Imager Research Articles

Ultralong Compositional Gradient Perovskite Nanowires Fabricated by Source-Limiting Anion Exchange.

Anion exchange in halide perovskites offers prospective approaches to band gap engineering for miniaturized and integrated optoelectronic devices. However, the band engineering at the nanoscale is uncontrollable due to the rapid and random exchange nature in the liquid or gas phase. Here, we report a source-limiting mechanism in solid-state anion exchange between low-dimensional perovskites, which readily gives access to ultralong compositional gradient nanowires (NWs) with lengths of up to 100 μm. The exchanged NWs remain single-crystalline with intact morphology, while the halogen content exhibits an apparent gradient distribution, leading to a tapered energy band profile along a NW. In the dynamic study of anion behavior, it is shown that the spatial stoichiometric composition can be precisely tuned following Fick's law of diffusion. In addition, self-powered, spectrally resolved photodetectors incorporating multiple detection units within a single gradient NW are demonstrated. This work provides a feasible strategy for the realization of perovskite-based ultracompact optoelectronics, imaging sensors, and other miniaturized semiconductor devices.

Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets.

Spatial distribution of gamma rays produced by axially symmetric polarized and optical vortex lasers

The polarization properties and spatial distribution of electromagnetic radiation emitted from a high-energy electron can be calculated using the Lienard-Wiechert potentials. These properties are characterized by changes in electron orbitals. In Thomson or Compton scattering by relativistic electrons, which involves the interaction between electrons and a laser, the transverse motion of the electrons is induced by the laser's electric field. The polarization characteristics and spatial distribution of emitted radiation in linearly and circularly polarized laser fields have been extensively studied. In this paper, using a CdTe image sensor, the spatial distribution of MeV gamma rays generated by a 90-degree collision between a 750 MeV electron beam and an axially symmetric polarized laser was measured. The axially symmetric polarized laser, which includes radially and azimuthally polarized components, was created from a linearly polarized laser using a spatially variant retarder known as an s-waveplate. The spatial distribution of gamma rays exhibited a peak at the center, similar to linearly and circularly polarized gamma rays, but differed in overall contour from both. This result suggested that axially symmetric polarized lasers generated gamma rays with different polarization states. Moreover, the contour was observed to change with the polarization axis of the axially symmetric polarized laser, likely owing to the nonuniform distribution of the laser's polarization intensity. Additionally, the spatial distribution of gamma rays produced by circularly polarized optical vortex lasers was measured, showing that their contours corresponded to those of nonvortex circularly polarized lasers. Published by the American Physical Society 2024

PE-MCAT: Leveraging Image Sensor Fusion and Adaptive Thresholds for Semi-Supervised 3D Object Detection

Existing 3D object detection frameworks in sensor-based applications heavily rely on large-scale annotated data to achieve optimal performance. However, obtaining such annotations from sensor data—like LiDAR or image sensors—is both time-consuming and costly. Semi-supervised learning offers an efficient solution to this challenge and holds significant potential for sensor-driven artificial intelligence (AI) applications. While it reduces the need for labeled data, semi-supervised learning still depends on a small amount of labeled samples for training. In the initial stages, relying on such limited samples can adversely affect the effective training of student–teacher networks. In this paper, we propose PE-MCAT, a semi-supervised 3D object detection method that generates high-precision pseudo-labels. First, to address the challenges of insufficient local feature capture and poor robustness in point cloud data, we introduce a point enrichment module. This module incorporates information from image sensors and combines multiple feature fusion methods of local and self-features to directly enhance the quality of point clouds and pseudo-labels, compensating for the limitations posed by using only a few labeled samples. Second, we explore the relationship between the teacher network and the pseudo-labels it generates. We propose a multi-class adaptive threshold strategy to initially filter and create a high-quality pseudo-label set. Furthermore, a joint variable threshold strategy is introduced to refine this set further, enhancing the selection of superior pseudo-labels.Extensive experiments demonstrate that PE-MCAT consistently outperforms recent state-of-the-art methods across different datasets. Specifically, on the KITTI dataset and using only 2% of labeled samples, our method improved the mean Average Precision (mAP) by 0.7% for cars, 3.7% for pedestrians, and 3.0% for cyclists.

