
        })
        .then(async()=>{
           return my_dongle.at_findscandata(sensorID,6).then((sd)=>{
                console.log('scandata',sd)
                let advData = sd[sd.length - 1].split(" ").pop()
                let positionOfID= advData.indexOf(sensorID);
                let tempHex = advData.substring(positionOfID+14, positionOfID+18)
                let temp = parseInt('0x'+tempHex.match(/../g).reverse().join(''))/10;

                let co2Hex = advData.substring(positionOfID+38, positionOfID+42)
                let co2 = parseInt('0x'+co2Hex);
                //console.log(temp,co2)
                return {
                    'CO2' :co2,
                    'Temp' :temp,                       
                  }
            })
        })
        .then((x)=>{
            console.log(x.CO2)
            console.log(x.Temp)
            var theVal = "L=1 SENSOR ID "+sensorID+"    TEMPERATURE " + x.Temp + ' °c    CO2 '+ x.CO2+' ppm';
            console.log('Message Send 1 ')
            // send command to show data
            my_dongle.at_spssend(theVal).then(()=>{
                console.log('Message Send '+theVal)
            })
        })
        
    })
})

document.getElementById('clearScreen').addEventListener('click', function(){
    my_dongle.ati().then((data)=>{
        //make central if not
        if(JSON.stringify(data).includes("Peripheral")){
            console.log('peripheral')
            my_dongle.at_central().then((x)=>{
                console.log('central now')
            })
        }
    })
    .then(()=>{
        // connect to dongle
        my_dongle.at_getconn().then((y)=>{
            if(JSON.stringify(y).includes(dongleToConnect)){
                console.log('already connected')
            }else{
                my_dongle.at_gapconnect(dongleToConnect).then(()=>{
                    console.log('connected successfully')
                })
            }
        })
        .then(()=>{
            // send command to clear the screen
            my_dongle.at_spssend('L=0').then(()=>{
                console.log('Screen Cleared')
            })
        })
        
    })
}) The script has a button to connect to the COM port on the computer. There is a text field where you can write the sensor ID of the air quality monitor device. Once connected, the script will try to get advertised data from the sensor and convert it to meaningful data. After that, it will send this data to the STM32 board, which is then displayed on the LCD screen. To connect to the BleuIO dongle on the STM32, make sure the STM32 is powered up, and a BleuIO dongle is connected to it. Get the MAC address Follow the steps to get the MAC address of the dongle that is connected to STM32. - Open this site https://bleuio.com/web_terminal.html and click connect to dongle.
- Select the appropriate port to connect.
- Once it says connected, type ATI. This will show dongle information and current status.
- If the dongle is on peripheral role, set it to central by typing AT+CENTRAL
- Now do a gap scan by typing AT+GAPSCAN
- Once you see your dongle on the list ,stop the scan by pressing control+c
- Copy the ID and paste it into the script (script.js) line #4 Run the web script You will need a web bundler. You can use parcel.js Once parcel js is installed, go to the root directory of the web script and type . This will start your development environment. “parcel index.html” Open the script on a browser. For this example, we opened http://localhost:1234. You can easily connect to the dongle and see air quality data on the LCD screen. The response will show on the browser console screen. The web script looks like this: Output The message will show on the LCD screen. Also published here.

