Podcasts about LabVIEW

Contact your host with questions, suggestions, or requests about sponsoring the AppleInsider Daily:charles_martin@appleinsider.com (00:00) - 01 - Intro (00:12) - 02 - Apple and Google's $20B secret (01:23) - 03 - Right to Repair in CA (02:06) - 04 - Australian under the thumb of big banks (02:56) - 05 - OLED use expanding? (03:32) - 06 - QN: Total War Pharoah for macOS (04:05) - 07 - QN: LabView leaving macOS (04:33) - 08 - OTN: India investigates Apple, Google (05:08) - 09 - IRS sneak at-tax (05:52) - 10 - Outro Links from the showApple could be out $20 billion a year if Google loses DOJ antitrust caseApple-backed right to repair bill now law in CaliforniaApple objects to Australia plan to regulate Apple PayMacBook Pro will get OLED in 2026 from new Samsung factory'Total War: Pharaoh' transports gamers to the Bronze Age on macOSLabView design & test app abandons the Mac after four decadesIndia's antitrust regulator investigating Apple's & Google's business practicesMicrosoft hammered with $29 billion back-tax billSubscribe to the AppleInsider podcast on: Apple Podcasts Overcast Pocket Casts Spotify Subscribe to the HomeKit Insider podcast on:•  Apple Podcasts•  Overcast•  Pocket Casts•  Spotify

It's an afternoon of new perspectives in the café, both from climbing the rock candy wall and chatting about LabVIEW. Climbing enthusiast and LabVIEW trainer Sam Taggart belays in to chat about both, and how code interacts with the messy real world.

Luca Balbo . Piemontese, classe '87, laureato prima in Organizzazione Aziendale e Risorse Umane, poi in Scienze del Lavoro, presso la Statale di Milano, Luca Balbo si occupa di di Head Hunting dal 2015 nella divisione ICT & Digital. È entrato, in Hunters Group, nel ruolo più operativo di Consultant e oggi è diventato Executive Manager. Appassionato di ICT & Digital, fa il possibile per aiutare le aziende a districarsi nella selezione di questi profili molto difficili da approcciare. Siti, app, libri e link utili Il sito di Hunters GroupLa pagina LinkedIn di Hunters GroupIl sito dell'Università degli Studi di MilanoIl sito dell'Università degli Studi di BolognaIl sito del Politecnico di Torino I libri da scegliere Firmware engineer Il Firmware engineer è responsabile dello sviluppo e del miglioramento del software per sistemi embedded e tipicamente si occupa di sviluppare e migliorare il software embedded per il sistema di gestione del prodotto, il firmware per sistemi a microprocessore e l'interfaccia di comunicazione. Redige, inoltre, la documentazione tecnica. In genere è un ingegnere con specializzazione elettronica, anche se non mancano gli informatici. Proviene dal settore energy storage e ha maturato esperienza pregressa nella progettazione di circuiti elettronici complessi. Deve, ovviamente avere un'ottima conoscenza del quadro normativo (ISO 26262 e IEC 61508) e di C++, Matlab, Labview, Altium, Python e IARi. Generalmente, la formazione necessaria per diventare un Firmware Engineer prevede il conseguimento di una laurea in Ingegneria Elettronica, Ingegneria Informatica o in una disciplina correlata.

This week's EYE ON NPI is on the right traq, ready to attaq, and gets no flaq: it's the Digilent USB-2001-TC Single Channel Thermocouple Measurement Device, otherwise known as a single-channel DAQ! (https://www.digikey.com/en/product-highlight/d/digilent/usb-2001-tc-single-channel-thermocouple-measurement-device) These rhymes may get me some flaq, but we can't help it, our lips smaq when we see a good DAQ. And this one is deliciously small and single-purpose. We wish we had one of these a few weeks ago when we wanted to check our reflow oven to calibrate the temperature curve! While we are featuring this particular DAQ board that is good for high temperature measurements via a thermocouple, there's a whole family of boards from Digilent/NI (https://www.digikey.com/en/products/filter/data-acquisition-daq/1017?s=N4IgTCBcDaICIEsDmCA2BTAdgFwAS4FcBnAIxAF0BfIA) All manufactured and supported by MCC (https://www.mccdaq.com/) - the OEM for this series. The others are a mix of Ethernet, Raspberry Pi or USB controlled ADCs & DACs (https://www.mccdaq.com/data-acquisition/low-cost-daq) with 12 to 24 bit resolution, and up to 500 kS/s. The USB DAQ board does not come with a thermocouple itself, you'll need to get a standard K, J, etc. type (https://www.digikey.com/en/products/filter/temperature-sensors-thermocouples-temperature-probes/513) and also have it with a mini thermocouple plug which is available on Digi-Key if you are getting bare thermocouple wires (https://www.digikey.com/short/9hbfrwht). The thermocouple wire can come on huge spools if desired: spot weld one end that will be attached to the hot thing being measured, and then screw the other ends into the mini plug. Inside is Silabs C8051F343 (https://www.digikey.com/en/products/detail/silicon-labs/C8051F343-GQ/990818), Analog Devices ADUM5401 (https://www.digikey.com/en/products/detail/analog-devices-inc/ADUM5401ARWZ-RL/1873647) isolator, and an AD7785 (https://www.digikey.com/en/products/detail/analog-devices-inc/AD7785BRUZ-REEL/1644783) 20-bit ADC. Data can be captured using the DAQami software available for download on Windows (https://www.mccdaq.com/daq-software/DAQami.aspx) although it seems like you can also interface through the hardware using an API or Python (https://www.mccdaq.com/MCC-Software.aspx). Of course, since its a Digilent / National Instruments product, it's also fully supported within LabVIEW. (https://www.mccdaq.com/daq-software/universal-library-extensions-lv.aspx) While normally we're happy to talk about individual interface chips for DAC/ADC/Thermocouple interfacing (https://blog.adafruit.com/?s=%23eyeonnpi+temperature), and there's tons of Adafruit guides on how to DIY this kind of data acquisition (https://learn.adafruit.com/search?q=thermocouple), it's pretty nice to have a ready-to-go USB device that streams data without having to open up an IDE or write any code at all. Particularly for automations where the data has to go into a computer anyways, it can save a lot of time to now cobble together your own setup. The Digilent USB-2001-TC Single Channel Thermocouple Measurement Device (https://www.digikey.com/short/23v0hp90) and other Digilent/MCC DAQs (https://www.digikey.com/en/products/filter/data-acquisition-daq/1017?s=N4IgTCBcDaICIEsDmCA2BTAdgFwAS4FcBnAIxAF0BfIA) are stocked by Digi-Key! They sold out while writing this EYE ON NPI, but they'll have more soon, so sign up to be notified by email when they come back in stock.

'Visual Programming' refers a style of programming that allows the user to specify a programs in a two-(or more)-dimensional fashion. Visual programming environments represent the data, control flow, or program state in a graphical way, allowing them to be directly manipulated. It has been a hot area of research from the very beginning of personal computing, to today.This week we will cover a few major visual programming environments, why visual programming has remained compelling over the decades, and whether there is untapped potential for VP today.Chapters:[00:00:00] Intros[00:03:50] What is Visual Programming?[00:05:42] Origins[00:14:34] Block-based Visual Programming[00:20:26] Wire and Dataflow-based Visual Programming[00:31:51] An Umbrella Term[00:36:31] Conceptual History[00:48:23] The Duality of Direct Manipulation[00:58:40] Direct Manipulation of Running State[01:11:25] Programming by Example[01:21:17] Fill in the Details for Me[01:28:49] Strengths of Visual Programming[01:43:36] Leveraging the Visual Cortex[01:50:58] Second Order EffectsLinks/Resources:SketchPad demo: https://www.youtube.com/watch?v=2Cq8S3jzJiQPygmilion Paper: http://worrydream.com/refs/Smith%20-%20Pygmalion.pdfDemo:: https://youtu.be/xNW8wUpbqQM?t=319GrailDemo: https://www.youtube.com/watch?v=2Cq8S3jzJiQHypercardDemo: https://www.youtube.com/watch?v=2Cq8S3jzJiQViewpoint https://scottkim.com/2020/06/07/viewpoint/Scratchhttps://www.bryanbraun.com/2022/07/16/scratch-is-a-big-deal/Labview (imperative control flow): https://www.ni.com/en-us/shop/labview.htmlUnreal Engine Blueprint (functional)https://docs.unrealengine.com/5.0/en-US/blueprints-visual-scripting-in-unreal-engine/https://blueprintsfromhell.tumblr.com/Max/MSP for musicianshttps://cycling74.com/products/maxOthershttps://cables.gl/https://nodes.io/===== About “The Technium” =====The Technium is a weekly podcast discussing the edge of technology and what we can build with it. Each week, Sri and Wil introduce a big idea in the future of computing and extrapolate the effect it will have on the world.Follow us for new videos every week on web3, cryptocurrency, programming languages, machine learning, artificial intelligence, and more!===== Socials =====WEBSITE: https://technium.transistor.fm/ SPOTIFY: https://open.spotify.com/show/1ljTFMgTeRQJ69KRWAkBy7 APPLE PODCASTS: https://podcasts.apple.com/us/podcast/the-technium/id1608747545

