As artificial intelligence (AI) technology advances, its applications expand into various fields, including robotic motion control. Self-balancing robots commonly employ PID or LQR control and typically require extensive parameter tuning for proper execution. An emerging alternative is using deep reinforcement learning algorithms, leveraging on Lightweight Neural Networks (NNs) and Catastrophic Forgetting Mitigation, in robot control. This paper details the process and outcomes of training a Q-network within a virtual environment via a deep Q-network (DQN) algorithm, preventing the network from getting overtrained and deploying it on a microcontroller to achieve a self-balancing robot. Additionally, it describes methods for facilitating interactions between the self-balancing robot and the real-world environment during training.

In this paper, we present an optimized implementation of Convolutional Neural Network (CNN) for road sign recognition on an FPGA platform, utilizing dynamic quantization and pruning techniques. Traditional CNN models are computationally intensive and require significant memory, which poses challenges for deployment on resource-limited hardware such as FPGA. To address these challenges, a hybrid approach was proposed combining dynamic quantization with pruning, allowing layer-specific precision adjustment while removing unnecessary network connections. This method significantly reduces the computational complexity and memory usage while maintaining high accuracy in road sign recognition tasks. The quantized and pruned model is implemented on the Tang Primer 25K FPGA, demonstrating efficient hardware utilization and real-time performance. Experimental results show that the proposed approach substantially reduces power consumption and resource utilization, with minimal impact on accuracy, making it highly suitable for embedded systems and edge computing applications.

This paper presents the design and implementation of a hardware-based memory game developed on an FPGA platform to promote cognitive stimulation among older adults. The game features a 3×3 LED and switch matrix that displays pseudo-random light patterns that users must recall and replicate. To enhance randomness, a seed is extracted from ambient electromagnetic noise using an Analog-to-Digital (ADC) and fed into a multi-Linear Feedback Shift Register (LFSR) pseudo-random number generator (PRNG). The PRNG employs XOR-combined LFSRs initialized with non-overlapping seed transformations to ensure statistical independence. A finite state machine (FSM) governs the game logic, translating 9-bit PRNG outputs into spatial LED patterns and evaluating user input for correctness. Statistical analyses of PRNG outputs, using normality tests and Central Limit Theorem-based smoothing, verify the Gaussian conformity and entropy quality of generated sequences. The fully integrated system includes custom PCBs for ADC input, a mechanical button interface, and an LED display, housed in a 3D-printed enclosure. Results from both simulation and hardware validation confirm the system's effectiveness in generating high-quality randomness and delivering a simple, tactile, and accessible cognitive exercise platform.

This project focused on the development of a Tetris-like game that incorporates a pseudo-random number generator (PRNG) implemented on a Field Programmable Gate Array (FPGA) to enhance the gameplay experience. By utilizing a hardware-generated seed instead of relying solely on software-based randomness, the project introduces a novel approach to determining the sequence in which Tetris blocks appear on the screen. This integration of hardware and software not only adds a layer of complexity to the game but also showcases the seamless real-time communication between a digital system powered by an FPGA and an interactive Python-based gaming application. Overall, this project represents a significant step forward in the realm of game development by showcasing the potential of integrating hardware and software technologies to enhance gameplay and create more engaging experiences for players. The fusion of FPGA-driven hardware components with a Python-based game exemplifies the innovative spirit driving advancements in the gaming industry, paving the way for future developments that blur the lines between virtual and physical gaming environments.

This paper introduces a novel hardware-based solution for generating one-time passwords (OTPs) using a field-programmable gate array (FPGA). By leveraging real-world analog noise sources like light, temperature, and sound sensors, the system ensures a high level of entropy to seed the random number generation process in a dedicated FPGA chaotic Chua's circuit. The design of this OTP generator is capable of producing secure 5-digit OTPs ranging from 00000 to 99999. These OTPs can serve various purposes, such as wireless applications when transmitted to an ESP32 microcontroller or authentication in access control systems. By integrating these OTPs directly into access control systems, organizations can enhance their security measures significantly. This integration allows for seamless and secure authentication processes, ensuring that only authorized individuals gain access to restricted areas. The proposed approach prioritizes high randomness and resistance to prediction, essential characteristics for secure embedded systems. By incorporating multiple noise sources and utilizing FPGA technology, the OTP generator guarantees a robust level of security. Overall, the hardware-based OTP generator presented in this paper stands as a reliable and innovative solution for enhancing security in embedded systems.

