#robot arduino

[nurawal]

Jasa Konsultasi Pemrograman Arduino. Bisa online

Kami melayani jasa konsultasi pemrograman robot dan peralatan elektronika berbasis arduino. Segala jenis peralatan elektronika dan segala jenis robot arduino dapat kami buat, sesuai permintaan anda.Kami juga melayani jasa konsultasi pembuatan proyek robot dan elektronika berbasis arduino, sekaligus pendampingan untuk event tertentu.Soal harga? Dijamin murah dan sesuai kantong anda. Kami…

[adafruit]

Meet the puppets of "Circuit Playground"! ​🤖🎭

Explore the fundamentals of electronics with "Circuit Playground," a video series by Adafruit. Each episode delves into the components and principles that make circuits function - beeping, blinking, and more. Ideal for beginners and enthusiasts eager to understand what makes circuits tick - great for engineers to watch with their kiddos -

https://youtube.com/playlist?list=PLjF7R1fz_OOXWHQhEVEI5Jqf18TQRr5Hu

[every-crocodiles-dad]

Desperately trying to find the tech AV/robotics/coding community of tumblr.

WHERE ARE YOU. PLEASE. GIVE ME LIFE. HEAR MY HASHTAGS AND FIND ME.

[a6y3ap]

I made an Arduino-based Animatronic Eye Mechanism

[generativetenebrousautomata]

[bdsmleatherman]

Progress continues apace. This model lacks robustness and the analog servos are too clumsy for precise aiming but it's a good stepping stone upward. It uses an existing simplistic survival crossbow design that Ive just sort of bolted a makeshift turret onto. Also happy that I got all the guts inside the box this time

[spark-hearts2]

I AM SO GOOD AT CIRCUIT BUILDING AND PROGRAMMING RAAAAA

C++ script under cut :3

int UpDown;       //value for the Y direction of controller

int LeftRight;    //value for the x direction of controller

int LR_neutral;   //value for the 0 position in the y direction of controller

int UD_neutral;   //value for the 0 position in the x direction of controller

int Bprev;        //value for button edge detection

int Bcurr;        //value for button edge detection

int R;

int Y;

int G;

int B;

void setup() {

  Serial.begin(9600);  //begin communication

  pinMode(A2,  INPUT); //button press detection

  pinMode(4, OUTPUT); //set pin 4 to power the Red LED

  pinMode(5, OUTPUT); //set pin 5 to power the Yellow LED

  pinMode(6, OUTPUT); //set pin 6 to power the Blue LED

  pinMode(7, OUTPUT); //set pin 7 to power the Green LED

  LR_neutral = analogRead(A1); //set zero position of controller

  UD_neutral = analogRead(A0); //set zero position of controller

  //WARNING!!! YOU CAN NOT TOUCH CONTROLER WHEN INITALIZATION HAPPENS!!!! WILL MESS CONTROLLER UP

}

void loop() {

LeftRight = analogRead(A0);  //read X position of controller

UpDown = analogRead(A1);     //read y position of controller

Bprev = Bcurr;               //set current button state to previous state

Bcurr = analogRead(A2);     //set current button state equal to actual button state

if ((Bprev == 0) && (Bcurr > 0)){

  //turns all LED on

  digitalWrite(4,HIGH);

  digitalWrite(5,HIGH);

  digitalWrite(6,HIGH);

  digitalWrite (7,HIGH);

  delay(100); //wait

  //turns all LED off

  digitalWrite (4,LOW);

  digitalWrite (5,LOW);

  digitalWrite (6,LOW);

  digitalWrite (7,LOW);

}

if (UpDown >= UD_neutral) {  // checks if controller is up

  B = 0; //if up turns blue LED off

  R = map(UpDown, UD_neutral,1023,0,255);  //if up turns red LED on

}

else {

  R = 0;  //if down turns red LED off

  B = map(UpDown, UD_neutral,0,0,255); //if down turns blue LED on

}

if (LeftRight >= LR_neutral) {  // checks if controller is right

  G = 0;  //if right turns green LED off

  Y = map(LeftRight, LR_neutral, 1023,0,255); //if right turns yellow LED on

}

else {

  Y = 0; //if left turns yellow off

  G = map(LeftRight, LR_neutral, 0,0,255); //if left turns green on

}

//writes values to LEDs

analogWrite(4,R);

analogWrite(5,Y);

analogWrite(6,B);

analogWrite (7,G);

}

[fortunerobotic]

Robotics Training

In the technologically advanced world of today, robotics has become a crucial sector that is reshaping industries and developing novel answers to difficult problems. Training in robotics has become crucial for both individuals and companies hoping to succeed in this ever-changing field. From novices to experts, systematic robotics instruction gives students the tools they need to efficiently design, program, and manage robotic systems.