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati (https://www.iaicenter.com/). In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when. If you liked this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation (https://www.augmentedpodcast.co/81). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati. In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Jay, it's a pleasure to have you here. How are you today? JAY: Good. Thank you for inviting me to have a good discussion about industrial AI. TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing. You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia. The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today. JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system. So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later. But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want. What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them. So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working. But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages. So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work. Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs. So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education. So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation. So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera. So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time. So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera. So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good. So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture. TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new. It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious. And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective. JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember? TROND: Yes, yes. JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000. So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back. Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name. But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs] So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment. So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI. But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today -- TROND: So they have achieved zero downtime after that project, essentially. JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year. TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that. Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You have very, very specific system elements. And they kind of have to work together in some architectural way before you're willing to call it an industrial AI because it may be a machine tool here, and a machine tool there, and some data here. But for you, unless it's put in place in a working architecture, you're not willing to call it, I mean, it may be an AI, but it is not an industrial AI. So how did this thinking then evolve for you? And what are the elements that you think are crucial for something that you even can start to call an industrial AI? Which you now have a book on, so you're the authority on the subject. JAY: Well, I think the real motivation was after you apply all the machine learning toolkits so long...and a company like National Instruments, NI, in Austin, Texas, they licensed our machine learning toolkits in 2015. And eventually, in 2017, they started using the embedding into LabVIEW version. So we started realizing, actually, the toolkit is very important, not just from the laboratory point of view but also from the production and practitioners' point of view from industry. Of course, researchers use it all the time for homework; I mean, that's fine. So eventually, I said...the question came to me about 2016 in one of our industry advisory board meeting. You have so many successes, but the successes that happen can you repeat? Can you repeat? Can you repeatably have the same success in many, many other sites? Repeatable, scalable, sustainable, that's the key three keywords. You cannot just have a one-time success and then just congratulate yourself and forget it, no. So eventually, we said, oh, to make that repeat sustainable, repeatable, you have a systematic discipline. TROND: I'm so glad you say this because I have taken part in a bunch of best practice schemes and sometimes very optimistically by either an industry association or even a government entity. And they say, "Oh yeah, let's just all go on a bunch of factory visits." Or if it's just an IT system, "Let's just all write down what we did, and then share it with other people." But in fact, it doesn't seem to me like it is that easy. It's not like if I just explain what I think I have learned; that's not something others can learn from. Can you explain to me what it really takes to make something replicable? Because you have done that or helped Foxconn do that, for example. And now you're obviously writing up case studies that are now shared in the World Economic Forum across companies. But there's something really granular but also something very systemic and structured about the way things have to be explained in order to actually make it repeatable. What is the sustainability factor that actually is possible to not just blue copy but turn it into something in your own factory? JAY: Well, I think that there are basically several things. The data is one thing. We call it the data technology, DT, and which means data quality evaluation. How do you understand what to use, what not to use? How do you know which data is useful? And how do you know where the data is usable? It doesn't mean useful data is usable, just like you have a blood donation donor, but the blood may not be usable if the donor has HIV. I like to use an analogy like food. You got a fish in your hand; wow, great. But you have to ask where the fish comes from. [chuckles] If it comes from polluted water, it's not edible, right? So great fish but not edible. TROND: So there's a data layer which has to be usable, and it has to be put somewhere and put to use. It actually then has to be used. It can't just be theoretically usable. JAY: So we have a lot of useful data people collect. The problem is people never realized lots of them are not usable because of a lack of a label. They have no background, and they're not normalized. So eventually, that is a problem. And even if you have a lot of data, it doesn't mean it is usable. TROND: So then I guess that's how you get to your second layer, which I guess most people just call machine learning, but for you, it's an algorithmic layer, which is where some of the structuring gets done and some of the machines that put an analysis on this, put in place automatic procedures. JAY: And machine learning to me it's like cooking ware like a kitchen. You got a pan fry; you got a steamer; you got the grill. Those are tools to cook the food, the data. Food is like data. Cooking ware is like AI. But it depends on purpose. For example, you want fish. What do you want to eat first? I want soup. There's a difference. Do you want to grill? Do you want to just deep fry? So depending on how you want to eat it, the cooking ware will be selected differently. TROND: Well, and that's super interesting because it's so easy to say, well, all these algorithms and stuff they're out there, and all you have to do is pick up some algorithms. But you're saying, especially in a factory, you can't just pick any tool. You have to really know what the effect would be if you start to...for example, on downtime, right? Because I'm imagining there are very many advanced techniques that could be super advanced, but they are perhaps not the right tool for the job, for the workers that are there. So how does that come into play? Are these sequential steps, by the way? So once you figure out what the data is then, you start to fiddle with your tools. JAY: Well, there are two perspectives; one perspective is predict and prevent. So you predict something is going to happen. You prevent it from happening, number one. Number two, understand the root causes and potential root causes. So that comes down to the visible and invisible perspective. So from the visible world, we know what to measure. For example, if you have high blood pressure, you measure blood pressure every day, but that may not be the reason for high blood pressure. It may be because of your DNA, maybe because of the food you eat, because of lack of exercise, because of many other things, right? TROND: Right. JAY: So if you keep measuring your blood pressure doesn't mean you have no heart attack. Okay, so if you don't understand the reason, measuring blood pressure is not a problem. So I'm saying that you know what you don't know. So we need to find out what you don't know. So the correlation of invisible, I call, visible-invisible. So I will predict, but you also want to know the invisible reason relationship so you can prevent that relationship from happening. So that is really called deep mining those invisibles. So we position ourselves very clearly between visible-invisible. A lot of people just say, "Oh, we know what the problem is." The problem is not a purpose. For example, the factory manufacturing there are several very strong purposes, number one quality, right? Worry-free quality. Number two, your efficiency, how much you produce per dollar. If you say that you have great quality, but I spent $10,000 to make it, it is very expensive. But if you spend $2 to make it, wow, that's great. How did you do it? So quality per dollar is a very different way of judging how good you are. You got A; I spent five days studying. I got A; I spent two hours studying. Now you show the capability difference. TROND: I agree. And then the third factor in your framework seems to be platform. And that's when I think a lot of companies go wrong as well because platform is...at least historically in manufacturing, you pick someone else's platform. You say I'm going to implement something. What's available on the market, and what can I afford, obviously? Or ideally, what's the state of the art? And I'll just do that because everyone seems to be doing that. What does platform mean to you, and what goes into this choice? If you're going to create this platform for industrial AI, what kind of a decision is that? JAY: So DT is data, AT is algorithm, and PT is platform, PT platform. Platform means some common things are used in a shared community. For example, kitchen is a platform. You can cook. I can cook. I can cook Chinese food. I can cook Italian food. I can cook Indian food. Same kitchen but different recipe, different seasoning, but same cooking ware. TROND: Correct. Well, because you have a good kitchen, right? JAY: Yes. TROND: So that's -- JAY: [laughs] TROND: Right? JAY: On the platform, you have the most frequently used tool, not everything. You don't need 100 cooking ware in your kitchen. You probably have ten or even five most daily used. TROND: Regardless of how many different cuisines you try to cook. JAY: Exactly. That's called the AI machine toolkit. So we often work with companies and say, "You don't need a lot of tools, come on. You don't need deep learning. You need a good logistic regression and support-vector machine, and you're done." TROND: Got it. JAY: Yeah, you don't need a big chainsaw to cut small bushes. You don't need it. TROND: Right. And that's a very different perspective from the IT world, where many times you want the biggest tool possible because you want to churn a lot of data fast, and you don't really know what you're looking for sometimes. So I guess the industrial context here really constrains you. It's a constraint-based environment. JAY: Yes. So industry, like I said, the industry we talked about three Ps like I said: problems, purposes, and processes. So normally, problem comes from...the main thing is logistic problems, machine, and factory problems, workforce problems, the quality problems, energy problem, ignition problem, safety problems. So the problem happens every day. That's why in factory world, we call it firefighting. Typically, you firefight every day. TROND: And is that your metaphor for the last part of your framework, which is actually operation? So operation sounds really nice and structured, right? JAY: [chuckles] Yes. TROND: As if that was like, yeah, that's the real thing, process. We got this. But in reality, it feels sometimes, to many who are operating a factory; it's a firefight. JAY: Sometimes the reason lean theme work, Six Sigma, you turn a problem into a process, five Ss process, okay? And fishbone diagram, Pareto chart, and Kaizen before and after. So all the process, SOP, so doesn't matter which year workforce comes in, they just repeat, repeat, repeat, repeat, repeat. So in Toyota, the term used to be called manufacturing is just about the discipline. It's what they said. The Japanese industry manufacturing is about discipline, how you follow a discipline to everyday standard way, sustainable way, consistent way, and then you make good products. This is how the old Toyota was talking about, old one. But today, they don't talk that anymore. Training discipline is only one thing; you need to understand the value of customers. TROND: Right. So there are some new things that have to be added to the lean practices, right? JAY: Yes. TROND: As time goes by. So talk to me then more about the digital element because industrial AI to you, clearly, there's a very clear digital element, but there's so many, many other things there. So I'm trying to summarize your framework. You have these four factors: data, algorithms, platforms, and operations. These four aspects of a system that is the challenge you are dealing with in any factory environment. And some of them have to do with digital these days, and others, I guess, really have to do more with people. So when that all comes together, do you have some examples? I don't know, we talked about Toyota, but I know you've worked with Foxconn and Komatsu or Siemens. Can you give me an example of how this framework of yours now becomes applied in a context? Where do people pick up these different elements, and how do they use them? JAY: There's a matrix thinking. So horizontal thinking is a common thing; you need to have good digital thread including DT, data technology, AT, algorithms or analytics, PT, platform, edge cloud, and the things, and OT operation like scheduling, optimizations, stuff like that. Now, you got verticals, quality vertical, cost vertical, efficiency verticals, safety verticals, emission verticals. So you cannot just talk about general. You got to have focus on verticals. For example, let me give you one example: quality verticals. Quality is I'm the factory manager. I care about quality. Yes, the customer will even care more, so they care. But you have a customer come to your shop once a month to check. You ask them, "Why you come?" "Oh, I need to see how good your production." "How about you don't have to come? You can see my entire quality." "Wow, how do I do that?" So eventually, we develop a stream of quality code, SOQ, Stream Of Quality. So it's not just about the product is good. I can go back to connect all the processes of the quality segment of each station. Connect them together. Just like you got a fish, oh, okay, the fish is great. But I wonder, when the fish came out of water, when the fish was in the truck, how long was it on the road? And how long was it before reaching my physical distribution center and to my home? So if I have a sensor, I can tell you all the temperature history inside the box. So when you get your fish, you take a look; oh, from the moment the fish came out of the boat until it reached my home, the temperature remained almost constant. Wow. Now you are worry-free. It's just one thing. So you connect together. So that's why we call SOQ, Stream Of Quality, like a river connected. So by the time a customer gets a quality product, they can trace back and say, "Wow, good. How about if I let you see it before you come? How about you don't come?" I say, "Oh, you know what? I like it." That's what this type of manufacturing is about. It just doesn't make you happy. You have to make the customer happy, worry-free. