Diy wifi setup on s/v deep playa

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Perhaps more importantly for power users, it's optimized for more efficient operation on crowded networks with a lot of competing traffic. The WiFi antenna designs with four-way positioning that enables better signal reception, and a magnetic base to secure the antenna on the top or side of the PC case. MyASUS offers a variety of support features to help you contact customer service, troubleshoot issues and optimize product performance.

Learn more. Onboard 2. This translates to incredibly fast file transfers, low-lag online gaming, and high-res video streaming. Distracting keyboard clatter, mouse clicks and other ambient noises are removed so you can hear and be heard with perfect clarity while gaming or during calls.

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Skip to content Accessibility help. Gaming Phone. Snapdragon Insiders. Thin and light. Everyday use Entry-level Vivobook. ProArt Studiobook Power up your imagination. For a single floor, your wifi antennae should always be sticking straight up and, if you have multiple antannae, parallel. You may need to lie them flat, parallel with the floor, to get the signal to go up and down. Always keep your antennae parallel if you have more than one. The key is that the reflectors need to made of something… well, reflective.

And the struts should be made of something very not reflective. The designer notes:. These extenders get the job done, but you might also be able to boost the signal from your wifi router by adjusting settings in the firmware. Depending on your firmware, you can amp up the throughput and achieve greater range and signal strength. Just be careful, as this will also make your router run hotter and could damage or shorten its lifespan.

Just use caution, as flashing the wrong firmware or flashing improperly could permanently brick the router. For internal antennae, it might not work. You can try building the parabola and experiment with placement around the modem. This site uses Akismet to reduce spam. Learn how your comment data is processed. Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts.

Perfect to run on a Raspberry Pi or a local server. Once started, Home Assistant will automatically scan your network for known devices and allow you to easily set them up. Home Assistant is not just limited to Home Assistant. Easily install other applications that will help you manage your home. Home Assistant communicates with your devices locally, and will fallback to pulling in data from the cloud if there is no other option.

No data is stored in the cloud, and everything is processed locally. So type the following into the terminal and press enter:. Now type ls "ls" is shorthand for "LiSt" and prints out all of the files in the current working directory. It's a great way to look around and see what changed on disk. You should see a list of filenames ending with. So let's look at one of these. To close the photo window from your terminal, press Ctrl-C Ctrl-C interrupts a running process and returns control back to the terminal prompt.

What is gpicview gpicview is an application that you can use to display an image. Not seeing anything on your monitor? Then try to view the image again by typing the command above. Stop the Joy Detector The Joy Detector runs by default, so you need to stop it before you can run another demo. To do this, type the following command and press enter:. After the demo stops, you are brought back to the command prompt. If you instead see an error, check the command for typos and try again.

Always stop any demos that are running before trying a new demo. However, the next time you reboot your kit, the Joy Detector demo will start running again. So if you want to disable it completely so that it does not start by default, type the following command into your prompt and press enter:. For more information about these commands, see run your app at bootup.

Type the following command into your prompt and press enter:. Copying and pasting in a terminal is a little different than other applications you may be used to.

If you are using the Secure Shell Extension, to copy some text, highlight what you want by clicking and dragging with the left mouse button, and as soon as you let go of it, it'll copy it.

To paste, click the right mouse button. On a touchpad this can be a little tricky, so try tapping or pressing in the lower right of the touchpad, or tapping with two fingers. To copy text using the terminal on your Raspberry Pi: select the text, right-click, and select 'copy' from the menu. Left click where you want to paste the text, then right click and select 'paste' from the pop up menu. These are the example demos written in Python Python is a programming language that we use for the majority of our demos and scripts.

It's a simple language and is very easy to learn. Start the image classification camera demo The image classification camera demo uses an object detection model to identify objects in view of the Vision Kit.

If it's working, a camera window pops up on your monitor if one is attached and the output from the model starts printing to your terminal. If you are brought back to the prompt after seeing error text, check the Using the Vision Kit section of the help page for troubleshooting tips. The camera is blocking my terminal window.

If you are connected directly to your Raspberry Pi via mouse, monitor, and keyboard, the camera window might block your terminal. Press Ctrl-C after pointing your camera at a few objects to stop the demo and close the camera window. Then you can scroll up in your terminal window to see what the camera identified. If you want to see the terminal and camera preview at the same time, you can connect your Raspberry Pi to Wi-Fi and then connect to it from another computer via SSH.

For information about that setup, see the login setup for the Voice Kit. Point your Vision Kit at a few objects, such as some office supplies or fruit. Check your terminal screen to see what the model A model is like a program for a neural network. It is a mathematical representation of all the different things the neural network can identify.

But unlike a program, a model can't be written, it has to be trained from hundreds or thousands of example images. When you show your Vision Kit a new image, the neural network uses the model to figure out if the new image is like any image in the training data, and if so, which one.

The number next to each guess is its confidence score The confidence score indicates how certain the model is that the object the camera is seeing is the object it identified.

The closer the number is to 1, the more confident it is. You might be surprised at the kinds of objects the model is good at guessing. What is it bad at? Try different angles of the same object and see how the confidence score changes. This will bring you back to the prompt. Start the face detection camera demo This demo enables your Vision Kit to identify faces. It prints out how many faces it sees in the terminal, and if you have a monitor attached, it draws a box around each face it identifies.