Show Bluetooth LE Sensor Readings on LCD Screen Connected to STM32 

Home automation involves automating household environment equipment. To achieve that, we have created a smart bulb that is easy to install, and the attached equipment can be controlled over a web browser or smartphone app. The aim of this project is to control different home appliances using a web browser or smartphone. 1. Introduction The project is showcasing a simple way of using the the BleuIO Dongle to turn on and off a light bulb that is connected to the STM32 Nucleo-144 via a 5V Relay. You will need two dongles, one connected to the Nucleo board and one connected to a computer, running the web script. When the BleuIO Dongle is connected to the Nucleo boards USB port the STM32 will recognize it and directly start advertising. This allows the other Dongle to connect to it. It will also accept 3 different inputs from the UART: 0	Send ATI (Request device information) command to BlueIO Dongle.
1	Manually turn the light bulb on
2	Manually turn the light bulb off input	result We have used a STM32 Nucleo-144 development board with STM32H743ZI MCU (STM32H743ZI micro mbed-Enabled Development Nucleo-144 series ARM® Cortex®-M7 MCU 32-Bit Embedded Evaluation Board) for this example. If you want to use another setup you will have to make sure it support USB Host and beware that the GPIO setup might be different and may need to be reconfigured in the .ioc file. WARNING – THIS PROJECT INVOLVES HIGH VOLTAGES THAT CAN CAUSE SERIOUS INJURY OR DEATH. PLEASE TAKE ALL NECESSARY PRECAUTIONS, AND TURN OFF ALL POWER TO A CIRCUIT BEFORE WORKING ON IT. What you will need Two BleuIO dongles ( ) https://www.bleuio.com/ The script for the dongle (available on the source code inside web script folder) A board with a STM32 Microcontroller with a USB port. (A Nucleo-144 development board: NUCLEO-H743ZI2, was used developing this example. ( ) https://www.st.com/en/evaluation-tools/nucleo-h743zi.html To connect the dongle to the Nucleo board a “USB A to Micro USB B”-cable with a USB A female-to-female adapter can be used.) STM32CubeIDE ( ) https://www.st.com/en/development-tools/stm32cubeide.html A 5V Relay A light bulb Connecting the relay NOTICE OF RISK; DISCLAIMER OF LIABILITY Always be very careful when experimenting with AC, electrical shock can result in serious injuries! For the DC part of the Relay circuit connect S (signal) to pin PE4 on the STM32 NUCLEO board, also connect the Power supply (+) and ground (-) to +5V and GND respectively. Pinout and Connection to STM32 About the Code You can get the project HERE https://github.com/smart-sensor-devices-ab/stm32_bleuio_lightbulb_example This project-based on our previous STM32 project ( ) with these changes in the .ioc file: https://github.com/smart-sensor-devices-ab/stm32_bleuio_example In the pinout view we set the GPIO PE4 to OUTPUT and labeled it to ‘lightbulb’. In the USBH_CDC_ReceiveCallback function in USB_HOST\usb_host.c we copy the CDC_RX_Buffer into a external variable called dongle_response that is accessible from the main.c file. void USBH_CDC_ReceiveCallback(USBH_HandleTypeDef *phost)
{
    if(phost == &hUsbHostFS)
    {
        // Handles the data recived from the USB CDC host, here just printing it out to UART
        rx_size = USBH_CDC_GetLastReceivedDataSize(phost);
        HAL_UART_Transmit(&huart3, CDC_RX_Buffer, rx_size, HAL_MAX_DELAY);

        // Copy buffer to external dongle_response buffer
        strcpy((char *)dongle_response, (char *)CDC_RX_Buffer);

        memset(CDC_RX_Buffer,0,RX_BUFF_SIZE);
        USBH_CDC_Receive(phost, CDC_RX_Buffer, RX_BUFF_SIZE);
    }

    return;
} In main.c we create a simple interpreter so we can react to the data we are receiving from the dongle. /**
  * @brief Simple dongle interpreter
  * @retval None
  */
void dongle_interpreter(uint8_t * input)
{

    if(strlen((char *)input) != 0)
    {
        if(strstr((char *)input, "\r\nADVERTISING...") != NULL)
        {
            isAdvertising = true;
        }
        if(strstr((char *)input, "\r\nADVERTISING STOPPED.") != NULL)
        {
            isAdvertising = false;
        }
        if(strstr((char *)input, "\r\nCONNECTED") != NULL)
        {
            isConnected = true;
            HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_SET);
        }
        if(strstr((char *)input, "\r\nDISCONNECTED") != NULL)
        {
            isConnected = false;
            HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_RESET);
        }
        if(strstr((char *)input, "L=0") != NULL)
        {
            isLightBulbOn = false;
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_RESET);

            writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_OFF);

