Resonance shifting due to refractive index changes is used quite often in terahertz sensing, but it does not show the advantages of substance identification of terahertz technology. Different from that approach, we ex...Resonance shifting due to refractive index changes is used quite often in terahertz sensing, but it does not show the advantages of substance identification of terahertz technology. Different from that approach, we explored the use of a cavity to enhance the sensitivity of terahertz sensing while retaining the original capability of substance identification. The defect mode of a one-dimensional photonic crystal cavity composed of periodic holes etched into a silicon wire wavegnide was investigated for this purpose. The resonance of the defect mode was designed to match one characteristic absorption frequency of the sample. Due to the high dependence of the defect mode transmission on the material loss, the transmission sensitivity to the quantity of target was amplified significantly. The detection of alactose was used as an example, which demonstrates steady detection with its thickness of a few microns.展开更多
Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and adva...Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,etc.Based on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.展开更多
Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricu...Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.展开更多
文摘Resonance shifting due to refractive index changes is used quite often in terahertz sensing, but it does not show the advantages of substance identification of terahertz technology. Different from that approach, we explored the use of a cavity to enhance the sensitivity of terahertz sensing while retaining the original capability of substance identification. The defect mode of a one-dimensional photonic crystal cavity composed of periodic holes etched into a silicon wire wavegnide was investigated for this purpose. The resonance of the defect mode was designed to match one characteristic absorption frequency of the sample. Due to the high dependence of the defect mode transmission on the material loss, the transmission sensitivity to the quantity of target was amplified significantly. The detection of alactose was used as an example, which demonstrates steady detection with its thickness of a few microns.
基金The work in Section III was supported by the National Science Foundation of China(NSFC)(Nos.52275116,52105112)The work in Section IV was supported by the National Science Foundation of China(NSFC)(Nos.52275117,12127801).
文摘Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,etc.Based on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.
基金This research was funded under EPSRC DTA studentship which is awarded to A.Z.for his PhD.Research Council(DTG EP/N509668/1 Eng).
文摘Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.
