Optic Detection Research Articles

Fiber optic quench detection for large-scale HTS magnets demonstrated on VIPER cable during high-fidelity testing at the SULTAN facility

Fiber-optic thermometry has the potential to provide rapid and reliable quench detection for emerging large-scale, high-field superconducting magnets fabricated with high-temperature-superconductor (HTS) cables. Developing non-voltage-based quench detection schemes, such as fiber Bragg grating (FBG) technology, are particularly important for applications such as magnetic fusion devices where a high degree of induced electromagnetic noise impose significant challenges on traditional voltage-based quench detection methods. To this end, two fiber optic quench detection techniques—FBG and ultra-long FBG (ULFBG)—were incorporated into two vacuum pressure impregnated, insulated, partially transposed, extruded, and roll-formed (VIPER) high-current HTS cables and tested in the SULTAN facility, which provides high-fidelity operating conditions to large-scale superconducting magnets. During surface heater induced quench-like events under a variety of operating conditions, FBG and ULFBG demonstrated strong signal-to-noise ratios (SNRs) ranging from 4 to 32 and measured single-digit temperature excursions; both the SNR and temperature sensitivity increase with temperature. Fiber thermal response times ranged between effectively instantaneous to a few seconds depending on the operating temperature. Strain sensitivity dominates the thermal sensitivity in the conditions achievable at SULTAN; however, measurements at higher quench evolution temperatures, coupled to future work to increase the thermal-to-strain signal, show promise for quench detection capability in full-scale magnets where temperature and strain may occur simultaneously. Overall, FBG and ULFBG were proven capable to quickly and reliably detect small temperature disturbances which induced quench initiation events for high current VIPER HTS conductors in realistic operating conditions, motivating further work to develop FBG and ULFGB quench detection systems for full-scale HTS magnets.

Optic disc detection based on visual saliency in fundus image

Optic disc detection based on visual saliency in fundus image

First Observations of Elves and Their Causative Very Strong Lightning Discharges in an Unusual Small‐Scale Continental Spring‐Time Thunderstorm

AbstractWe show for the first time that elves can be produced by an unusual small‐scale continental spring‐time thunderstorm. The storm occurred in Central Europe, covered a very small area of ∼50 × ∼30 km and lasted only for ∼4 h on April 2, 2017. The fraction of intense positive cloud‐to‐ground lightning strokes was unusually high, reaching 55%, with a mean peak current of 64 kA. The peak currents of return strokes (RS) associated with elves exceeded ∼300 kA. Elves and their causative RS have been observed with different optical and electromagnetic recordings. Signatures of ionospheric disturbances indicating the presence of elves were found in measurements of displacement currents, ionospheric reflections of sferics and man‐made narrow‐band transmissions. All these electromagnetic observations coincide with four optical detections of elves and strongly suggest the occurrence of two more elves later in the decaying phase of the storm. Surprisingly, the same electromagnetic measurements indicate that other strong strokes did not produce any elves. Our simulation results show that the formation of an elve is not only determined by the high‐peak current of their causative strokes but that it is also controlled by the conductivity of the lightning channels and velocity of the current wavefront. We hypothesize that because of a lower conductivity of RS lightning channels and/or slower current waves only very strong strokes with peak currents above ∼300 kA might have been capable to produce observable elves during this thunderstorm.

Comparative study of 1 DD and 2 DD radius conventional internal limiting membrane peeling in large idiopathic full-thickness macular holes: a randomized controlled trial.

To compare the surgical outcomes of 1-disc diameter (DD) and 2-DD conventional internal limiting membrane peeling (C-ILMP) in large full-thickness macular holes (FTMHs). A prospective randomized controlled trial. One hundred patients with large idiopathic full-thickness macular hole (FTMH) were randomized into C-ILMP and extended C-ILMP (EC-ILMP) groups. The primary outcome was closure rate at 6 months after surgery. Secondary outcomes were visual acuity (VA), closure type, consequence of ILMP and complications. The mean symptom duration was 12.19 ± 9.64 months. Mean preoperative VA was 1.25 ± 0.37 logMAR. The average minimum linear diameter was 633.05 ± 129.82 µm and basal linear dimension was 1158.49 ± 249.07 µm. The two groups did not differ in term of demographic data. Closure rate was significantly higher in the EC-ILMP group (76.47% vs. 51.02%, 95% CI 7.24-43.66; p = 0.008). There were also no significant differences in closure type, central foveal thickness, dissociated optic nerve fibre layer detection, or change in fovea-to-disc distance. There were also no significant differences in postoperative VA (p = 0.069) or visual improvement (mean 0.39 ± 0.43 logMAR; p = 0.286). According to subgroup analysis, EC-ILMP resulted in a higher closure rate in patients with chronic FTMH for >6 months, (p = 0.008). Furthermore, EC-ILMP resulted in better anatomical closure and visual result in patients with FTMH with macular hole closure index ≤0.5, p = 0.003 and p = 0.010, respectively. Extended C-ILMP yielded a significantly higher closure rate in large FTMHs, but visual outcome did not differ significantly. According to subgroup analysis, extended C-ILMP was more effective in chronic large FTMH with MHCI ≤ 0.5.