This paper proposes a cost-effective Cryptographically Secure Random Number Generator (CSRNG) utilizing a Field-Programmable Gate Array (FPGA) integrated with a Geiger counter. The front end of this system is designed to capture ambient radioactivity through a Geiger counter system that emits pulses in response to such environmental stimuli. To complement this setup, a neoTRNG-based ring oscillator RNG is incorporated to enhance the randomness of the generated numbers. By combining these elements, the CSRNG can achieve a high level of unpredictability crucial for cryptographic applications. Moving to the system's backend, utilizing the BLAKE2s hash function is pivotal in counteracting any potential biases introduced by the frontend components. This hashing function ensures that the output random numbers remain robust and secure, free from discernible patterns or vulnerabilities. In essence, the methodology outlined in this paper offers a comprehensive guide to conceptualizing and implementing a cost-effective CSRNG. The challenges inherent in generating cryptographically secure random numbers can be effectively navigated through a meticulous integration of hardware components and cryptographic techniques. This approach sheds light on the intricate processes in creating a reliable source of randomness, essential for safeguarding sensitive information in various digital systems.

The project is about integrating biometric security, hardware-based randomness, and symbolic visualization through tarot for a unique user experience. It utilizes a field-programmable gate array (FPGA) for biometric authentication using fingerprint input to enhance security. Sensitive data is encrypted within the FPGA, ensuring tamper resistance and mitigating threats such as replay attacks. The system utilizes the entropy from the fingerprint as a seed for a pseudorandom number generator (PRNG) to select a tarot card displayed on a Raspberry Pi and a narrative. The project explores the fusion of digital security and human meaning by combining secure biometrics, hardware-accelerated cryptography, and symbolic storytelling. It aims to provide personalized authentication, interactive installations, and secure entertainment interfaces for users. A custom enclosure CAD design was developed for the FPGAintegrated biometric system to improve user-friendliness. The design focused on touch-based interaction using a touchscreen and fingerprint scanner to simplify the user interface and enhance user enjoyment. This approach allowed the team to concentrate on perfecting the PRNG code rather than dealing with moving parts and manual updates for user instructions. The Hénon Map-based PRNG is implemented in the FPGA for real-time applications.

Auto-infusion systems stand as instrumental entities in the medical sector, facilitating a streamlined and automated approach towards patient infusion processes. The evolution of these systems has been significantly influenced by the integration of Internet of Things (IoT) technologies [1], heralding a new era of enhanced reliability, precision, and functionality. This research aims to elucidate the development and operation of an IoT-based auto-infusion system equipped with a myriad of features optimized for real-time monitoring and responsiveness. Central to the system's operation is a peristaltic pump, meticulously regulated by a stepper motor, ensuring precise control over the infusion process. The system boasts a multitude of monitoring and alarm functionalities, such as flow rate detection, obstruction alerts, leakage notifications, low fluid level warnings, and bubble detection features. These capabilities ensure the provision of a robust and secure operational environment, minimizing potential risks and enhancing the reliability of the infusion process. An added innovative facet of the system is the incorporation of a UPS (Uninterruptible Power Supply), ensuring continuous operation even in scenarios afflicted by power outages, thereby enhancing the resilience and reliability of the infusion system.

Acoustic levitation, an emerging technology that employs sound waves to suspend solid particles and liquid droplets in mid-air, holds immense promise. Its potential applications span diverse fields, including environmental science, mechanical engineering, biochemistry, food science, and cancer therapy. The method involves manipulating matter using sound waves to create novel forms of energy and material. However, standing wave acoustic levitation faces challenges, particularly unstable soundwaves that hinder stationary levitation. This study delves into the intricacies of the standing wave acoustic levitation system. Additionally, it compares this method with near-field and parametric array levitation techniques. Each approach utilizes sound waves to create “invisible fingers” that hold objects, but they have distinct advantages and limitations. To address instability, two standing wave acoustic levitation systems are constructed and tested: a simpler version and an expert-level setup. The goal is to enhance accuracy and stability. Ultimately, this research aims to unlock the full potential of acoustic levitation.

This study explores how Artificial Intelligence (AI) can be used to enhance diversity and inclusion in engineering education. By using AI, schools can find and reduce biases, tailor learning to individual needs, and support students from underrepresented backgrounds. AI can also improve accessibility and help teachers adopt more inclusive methods. We discuss the benefits and challenges of using AI in this context, showing its potential to make engineering education more equitable.