            uart_buf_len = sprintf(uart_tx_buf, "\r\nLight bulb is %s\r\n", isLightBulbOn ? "on":"off");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
        }
        if(strstr((char *)input, "L=1") != NULL)
        {
            isLightBulbOn = true;
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_SET);

            writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_ON);

            uart_buf_len = sprintf(uart_tx_buf, "\r\nLight bulb is %s\r\n", isLightBulbOn ? "on":"off");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
        }
    }
    memset(&dongle_response, 0, RSP_SIZE);
} In main.c we create a simple interpreter so we can react to the data we are receiving from the dongle. /**
  * @brief Simple dongle interpreter
  * @retval None
  */
void dongle_interpreter(uint8_t * input)
{

    if(strlen((char *)input) != 0)
    {
        if(strstr((char *)input, "\r\nADVERTISING...") != NULL)
        {
            isAdvertising = true;
        }
        if(strstr((char *)input, "\r\nADVERTISING STOPPED.") != NULL)
        {
            isAdvertising = false;
        }
        if(strstr((char *)input, "\r\nCONNECTED") != NULL)
        {
            isConnected = true;
            HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_SET);
        }
        if(strstr((char *)input, "\r\nDISCONNECTED") != NULL)
        {
            isConnected = false;
            HAL_GPIO_WritePin(GPIOE, GPIO_PIN_1, GPIO_PIN_RESET);
        }
        if(strstr((char *)input, "L=0") != NULL)
        {
            isLightBulbOn = false;
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_RESET);

            writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_OFF);

            uart_buf_len = sprintf(uart_tx_buf, "\r\nLight bulb is %s\r\n", isLightBulbOn ? "on":"off");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
        }
        if(strstr((char *)input, "L=1") != NULL)
        {
            isLightBulbOn = true;
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_SET);

            writeToDongle((uint8_t*)DONGLE_SEND_LIGHT_ON);

            uart_buf_len = sprintf(uart_tx_buf, "\r\nLight bulb is %s\r\n", isLightBulbOn ? "on":"off");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
        }
    }
    memset(&dongle_response, 0, RSP_SIZE);
} We also update the handleUartInput function so we can have manual control over the light bulb via the UART. /**
  * @brief Simple uart input handler
  * @retval None
  */
void handleUartInput(UARTCommandTypeDef cmd)
{
    switch(cmd)
    {
        case UART_RX_0:
        {
            // 0
            uart_buf_len = sprintf(uart_tx_buf, "\r\n(0 pressed)\r\n");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
            if(isBleuIOReady)
            {
                writeToDongle((uint8_t*)DONGLE_CMD_ATI);
            } else
            {
                uart_buf_len = sprintf(uart_tx_buf, BLEUIO_NOT_READY_MSG);
                HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
            }
            uartStatus = UART_RX_NONE;
            break;
        }
        case UART_RX_1:
        {
            // 1
            uart_buf_len = sprintf(uart_tx_buf, "\r\n(1 pressed light bulb on!)\r\n");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_SET);
            uartStatus = UART_RX_NONE;
            break;
        }
        case UART_RX_2:
        {
            // 2
            uart_buf_len = sprintf(uart_tx_buf, "\r\n(2 pressed light bulb off!)\r\n");
            HAL_UART_Transmit(&huart3, (uint8_t *)uart_tx_buf, uart_buf_len, HAL_MAX_DELAY);
            HAL_GPIO_WritePin(Lightbulb_GPIO_Port, Lightbulb_Pin, GPIO_PIN_RESET);

            uartStatus = UART_RX_NONE;
            break;
        }
        case UART_RX_NONE:
        {
            break;
        }
        default:
        {
            uartStatus = UART_RX_NONE;
            break;
        }
    }
} We put the interpreter function inside the main loop. /* Infinite loop */
  /* USER CODE BEGIN WHILE */
  while (1)
  {
    /* USER CODE END WHILE */
    MX_USB_HOST_Process();

    /* USER CODE BEGIN 3 */
    // Simple handler for uart input
    handleUartInput(uartStatus);
    // Inteprets the dongle data
    dongle_interpreter(dongle_response);