If it's working, you will see a camera window pop up on your monitor if one is attached and the output from the model will start printing to your terminal. If you are brought back to the prompt after seeing error text, check out the Using the Vision Kit section of the help page for troubleshooting tips.

Point the camera toward some faces and watch the demo output. Iteration tells you the number of times the model has run. Try moving the camera quickly, or farther away. Does it have a harder time guessing the number of faces? Run the face camera trigger demo With this demo, your Vision Kit automatically takes a photo when it detects a face. To start it, type the following command and press enter:. It will remain in this state until the camera sees a face and captures a photo.

Point the camera at yourself or a friend. Try making a bunch of faces and experiment with what the machine considers to be a face. When it sees a face, it will take a photo and create an image called faces.

Seeing an error? Check out the Using the Vision Kit section of the help page for troubleshooting tips. To open the photo, see the instructions for how to View an image on your Pi. Take a photo The following demos show you how to use existing image files as input instead of using the live camera feed. So you need to first capture a photo with the camera or save a file into the same directory.

What should I name my file? You can name your file anything you want, as long as you use only letters, numbers, dashes, and underscores. You should end your filename with. What does this command mean? The -w flag and -h flags specify the width and height for the image. The -o flag specifies the filename. For more information, see the raspistill documentation. To verify that a photo was created, type ls at the prompt and press enter.

You should see the filename you used in the step above. Tip: Press the up and down arrow keys at the prompt to scroll through a history of commands you've run. To rerun a command, it's easier to press the arrows until the one you want is shown. You can edit the command if needed, then press enter. If you skipped that step, go back and take a photo or make sure you have a photo with a face on your SD card. If you named your image file something different, replace image.

Try taking a new photo and then running the command again. Be sure your subject is well lit from the front and there are no bright lights directly behind them. First, you need an image ready: take a photo with the camera or save a photo on the SD card.

Then type the following command and press enter, replacing image. Run the dish classifier demo The dish classifier model can identify food from an image. Try again with a different photo. Run the image classification demo This is the same image classifier from above but now running against a captured image. If you've connected your kit to a monitor, mouse, and keyboard, you can shut it down by opening the applications menu the Raspberry Pi icon in the top-left corner of the desktop and then clicking Shutdown.

Otherwise, if you're connected to the kit with an SSH terminal, type the following command and press enter:. To reconnect your kit, plug your kit back into the power supply and wait for it to boot up about 2 minutes. Once your kit is booted, reconnect via the Secure Shell Extension review the steps to connect to your kit. Note: You might have to re-pair your kit via the app.

It also describes how you can train your own TensorFlow model to perform new machine vision tasks. Heads up! This section assumes a much higher level of technical experience. So if you're new to programming, don't be discouraged if this is where you stop for now.

To support various features in the Vision Kit, we've built a Python library that handles a lot of programming dirty work for you.

It makes it easy to perform an inference with a vision model and draw a box around detected objects, and to use kit peripherals such as the button, LEDs, and extra GPIO pins. These APIs are built into a Python package named aiy , which is pre-installed in the kit's system image.

Just be sure that you've installed the latest system image. You might find it easier learn the aiy Python API if you start with an existing demo and modify it to do what you want. You can also browse the examples on GitHub , where you'll find the source code for all the examples and more.

For instance, to learn more about the aiy. For each face detected in image. It also creates an image to the output location, which is a copy of the image that includes a box around each face. To see how it works, open this file on your Raspberry Pi or see the source code here.

Then start tweaking the code. If you're more interested in programming hardware such as buttons and servos, see the section below about the GPIO expansion pins , which includes some other example code. To further customize your project, you can train a TensorFlow model to recognizes new types of objects, and use our Vision Bonnet compiler to convert the model into binary file that's compatible with the Vision Bonnet.

Give it a try right now by following our tutorial to retrain a classification model. If you want to build your own TensorFlow model, beware that due to limited hardware resources on Vision Bonnet, there are constraints on what type of models can run on device. We have tested and verified that the following model structures are supported on the Vision Bonnet. For an example of how to retrain and compile a TensorFlow model for the Vision Bonnet, follow this Colab tutorial to retrain a classification model for the Vision Kit.

The tutorial uses Google Colab to run all the code in the cloud, so you don't need to worry about installing and running TensorFlow on your computer. At the end of the tutorial, you'll have a new TensorFlow model that's trained to recognize five types of flowers and compiled for the Vision Bonnet, which you can download and run on the Vision Kit as explained in the tutorial.

You can also modify the code directly in the browser or download the code to adjust the training parameters and provide your own training data. For example, you can replace the flowers training data with something else, like photos of different animals to train a pet detector. Beware that although this script retrains an existing classification model, it still requires a large amount of training data to produce accurate results usually hundreds of photos for each class.

You can often find good, freely-available datasets online, such as from the Open Images Dataset. Download the Vision Bonnet model compiler here. Due to the Vision Bonnet model constraints , it's best to make sure your model can run on Vision Bonnet before you spend a lot of time training the model. You can do this as follows:. Use the checkpoint generated at training step 0 and export as a frozen graph ; or export a dummy model with random weights after defining your model in TensorFlow.

Use our compiler to convert the frozen graph into binary format, and copy it onto the Vision Kit. Note: Vision Bonnet handles down-scaling, therefore, when doing inference, you can upload image that is larger than model's input image size. And inference image's size does not need to be a multiple of 8. The following subset of TensorFlow operators can be processed by the model compiler and run on device.