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Danaher Corporation designs, manufactures, and markets professional, medical, industrial, and commercial products and services worldwide. The company operates through three segments: Life Sciences, Diagnostics, and Environmental & Applied Solutions. The Life Sciences segment provides mass spectrometers; flow cytometry, genomics, lab automation, centrifugation, particle counting and characterization; microscopes; genomics consumables; and Gene and Cell Therapy. This segment also offers bioprocess technologies, consumables, and services; and filtration, separation, and purification technologies to the pharmaceutical and biopharmaceutical, food and beverage, medical, and life sciences companies, as well as universities, medical schools and research institutions, and various industrial manufacturers. The Diagnostics segment provides chemistry, immunoassay, microbiology, and automation systems, as well as hematology, molecular, acute care, and pathology diagnostics products. This segment offers clinical instruments, reagents, consumables, software, and services for hospitals, physicians' offices, reference laboratories, and other critical care settings. The Environmental & Applied Solutions segment offers instrumentation, consumables, software, services, and disinfection systems to analyze, treat, and manage ultra-pure, potable, industrial, waste, ground, source, and ocean water in residential, commercial, industrial, and natural resource applications. This segment also provides instruments, software, services, and consumables for various color and appearance management, packaging design and quality management, packaging converting, printing, marking, coding, and traceability applications for consumer, pharmaceutical, and industrial products. The company was formerly known as Diversified Mortgage Investors, Inc. and changed its name to Danaher Corporation in 1984. Danaher Corporation was founded in 1969 and is headquartered in Washington, the District of Columbia.

Danaher

danaher

Johnathan Milner suspended his vote against campaign today in the wake of "extensive feedback" from Abcam shareholders.

Signal: Abcam likely to be sold as founder suspends bid to block takeover

Danaher Corporation (NYSE: DHR) shares are trading higher on Tuesday, alongside shares of healthcare equipment and lifesciences tools stocks.

Why Danaher Corporation Stock Gained Today

WASHINGTON, Oct. 31, 2023 /PRNewswire/ -- Danaher Corporation (NYSE: DHR) ("Danaher" or the "Company"), a leading global life sciences ... off-label marketing and other regulations relating to medical devices and the health care industry), the results ...

Danaher appoints Christopher Riley to Executive Vice President for Biotechnology

Danaher and Thermo Fisher Scientific reported Q3 revenue declines, touching off price target cuts and one downgrade by analysts.

StockWatch: Danaher, Thermo Fisher Revenue Declines Rattle Analysts

and Danaher (DHR – Research Report). Globus Medical (GMED) In a report released today, Craig Bijou from Bank of America Securities reiterated a Sell rating on Globus Medical, with a price target of $55.00. The company’s shares closed last Tuesday at $ ...

Analysts Offer Insights on Healthcare Companies: Globus Medical (GMED), HCA Healthcare (HCA) and Danaher (DHR)

TD Cowen analyst Daniel Brennan has reiterated their bullish stance on DHR stock, giving a Buy rating yesterday. Daniel Brennan has given his

Daniel Brennan Reiterates Buy Rating for Danaher Despite Challenges: Anticipates 2024 Upswing

At this time, I would like to welcome everyone to Danaher Corporation's Third Quarter 2023 Earnings Results Conference Call. (Operator Instructions) I will now turn the call over to Mr. John Bedford,

Q3 2023 Danaher Corp Earnings Call

Danaher presently carries a Zacks Rank #3 (Hold). Here are some better-ranked stocks from the Industrial Products sector: Applied Industrial Technologies AIT flaunts a Zacks Rank #1 (Strong Buy) at present. The company delivered a trailing four-quarter ...

Danaher's (DHR) Q3 Earnings & Revenues Beat, Decline Y/Y

WASHINGTON (AP) — WASHINGTON (AP) — Danaher Corp. (DHR) on Tuesday reported third-quarter profit of $1.13 billion. On a per-share basis, the Washington-based company said it had profit of $1.51. Earnings, adjusted for non-recurring costs, were $2.02 per share.

Danaher: Q3 Earnings Snapshot

Danaher (DHR) came out with quarterly earnings of $2.02 per share, beating the Zacks Consensus Estimate of $1.83 per share. This compares to earnings of $2.56 per share a year ago. These figures are adjusted for non-recurring items.