    // Starts advertising as soon as the Dongle is ready.
    if(!isAdvertising && !isConnected && isBleuIOReady)
    {
        HAL_Delay(200);
        writeToDongle((uint8_t*)DONGLE_CMD_AT_ADVSTART);
        isAdvertising = true;
    }
  }
  /* USER CODE END 3 */ How to setup project Downloading the project from GitHub Either clone the project, or download it as a zip file and unzip it, into your STM32CubeIDE workspace. Get project HERE https://github.com/smart-sensor-devices-ab/stm32_bleuio_lightbulb_example Importing as an Existing Project From STM32CubeIDE choose File>Import… Then choose General>Existing Projects into Workspace then click ‘Next >’ Make sure you’ve chosen your workspace in ‘Select root directory:’ You should see the project “stm32_bleuio_example”, check it and click ‘Finish’. Running the example In STMCubeIDE click the hammer icon to build the project. Open up the ‘STMicroelectronics STLink Virtual COM Port’ with a serial terminal emulation program like TeraTerm, Putty or CoolTerm. Serial port Setup: Baudrate: 115200 Data Bits: 8 Parity: None Stop Bits: 1 Flow Control: None Connect the BleuIO Dongle before running the example. In STMCubeIDE click the green play button to flash and run it on your board. The first time you click it the ‘Run Configuration’ window will appear. You can just leave it as is and click run. You should be greeted by this welcome message: Wait until the message: “[BleuIO Dongle Ready]” is shown. You can now connect with the other dongle using the script. You can also use the uart commands (0, 1 or 2): Press 0 to get device information. 1 to turn on lightbulb. 2 to turn off lightbulb. Dongle response will be printed to UART. Controlling the light from a web browser We wrote a simple script that connects to the dongle and sends signals to toggle the light from the web browser. For this script to work, we need connected to the computer. BleuIO USB dongle BleuIO javascript library Chrome 78 or later, and you need to enable the flag in #enable-experimental-web-platform-features chrome://flags A web bundler – ( ) parcel js Steps Create a simple Html file called index.html which will serve as the frontend of the script. This Html file contains some buttons that help connect and signal to the remote dongle, which is connected to stm32. `<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta http-equiv="X-UA-Compatible" content="IE=edge" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3" crossorigin="anonymous" /> <title>Control Light using Bleutooth Low Energy</title> </head> <body class="mt-5"> <div class="container mt-5"> <h1 class="mb-5">Control Light using Bleutooth Low Energy</h1> <button class="btn btn-success" id="connect">Connect</button> <button class="btn btn-warning" id="lightOn" disabled>Turn On</button> <button class="btn btn-danger" id="lightOf" disabled>Turn Off</button> </div> <div class="container mt-5"> <img id="light" src="light_off.png" alt="" /> </div> <script src="script.js"></script> </body> </html>` Create a js file called script.js and include it at the bottom of the Html file. This js file uses the BleuIO js library to write AT commands and communicate with the other dongle. import * as my_dongle from "bleuio";
const dongleToConnect = "[0]40:48:FD:E5:35:A5";
import lightOnImg from "./light_on.png";
import lightOfImg from "./light_off.png";
document.getElementById("connect").addEventListener("click", function () {
  my_dongle.at_connect();
  document.getElementById("lightOn").disabled = false;
  document.getElementById("lightOf").disabled = false;
  document.getElementById("connect").disabled = true;
});

document.getElementById("lightOn").addEventListener("click", function () {
  my_dongle
    .ati()
    .then((data) => {
      //make central if not
      if (JSON.stringify(data).includes("Peripheral")) {
        console.log("peripheral");
        my_dongle.at_central().then((x) => {
          console.log("central now");
        });
      }
    })
    .then(() => {
      // connect to dongle
      my_dongle
        .at_getconn()
        .then((y) => {
          if (JSON.stringify(y).includes(dongleToConnect)) {
            console.log("already connected");
          } else {
            my_dongle.at_gapconnect(dongleToConnect).then(() => {
              console.log("connected successfully");
            });
          }
        })
        .then(() => {
          // send command to control light
          my_dongle.at_spssend("L=1").then(() => {
            console.log("Turned on");
            document.getElementById("light").src = lightOnImg;
          });
        });
    });
});

