Irys Software Manual

[Efraine Ton]

Irysis a contactless system that using Terahertz waves and proprietary algorithms developed by das-Nano provides the thickness measurement of every painting layer in a car body in a non-destructive way.

With previous methods, it was still necessary to make scratch bodies, mask them off and manually measure the layer thickness. So now we have significant time and material savings with this novel system. You get much more information compared to previous systems.

Due to the obsolescence of our previous measurement systems, the Wolfsburg planning department and our technology partner das-Nano, decided to introduced an installation based on Terahertz technology. This system allows us the simultaneous measurement of all paint layers in a fully automated way eliminating manual processes.

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Summary: Irys Extract is a software tool for generating genomic information from data collected by the BioNano Genomics Irys platform. The tool allows the user easy access to the raw data in the form of cropped images and genetically aligned intensity profiles. The latter are also made compatible with the BED format for using with popular genomic browsers such as the UCSC Genome Browser.

Availability and implementation: Irys Extract has been developed in Matlab R2015a, it was tested to work with IrysView 2.4.0.15879 and AutoDetect 2.1.4.9159, and it currently runs under Microsoft Windows operating systems (7-10). Irys Extract can be downloaded alongside its manual and a demo dataset at and

Arweave allows for decentralized data storage. Users pay a one-time fee to upload their data and are guaranteed permanent storage. Miners ensure data permanence by storing and replicating data in exchange for Arweave's native AR token. You can think of Arweave as a global hard disk or a decentralized Amazon S3.

Irys, formerly known as Bundlr, is a provenance layer on Arweave. It scales uploads to Arweave via bundling, lets you pay with different tokens than AR, and gives you strong provenance capabilities for your uploads.

It is also cheaper and more convenient to use Irys for Arweave upload as you are not charged per chunk as when you upload to Arweave directly. If you only upload 1KB, you only pay for that, not the whole 256KB chunk used to store it.

Still, the fee is dynamic and dependent on several factors, like the exact number of bytes you are uploading and Arweave's cost, which is the cost of storing X amount of bytes on Arweave using linear pricing, etc. You can learn how the cost is computed in the Irys docs.

Transactions uploaded via Irys are deemed finalized once uploaded, as Irys guarantees that the data gets uploaded to Arweave. In contrast to Arweave, which has a block time of 2 minutes and takes 50 block confirmations for data to be finalized and stored permanently on Arweave, Irys does not have such restrictions, as it uses optimistic finality and retries the upload of data to Arweave until the data is included in a block and confirmed. You can safely assume that Irys finalizes any data uploaded via the network.

You should be familiar with working with JavaScript and know how to build React apps. You should know your way around the command line and be able to install NPM packages via the terminal. You should install Node.js v16 and NPM on your development machine and be familiar with an EVM-based wallet like MetaMask.

Navigate to your working directory, create a new directory, and initiate a new NPM project in the newly created directory, where we will install the NextJS application. Run the following command at the terminal:

Add the .env-local file to your gitignore file, and never push it to a remote repository. It is advisable to use a disposable wallet key for this exercise; don't use the wallet key that contains your crypto assets!

This code initializes Irys creating an Irys constructor that accepts an object with keys of url, token, key and config. The url is the Irys node we want to connect to, token is the currency to use for payment, key is the private key of the wallet and config is only necessary if we are connecting to a testnet which we are doing in this tutorial.

You could also fund the node in advance, but you can only use the node you have funded, and you are also allowed to withdraw any unused funds from the node.

Open the file utils/utils.js and create a new function called lazyFundNode.

The function is async and takes the size of the data you are uploading as a parameter. It calls the getIrysClient method, which we have previously defined, to obtain a new Irys client that uses Mumbai Matic to pay for uploads. Next, you await a call to the getPrice method to get the price for uploading an image/data of the passed parameter size. Finally, you await irys.fund(price), which causes the token to be withdrawn from your wallet to fund the node.

Create and export a new function inside the utils/utils.js file called uploadFileToArweave. This is a simple function that does the file upload.

At the top of the utils.js, add an import statement for fs.

The function uploadFileToArweave takes in two parameters: the filepath and tags. The function reads the file from the file system using fs.readFileSync(filepath). After reading the file into a buffer, it calls the irys.upload, passing the file buffer and Arweave tags.

The allowedFiles function checks if the mime type is "image/png", "image/jpeg", "image/jpg" or "image/gif". This check ensures simplicity, as the best way to check the content and type of a file is on the server side.

The browser's FileReader class reads the selected file and saves the result in the imageSource React state. The image file is saved in another state called selectedFile. The selected image is displayed with an image tag. After the image is selected, a form is indicated for the user to enter a caption and a description for the image. The caption and description are saved in a React state named caption and description.

The uploadFileToArweave function is attached to the UploadImage button's click event handler of the image details form. The function checks for the existence of a selected image, caption, and image description. You create a new FormData() to be passed to the server. The selected image and the metadata are appended to the FormData().

You are creating three tags describing the image and one tag describing the application name. You can define an arbitrary number of tags; the only restriction is that the total size of the tags should not be more than 2KB. In defining tags we could include the creator of the particular data which then becomes associated with that piece of data.

You manually parse the client request using formidable; remember, as the request will contain the uploaded file and the metadata fields, you will define a handler function to process the client request.

The handler is asynchronous and receives a request and a response object as parameters. At the top of the file, we create a directory to store the uploaded image. The directory is created if it does not exist inside your application folder.

path.join adds the current working directory to the newly created directory "uploads/images" to get an absolute path. The function readFile processes the file and returns the file object and the fields sent from the client. Copy the code below and paste it into the upload.js file.

The readFile function returns a Promise, which resolves to the fields and files. You create an emptyoptionsobject to configureformidable. First, you set theuploadDir, which equates to theupload/imagesdirectory you previously created. You also set thefilename` and the maximum file size we want to upload to the server, in this case, 4MB.

The Promise resolves with the files and fields if successful or rejects with an error. After reading and processing the file, we get the file size using the fs.statsSync method, passing in the file path.

You must fund the Irys node with the token amount required to upload the file. In this example, you are operating a pay-as-you-go method. You await the result of the function await lazyFundNode(size). Remember, this is one of the utility functions created earlier that accepts a size parameter to fund a node.

We will use the Irys query package to retrieve data instead of using Graphql. Create a new file in the root of your project folder called queries.js. Inside the newly created queries.js file, we will create and export an instance of the query package; this exported instance will be used throughout our application.

The data returned from the node is destructured to get the uploaded image with the caption and description displayed on the page. Open the index.js page, and let's add the ImageViewer component to the page. Add the ImageViewer before the closing .

Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy[1] they are able to encode and use in matching. Iris recognition is exceptional in this regard, enabling the avoidance of "collisions" (False Matches) even in cross-comparisons across massive populations.[2] Its major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult. However, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed.[3]

Retinal scanning is a different, ocular-based biometric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.[4] Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with remarkably low false match rates.

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