The Importance of Robotics Training

Training in robotics helps close the gap between theory and real-world implementation. Understanding robotics is now essential as automation and artificial intelligence become more pervasive in industries including manufacturing, healthcare, agriculture, and logistics.

Types of Robotics Training Programs

Beginner Courses

Advanced Training

Industry-Specific Training

Certifications and Diplomas

Benefits of Robotics Training

Improved Opportunities for Careers:

Experts in robotics are highly sought after in a variety of businesses.

Certification programs enhance employment opportunities and give resumes more legitimacy.

Creativity & Innovation:

Learning robotics inspires students to think creatively and develop ground-breaking technology.

Practical Education:

Training programs have a strong emphasis on hands-on experience, enabling students to construct working robots and work on real-world projects.

Teamwork:

Interdisciplinary collaboration is common in robotics, which promotes communication and teamwork abilities.

Popular Robotics Training Platforms

Coursera and edX

Udemy and Skillshare

VEX Robotics and FIRST Robotics

University Programs

Learning robotics is more than just becoming proficient with technology; it's about getting ready for a time when automation and robots will be a big part of our everyday life. Investing in robotics training can result in rewarding opportunities and a happy career, regardless of whether you're a professional trying to advance your skills or a student hoping to enter the sector. Maintaining your edge with the correct training will enable you to participate in this revolutionary journey as robotics continues to advance.

To know more, click here.

[quartz-components]

Electronics Components #components #electronics #engineering #electrical #arduino #electronic #innovation #design #robotics #electroniccomponents #raspberry #technology #manufacturing #industrial #gaming #school #learningbydoing #solar #bahrain #pc #pcb #budaiya #muhharaq #manama #engineeringforkids #saar #riffa #makersgonnamake #isatown #summerprogram

[creativespot123]

My nano which I bought with my own money:

[adafruit]

QT adapter for Sensirion SEN6x 🔌🌡️

Sensirion just came out with the new SEN6x series of 'everything including the kitchen sink' environmental sensors - and you can pick them up at DigiKey right now

https://www.digikey.com/short/c4tndnd4

We noted that the cable for the 6 series is the same as the SEN5x, BUT power supply requirements differ, so our existing SEN5x adapter won't work

Here's a simple level-shifting breakout that converts to the JST GH cable connector

[learning-robotics]

Mastering Neural Networks: A Deep Dive into Combining Technologies

How Can Two Trained Neural Networks Be Combined?

Introduction

In the ever-evolving world of artificial intelligence (AI), neural networks have emerged as a cornerstone technology, driving advancements across various fields. But have you ever wondered how combining two trained neural networks can enhance their performance and capabilities? Let’s dive deep into the fascinating world of neural networks and explore how combining them can open new horizons in AI.

Basics of Neural Networks

What is a Neural Network?

Neural networks, inspired by the human brain, consist of interconnected nodes or "neurons" that work together to process and analyze data. These networks can identify patterns, recognize images, understand speech, and even generate human-like text. Think of them as a complex web of connections where each neuron contributes to the overall decision-making process.

How Neural Networks Work

Neural networks function by receiving inputs, processing them through hidden layers, and producing outputs. They learn from data by adjusting the weights of connections between neurons, thus improving their ability to predict or classify new data. Imagine a neural network as a black box that continuously refines its understanding based on the information it processes.

Types of Neural Networks

From simple feedforward networks to complex convolutional and recurrent networks, neural networks come in various forms, each designed for specific tasks. Feedforward networks are great for straightforward tasks, while convolutional neural networks (CNNs) excel in image recognition, and recurrent neural networks (RNNs) are ideal for sequential data like text or speech.

Why Combine Neural Networks?

Advantages of Combining Neural Networks

Combining neural networks can significantly enhance their performance, accuracy, and generalization capabilities. By leveraging the strengths of different networks, we can create a more robust and versatile model. Think of it as assembling a team where each member brings unique skills to tackle complex problems.

