Smart Receptionist with smart lock system is mainly designed and developed for security system. This smart security system is used to see a visitor when the main door of the office or Home is closed. Wi-Fi (Short for Wireless Fidelity) is a wireless technology that uses radio frequency to transmit data through the air. Wi-Fi has initial speeds of 1mbps to 2mbps. Wi-Fi transmits data in the frequency band of 2.4 GHz. It implements the concept of frequency division multiplexing technology. Range of Wi-Fi technology is 40-300 feet.

The controlling device of the whole system is a raspberry pi processor; it has an inbuilt Ethernet. LCD display, Pi-camera, servo Motor is interfaced to the raspberry pi processor. In this system whenever a person presses the calling bell, image of the person is captured by the Pi-camera and it will send the raspberry pi then the processor mails the person image to owner. Once the image is uploaded user need to check the image and take decision accordingly like access/denies through mail. If the person denies the permission to open the door, door will remain locked and the system will give buzzer alert. The status of the project is displayed on LCD display. Raspberry Pi is the controlling section in our project. We program it using Python Language and the OS used here is Linux.

Objectives of the project:

• Buzzer alert for unknown persons.
• Door access/denies from MAIL.
• Using Pi camera to capture the image.
• Servo motor-based door lock/unlock.
• Using raspberry pi ZERO W to achieve this task.

The major building blocks of the project are:

• Power Supply. u Raspberry pi ZERO W.
• SD card. u Pi camera. u Buzzer.
• LCD display.
• Servo motor.

Software’s used:

• Raspbian OS.
• Python programming language.
• Express SCH for Circuit design.
• IOT technology.

Block diagram:

video:

This system to detect the object using machine learning algorithm which is written in embedded Linux platform. All images like table, chair, bottle…..we will prepared dataset and stored into the raspberry pi and trained for preprocessing. This system uses Ultrasonic sensor to detect the nearest obstacle and announce the voice through speaker.

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It mainly focuses on image processing; video capture and analysis including features like face detection and object detection.

In this project we are using python language to write the program. Python is an interpreter, object-oriented, high-level programming language with dynamic semantics. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse.

The controlling device of the whole unit is a Raspberrypi3 processor to which input and output modules are interfaced. Raspberrypi3 has Broadcom BCM2837B0, Cortex-A53 (ARMv8) 1GB LPDDR2 SDRAM 1.4GHz 64-bit quad-core processor, dual-band wireless LAN, Bluetooth 4.2/BLE, faster Ethernet, and Power-over-Ethernet support (with separate PoE HAT).

The processor is programmed in PYTHON language which intelligently performs the specific task. Here, the processor gets input from the sensor and pi camera attached to the raspberry pi. This input is processed by processor and announce the voice appropriately.

The major features of this project are:

• 1. Automatic obstacle sensing.
  2. Automatic object detection and classification.
  3. Using Raspberrypi3 processor to achieve this task.
  4. Using pi camera and openCV machine learning to detect the objects
  5. Using Ultrasonic sensor to detect the obstacle.
  6. Voice alerts for obstacle detection.
  7. Voice announce for object detection.

The major building blocks of this project:

• Battery power supply.
• Raspberry Pi3 Processor.
• SD Card.
• Ultrasonic sensor.
• Pi camera.
• LED indicators.

Software’s used in the project:

1. Python Programming.
2. OpenCV MACHINE LEARNING Image processing.
3. Embedded Linux operating system.
4. Express SCH for Circuit design.

Block Diagram:

video:

The objectives of the project are:

• To design and implement of fingerprint and face authenticated based real time security system.
• To design and implement face authentication of captured image using pi camera by Python platform on Raspberry Pi.
• The captured image is compared and verified with the database, if found matching then system gives the Green LED indications.
• In case failure of face authentication then system gives the RED led indication.
• To design and implement fingerprint authentication using finger print module by python platform and raspberry.
• By using two buttons we can enroll/delete the finger prints.
• This system also give the red, green led indications for fingerprint access and denied.
• Visible alerts using LCD display.

The major building blocks of the project are:

• Power supply.
• Raspberry Pi
• Pi Camera
• Finger print module
• LCD display.
• Two push buttons.
• Red, Green LEDs.

Software’s used:

1. Embedded Linux programming.
2. Express SCH for Circuit design.

Block diagram:

video: