Search Results (43)

We present a Genetic Algorithm (GA)-based inverse design framework for creating a single-layer, fabrication-compatible dielectric nano-patterned surface that enables efficient color routing in both transmissive and reflective optical systems. Unlike traditional multilayer or absorption-based color filters, the proposed structure employs a fabrication-compatible architecture that spatially routes red, green, and blue light into designated output channels, significantly enhancing light utilization and color fidelity. The design process integrates a GA with full-wave finite-difference time-domain (FDTD) simulations to optimize the structural pillar height distribution, using a figure of merit that simultaneously maximizes optical efficiency and minimizes spectral crosstalk. For CMOS image sensor-scale designs, the nano-patterned surface achieved peak optical efficiencies of 76%, 72%, and 78% for blue, green, and red channels, respectively, with an average efficiency of 75.5%. Parametric studies further revealed the dependence of performance on pillar geometry, refractive index, and unit cell scaling, providing practical design insights for scalable fabrication using nanoimprint or grayscale lithography. Extending the approach to reflective displays, we demonstrate tunable-mirror-based architectures that emulate electrophoretic microcapsules, achieving efficient color reflection and an expanded color gamut beyond the sRGB standard. This single-layer, inverse-designed nano-patterned surface offers a high-performance and fabrication-ready solution for compact, energy-efficient imaging and display technologies. Full article

To address the power constraints of the emerging Internet of Things (IoT) era, we propose a compression-efficient feature extraction method for a CMOS image sensor that can extract binary feature data. This sensor outputs six-channel binary feature data, comprising three channels of binarized luminance signals and three channels of horizontal edge signals, compressed via a run length encoding (RLE) method. This approach significantly reduces data transmission volume while maintaining image recognition accuracy. The simulation results obtained using a YOLOv7-based model designed for edge GPUs demonstrate that our approach achieves a large object recognition accuracy (${\mathrm{AP}}_{\mathrm{L}}50$) of 60.7% on the COCO dataset while reducing the data size by 99.2% relative to conventional 8-bit RGB color images. Furthermore, the image classification results using MobileNetV3 tailored for mobile devices on the Visual Wake Words (VWW) dataset show that our approach reduces data size by 99.0% relative to conventional 8-bit RGB color images and achieves an image classification accuracy of 89.4%. These results are superior to the conventional trade-off between recognition accuracy and data size, thereby enabling the realization of low-power image recognition systems. Full article

This work presents a method for measuring flame temperatures through an imaging technique that combines spectral analysis with two-color pyrometry. Initially, we employed Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the radiation spectrum of nitrocellulose, selecting 694 nm and 768 nm as the two spectral lines for temperature measurement. Subsequently, we constructed a temperature measurement system utilizing two sCMOS cameras and conducted calibration within the range of 600 to 1000 °C, achieving a maximum temperature measurement uncertainty of 3.43%. Finally, we successfully performed two-dimensional temperature field detection and imaging of nitrocellulose flames of varying qualities, achieving a flame image resolution of 2048 (H) × 2048 (V). In comparison to traditional two-color infrared thermometers and Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, the maximum relative temperature measurement error was 2.1%. This work provides technical insights into the development of high-resolution, low-cost flame temperature imaging technology applicable across a wide range of fields. Full article

We report an optical immunosensing system for oxytocin (OXT) based on image analysis of color reactions in an enzyme-linked immunosorbent assay (ELISA). We employed a miniaturized optical immunosensing unit that was functionally connected to an LED and a smartphone camera. Our system measures OXT levels using a metric called the RGBscore, which is derived from the red, green, and blue (RGB) information in the captured images. By calculating the RGBscore regressively using the brute-force method, this approach can be applied to smartphones with various CMOS image sensors and firmware. The lower detection limit was determined to be 5.26 pg/mL, and the measurement results showed a higher correlation (r = 0.972) with those obtained from conventional ELISA. These results suggest the potential for its application in a simplified health management system for individuals. Full article

