#computer hardware engineer

[computer-nerd-girl]

[teknewsfeed]

Interactive floor concept!

[adafruit]

Desk of Ladyada - I2S DACs, Claude API, and Compute Module Backpack 🤖🎒🥧 https://youtu.be/XihMNhTyUlg

Ladyada explores I2S DACs, testing PCM51xx as a UDA1334A alternative. Work continues on the TLV320DAC3100, we test an AI API interface for setters/getters for Claude with pay per token. A new Pi Compute Module backpack is in progress - And we search for tall connectors for CM4/CM5.

[bitstitchbitch]

here’s my pro tip

everyone keeps pushing computer science without recognizing that the field is quickly becoming oversaturated. If you love CS, than sure, do that. But if you’re looking for a really marketable degree that will let you do CS but also leave the door open for other stem careers, then I highly recommend computer engineering or electrical engineering with a CS minor (optional - have an ee degree without a minor and I still work in software). You can still get a software job if you want, with the added bonus that a lot of CS people will think you’re a wizard for having a working knowledge of hardware. And as software jobs get harder to find and get, you can diversify and apply for hardware jobs. And the hardware jobs will be easier to get if you know how to code. Also, circuits are really fucking cool guys.

[attila-werther]

photos taken five minutes before disaster

[1o1percentmilk]

i actually think hatori is more of an electrical/hardware engineer than an informatics/information technology/software engineering person

[gmos]

i keep seeing posts that are like "kids these days dont know how to use computers" do you realize that Most People dont know how to use computers. this is not a generational issue. computer literacy is a complex and evolving skillset that has to be taught and maintained. most people are doing other stuff in their lives besides going on computer. insisting that kids only know about apps and its making them stupid is wildly reductive. and honestly pathetic bc they are children they dont have control of their learning environment how are you mad at them

[cerulity]

Making your own Home Assistant (theorising)

Disclaimer: This is all theory and speculation. I have not tested anything or made my own home assistant yet, I just looked around for libraries and hardware that are likely compatible. I have not fully tested the compatibility or quality of these, this is simply the first iteration of an idea I have.

I just got news that Amazon Alexa has lost 10 billion dollars because their business model failed. This makes me happy, and has also made me realise that you can make your own home assistant.

Here are some of the links to things (I am aware that some are amazon, but it's the most global I could find. I encourage you to find other sellers, this is just what you should need. If you find anything cheaper or more local to where you are, go for it):

Hardware:

Raspberry Pi 4: https://www.canakit.com/raspberry-pi-4-2gb.html (RAM requirements may differ, I may do testing to see what comfortably runs)

8GB MicroSD: https://www.amazon.ca/Verbatim-Premium-microSDHC-Adapter-10-44081/dp/B00CBAUI40/ref=sr_1_3?crid=3CTN6X9TJXRR2&keywords=microsd%2Bcard%2B8gb&qid=1699209597&s=electronics&sprefix=microsd%2Bcard%2B8gb%2Celectronics%2C91&sr=1-3&th=1

Microphone: https://www.amazon.ca/SunFounder-Microphone-Raspberry-Recognition-Software/dp/B01KLRBHGM?th=1

Software:

Coqui STT: https://github.com/coqui-ai/STT

Coqui TTS: https://github.com/coqui-ai/TTS

If you have it set up correctly, you should be able to run both the STT and TTS in realtime (see https://github.com/coqui-ai/TTS/discussions/904).

After all of them are set up, the only thing to do is bridge it all together with software. There are bindings to Rust for both Coqui libraries (https://github.com/tazz4843/coqui-stt and https://github.com/rowan-sl/coqui-rs), and all that's left to do is implement parsing.

The libraries can also be swapped out for different ones if you like. If you can find and implement a DECtalk library that works for the Raspberry Pi, you can use that.

If I ever figure out how to manifest this idea, I will likely make the project modular so that you can use whatever library you want. You can even fork the project and include your own library of choice (if you can bind it to Rust).