Danaher (DHR) Surpasses Q3 Earnings and Revenue Estimates

Abcam plc (ABCM), a supplier of life science research tools, announced on Monday that proxy advisor Glass Lewis has recommended that

Proxy Advisor Glass Lewis Recommends Abcam Shareholders To Vote 'for' Danaher Acquisition

Danaher (DHR) closed the most recent trading day at $207.30, moving +1.22% from the previous trading session. This change outpaced the S&P 500's 0.85% loss on the day. Elsewhere, the Dow saw a downswing of 0.

Why the Market Dipped But Danaher (DHR) Gained Today

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Microcephaly Industry Analysis; Technology Advancement, Trends and Growth 2029

🚀 This is issue #104 of THE EXPONENTIAL VIEW. Sign-up for the newsletter here. Darknet is more decentralised than Internet 🙊 : . “People want voice to be the new thing …. the tech industry is casting around looking for the Next Big Thing. I suspect that voice is certainly a big thing, but we’ll have to wait a bit longer for the next platform shift.” Benedict Evans Voice & the uncanny valley of AI 👨🏽‍🌾 How . “These superstars can offer more variety, cheaper prices and convenience, but the bigger chunk of profits that they capture is split among fewer workers.” superstar firms have shrunk labour’s share of the pie 👫 ? John Danaher takes an Aristotelian view. Will robots help our hinder the creation and maintenance of meaningful human friendships 🌟 Tim Berners-Lee: (Paradoxically, the Darknet now which may make it more resilient to attack or oligopoly power.) We need to take control of our data, tackle misinformation and manage political messaging in order to save the Web. has a more decentralized structure than the mainstream Internet 🔥 argues . “It finally recognizes its power and the ways in which that power has caused problems in the world, but its instinct is to wield that power even more, rather than back off.” Facebook wants more power in our lives, and we should resist, Jan Dawson Dept of artificial intelligence from Singularity. Says Ng: Interview with Andrew Ng from Baidu and Neil Jacobstein Just as about 100 years ago electrification changed every single major industry, I think we’re in the phase where AI will change pretty much every major industry. And he continues Things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either now or in the very near future automate with AI.This is a far cry from all work. But there are a lot of jobs that can be accomplished by stringing together many one-second tasks. 📈 Put this together with that “machine and AI algorithmic innovation transform analytics … to supercharge the [Pareto 80/20 principle]”, and it paints a picture of how important it is for firms to get the grips with AI’s impact. Michael Schrage’s argument in Harvard Business Review learning In essence, the availability of data across organisations will identify opportunities where the optimisation is not an 80/20 but perhaps a super-pareto (a 99/1). In other words, taking Pareto’s observation that small components may have substantial impact and lasering in on where those effects are more significant. 🔮 To see how this works in practice, I really recommend . Lange has run machine learning platforms at Microsoft, Amazon, Uber and Unity. (I recently saw him speak at an excellent .) Danny Lange ’s excellent presentation on AI in the enterprise Nordic.AI event The arrival of AI is in firms. also changing organisational structures Many companies are now putting greater emphasis on cultural fit and adaptability, knowing that individual roles will have to evolve along with the implementation of AI. Both Ng and Schrage describe AI in the terms that “potent tools that promise a more prosperous and comfortable ”. Kaplan reckons that “[p]ublic discourse about AI has become untethered from reality.” Jerry Kaplan calls future Or as says in this lovely essay: “ ” Ines Montani please, finally, stop using that same old fucking wired brain illustration. Elsewhere: 🚗 How Drive.AI is . There has been much discussion about whether deep learning on its own can handle the complexities of autonomous driving or needs to be married with more traditional robotics approaches. Drive seems to be one firm emphasising a holistic deep learning approach. using deep-learning from soup-to-nuts in their new autonomous vehicle tech FASCINATING 🗼 How could AI could improve the understanding of a site and its environs, support design decision making and the creative endeavour, foster better client relations and facilitate novel AI-robot enhanced craftsmanship. artificial intelligence change architecture? . Moving into trial in 50 burger joints over the next two years. (Video) Flippy the burger making robot Nice application of machine learning to assist in fairer bail and sentencing IBM’s achieves a 5.5% error rate continuous speech recognition system A look at Graphcore’s chip architectures which are optimised for machine learning Did ? Possibly not. Cambridge Analytica’s secret psychographic sauce really help Trump’s victory 48m twitter accounts are actually bots with bots Shanghai factory plans to replace all humans Quantum dept of maybe I’ve been meaning to cover more quantum computing for a few weeks but this week and my buddies at went to town on it. You can access here. Nature The Economist Jason Palmer’s survey in The Economist which promises to make quantum computing more widely accessible. It won’t outperform conventional computing (yet) but could be a useful playground for developers. IBM has announced IBM Q, ArsTechnica has more details on IBM’s 50-qubit computer. Time crystals, the seventh state of matter, are official. (We hinted at this in ). Simple ; more . EV#98 explanation from WSJ detailed explanation from Nature 🔬 Obituary of Peter Mansfield, pioneer of magnetic resonance imaging Small morsels to appear smart at dinner parties We a re watching more video, just not more TV The turning to new chip architectures at Intel’s cost. Wintel marriage is falling apart and Microsoft is Handy brief history of Blockchain within 100 days. Tesla reckons it can solve South Australia’s energy problems 🇨🇳 China will have more than a quarter of a billion over-60s within 3 years How the food (and wider) trade blockchain might improve (Machine learning meets psychology with a surprising result.) Your name might affect your facial appearance. 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🔮 How AI will change the enterprise and robot friendships ++ #104