Fluorescent Nanodiamonds Embedded in Poly-ε-Caprolactone Fibers as Biomedical Scaffolds

Fluorescent nanodiamonds (fNDs) are emerging as important tools for imaging and sensing in biology, which enable the optical detection, for example, of temperature and magnetic fields at the nanoscale. At the same time, their unique physicochemical properties allow fNDs to drastically improve the properties of nanocomposites. Here, we report the integration of fNDs into electrospun poly-ϵ-caprolactone (PCL) fibers and the use of the resulting hybrid material as a nontoxic and multifunctional bioscaffold. We investigate the morphology, size distribution, optical properties, wettability, and biocompatibility of PCL fibers containing 0.2 and 0.4 wt % fNDs and demonstrate the quantum sensing capability of the nanocomposite via optically detected magnetic resonance measurements of the nitrogen-vacancy center in fNDs. We find that the use of ethanol as a cosolvent improves the dispersion of fNDs into PCL fibers and reduces the occurrence of fiber defects. We demonstrate that the PCL/fND scaffold is not cytotoxic for mesenchymal stem cells and even promotes cell adhesion and cell proliferation up to 21 days because of an increase in wettability compared to pure PCL fibers. Our results highlight the immense potential of PCL/fND nanocomposites as smart bioscaffolds for advanced biomedical applications such as tissue engineering, biosensing, and theranostics.

Switchable and adjustable AIE activity of Pt(II) complexes achieving swift-responding and highly sensitive oxygen sensing

AIE-active luminogens are attractive for bioimaging, sensors, and optical detections. However, controllably switching and adjusting the AIE activity of luminogens in aqueous media to perform the best of AIE performance under appropriate water fraction remains a challenge. Herein, we report the first case of switchable and adjustable AIE activity based on Pt(II) complexes. The AIE activity of Pt(II) complexes in aqueous media can be flexibly switched and adjusted via finely tuning the rotation activity of the substituents. And the rotation activity of the substituents have been experimentally confirmed by the luminescent lifetimes and theoretically verified by the dipole moments of Pt(II) complexes. Furthermore, luminescent oxygen sensing films were constructed by combining the AIE-active Pt(II) complexes with commercially available, low-cost ethyl cellulose (EC). With the attenuation of EC film thickness, swift-responding quenching/recovering cycles (7.7 s) of the PtTPA-EC(N10) oxygen sensing film were demonstrated. And the controllable AIE property of Pt(II) complexes can effectively improve the oxygen sensitivity and the homogeneity of oxygen sensing films.

Diagnostic performance of MRI of post-laminar optic nerve invasion detection in retinoblastoma: A systematic review and meta-analysis.

Preoperative MRI detection of post-laminar optic nerve invasion (PLONI) offers guidance in assessing the probability of total tumor resection, an estimation of the extent of surgery, and screening of candidates for eye-preserving therapies or neoadjuvant chemotherapies in the patients with retinoblastoma (RB). The purpose of this systematic review and meta-analysis was to evaluate the diagnostic performance of MRI for detecting PLONI in patients with RB and to demonstrate the factors that may influence the diagnostic performance. Ovid-MEDLINE and EMBASE databases were searched up to January 11, 2020, for studies identifying the diagnostic performance of MRI for detecting PLONI in patients with RB. The pooled sensitivity and specificity of all studies were calculated followed by meta-regression analysis. Twelve (1240 patients, 1255 enucleated globes) studies were included. The pooled sensitivity was 61%, and the pooled specificity was 88%. Higgins I2 statistic demonstrated moderate heterogeneity in the sensitivity (I2= 72.23%) and specificity (I2= 78.11%). Spearman correlation coefficient indicated the presence of a threshold effect. In the meta-regression, higher magnetic field strength (3T than 1.5T), performing fat suppression, and thinner slice thickness (< 3mm) were factors causing heterogeneity and enhancing diagnostic power across the included studies. MR imaging was demonstrated to have acceptable diagnostic performance in detecting PLONI in patients with RB. The variation in the magnetic field strength and protocols was the main factor behind the heterogeneity across the included studies. Therefore, there is room for developing and optimizing the MR protocols for patients with RB.