Go FOSS!

[welcometogrouchland]

Hey audio engineering side of Tumblr. Theoretically how would a young dumb lass record audio directly from an instrument (such as an electric guitar 🎸) to a computer. Secondly how would said young dumb lass layer different recordings on top of eachother without forking over her organs.

[literary-potato]

I’m a privacy engineer and this is a poster I printed out to hang on my wall because it was so perfect:

Source

I don't know who needs to hear this but please please please please please explore the settings. Of your phone, computer, of every app you use. Investigate the UI, toggle some things around and see what happens. You won't break anything irreperably without a confirmation box asking you if you really mean to do that thing. And you can just look up what a setting will do before touching it if you're really worried ok?

Worst case scenario you just have to change the settings back if you don't like what happened but it is so so so important to explore the tools available to you and gain a better understanding for how the stuff you use works.

Even if you already know. Even if you're comfortable with how you use it now. You don't just have to accept whtever experience has been handed to you by default and it's good for you to at least know what's available to you.

[nullthevoidsheep]

Even native speakers of a language had to learn it from those who came before them.

So this was originally a response to this post:

****

****

Which is about people wanting an AO3 app, but then it became large and way off topic, so here you go.

Nobody under the age of 20 knows how to use a computer or the internet. At all. They only know how to use apps. Their whole lives are in their phones or *maybe* a tablet/iPad if they're an artist. This is becoming a huge concern.

I'm a private tutor for middle- and high-school students, and since 2020 my business has been 100% virtual. Either the student's on a tablet, which comes with its own series of problems for screen-sharing and file access, or they're on mom's or dad's computer, and they have zero understanding of it.

They also don't know what the internet is, or even the absolute basics of how it works. You might not think that's an important thing to know, but stick with me.

Last week I accepted a new student. The first session is always about the tech -- I tell them this in advance, that they'll have to set up a few things, but once we're set up, we'll be good to go. They all say the same thing -- it won't be a problem because they're so "online" that they get technology easily.

I never laugh in their faces, but it's always a close thing. Because they are expecting an app. They are not expecting to be shown how little they actually know about tech.

I must say up front: this story is not an outlier. This is *every* student during their first session with me. Every single one. I go through this with each of them because most of them learn more, and more solidly, via discussion and discovery rather than direct instruction.

Once she logged in, I asked her to click on the icon for screen-sharing. I described the icon, then started with "Okay, move your mouse to the bottom right corner of the screen." She did the thing that those of us who are old enough to remember the beginnings of widespread home computers remember - picked up the mouse and moved it and then put it down. I explained she had to pull the mouse along the surface, and then click on the icon. She found this cumbersome. I asked if she was on a laptop or desktop computer. She didn't know what I meant. I asked if the computer screen was connected to the keyboard as one piece of machinery that you can open and close, or if there was a monitor - like a TV - and the keyboard was connected to another machine either by cord or by Bluetooth. Once we figured it out was a laptop, I asked her if she could use the touchpad, because it's similar (though not equivalent) to a phone screen in terms of touching clicking and dragging.

Once we got her using the touchpad, we tried screen-sharing again. We got it working, to an extent, but she was having trouble with... lots of things. I asked if she could email me a download or a photo of her homework instead, and we could both have a copy, and talk through it rather than put it on the screen, and we'd worry about learning more tech another day. She said she tried, but her email blocked her from sending anything to me.

This is because the only email address she has is for school, and she never uses email for any other purpose. I asked if her mom or dad could email it to me. They weren't home.

(Re: school email that blocks any emails not whitelisted by the school: that's great for kids as are all parental controls for young ones, but 16-year-olds really should be getting used to using an email that belongs to them, not an institution.)

I asked if the homework was on a paper handout, or in a book, or on the computer. She said it was on the computer. Great! I asked her where it was saved. She didn't know. I asked her to search for the name of the file. She said she already did that and now it was on her screen. Then, she said to me: "You can just search for it yourself - it's Chapter 5, page 11."

