⚡ Tech Pioneers ⚡

// Advanced IoT System with MQTT and Cloud Integration import paho.mqtt.client as mqtt import json from azure.iot.device import IoTHubDeviceClient class IndustrialIoTSystem: def __init__(self, connection_string): self.client = IoTHubDeviceClient.create_from_connection_string(connection_string) self.mqtt_client = mqtt.Client() self.sensor_data = {} def setup_sensor_network(self): # Configure distributed sensor array sensors = [ {'id': 'temp_01', 'type': 'temperature', 'location': 'factory_floor'}, {'id': 'humid_01', 'type': 'humidity', 'location': 'warehouse'}, {'id': 'pressure_01', 'type': 'pressure', 'location': 'pipeline'} ] return sensors async def real_time_analytics(self, data_stream): # Advanced data processing and anomaly detection for data_point in data_stream: processed_data = self.apply_machine_learning(data_point) if self.detect_anomaly(processed_data): await self.trigger_alert(processed_data) await self.send_to_cloud(processed_data)

// Advanced Drone Control with ROS Integration #include <ros/ros.h> #include <mavros_msgs/CommandBool.h> #include <geometry_msgs/PoseStamped.h> class AutonomousDrone { private: ros::NodeHandle nh; ros::Publisher local_pos_pub; ros::Subscriber state_sub, local_pos_sub; ros::ServiceClient arming_client, set_mode_client; public: AutonomousDrone() { local_pos_pub = nh.advertise<geometry_msgs::PoseStamped> ("mavros/setpoint_position/local", 10); state_sub = nh.subscribe<mavros_msgs::State> ("mavros/state", 10, &AutonomousDrone::state_cb, this); } void autonomous_mission() { // Advanced waypoint navigation with obstacle avoidance std::vector<geometry_msgs::PoseStamped> waypoints = generate_flight_path(); for (const auto& waypoint : waypoints) { navigate_to_waypoint(waypoint); perform_sensor_scan(); if (detect_obstacles()) { execute_avoidance_maneuver(); } } } void swarm_coordination() { // Multi-drone swarm intelligence broadcast_position_to_swarm(); receive_swarm_data(); calculate_formation_flight(); } };

# Advanced Raspberry Pi Cluster Computing import RPi.GPIO as GPIO import multiprocessing as mp from mpi4py import MPI import docker class PiClusterManager: def __init__(self, cluster_nodes): self.comm = MPI.COMM_WORLD self.rank = self.comm.Get_rank() self.size = self.comm.Get_size() self.docker_client = docker.from_env() def distributed_computing(self, task_data): # Distribute computational tasks across Pi cluster if self.rank == 0: # Master node - distribute work chunks = self.split_data(task_data, self.size) for i in range(1, self.size): self.comm.send(chunks[i], dest=i, tag=11) result = self.process_chunk(chunks[0]) else: # Worker nodes - process assigned chunk chunk = self.comm.recv(source=0, tag=11) result = self.process_chunk(chunk) self.comm.send(result, dest=0, tag=22) def edge_ai_deployment(self): # Deploy AI models at the edge container = self.docker_client.containers.run( "tensorflow/tensorflow:latest-gpu", "python /app/edge_inference.py", detach=True, device_requests=[docker.types.DeviceRequest(count=-1, capabilities=['gpu'])] ) return container def industrial_automation(self): # Professional industrial control systems GPIO.setmode(GPIO.BCM) self.setup_industrial_io() self.implement_safety_protocols() self.start_real_time_control_loop()

# Advanced Deep Learning with PyTorch import torch import torch.nn as nn import torchvision.transforms as transforms from transformers import GPTNeoForCausalLM, GPT2Tokenizer class AdvancedNeuralNetwork(nn.Module): def __init__(self, input_size, hidden_layers, num_classes): super(AdvancedNeuralNetwork, self).__init__() # Transformer-based architecture self.transformer = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model=512, nhead=8), num_layers=6 ) # Attention mechanism self.attention = nn.MultiheadAttention(embed_dim=512, num_heads=8) # Residual connections and layer normalization self.layer_norm = nn.LayerNorm(512) self.dropout = nn.Dropout(0.1) # Output layers self.classifier = nn.Sequential( nn.Linear(512, 256), nn.ReLU(), nn.Dropout(0.2), nn.Linear(256, num_classes) ) def forward(self, x): # Advanced forward pass with attention x = self.transformer(x) attended_x, _ = self.attention(x, x, x) x = self.layer_norm(x + attended_x) x = self.dropout(x) output = self.classifier(x.mean(dim=1)) return output # Production deployment pipeline def deploy_to_production(model, model_name): # Convert to ONNX for cross-platform deployment torch.onnx.export(model, dummy_input, f"{model_name}.onnx") # Deploy to cloud inference endpoint deploy_to_aws_sagemaker(model_name) setup_monitoring_and_logging()