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com and pick up the book in a bookstore near you. TROND: So, Jay, you took the words out of my mouth because I wanted to talk about the future. I'm imagining when you say worry-free, I mean, you're talking about a soon-to-be state of manufacturing. Or are you literally saying there are some factories, some of the excellence factories where you've won awards in the World Economic Forum or other places that are working towards this worry-free manufacturing, and to some extent, they have achieved it? Well, elaborate for me a little bit about the future outlook of manufacturing and especially this people issue because you know that I'm engaged...The podcast is called Augmented Podcast. I'm engaged in this debate about automation. Well, is there a discrepancy between automation and augmentation? And to what extent is this about people running the system? Or is it the machines that we should optimize to run all the system? For you, it's all about worry-free. First of all, just answer this question, is worry-free a future ideal, or is it actually here today if you just do the right things? JAY: Well, first of all, worry-free is our mindset where the level of satisfaction should be, right? TROND: Yep. JAY: So to make manufacturing happen is not about how to make good quality, how to make people physically have less worry, how to make customers less worry is what is. But the reason we have a problem with workforce today, I mean, we have a hard time to hire not just highly skilled workers but even regular workforce. Because for some reason, not just U.S., it seems everywhere right now has similar problems. People have more options these days to select other living means. They could be an Uber driver. [laughs] They could be...I don't know. So there are many options. You don't have to just go to the factory to make earnings. They can have a car and drive around Uber and Lyft or whatever. They can deliver the food and whatever. So they can do many other things. And so today, you want to make workforce work environment more attractive. You have to make sure that they understand, oh, this is something they can learn; they can grow. They are fulfilled because the environment gives them a lot of empowerment. The vibe, the environment gives them a wow, especially young people; when you attract them from college, they'd like a wow kind of environment, not just ooh, okay. [laughs] TROND: Yeah. Well, it's interesting you're saying this. I mean, we actually have a lack of workers. So it's not just we want to make factories full of machines; it's actually the machines are actually needed just because there are no workers to fill these jobs. But you're looking into a future where you do think that manufacturing is and will be an attractive place going forward. That seems to be that you have a positive vision of the future we're going into. You think this is attractive. It's interesting for workers. JAY: Yeah. See, I often say that there are some common horizontal we have to use all the day. Vertical is the purpose, quality. I talked about vertical quality first, quality. But what are the horizontal common? I go A, B, C, D, E, F. What's A? AI. B is big data. C is cyber and cloud. D is digital or digital twin, whatever. E is environment ecosystem and emission reduction. What's F? Very important, fun. [laughs] If you miss that piece, who wants to work for a place there's no fun? You tell me would you work for...you and I, we're talking now because it's fun. You talk to people and different perspectives. I talk to you, and I say, wow, you've built some humongous network here in the physical...the future of digital, not just professional space but also social space but also the physical space. So, again, the fun things inspire people, right? TROND: They do. So talking about inspiring people then, Jay, if you were to paint a picture of this future, I guess, we have talked just now about workers and how if you do it right, it's going to be really attractive workplaces in manufacturing. How about for, I guess, one type of worker, these knowledge workers more generally? Or, in fact, is there a possibility that you see that not just is it going to be a fun place to be for great, many workers, but it's actually going to be an exciting knowledge workplace again? Which arguably, industrialization has gone through many stages. And being in a factory wasn't always all that rosy, but it was certainly financially rewarding for many. And it has had an enormous career progression for others who are able to find ways to exploit this system to their benefit. How do you see that going forward? Is there a scope, is there a world in which factory work can or perhaps in an even new way become truly knowledge work where all of these industrial AI factors, the A to the Fs, produce fun, but they produce lasting progression, and career satisfaction, empowerment, all these buzzwords that everybody in the workplace wants and perhaps deserves? JAY: That's how we look at the future workforce is not just about the work but also the knowledge force. So basically, the difference is that people come in, and they become seasoned engineers, experienced engineers. And they retire, and the wisdom carries with them. Sometimes you have documentation, Excel sheet, PPT in the server, but nobody even looks at it. That's what today's worry is. So now what you want is living knowledge, living intelligence. The ownership is very important. For example, I'm a worker. I develop AI, not just the computer software to help the machine but also help me. I can augment the intelligence. I will augment it. When I make the product happen, the inspection station they check and just tell me pass or no pass. They also tell me the quality, 98, 97, but you pass. And then you get your score. You got a 70, 80, 90, but you got an A. 99, you got an A, 91, you got an A, 92. So what exactly does A mean? So, therefore, I give you a reason, oh, this is something. Then I learn. Okay, I can contribute. I can use voice. I can use my opinion to augment that no, labeled. So next time people work, oh, I got 97. And so the reason is the features need to be maintained, to be changed, and the system needs to be whatever. So eventually, you have a human contribute. The whole process could be consisting of 5 experts, 7, 10, 20, eventually owned by 20 people. That legacy continues. And you, as a worker, you feel like you're part of the team, leave a legacy for the next generation. So eventually, it's augmented intelligence. The third level will be actual implementation. So AI is not about artificial intelligence; it is about actual implementation. So people physically can implement things in a way they can make data to decisions. So their decision mean I want to make an adjustment. I want to find out how much I should adjust. Physically, I can see the gap. I can input the adjustment level. The system will tell me physically how could I improve 5%. Wow, that's good. I made a 5% improvement. Your boss also knows. And your paycheck got the $150 increase this month. Why? Because my contribution to the process quality improved, so I got the bonus. That's real-world feedback. TROND: Let me ask you one last question about how this is going to play out; I mean, in terms of how the skilling of workers is going to allow this kind of process. A lot of people are telling me about the ambitions that I'm describing...and some of the guests on the podcasts and also the Tulip software platform, the owner of this podcast, that it is sometimes optimistic to think that a lot of the training can just be embedded in the work process. That is obviously an ideal. But in America, for example, there is this idea that, well, you are either a trained worker or an educated worker, or you are an uneducated worker. And then yes, you can learn some things on the job. But there are limits to how much you can learn directly on the job. You have to be pulled out, and you have to do training and get competencies. As you're looking into the future, are there these two tracks? So you either get yourself a short or long college degree, and then you move in, and then you move faster. Or you are in the factory, and then if you then start to want to learn things, you have to pull yourself out and take courses, courses, courses and then go in? Or is it possible through these AI-enabled training systems to get so much real-time feedback that a reasonably intelligent person actually never has to be pulled out of work and actually they can learn on the job truly advanced things? So because there are two really, really different futures here, one, you have to scale up an educational system. And, two, you have to scale up more of a real-time learning system. And it seems to me that they're actually discrepant paths. JAY: Sure. To me, I have a framework in my book. I call it the four P structure, four P. First P is principle-based. For example, in Six Sigma, in lean manufacturing, there's some basic stuff you have to study, basic stuff like very simple fishbone diagram. You have to understand those things. You can learn by yourself what that is. You can take a very basic introduction course. So we can learn and give you a module. You can learn yourself or by a group, principle-based. The second thing is practice-based. Basically, we will prepare data for you. We will teach you how to use a tool, and you will do it together as a team or as individual, and you present results by using data I give to you, the tool I give to you. And it's all, yeah, my team A presented. Oh, they look interesting. And group B presented, so we are learning from each other. Then after the group learning is finished, you go back to your team in the real world. You create a project called project-based learning. You take a tool you learn. You take the knowledge you learn and to find a project like a Six Sigma project you do by yourself. You formulate. And then you come back to the class maybe a few weeks later, present with a real-world project based on the boss' approval. So after that, you've got maybe a black belt but with the last piece professional. Then you start teaching other people to repeat the first 3ps. You become master black belt. So we're not reinventing a new term. It really is about a similar concept like lean but more digital space. Lean is about personal experience, and digital is about the data experience is what's the big difference. TROND: But either way, it is a big difference whether you have to rely on technological experts, or you can do a lot of these things through training and can get to a level of aptitude that you can read the signals at least from the system and implement small changes, perhaps not the big changes but you can at least read the system. And whether they're low-code or no-code, you can at least then through learning frameworks, you can advance, and you can improve in not just your own work day, but you can probably in groups, and feedbacks, and stuff you can bring the whole team and the factory forward perhaps without relying only on these external types of expertise that are actually so costly because they take you away. So per definition, you run into this; I mean, certainly isn't worry-free because there is an interruption in the process. Well, look, this is fascinating. Any last thoughts? It seems to me that there are so many more ways we can dig deeper on your experience in any of these industrial contexts or even going deeper in each of the frameworks. Is there a short way to encapsulate industrial AI that you can leave us with just so people can really understand? JAY: Sure. TROND: It's such a fundamental thing, AI, and people have different ideas about that, and industry people have something in their head. And now you have combined them in a unique way. Just give us one sentence: what is industrial AI? What should people leave this podcast with? JAY: AI is a cognitive science, but industrial AI is a systematic discipline is one sentence. So that means people have domain knowledge. Now we have to create data to represent our domain then have the discipline to solve the domain problems. Usually, with domain knowledge, we try with our experience, and you and I know; that's it. But we have no data coming out. But if I have domain become data and data become discipline, then other people can repeat our success even our mistake; they understand why. So eventually, domain, data, discipline, 3 Ds together, you can make a good decision, sustainable and long-lasting. TROND: Jay, this has been so instructive. I thank you for spending this time with me. And it's a little bit of a never-ending process. JAY: [laughs] TROND: Industry is not something that you can learn it and then...because also the domain changes and what you're doing and what you're producing changes as well. So it's a lifelong -- JAY: It's rewarding. TROND: Rewarding but lifelong quest. JAY: Yeah. Well, thank you for the opportunity to share, to discuss. Thank you. TROND: It's a great pleasure. You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Industrial AI. And our guest was Professor Jay Lee from University of Cincinnati. In this conversation, we talked about how AI in industry needs to work every time and what that means. My takeaway is that industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and is empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Jay Lee.

