Browse Topic: Optics
Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC. Experimental
Oil churning and windage power losses in dip-lubricated gearboxes can significantly affect overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are
This SAE Aerospace Standard (AS) will specify what type of NVGs are required and minimum requirements for compatible crew station lighting, aircraft exterior lighting such as anti-collision lights, and position/navigation lights that are “NVG compatible.” Also, this document is intended to set standards for NVG utilization for aircraft so that special use aircraft such as the Coast Guard, Border Patrol, Air Rescue, Police Department, Medivacs, etc., will be better equipped to chase drug smugglers and catch illegal immigrants, rescue people in distress, reduce high-speed chases through city streets by police, etc. Test programs and pilot operator programs are required. For those people designing or modifying civil aircraft to be NVG compatible, the documents listed in 2.1.3 are essential.
A new Microelectromechanical system (MEMS) grating modulator has been developed, offering significant advancements in optical efficiency and scalability for communication systems. By integrating a tunable sinusoidal grating with broadside-constrained continuous ribbons, a large-scale aperture of 30 × 30 mm is achieved and supports high-speed modulation up to 250 kHz.
Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge
This paper presents a novel AI-based parking management system designed to enhance efficiency, reduce manual intervention, and optimize operational costs in modern parking facilities. By integrating computer vision with infrared (IR) sensors, the system continuously monitors parking areas in real time, accurately detecting vehicle occupancy and dynamically updating the space availability. The hybrid approach minimizes reliance on conventional sensors, improving accuracy and environmental robustness. Additional features include intelligent navigation assistance guiding drivers to available spots and integrated video surveillance for enhanced security through AI-driven suspicious activity detection. The user interface provides real-time updates ensuring a seamless and convenient parking experience. Overall, this system offers a comprehensive solution that advances parking technology through automation, real-time monitoring, and secure, user-friendly operation.
Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized
Hydrogenated nitrile butadiene rubbers (HNBR) and their derivatives have gained significant importance in automotive compressed natural gas (CNG) valve applications. In one of the four-wheelers, CNG valve application, HNBR elastomeric diaphragms are being used for their excellent sealing and pressure regulation properties. The HNBR elastomeric diaphragm was developed to sustain CNG higher pressure However, it was found permanently deformed under lower pressures. In this research work, number of experiments was carried out to find out the primary root cause of diaphragm permanent deformation and to prevent the failure for safe usage of the CNG gas. HNBR diaphragm deformation investigation was carried out using advanced qualitative and quantitative analysis methods such as Soxhlet Extraction Column, Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Optical Microscopy (OM), Scanning Electron Microscopy (SEM), and Thermogravimetric Analysis (TGA). For
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
The objective of this study was to examine the effect of Correlated Colour Temperature (CCT) of automotive LED headlamps on driver’s visibility and comfort during night driving. The experiment was conducted on different headlamps having different correlated colour temperatures ranging from 5000K to 6500K in laboratory. Further study was conducted involving participants of different age group and genders for understanding their perception to identify objects when observed in light of different LED headlamps with different CCTs. Studies have shown that both Correlated Colour Temperature and illumination level affect driver’s alertness and performance. Further study required on headlamps with automatically varying CCT to get better solution on driver’s visibility and safety.
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