In machine learning, it is crucial to have a large amount of data in order to achieve strong model performance. Using a method known as data augmentation, you can create more data for your machine learning project. Data augmentation is a collection of techniques that manage the process of automatically generating high-quality data on top of existing data. In computer vision applications, augmenting approaches are extremely prevalent.If you are working on a computer vision project (e.g Image classification), for instance, you can apply dozens of techniques to each image: shift, modify color intensities, scale, rotate, crop, etc. If you have a tiny dataset for your ML project or wish to reduce overfitting in your machine learning models, it is recommended that you may apply data augmentation approaches. “We don’t have better algorithms. We just have more data.”- Peter Norvig In the field of Natural Language Processing (NLP),the tremendous level of complexity that language possesses, makes it difficult to augment text. The process of augmenting text data is more challenging and not as straightforward as some might expect. In this article, you will learn how to use a library called to improve data for natural language processing. TextAttack What is TextAttack? TextAttack is a Python framework that was built by the for the purpose of conducting adversarial attacks, adversarial training, and data augmentation in natural language processing. TextAttack has components that can be utilized independently for a variety of basic natural language processing tasks, including sentence encoding, grammar checking, and word substitution. QData team TextAttack excels in performing the following three functions: Adversarial attacks (Python: , Bash: ). textattack.Attack textattack attack Data augmentation (Python: , Bash: ). textattack.augmentation.Augmenter textattack augment Model training (Python: , Bash: ). textattack.Trainer textattack train For this article, we will focus on how to use TexAttack library for Data augmentation. Note: How to Install TexAttack To use this library make sure you have python 3.6 or above in your environment. Run the following command to install textAttack. pip install textattack Once you have installed TexAttack, you can run it via python module or via command-line. Note: Data Augmentation Techniques for Text Data TextAttack library has various augmentation techniques that you can use in your NLP project to add more text data.Here are some of the techniques that you can apply: It augments words by swapping characters out for other characters. 1.CharSwapAugmenter charswap_aug = CharSwapAugmenter()
charswap_aug.augment(text) from textattack.augmentation import CharSwapAugmenter text = "I have enjoyed watching that movie, it was amazing." ['I have enjoyed watching that omvie, it was amazing.'] The Augmenter has swapped the word to . "movie" "omvie" It augments the text by deleting some parts of the text to make new text. 2.DeletionAugmenter deletion_aug = DeletionAugmenter()
deletion_aug.augment(text) from textattack.augmentation import DeletionAugmenter text = "I have enjoyed watching that movie, it was amazing." ['I have watching that, it was amazing.'] This method has removed the word to create a new augmented text. "enjoyed" This augments the text with a combination of different methods, such as 3.EasyDataAugmenter Randomly swap the positions of the words in the sentence. Randomly remove words from the sentence. Randomly insert a random synonym of a random word at a random location. Randomly replace words with their synonyms. eda_aug = EasyDataAugmenter()
eda_aug.augment(text) from textattack.augmentation import EasyDataAugmenter text = "I was billed twice for the service and this is the second time it has happened" ['I was billed twice for the service and this is the second time it has happen', 'I was billed twice for the one service and this is the second time it has happened', 'I billed twice for the service and this is the second time it has happened', 'I was billed twice for the this and service is the second time it has happened'] As you can see from the augmented texts, it show different results based on the methods applied.For example in the first augmented text, the last word has been modified from to . "happened" "happen" It can augment the text by replacing it with synonyms from the WordNet thesaurus. 4.WordNetAugmenter wordnet_aug = WordNetAugmenter()
wordnet_aug.augment(text) from textattack.augmentation import WordNetAugmenter text = "I was billed twice for the service and this is the second time it has happened" ['I was billed twice for the service and this is the second time it has pass'] This method has changed the word to in order to create a new augmented text. "happened" "pass" Importing transformations and constraints from the and t allows you to build your own augmenter from the ground up. The following is an illustration of the use of the to produce augmentations of a string: 5.Create your Own Augmenter textattack.transformations extattack.constraints WordSwapRandomCharacterDeletion algorithm my_transformation = CompositeTransformation([WordSwapRandomCharacterDeletion()]) augmenter.augment(text) from textattack.transformations import WordSwapRandomCharacterDeletion from textattack.transformations import CompositeTransformation from textattack.augmentation import Augmenter augmenter = Augmenter(transformation=my_transformation, transformations_per_example= 3 ) text = 'Siri became confused when we reused to follow her directions.' ['Siri became cnfused when we reused to follow her directions.', 'Siri became confused when e reused to follow her directions.', 'Siri became confused when we reused to follow hr directions.'] The output show different augmented texts after implementing the method.For example, in the first augmented text, the method randomly removes a character in the word WordSwapRandomCharacterDeletion "o" "confused". Conclusion In this article, you have learned the significance of data augmentation for your Machine Learning project. In addition, you have learnt how to execute data augmentation for textual data using the TextAttack library. To the best of my knowledge, these techniques are the most effective approaches available to do the task for your NLP project.Hopefully, they'll be of use to you in your work. You can also try to use other available augmentation techniques from TextAttack library such as: EmbeddingAugmenter CheckListAugmenter CLAREAugmenter If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post! You can also find me on Twitter . @Davis_McDavid