3D shape measurement method for high-reflective surface based on a color camera

Fringe projection profilometry (FPP) has been widely utilized in many fields due to its non-contact, high accuracy, high resolution and full-field measurement capabilities. However, the limited dynamic range of the camera sensor can result in overexposure of high-reflective parts in industrial production measurement. To effectively solve the above issue, this paper proposes a 3D shape measurement method for the high-reflective surface based on a color camera. Firstly, the optimal exposure time for the low-reflective region is estimated using the relationship between the standard variance of the phase error and the modulation. Twelve blue phase-shifted fringe patterns and a uniform blue image are utilized to obtain 3D shape of high-reflective surface. Secondly, captured images are separated into red, green and blue components. The fused Gaussian low-pass filtering method with different cut-off frequencies is used to filter and denoise the green or red components and the true intensity of the blue component in the saturated regions is calculated by the unsaturated intensity of the green or red components based on the calibrated crosstalk matrix. Then the image in saturated regions is fused and normalized with the unsaturated region. The absolute phase obtained from the fused normalized fringe patterns is converted into 3D data. Finally, experiments have been carried out on measuring. The experimental results show that the proposed method is capable of obtaining the 3D shape of the surface of a high-reflective object with fewer patterns and high measurement accuracy.

A high consistency ramp circuit design method for negative feedback adaptive adjustment mechanism applied to large area array CMOS image sensors

A high consistency ramp circuit design method for negative feedback adaptive adjustment mechanism applied to large area array CMOS image sensors

Quasi-single-crystal Tin Halide Perovskite Films with High Structural Integrity for Near-infrared Imaging Array enabling Hidden Object Recognition.

Near-infrared (NIR) image sensors based on solution-processed thin film are gaining prominence in various applications, including security detection, remote sensing, medical imaging, and environmental monitoring, owing to superior penetration capabilities of NIR light. However, the reported perovskite image sensors suffer from limited resolution and performance due to poor structural integrity, bioincompatibility, and constrained response wavelength range from lead-based perovskite materials employed. In this study, we present non-toxic quasi-single-crystal (QSC) CH(NH2)2SnI3 (FASnI3) perovskite films, prepared via a simple spin-coating technique, that demonstrate high structural integrity and effective NIR response. Through a series of in situ characterizations, we reveal that the orderly growth mode of QSC-FASnI3 perovskite films enables a more oriented crystal growth with reduced trap and grain boundaries. Consequently, self-powered NIR photodetector based on QSC-FASnI3 films exhibited large detectivity over 1013 Jones in the NIR range (780-890 nm). Ultimately, due to the high structural integrity, the high-resolution 64 × 64 (4096) pixels NIR imaging array, exhibiting reduced photo and dark response non-uniformity value, was fabricated on the thin-film transistors (TFT) backplanes, enabling ultraweak NIR light (63 nW cm-2) real-time imaging, fingerprint imaging, and hidden object recognition. This work pioneers the application of lead-free perovskite for high-resolution NIR imaging arrays.

Quasi‐single‐crystal Tin Halide Perovskite Films with High Structural Integrity for Near‐infrared Imaging Array enabling Hidden Object Recognition

Near‐infrared (NIR) image sensors based on solution‐processed thin film are gaining prominence in various applications, including security detection, remote sensing, medical imaging, and environmental monitoring, owing to superior penetration capabilities of NIR light. However, the reported perovskite image sensors suffer from limited resolution and performance due to poor structural integrity, bioincompatibility, and constrained response wavelength range from lead‐based perovskite materials employed. In this study, we present non‐toxic quasi‐single‐crystal (QSC) CH(NH2)2SnI3 (FASnI3) perovskite films, prepared via a simple spin‐coating technique, that demonstrate high structural integrity and effective NIR response. Through a series of in situ characterizations, we reveal that the orderly growth mode of QSC‐FASnI3 perovskite films enables a more oriented crystal growth with reduced trap and grain boundaries. Consequently, self‐powered NIR photodetector based on QSC‐FASnI3 films exhibited large detectivity over 1013 Jones in the NIR range (780‐890 nm). Ultimately, due to the high structural integrity, the high‐resolution 64 × 64 (4096) pixels NIR imaging array, exhibiting reduced photo and dark response non‐uniformity value, was fabricated on the thin‐film transistors (TFT) backplanes, enabling ultraweak NIR light (63 nW cm‐2) real‐time imaging, fingerprint imaging, and hidden object recognition. This work pioneers the application of lead‐free perovskite for high‐resolution NIR imaging arrays.

Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network “HSC3D” for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

Water-to-air unmanned aerial vehicle (UAV) based rolling shutter optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN)

Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted in oceanic research activities. As the number of underwater sensors and AUVs is growing quickly, the bandwidth requirements are increasing accordingly. In this work, we put forward and demonstrate a large field-of-view (FOV) water-to-air unmanned aerial vehicle (UAV) based optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN) for the first time to the best of our knowledge. As the UAVs are embedded with complementary-metal-oxide-semiconductor (CMOS) cameras, there is no need to install OCC receivers (Rxs), reducing the deployment cost. Moreover, the large photo-sensitive area of the CMOS camera can support a large FOV OCC transmission without the need for precise optical alignment. Here, by utilizing the column matrix identification during the rolling shutter pattern decoding in the CMOS image sensor, the scintillation caused by water turbulence can be reduced. Besides, in the outdoor and windy environment, the UAV will experience significant movement caused by the wind making it very difficult to capture stable OCC frames in the CMOS image sensor. Here, we propose and demonstrate utilizing GRU-NN, which is a special realization of the recurrent neural network (RNN) with memory cells capable of learning the time-domain dependent signals. It is shown that the GRU-NN can learn effectively from successive image frames in time-domain and produce correct prediction even under the windy and unstable UAV flying environment. Experimental results reveal that the proposed GRU-NN can outperform the previous pixel-per-symbol labeling neural network (PPS-NN), and also can significantly reduce the computation time when compared with long-short-term-memory-neural-network (LSTM-NN). The proposed system can decode 4-level pulse-amplitude-modulation (PAM4) rolling shutter OCC patterns at data rates of 5.4 kbit/s and 3.0 kbit/s under clear and cloudy water, respectively, fulfilling the pre-forward error correction bit-error rate (pre-FEC BER = 3.8 × 10−3). We also demonstrate that the UAV based OCC system can support data rates of 5.4 kbit/s, 4.2 kbit/s, and 3.0 kbit/s at distances of 2.2 m, 3.2 m, and 4.2 m, respectively, at outdoor and windy environments.

Recent Advances in Thermally Activated Delayed Fluorescence-Based Organic Afterglow Materials.

Thermally activated delayed fluorescence (TADF)-based materials are attracting widespread attention for different applications owing to their ability of harvesting both singlet and triplet excitons without noble metals in their structures. As compared to the conventional fluorescence and room-temperature phosphorescence pathways, TADF originates from the reverse intersystem crossing process from the excited triplet state (T1) to the singlet state (S1). Therefore, TADF emitters enabling activated and long lifetime T1 excitons are potential candidates for generating long-lived afterglow emission, an effect that can still be observed for a while by the naked eye after the removal of the excitation light source. Recently, TADF-based organic afterglow materials featuring high photoluminescence quantum yields and long lifetimes above 100 ms under ambient conditions, have emerged for advanced information security, high-contrast biological imaging, optoelectronic devices, and intelligent sensors, whereas the related systematic review is still lacking. Herein, the recent progress in TADF-based organic afterglow materials is summarized and an overview of the photophysical mechanism, design strategies, and the performances for relevant applications is given. In addition, the challenge and perspective of this area are given at the end of the review.

High-Performance Processing Electronics for Cooler Temperature Sensors in Infrared Payloads

Remote Sensing Infra-Red (IR) imaging sensors operate at very low temperatures (40–100 K). Generally, these types of imaging sensors are integrated with cryo-coolers. These sensors have an inbuilt 2N2222 diode-based cooler temperature sensor (CTS). This paper presents details of low-noise, high=precision processing electronics developed for CTS. Critical design aspects, simulation and hardware results of the system are discussed in this paper. Developed electronics systems achieve ≈5mK resolution and the power dissipation recorded is < 200 mW.