This is because homework is on the school's website, in her math class's homework section, which is where she searched. For her, that was "searching the internet."

Her concepts of "on my computer" "on the internet" or "on my school's website" are all the same thing. If something is displayed on the monitor, it's "on the internet" and "on my phone/tablet/computer" and "on the school's website."

She doesn't understand "upload" or "download," because she does her homework on the school's website and hits a "submit" button when she's done. I asked her how she shares photos and stuff with friends; she said she posts to Snapchat or TikTok, or she AirDrops. (She said she sometimes uses Insta, though she said Insta is more "for old people"). So in her world, there's a button for "post" or "share," and that's how you put things on "the internet".

She doesn't know how it works. None of it. And she doesn't know how to use it, either.

Also, none of them can type. Not a one. They don't want to learn how, because "everything is on my phone."

And you know, maybe that's where we're headed. Maybe one day, everything will be on "my phone" and computers as we know them will be a thing of the past. But for the time being, they're not. Students need to learn how to use computers. They need to learn how to type. No one is telling them this, because people think teenagers are "digital natives." And to an extent, they are, but the definition of that has changed radically in the last 20-30 years. Today it means "everything is on my phone."

[jcmarchi]

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

New Post has been published on https://thedigitalinsider.com/photonic-processor-could-enable-ultrafast-ai-computations-with-extreme-energy-efficiency/

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.

Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

The optical device was able to complete the key computations for a machine-learning classification task in less than half a nanosecond while achieving more than 92 percent accuracy — performance that is on par with traditional hardware.

The chip, composed of interconnected modules that form an optical neural network, is fabricated using commercial foundry processes, which could enable the scaling of the technology and its integration into electronics.

In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.

“There are a lot of cases where how well the model performs isn’t the only thing that matters, but also how fast you can get an answer. Now that we have an end-to-end system that can run a neural network in optics, at a nanosecond time scale, we can start thinking at a higher level about applications and algorithms,” says Saumil Bandyopadhyay ’17, MEng ’18, PhD ’23, a visiting scientist in the Quantum Photonics and AI Group within the Research Laboratory of Electronics (RLE) and a postdoc at NTT Research, Inc., who is the lead author of a paper on the new chip.

Bandyopadhyay is joined on the paper by Alexander Sludds ’18, MEng ’19, PhD ’23; Nicholas Harris PhD ’17; Darius Bunandar PhD ’19; Stefan Krastanov, a former RLE research scientist who is now an assistant professor at the University of Massachusetts at Amherst; Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research; Matthew Streshinsky, a former silicon photonics lead at Nokia who is now co-founder and CEO of Enosemi; Michael Hochberg, president of Periplous, LLC; and Dirk Englund, a professor in the Department of Electrical Engineering and Computer Science, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE, and senior author of the paper. The research appears today in Nature Photonics.

Machine learning with light

Deep neural networks are composed of many interconnected layers of nodes, or neurons, that operate on input data to produce an output. One key operation in a deep neural network involves the use of linear algebra to perform matrix multiplication, which transforms data as it is passed from layer to layer.

But in addition to these linear operations, deep neural networks perform nonlinear operations that help the model learn more intricate patterns. Nonlinear operations, like activation functions, give deep neural networks the power to solve complex problems.

In 2017, Englund’s group, along with researchers in the lab of Marin Soljačić, the Cecil and Ida Green Professor of Physics, demonstrated an optical neural network on a single photonic chip that could perform matrix multiplication with light.

But at the time, the device couldn’t perform nonlinear operations on the chip. Optical data had to be converted into electrical signals and sent to a digital processor to perform nonlinear operations.

“Nonlinearity in optics is quite challenging because photons don’t interact with each other very easily. That makes it very power consuming to trigger optical nonlinearities, so it becomes challenging to build a system that can do it in a scalable way,” Bandyopadhyay explains.