How to Perform Data Augmentation in NLP Projects

From the most popular seats to the most popular viewing times, we wanted to find out more about the movie trends in . So we created — a website to get a glimpse of Singapore’s Movie trends — by scraping data, finding interesting insights, and visualizing them. Singapore PopcornData Ever felt the crippling disappointment of finding out your favorite seat at the theater has been booked? How popular really is your favorite seat? On the website you can see how people watched different movies, at different halls, theaters and timings! Some unique aspects include to show the and to show the . This 2-part article explains how we obtained the data for the website and our analysis of the data. heat maps most popular seats animations order in which seats were bought Scraping the Data To implement our idea, the first and maybe even most crucial step was to collect the data. We decided to scrape the website of , one of the biggest cinema chains in Singapore. Shaw Theatres Starting out with basic knowledge of scraping in python, we initially tried using python’s library to get the site’s HTML and library to parse it but quickly realized that the data we required was not present in the HTML we requested. This was because the website was dynamic — . When we request the HTML directly, the dynamic part of the website is not being rendered, hence the missing data. requests BeautifulSoup it requests the data from an external source using Javascript and renders the HTML dynamically To fix this issue, we used — A web browser automation tool that can first render the website with the dynamic content before getting the HTML. Selenium Issues With Selenium Getting the selenium driver to work and fixing minor issues with it had a big learning curve. After countless StackOverflow searches and ‘giving up’ multiple times we managed to scrape through (pun intended) and get it to work. The main issues we faced were: Scrolling to a specific portion of the screen to click a button so that the data will be found in the HTML Figuring out how to run headless Selenium on the cloud. After deploying the script on Heroku, some of the data was not being scraped when in fact the script was working properly on the local machine. After racking our brains we figured out that some pages loaded by Selenium were defaulting to the mobile version of the page. We fixed it by explicitly mentioning the screen size. With Selenium and BeautifulSoup, we were finally able to get the data for all the available movie sessions for a particular day! Sample movie session data: { : , : , : , : , : , : } "theatre" "Nex" "hall" "nex Hall 5" "movie" "Jumanji: The Next Level" "date" "18 Jan 2020" "time" "1:00 PM+" "session_code" "P00000000000000000200104" Now we needed to collect the seat data for each movie slot to see which seats were occupied and when they were bought. After going through the Network tab of the website in the Developer Tools, we found that the seat data was being requested from Shaw’s API. We were halfway there! The data could be obtained by requesting the URL where the session code was the unique code for each movie session which we had already scraped earlier. https://www.shaw.sg/api/SeatingStatuses?recordcode=<session_code> The data we got was in JSON format and we parsed it and reordered the seats in ascending order of seat buy time to obtain an array of JSON objects where each object contained information about each seat in the movie hall, including seat_number, seat_buy_time, and seat_status. Sample seat data: [
  { : , : , : , : , : },
   ...,
  { : , : , : , : , : }
