Eclipse

An ecosystem of camera and access control modules offers a revolutionary approach to managing access to spaces, providing enhanced flow, granular control, and unprecedented security levels. This advanced system leverages cutting-edge technologies to create a seamless and highly efficient access control environment. The Eclipse A1 and A2 are two tiers of the same Raspberry Pi powered cutting edge system designed to enhance authentication and authorization processes. One of the primary benefits of this ecosystem is its ability to enable controlled access with increased flow. Traditional access control systems often create bottlenecks, as individuals need to present physical credentials such as key cards or badges. In contrast, an integrated camera and access control system utilizes facial recognition and other biometric technologies to streamline the entry process. This allows authorized individuals to move through access points swiftly without the need for manual verification, significantly reducing wait times and improving overall efficiency. In summary, an ecosystem of camera and access control modules transforms access management by enabling controlled access with increased flow, offering granular control over permissions, and delivering security at unprecedented ranges. This advanced solution enhances operational efficiency, security, and adaptability, making it an ideal choice for modern, high-security environments.

UN SUSTAINABILITY DEVELOPMENT GOALS

Empowered and Driven:
Delivering Solutions for Global Issues

Reducing the usage of plastic cards for authentication in business and educational settings offers numerous environmental and operational benefits. Firstly, by minimizing reliance on plastic cards, we significantly cut down on the demand for their production. This, in turn, reduces the consumption of raw materials, such as petroleum, which is a key component in plastic manufacturing. Consequently, the environmental impact associated with extracting and processing these materials is also diminished.

Additionally, the transportation of plastic cards from manufacturing facilities to end-users involves considerable logistics, including packaging, shipping, and distribution. By decreasing the need for physical cards, we lessen the carbon footprint associated with these transportation activities. This reduction in transportation not only saves energy but also decreases greenhouse gas emissions, contributing to a more sustainable environment.

Furthermore, the lifecycle of plastic cards involves several stages, including production, usage, and eventual disposal. Once these cards are no longer needed, they often end up in waste streams, requiring processing for recycling. Recycling plastic cards is a complex and energy-intensive process, involving sorting, cleaning, and reprocessing the material into usable forms. By reducing the initial production of plastic cards, we alleviate the burden on recycling facilities, thereby conserving energy and resources required for recycling operations.

In educational and business environments, transitioning to digital authentication methods, such as mobile apps or biometric systems, can enhance security and efficiency. Digital solutions are typically more flexible and can be easily updated or revoked without the need for physical replacements. This reduces administrative overhead and lowers the overall cost of managing authentication systems.

In summary, reducing the use of plastic cards for authentication purposes leads to a decrease in material consumption, transportation emissions, and the complexities of recycling processes. Simultaneously, it fosters a more secure, efficient, and sustainable approach to authentication in both business and educational settings.

DESIGN STORY

Challenges

Hardware Challenges: 1. Performance Limitations: - Raspberry Pi: While versatile, the Raspberry Pi may struggle with real-time processing of high-resolution video feeds for facial recognition, especially when dealing with multiple cameras simultaneously. - ESP32: Primarily used for IoT applications, the ESP32 has limited processing power and memory, making it challenging to handle complex computations required for biometric authentication. 2. Scalability: - Ensuring the hardware setup can scale to handle an increasing number of access points and users without significant performance degradation. 3. Reliability and Durability: - Ensuring the Raspberry Pi and ESP32 devices can operate reliably in various environmental conditions (temperature, humidity, dust). Software Challenges: 1. Processing Power: - TensorFlow and Deep Learning Models: Running deep learning models on limited hardware like the Raspberry Pi can be resource-intensive and slow. Optimizing these models for performance without sacrificing accuracy is challenging. 2. Real-Time Processing: - Implementing real-time video processing with MediaPipe and TensorFlow to ensure quick and accurate recognition under various lighting and environmental conditions. 3. Data Management: - Efficiently managing and storing large amounts of video and biometric data. This includes ensuring fast access times and securing sensitive data. Integration Challenges: 1. Interoperability: - Ensuring seamless communication between various hardware components (Raspberry Pi, ESP32) and software modules. - Developing a reliable communication protocol for the Raspberry Pi and ESP32 to handle data exchange securely and efficiently. 2. containerization with Docker: - Ensuring Docker containers run efficiently on the Raspberry Pi, which may have limitations in handling multiple containers due to limited RAM and CPU resources. - Managing dependencies and ensuring consistent environments across different devices using Docker. 3. Complex Workflows: - Developing complex workflows in Bash for automating system updates, monitoring, and maintenance tasks across a distributed network of devices. Creating an advanced access control system with the specified technologies is a complex task requiring careful consideration of hardware limitations, software integration, security, and maintenance challenges. Addressing these challenges effectively requires a multi-disciplinary approach, involving expertise in hardware engineering, software development, machine learning, cybersecurity, and system design.