LabVIEW is typically used by companies that manufacture some type of hardware and/or complex systems. This includes industries like medical devices, military/aerospace, automotive, telecom, energy and power, and consumer electronics.  LabVIEW uses intuitive graphical programming that makes it easy for non-developers to understand. At Dev.co., we begin each project with an in-depth discovery phase that helps uncover wants, needs, goals, and expectations.  We then construct a plan for what your application will look like and how we can move from idea to finished product with minimal friction. More info about LabVIEW development services:   https://dev.co/labview/   Connect with us:  SEO // PPC // DEV // WEBSITE DESIGN

Episode 18 of the Modern Chemistry podcast features Dr. Sebastian Gross. Sebastian is a consultant at Wega IT, (https://www.wega-it.com/en/). Where he supports clients, using his advanced experience in biotechnology methods, bioprocess development, lab & assay automation and kinetic modelling. Sebastian has strong experience in tools like Liquid Handling station, SiLA, Python, MATLAB, LabVIEW, SQL, and Data modelling.Prior to Wega, Sebastian was head of process development at TUB (Technische Universität Berlin), where he also did his PhD. Sebastian is contactable on social media, and you can find him on LinkedIn at https://www.linkedin.com/in/sebastian-hans/You can also connect with Sebastian via the Wega website link above.Sebastian's web linkOur theme music is "Wholesome" by Kevin MacLeod (https://incompetech.com)Music from https://filmmusic.ioLicense: CC BY (http://creativecommons.org/licenses/by/4.0/) Connect with me (Paul) at https://www.linkedin.com/in/paulorange/H.E.L. group can be found at www.helgroup.com online,on LinkedIn at https://www.linkedin.com/company/hel-group/ on Twitter, we're @hel_group, https://twitter.com/hel_groupor search for us on Facebook 

The lightning-fast pace of innovation brought on by the rapid advances in our testing technology owes at least part of its success to building, writing, and deploying a coding language that allows humans to follow and track it in a way that resembles how we think. This week, we explore how software-defined test has helped us improve and optimize our test, the differences between spoken language and coding, and the opportunities that arise when you are armed with a new vocabulary. To explore how we can better speak the language of test, host Derek Burrows welcomes Kimberly Bryant, founder and CEO of Black Girls Code, and NI's  Eli Kerry.  Learn More About: What is Kimberly's perspective on the differences between spoken language and coding language? What are the similarities? How has software-defined test helped us improve and organize in the automotive domain and beyond? While spoken languages may help us navigate and define the world around us, coding languages help us build them.Why was LabView created, and what does LabView code look like? With novel technologies like AI, we are teaching the computer how to solve problems sequentially by following a set of algorithmic steps, much like the teacher teaches their student. As our computers get smarter, will we see the line between our language and theirs start to blur?  Resources Mentioned: NIKimberly BryantBlack Girls Code Eli Kerry 

Welcome to the second season of Testing 1-2-3 from NI, where we speak to engineers, experts, and those on the forefront of some of the world's biggest inventions and possibilities to look at the world around us from the lens of testing. In this episode, we explore the meteoric rise in space travel and exploration, and what that means for testing and the future of sending humans to space. Host Derek Burrows talks with author and founder of The Mars Generation, Astronaut Abby, about the unique challenges that come with testing for space, and specifically Mars. The conversation then shifts to Omar Mussa from Virgin Orbit, who touches upon the ethics of testing in space, and the one area where big hairy failures happen most. It's an out-of-this-world conversation this week, get ready for blast off!  Learn More About: How space exploration is one of the most extreme environments that we can explore.What are some of the rigorous physical testing requirements that go into astronaut selection?Last year, there were 133 successful orbital launches around the world, beating a record for annual launches that dates back to the Space Race.Is there even space for human beings in space?With only 5% of space launches being crewed, how much can we really do without human presence?We are now testing both for longer explorations in space, but also shorter missions with commercial tourism.Omar talks about what it's like building something that blasts off completely unsupervised.What is vibration testing?Why do we need people in space if it's so hard, expensive, and dangerous?What do Abby and Omar think the future of test will look like in space?Resources Mentioned: NIAstronaut Abby The Mars GenerationOmar MussaVirgin Orbit | Virgin Orbit YouTube | Above the Clouds | Blue Skies Went To Black We're hiring! If you're interested in exploring a future career supporting a small sat launch, check out Virgin Orbit Careers.A special thank you to one of Omar's LabVIEW mentors, Fabiola de la Cueva, for building the “Our Giants are Female” movement within the LabView community of presenters.GDevCon 

In this holiday episode, Jason and Patrick answer questions from listeners. They also look back at the past year's challenges and victories.00:15:35 (Kevin)What's been the biggest thing that pushed you to learn more during your career?Was it taking a new job and moving somewhere, doing stuff in your spare time or something like a new hobby or anything else?00:29:38 (Kevin)Favorite city to live in or visit?00:31:29 First Winner (James B.)00:32:21 (Clever Clover/James)Next biggest tech prediction.00:36:28 (Paul) If we could standardize all the code there is out there to one particular language, which language would it be and why would it be Python?00:40:40 Second Winner (Collin G.)00:41:21 (Necrous)If you could redo your career and education path, what would you change?00:47:12 Third Winner (Matt I.)00:47:48 (MQNC)What is the dirtiest hackiest anti-pattern piece of code you ever wrote in full consciousness and even maybe enjoying the thrill and why was it the way to go?00:54:36 (Leedle)Thoughts on server side rendering React and NextJS?00:57:00 Fourth Winner (Glenn S.)00:57:25 (NC Plattipus)The visual programming language, LabVIEW?01:05:02 Fifth Winner (James F.)01:05:53 (Gethan)Future technology or big technologies, what about AR? 01:10:18 (Gethan)On the topic of getting a master's degree or classes, do you see a benefit of getting certifications? 01:18:16 Sixth Winner (Don R.)01:19:38Predictions we made last 2020 and how they held up.01:26:00FarewellsIf you've enjoyed this episode, you can listen to more on Programming Throwdown's website: https://www.programmingthrowdown.com/Reach out to us via email: programmingthrowdown@gmail.comYou can also follow Programming Throwdown on Facebook | Apple Podcasts | Spotify | Player.FM Join the discussion on our DiscordHelp support Programming Throwdown through our Patreon★ Support this podcast on Patreon ★

In our final episode for 2021, we speak to Dan Press from the newest DQMH® Trusted Advisor, PrimeTest Automation. Dan has been in the game for 25+ years with a wealth of experience, and has been a G programmer since LabVIEW 3! Primetest now use DQMH in their solutions. Hear about how Dan is extending the DTS (DQMH Test Sequencer), and looking into a robust approach to implementing state machines using DQMH. We hope you enjoy the last episode for the year!

