Multispectral Module Research Articles

High transmission efficiency long-wave infrared multispectral modulation array based on nanogap engineering

The long-wave infrared spectrum’s unique advantages in molecular fingerprinting and atmospheric transmission have led to its widespread application in detection, identification, medical diagnosis, and environmental monitoring. Consequently, there has been a strong focus on developing multispectral structure arrays with high transmission efficiency. In this study, we introduce a high-transmission-efficiency nanocoaxes field enhancement structures array based on complex nanogap engineering, exhibiting a transmission of 21.59 % within an open area of 3 % and accompanied by a 90-fold enhancement of the nanogap field. With the modulation of nanogaps, the absolute transmission can exceed 60 %. The experimental data and finite element simulation results of the spectrally tunable array confirm that the origin of resonance is attributed to the enhanced localized electromagnetic modes supported by nanogaps. Furthermore, we demonstrated the proposed nanocoaxes field-enhancement structures in practical applications such as molecular resonance absorption enhancement and material composition analysis. Our work not only provides a method for on-chip multispectral tuning in the long-wave infrared range but also contributes to further advancing the development of long-wave infrared nanophotonic structures.

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor.

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.

Bio-inspired thermal actuator for dynamic multispectral modulation from visible to microwave wavelengths

With the rapid development of photoelectric technology, multispectral modulation materials are highly urgent to eliminate detection probability. However, it remains an extreme challenge to establish the platform for dynamic multispectral modulation from visible to microwave wavelengths. Herein, inspired by the lodging of plants, a thermal actuator (TA) was fabricated by gold layer coating MXene/Fe3O4@polyaniline (MFP) hybrids with the thermal-responsive polyurethane (TPU)-polycaprolactone (PCL) polymer blends. MFP exhibits excellent electromagnetic (EM) wave absorption due to excellent magnetic loss of Fe3O4, electricity loss of MXene, and dielectric loss of polyaniline, respectively. By applying an external thermal field, TA can change the shape from a lodging state to a vertical state due to the shape memory effect of TPU-PCL composite, which endows the TA with an excellent dynamic multispectral modulation ability. In detail, TA achieves a color change from yellow to black in the visible region, a transformation from high reflectance to high emittance in the infrared (IR) region, and a reflection loss (RL) modulation of 54.7 dB in the microwave region. Moreover, the TA exhibits excellent extensibility and shape reconfigurability. It is anticipated that the developed TA may serve as a guide for designing dynamic multispectral camouflage materials.

Double‐Layer Structural Compatible Protective Material with Excellent Microwave Absorption and Heat Shielding Performance

AbstractIn view of the practical demands and mechanistic conflicts between microwave absorption and heat shielding compatibility, a double‐layer composite coating based on moth‐eyed hexagonal anti‐reflective structure is prepared by combining the advantages of metamaterials and coatings. The hexagonal periodic sequential configuration of the upper infrared shield layer (IRSL) can break the continuity of the electric field, induce a networked concentration of the surface current and internal electric field of the microwave absorption layer, thus optimizing the impedance matching and counteract the attenuated absorption performance due to the high dielectric IRSL. In addition, the emissivity of IRSL can be tuned artificially by changing the filler composition and particle size. The structural double‐layer composite coatings have a maximum effective absorption (reflection loss←10 dB) bandwidth of 7.4 GHz in the SKu bands at deep subwavelength thickness, while the infrared emissivity is 0.292. This work provides a novel approach for multispectral modulation in the field of advanced compatible protection systems.

The Efficiency and Repeatability of Multispectral Module for Close-up Photography to Latent Fingerprints

다양한 색상의 배경에 유류된 잠재지문을 여러 가지 색상의 분말로 현출한 후, 기존의 지문촬영 방법(A)과 신규 개발된 복합광원 모듈장비(B)를 이용하여 촬영한 결과를 비교하여 장치의 효율성을 확인하였다. 그리고 장치의 정확성 및 정밀성을 확인하기 위하여 상대표준편차(RSD)를 이용하여 재현성을 검토하였다. 개발된 장치(B)는 시간적 및 공간적으로 많은 절약이 가능하였고, 지문의 다양한 유류 조건과 현출 조건에서 대조비가 좋은 이미지를 얻을 수 있어 자동지문인식시스템(AFIS) 분석결과, 우수한 매칭점수와 특징점수를 확인할 수 있었다. 반복실험을 통하여 재현성을 검토한 결과, 기존의 A 방법에 비하여 B장비의 결과가 유사하거나 다소 우수한 결과를 보여 개발된 B 장비의 정밀성을 나타내었다. 결론적으로, 제한된 범죄현장에서 모듈장치(B)의 활용은 과학수사요원들의 업무 효율을 증가시킬 수 있었다.

Differentiation of basal cell carcinoma and healthy skin using multispectral modulation autofluorescence imaging: A pilot study

An approach for differentiating basal cell carcinoma (BCC) and healthy skin by combining a multispectral modulation autofluorescence imaging with the linear discriminant analysis has been proposed. The experimental setup, which employs a 365-nm narrowband excitation, 4 replaceable bandpass filters and a digital camera, has been assembled and applied to study freshly excised samples of BCC. In the experimental setup, modulation of the UV-excitation and demodulation of the visible light images allow for both increasing a signal-to-noise ratio and suppressing a non-fluorescence background in the autofluorescence images of tissues. The observed results demonstrate an ability for distinguishing both ordinary and keratinized BCC from healthy skin justifying the perspectives of the multispectral modulation autofluorescence imaging use for non-invasive and intraoperative diagnosis of BCC and other low-pigmented malignancies of the skin.

Multispectral Image Processing System Developed in CARTOMORPH Software—NDVI Module

In this paper, we have evaluated the multispectral module implemented in the CARTOMORPH software, which is a public domain software under development by the mathematical morphology research group at FCT/UNESP. The aim of CARTOMORPH is to provide an open software with feature extraction methods and a library that contains a variety of implemented code (functions) that can be easily operated by users through graphical interface. The multispectral image processing system has been developed to allow feature discrimination by operations between bands from Remote Sensing images. The normalized difference vegetation index (NDVI) was selected to be implemented on CARTOMORPH because of its acknowledged performance for monitoring of vegetation and cartographic applications. The experiments were applied to multispectral bands from Quick Bird image and the results were compared with those provided by the SPRING and ENVI software. Former is a Brazilian free software, developed by National Institute for Space Research-INPE and dedicated to image processing and Geographic Information Systems (GIS) analysis, as well as ENVI, which is a traditional remote sensing image analysis system. The results prove that the implementation of this module is correct, allowing potential usage in the field of cartography and for environmental applications.