Single-layer dielectric metasurface with giant chiroptical effects combining geometric and propagation phase

Planar chiral optics play an important role in modern optical detections, for example, imaging systems. Here, we extend the scheme to design planar chiral devices by combining both the geometric phase and propagation phase. Both the propagation and geometric phase can be used to modulate wavefront for the single-layer chiral metasurface. The supercells, containing interference effect among adjacent building blocks inside, are constructed to achieve a strong chiral effect. The circular dichroism in transmission of metasurface with periodic arrangement of supercells can reach as high as 0.937 at mid-infrared range. Meanwhile, the supercells on metasurface exhibit the ability of geometric and propagation phase modulation, which is employed to realize chiral metalenses supporting spin-selective focusing effects. We believe that the proposed scheme could be readily extended for designing compact and miniaturized chiral devices and promote their practical applications.

Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection

The retina is the part of the eye that protects parts of light-sensitive cells. The retina consists of four main parts, namely the blood vessel system, fovea, macula and optic discs. Blood vessels are one of the characteristics that can help in the diagnosis of various retinal diseases. This research discusses the application of algorithms for detection of blood vessels in optic discs. The blood vessels are segmented using the Maximum Principal Curvature algorithm. Before the segmentation process, images are filtered using a Gaussian filters and the optic disc removal is performed. After that the segmentation of vessels uses Wolf thresholding to convert images into binary images. The final step in blood vessels segmentation is removing fine lines that are not vessels using morphological operations. Optic disc detection is done using Prewitt edge detection and circular Hough transform. In optic disc detection, the input image is converted to grayscale and then complemented and improved contrast using contrast-limited adaptive histogram equalization. Then, the opening morphology and median filter were performed. After that, the Prewitt edge and circular Hough transform methods are applied to detect the location of the optic disc. After getting the blood vessel segmentation and knowing the location of optic disc, the last stage is combining the results of blood vessel segmentation with the location of the optic disc. The methods applied are quite efficient in detecting blood vessels at the optic location of the disc.

Photon-echo detected nuclear quadrupole resonance spectroscopy

Rare-earth-ion-doped crystals (REICs) have played an important role in quantum information processing due to their excellent coherent properties. In order to obtain the information regarding the hyperfine structures of the rare-earth ions in REICs, optically detected nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) techniques based on RF resonance and various optical detection methods are widely employed in previous works. Here we demonstrate a new method of NQR spectroscopy based on the photon-echo detection. The hyperfine spectra of the ground state (7F0) and the optically-excited state (5D0) of 151Eu3+ in Y2SiO5 at zero field are obtained. This method can determine the hyperfine splittings within the ground state and the optically-excited state and is shown to be robust against electrical noise. Our results provide an alternative way for optical detection of NMR and NQR with high signal-to-noise ratio.

Lake area monitoring based on land surface temperature in the Tibetan Plateau from 2000 to 2018

Lake area change in the Tibetan plateau is an important indicator for climate change assessment. To overcome the temporal inconsistency of optical remote sensing-based lake area detections, a land surface temperature (LST)-based detection scheme was proposed by utilizing the big difference between land and water surface temperatures. A trend test conducted by the Mann–Kendall (MK) method was successfully applied to investigate lake area variation from 2000 to 2018 with the use of the annual mean temperature information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST daily product. A comparison with the monitoring results from Landsat images indicates the proposed method can provide spatial distributions of lake area change with high accuracy. More importantly, the temporal variation of annual mean LST provides a special way to detect the abrupt change year (ACY) in lake area.The ACYs of most lakes mainly occur from 2004 to 2012. For an individual lake, the ACY offers vital information about the lake area change process. In summary, this work demonstrates the good potential of the LST-based method for lake area monitoring and assessment.

A fiber optic nanoplasmonic biosensor for the sensitive detection of ampicillin and its analogs.