Stephen is the go-to person for creative design work at the Mayo Clinic and has been working at Mayo for the past 20 years. He's also the Chair of documentations standards workgroup, he figures out how to document SolidWorks designs. Stephen is a mechanical engineer with a P.E. license. In this episode we discuss the advantages of Model-Based Definition (MBD), and specializing vs. being a jack of all trades.  ABOUT BEING AN ENGINEERThe Being an Engineer podcast is a repository for industry knowledge and a tool through which engineers learn about and connect with relevant companies, technologies, people resources, and opportunities. We feature successful mechanical engineers and interview engineers who are passionate about their work and who made a great impact on the engineering community.The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical & other device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.teampipeline.us***Valued listener, we need your help getting to 100 podcast reviews. Win a $50 Amazon Gift card if you leave us a review on the Apple Podcasts. Simply email a screenshot of your 5-star review to Podcast@teampipeline.us , the email will be in the show notes. We will announce 5 lucky winners at the end of the first quarter in 2022.LINKS:SolidWorks luggage handle mechanism by Rafael Testai (video sponsored by Pipeline)Rafael Testai (co-host) on Linkedin

In this month's episode, we talk with newest DQMH Trusted Advisor, Enrique Noé Arias from PantherLAB. Hear about Enrique's LabVIEW journey, working as part of the DQMH development team as a tester, setting up his new business in Mexico to provide LabVIEW expertise, and his brand new product Panther Sniffer for DQMH. Panther Sniffer for DQMH allows LabVIEW/DQMH developers to extend their applications with a MQTT based application, a Flutter developed app downloadable from the Google Play store, so that developers can broadcast status messages to their smart device - great for factory environments. Hope you enjoy this one!

In Episode 11, we talk to two of GDevCon N.A. directors - Jeff DeBuhr and Sam Taggart. First up, we hear from Jeff about how DQMH is helping his organization manage their large array of Data Acquisition / Measurement systems, and how he brings up interns and young engineers on learning DQMH. Then we hear from Sam about his upcoming 2-day DQMH workshop in October. This workshop is an add-on to the GDevCon N.A. conference. Finally Jeff and Sam give us an overview of what one can expect at GDevCon N.A., to be hosted in Boulder Colorado on 20&21 October 2021, and how it will be great to re-connect with the LabVIEW community after such a long hiatus.

This week's EYE ON NPI is ready to be tested - it's a super powerful piece of test equipment from Digilent - we are happy to feature the Digilent Analog Discovery Pro 3000 Series (https://www.digikey.com/en/product-highlight/d/digilent/analog-discovery-pro-3000-series). The Analog Discovery Pro 3000 (which we will shorten to ADP3000) is a huge upgrade from the EYE ON NPI we had last year with Digilent's Digital Discovery pod (https://blog.adafruit.com/2020/09/03/eye-on-npi-digilent-digital-discovery-eyeonnpi-digikey-digikey-digilentinc-adafruit/). That product was a small digital logic analyzer, great for doing firmware debugging with up to 32 channels of 200 MS/s data capture and lots of built-in bus decoding. To match with the Digital Discovery, Digilent also came out with the Analog Discovery 2 (https://www.adafruit.com/product/4652) with two analog input channels of 14-bit, 100MS/s, 30MHz ADC and 16-channel logic analyzer. The Analog Discovery 2 is a great student/maker/beginner mixed-signal tool, and compact enough that you can fit it into your laptop bag for analysis anywhere. But if you want something that is comparable to a 'real' scope, you'll find that you'll want something that can take real scope probes and BNC output for those high speed signal generations. That's why there's now the ADP3000 (https://www.digikey.com/en/product-highlight/d/digilent/analog-discovery-pro-3000-series) - the 3000 means its 1500x 'more extra' than the Analog Discovery 2? Which gives benchtop-ruggedness to a digital USB scope. The ADP3000 series is a 'little bit of everything': Two or four analog inputs with 100MS/s sample rate, 50 MHz bandwidth, 14-bit, +-25V input 16-channel logic analyzer and waveform pattern generator, 1.8 to 5V input, 125 MS/s sample rate Two analog waveform outputs, +-5V, 14-bit, 15MHz bandwidth Now, to be completely fair - this isn't going to replace a benchtop scope. Even my trusty old Tek TDS2014 (https://www.tek.com/oscilloscope/tds2000-digital-storage-oscilloscope), bought 15 years ago, has 1 GS/s and 100 MHz sample rate. And, personally, I still really like twiddling physical knobs when debugging a circuit. So we think that while it could be used as a benchtop toolkit, the ADP3000 isn't designed for that. Instead where we think this tool would really shine is automation or test engineering, where components or setups need to be analyzed or as part of a integration test. That's because Digilent is a wholly-owned subsidiary of National Instruments (https://www.ni.com) who make LabVIEW (https://www.ni.com/en-us/shop/labview.html) an extremely-popular lab/data capture/analysis program. So you know that there's going to be excellent integration, with the Digilent tools getting "First Class" support in LabVIEW. Having seen physicists/biologists/mech e's struggle with how to automate their experiments, this tool would work very nicely in a graduate or company lab. One feature that really stands out for the ADP3000 is that, yes, you can plug it into a computer via USB like you'd expect to capture data on a host PCB, but you can also log in directly into the scope in "Linux Mode". (https://reference.digilentinc.com/test-and-measurement/analog-discovery-pro-3x50/linux-mode) Not surprisingly, this scope runs embedded Linux, that's how it can do stuff like have USB host and Ethernet. But even with Ethernet, there's always the risk of bandwidth or dropped packets. What if you want to get some large data capture going, where you don't want to be restricted by USB? Or if you wanted to connect some additional hardware? We like that Digilent opened up this capability - most test equipment companies would not be excited to let the user log into the device itself and mess around! For scientists and automation and test engineers, this is a very promising tool - and you can pick up one from Digi-Key for immediate shipment right now! There are four 'versions' of this product (https://www.digikey.com/en/products/result?s=N4IgjCBcoLQCxVAYygMwIYBsDOBTANCAG4B2aWehA9lANogCcYADAMwDsIAuoQA4AuUECAC%2BYoA): you can get 2 or 4 analog input and with or without probes. For many uses, ADP3250 2-input version without probes will do a fine job (https://www.digikey.com/short/vmvw4mv3) but check out the other options before checking out. Order today and you can be automating your data acquisition by tomorrow. See on Digi-Key at https://www.digikey.com/short/vmvw4mv3 See Digilent's video at https://www.youtube.com/watch?v=-lMlnb6_Kdw

In episode 10 we have Fabiola De la Cueva, Joerg Hampel, Matthias Baudot, and Olivier Jourdan joining us to discuss the imminent release of DQMH 6. Tune in to hear about the new features coming in the popular LabVIEW development framework, including some features posted at the DQMH Feature Requests web page. And following the DQMH 6 review, we will reveal the big announcement for DQMH. So stick around to hear about this exciting new DQMH development.

This is the episode we talk - to ourselves - finally! We chat to a selection of LabVIEW developers from Wired-in Software. Join Stuart, Kim, Parag, and Chris in the discussion of what makes Wired-in Software tick, and how DQMH forms a critical foundation of our work. We talk about the sort of problems we help our customers solve, the application types, our design process, how we learn DQMH, and what the future holds. Check it out!

Vincent Carpentier and Cyril Gambini from Neosoft Technologies from Quebec are our guests on this episode. Neosoft Technologies are the newest DQMH Trusted Advisor, and have been using DQMH in their varied solutions, from pure LabVIEW, to TestStand and VeriStand, and also Flexlogger plug-ins. Neosoft are pushing the boundaries of how one might use DQMH, including building their own VI scripting over the top of DQMH to further automate the development of their applications, and using Antidoc as a tool to help prepare initial design documentation for their internal engineers and customers. This episode is definitely worth a listen!

Today we speak with Olivier Jourdan from Wovalab in France. Wovalab is a young company, and relatively new to the DQMH Trusted Advisors. But already their open source product Antidoc is making waves in the LabVIEW community. Antidoc is a free tool to help LabVIEW developers automate the generation of documentation of their LabVIEW projects, and can be downloaded from vipm.io. It is specifically designed for DQMH, but plans are in place to extend it to other frameworks like Actor Framework in the future. Hear all about Antidoc, and how Wovalab uses DQMH to help their customers.