Artificial Intelligence (AI) is transforming the educational landscape by offering personalized learning experiences that cater to individual student needs. This report delves into the psychological aspects of AI -enhanced learning experiences, focusing on their impact on student motivation, engagement, cognitive load, and emotional well-being. By analyzing various case studies and existing literature, we explore the benefits and challenges associated with integrating AI into educational settings. The findings indicate that AI-driven tools can significantly enhance motivation and engagement by providing tailored learning paths and real-time, relevant feedback. Additionally, AI can help manage cognitive load and offer emotional support, fostering a supportive and effective learning environment. However, challenges such as dependency on technology, data privacy concerns, and potential stress must be addressed to fully harness the benefits of AI in education. This report underscores the importance of strategic implementation and continuous evaluation of AI tools to ensure they contribute positively to students' psychological well-being and academic success.

This paper investigates how Collaborative Learning environments enriched by AI technologies have emerged as a pivotal advancement in modern education, promising enhanced learning experiences and outcomes. AI facilitates personalized learning pathways, fosters engagement through interactive tools, and supports peer interaction in dynamic educational settings. However, integrating AI into educational practices poses concerning challenges. Despite these challenges, AI holds immense potential to revolutionize collaborative learning by optimizing educational resources, promoting student engagement, and preparing learners for future challenges.

Signals are crucial in conveying information across various fields. This paper presents new methods for processing electromyographic (EMG) signals to create AI systems that decode muscle activity for arm movement control. Using an advanced dataset, the study focuses on enhancing prosthetic control and rehabilitation technologies through sophisticated signal processing and machine learning techniques. Various preprocessing steps improved signal quality, and a diverse set of features was extracted and classified. The results highlight the potential for more intuitive and responsive robotic arm movements, contributing to better prosthetic and rehabilitation solutions.

This paper investigates how clinical technologies focusing in radiology enriched by AI technologies have emerged as a pivotal advancement in the field of radiology, offering advancements that enhance both diagnostic accuracy and operational efficiency. These AI systems improve diagnostic performance by identifying subtle patterns that might be overlooked by human radiologists. Additionally, AI integration with Picture Archiving and Communication Systems (PACS) streamlines image management, automating routine tasks such as image analysis and report generation, which boosts workflow efficiency and productivity. Despite these benefits, challenges such as data privacy, algorithm transparency, and the need for continuous validation of AI models remain pertinent. As AI technology evolves, it holds the promise of further enhancing radiological practices, improving patient outcomes, and contributing to a more efficient healthcare system.

This paper investigates how AI-driven tools and platforms are integrated into engineering curricula to ready future engineers for the digital age. It emphasizes the advantages of improved learning experiences and tailored education, stressing the significance of updating curriculum content and training faculty. It also addresses challenges such as ethical concerns and integration complexities, highlighting the necessity for strategic planning to optimize AI's role in encouraging innovation and preparing students for careers in technology.

This paper explores the integration of AI-based assessment techniques in engineering education, highlighting their potential to enhance personalized feedback, improve learning outcomes, and streamline assessment processes. It examines various AI tools and their applications in automated grading, adaptive testing, and intelligent tutoring systems. Additionally, it addresses the challenges of implementing AI in educational settings, including fairness, data privacy, and integration with existing systems. The paper concludes with a discussion on strategic planning and continuous improvement to optimize AI's role in fostering an effective assessment framework for engineering education.

The self-balancing robot represents a significant advancement in the realm of mobile robotic platforms. This paper introduces the design of an intelligent embedded system aimed at managing the direction and speed of the stepper motor that drives the self-balancing robot. Controlling the speed and direction of the stepper motor is crucial for maintaining the stability of the two-wheeled robot. Stability is achieved by keeping the robot in an upright walking position. The proposed smart embedded system is engineered to handle sensing, control, and actuation functions.

This paper explores the transformative potential of gamification and immersive learning experiences, enhanced by artificial intelligence (AI), in modern education. Gamification leverages game design elements to boost engagement, motivation, and learning outcomes, while immersive technologies such as virtual reality (VR) and augmented reality (AR) create interactive, experiential learning environments. AI plays a pivotal role by personalizing learning experiences, adapting content to individual needs, and providing real-time feedback. This study reviews existing literature presents case studies of successful implementations, and discusses the benefits and challenges associated with these technologies. By integrating AI with gamification and immersive learning, educators can create dynamic, engaging, and effective educational experiences. The paper also addresses ethical considerations, accessibility issues, and future research directions, ultimately highlighting the significant impact of AI-driven gamification and immersive learning on the future of education.