document.getElementById("lightOf").addEventListener("click", function () {
  my_dongle
    .ati()
    .then((data) => {
      //make central if not
      if (JSON.stringify(data).includes("Peripheral")) {
        console.log("peripheral");
        my_dongle.at_central().then((x) => {
          console.log("central now");
        });
      }
    })
    .then(() => {
      // connect to dongle
      my_dongle
        .at_getconn()
        .then((y) => {
          if (JSON.stringify(y).includes(dongleToConnect)) {
            console.log("already connected");
          } else {
            my_dongle.at_gapconnect(dongleToConnect).then(() => {
              console.log("connected successfully");
            });
          }
        })
        .then(() => {
          // send command to control light
          my_dongle.at_spssend("L=0").then(() => {
            console.log("Turned off");
            document.getElementById("light").src = lightOfImg;
          });
        });
    });
}); The script js file has three button actions; connect and control light. Now we need to know the ID of the other dongle connected to STM32 so that we can connect to it. You can use this web terminal to get the dongle ID. Open this site and click connect to dongle. https://bleuio.com/web_terminal.html Select the appropriate port to connect. Once it says connected, type . This will show dongle information and current status. "ATI" If the dongle is on peripheral role, set it to central by typing "AT+CENTRAL" Now do a gap scan by typing "AT+GAPSCAN" Once you see your dongle on the list ,stop the scan by pressing control+c Copy the ID and paste it into the script (script.js) line #2 You will need a web bundler. You can use parcel.js Once parcel js installed, go to the root directory and type . This will start your development environment. “parcel index.html” Open the script on a browser using parcel js. You can easily connect to the dongle and turn on-off light from there.

A Guide to Making a BLE-Enabled Smart Bulb with STM32

Signal filtering is the bread and butter of digital signal processing, and ARM Cortex-M cores provide a very efficient way to implement IIR filters, especially for cores that come with an FPU. ARM maintains the library as a collection of useful DSP functionality, including IIR filters implemented using a Direct Form II transposed structure. Big words, right? CMSIS-DSP In fact, the maths needed to go from filter specification to running code are not necessarily straightforward to do on paper. The process is complex and error prone, plus there is not a lot of value in doing it yourself for every minimal parameter variation. That's why at we created a collection of scripts to harness the power of to design IIR filters and automatically get "ready-to-run" C code for our applications. Elimo Engineering GNU Octave Being very creative people, we called it and, in the spirit of open source, we made it available with examples on GitHub. iir-designer-cmsis-dsp The repo includes a file with example inputs like the following for a DC blocking filter: / % Example % nd order DC blocker, Hz, sampling frequency = kHz
% Plots the filter response sample signals / pkg load signal
order= f1= fs= plot_results= %// // // // // // // // // // // // // // // // // // // // // // // // // // // // // // // 1 2 20 44.1 and %// // // // // // // // // // // // // // // // // // // // // // // // // // // // // // // 2 20 44100 true design ; _iir_highpass_cmsis_butter( , , , ) order f1 fs plot_results which will give you the following output: coeffs = 0.99799 -1.99597 0.99799 1.99597 - . 0 99598 That array of coefficients can be directly used in ARM code like the following: #define IIR_ORDER #define IIR_NUMSTAGES (IIR_ORDER/ )

static 32_t m_biquad_state[IIR_ORDER];
static 32_t m_biquad_coeffs[ *IIR_NUMSTAGES] =
{ , , , , };

arm_biquad_cascade_df2T_instance_f32 iir_inst = 
{
  IIR_ORDER/ ,
  m_biquad_state,
  m_biquad_coeffs
};

extern 32_t* pSrc;
extern 32_t* pDst;
extern 16_t blockSize;

arm_biquad_cascade_df2T_f32(&amp;iir_inst, pSrc, pDst, blockSize); 2 2 float float 5 0.99799 -1.99597 0.99799 1.99597 -0.99598 const 2 float float uint Note that this code is mostly boilerplate, and you just need to provide an input array of floats (pSrc in our example), a buffer for the filtered signal (pDst) and their length (blockSize) Let us know if you find it useful, we would love to know what people are using it for!

