#Arrays

[adafruit]

Driving HUB75 RGB Matrices on Raspi 5 with PIO 💡😎

Since the latest release of 'piolib' we can do things like drive NeoPixels on any pin on the Raspberry Pi 5

which rocks, and means we can tackle the next, more complex, project: driving HUB75 RGB Matrix displays

these require even MORE timing freakiness: using 10 pins, and 'manual' PWM means we have to constantly blit out the color dithering. Historically this was done with mmap'd memory to the GPIO controller bitbanging, which required a full core and could jitter depending on load. But now we can use the PIO peripheral! We can drive massive display arrays at high speeds and color depths using just about any pins. The future is looking bright 😎

[jcmarchi]

From Tweets to Calls: How AI is Transforming the Acoustic Study of Migratory Birds

New Post has been published on https://thedigitalinsider.com/from-tweets-to-calls-how-ai-is-transforming-the-acoustic-study-of-migratory-birds/

From Tweets to Calls: How AI is Transforming the Acoustic Study of Migratory Birds

Every year, billions of birds travel across continents and oceans. These journeys are not only fantastic to watch, but they are also essential for keeping nature in balance. Birds pollinate flowers, spread seeds, and help control pests, playing a big part in keeping our environment healthy. However, their survival is threatened by problems like losing their habitats, climate change, and expanding cities. Understanding how they travel and live has never been more critical.

Scientists used traditional methods to study bird migration in the past, but these were often slow and limited in how much they could tell us. Now, Artificial Intelligence (AI) is changing everything. By listening to bird calls and songs, AI tools are helping researchers understand where birds go, how many there are, and what they need to survive. This new technology is bringing powerful ways to protect and study the birds.

The Significance of Migratory Birds and Acoustic Monitoring

Migratory birds are vital to ecosystems worldwide. They act as indicators of environmental health, with changes in their migration patterns often showing more significant natural shifts. For example, North America’s declining numbers of barn swallows point to issues like habitat loss and insect population changes. Similarly, the Arctic Tern’s 40,000-kilometer migration helps scientists understand the health of polar and ocean ecosystems.

Listening to bird calls has become a keyway to studying these migrations. Each bird species has unique sounds, or acoustic fingerprints, that researchers can use to identify them without needing to see them. This is especially useful because most bird migrations happen at night. Scientists can learn about where birds go and how they behave by recording their flight calls.

However, sorting through vast amounts of recorded sounds was slow and difficult with traditional methods. AI has solved this problem by quickly analyzing audio data and identifying bird species with remarkable accuracy. This breakthrough has opened new ways to study migratory birds, making research faster, more detailed, and more effective.

AI-Powered Innovations in Acoustic Research

AI-powered tools like BirdVoxDetect and BirdNET have transformed how researchers study migratory birds through their calls. BirdVoxDetect developed through a collaboration between New York University, the Cornell Lab of Ornithology, and École Centrale de Nantes, shows how powerful machine learning can be in bird research. This tool uses a neural network to detect and classify nocturnal flight calls with impressive accuracy, even in noisy environments. It can filter out background sounds like car alarms and raindrops while isolating and identifying bird-specific calls. By 2024, BirdVoxDetect had analyzed more than 6,600 hours of audio recordings, identifying hundreds of thousands of bird calls. Its ability to estimate bird biomass is as practical as Doppler radar but with the added advantage of providing species-specific data at a much lower cost.

BirdNET is another significant tool designed for both researchers and bird enthusiasts. It allows users to record and identify bird calls using just their smartphones. During the Global Big Day in 2024, BirdNET helped participants identify over 900 bird species in real-time, demonstrating the scalability and inclusivity of AI in bird research. Powered by neural networks and extensive training datasets, BirdNET has made bird studies accessible to a global community, inspiring new conservation engagement levels.

These tools do more than just identify bird species. They also improve the accuracy of tracking migratory routes. For example, researchers studying Arctic Terns have used AI to find key stopover locations and understand the environmental factors that influence their journeys. This information is essential for conservation efforts because it helps protect critical habitats and ensures resources are used effectively.

How AI is Transforming Conservation Efforts

AI-powered tools are changing the way we protect birds and their habitats. Real-time monitoring systems help cities take steps like turning off building lights at night during migration seasons. These Lights Out programs have worked well in cities like Chicago, where fewer birds now collide with skyscrapers.

Tools like BirdVoxDetect can also adapt to different regions. With only a small amount of training data, they can identify bird species even in areas without traditional monitoring systems. This flexibility has allowed scientists to study birds in the Amazon rainforest and sub-Saharan Africa. By automating data collection and analysis, these tools save time and effort, making it easier to carry out large-scale studies. Open-source platforms like BirdVoxDetect allow researchers to share and improve these technologies worldwide.

Other tools are also significantly advancing how AI is used for bird conservation. Nighthawk, an advanced system built on BirdVox, provides faster results and is more straightforward to use. Researchers studying birds in areas like the Great Lakes have reported improved accuracy with this tool. Merlin, developed by the Cornell Lab of Ornithology, uses AI to assist both scientists and bird watchers identify species. Its mobile app has made bird studies more accessible, inspiring people worldwide to participate in conservation efforts.

New technologies, such as microphone arrays, are further improving bird research. These systems can determine a bird’s location by detecting its altitude and direction of flight. Institutions like the University of Windsor are among the leaders of these innovations, enhancing our ability to monitor bird migrations.

Scientists are also working on foundation models for bioacoustics. These models are designed to study various species and ecosystems, beyond birds to animals like bats and whales. With these tools, researchers aim to deepen our understanding of biodiversity and develop better strategies for its protection.

AI is making bird conservation more efficient and effective. It is helping us gather essential insights to protect migratory species and ensure survival amidst rapid ecological changes.

The Bottom Lin

AI is revolutionizing the study and conservation of migratory birds, providing powerful tools that enhance our understanding of their behaviors and habitats. By automating the analysis of bird calls and migration patterns, technologies like BirdVoxDetect and BirdNET are making it easier for researchers and enthusiasts alike to engage in conservation efforts. These innovations improve the accuracy of tracking migratory routes and facilitate real-time monitoring, enabling cities to implement effective strategies like “Lights Out” programs to reduce bird collisions with buildings.

AI is creating new ways to protect these critical species and their habitats. This ensures that future generations can enjoy the amazing journeys of birds around the world. It also helps build a stronger connection with nature and supports efforts to preserve it.

[kvin2phens]

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High-voltage transients, TVS diode selection, Bi-Directional TVS Diode

SMCJ Series 6.5 W 185 V Bi-Directional Surface Mount TVS Diode - SMC

[appwebcoders]

What is array_diff() Function in PHP and How to Use.

Introduction

array_diff — Computes the difference of arrays

Supported Versions: — (PHP 4 >= 4.0.1, PHP 5, PHP 7, PHP 8)

In Today’s Blog, We are going to discuss about array_diff() function in php. When it comes to working with arrays in PHP, developers often encounter situations where they need to compare arrays and find the differences between them. This is where the array_diff() function comes to the rescue. In this comprehensive guide, we will delve into the intricacies of the array_diff() function, understanding its syntax, functionality, and usage with real-world examples.

Understanding the array_diff() Function:

When working with arrays in PHP, the array_diff function emerges as a powerful tool for array comparison and manipulation. array_diff function enables developers to identify the disparities between arrays effortlessly, facilitating streamlined data processing and analysis.

The array_diff function allows you to compare arrays, pinpointing differences across elements while efficiently managing array operations. By leveraging this function, developers can identify unique values present in one array but absent in another, paving the way for comprehensive data management and validation.

One remarkable feature of array_diff is its ability to perform comparisons based on the string representation of elements. For instance, values like 1 and ‘1’ are considered equivalent during the comparison process. This flexibility empowers developers to handle diverse data types seamlessly.