] "seat_status" "AV" "last_update_time" "2020-01-20 14:34:53.704117" "seat_buy_time" "1900-01-01T00:00:00" "seat_number" "I15" "seat_sold_by" "" "seat_status" "SO" "last_update_time" "2020-01-20 14:34:53.705116" "seat_buy_time" "2020-01-18T13:12:34.193" "seat_number" "F6" "seat_sold_by" "" : Unique identifier for a seat in a hall seat_number : Indicates the availability of a seat ( -seat occupied, -Available) seat_status SO AV : time seat was purchased by the customer seat_buy_time time seat data was last scraped last_update_time: Halls have anywhere between 28 – 502 seats and each seat corresponds to a JSON object in the array. Add this to the fact that there are upwards of 350 movie sessions in a single day, and the amount of data generated is pretty big. Storing data for a single day took about 10MB. The movie session data was combined with the seat data and stored on a MongoDB database. We managed to scrape all the movie data from Shaw for January 2020. A single document in the database { : , : , : , : , : , : :[
   { : , : , : , : , : },
   ...,
   { : , : , : , : , : }
 ]
} "theatre" "Nex" "hall" "nex Hall 5" "movie" "Jumanji: The Next Level" "date" "18 Jan 2020" "time" "1:00 PM+" "session_code" "P00000000000000000200104" "seats" "seat_status" "AV" "last_update_time" "2020-01-20 14:34:53.704117" "seat_buy_time" "1900-01-01T00:00:00" "seat_number" "I15" "seat_sold_by" "" "seat_status" "SO" "last_update_time" "2020-01-20 14:34:53.705116" "seat_buy_time" "2020-01-18T13:12:34.193" "seat_number" "F6" "seat_sold_by" "" To view the full document . click here The complete raw data collected can be downloaded here: Raw Movie Data.zip Time to Get Our Hands Dirty by and pulling out relevant information. Using pandas, we parsed the JSON, cleaned it and made a DataFrame with the data to improve readability and filter it easily. It was now time to get our hands dirty cleaning the data Since the seat data took a lot of memory, we could not include all of it in the DataFrame. Instead, we aggregated the seat data using python to obtain the following: 1. : Total number of seats available for a movie session Total Seats 2. : Number of seats sold for a movie session Sold Seats 3. : 2 dimensional array showing the order in which seats were bought Seat Buy Order [[ , ], [ ], [ , , , ], [ , , ], [ , ], [ , ], [ , ], [ , , ], [ ], [ , , ]] 'A_10' 'A_11' 'A_12' 'B_4' 'B_7' 'B_6' 'B_5' 'C_8' 'C_10' 'C_9' 'B_1' 'B_2' 'C_6' 'C_7' 'C_5' 'C_4' 'B_8' 'B_10' 'B_9' 'D_8' 'A_15' 'A_14' 'A_13' Each element in the array represents the seats bought at the same time and the order of elements represents the order in which the seats were purchased. 4. : Dictionary showing the number of seats that were bought together (in groups of 1, 2, 3 or more) Seat Distribution { : , : , : , : , : } 'Groups of 1' 8 'Groups of 2' 30 'Groups of 3' 9 'Groups of 4' 3 'Groups of 5' 1 5. : Dictionary showing the number of times each seat in a hall was bought over the course of the month Seat Frequency { : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : } 'E_7' 4 'E_6' 5 'E_4' 11 'E_5' 9 'E_2' 2 'E_1' 2 'E_3' 7 'D_7' 15 'D_6' 17 'C_1' 33 'D_2' 15 'D_1' 14 'B_H2' 0 'B_H1' 0 'D_4' 45 'D_5' 36 'D_3' 32 'C_3' 95 'C_4' 94 'A_2' 70 'A_1' 70 'B_2' 50 'B_1' 47 'C_2' 37 'C_6' 53 'C_5' 61 'B_4' 35 'B_3' 40 6. : Two dictionaries, with the first dictionary showing the time left to a movie showing (in days) with the corresponding accumulated number of tickets bought in the second dictionary Rate of Buying {“ ”: [ ..., , , , , ....],
...}
{“ ”: [ , , , , , ...],
...} 1917 4.1084606481481485 2.566423611111111 2.245578703703704 2.0319560185185184 1.9269907407407407 1.8979513888888888 1917 1 3 8 10 11 The cleaned data can be viewed here: Cleaned Movie Data Finally we were done! (with 20% of the work) With our data scraped and cleaned, we could now get to the fun part — to find patterns amongst the popcorn litter. analyzing the data To learn more about our analysis of the data and interesting patterns we found, check out of this article. In Part II, we analyze the data, visualize it, and then build a website for our findings. Part II The code for the scraper can be found in this . github repo Don’t forget to check out our website at http://popcorn-data.herokuapp.com ! This article was written by and Noel Mathew Isaac Vanshiqa Agrawal Previously published at https://medium.com/@noelmathewisaac/popcorn-data-analysing-cinema-seating-patterns-part-1-a0b2a5c2c19a

