Chien-Yu Lu

This paper presents a modular system architecture and an automated signal processing pipeline designed to quantify neurophysiological relaxation responses to fragrance using consumer-grade wearable electroencephalography (EEG). By integrating real-time data streaming via Open Sound Control (OSC) with a high-performance backend, the platform enables objective assessment of olfactory stimuli through a reproducible Sleep Readiness Index (SRI) derived from spectral power shifts. To mitigate the signal quality constraints inherent in portable hardware, the framework utilizes a robust suite of engineering controls, including zero-phase filtering and automated artifact rejection, ensuring data integrity across short-window trials. Validation through construct-level analysis of public sleep datasets and synthetic sensitivity testing confirms the index’s directional reliability, while runtime benchmarking demonstrates sub-millisecond compute times suitable for interactive wellness applications. Ultimately, this framework provides a transparent, auditable engineering scaffold that replaces subjective self-reports with a standardized, within-session proxy metric for comparative fragrance evaluation.

This study addresses the increasingly complex challenge of multiple-fault diagnosis in modern intelligent automotive electronic systems by proposing an innovative deep learning-based solution. The research integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and the Transformer architecture to construct a multi-modal fault diagnosis model. By collecting real-world operational data from vehicle electronic systems, including fault samples from key modules such as the Engine Control Unit (ECU), Body Control Module (BCM), and safety systems, a comprehensive dataset comprising 12 major fault types was established. Experimental results demonstrate that the proposed hybrid deep learning model achieves a multiple-fault identification accuracy of 96.8%, representing a 23% performance improvement over traditional diagnostic methods. The integration of Explainable AI (XAI) techniques provides the diagnostic results with visual interpretability, aiding maintenance technicians in understanding the model’s diagnostic logic. The findings of this research can be applied in smart factories, automotive service centers, and on-board diagnostic (OBD) systems, offering significant practical value in enhancing vehicle safety and reducing maintenance costs.

In this paper, we report the design and verification methodology of a SystemVerilog-based I²C protocol Verification IP (VIP) based not only on assertion-based verification but also on the new checkout error injection techniques. The resulting VIP is designed as a set of loose-coupled modules for protocol description, transaction generation, and automatic protocol checking with SystemVerilog Assertions (SVAs). Timing, multi-master arbitration, and error recovery related to I²C protocol verification challenges are achieved using embedded assertion monitors and focused error injection scenarios on the testbench. The paper describes the inclusion of assertion-based monitors used for checking real-time protocol compliance as well as the ability for systematic error injection to expose corner-case bugs and verify the strength of the DUT and the verification environment. We present our experimental results that demonstrate the effectiveness of proposed strategies on coverage, bug leakage, and reduction in debug cycles. The approach also provides a useful guideline for verification engineers who need to build protocol VIPs or wish to improve the efficiency of their verification flow with assertion-led methods.

Because the thread is easy to install, disassemble, and replace, and most of the threads have been standardized, the miniaturized internal thread structure is quite special, the size is extremely small, and the space position is limited, so that the current common measurement mechanism cannot achieve effective measurement. Based on the above reasons, this research aims to develop a portable miniaturized metric internal thread contact detector. The parameter values that can be detected by the detector respectively include the hole diameter value under the inner thread before tapping, the minor diameter value and the major diameter value of the internal thread after tapping. Then, use the quadratic element measuring instrument to measure three parameter values, namely the lower diameter value, the smaller diameter value of the internal thread, and the value of the major diameter of the internal thread. It is then verified with the theoretical value of the metric internal thread, which proves that the experimental parameter values designed in this study are consistent with the theoretical values. That is to say, the miniaturized metric internal thread detector can be applied to any miniaturized metric internal thread detection place, and can accurately detect the obtained parameter values, and can overcome the detection limitation given by the miniaturized internal thread in the space structure. Finally, the easy availability of the detector and the relatively low cost are its main development advantages.

This paper developed a set of size detection systems with a computer vision method based on the accuracy requirements of the hole saw caps to meet the needs of the accuracy detection machine. The results allow manufacturers to build a digitalized hole saw cap detection system at a low cost. We have designed a measurement system for the hole saw caps with the computer vision method to measure the dimensions of the hole saw caps. However, a valid measurement value of the hole saw caps must be positioned symmetrically. However, in fact, when the measurement system is positioned accurately asymmetrically, it will cause a problem in the measurement data. Therefore, the dark box environment made of a light source and the back plate of the hole saw caps material and two cameras are employed to observe the hole saw caps from both above and the side views. Then, personal desktop computers calculate the size of the hole saw caps based on the camera screen vision with a Python program. The results of the proposed methodology are obtained by measuring 10 workpieces of different sizes, and all the errors within a range of 2 pixels (pixel, px) met the detection standards. Therefore, the developed hole saw cap detection system is in line with expectations.