Applications in Real-World Scenarios

In real-world applications, combining neural networks can lead to breakthroughs in fields like healthcare, finance, and autonomous systems. For example, in medical diagnostics, combining networks can improve the accuracy of disease detection, while in finance, it can enhance the prediction of stock market trends.

Methods of Combining Neural Networks

Ensemble Learning

Ensemble learning involves training multiple neural networks and combining their predictions to improve accuracy. This approach reduces the risk of overfitting and enhances the model's generalization capabilities.

Bagging

Bagging, or Bootstrap Aggregating, trains multiple versions of a model on different subsets of the data and combines their predictions. This method is simple yet effective in reducing variance and improving model stability.

Boosting

Boosting focuses on training sequential models, where each model attempts to correct the errors of its predecessor. This iterative process leads to a powerful combined model that performs well even on difficult tasks.

Stacking

Stacking involves training multiple models and using a "meta-learner" to combine their outputs. This technique leverages the strengths of different models, resulting in superior overall performance.

Transfer Learning

Transfer learning is a method where a pre-trained neural network is fine-tuned on a new task. This approach is particularly useful when data is scarce, allowing us to leverage the knowledge acquired from previous tasks.

Concept of Transfer Learning

In transfer learning, a model trained on a large dataset is adapted to a smaller, related task. For instance, a model trained on millions of images can be fine-tuned to recognize specific objects in a new dataset.

How to Implement Transfer Learning

To implement transfer learning, we start with a pretrained model, freeze some layers to retain their knowledge, and fine-tune the remaining layers on the new task. This method saves time and computational resources while achieving impressive results.

Advantages of Transfer Learning

Transfer learning enables quicker training times and improved performance, especially when dealing with limited data. It’s like standing on the shoulders of giants, leveraging the vast knowledge accumulated from previous tasks.

Neural Network Fusion

Neural network fusion involves merging multiple networks into a single, unified model. This method combines the strengths of different architectures to create a more powerful and versatile network.

Definition of Neural Network Fusion

Neural network fusion integrates different networks at various stages, such as combining their outputs or merging their internal layers. This approach can enhance the model's ability to handle diverse tasks and data types.

Types of Neural Network Fusion

There are several types of neural network fusion, including early fusion, where networks are combined at the input level, and late fusion, where their outputs are merged. Each type has its own advantages depending on the task at hand.

Implementing Fusion Techniques

To implement neural network fusion, we can combine the outputs of different networks using techniques like averaging, weighted voting, or more sophisticated methods like learning a fusion model. The choice of technique depends on the specific requirements of the task.

Cascade Network

Cascade networks involve feeding the output of one neural network as input to another. This approach creates a layered structure where each network focuses on different aspects of the task.

What is a Cascade Network?

A cascade network is a hierarchical structure where multiple networks are connected in series. Each network refines the outputs of the previous one, leading to progressively better performance.

Advantages and Applications of Cascade Networks

Cascade networks are particularly useful in complex tasks where different stages of processing are required. For example, in image processing, a cascade network can progressively enhance image quality, leading to more accurate recognition.

Practical Examples

Image Recognition

In image recognition, combining CNNs with ensemble methods can improve accuracy and robustness. For instance, a network trained on general image data can be combined with a network fine-tuned for specific object recognition, leading to superior performance.

Natural Language Processing

In natural language processing (NLP), combining RNNs with transfer learning can enhance the understanding of text. A pre-trained language model can be fine-tuned for specific tasks like sentiment analysis or text generation, resulting in more accurate and nuanced outputs.

Predictive Analytics

In predictive analytics, combining different types of networks can improve the accuracy of predictions. For example, a network trained on historical data can be combined with a network that analyzes real-time data, leading to more accurate forecasts.

Challenges and Solutions

Technical Challenges

Combining neural networks can be technically challenging, requiring careful tuning and integration. Ensuring compatibility between different networks and avoiding overfitting are critical considerations.

Data Challenges

Data-related challenges include ensuring the availability of diverse and high-quality data for training. Managing data complexity and avoiding biases are essential for achieving accurate and reliable results.

Possible Solutions

To overcome these challenges, it’s crucial to adopt a systematic approach to model integration, including careful preprocessing of data and rigorous validation of models. Utilizing advanced tools and frameworks can also facilitate the process.