How to Use Data Science to Find the Best Seat in the Cinema (Part I)

Water your plants, get alarms, and analyze the data Having plants at home is a luxury that only households that  always count with someone's presence can enjoy. If you live alone and travel for a few days, you are likely to return home and find a mumified version of your beloved basil plant. One may argue: "Why not buy a timer on Home Depot for 5 bucks?". That's an excellent point, but why not make it overly complicated and learn a couple of things during the process? In this article I will show you how I created an IoT application that controls the irrigation of the plants in my house. This application independently irrigates 5 pots: 1 lemon tree, 2 passion fruit plants, 1 gardenia and 1 pot with herbs (basil, fennel and oregano). Because I'm planting these plants on the balcony of my apartment, I don't have running water, so I'm using a 40 liters tank. This tank has an ultrasonic sensor that monitors the volume and once the water level is below 30%, my Losant app sends me an email telling me to fill the tank. Well, to be honest, it sends an email to my wife, I hate filling that tank. Besides that, the system gets the current temperature and humidity here in Houston, TX and based on that increases the volume of irrigation water to compensate for the extra heat. I realized that I needed to implement this feature once I was done with the hardware, so I'm pulling the temp info from a free weather API. It's pretty cool that Losant allows you to integrate info from so many different sources. The hardware The general system diagram is shown below: The electronic enclosure The electronics are very simple, these are the components of this project: Microprocessor: Particle Photon (https://www.particle.io) Power supplies (5 and 12 Volts) 6 Relays. Caveat: I have used mechanical relays because I've also used a switching power supply. SSD relays can switch as fast as your switching power supply and mess up with the output. Wifi antenna The basic idea is the following: 5V feed the Particle Photon Photon's outputs connect to the low power side of the relays driving 12V to the valve solenoids upon activation. Similarly, the pump (underwater aquarium pump) is controlled by the Photon which drives AC current through its motor. Lets put everything together in a nice, presentable box. Remember, if you use a metallic enclosure like I did, you need to ground the box. I can't stress this enough! The Water Manifold I got the cheapest solenoid valves I could get from AliExpress.com, I think I paid 3 bucks for each. This is not bad at all, in the US they go for 10 bucks each. I also got some valves, tubing and an aquarium manifold. I've put everything together in a fancy plastic box. Note that the electrical connection from this wet module to the electronics in done by a multi-core cable and a connector. The final result is pretty neat. Firmware The Particle Photon, like Arduino, uses C++. It has a some conveniences like Particle.function() and Particle.publish(). These features allow the user to trigger functions from external events and publish local events respectively. In order to trigger functions remotely you'll need to declare them in the void setup. In my system I have 7 Particle functions, they are: tooHot: changes the value of the boolean variable hot to TRUE if the local temperature is greater than 30 Celsius. The data is retrieved from the weather API and the logic is executed in Losant. mode: activates the manual mode. pf1, lemon, herbs, gardenia, pf2: water each plant in manual mode. These Particle functions only accept one argument and it must be a String. Conversaly, you can publish events with Particle.publish(). Please check out my code below and next we will see how to create an interface to interact with the microcontroller. V1 = ; V2 = ; V3 = ; V4 = ; V5 = ; pump = ; trigger = ; echo = ; volume; hot = FALSE; manual_mode = FALSE; { Particle.function( , tooHot);
    Particle.function( , mode);
    Particle.function( , pf1);
    Particle.function( , lemon);
    Particle.function( , herbs);
    Particle.function( , gardenia);
    Particle.function( , pf2); pinMode(trigger, OUTPUT);
    pinMode(echo, INPUT);
    pinMode(pump, OUTPUT);
    pinMode(V1, OUTPUT);
    pinMode(V2, OUTPUT);
    pinMode(V3, OUTPUT);
    pinMode(V4, OUTPUT);
    pinMode(V5, OUTPUT); Time.zone( ); digitalWrite(V1, HIGH);
    digitalWrite(V2, HIGH);
    digitalWrite(V3, HIGH);
    digitalWrite(V4, HIGH);
    digitalWrite(V5, HIGH);
    digitalWrite(pump, HIGH);
    
