🥉 3rd Place IEEE Competition Winner

Transform your motion data into actionable health insights using advanced AI algorithms.

Tech Stack:

class MotionSenseAI:
    def __init__(self):
        self.model = load_trained_model()
    
    def analyze_motion(self, sensor_data):
        insights = self.model.predict(sensor_data)
        return generate_health_recommendations(insights)

Features:

• 🏃‍♂️ Real-time activity recognition
• 📊 Health insights dashboard
• 🎯 Personalized recommendations
• 📱 Cross-platform mobile app

🏅 6th Place Crown Prince Award

Connecting citizens with decision makers through digital democracy.

Tech Stack:

class SawtakApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Sawtak - Your Voice Matters',
      home: CivicEngagementDashboard(),
    );
  }
}

Features:

• 🏛️ Government integration
• 📝 Digital voting system
• 💬 Citizen feedback portal
• 📊 Real-time analytics

Native Android applications with advanced features

Tech Stack:

public class WeatherService {
    private WeatherAPI weatherAPI;
    
    public WeatherData getCurrentWeather(String location) {
        return weatherAPI.fetchWeatherData(location);
    }
    
    public void setupSmartNotifications() {
        // Advanced notification system
    }
}

Features:

• 🌡️ Real-time weather data
• 📞 Smart call management
• 🔔 Intelligent notifications
• 🎨 Material Design UI

Interactive Bluetooth-enabled games

Tech Stack:

class BluetoothGameController {
  BluetoothConnection? connection;
  
  Future<void> connectToDevice() async {
    connection = await BluetoothConnection.toAddress(deviceAddress);
    startGameSession();
  }
  
  void handleJoystickInput(JoystickData data) {
    // Process gaming input
  }
}

Features:

• 🎮 Joystick control interface
• 💬 Real-time chat system
• 🔗 Bluetooth connectivity
• 🏆 Multiplayer gaming

Advanced processor architecture in Verilog

Features:

• ⚡ Dual-issue execution engine
• 🔄 Out-of-order processing
• 🧠 Branch prediction unit
• 💾 Advanced cache optimization

module dual_issue_processor(
    input clk, rst,
    input [31:0] instruction1, instruction2,
    output [31:0] result1, result2,
    output reg pipeline_stall
);

// Instruction decode and dispatch logic
always @(posedge clk) begin
    if (!pipeline_stall) begin
        // Dual instruction processing
        dispatch_unit(instruction1, instruction2);
    end
end

endmodule

IoT-based weather-aware garden automation

Features:

• 🌦️ Weather API integration
• 💧 Smart water management
• 📊 Real-time monitoring
• 🔌 IoT connectivity

#include <stdio.h>
#include <wifi.h>
#include <sensors.h>

void smartIrrigation() {
    float rainfall_prob = getWeatherData();
    float soil_moisture = readSoilSensor();
    
    if (rainfall_prob < RAIN_THRESHOLD && 
        soil_moisture < MOISTURE_THRESHOLD) {
        activateIrrigation();
        logActivity("Irrigation activated");
    }
}

Secure client-server medical data management

Tech Stack:

<?php

class MedicalController extends Controller {
    public function secureDataTransfer(Request $request) {
        $encryptedData = encrypt($request->medicalData);
        
        // TCP secure transmission
        return response()->json([
            'status' => 'secure_transfer_complete',
            'data' => $encryptedData
        ]);
    }
    
    public function generateHealthReport($patientId) {
        return HealthReportService::generate($patientId);
    }
}

Features:

• 🔐 End-to-end encryption
• 👨‍⚕️ Multi-role authentication
• 📊 Health analytics dashboard
• 🏥 Hospital integration

NLP-powered conversational AI from scratch

Tech Stack:

class IntelligentChatbot:
    def __init__(self):
        self.nlp_model = self.load_trained_model()
        self.context_manager = ContextManager()
    
    def process_message(self, user_input):
        intent = self.classify_intent(user_input)
        context = self.context_manager.get_context()
        
        response = self.generate_response(intent, context)
        self.context_manager.update_context(user_input, response)
        
        return response
    
    def continuous_learning(self, feedback):
        self.retrain_model(feedback)

Features:

• 🧠 Advanced NLP processing
• 💬 Context-aware conversations
• 📈 Continuous learning
• 🎯 Intent classification