They overcame that challenge by designing devices called nonlinear optical function units (NOFUs), which combine electronics and optics to implement nonlinear operations on the chip.

The researchers built an optical deep neural network on a photonic chip using three layers of devices that perform linear and nonlinear operations.

A fully-integrated network

At the outset, their system encodes the parameters of a deep neural network into light. Then, an array of programmable beamsplitters, which was demonstrated in the 2017 paper, performs matrix multiplication on those inputs.

The data then pass to programmable NOFUs, which implement nonlinear functions by siphoning off a small amount of light to photodiodes that convert optical signals to electric current. This process, which eliminates the need for an external amplifier, consumes very little energy.

“We stay in the optical domain the whole time, until the end when we want to read out the answer. This enables us to achieve ultra-low latency,” Bandyopadhyay says.

Achieving such low latency enabled them to efficiently train a deep neural network on the chip, a process known as in situ training that typically consumes a huge amount of energy in digital hardware.

“This is especially useful for systems where you are doing in-domain processing of optical signals, like navigation or telecommunications, but also in systems that you want to learn in real time,” he says.

The photonic system achieved more than 96 percent accuracy during training tests and more than 92 percent accuracy during inference, which is comparable to traditional hardware. In addition, the chip performs key computations in less than half a nanosecond.     

“This work demonstrates that computing — at its essence, the mapping of inputs to outputs — can be compiled onto new architectures of linear and nonlinear physics that enable a fundamentally different scaling law of computation versus effort needed,” says Englund.

The entire circuit was fabricated using the same infrastructure and foundry processes that produce CMOS computer chips. This could enable the chip to be manufactured at scale, using tried-and-true techniques that introduce very little error into the fabrication process.

Scaling up their device and integrating it with real-world electronics like cameras or telecommunications systems will be a major focus of future work, Bandyopadhyay says. In addition, the researchers want to explore algorithms that can leverage the advantages of optics to train systems faster and with better energy efficiency.

This research was funded, in part, by the U.S. National Science Foundation, the U.S. Air Force Office of Scientific Research, and NTT Research.

[spaaztech1]

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[electricalinsightsdaily]

RN42 Bluetooth Module: A Comprehensive Guide

The RN42 Bluetooth module was developed by Microchip Technology. It’s designed to provide Bluetooth connectivity to devices and is commonly used in various applications, including wireless communication between devices.

Features Of RN42 Bluetooth Module

The RN42 Bluetooth module comes with several key features that make it suitable for various wireless communication applications. Here are the key features of the RN42 module:

Bluetooth Version:

The RN42 module is based on Bluetooth version 2.1 + EDR (Enhanced Data Rate).

Profiles:

Supports a range of Bluetooth profiles including Serial Port Profile (SPP), Human Interface Device (HID), Audio Gateway (AG), and others. The availability of profiles makes it versatile for different types of applications.

Frequency Range:

Operates in the 2.4 GHz ISM (Industrial, Scientific, and Medical) band, the standard frequency range for Bluetooth communication.

Data Rates:

Offers data rates of up to 3 Mbps, providing a balance between speed and power consumption.

Power Supply Voltage:

Operates with a power supply voltage in the range of 3.3V to 6V, making it compatible with a variety of power sources.

Low Power Consumption:

Designed for low power consumption, making it suitable for battery-powered applications and energy-efficient designs.

Antenna Options:

Provides options for both internal and external antennas, offering flexibility in design based on the specific requirements of the application.

Interface:

Utilizes a UART (Universal Asynchronous Receiver-Transmitter) interface for serial communication, facilitating easy integration with microcontrollers and other embedded systems.

Security Features:

Implements authentication and encryption mechanisms to ensure secure wireless communication.

Read More: RN42 Bluetooth Module

[sirtbhopal]

Hands-on training on Computer Hardware and Networking Concepts

Department of Computer Science and Engineering, SIRT successfully conducted hands-on training on "Computer Hardware and Networking Concepts".