Tools and Frameworks

Popular Tools for Combining Neural Networks

Tools like TensorFlow, PyTorch, and Keras provide extensive support for combining neural networks. These platforms offer a wide range of functionalities and ease of use, making them ideal for both beginners and experts.

Frameworks to Use

Frameworks like Scikit-learn, Apache MXNet, and Microsoft Cognitive Toolkit offer specialized support for ensemble learning, transfer learning, and neural network fusion. These frameworks provide robust tools for developing and deploying combined neural network models.

Future of Combining Neural Networks

Emerging Trends

Emerging trends in combining neural networks include the use of advanced ensemble techniques, the integration of neural networks with other AI models, and the development of more sophisticated fusion methods.

Potential Developments

Future developments may include the creation of more powerful and efficient neural network architectures, enhanced transfer learning techniques, and the integration of neural networks with other technologies like quantum computing.

Case Studies

Successful Examples in Industry

In healthcare, combining neural networks has led to significant improvements in disease diagnosis and treatment recommendations. For example, combining CNNs with RNNs has enhanced the accuracy of medical image analysis and patient monitoring.

Lessons Learned from Case Studies

Key lessons from successful case studies include the importance of data quality, the need for careful model tuning, and the benefits of leveraging diverse neural network architectures to address complex problems.

Online Course

I have came across over many online courses. But finally found something very great platform to save your time and money.

1.Prag Robotics_ TBridge

2.Coursera

Best Practices

Strategies for Effective Combination

Effective strategies for combining neural networks include using ensemble methods to enhance performance, leveraging transfer learning to save time and resources, and adopting a systematic approach to model integration.

Avoiding Common Pitfalls

Common pitfalls to avoid include overfitting, ignoring data quality, and underestimating the complexity of model integration. By being aware of these challenges, we can develop more robust and effective combined neural network models.

Conclusion

Combining two trained neural networks can significantly enhance their capabilities, leading to more accurate and versatile AI models. Whether through ensemble learning, transfer learning, or neural network fusion, the potential benefits are immense. By adopting the right strategies and tools, we can unlock new possibilities in AI and drive advancements across various fields.

FAQs

What is the easiest method to combine neural networks?

The easiest method is ensemble learning, where multiple models are combined to improve performance and accuracy.

Can different types of neural networks be combined?

Yes, different types of neural networks, such as CNNs and RNNs, can be combined to leverage their unique strengths.

What are the typical challenges in combining neural networks?

Challenges include technical integration, data quality, and avoiding overfitting. Careful planning and validation are essential.

How does combining neural networks enhance performance?

Combining neural networks enhances performance by leveraging diverse models, reducing errors, and improving generalization.

Is combining neural networks beneficial for small datasets?

Yes, combining neural networks can be beneficial for small datasets, especially when using techniques like transfer learning to leverage knowledge from larger datasets.

[a6y3ap]

new body and added joystick to control the movements

[generativetenebrousautomata]

[ainow]

Arduino Nano Board R3 with CH340 chip

Based on the Arduino platform, the Nano R3 Board CH340 Chip without USB Cable does all the functions of the Uno, but has a smaller footprint.

Using Nano CH340 Soldered Board R3 Chip without USB cable is essential for your small project where you don’t need many pins, but the small size is very important to make it look nice.

In comparison to older versions of Arduino Nano with FTDI USB-Serial Chip, the Nano uses a low-cost USB-Serial Chip.

With the ATmega328 (Arduino Nano R3), the Nano is small, complete, and breadboard-friendly. It has more or less the same functionality as the Arduino Uno but in a different package. In addition to lacking a DC power jack, it uses a Mini-B USB cable instead of a standard one.

This device can be powered by a mini-USB cable, a 6-20V unregulated external power supply (pin 30), or a 5V regulated external power supply (pin 27).

Six PWM I/O are included from a total of 14 digital I/O, eight analog inputs, 16Mhz clock speed, and 32kB flash memory.

Please note:

The CH340 chip might not work directly with some PCs/laptops. You will need some drivers for the CH340 chip.

Arduino nano features :

TTL level serial transceiver ports (RX / TX)

Atmel Atmega328P-AU MCU used

It has a bootloader installed

Supports USB download and power supply

Provides support for external DC power supplies of 5V and 12V

[pepperysart]

Wip animatronic sylveon head that sings :3