This paper presents the implementation of a hardware pseudo-random number generator (PRNG) using multiple linear feedback shift registers (LFSRs) for generating pseudo-random numbers and ambient electromagnetic noise for seed generation. The PRNG algorithm is deployed on a Digilent CMOD A7 field-programmable gate array (FPGA) board, producing 16-bit random numbers at a frequency of 50 kHz, which are then displayed on a 7-segment display. To facilitate the transfer of generated numbers to a laptop for further use in different applications, a dedicated high-speed UART with a baud rate of 1,152,000 is designed. The performance and effectiveness of the PRNG are evaluated in this setup.

The adoption of IoT enabled devices have led to higher risks of EMI issues, especially in their power management. Particularly, rectifiers for IoT devices are especially concerning. To mitigate this, one approach is to use wide-bandgap semiconductor devices for power management, which reduces the devices' susceptibility and emission of conducted EMI. One such example are GaN semiconductors. However, for low voltage, low frequency systems, the literature concerning GaN performance with regards to conducted EMI have been sparse. This is particularly important for industries such as the medical field which might require low voltage power circuits with components more adept to handling EMI. In this paper, an evaluation will be carried out to compare transistors' performance in the design of a low voltage, low frequency rectifier of 12 Vac at 50 Hz. The results of the simulation using IGBT, GaN FETs and MOSFETs are then shown and discussed.

One of the problems faced in the product de-velopment design cycle is the requirement that the devices would have to be electromagnetically compliant. With more and more pyroelectric sensors implemented in smart systems due to their attractiveness in reducing costs and increasing energy conservation, there have been more instances of these sensors failing due to EMI. As such, there is an increasing need to explore the EMS of these sensors, especially when they are implemented in IoT enabled devices. This paper therefore seeks to explore the EMS of pyroelectric sensors to conducted noise injection in IoT applications. This is done by devising a noise injection circuit to be implemented in the setup. The experiment and the results of this setup is presented accordingly.

VLC systems often requires costly devices and a lengthy design process due to its high performance and precision requirement. In this work, low-cost devices and rapid prototyping have been adopted to realise a system which can transmit real time video streaming. A VLC transmitter and receiver circuit has been designed and implemented. The full system consists of a mechanical enclosure, which was fabricated using rapid prototyping techniques such as 3D printing and laser cutting, electrical circuitry system and the optical modules. The prototype was thus able to transmit video signals compatible with the National Television System Committee (NTSC) standard, up to a distance of 3.1 m.

RF technologies has facilitated communication including audio, images, video and data transmission. However, the RF spectrum is getting increasingly crowded, and alternative methods to transfer data need to be explored. In this paper, software-hardware co-design is implemented to design the audio data synchronization and transmission for an optical-through-air transceiver system. Besides audio transmission, the performance of the VLC system is verified through testing serial data transmission and text transmission. The experimental results show that the VLC system can achieve up to 4 Mb/s data rate using OOK modulation with red light. This system provides a practical step-by-step guidance for setting up a VLC system for data transmission.

Ultra low power sensor node for wireless health monitoring system was designed and implemented in 0.18-μm CMOS. The sensor node functions as an interface circuit to both sensor and RF transceiver. The sensor node consists of an amplifier, an ADC (analog-to-digital converter) as well as digital system. The digital system is embedded with DSP (digital signal processing) for heart rate processing and RF interface for transceiver. The sensor node draws a total current of 7.5-μA from a 0.9-V single supply. The decoding scheme in the RF transceiver can tolerate up to ±22% clock frequency variation.

A low-power source follower variable attenuator (SFVA) is proposed and implemented using standard 0.18μm CMOS. Using a PMOS source follower, a bandwidth of 200MHz is achieved with 500μA current from a 1.8V single supply. Accurate attenuation can be varied with a digital control signal. Dc coupling at the input is made possible with a self-biasing technique. A single stage SFVA with a 2-bit control is demonstrated for 0dB to 6dB attenuation. The number of control bit and attenuation is expandable. The active area is merely 120μm×60μm which is very cost effective.