A novel optical immunosensor for the screening of ampicillin (Amp) residues has been developed. The biosensor is based on fiber optic particle plasmon resonance detection and uses an enhancement method called as fiber optic nanogold-linked immunosorbent assay (FONLISA) for the sensitive detection of antibiotics. A commercial antibody which had a higher affinity for ampicillin than for other β-lactam antibiotics was chosen. A surface competitive binding assay was used in which a fixed concentration of antibiotic-conjugated gold nanoparticles (AuNPs) competes with free unlabeled antibiotic molecules to measure the amount of binding with antibody molecules immobilized on an optical fiber. The synthesis of the 11-mercaptoundecanoic acid (MUA)-ampicillin conjugate facilitates the attachment of the Amp molecules to AuNPs via MUA which acts as a linker between them. This AuNP-Amp conjugate was then used for the detection of β-lactam antibiotics. The practical limit of detection obtained for Amp was 0.74ppb (7.4 × 10-10g/mL) which is lower than the recommended maximum residue limit (MRL) for β-lactams. The method also shows a wide linear range of 4 orders. Its applicability to the determination of ampicillin in spiked milk samples has been demonstrated with good recovery and reproducibility. Graphical abstract.

Optical Capacitance/Conductance-Voltage Characteristics of Stored Charges in Organic Light-Emitting Diodes.

In this paper, capacitance/conductance-voltage characteristics (C/G-V) under illumination was achieved to investigate the dynamic mechanism of stored charges in OLEDs with a structure of ITO/ PEDOT:PSS/PMMA/Alq3/Al. For all devices, at least two peaks presented in the optical capacitance-voltage curve. Compared to curves of devices under dark, the first peak increased remarkably with a deviation to Vbi, which can be explained in the form of stored charges combined with the optical conductance characteristics. It was also found that a great decrease in capacitance is followed by the collapse of the first peak with PMMA thickness increased. It can account for the presence of interfacial charges, which is proved further by the conductance curves. To the device with 10 nm PMMA, a third peak took place in optical capacitance and it was due to the storage of electrons by PMMA. Also, the first capacitance peak enhanced approximate linearly as the illumination power increased, which can verify the contribution of the stored charges. Additionally, it shows the potential for the stored charges in optical detections.

A novel hybrid segmentation approach for optic papilla detection in high resolution fundus images of retina

The paper proposes a novel method for segmenting optic papilla (OP) from the high-resolution fundus (HRF) image. For diagnosing eye-related diseases like Glaucoma, Diabetic Retinopathy, fine changes in OP must be examined. To examine the OP, its region should be exactly segmented from the fundus images of the retina. Major problems in accurately segmenting the OP are: 1) features of OP and exudates are similar and 2) the region behind the optic nerve head is difficult to locate. To overcome these problems and acquire precise segmentation a novel hybrid segmentation algorithm is developed using morphological image processing techniques and entropy filtering. A novel region selection algorithm based on Euclidean distance is proposed to remove the regions around the OP. When these regions are removed, the papilla region can be located easily. Then, active contour is applied to segment the OP. Most of the researchers have done OP localization than segmentation. The proposed method automatically locates the OP and segments it from the image. The proposed algorithm is evaluated by computing sensitivity, specificity, and accuracy. These metrics are computed using the proposed segmentation results against the ground truth data. To check the efficiency of the proposed algorithm, it is tested with low-resolution images in DRIONS. The proposed algorithm achieves 99.45% and 99.51% of accuracy for HRF and DRIONS datasets respectively.

A novel method for optic disc detection in retinal images using the cuckoo search algorithm and structural similarity index

Accurate and reliable optic disk (OD) localization is vital for eye disease monitoring and fundus image analysis. This paper describes a novel technique to localize the OD in retinal images using the cuckoo search algorithm and the structural similarity index measure (SSIM). SSIM uses the average OD value to compare with candidate OD. Hence, the average OD values were calculated from randomly selected images. The average OD values and the colored retina fundus images were given as input to the proposed algorithm. The adaptive histogram equalization method was applied to ensure that the brightness and contrast values in all images were within a similar range. Next, candidate OD centers were calculated using the search algorithm and the similarity value between each candidate OD and the average OD was determined. Finally, the computed similarity was maximized by the search algorithm and the true OD center was found. The performance of the OD detection algorithm was evaluated on three public datasets. The experimental results showed that proposed method achieved comparable performance, without employing complex image pre-processing, compared with the state-of-the-art techniques. Specifically, the accuracy of 100%, 100%, and 97.5% were obtained for ONHSD, DRIONS and DRIVE datasets, respectively.

