#iot tutorial

[tutorialwithexample]

Navigating the IoT Landscape: A Practical Tutorial for Developers and Enthusiasts

Embarking on the journey into the world of the Internet of Things (IoT) might seem daunting, but fear not – our IoT tutorial is here to guide you every step of the way. Let's unravel the mysteries and explore the fascinating realm of connected devices!

Understanding the Basics: Start by grasping the fundamental concepts of IoT. Learn how everyday objects become 'smart' with sensors and the ability to communicate over the internet.

Hands-On Experience: Our tutorial isn't just theoretical; it's hands-on. Dive into practical examples that showcase the implementation of IoT in real-life scenarios. Connect devices, gather data, and witness the magic unfold.

Building Your Knowledge: Progress from the basics to advanced topics. Discover how IoT is revolutionizing industries, from smart homes to healthcare and beyond. Gain insights into the future of technology.

Why Choose TAE's Tutorial: Trust Tutorial and Example (TAE) as your go-to source for a reliable and comprehensive IoT tutorial. Our expert-led guide ensures a smooth learning experience, empowering you to navigate the IoT landscape confidently.

Ready to embark on your IoT journey? Head over to Tutorial and Example's IoT Tutorial and unlock the potential of the Internet of Things today!

[jtpoint]

Unlock the potential of Robotic Process Automation (RPA) with our tutorial. From basics to advanced techniques, discover a step-by-step guide, empowering you to streamline processes and enhance efficiency effortlessly.

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Iam paulalice working for theredditblog as PR consultant.With more than 6 year’s experience in PR and Digital Industry,helping teams to achieve goals by streamlining the process.

[greatonlinetrainingsposts]

SAS and IoT: Transforming Real-Time Data Analytics

The Internet of Things (IoT) is transforming industries by generating massive volumes of real-time data. From smart devices to industrial sensors, IoT provides continuous data streams that businesses must analyze efficiently. SAS tutorial online resources help professionals utilize SAS programming tutorials to extract insights from IoT-generated data.

This article explores how SAS tutorials for beginners are helping businesses process IoT data effectively and make real-time decisions.

What is IoT and Why is It Important?

IoT refers to interconnected devices that collect and exchange data in real time. This data helps businesses optimize operations, improve efficiency, and enhance customer experiences. However, managing and analyzing this data requires advanced analytics tools, and SAS is a leading solution for IoT data processing.

The integration of SAS tutorials with IoT allows businesses to:

Monitor operations in real time.

Use predictive analytics to anticipate system failures.

Optimize business processes using AI-driven insights.

With the increasing reliance on IoT, learning SAS programming tutorials focused on IoT analytics is crucial for data professionals.

How SAS is Used for IoT Data Analytics

Processing IoT data requires handling massive datasets in real time. SAS provides powerful tools to analyze IoT data efficiently.

Real-Time Data Processing

SAS captures and processes data streams as they are generated.

Businesses can make real-time adjustments based on live analytics.

Predictive Maintenance

IoT sensors detect equipment failures before they happen.

SAS analyzes sensor data to predict when maintenance is needed.

Machine Learning for IoT

AI-powered SAS tools analyze IoT data to optimize business strategies.

Machine learning improves predictive modeling for better decision-making.

Cloud Integration for Scalability

SAS integrates with cloud platforms like AWS and Azure to manage large-scale IoT data processing.

With the rise of IoT applications, businesses are increasingly investing in SAS tutorial online training to build expertise in IoT analytics.

Industries Benefiting from SAS in IoT

IoT and SAS are transforming several industries:

Smart Cities – Traffic monitoring, energy optimization.

Manufacturing – Predictive maintenance and supply chain management.

Healthcare – Remote patient monitoring and predictive diagnostics.

Automotive – Connected vehicles and AI-driven safety systems.

With SAS tutorials for beginners, professionals can learn how to apply SAS analytics in real-world IoT scenarios.

Challenges of IoT Data Analytics with SAS

While SAS is a powerful tool for IoT data processing, businesses face several challenges:

Data Overload – IoT generates enormous datasets that require optimized SAS models.

Security Risks – IoT devices are vulnerable to cyber threats, requiring encrypted data processing.

Integration Complexity – Connecting IoT platforms with SAS demands seamless integration strategies.

Businesses can mitigate these challenges by following best practices outlined in SAS programming tutorials and implementing secure analytics frameworks.

Future of SAS and IoT Analytics

As IoT adoption expands, SAS will continue to enhance its real-time analytics capabilities. Professionals skilled in SAS tutorial online training will be in high demand as businesses seek experts to manage IoT data effectively.