Paul Schaffner has worked for Benchmark Electronics for over 30 years. These days it’s hard to find individuals who have stayed with a company for effectively their entire careers. Join us on the episode and listen to what factors have motivated Paul to develop his long-term strategy, and the benefits he has enjoyed through doing so. Along the way, learn a little about what it’s like to work in a field developing test equipment for electronics hardware and why it’s more fun than working at Tesla!The Being An Engineer podcast is brought to you by Pipeline Design & Engineering. Pipeline partners with medical device engineering teams who need turnkey equipment such as cycle test machines, custom test fixtures, automation equipment, assembly jigs, inspection stations and more. You can find us on the web at www.testfixturedesign.com and www.designtheproduct.com Physics, Electrical engineering, Mechanical engineering, Benchmark Electronics, Electronics testing, In-circuit Test Development, Functional Test Development, LabVIEW

Matthias Baudot from Studio Bods joins us in this episode. Matthias is the founder of Studio Bods, creator of the excellent LabVIEW tool Build License Track (BLT) - a tool for managing your LabVIEW distributions, licensing, and status. Studio Bods - a DQMH Trusted Advisor - deliver company management/project management/Enterprise Resource Planning tools for their customers - something not often created with the LabVIEW development environment, and proving that LabVIEW can do a lot more than you might think. Studio Bods can now offer more in this space, with the capability of LabVIEW NXG Web applications, and they are pioneers in this space. Matthias is a keen user of DQMH, and uses it extensively in the majority of his solutions.

In this episode, we introduce you to all of the DQMH Trusted Advisors (except Neosoft Technologies who unfortunately was a last minute cancellation) . We talk to Fabiola De La Cueva from Delacor about what a DQMH Trusted advisor actually is. We hear from Joerg Hampel from Hampel Software Engineering, Sam Taggart from SAS Workshops, Matthias Baudot from Studiobods, and Olivier Jourdan from Wovalab, about their experiences with DQMH for LabVIEW, and what it means to be part of the DQMH Trusted Advisors network. It's an extra long episode this one, but each guest has a lot to offer. I hope you enjoy it! Hosted by Chris Farmer from Wired-in Software

This is the first episode of the DQMH® Podcast - a podcast dedicated to the LabVIEW™ community. The purpose of this episode is to introduce you to DQMH® . Learn about what DQMH® is, why you would use it, how you use it, how to go about learning it, and it's key benefits and features.

Don't think for a second that DevOps has boundaries, becasue it doesn't. I sat down with Sam Taggart to talk about how DevOps can be used with enterprise and client/server development environments like LabView. Web: https://sweetcode.io/developers-eating-the-world/ Youtube: https://youtu.be/xXmMg1HVhYk Instagram: https://www.instagram.com/devseatworld/ SC Twitter: https://www.twitter.com/sweetcodehq Chris Twitter: https://twitter.com/hoardinginfo Chris LinkedIn: https://www.linkedin.com/in/cloudproductandmarketing/

In this episode, our hosts discuss the benefits of using Labview in a workplace environment. LabVIEW is an easy-to-use, interactive, graphical programming language that is ideal for engineers and scientists. It is used in many areas of science and engineering from launching rockets to measuring temperatures. Tune in as the hosts discuss real world applications as well as their own experiences with Labview!

Шоу нотес SPA (не) нужны https://tonsky.livejournal.com/317029.html https://twitter.com/AirbnbEng/status/1019670820065402880 https://twitter.com/giacomotesio/status/1021695798072025089 Заменяем lodash используя ES6 https://www.sitepoint.com/lodash-features-replace-es6/ https://github.com/tc39/proposal-flatMap/pull/56#issue-173327251 https://www.youtube.com/watch?v=TS1lpKBMkgg String#split с блоком https://blog.bigbinary.com/2018/07/17/ruby-2-6-adds-split-with-block.html netflix/pollyjs https://github.com/Netflix/pollyjs stalniy/bdd-lazy-var https://github.com/stalniy/bdd-lazy-var Snapshot testing https://jest-bot.github.io/jest/docs/snapshot-testing.html Elements of Clojure by Zach Tellman https://leanpub.com/elementsofclojure Мемы и телепатия https://www.dropbox.com/s/tyhhwe199obd80s/distraction.jpg?dl=0 https://ru.wikipedia.org/wiki/LabVIEW Гипотеза лингвистической относительности Приватная rake-драма https://supergood.software/dont-step-on-a-rake/ https://github.com/erikhuda/thor https://github.com/ruby/rake/blob/4f9c156/lib/rake/rake_module.rb#L28L30 load.c Кому нужен RubyMotion http://www.rubymotion.com/developers/samples/ https://github.com/HipByte/RubyMotionSamples Active Interractor или нет https://github.com/AaronLasseigne/active_interaction https://github.com/thalamusai/mandate http://www.infoq.com/presentations/Simple-Made-Easy Менторство на exercism.io https://exercism.io/tracks https://twitter.com/razum2um/status/1020210374216486912 Послушал? Оставь отзыв На hardcode.fm hardcodefm@telegram + группа hardcodefm@facebook hardcodefm@vkontakte

Malcolm Myers of AMH Test System takes us on a learning journey of how applicable and scaleable LabVIEW software and testing equipment is for him and hundreds of other engineering and electrical business around the UK and World. Recognised as a Chartered Engineer, Certified LabVIEW Architect and National Instruments Alliance Partner, he knows his craft. Working with audio, acoustics, power tools, telecommunications and renewable technologies around the UK since 1996. We talk about what you need to get started and how an engagement works for him. Using this graphical programming software and wiring up the testing devices on site. So join us and see if we PASS/FAIL the test. Read more at http://www.amhtestsystems.co.uk/about-us/ Follow on twitter https://twitter.com/photovalve Contact https://holdingbay.co.uk/contact/ Tweet at @cliffnotespod This episode of Cliff Notes Podcast: Ask a leader, host and founder of Holdingbay Tristan Bailey talks Malcolm Myers. He has enjoyed a varied career with a broad engineering background, particularly in the areas of audio, acoustics, power tools, telecommunications and renewable technologies. Having used LabVIEW since 1996 he has now branched out on his own to provide expert testing services to industry. Show Notes: https://holdingbay.co.uk/cliff-notes/podcasts/11/

The AWS Well-Architected Framework enables customers to understand best practices around security, reliability, performance, and cost optimization when building systems on AWS. This approach helps customers make informed decisions and weigh the pros and cons of application design patterns for the cloud. In this session, you'll learn how National Instruments used the Well-Architected Framework to follow AWS guidelines and best practices. By developing a strategy based on the AWS Well-Architected Framework, National Instruments was able to triple the number of applications running in the cloud without additional head count, significantly increase the frequency of code deployments, and reduce deployment times from two weeks to a single day. As a result, National Instruments was able to deliver a more scalable, dynamic, and resilient LabVIEW platform with agility.

Download Episode We get a bit rant-tastic in this episode; Ryan’s upset about LabView; Scarlet says “Hockey isn’t for weiners” Haven Head (intro & outro music) The post Episode 56 appeared first on Honest, Open & Vulnerable.

Design World editor Miles Budimir talks with Jeff Phillips, Section Manager for Software Platforms at National Instruments, about the release of LabVIEW 2015.

02:32 - Julian Cheal Introduction Twitter GitHub Blog 02:49 - Julian’s Background with Robots and Drones Arduino AR.Drone 03:32 - NodeCopter Events 04:31 - Traveling with Robots 05:35 - Julian’s Collection and Projects Julian Cheal: Dancing with Robots Raspberry Pi BeagleBone 07:46 - Giving Demos 09:12 - What Makes Robots? Sinon.JS MQTT Protocol 10:21 - Where is IoT (Internet of Things) Heading? Security 13:11 - Programming Languages NodeBots 14:15 - Tools and Protocols The MIDI Protocol Spark Core voodoospark 17:31 - Programming Challenges Around Hardware Hacking Artoo celluloid 18:49 - Barrier to Entry 20:41 - Getting Kids Started Kids Ruby Arduino Starter Kit 22:09 - Wearables EL Wire (Electroluminescent Wire) 23:18 - LEGO Robotics Mindstorms LabVIEW National Instruments 25:01 - Issues with Hardware Hacking 28:22 - Rubyists and Hardware Julian Cheal: Dancing with Robots JRuby Rubinius 29:45 - Interfacing with Humans iBeacon OpenCV 33:27 - [Kickstarter] CHIP - The World's First Nine Dollar Computer 34:01 - Connectivity  Sphero Carin Meier: The Joy of Flying Robots with Clojure @ OSCON 2013 36:55 - More Interesting Projects Aaron Patterson: Using chicken scheme to read sausagebox values Oscilloscope Picks Jacob Kaplan-Moss Keynote @ Pycon 2015 (Jessica) Kobo Aura H20 (Avdi) Liz Abinante: Unicorns Are People, Too (Re-Thinking Soft and Hard Skills) @ Madison+ Ruby 2014 (Coraline) littleBits (Julian) Jewelbots (Julian) Ruby Rogues Episode #156: Hardware Hacking with Julia Grace (Julian) The End of Mr. Y by Scarlett Thomas (Julian)        