Fast IIR filtering on ARM Cortex-M with CMSIS-DSP and GNU Octave

In 2017, the cryptocurrency rage reached to the highest point when Bitcoin surged to USD19,783, many speculators and miners started to invest an enormous amount of money into mining hardware and chips hoping to get on the craze. Cryptocurrency mining was originally designed to be doable with general-purpose computers. Yet with the increase in competition to the limited resources, miners have been seeking an effective way to improve their chances. A , known as ASIC, has taken over the mining market since. ASIC are chips that are designed with a singular purpose. For cryptocurrency mining, these chips are built into specifically designed motherboards and power supplies to efficiently . Each cryptocurrency has its own cryptographic hash algorithm and ASIC hardware are designed to excel at calculating that algorithm, in the case of Bitcoin, ASIC hardware is designed to calculate the SHA-256 hash algorithm. pplication-specific integrated circuit chips solve a specific algorithm However, in 2018, the bearish performance in the cryptocurrency market has put them in a difficult situation. Though with better performance comparing the general-purpose computer using GPU or CPU cards, there is a limited room of adjustment for the ASIC hardware. ASIC miners were seeking ways to deal with their machines and spaces they rent for running the mining machines. In contrast, GPU miners can easily adapt to new PoW algorithms and tokens due to better compatibility with different hash algorithms (in the cost of performance). Furthermore, the emerging of Cuckoo Cycle algorithm sheds light on the GPU mining to compete against the ASIC miners. What is Cuckoo Cycle PoW algorithm? is one of the most promising PoW algorithm created by Dutch computer scientist, John Tromp. Cuckoo cycle was originally applied as a measure to prevent email spams. Cuckoo cycle ASIC-resistant The email sender has to perform a certain amount of work that consumes computational power to prove that he does have the need to send out the email to the recipient. Yet, with its unique design, it sees fit for GPU mining. It focuses more on memory use, meaning the solution time is bound to memory bandwidth rather than the raw processor or GPU speed. Unlike other PoW algorithms that requires maxing out the capacity of your hardware that drains a lot of power which produces heat (which is why most mining factories are located in colder locations/or locations with cheap electricity that can turn A/C on 24/7), a memory-bound algorithm like Cuckoo cycle require far less energy than most other GPU, CPU or ASIC-bound PoW algorithms. The most up-to-date implementations and findings can be found on his . GitHub Difference between the Cuckoo Cycle PoW algorithm and the SHA256 used by Bitcoin? The SHA256 used by Bitcoin is a simple and pure hash function. The computational process of the solution is easily migratable from the CPU to GPU, FPGA, and ASIC. The above picture is an 8-node, 4-sided Cuckoo Cycle bipartite graph Cuckoo Cycle is a graph theory-based algorithm. It tries to find a fixed length L ring in the Cuckoo Cycle bipartite graph randomly generated by Siphash. As the scale of the graph increases, the L value increases and becomes more difficult to find a ring that fits the length of L. Solving the solution (i.e., the process of finding a loop) itself requires a relatively large amount of memory to store the sorting of data. The Cuckoo Cycle algorithm is to ASIC chips. The difficulty and cost to make Cuckoo Cycle compatible on ASIC are much higher than the SHA256 of Bitcoin. It is possible to reduce the anti-ASIC capability by deformation and become an ASIC-friendly algorithm, but it is also possible to further improve its ASIC resistance through deformation as well. resisted For example, simplifies and discards the binary end of nodes in the bipartite graph which greatly reduce the difficulty of the solution. However, if all the nodes in the bipartite graph are grouped where all the nodes in the group have mutually dependent relationships, it is necessary to calculate the hash of all the nodes in the group. The difficulty of the solution and the requirements for data cache are then