Multi-Scale and Multi-Network Deep Feature Fusion for Discriminative Scene Classification of High-Resolution Remote Sensing Images

The advancement in satellite image sensors has enabled the acquisition of high-resolution remote sensing (HRRS) images. However, interpreting these images accurately and obtaining the computational power needed to do so is challenging due to the complexity involved. This manuscript proposed a multi-stream convolutional neural network (CNN) fusion framework that involves multi-scale and multi-CNN integration for HRRS image recognition. The pre-trained CNNs were used to learn and extract semantic features from multi-scale HRRS images. Feature extraction using pre-trained CNNs is more efficient than training a CNN from scratch or fine-tuning a CNN. Discriminative canonical correlation analysis (DCCA) was used to fuse deep features extracted across CNNs and image scales. DCCA reduced the dimension of the features extracted from CNNs while providing a discriminative representation by maximizing the within-class correlation and minimizing the between-class correlation. The proposed model has been evaluated on NWPU-RESISC45 and UC Merced datasets. The accuracy associated with DCCA was 10% and 6% higher than discriminant correlation analysis (DCA) in the NWPU-RESISC45 and UC Merced datasets. The advantage of DCCA was better demonstrated in the NWPU-RESISC45 dataset due to the incorporation of richer within-class variability in this dataset. While both DCA and DCCA minimize between-class correlation, only DCCA maximizes the within-class correlation and, therefore, attains better accuracy. The proposed framework achieved higher accuracy than all state-of-the-art frameworks involving unsupervised learning and pre-trained CNNs and 2–3% higher than the majority of fine-tuned CNNs. The proposed framework offers computational time advantages, requiring only 13 s for training in NWPU-RESISC45, compared to a day for fine-tuning the existing CNNs. Thus, the proposed framework achieves a favourable balance between efficiency and accuracy in HRRS image recognition.

New Yellow Azo Pyridone Derivatives with Enhanced Thermal Stability for Color Filters in Image Sensors

Two new yellow azo pyridone derivatives, (E)-6-hydroxy-1-(3-methoxypropyl)-4-methyl-2-oxo-5-(p-tolyldiazenyl)-1,2-dihydropyridine-3-carbonitrile (APY-M) and 5,5′-((1E,1′E)-(methylenebis(4,1-phenylene))bis(diazene-2,1-diyl))bis(6-hydroxy-4-methyl-2-oxo-1,2-dihydropyridine-3-carbonitrile) (APY-D), were designed and synthesized as yellow colorants for image sensors. The properties of these new compounds were evaluated in both solution and color filter film forms, focusing on their optical and thermal characteristics. The molar extinction coefficient values of APY-M and APY-D in solution were found to be 2.7 × 105 and 3.0 × 105 L/mol·cm, respectively. The transmittance of the newly synthesized compounds met commercial requirements, showing values below 0.21% at 435 nm and above 97.1% at 530 nm. APY-D exhibited a molar extinction coefficient value in solution that was 1.15 times higher than that of the commercially used yellow colorant Disperse Yellow 241. Both newly synthesized compounds satisfied the decomposition temperature requirement of over 230 °C, which is essential for the color filter manufacturing process in image sensors. In particular, APY-D, with its dimeric structure and increased molecular weight, demonstrated enhanced thermal stability, with a 50 °C increase in decomposition temperature compared to Disperse Yellow 241. Color filter films for image sensors were fabricated using the new compounds, and their thermal resistance was evaluated. APY-D maintained its transmittance due to the enhanced thermal stability provided by its dimer structure and increased molecular weight. Consequently, APY-D is anticipated to be a promising candidate for use as a yellow colorant in image sensors, owing to its excellent optical and thermal properties.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

Critically Reckoning Spectrophotometric Detection of Asymptomatic Cyanotoxins and Faecal Contamination in Periurban Agrarian Ecosystems via Convolutional Neural Networks