02:32 - Julian Cheal Introduction Twitter GitHub Blog 02:49 - Julian’s Background with Robots and Drones Arduino AR.Drone 03:32 - NodeCopter Events 04:31 - Traveling with Robots 05:35 - Julian’s Collection and Projects Julian Cheal: Dancing with Robots Raspberry Pi BeagleBone 07:46 - Giving Demos 09:12 - What Makes Robots? Sinon.JS MQTT Protocol 10:21 - Where is IoT (Internet of Things) Heading? Security 13:11 - Programming Languages NodeBots 14:15 - Tools and Protocols The MIDI Protocol Spark Core voodoospark 17:31 - Programming Challenges Around Hardware Hacking Artoo celluloid 18:49 - Barrier to Entry 20:41 - Getting Kids Started Kids Ruby Arduino Starter Kit 22:09 - Wearables EL Wire (Electroluminescent Wire) 23:18 - LEGO Robotics Mindstorms LabVIEW National Instruments 25:01 - Issues with Hardware Hacking 28:22 - Rubyists and Hardware Julian Cheal: Dancing with Robots JRuby Rubinius 29:45 - Interfacing with Humans iBeacon OpenCV 33:27 - [Kickstarter] CHIP - The World's First Nine Dollar Computer 34:01 - Connectivity  Sphero Carin Meier: The Joy of Flying Robots with Clojure @ OSCON 2013 36:55 - More Interesting Projects Aaron Patterson: Using chicken scheme to read sausagebox values Oscilloscope Picks Jacob Kaplan-Moss Keynote @ Pycon 2015 (Jessica) Kobo Aura H20 (Avdi) Liz Abinante: Unicorns Are People, Too (Re-Thinking Soft and Hard Skills) @ Madison+ Ruby 2014 (Coraline) littleBits (Julian) Jewelbots (Julian) Ruby Rogues Episode #156: Hardware Hacking with Julia Grace (Julian) The End of Mr. Y by Scarlett Thomas (Julian)        

02:32 - Julian Cheal Introduction Twitter GitHub Blog 02:49 - Julian’s Background with Robots and Drones Arduino AR.Drone 03:32 - NodeCopter Events 04:31 - Traveling with Robots 05:35 - Julian’s Collection and Projects Julian Cheal: Dancing with Robots Raspberry Pi BeagleBone 07:46 - Giving Demos 09:12 - What Makes Robots? Sinon.JS MQTT Protocol 10:21 - Where is IoT (Internet of Things) Heading? Security 13:11 - Programming Languages NodeBots 14:15 - Tools and Protocols The MIDI Protocol Spark Core voodoospark 17:31 - Programming Challenges Around Hardware Hacking Artoo celluloid 18:49 - Barrier to Entry 20:41 - Getting Kids Started Kids Ruby Arduino Starter Kit 22:09 - Wearables EL Wire (Electroluminescent Wire) 23:18 - LEGO Robotics Mindstorms LabVIEW National Instruments 25:01 - Issues with Hardware Hacking 28:22 - Rubyists and Hardware Julian Cheal: Dancing with Robots JRuby Rubinius 29:45 - Interfacing with Humans iBeacon OpenCV 33:27 - [Kickstarter] CHIP - The World's First Nine Dollar Computer 34:01 - Connectivity  Sphero Carin Meier: The Joy of Flying Robots with Clojure @ OSCON 2013 36:55 - More Interesting Projects Aaron Patterson: Using chicken scheme to read sausagebox values Oscilloscope Picks Jacob Kaplan-Moss Keynote @ Pycon 2015 (Jessica) Kobo Aura H20 (Avdi) Liz Abinante: Unicorns Are People, Too (Re-Thinking Soft and Hard Skills) @ Madison+ Ruby 2014 (Coraline) littleBits (Julian) Jewelbots (Julian) Ruby Rogues Episode #156: Hardware Hacking with Julia Grace (Julian) The End of Mr. Y by Scarlett Thomas (Julian)        

Introducción a LabVIEW (3/5). Asignatura: Sensores y Acondicionamiento de Señal. Grado en Ingeniería de Tecnologías de Telecomunicación. Profesor: José Luis Alonso Serrano. Dpto. de Ingeniería de Comunicaciones Área de Electrónica. Proyecto PLE. Universidad Miguel Hernández de Elche. Se realiza una introducción al entorno de programación gráfico LabVIEW.

Introducción a LabVIEW (4/5). Asignatura: Sensores y Acondicionamiento de Señal. Grado en Ingeniería de Tecnologías de Telecomunicación. Profesor: José Luis Alonso Serrano. Dpto. de Ingeniería de Comunicaciones Área de Electrónica. Proyecto PLE. Universidad Miguel Hernández de Elche. Se realiza una introducción al entorno de programación gráfico LabVIEW.

Introducción a LabVIEW (2/5). Asignatura: Sensores y Acondicionamiento de Señal. Grado en Ingeniería de Tecnologías de Telecomunicación. Profesor: José Luis Alonso Serrano. Dpto. de Ingeniería de Comunicaciones Área de Electrónica. Proyecto PLE. Universidad Miguel Hernández de Elche. Se realiza una introducción al entorno gráfico de programación LabVIEW.

Introducción a LabVIEW (1/5). Asignatura: Sensores y Acondicionamiento de Señal. Grado en Ingeniería de Tecnologías de Telecomunicación. Profesor: José Luis Alonso Serrano. Dpto. de Ingeniería de Comunicaciones Área de Electrónica. Proyecto PLE. Universidad Miguel Hernández de Elche. Se realiza una introducción al entorno de programación gráfico LabVIEW.

Introducción a LabVIEW (5/5). Asignatura: Sensores y Acondicionamiento de Señal. Grado en Ingeniería de Tecnologías de Telecomunicación. Profesor: José Luis Alonso Serrano. Dpto. de Ingeniería de Comunicaciones Área de Electrónica. Proyecto PLE. Universidad Miguel Hernández de Elche. Se realiza una introducción al entorno de programación gráfico LabVIEW.

Are you excited about NIWeek? Here at VI Shots, we definitely are and to prepare you for what's to come. I assembled 3 session speakers for you to talk about their sessions and what to expect. I also go through a run-down of some sessions that I recommend. There are also some activities that are […]

NI recently announced a small form factor instrument that packs a punch called VirtualBench. I interviewed Chris Delvizis, a Senior Product Manager at National Instruments about this new hardware. Aside from containing several instruments essential to a typical benchtop lab setup. It also works right out of the box with built-in software for the PC that loads […]

The Actor Framework, is a LabVIEW framework that has a growing following. It allows you to build powerful applications that can contain asynchronous processes and allow them to communicate in a more robust manner. Jack Dunaway and I talk with Allen Smith, Dr. James Powell and Dave Snyder about what exactly is the Actor Framework and how […]

How do you support your LabVIEW applications remotely? Have you found the right tools and methods that make your job easier? In this episode of VI Shots Live, we look at some of the benefits and pitfalls of remote collaboration and support. Jack Dunaway and I talk with Fabiola De La Cueva and Justin Goeres […]

Waterloo labs is at it again with another creative project that merges LabVIEW, NI hardware and the maker mind-set. How about standing in front of 3 paintball guns as they automatically “draw” an outline of you? How about if the software controlling them was written by an NI intern? It turns out, this system is […]

I'd like to thank everyone for the positive feedback on our first VI Shots live, both in personal emails and also on our Google+ page. We're back with a new live episode which was recorded on Feb. 26, 2014. You can listen to the podcast version of the show above or watch the embedded video […]

We've all heard of the Agile Software development methodology; but how many of us are actually using these principles while developing LabVIEW code? My guest, John Sextro is an Agile coach and an expert in the field of Agile Software development. Listen to this episode of the VI Shots podcast where I ask John about […]

I've started something new here with Jack Dunaway from Wirebird Labs. We've decided to host monthly Google hangout sessions to discuss the business of software engineering with LabVIEW. The first one this month was held on Jan 29th, 2014 and was called: Sustainable Careers in LabVIEW. You can listen to the podcast version of the […]

Using Subversion with LabVIEW is a challenge when it comes to some of the most popular free tools like Subversion (SVN). Eric Metzler from Viewpoint Systems took on the challenge of updating an older version of an internal company tool. The new free version of the TSVN tool won  the LabVIEW Tools Network Product of […]

Jack Dunaway from Wirebird Labs is on this episode where we discuss his Deploy product and what it takes to develop great software products. Jack also announces a new “framework” he will be releasing in 2014 called Featherweight. Links to Content Mentioned: Deploy – LabVIEW Tools by Wirebird Labs Inspired –  Book by Marty Cagan VIPM Idea […]

If you think you know everything there is to know about functional globals, then you're wrong. In this episode of the VI Shots podcast, Nancy Hollenback and I take functional global variables to the next level. Find out how to safely use native globals, have multiple instantiation capabilities and speed optimize your look-up tables. I […]

Christina Rogers is a software engineer in the LabVIEW R&D group. She primarily works under the hood of LabVIEW adding much needed features and capabilities using C++. Sometimes she dives into G code, as she did to implement the LabVIEW Getting Started window. She's worked on features that you may have not noticed. In this […]