Chemical Oxygen Demand (COD) serves as a crucial metric for assessing the extent of water pollution attributable to organic substances. This study introduces an innovative approach for the detection of low-concentration COD in aqueous environments through the application of Laser-Induced Fluorescence (LIF) image processing. The technique employs an image sensor to capture fluorescence image data generated by organic compounds in water when excited by ultraviolet laser radiation. Subsequently, the COD value, indicative of the concentration of organic matter in the water, is derived via image processing techniques. Utilizing this methodology, an LIF image processing COD detection system has been developed. The system is primarily composed of a CMOS image sensor, an STM32 microprocessor, a laser emission module, and a display module. In this study, the system was employed to detect mixed solutions of sodium humate and glucose at varying concentrations, resulting in the acquisition of corresponding fluorescence images. By isolating color channels and processing the image data features, variations in RGB color characteristics were analyzed. The Partial Least Squares Regression (PLSR) analysis method was utilized to develop a predictive model for COD concentration values based on the average RGB color feature values from the characteristic regions of the fluorescence images. Within the COD concentration range of 0–12 mg/L, the system demonstrated a detection relative error of less than 10%. In summary, the system designed in this research, utilizing the LIF image processing method, exhibits high sensitivity, robust stability, miniaturization, and non-contact detection capabilities for low-concentration COD measurement. It is well-suited for rapid, real-time online water quality monitoring. Full article

Fluorescence-guided surgery (FGS) is an innovative technique for accurately localizing tumors during surgery, particularly valuable in brain tumor detection. FGS uses advanced spectral and imaging tools to provide precise, quantitative fluorescence measurements that enhance surgical accuracy. However, the current challenge with these advanced tools lies in their lack of miniaturization, which limits their practicality in complex surgical environments. In this study, we present a miniaturized fluorescence detection system, developed using state-of-the-art CMOS color sensors, to overcome this challenge and improve brain tumor localization. Our 3.1 × 3 mm multispectral sensor platform measures fluorescence intensity ratios at 635 nm and 514 nm, producing a high-resolution fluorescence distribution map for a 16 mm × 16 mm area. This device shows a high correlation (R2 > 0.98) with standard benchtop spectrometers, confirming its accuracy for real-time, on-chip fluorescence detection. With its compact size, our system has strong potential for integration with existing handheld surgical tools, aiming to improve outcomes in tumor resection and enhance intraoperative tumor visualization. Full article

Technological advances in the production of printed circuit boards (PCBs) are increasing the number of components inserted on the surface. This has led the electronics industry to seek improvements in their inspection processes, often making it necessary to increase the level of automation on the production line. The use of machine vision for quality inspection within manufacturing processes has increasingly supported decision making in the approval or rejection of products outside of the established quality standards. This study proposes a hybrid smart-vision inspection system with a machine vision concept and vision sensor equipment to verify 24 components and eight screw threads. The goal of this study is to increase automated inspection reliability and reduce non-conformity rates in the manufacturing process on the assembly line of automotive products using machine vision. The system uses a camera to collect real-time images of the assembly fixtures, which are connected to a CMOS color vision sensor. The method is highly accurate in complex industry environments and exhibits specific feasibility and effectiveness. The results indicate high performance in the failure mode defined during this study, obtaining the best inspection performance through a strategy using Vision Builder for automated inspection. This approach reduced the action priority by improving the failure mode and effect analysis (FMEA) method. Full article

Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters. Full article

Digital non-metric cameras with high-resolution capabilities are being used in various domains such as digital heritage, artifact documentation, art conservation, and engineering applications. In this study, a novel approach consisting of the application of the combined use of close-range photogrammetry (CRP) and mapping techniques is used to capture the depth of a mural digitally, serving as a database for the preservation and archiving of historic murals. The open hall next to the main sanctuary of the Virupaksha temple in Hampi, Karnataka, India, which is a UNESCO World Heritage site, depicts cultural events on a mural-covered ceiling. A mirrorless Sony Alpha 7 III camera with a full-frame 24 MP CMOS sensor mounted with a 50 mm lens and 24 mm lens has been used to acquire digital photographs with an image size of 6000 × 6000 pixels. The suggested framework incorporates five main steps: data acquisition, color correction, image mosaicking, orthorectification, and image filtering. The results show a high level of accuracy and precision attained during the image capture and processing steps. A comparative study was performed in which the 24 mm lens orthoimage resulted in an image size of 9131 × 14,910 and a pixel size of 1.05 mm, whereas the 50 mm lens produced a 14,283 × 21,676 image size and a pixel size of 0.596 mm of the mural on the ceiling. This degree of high spatial resolution is essential for maintaining the fine details of the artwork in the digital documentation as well as its historical context, subtleties, and painting techniques. The study’s findings demonstrate the effectiveness of using digital sensors with the close-range photogrammetry (CRP) technique as a useful method for recording and preserving historical ceiling murals. Full article