    Serial.begin( );
} { STARTUP(WiFi.selectAntenna(ANT_EXTERNAL)); (manual_mode == FALSE){ (Time.hour() == && Time.minute() == ){
            auto_irrigation();
        } (hot == TRUE){ (Time.hour() == && Time.minute() == ){
                auto_irrigation();
            }
        }
    } ((Time.hour() == || Time.hour() == ) && Time.minute() == ){
        volume = measure_level();
        Particle.publish( , (String)volume);
    }

} {
    digitalWrite(V1, LOW);
    digitalWrite(pump, LOW);
    delay( );
                
    digitalWrite(V1, HIGH);
    digitalWrite(V2, LOW);
    delay( );
                
    digitalWrite(V2, HIGH);
    digitalWrite(V3, LOW);
    delay( );
                
    digitalWrite(V3, HIGH);
    digitalWrite(V4, LOW);
    delay( );
                
    digitalWrite(V4, HIGH);
    digitalWrite(V5, LOW);
    delay( );
                
    digitalWrite(V5, HIGH);
    digitalWrite(pump, HIGH);
} { result; duration = ; ( i = ; i <= ; i++)
    {
        digitalWrite(trigger, LOW);
        delayMicroseconds( );
        digitalWrite(trigger, HIGH);
        delayMicroseconds( );
        digitalWrite(trigger, LOW);
        duration = duration + (pulseIn(echo, HIGH)/ );
        delay( );
    }
    result = *( -duration* )/ ; result;
} { (command == ){
        hot = TRUE; ;
    } (command == ){
        hot = FALSE; ;
    } { ;
    }
    