Do you want to create a better software experience for your customers? Do you want to create maintainable code? Listen to this interview with Fabiola De la Cueva. She's a Certified LabVIEW Architect, a LabVIEW Champion and an influential voice in the LabVIEW community. In this interview, I asked Fabiola to list the five things […]

Here's a recording of the VI Shots Live panel I did way back at NIWeek 2012. You'll hear me mention in the recording that this should go live soon after NIWeek. I guess soon meant a year, because here it is. On the panel are Jack Dunaway, Justin Goeres, me, Chris Relf and Brian Powell. We […]

Thomas Bress is the author of a new book out on LabVIEW called: “Effective LabVIEW Programming”. This book was released recently and in this episode of the VI Shots podcast, I interview the author, so we can get a better understanding of how it can help you transition from the CLAD level of certification to […]

During this past NIWeek 2013, the Raima Database API for LabVIEW was awarded the LabVIEW Tools Network Embedded Systems Product of the Year Award. I sat down with Scott Meeder who's the Director of Sales at Raima to find out what this toolkit is and how we, as LabVIEW application developers can take advantage of […]

I had the pleasure of interviewing Steve Watts. I recorded this interview back in February, but only now had a chance to publish it. Steve has been writing industrial software for more than 25 years and began programming with LabVIEW in 1998. He is the co-owner of SSDC Ltd (Structured Software Design Consultants). His book, […]

In this episode I dive into the world of Lego robotics and specifically, I learn about a new version of the Mindstorms educational robotic toy from Lego. At this past consumer electronics show (CES) in January, Lego announced and displayed the Mindstorms EV3. Also, this past NIWeek 2013, I saw a demo of the new EV3 […]

NIWeek 2013 is over and one of the biggest announcements was the release of LabVIEW 2013 with over 100 new features. In addition, a new cRIO-9068 controller which runs the Linux operating system, was announced. Now, a LabVIEW developer can tap into the vast community of Linux libraries available. In this podcast episode I chat […]

I've long anticipated this interview with Brian Powell. The impetus for this interview was a series of blog articles Brian wrote recently. BTW, I suggest everyone go to labviewjournal.com and read the entire series he wrote there. Brian of course is well known as one of the original team members that helped bring LabVIEW 2.0 to market […]

One of the benefits of being a CLA (Certified LabVIEW Architect), is that you get to go to the exclusive CLA summit. This is a yearly event held in Austin and Europe for the purpose of discussing topics and issues affecting LabVIEW developers today. Topics range from LabVIEW frameworks to source code control techniques. Best […]

In this episode, I interview Matthias Baudot from Studio Bods. He's started a new company that just announced a product on the LabVIEW Tools Network called BLT for LabVIEW. From the LabVIEW Tools Network Description: License your own LabVIEW application in a few clicks – no coding required Automatically (and remotely) update your applications when you […]

Here at VI Shots, one of my goals is to always be expanding my knowledge into LabVIEW and learning new software engineering techniques and ideas. To that goal, I try to bring people on the show that can teach me, and in turn you, something new. This episode of the VI Shots LabVIEW podcast is no […]

In this episode of the VI Shots Podcast. I sit down with Wendy Covey, from Trew Marketing. Wendy founded Trew Marketing with Rebecca Geier back in 2008. Since then, their company has focussed on helping engineering companies uncover their unique voice in the marketplace. As you'll hear in this interview. Sometimes in order to grow your […]

I had the pleasure of interviewing John Bergmans from Bergmans Mechatronics. He's been working with websockets and LabVIEW over the past few years and has evolved his knowledge into a product he calls LabSockets. From his website: The LabSocket System enables LabVIEW applications to be monitored and controlled remotely using a web browser. The system […]

National Instruments has been exploring the mobile space for a couple of years now. They started off with simple utility apps such as the Service Request tracker and NI device pinout documentation tool. They released a mobile app last year which allowed you to view data on an iOS and Android device in a limited […]

I had the pleasure of  interviewing Jeff C Jensen, who is a Senior Lead User Manager for Embedded Systems at National Instruments. We had a very interesting discussion about some of the work he's done with various students and NI employees to interface computers with everyday consumer devices using LabVIEW. All of the work can […]

I had the pleasure of speaking with Jeffrey Travis on this podcast episode. Jeffrey has written several LabVIEW related books, has released several open source LabVIEW tools such as LabVNC, LabSQL and LabPerl. He also has a very successful engineering business (Jeffrey Travis Studios ) developing LabVIEW based automation systems. Over the years, he’s transitioned […]

LabVIEW 2012 was announced today! You can download it from here. I invited Elijah Kerry, the senior product manager for LabVIEW, on the show so we can find out first-hand about all the new features of LabVIEW 2012. One of the most significant features, this year is the LabVIEW Templates and sample projects. I was […]

[Scroll Down for Video] I’m very excited today to bring you an interview I did last year at NIWeek 2011. I sat down with a group of engineering interns at National Instruments. Now these intern positions were not typical. Hunter Smith and Ben James who managed this group of students, gave them the flexibility to […]

I had the pleasure of talking with Brian Spears and Jassem Shahrani of Sixclear on today's podcast. I had a great time talking about how they got started running their successful business which has LabVIEW as its core. Powered by LabVIEW, if you will. In addition to getting some insights into how they work and […]

A quick podcast interview with Hassan Atassi, who is managing the latest LabVIEW Coding Challenge for the Winter of 2011. Listen to the podcast and find out what the two different challenges are and how you can get involved. The winner gets a sweet Sony Cyber-shot HX9V Digital Camera ($300). Some Deadlines to be aware of: Dec […]

I'm back from a very long absence. I've been very busy on various projects so I apologize for leaving you with such a huge gap. However, now I'm back and starting the flow of new audio podcast episodes and soon, new videos. In this episode of the VI Shots podcast, I interview the three members of the […]

Gary reports on NI Week, National Instruments' annual user conference. He discusses the large turnout, 25 years of LabView, multicore and FPGA parallel programming with LabView and dataflow programming, and NI's growth into a big company.

I've reached the 10th podcast episode! That's a big milestone for VI Shots. Another big milestone is the 25th anniversary of LabVIEW and the release of LabVIEW 2011. To get the rundown of all the new features, I asked Jeffrey Philipps from National Instruments to join me in this podcast episode. Do you know why […]

In this episode of the VI Shots podcast, I invited three members of the LabVIEW community to talk about how to get the most out of NIWeek 2011. I have Darren Natinger from National Instruments, Christopher Relf from VI Engineering and Justin Goeres from JKI. There are a lot of things to see at NIWeek and […]

In this episode of the VI Shots podcast I sit down with Terry Stratoudakis who is the CEO of Wall Street FPGA. A company that specializes in the acceleration of trading and financial analytics software using LabVIEW FPGA. This is a new industry in general for LabVIEW and specifically LabVIEW FPGA. I was skeptical at first and a […]

In this episode of the VI Shots LabVIEW podcast I chat with Sam Kristoff and Ben James from National Instruments. Sam was responsible for most of the development in the LabVIEW interface for Arduino toolkit that was released a few weeks ago. I mention in the interview that I was having problems getting LabVIEW to communicate […]

In this episode of the VI Shots LabVIEW podcast I chat with Crystal Drumheller from W.L.Gore and Associates in Flagstaff Arizona and Justin Goeres from JKI. Before Crystal worked for her current employer, she worked for National Instruments. That's where she got the LabVIEW bug and hasn't looked back. Both Crystal and Justin mentor FIRST Robotics teams and […]

Several weeks ago, National Instruments hosted an online coding challenge called: Code Madness 2011. In this episode of the VI Shots LabVIEW Podcast, my guests are Grant Heimbach who organized the challenge and Peter Kovacs who was the winner. Peter wrote some awesome code that scans through the NI Community site and returns all the documents available […]

In this episode of the VI Shots LabVIEW podcast, I have Darren Nattinger of National Instruments back to chat about his favorite feature of LabVIEW – Quickdrop. Darren is not new to this podcast. I had him on the show back in episode 002 where he gave us some interesting insight to his background and […]

In this 3rd episode of the VI Shots LabVIEW podcast. I sat down with Justin Goeres and we chatted about the NI 2011 CLA (Certified LabVIEW Architect) summit, we both attended, which happened in Austin March 7-8. Justin gives us a run down of all the reasons why you should be attending next year if you are […]

In this episode of VI Shots we sit down with Darren Nattinger of National Instruments to see why he is known as the fastest LabVIEW developer around. Darren is a senior software engineer and a Certified LabVIEW Architect and among the few people at National Instruments who codes in G. He shares some of his tips […]

In this (our first!) episode of the VI Shots podcast, we chat with Ben Zimmer of Enable training and Consulting. We discuss how he started using LabVIEW and how he’s built a growing business around providing training materials. He also talks about his interesting journey as a mentor to FIRST robotics teams and how that […]

In this episode, Gary discusses the National Instruments NI Week reporting on LabView upgrades and parallel programming.

Laboratorio di Misure Laboratorio 3: Ambiente di sviluppo National Instruments LabVIEWTM Laboratorio

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Aprender a manejar la herramienta Labview DSP