Diagnosis of Glaucoma using ARM Processor on Retinal Image

The abstract is to be in fully-justified italicized text as it is here, below the author information. Use the word Glaucoma is the disease which effect on the human eye which may cause blindness if not treated properly. Glaucoma occurs due to increased pressure called Intra Ocular Pressure (IOP). The glaucoma detection will be carried out using Image Processing Methods like NFL (Nerve Fibre Layer) defects detection and texture analysis, Neuro Retinal Optic Cup Detection and Image Segmentation. For the development of the system, ARM based processor and high-resolution night vision camera is used. Using Camera module will capture fundus images, image processing algorithm will process this image further with edge detection and apply various techniques for detection and correction of medical images. This research work can detect Glaucoma at preliminary stages. It is helpful for medical practitioner and researchers as well as patients. The proposed method is automated, portable, cheapest and more accurate and efficient.

Automated macula proximity diagnosis for early finding of diabetic macular edema

Diabetic retinopathy (DR) is non-recoverable in nature. One of the advanced sight threatening condition in diabetic patient is defined in terms of diabetic macular edema (DME), where the macula gets deposited with fluid rich in proteins called exudates. It is indisputably required to find and treat occurrence of exudates near macula in time, to avoid further complications of retina and vision loss at later stage. However, presence of various dark and bright lesions awakes the need of reliable macula and exudate detection process. Proposed work intends to present a robust, scale, and rotation invariant tool for early diagnosis of DME. Optic disc (OD) diameter is one of the important parameters; it is always proportional to the size of the retina; an adaptive method based on pixel count and histogram-based segmentation is used for optic disc detection. Center of macula is detected based on estimated optic disc radius calculations and anatomical priors. In order to reduce computational burden, two disc diameter (DD) region near macula is extracted. Hard exudates are extracted with unique combination of CLAHE and morphological operations, followed by Kirsch’s mask for sharp edge detection of hard exudates, and further morphological operations for noise removal. OD detection algorithm is evaluated on four different databases DiaretDB1, MESSIDOR, DRIONS-DB, and images from local hospital, overall sensitivity of 95.71% is achieved, highest sensitivity value is 98%, and is achieved on first 100 images from MESSIDOR database. Further DiaretDB1 and images from local hospital are processed further for macula detection and exudates finding in macular proximity, as DiaretDB1 is rich in variety of DR lesions and images from the local hospital to deal with real-time issues. Finding macula region is achieved with an overall success rate of 91.93% and overall sensitivity of 99% is reported on both the chosen databases. Proposed work provides an automated tool, for early diagnosis of diabetic macular edema. The method is straightforward, robust and computationally less complex and improved success rates for feature, and lesion detections are achieved as compared with state-of-the-art methods.

Deep Learning and Transfer Learning for Optic Disc Laterality Detection: Implications for Machine Learning in Neuro-Ophthalmology.

Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood vessel segmentation, can effectively identify these diseases. The existing methods lack sufficient discrimination power for the fundus image and are easily affected by pathological regions. This paper proposes a novel multi-path recurrent U-Net architecture to achieve the segmentation of retinal fundus images. The effectiveness of the proposed network structure was proved by two segmentation tasks: optic disc and optic cup segmentation and retinal vessel segmentation. Our method achieved state-of-the-art results in the segmentation of the Drishti-GS1 dataset. Regarding optic disc segmentation, the accuracy and Dice values reached 0.9967 and 0.9817, respectively; as regards optic cup segmentation, the accuracy and Dice values reached 0.9950 and 0.8921, respectively. Our proposed method was also verified on the retinal blood vessel segmentation dataset DRIVE and achieved a good accuracy rate.

Dual Photonic–Phononic Crystal Slot Nanobeam with Gradient Cavity for Liquid Sensing

A dual photonic–phononic crystal slot nanobeam with a gradient cavity for liquid sensing is proposed and analyzed using the finite-element method. Based on the photonic and phononic crystals with mode bandgaps, both optical and acoustic waves can be confined within the slot and holes to enhance interactions between sound/light and analyte solution. The incorporation of a gradient cavity can further concentrate energy in the cavity and reduce energy loss by avoiding abrupt changes in lattices. The newly designed sensor is aimed at determining both the refractive index and sound velocity of the analyte solution by utilizing optical and acoustic waves. The effect of the cavity gradient on the optical sensing performance of the nanobeam is thoroughly examined. By optimizing the design of the gradient cavity, the photonic–phononic sensor has significant sensing performances on the test of glucose solutions. The currently proposed device provides both optical and acoustic detections. The analyte can be cross-examined, which consequently will reduce the sample sensing uncertainty and increase the sensing precision.