Solution

1. Performance Limitations: Raspberry Pi: - Edge Computing: Offload intensive computations to more powerful edge devices or cloud servers while using Raspberry Pi for basic processing and communication. - Hardware Acceleration: Utilize Raspberry Pi’s GPU for accelerating tasks like image processing with frameworks that support hardware acceleration. ESP32: - Task Delegation: Use ESP32 primarily for simple tasks like sensor data acquisition and communication, leaving more complex computations to Raspberry Pi or cloud services. 2. Scalability: - Modular Design: Design the system with modular components that can be easily added or replaced as the system scales. - Load Balancing: Distribute computational loads across multiple Raspberry Pi devices or use cloud-based services to handle peaks in processing demand. 3. Reliability and Durability: - Environmental Protection: Use ruggedized enclosures designed with Fusion 360 to protect devices from harsh environmental conditions. - Redundant Systems: Implement redundant hardware setups to ensure continuous operation in case of device failures. Software Challenges: 1. Processing Power: - Model Optimization: Use TensorFlow Lite to optimize deep learning models for lower - Pre-processing: Perform pre-processing steps (like resizing and filtering) on the edge devices before sending data to the more powerful processors for analysis. 2. Real-Time Processing: - Efficient Algorithms: Use efficient algorithms and libraries like MediaPipe optimized for real-time performance. - Distributed Processing: Distribute the processing tasks between edge devices and cloud servers to balance the load and reduce latency.

Use Case

A multinational corporation, XYZ Enterprises, headquartered in a large metropolitan area, seeks to upgrade its access control system to enhance security, improve employee flow, and ensure compliance with stringent regulatory requirements. The company’s headquarters houses critical departments such as R&D, finance, executive offices, and sensitive data centers that require varying levels of access control. Solution: XYZ Enterprises implements an ecosystem of camera and access control modules to manage access throughout its headquarters. This advanced system incorporates high-resolution cameras, facial recognition, and other biometric technologies to create a seamless, secure, and efficient access control environment. Implementation: 1.Entrances and Exits: - High-resolution cameras with facial recognition capabilities are installed at all building entrances and exits. - Employees and visitors are pre-registered in the system, allowing for quick and accurate identification upon entry. - The system enables touchless access, enhancing hygiene and reducing the need for physical contact with access control devices. 2.Departmental Access: - Each department within the headquarters is equipped with access control modules tailored to its security needs. - Sensitive areas like the R&D labs and data centers require multi-factor authentication, combining facial recognition with iris scans for added security. - Regular office areas use facial recognition alone, ensuring quick and efficient access for employees. 3.Visitor Management: - Visitors are pre-registered by the hosting employee, receiving a temporary access profile. - Upon arrival, visitors are identified by the facial recognition system and granted access to specific areas based on their profile. - The system tracks visitor movement within the building, ensuring they remain within authorized zones. 4.Granular Access Control: - Access permissions are customized based on employee roles and responsibilities. - Executives have broader access to various departments, while other employees are limited to their designated areas. - Changes in access permissions are managed centrally and updated in real-time, allowing for quick adjustments as needed. 5. Monitoring and Alerts: - The integrated system continuously monitors all access points and records entry and exit data. - In the event of an unauthorized access attempt, the system generates real-time alerts and notifies security personnel. - The system can track movement patterns and detect anomalies, enabling proactive security management. 6. Scalability and Future-proofing: - The modular nature of the system allows XYZ Enterprises to scale and adapt the access control system as the company grows. - New technologies and features can be integrated into the existing ecosystem without significant overhauls.

