Part 1: Coding Challenge - Wall Validation Problem Statement Develop a function to validate a "wall" structure composed of 2D video tiles. The function receives a list of integers representing the (x, y) top-left coordinates of fixed-size 1x1 tiles. Validation Criteria The function must return true if all the following conditions are met, and false otherwise: 1. Non-Empty: The wall contains at least one tile. 2. Origin: The shape starts at coordinate [0,0]. 3. Shape: The tiles form a solid square or rectangle with no holes. 4. Uniqueness: No tiles overlap (coordinates must be unique). Input/Output Examples * Valid: [[0,0], [0,1], [1,0], [1,1]] (2x2 Square) * Valid: [[1,1], [0,1], [0,0], [1,0]] (Unsorted input is acceptable) * Valid: [[0,0], [0,1]] (1x2 Rectangle) * Invalid: [[0,0], [1,1]] (Contains holes) * Invalid: [[0,0], [0,1], [1,0]] (Not a perfect rectangle) * Invalid: [[0,0], [0,1], [0,1], [1,1]] (Contains overlaps) *** # Part 2: System Design - Camera Detection Events Role: Full Stack Engineer Data Context The system processes batched detection events from cameras running AI algorithms. * Event Data: Camera hash (string), Detection type (Person, car, bicycle), Bounding Box (startX, startY, endX, endY), Timestamp. * Existing Camera Metadata: Camera hash, Camera name, Last seen time. Backend Requirements Design an architecture to ingest and process detection data with the following considerations: * Ingestion: Handle batched events sent from cameras (e.g., every second). * Storage: Persist detection events. * Architecture: Define communication protocols, redundancy strategies, and scaling capabilities. Frontend Requirements Design the following user interfaces: 1. Landing Page (Camera List) * List all cameras in the system. * Display "Online" status based on last_seen_time (Online if seen < 30 seconds ago). * Handle dynamic updates (cameras added/removed). * Link to the Daily Activity page. 2. Camera Daily Activity Page * Display detection analytics for a specific camera on a specific day. * Required Metrics/Visualizations: * Timestamps for the first and last detection of each type (person/car/bike). * Distribution of detections per type throughout the day. * Concurrency analysis (e.g., identifying times when at least 4 people were detected simultaneously).