Moreover, array_diff simplifies array comparisons regardless of element repetition. Whether an element is repeated several times in one array or occurs only once in another, the function ensures accurate differentiation, contributing to consistent and reliable results.

For more intricate data structures, such as multi-dimensional arrays, array_diff proves its versatility by facilitating dimension-specific comparisons. Developers can effortlessly compare elements across various dimensions, ensuring precise analysis within complex arrays.

Incorporating the array_diff function into your PHP arsenal enhances your array management capabilities, streamlining the identification of differences and enabling efficient data manipulation. By seamlessly integrating array_diff into your codebase, you unlock a world of possibilities for effective array handling and optimization.

The array_diff function in PHP is a powerful tool that allows developers to compare two or more arrays and return the values that exist in the first array but not in the subsequent arrays. It effectively finds the differences between arrays, making it an essential function for tasks like data validation, data synchronization, and more.

Note

VersionDescription8.0.0This function can now be called with only one parameter. Formerly, at least two parameters have been required.Source: https://www.php.net/

Syntax:

array_diff(array $array1, array $array2 [, array $... ])

Parameters:

array1: The base array for comparison.

array2: The array to compare against array1.

…: Additional arrays to compare against array1.

Example 1: Basic Usage:

$array1 = [1, 2, 3, 4, 5]; $array2 = [3, 4, 5, 6, 7]; $differences = array_diff($array1, $array2); print_r($differences);

Output

Array ( [0] => 1 [1] => 2 )

Example 2: Associative Arrays:

$fruits1 = ["apple" => 1, "banana" => 2, "orange" => 3]; $fruits2 = ["banana" => 2, "kiwi" => 4, "orange" => 3]; $differences = array_diff_assoc($fruits1, $fruits2); print_r($differences);

Output

Array ( [apple] => 1 )

Example 3: Multi-dimensional Arrays:

$books1 = [ ["title" => "PHP Basics", "author" => "John Doe"], ["title" => "JavaScript Mastery", "author" => "Jane Smith"] ]; $books2 = [ ["title" => "PHP Basics", "author" => "John Doe"], ["title" => "Python Fundamentals", "author" => "Michael Johnson"] ]; $differences = array_udiff($books1, $books2, function($a, $b) { return strcmp($a["title"], $b["title"]); }); print_r($differences);

Output

Array ( [1] => Array ( [title] => JavaScript Mastery [author] => Jane Smith ) )

Important Points

It performs a comparison based on the string representation of elements. In other words, both 1 and ‘1’ are considered equal when using the array_diff function.

The frequency of element repetition in the initial array is not a determining factor. For instance, if an element appears 3 times in $array1 but only once in other arrays, all 3 occurrences of that element in the first array will be excluded from the output.

In the case of multi-dimensional arrays, a separate comparison is needed for each dimension. For instance, comparisons should be made between $array1[2], $array2[2], and so on.

Conclusion

The array_diff() function in PHP proves to be an invaluable tool for comparing arrays and extracting their differences. From simple one-dimensional arrays to complex multi-dimensional structures, the function is versatile and easy to use. By understanding its syntax and exploring real-world examples, developers can harness the power of array_diff() to streamline their array manipulation tasks and ensure data accuracy. Incorporating this function into your PHP toolkit can significantly enhance your coding efficiency and productivity.

Remember, mastering the array_diff() function is just the beginning of your journey into PHP’s array manipulation capabilities. With this knowledge, you’re better equipped to tackle diverse programming challenges and create more robust and efficient applications.

[tinchicus]

Esta funcion nos permite llenar un array con un valor. Espero les sea de utilidad!

[justnshalom]

When to Use an Array and When to Use a Linked List in JavaScript

Introduction As a JavaScript developer, you are likely familiar with arrays and linked lists. Both of these data structures have their own advantages and use cases. In this article, we will discuss when to use an array and when to use a linked list in JavaScript, along with example code to demonstrate their usage. When to Use an Array Arrays are one of the most commonly used data structures in…

[colisty]

PHP Introduction II: PHP Basics On Demand | CoListy

Learn PHP basics including script building variable definition array usage and writing readable code for dynamic web development and career growth.