greatly increased. This transformation of Cuckoo Cycle is called . CuckAToo algorithm CuckARoo algorithm Projects using the Cuckoo Cycle algorithm On the official Cuckoo Cycle page, a list of projects using this algorithm was announced including AE, Grin, and Cortex. GitHub The list of projects using Cuckoo Cycle on GitHub , also known as , is a new blockchain technology, designed to deliver unmatched efficiency, transparent governance, and global scalability. (2.0.0) was released a couple weeks ago. AE has a total of 254,396,431 AE and aims to boost 1,000 TPS. AE æternity Minerva Mainnet upgrade is currently live with its Grin uses the Mimblewimble protocol to improve scalability and privacy. It empowers anyone to transact or save modern money without the fear of external control or oppression. Grin is designed for the decades to come, not just tomorrow. Grin wants to be usable by everyone, regardless of borders, culture, skills or access. Grin does not have circulating limit, which means that there is no limit on the total amount, and 60 Grins are mined per minute. Grin mainnet . is building something unique and original. Cortex aims to create a decentralized AI autonomous system that continues to expand the performance boundaries of smart contracts, allowing smart contracts to be actually smart with AI algorithms. In Cortex’s AI on Blockchain ecosystem, AI model developers, AI DApp developers, miners, and users can be linked together through public blockchain to create more AI DApps with real-world use cases. It also help stimulating more high-quality AI models to open source through the tokenomic model and incentive mechanism, which helps to democratize AI research which often led to concentrations of power and money. This is a new AI on Blockchain ecosystem that is independently governed by AI smart contracts. Cortex follows the four-year halving mechanism of Bitcoin. The total amount of CTXC mined through PoW is 150 million, and the first four years yields 75 million, which is halved every four years. Currently, the is live and the are live. Cortex TestNet first AI DApp Grin and Cortex's take on ASIC Grin uses CuckARoo (anti-ASIC) and CuckAToo (ASIC-friendly) algorithms in parallel where 90% of Grin is mined by ASIC-resistant CuckARoo and 10% of Grin is mined by ASIC-friendly CuckAToo. This ratio will be adjusted every six months to the point that will fully adopt ASIC-friendly CuckAToo. Grin Cortex In contrast, goal is to try to establish an environment where any AI equipment or conventional hardware already owned by everyone to be able to mine. Based on this goal, anti-ASIC is a firm belief in Cortex. The reason for this is that Cortex hopes to allow the mining machine investors in the Cortex ecosystem to have better general-purpose computing capabilities and lay the foundation for the future large-scale AI computing demand market. Cortex’s Cortex has released a full version of the which is already using a version of Cuckoo Cycle for consensus mechanism. The AI smart contract feature has also been deployed. The first fully available has been created on the TestNet. The development progress of the MainNet is working in progress according to the roadmap, which will be launched in Q2 of 2019. The launched of Cortex MainNet will bring a new wave of not just to AI developers and DApp developers but also to GPU miners. TestNet AI DApp The first AI DApp on the Cortex TestNet Conclusion Cuckoo Cycle is the newly emerging Proof-of-Work algorithm adopt by many projects to establish a friendly mining environment. It generates random bipartite graphs from a provided message and tries to find whether a subgraph with required property exists or not. Different projects have their objectives using Cuckoo Cycle. The algorithm itself has a relatively high anti-ASIC capability and can be modified into other algorithms to adjust the anti-ASIC capability. For Grin, Cuckoo Cycle was used to the Bitcoin-style “hardware arms-race” by making it ASIC resistant. For Cortex, it is to the mining requirements to lay the foundation for future scalability in AI computing demand market. Cukoo Cycle and GPU mining will lead us to a better mining environment where anyone can be a part of. mitigate against lower