For those looking to specialize in IoT analytics, our online training programs offer expert-led SAS programming tutorial courses to help professionals gain hands-on experience.

[bob3160]

The Dark Side of Convenience

[electricalinsightsdaily]

MPU-6050: Features, Specifications & Important Applications

The MPU-6050 is a popular Inertial Measurement Unit (IMU) sensor module that combines a gyroscope and an accelerometer. It is commonly used in various electronic projects, particularly in applications that require motion sensing or orientation tracking.

Features of MPU-6050

The MPU-6050 is a popular Inertial Measurement Unit (IMU) that combines a 3-axis gyroscope and a 3-axis accelerometer in a single chip.

Here are the key features of the MPU-6050:

Gyroscope:

3-Axis Gyroscope: Measures angular velocity around the X, Y, and Z axes. Provides data on how fast the sensor is rotating in degrees per second (°/s).

Accelerometer:

3-Axis Accelerometer: Measures acceleration along the X, Y, and Z axes. Provides information about changes in velocity and the orientation of the sensor concerning the Earth's gravity.

Digital Motion Processor (DMP):

Integrated DMP: The MPU-6050 features a Digital Motion Processor that offloads complex motion processing tasks from the host microcontroller, reducing the computational load on the main system.

Communication Interface:

I2C (Inter-Integrated Circuit): The MPU-6050 communicates with a microcontroller using the I2C protocol, making it easy to interface with a variety of microcontrollers.

Temperature Sensor:

Onboard Temperature Sensor: The sensor includes an integrated temperature sensor, providing information about the ambient temperature.

Programmable Gyroscope and Accelerometer Range:

Configurable Sensitivity: Users can adjust the full-scale range of the gyroscope and accelerometer to suit their specific application requirements.

Low Power Consumption:

Low Power Operation: Designed for low power consumption, making it suitable for battery-powered and energy-efficient applications.

Read More: MPU-6050

[hacks260d]

DIY ESP-01 Temperature Monitoring Using Wi-Fi, MQTT, and Node-Red | #sma...

[virtualizationhowto]

Microk8s vs k3s: Lightweight Kubernetes distribution showdown

Microk8s vs k3s: Lightweight Kubernetes distribution showdown #homelab #kubernetes #microk8svsk3scomparison #lightweightkubernetesdistributions #k3sinstallationguide #microk8ssnappackagetutorial #highavailabilityinkubernetes #k3s #microk8s #portainer

Especially if you are into running Kubernetes in the home lab, you may look for a lightweight Kubernetes distribution. Two distributions that stand out are Microk8s and k3s. Let’s take a look at Microk8s vs k3s and discover the main differences between these two options, focusing on various aspects like memory usage, high availability, and k3s and microk8s compatibility. Table of contentsWhat is…

[quartz-components]

How to Use AHT10 High Precision Digital Temperature & Humidity Sensor with Arduino

Looking to measure temperature and humidity with high accuracy using Arduino? The AHT10 sensor is a compact, I2C-based module that provides reliable data, making it perfect for IoT projects, weather stations, and smart home automation.

What You’ll Learn: ✔️ How the AHT10 sensor works ✔️ Wiring it to an Arduino board ✔️ Writing & uploading the code to get readings ✔️ Tips for stable and accurate measurements

What You Need:

AHT10 Temperature and Humidity Sensor Module

Arduino Nano

0.96 inch SSD1306 OLED Display (128x64, I2C)

Breadboard

Connecting/Jumper Wires

Arduino Nano Cable

Download the Code & Library Arduino AHT10 Temperature and Humidity Sensor Module

Watch the full tutorial on YouTube:

Follow for more DIY electronics tutorials & Arduino projects!

[techaircraft]

🚀🔧 Dive into the World of Embedded Systems with TechAircraft! 🔧🚀

Are you fascinated by the technology that powers everything from smart gadgets to automotive systems? At TechAircraft, we explore the cutting-edge realm of embedded systems, where hardware meets software to create innovative solutions. Our comprehensive resources, tutorials, and expert insights are designed to help you master this exciting field.

Whether you're a beginner looking to get started or an experienced engineer aiming to enhance your skills, we have something for everyone! Join us as we decode complex concepts, share industry trends, and provide hands-on projects that will elevate your expertise.