[frog707]

Hey Siri

"Please explain to my manager why I need a raise."

"Arrays are fundamental data structures that allow you to access multiple values by means of non-negative indices ..."

[brin2atte]

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Capacitors, Ceramic Capacitors, what is a multilayer ceramic capacitor, MLCC,

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[ul2gherty]

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[jcmarchi]

MIT engineers grow “high-rise” 3D chips

New Post has been published on https://thedigitalinsider.com/mit-engineers-grow-high-rise-3d-chips/

MIT engineers grow “high-rise” 3D chips

The electronics industry is approaching a limit to the number of transistors that can be packed onto the surface of a computer chip. So, chip manufacturers are looking to build up rather than out.

Instead of squeezing ever-smaller transistors onto a single surface, the industry is aiming to stack multiple surfaces of transistors and semiconducting elements — akin to turning a ranch house into a high-rise. Such multilayered chips could handle exponentially more data and carry out many more complex functions than today’s electronics.

A significant hurdle, however, is the platform on which chips are built. Today, bulky silicon wafers serve as the main scaffold on which high-quality, single-crystalline semiconducting elements are grown. Any stackable chip would have to include thick silicon “flooring” as part of each layer, slowing down any communication between functional semiconducting layers.

Now, MIT engineers have found a way around this hurdle, with a multilayered chip design that doesn’t require any silicon wafer substrates and works at temperatures low enough to preserve the underlying layer’s circuitry.

In a study appearing today in the journal Nature, the team reports using the new method to fabricate a multilayered chip with alternating layers of high-quality semiconducting material grown directly on top of each other.

The method enables engineers to build high-performance transistors and memory and logic elements on any random crystalline surface — not just on the bulky crystal scaffold of silicon wafers. Without these thick silicon substrates, multiple semiconducting layers can be in more direct contact, leading to better and faster communication and computation between layers, the researchers say.

The researchers envision that the method could be used to build AI hardware, in the form of stacked chips for laptops or wearable devices, that would be as fast and powerful as today’s supercomputers and could store huge amounts of data on par with physical data centers.

“This breakthrough opens up enormous potential for the semiconductor industry, allowing chips to be stacked without traditional limitations,” says study author Jeehwan Kim, associate professor of mechanical engineering at MIT. “This could lead to orders-of-magnitude improvements in computing power for applications in AI, logic, and memory.”

The study’s MIT co-authors include first author Ki Seok Kim, Seunghwan Seo, Doyoon Lee, Jung-El Ryu, Jekyung Kim, Jun Min Suh, June-chul Shin, Min-Kyu Song, Jin Feng, and Sangho Lee, along with collaborators from Samsung Advanced Institute of Technology, Sungkyunkwan University in South Korea, and the University of Texas at Dallas.

Seed pockets

In 2023, Kim’s group reported that they developed a method to grow high-quality semiconducting materials on amorphous surfaces, similar to the diverse topography of semiconducting circuitry on finished chips. The material that they grew was a type of 2D material known as transition-metal dichalcogenides, or TMDs, considered a promising successor to silicon for fabricating smaller, high-performance transistors. Such 2D materials can maintain their semiconducting properties even at scales as small as a single atom, whereas silicon’s performance sharply degrades.

In their previous work, the team grew TMDs on silicon wafers with amorphous coatings, as well as over existing TMDs. To encourage atoms to arrange themselves into high-quality single-crystalline form, rather than in random, polycrystalline disorder, Kim and his colleagues first covered a silicon wafer in a very thin film, or “mask” of silicon dioxide, which they patterned with tiny openings, or pockets. They then flowed a gas of atoms over the mask and found that atoms settled into the pockets as “seeds.” The pockets confined the seeds to grow in regular, single-crystalline patterns.

But at the time, the method only worked at around 900 degrees Celsius.

