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In the digital era, the demand for practical and efficient security systems has significantly increased, particularly in the context of access control for restricted rooms or buildings. This research aims to develop an automatic door locking system utilizing an RFID card and a NodeMCU ESP32 microcontroller integrated with Internet of Things (IoT) technology through real-time notifications using the Telegram application. The system is designed to replace conventional locking methods that often present various weaknesses, such as key loss, physical duplication, and lack of remote access capabilities. The development method employed is the Research and Development (R&D) approach, consisting of needs analysis, system design, hardware and software implementation, followed by testing and evaluation. The main components used in the system include the RC522 RFID reader for user identification, Espressif manufactures the ESP-32 microcontroller, which is equipped with Wi-Fi and Bluetooth modules to enable wireless internet connections. NodeMCU ESP32 as the control center and internet connector, a relay module as an electronic switch, and a solenoid door lock as the actuator. The results show that the system is capable of accurately reading RFID card UIDs, granting access to registered cards, activating the solenoid to unlock the door, and sending access status notifications to Telegram in an average of less than three seconds. The system also effectively denies access to unregistered cards and sends warning messages accordingly. Therefore, this system enhances the security and efficiency of room access control and has the potential to be adopted as a prototype solution in the development of smart homes or modern access control systems.

Android continues to innovate, and biometric systems particularly fingerprint sensors must operate with high efficiency. However, the main challenge lies in the communication between hardware and software, which often causes high latency, excessive power consumption, and protocol incompatibility between components. As a result, authentication speed decreases and system stability is compromised, especially on mid- to low-range devices. This study explores various implementations of Android-based fingerprint systems, focusing on how sensor modules interact with the microcontroller or Trusted Execution Environment (TEE) and the operating system through a hardware-software co-design approach to evaluate integration efficiency across all layers. The analysis reveals that conventional protocols such as Bluetooth or serial connections still cause delays, while improvements in drivers and the Hardware Abstraction Layer (HAL) can significantly reduce latency. As a solution, the researchers propose a co-design optimization approach that utilizes data flow normalization within the HAL and adopts lightweight communication protocols to accelerate the verification process. Based on the test results, this approach successfully improves efficiency—authentication time is reduced by up to 35% and power consumption decreases by approximately 15%. Therefore, the efficiency of communication between hardware and software becomes a key factor in enhancing the performance and reliability of fingerprint systems on Android devices.

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.

Stroke is a physical disability disease, which affects the upper extremity motor system. To support post-stroke motor rehabilitation, research on IoT-based elbow exoskeleton therapy devices that help elbow therapy with flexion and extension movements. The therapy device uses DS-SERVORD5160 as the main driver to provide flexible movement. By using the ESP32 microcontroller control system equipped with a Bluetooth connection that is connected via the MIT App Inventor application from a smartphone that can be done at home to increase the effectiveness of post-stroke recovery. The results of this study with low (20°), medium (40 °) and high (60 °) modes by providing a maximum load of 2 Kg. It can be concluded that the device shows stable performance in low (20 °) and medium (40 °) modes, but in testing with high mode (60 °) with a load of 2 Kg there is a decrease in servo performance which is indicated by the instability of the servo movement which shows an angle of 56 °.