Based on a systematic review of convolutional neural networks (CNN), this study explores the efficacy of small imaging sensors in monitoring the real-time presence of cyanotoxins and hazardous contaminants in urban ecosystems. To develop a machine learning-based CNN, this study first investigated the relationships between the prevalence of hazardous algal blooms (HABs) and faecal indicator bacteria (FIB) in waterways and aquifers of certain semi-arid zones of Sri Lanka, Sweden and New York (United States). By incorporating a popularly known AbspectroscoPY framework to effectively process the spectrophotometric data of the obtained samples, the formulation subsequently reveals strong positive correlations between FIB coliforms and nutrient loads (particularly nitrate and phosphate). A corroborative association with the incidence of chronic kidney disease of uncertain aetiology (CKDu) among the residents of the studied regions further affirms the reliability of the methodology. These findings underline the need for policymakers to consider the geographical and land-use traits of urban habitats in strategies aimed at reducing water-borne health hazards. HIGHLIGHTS This study examines the link between hazardous algal blooms (HABs) and faecal indicator bacteria (FIB) in the semi-arid habitats of Sri Lanka, Sweden, and New York, USA. Quantitative Phase Imaging based on a convolutional neural network (CNN) model helps monitor cyanobacterial incursions in peri-urban agrarian ecosystems. AbspectroscoPY-enabled spectrophotometric data analysis reveals strong positive correlations between the prevalence of FIB coliforms and nutrient loads, particularly those of nitrates and phosphates. Reliability of proposed machine learning-based CNNs is validated by the corroborative incidences of chronic kidney diseases among residents of the studied regions. GRAPHICAL ABSTRACT

Highly Amplified Broadband Ultrasound in Antiresonant Hollow Core Fibers

High‐frequency broadband ultrasound in nested antiresonant hollow core fibers (NANFs) is investigated for the first time. NANFs have remarkable features enabling high‐resolution microscale optoacoustic imaging sensors and neurostimulators. Solid optical fibers have been successfully employed to measure and generate ultrasonic signals, however, they face issues concerning attenuation, limited frequency range, bandwidth, and spatial resolution. Herein, highly efficient ultrasonic propagation in NANFs from 10 to 100 MHz is numerically demonstrated. The induced pressures and sensing responsivity are evaluated in detail, and important parameters for the development of ultrasonic devices are reviewed. High pressures (up to 234 MPa) and sensing responsivities (up to −207 dB) are tuned over 90 MHz range by changing the diameters of two distinct NANF geometries. To the best of knowledge, this is the widest bandwidth reported using similar diameter fibers. The results are a significant advance for fiber‐based ultrasonic sensors and transmitters, contributing to improve their efficiency and microscale spatial resolution for the detection, diagnosis, and treatment of diseases in biomedical applications.

Detection of morphine and data processing using surface plasmon resonance imaging sensor

Abstract Based on the surface plasmon resonance imaging (SPRi) instrument, we established a new method of analyzing morphine in urine by processing a calibration curve. According to an inhibition immunoassay, gradient concentration of morphine and morphine-BSA fixed on the chip competitively combine with morphine antibody on the chip. Given the three mathematical models, the data of SPRi signals generated from SPRi with morphine was processed to obtain the calibration curve. Ultrafiltration was used to pretreat blank urine samples with adding morphine, and then investigated the advantages and disadvantages of each model. With a limit detection of 6.57 ng·mL−1, the method and mathematical models can provide robust support for SPRi sensors used in further environmental detection, such as the epidemiological study of sewage.

Research on high dynamic pixel structure based on adaptive integral capacitor

Infrared image sensing technology has attracted wide attention due to its advantages such as being unaffected by the environment, good target recognition, and strong anti-interference ability. However, with the improvement of the integration of infrared focal planes, the constraints between the dynamic range, noise, and full-shade of the optoelectronic system are particularly prominent. Therefore, in order to solve the contradiction between noise in weak light and full-shade capacity in strong light, in a 5 T infrared pixel circuit, the relationship between the capacitance value and voltage of the inverted MOS capacitor in a specific voltage range is used to make the integral capacitance of the infrared image sensor automatically change from 6.5 fF to 37.5 fF. A high dynamic pixel structure based on adaptive integral capacitance is proposed. Based on 55 nm CMOS process technology, 12288 × 12288its performance parameters are studied in a pixel-scale infrared sensor. The research results show that 5.5m × 5.5mthe small-size pixel has a large full-shade capacity of 1.31 Me- and a variable conversion gain, a noise of less than 0.43 e, and a dynamic range of more than 130 dB.

Reliable person identification using a novel multibiometric image sensor fusion architecture

Reliable person identification using a novel multibiometric image sensor fusion architecture