} { (command == ){
        manual_mode = TRUE; ;
    } (command == ){
        manual_mode = FALSE; ;
    } { ;
    }
} { (manual_mode == TRUE){ (command == ){
            digitalWrite(V1, LOW);
            digitalWrite(pump, LOW);
        } (command == ){
            digitalWrite(V1, HIGH);
            digitalWrite(pump, HIGH);
        }
    }
} { (manual_mode == TRUE){ (command == ){
            digitalWrite(V2, LOW);
            digitalWrite(pump, LOW);
        } (command == ){
            digitalWrite(V2, HIGH);
            digitalWrite(pump, HIGH);
        }
    }
} { (manual_mode == TRUE){ (command == ){
            digitalWrite(V3, LOW);
            digitalWrite(pump, LOW);
        } (command == ){
            digitalWrite(V3, HIGH);
            digitalWrite(pump, HIGH);
        }
    }
} { (manual_mode == TRUE){ (command == ){
            digitalWrite(V4, LOW);
            digitalWrite(pump, LOW);
        } (command == ){
            digitalWrite(V4, HIGH);
            digitalWrite(pump, HIGH);
        }
    }
} { (manual_mode == TRUE){ (command == ){
            digitalWrite(V5, LOW);
            digitalWrite(pump, LOW);
        } (command == ){
            digitalWrite(V5, HIGH);
            digitalWrite(pump, HIGH);
        }
    }
} // ports to control and read hardware const int 0 const int 1 const int 2 const int 3 const int 4 const int 5 const int 6 const int 7 // tank volume measurement int // temperature alarm bool // manual mode bool void setup () // declaring particle functions "tooHot" "mode" "pf1" "lemon" "herbs" "gardenia" "pf2" // defining i/o's // setting time zone -5 // initiallizing actuators 9600 void loop () // selects the u.FL antenna if // morning irrigation schedule if 7 00 // if it's warm, the system water the plants again at 3pm if if 15 00 // measure water tank level twice a day if 8 16 00 "tank_volume" /* FUNCTIONS */ // irrigation int auto_irrigation () 80000 50000 80000 40000 40000 // measure the tank level, averages 100 measurements int measure_level () int long 0 for int 0 100 2 10 100 50 100 25.4 0.017 25.4 return /* this function changes the boolean value of the variable hot and its logic is executed on Losant. 
the weather api provides the temp and if greater than 30C, it will trigger toohot. */ int tooHot (String command) if "yes" return 1 else if "no" return 0 else return -1 // this function changes the logic value of manual_mode and it's triggered on losant. int mode (String command) if "true" return 1 else if "false" return 0 else return -1 // passion fruit 1 manual irrigation int pf1 (String command) if if "true" else if "false" // lemon tree manual irrigation int lemon (String command) if if "true" else if "false" // herbs manual irrigation int herbs (String command) if if "true" else if "false" // gardenia manual irrigation int gardenia (String command) if if "true" else if "false" // passion fruit 2 manual irrigation int pf2 (String command) if if "true" else if "false" IoT Workflow Receive an email notification Losant is an IoT platform that allows the developper to create a workflow logic that will receive and trigger events. It's beyond the scope of this article to provide a detailed Losant tutorial, however I'll explain the intuition behind it and give you references in case you decide to develop your own IoT app. Let's start checking how to implement an email notification to alert the user that the water level is getting low. First you'll need to create a device and a particle integration in Losant. Upon doing that, go to your workflow and build the following block logic. Let's examinate how to set the nodes to implement this logic. This node establishes communication with your device. Click on it and select the integration you have created. 1. Particle: Remember Particle.publish()? We will set up this node to listen for events published from the Photon. In order to do that, we need to define the structure of the payload with some javascript. The event published by the microcontroller will be transmitted in a json package, more precisely inside data. In our case, the variable being passed is 'volume', so we can retrieve this data by accessing 'data.volume'. 2. Function: Click on the function node and add the following code. parts = payload.data.data.split( );
payload.data.volume = parts[ ]; var ':' 0 : This node will attribude the data from you payload to a device and the attributed you've defined upon its creation. This will allow us to create a dashboard later. 3. Device Get Select the query method to: 'Match all tags query' Set 'Key Templates' to 'particle_device_id' and 'Value Templates' to '{{ data.coreid }}' Set 'Result Path' to 'device_to_update' : Stores the state in a device attribute. 4. Device State 'Device ID JSON path' = 'device_to_update.id' 'Attribute' = '{{data.volume}}' This block allows you to debug and understand what is going on with you payload. You can trigger events from the workflow and see how your payload is being constructed. This block is not necessary to make the app work. 5. Debug: Executes a conditional logic. Remember you'll need to refer to the variable according to the way that your JSON payload was defined, in our case {{data.volume}}. 6. Conditional: This one is pretty self explanatory. Upon the activation of the condition previously defined, this block will send an email. 7. Email: To summarize, we will retrieve the variable 'volume' by setting up a logic to listen to the events being published from the IoT microcontroller. Upon getting this data from a JSON package we will compare it to a threshold and if the value is less than 30% of the total volume, an email will be sent asking the user to re-fill the tank. Get local temperature from an API First you'll need an API key, go to and create an account. You can use their free plan for this project. If I'm not wrong, the free plan includes 1000 api calls a day. If you're not in the agricultural business, this should be more than enough. https://openweathermap.org/api Build the following workflow: The http block is responsible from calling the API, let's check on how to set it up. In the 'URL template', add the following: http://api.openweathermap.org/data/2.5/weather?q='YOUR CITY'&appid='YOUR API KEY' The 'request method' should be set to GET and the API response should be stored in the 'Payload path to store response' field. Set it to 'working.weather'. Set the timer to call the API every hour so you don't exceed the daily limit of calls. Now your payload should look like this: Now the temperature should be accessible through: {{working.weather.body.main.temp}} Triggering Functions Remotely Now it's time to finally trigger our Particle functions previously defined in our firmware. Let's start by building a workflow: Basically we have a 'Virtual Button' node triggering a 'Particle Call' node for each one of our remotely operated functions. In the 'Particle Call' node, we need to: Inform the node which firmware function we will call by adding its name to the field 'Function Name'. Tell the node what argument we will be passing by specifying it directly or adding a payload path. We will pass a payload path since we will be using a toggle switch to activate the system. We will create variables for each plant and add them to the payload. They will be located at the root data, so they will be accessible as data.pf1, data.lemon, data.herbs, data.gardenia and data.pf2. Now, we need to create an interface to trigger this workflow we've just created. Go to Dashboard and add an input control. Add toggles and buttons like so: The toggle state is sent via payload to the microcontroller upon triggering the workflow via the button. Set the button like so (note, the value between the double brackets should be the same of your toggle, my toggle is also named 'lemon'). Do the same for all the other inputs and test it: Data visualization Add some charts and link then to your devices attributes. Conclusion I hope you guys enjoyed this article. This project has several moving parts and I know that I have covered some of them too fast without providing the level of detail that the reader deserves, for that reason do not hesitate to contact me if you'd like to get more info or simply ask a question. Moving forward I'd like to add a strain gauge to monitor the weight of the plant. Over 98% of the mass of plants comes from the air (interesting fact isn't it?). That would be an interesting step into starting optimizing fertilization and irrigation. If you are interested in learning more about Losant, check this tutorial out: https://www.losant.com/blog/how-to-integrate-particle-with-losant See y'all soon!

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