Let’s build the future together! 🌟

TechAircraft #EmbeddedSystems #Innovation #TechTrends #Engineering #IoT #SmartTechnology #LearnToCode #FutureTech #TechCommunity #TechEducation #STEM #Electronics

[theredditblog]

https://www.theredditblog.com/

[govindhtech]

Intel VTune Profiler For Data Parallel Python Applications

Intel VTune Profiler tutorial

This brief tutorial will show you how to use Intel VTune Profiler to profile the performance of a Python application using the NumPy and Numba example applications.

Analysing Performance in Applications and Systems

For HPC, cloud, IoT, media, storage, and other applications, Intel VTune Profiler optimises system performance, application performance, and system configuration.

Optimise the performance of the entire application not just the accelerated part using the CPU, GPU, and FPGA.

Profile SYCL, C, C++, C#, Fortran, OpenCL code, Python, Google Go, Java,.NET, Assembly, or any combination of languages can be multilingual.

Application or System: Obtain detailed results mapped to source code or coarse-grained system data for a longer time period.

Power: Maximise efficiency without resorting to thermal or power-related throttling.

VTune platform profiler

It has following Features.

Optimisation of Algorithms

Find your code’s “hot spots,” or the sections that take the longest.

Use Flame Graph to see hot code routes and the amount of time spent in each function and with its callees.

Bottlenecks in Microarchitecture and Memory

Use microarchitecture exploration analysis to pinpoint the major hardware problems affecting your application’s performance.

Identify memory-access-related concerns, such as cache misses and difficulty with high bandwidth.

Inductors and XPUs

Improve data transfers and GPU offload schema for SYCL, OpenCL, Microsoft DirectX, or OpenMP offload code. Determine which GPU kernels take the longest to optimise further.

Examine GPU-bound programs for inefficient kernel algorithms or microarchitectural restrictions that may be causing performance problems.

Examine FPGA utilisation and the interactions between CPU and FPGA.

Technical summary: Determine the most time-consuming operations that are executing on the neural processing unit (NPU) and learn how much data is exchanged between the NPU and DDR memory.

In parallelism

Check the threading efficiency of the code. Determine which threading problems are affecting performance.

Examine compute-intensive or throughput HPC programs to determine how well they utilise memory, vectorisation, and the CPU.

Interface and Platform

Find the points in I/O-intensive applications where performance is stalled. Examine the hardware’s ability to handle I/O traffic produced by integrated accelerators or external PCIe devices.

Use System Overview to get a detailed overview of short-term workloads.

Multiple Nodes

Describe the performance characteristics of workloads involving OpenMP and large-scale message passing interfaces (MPI).

Determine any scalability problems and receive suggestions for a thorough investigation.

Intel VTune Profiler

To improve Python performance while using Intel systems, install and utilise the Intel Distribution for Python and Data Parallel Extensions for Python with your applications.

Configure your Python-using VTune Profiler setup.

To find performance issues and areas for improvement, profile three distinct Python application implementations. The pairwise distance calculation algorithm commonly used in machine learning and data analytics will be demonstrated in this article using the NumPy example.

The following packages are used by the three distinct implementations.

Numpy Optimised for Intel

NumPy’s Data Parallel Extension

Extensions for Numba on GPU with Data Parallelism

Python’s NumPy and Data Parallel Extension

By providing optimised heterogeneous computing, Intel Distribution for Python and Intel Data Parallel Extension for Python offer a fantastic and straightforward approach to develop high-performance machine learning (ML) and scientific applications.

Added to the Python Intel Distribution is:

Scalability on PCs, powerful servers, and laptops utilising every CPU core available.

Assistance with the most recent Intel CPU instruction sets.

Accelerating core numerical and machine learning packages with libraries such as the Intel oneAPI Math Kernel Library (oneMKL) and Intel oneAPI Data Analytics Library (oneDAL) allows for near-native performance.

Tools for optimising Python code into instructions with more productivity.

Important Python bindings to help your Python project integrate Intel native tools more easily.

Three core packages make up the Data Parallel Extensions for Python:

The NumPy Data Parallel Extensions (dpnp)

Data Parallel Extensions for Numba, aka numba_dpex

Tensor data structure support, device selection, data allocation on devices, and user-defined data parallel extensions for Python are all provided by the dpctl (Data Parallel Control library).

It is best to obtain insights with comprehensive source code level analysis into compute and memory bottlenecks in order to promptly identify and resolve unanticipated performance difficulties in Machine Learning (ML),  Artificial Intelligence ( AI), and other scientific workloads. This may be done with Python-based ML and AI programs as well as C/C++ code using Intel VTune Profiler. The methods for profiling these kinds of Python apps are the main topic of this paper.