“You have to grow this single-crystalline material below 400 Celsius, otherwise the underlying circuitry is completely cooked and ruined,” Kim says. “So, our homework was, we had to do a similar technique at temperatures lower than 400 Celsius. If we could do that, the impact would be substantial.”

Building up

In their new work, Kim and his colleagues looked to fine-tune their method in order to grow single-crystalline 2D materials at temperatures low enough to preserve any underlying circuitry. They found a surprisingly simple solution in metallurgy — the science and craft of metal production. When metallurgists pour molten metal into a mold, the liquid slowly “nucleates,” or forms grains that grow and merge into a regularly patterned crystal that hardens into solid form. Metallurgists have found that this nucleation occurs most readily at the edges of a mold into which liquid metal is poured.

“It’s known that nucleating at the edges requires less energy — and heat,” Kim says. “So we borrowed this concept from metallurgy to utilize for future AI hardware.”

The team looked to grow single-crystalline TMDs on a silicon wafer that already has been fabricated with transistor circuitry. They first covered the circuitry with a mask of silicon dioxide, just as in their previous work. They then deposited “seeds” of TMD at the edges of each of the mask’s pockets and found that these edge seeds grew into single-crystalline material at temperatures as low as 380 degrees Celsius, compared to seeds that started growing in the center, away from the edges of each pocket, which required higher temperatures to form single-crystalline material.

Going a step further, the researchers used the new method to fabricate a multilayered chip with alternating layers of two different TMDs — molybdenum disulfide, a promising material candidate for fabricating n-type transistors; and tungsten diselenide, a material that has potential for being made into p-type transistors. Both p- and n-type transistors are the electronic building blocks for carrying out any logic operation. The team was able to grow both materials in single-crystalline form, directly on top of each other, without requiring any intermediate silicon wafers. Kim says the method will effectively double the density of a chip’s semiconducting elements, and particularly, metal-oxide semiconductor (CMOS), which is a basic building block of a modern logic circuitry.

“A product realized by our technique is not only a 3D logic chip but also 3D memory and their combinations,” Kim says. “With our growth-based monolithic 3D method, you could grow tens to hundreds of logic and memory layers, right on top of each other, and they would be able to communicate very well.”

“Conventional 3D chips have been fabricated with silicon wafers in-between, by drilling holes through the wafer — a process which limits the number of stacked layers, vertical alignment resolution, and yields,” first author Kiseok Kim adds. “Our growth-based method addresses all of those issues at once.” 

To commercialize their stackable chip design further, Kim has recently spun off a company, FS2 (Future Semiconductor 2D materials).

“We so far show a concept at a small-scale device arrays,” he says. “The next step is scaling up to show professional AI chip operation.”

This research is supported, in part, by Samsung Advanced Institute of Technology and the U.S. Air Force Office of Scientific Research. 

[wliam2ver]

https://www.futureelectronics.com/p/passives--capacitors--ceramic-capacitors--multilayer-ceramic-capacitors/grt188r61e106me13d-murata-4144114

Low ESR capacitor, capacitors ceramic, MLCCs, ceramic capacitors

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[gagande]

In JavaScript, you can perform a variety of operations on arrays. Here are some commonly used methods:

The push() method allows you to add one or more elements to the end of an array.

The unshift() method lets you add one or more elements to the beginning of an array.

The filter() method is often used in conjunction with arrow functions to create a new array with all elements that pass the test implemented by the provided function.

[tinchicus]

Esta funcion nos permite comparar dos arrays para verificar si son iguales. Espero les sea de utilidad!

[justnshalom]

Ace Your Amazon Interview with Data Structures and Algorithms

Overview Preparing for a job interview at Amazon? One crucial aspect to focus on is data structures and algorithms. Amazon places a strong emphasis on candidates’ understanding of these foundational concepts. In this article, we will guide you through mastering data structures and algorithms to ace your Amazon interview. Why are Data Structures and Algorithms Important for Amazon…

[notescom]

003 GRAPH THEORY

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