TECHNOLOGIES

List of Essential Tools and Technologies

Raspberry Pi

Python

Fusion360

THE PROCESS

PROTOTYPE DESIGN

The prototype for an advanced access control system integrates multiple technologies to enhance security, efficiency, and scalability. Central to the system are Raspberry Pi units equipped with high-resolution cameras and ESP32 microcontrollers. The Raspberry Pi handles real-time video processing using TensorFlow and MediaPipe for facial recognition, while the ESP32 manages sensor data and controls door locks and alarms. These devices are interconnected via a secure network to ensure reliable communication and data exchange.

At each access point, the system captures and processes video feeds to authenticate individuals. The entrance module detects motion, captures the video, and verifies identities against a central database. Upon successful authentication, the ESP32 triggers the door lock to grant access. Internal access points function similarly, ensuring secure entry to sensitive areas like R&D labs and data centers. The central control unit, another Raspberry Pi, manages user data, permissions, and access logs, offering real-time monitoring and alerts for unauthorized attempts.

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PLEDGE

Impact on the market

he introduction of an advanced access control system leveraging technologies like Raspberry Pi, ESP32, TensorFlow, and Docker has the potential to significantly disrupt the security market. By using affordable hardware components, the system offers a cost-effective alternative to traditional high-end security solutions, making advanced security technology accessible to a broader range of businesses, including small and medium-sized enterprises (SMEs). This affordability could democratize access to sophisticated security measures, expanding the market and driving growth in sectors that previously relied on less advanced systems due to cost constraints.

The introduction of this system can spur further innovation in the security industry, encouraging competitors to integrate AI, IoT, and edge computing into their offerings. This could lead to a wave of next-generation security solutions that are smarter, more efficient, and more user-friendly. The use of open-source technologies and the potential for customization might foster collaboration between tech companies, security firms, and academic institutions to develop tailored solutions for specific industry needs, driving technological advancement and market evolution.

Finally, the system's touchless, automated access enhances user experience by reducing bottlenecks and wait times, particularly in high-traffic areas, leading to higher customer satisfaction and trust in the reliability and efficiency of the security measures. With robust encryption and real-time monitoring, the system ensures high levels of data security, which is crucial for maintaining user trust and compliance with data protection regulations. In summary, this advanced access control system can disrupt the security market by providing a cost-effective, scalable, and technologically advanced solution, driving widespread adoption, fostering innovation, and elevating overall market standards for security technology.

RESULTS

FINAL PRODUCT

The MVP of the advanced access control system represents a culmination of cutting-edge technologies harmonized to deliver a seamless and secure access management solution. With Raspberry Pi units at its core, equipped with high-resolution cameras and running TensorFlow models, the system efficiently captures and processes facial data for real-time authentication. Complemented by ESP32 microcontrollers managing sensors and door locks, it ensures a holistic security approach. This MVP prioritizes scalability and modularity, allowing for easy integration with existing systems and expansion as needs evolve. Touchless entry, stringent data security measures, and centralized user management underscore its commitment to user convenience, privacy, and regulatory compliance. Real-time monitoring and alerting mechanisms provide proactive threat detection, enhancing overall security posture. With its robust features and user-centric design, the MVP sets a strong foundation for further development, promising to revolutionize access control in diverse business and institutional settings.