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Arduino pulse rate monitor

1. System Overview

A pulse rate monitor measures heartbeats using an optical sensing technique called photoplethysmography (PPG). The system works as follows:

  1. An LED emits light into the skin.

  2. A photodiode detects variations in reflected light.

  3. The signal is amplified and filtered.

  4. The microcontroller processes the signal.

  5. Beats per minute (BPM) are calculated.

  6. The result is displayed or transmitted.

The microcontroller used in this guide is the Arduino Uno, but other compatible boards such as the Arduino Nano can also be used.

2. Required Components

Core Components

  • Arduino Uno

  • Pulse Sensor Amped

  • Breadboard

  • Jumper wires

  • USB cable

Optional Components

  • 16×2 LCD display

  • OLED display (I2C)

  • 10kΩ potentiometer (for LCD contrast)

  • Buzzer (heartbeat indication)

  • External power supply (9V battery)

  • Enclosure case

3. Working Principle of the Pulse Sensor

The Pulse Sensor typically contains:

  • A green LED (light source)

  • A photodiode (light detector)

  • Amplifier circuitry

  • Noise filtering stage

Principle of Operation

When the heart pumps blood:

  • Blood volume in the capillaries increases.

  • Light absorption increases.

  • Reflected light decreases.

  • The photodiode output voltage changes.

This produces a waveform known as a PPG signal, which contains periodic peaks corresponding to heartbeats.

4. Circuit Connections

Pulse Sensor to Arduino

Pulse Sensor Pin Arduino Pin
VCC 5V
GND GND
SIGNAL A0

The signal pin connects to analog input A0.

Optional: 16×2 LCD (Parallel Mode)

LCD Pin Arduino Pin
RS 12
EN 11
D4 5
D5 4
D6 3
D7 2
VSS GND
VDD 5V

5. Signal Processing Logic

The sensor outputs an analog waveform that includes noise. The Arduino must:

  1. Continuously read analog values.

  2. Detect peaks above a defined threshold.

  3. Measure time between successive peaks.

  4. Calculate BPM.

BPM Calculation

If IBI is the inter-beat interval in milliseconds:

BPM = 60000 / IBI

Where:

  • 60000 = milliseconds per minute

  • IBI = time between two detected heartbeats

6. Arduino Code (Basic Version)

const int pulsePin = A0;
int signal;
int threshold = 550;unsigned long lastBeatTime = 0;
unsigned long currentTime;
int BPM = 0;

void setup() {
Serial.begin(9600);
}

void loop() {
signal = analogRead(pulsePin);
currentTime = millis();

if(signal > threshold) {
if(currentTime – lastBeatTime > 300) {
unsigned long IBI = currentTime – lastBeatTime;
lastBeatTime = currentTime;
BPM = 60000 / IBI;
Serial.print(“BPM: “);
Serial.println(BPM);
}
}
}

7. Code Explanation

Threshold

The threshold filters out noise. It must be adjusted depending on:

  • Finger placement

  • Ambient light conditions

  • Individual physiology

Debounce Interval (300 ms)

This prevents double counting. A 300 ms minimum interval limits detection to a maximum of approximately 200 BPM.

8. Adding LCD Display

Include the library:

#include <LiquidCrystal.h>
LiquidCrystal lcd(12, 11, 5, 4, 3, 2);

In setup():

lcd.begin(16, 2);

In loop():

lcd.setCursor(0, 0);
lcd.print("Heart Rate:");
lcd.setCursor(0, 1);
lcd.print(BPM);
lcd.print(" BPM ");

9. Improving Accuracy

1. Moving Average Filtering

BPM = (BPM + previousBPM) / 2;

This smooths fluctuations.

2. Use Interrupt-Based Sampling

Provides more precise timing than polling in the main loop.

3. Use Hardware Timers

Using Timer2 improves sampling stability and reduces jitter.

4. Reduce Ambient Light

Use a finger clip or dark enclosure to block external light interference.

10. Calibration Procedure

  1. Open Serial Monitor

  2. Observe raw analog values

  3. Identify:

    • Resting signal level

    • Peak signal level

  4. Set threshold midway between these values.

Example:

  • Resting value: 520

  • Peak value: 620

  • Threshold: 570

11. Expected Output

Normal adult resting heart rate:

  • 60–100 BPM

Athletes:

  • 40–60 BPM

If readings fluctuate significantly:

  • Check noise filtering

  • Recalibrate threshold

  • Improve finger positioning

12. Troubleshooting

Problem Possible Cause Solution
No reading Wiring issue Check connections
Unstable BPM Noise Improve filtering
Constant zero Threshold too high Lower threshold
Very high BPM Threshold too low Increase threshold
[mai mult...]