In order to improve image recognition technologies in an IoT environment, we propose a data reduction scheme for a feature-extractable CMOS image sensor and present simulation results for object recognition using feature data. We evaluated the accuracy of the simulated feature data in object recognition based on YOLOX trained with a feature dataset. According to our simulation results, the obtained object recognition accuracy was $56.6\%$ with the large-scale COCO dataset, even though the amount of data was reduced by $97.7\%$ compared to conventional RGB color images. When the dataset was replaced with the RAISE RAW image dataset for more accurate simulation, the object recognition accuracy improved to $76.3\%$. Furthermore, the feature-extractable CMOS image sensor can switch its operation mode between RGB color image mode and feature data mode. When the trigger for switching from feature data mode to RGB color image mode was set to the detection of a large-sized person, the feature data achieved an accuracy of $93.5\%$ with the COCO dataset. Full article

This paper introduces a new design of silicon nanowire (Si NW) phototransistor (PT) arrays conceived explicitly for improved CMOS image sensor performance, and comprehensive numerical investigations clarify the characteristics of the proposed devices. Each unit within this array architecture features a top-layer vertical Si NW optimized for the maximal absorption of incoming light across the visible spectrum. This absorbed light generates carriers, efficiently injected into the emitter–base junction of an underlying npn bipolar junction transistor (BJT). This process induces proficient amplification of the output collector current. By meticulously adjusting the diameters of the NWs, the PTs are tailored to exhibit distinct absorption characteristics, thus delineating the visible spectrum’s blue, green, and red regions. This specialization ensures enriched color fidelity, a sought-after trait in imaging devices. Notably, the synergetic combination of the Si NW and the BJT augments the electrical response under illumination, boasting a quantum efficiency exceeding 10. In addition, by refining parameters like the height of the NW and gradient doping depth, the proposed PTs deliver enhanced color purity and amplified output currents. Full article

We propose a new concept image sensor suitable for viewing and sensing applications. This is a report of a CMOS image sensor with a pixel architecture consisting of a 1.5 μm pixel with four-floating-diffusions-shared pixel structures and a 3.0 μm pixel with an in-pixel capacitor. These pixels are four small quadrate pixels and one big square pixel, also called quadrate–square pixels. They are arranged in a staggered pitch array. The 1.5 μm pixel pitch allows for a resolution high enough to recognize distant road signs. The 3 μm pixel with intra-pixel capacitance provides two types of signal outputs: a low-noise signal with high conversion efficiency and a highly saturated signal output, resulting in a high dynamic range (HDR). Two types of signals with long exposure times are read out from the vertical pixel, and four types of signals are read out from the horizontal pixel. In addition, two signals with short exposure times are read out again from the square pixel. A total of eight different signals are read out. This allows two rows to be read out simultaneously while reducing motion blur. This architecture achieves both an HDR of 106 dB and LED flicker mitigation (LFM), as well as being motion-artifact-free and motion-blur-less. As a result, moving subjects can be accurately recognized and detected with good color reproducibility in any lighting environment. This allows a single sensor to deliver the performance required for viewing and sensing applications. Full article

An image processing method was employed to obtain wavelength information using light irradiated during camera exposure. Physically, hue (H) and wavelength (W) are closely related. Once the H value is known through image pixel analysis, the wavelength can be obtained. In this paper, the H-W curve was investigated from 400 to 650 nm using raw image data with a complementary metal oxide semiconductor (CMOS) sensor technology. We reconstructed the H-W curve from raw image data based on a demosaicing method with 2 × 2 pixel images. To date, no study has reported on reconstructing the H-W curve using several different interpolation algorithms in the 400~650 nm wavelength region. In addition, several factors affecting the H-W curve with a raw digital image, such as exposure time, aperture, and international organization for standardization (ISO) settings, were investigated for the first time. Full article

Stand-off detection of latent traces avoids the scene alteration that might occur during close inspection by handheld forensic lights. Here, we describe a novel sensor, named Crime Light Imaging (CLI), designed to perform high-resolution photography of targets at a distance of 2–10 m and to visualize some common latent traces. CLI is based on four high-power illumination LEDs and one color CMOS camera with a motorized objective plus frontal filters; the LEDs and camera could be synchronized to obtain short-exposure images weakly dependent on the ambient light. The sensor is integrated into a motorized platform, providing the target scanning and necessary information for 3D scene reconstruction. The whole system is portable and equipped with a user-friendly interface. The preliminary tests of CLI on fingerprints at distance of 7 m showed an excellent image resolution and drastic contrast enhancement under green LED light. At the same distance, a small (1 µL) blood droplet on black tissue was captured by CLI under NIR LED, while a trace from 15 µL semen on white cotton became visible under UV LED illumination. These results represent the first demonstration of true stand-off photography of latent traces, thus opening the way for a completely new approach in crime scene forensic examination. Full article

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor’s operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e⁻rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e⁻·nJ. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications. Full article