Using highly optimised Intel Optimised Numpy and Data Parallel Extension for Python libraries, developers can replace the source lines causing performance loss with the help of Intel VTune Profiler, a sophisticated tool.

Setting up and Installing

1. Install Intel Distribution for Python

2. Create a Python Virtual Environment

   python -m venv pyenv

   pyenv\Scripts\activate

3. Install Python packages

   pip install numpy

   pip install dpnp

   pip install numba

   pip install numba-dpex

   pip install pyitt

Make Use of Reference Configuration

The hardware and software components used for the reference example code we use are:

Software Components:

dpnp 0.14.0+189.gfcddad2474

mkl-fft 1.3.8

mkl-random 1.2.4

mkl-service 2.4.0

mkl-umath 0.1.1

numba 0.59.0

numba-dpex 0.21.4

numpy 1.26.4

pyitt 1.1.0

Operating System:

Linux, Ubuntu 22.04.3 LTS

CPU:

Intel Xeon Platinum 8480+

GPU:

Intel Data Center GPU Max 1550

The Example Application for NumPy

Intel will demonstrate how to use Intel VTune Profiler and its Intel Instrumentation and Tracing Technology (ITT) API to optimise a NumPy application step-by-step. The pairwise distance application, a well-liked approach in fields including biology, high performance computing (HPC), machine learning, and geographic data analytics, will be used in this article.

Summary

The three stages of optimisation that we will discuss in this post are summarised as follows:

Step 1: Examining the Intel Optimised Numpy Pairwise Distance Implementation: Here, we’ll attempt to comprehend the obstacles affecting the NumPy implementation’s performance.

Step 2: Profiling Data Parallel Extension for Pairwise Distance NumPy Implementation: We intend to examine the implementation and see whether there is a performance disparity.

Step 3: Profiling Data Parallel Extension for Pairwise Distance Implementation on Numba GPU: Analysing the numba-dpex implementation’s GPU performance

Boost Your Python NumPy Application

Intel has shown how to quickly discover compute and memory bottlenecks in a Python application using Intel VTune Profiler.

Intel VTune Profiler aids in identifying bottlenecks’ root causes and strategies for enhancing application performance.

It can assist in mapping the main bottleneck jobs to the source code/assembly level and displaying the related CPU/GPU time.

Even more comprehensive, developer-friendly profiling results can be obtained by using the Instrumentation and Tracing API (ITT APIs).

Read more on govindhtech.com

[bob3160]

The Rise of Edge Computing - It's Like Having a Mini Data Center in Your...

[electricalinsightsdaily]

HC-05 Bluetooth Module: A Comprehensive Guide

The HC-05 Bluetooth module is a widely used and versatile wireless communication module that enables Bluetooth connectivity in electronic projects. Manufactured by various suppliers, this module is popular among hobbyists, engineers, and developers for its ease of use, cost-effectiveness, and compatibility with microcontrollers like Arduino.

Below are some key features and details about the HC-05 Bluetooth module:

Key Features HC-05 Bluetooth Module:

Bluetooth Standard:

The HC-05 Bluetooth module operates on Bluetooth version 2.0 + EDR (Enhanced Data Rate), supporting reliable and efficient wireless communication.

Operating Modes:

The module can operate in both Master and Slave modes. In Slave mode, it can pair with other Bluetooth devices, while in Master mode, it can initiate connections.

Communication Range:

The HC-05 is classified as a Class 2 Bluetooth device, providing a communication range of approximately 10 meters (33 feet). This makes it suitable for short to medium-range applications.

Serial Communication:

It communicates with other devices using a serial communication interface, making it compatible with microcontrollers like Arduino. The module typically supports standard baud rates like 9600 bps.

AT Command Configuration:

The HC-05 Bluetooth module can be configured using AT commands, allowing users to customize various parameters such as the device name, pairing code, and operating mode.

Voltage Compatibility:

The module operates within a voltage range of 3.6V to 6V, making it compatible with a variety of power sources.

Security Features:

The HC-05 supports basic security features, including the ability to set a PIN code for pairing and configuring security modes.

LED Indicator:

Many HC-05 modules have an onboard LED indicator that provides visual feedback on the pairing status and communication activity.

HC-05 Bluetooth Module Pinout Configuration

HC-05 Bluetooth module stands out as a compact marvel, bridging the gap between traditional wired connections and the boundless realm of wireless communication. If you’re venturing into the exciting domain of IoT projects, robotics, or any application where wireless connectivity is paramount, understanding the HC-05 Bluetooth module pinout configuration is essential.

Read More: HC-05 Bluetooth Module