WTF is Cuckoo Cycle PoW algorithm that attract projects like Cortex and Grin?

Your brain is a magical thing. It is the computer that you, that defines your world, and creates the interface for which you interpret “reality.” Technology and culture are starting to make us aware of just machine-like we are. In the movie “ ,” Nathan, shows Caleb the artificial wetware brain he crafted out of an exotic gel, essentially distilling our consciousness down to just a very expensive piece of tech. ( ) Treating your mind like a machine is an approach that is logical and highly beneficial. is how Ex Machina Apparently you can build one if you are so inclined. My Mom’s Desktop on her is a mess. I just counted and there are over 8,000 files sitting there just on the Desktop. But, she gets a lot done. She runs a youth theatre, is CFO and COO for my Dad’s business, and despite my bias, is genuinely one of the most productive people I know. But, back to the subject, her Desktop on that MacBook gives me instant anxiety. It’s a clusterfuck. So, every 2 or 3 years she gets a new laptop and I end up cloning her old computer onto her new one, except I always put her Desktop folder into a sub folder. At some point, I delete the old ones. She never notices. MacBook Many times, when working on a collaborative project, I hear someone say “I just didn’t have time to get to it,” or “I was distracted.” 9 times out of 10, their life is just too busy, their office too messy, and they watch The News every night before bed. Our minds (most of them) can’t deal with a Desktop like my mom’s. Our minds need clean input to obtain clean output. Our brains take in everything, and we are very much at the peril of the world. But, we have mechanisms to clean our mind’s Desktop. Reduce Distraction There are lots of things I do to reduce distraction: I don’t have sounds on my phone. If you call me, my phone doesn’t ring. If you message me, my phone doesn’t even vibrate. In fact, there is only 1 person that my phone makes sound for. This reduces distraction. But the most important piece of the input puzzle is not the amount of input, but the quality of the input. Even just a little positive input in a crowded life can completely change everything. We have deliberate input sources, like TV, books, and other media. I stopped watching TV for years. I don’t read Facebook, and my Twitter usage is limited. I read books with purpose and intent, and try to only interact with positive people. But there is so much input you receive without consent. Reprogram To combat the input you receive without your consent, you need to employ some reprogramming techniques. Can you remember the last time you had such an amazing day, that you went to bed, slept like a baby, and woke up the next morning feeling deep love in your heart? It would be amazing if we could operate this way, almost irregardless, every single day. We can’t always feel good, but we can rewire our brain to function more positively. The 2 biggest ideas for reprogramming your mind are: Mind follows body. If you start moving your body, your mind will follow suit. Subconscious programming through self hypnosis is real, it works, and you can change your life with it. The aim is to reprogram our brain more positively. Think of someone that makes you extremely happy and picture the essence and joy they conjure in your mind. They aren’t around right now, but you can still feel that happiness. All of that wonderfulness was fed straight to your brain, and you can recall it. People subtly feed you information about who they are and their intentions. How people sit in our minds has a lot to do with the subconscious information we’ve gathered on them, and how our brain processes it. People are hypnotizing. They work their way into our minds through very hypnotic, subconscious methods. We can also program our own vision of ourselves, the outlook we have on the day, and the perspective we take on life in a similar fashion through self hypnosis. The Hypnosis Experience I was laying in a chair, feeling my body floating perhaps 4 feet in the air. Colors of yellow were washing over my eyes and words being echoed through the air were entering and exiting my mind simultaneously. I saw a version of myself that I had not seen in a long time. Waves of energy pulsed through my body. And, then a finger snapped… I was awake and I felt “good.” What I experienced was similar to meditation experiences I had in the past, so I didn’t think it was absolutely crazy, but I was bewildered. I didn’t think I would see a dramatic change in my life by listening to this hypnosis session every day, but I continued to every day since that day. In 3 years, there is not a single thing I can contribute to my health, happiness, and success more than self hypnosis. I was told many years ago by my father that self hypnosis is something you are doing whether you think you are or not. You can be in control of the content your brain receives, or let the world decide for you. Hypnosis is real and it works. What does it mean? Mental health is something we don’t talk about enough. It’s not just about beating depression, but about finding ways to optimally live in the moment. There are wonderful things out there, like hypnosis, meditation and exercise. Take control of your brain’s input and output, and do everything and anything you need to attain the optimal life you desire. Originally published at https://noeljackson.com/io-cerebral-cortex/ Noël Jackson is an engineer, designer, and hacker working on the web since 1998, with a focus on optimization through thoughtfulness and logic. He currently works at studio.zeldman as director of development.

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