Short note space

  If you're running Docker containers on a server, monitoring their performance using command-line tools can be time-consuming and difficult to visualize. In this guide, I’ll show you how to monitor your Docker services using Grafana, Prometheus, and cAdvisor—all through a web interface, without relying on CLI tools after setup. You’ll be able to: Track real-time Docker container metrics Visualize system performance through a Grafana dashboard Use Prometheus to scrape and expose container metrics Deploy everything using simple Docker commands Step 1: Run Your Docker Containers Make sure your Docker containers are already running. Here’s a snapshot of my currently active containers: Step 2: Install cAdvisor in a Docker Container To collect container metrics in real time, we’ll run cAdvisor inside a dedicated Docker container. Run the cAdvisor Container: sudo docker run   --volume=/:/rootfs:ro   --volume=/var/run:/var/run:rw   --volume=/sys:/sys:ro   --volume=/var...

  LoRa (Long Range) is a wireless communication technology designed for long-distance, unlicensed frequency, low-power data transmission. It’s widely used in IoT (Internet of Things) applications where devices need to send small amounts of data over several kilometers without relying on Wi-Fi or cellular networks. With its low power consumption and extended range, LoRa is ideal for smart agriculture, environmental monitoring, and remote sensing projects. In this guide, I’ll walk you through my LoRa implementation setup using the Heltec ESP32 V3 boards. If you haven’t already installed the Arduino IDE on your computer, make sure to install it first. it’s essential for uploading code to your LoRa boards. Once the Arduino IDE is successfully installed, it should look like the screenshot below (or similar depending on your version). From there, follow the steps outlined in this tutorial to configure your boards for peer-to-peer communication. For this project, I used two Heltec ESP32 V...

  In this test, I used an ESP32 development board, an ADS1115 16-bit analog-to-digital converter, and an SM-24 geophone sensor to capture seismic signals generated by human footfalls. Rather than relying on an external analog bandpass filter, which often introduces additional electrical noise. I implemented a digital bandpass filter directly on the ESP32 microcontroller. This software-based signal conditioning approach enabled more precise control over frequency filtering and reduced hardware complexity. The filter was designed to pass frequencies in the 5 Hz to 60 Hz range, which corresponds to the dominant frequency band of human footfall vibrations. The complete sensor setup for footfall vibration acquisition is illustrated below: Based on insights from prior research studies, the SM-24 geophone sensor exhibits higher accuracy and signal stability when buried at a depth of approximately 20 centimeters. To maximize seismic wave sensitivity, the sensor in this experiment was embed...

In this case study, I investigate the performance of a 4-phase interleaved boost converter using MATLAB Simulink. This type of DC-DC converter architecture is widely used in high-efficiency power systems due to its improved current sharing and reduced output ripple. Each phase of the converter is symmetrically designed using identical passive components: Inductors: L₁ = L₂ = L₃ = L₄ = 1.5 mH Capacitors: C₁ = C₂ = 200 μF The converter supplies an inductive load that draws a constant output current of 5 A. The input voltage is fixed at 30 V, and the converter is operated at a duty cycle (D) of 0.6. The primary simulation objectives are to analyze: The voltage gain of the converter, The output voltage ripple characteristics, And the phase current sharing behavior under these operating conditions. Simulink design of 4-phase interleaved boost converter Simulink inputs and output of design Through this design and simulation, the following steady state waveforms can be evaluated: Load voltage...

In this project, I used the ESP32 WROOM-32 development board in combination with the Neo-6M GPS module to acquire real-time satellite-based location data. The hardware components were interconnected using hardware serial (UART) to ensure reliable communication between the ESP32 and the GPS module. The wiring setup is illustrated below. Neo-6M GPS Module     |      ESP32 WROOM-32        Vcc      =====>   VIN (3.3V) GND   =====>     GND     TX      =====>    GPIO16     RX     =====>   GPIO17 Snapshot of the NEO-6M GPS Module used in this project: Front View of the NEO-6M GPS Module Backside of the NEO-6M GPS Module Snapshot of the ESP32 WROOM-32 Arduino board used in this project: After the interconnecting these two boards you have to follow following procedures. Install TinyGPS++ library: Open Ardui...