Tailscale: The Easiest way to access your Homelab from Anywhere

If you’ve ever tried to access your homelab, NAS, or Proxmox server from outside your house, you probably know the pain. Open ports on the router, deal with NAT, hope your ISP didn’t throw you behind CG-NAT, and then sit there wondering how exposed your services really are.

On top of that, you have to think about dynamic IPs, firewall rules, and whether you just made your setup visible to the whole internet.

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The new AI feature of Kaspersky Premium Antivirus

In the latest Kaspersky Antivirus and broader security suites (especially Kaspersky Premium), AI and machine-learning enhancements have been integrated across multiple protection layers:

  1. AI-Powered Threat Detection (Malware & Zero-Day):
    Kaspersky now uses machine learning models (beyond traditional signatures) to detect evolving malware patterns and suspicious behavior in real time. The AI engine can identify threats without relying solely on known signatures, which helps catch previously unseen malicious files and behaviors.
  2. Advanced AI Phishing and Web Protection:
    The phishing engine incorporates AI to analyse webpage content—including hidden text within images and complex HTML structures—improving detection of scam sites that use obfuscation or mimic legitimate services. In independent testing, Kaspersky Premium scored high in phishing detection (e.g., a 93 % block rate in an AV-Comparatives test).
  3. Behavioral & Contextual Analysis:
    AI looks at how processes behave over time (system changes, login patterns, resource usage) and flags anomalies not resembling typical legitimate activity. This goes beyond classic signature matching and reduces blind spots for novel threats.
  4. SIEM & Enterprise-Level AI Insights (for Pro Users):
    For business users of Kaspersky SIEM, there’s a more advanced AI module that improves alert triage and accounts-compromise detection by learning normal behavior baselines and highlighting deviations—important for enterprise security teams.

 How It feels in daily use

Strengths & Positive Outcomes

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  • Multi-layered protection: Even when malware changes its code to evade detection, behavioral analysis improves the chance of blocking it.

 Drawbacks & Real User Feedback

User Experience Issues

  • AI support can disappoint: Some users criticize Kaspersky’s AI-based support/bot interactions as unhelpful when dealing with product issues—e.g., login problems or account recovery.
  • Feature visibility & control: Community posts indicate that AI icons and automated features appear broadly in the UI with unclear toggles for advanced users, leading some to feel they can’t easily turn off “AI stuff” if they want more control.

 Customer Support Frustrations

  • Independent reviews and forum comments reflect frustration with support responsiveness when hardware or performance issues occur. AI support bots don’t always provide a satisfying resolution path.

Market & Competitive Context

Even before these AI boosts, Kaspersky products consistently scored well in core antivirus tests, web protection, and real-time scanning, and customer reviews are generally positive outside support issues. But Kaspersky’s use of AI is now matched by major competitors like Bitdefender and others who also apply machine learning extensively.

 Overall Assessment

Pros:

  • Strong phishing and malware detection empowered by AI that goes beyond signatures.
  • More adaptive behavior analysis which helps catch sophisticated threats.
  • Enterprise-level AI features give advanced context and anomaly detection.

Cons:

  • UI and AI indicators can feel opaque; some users want clearer control over AI components.
  • Support experiences with AI chat assistants are mixed and sometimes frustrating.
  • Not a silver bullet—AI improves detection but can still produce alerts that need human interpretation.
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Cum oprim monitorul doar cu un simplu click

In loc sa asteptam ca temporizatorul monitorului sa stinga ecranul, putem alege o alta optiune care ne ofera si mai mult control pe acesta. Putem face asta cu un simplu fisier batch care o sa opreasca monitorul nostru doar cu un simplu dublu-click.

Aceasta optiune este foarte placuta deoarece putem pleca rapid de la computer, in loc sa asteptam ca temporizatorul monitorului sa se opreasca. Utilizatorii de laptopuri si tablete pot beneficia si de o durata mai lunga de viata a bateriei. Acest fisier batch opereaza apeland o comanda PowerShell.

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