Colorimetric characterization of imaging device by total color difference minimization

Colorimetric characterization is to transform the device-dependent responses to device-independent colorimetric values, and is usually conducted in CIEXYZ space. However, the optimal solution in CIEXYZ space does not mean the mini-mization of perceptual error. A novel method for colorimetric characterization of imaging device based on the minimization of total color difference is proposed. The method builds the transform between RGB space and CIELAB space directly using the downhill simplex algorithm. Experimental results showed that the proposed method performs better than traditional least-square (LS) and total-least-square (TLS) methods, especially for colors with low luminance values.

Optimized cardiac magnetic resonance imaging inversion recovery sequence for metal artifact reduction and accurate myocardial scar assessment in patients with cardiac implantable electronic devices

Late gadolinium enhancement(LGE) cardiovascular magnetic resonance(CMR) is the gold standard for imaging myocardial viability.An important application of LGE CMR is the assessment of the location and extent of the myocardial scar in patients with ventricular tachycardia(VT), which allows for more accurate identification of the ablation targets.However, a large percentage of patients with VT have cardiac implantable electronic devices(CIEDs), which is a relative contraindication for cardiac magnetic resonance imaging due to safety and image artifact concerns.Previous studies showed that these patients can be safely scanned on 1.5 T scanners provided that an adequate imaging protocol is adopted.Nevertheless, imaging patients with a CIED result in metal artifacts due to the strong frequency off-resonance effects near the device; therefore, the spins in the surrounding myocardium are not completely inverted, and thus give rise to hyperintensity artifacts.These artifacts obscure the myocardial scar tissue and limit the ability to study the correlation between the myocardial scar structure and the electro-anatomical map during catheter ablation.In this study, we developed a modified inversion recovery technique to alleviate the CIED-induced metal artifacts and improve the diagnostic image quality of LGE images in patients with CIEDs without increasing scan time or requiring additional hardware.The developed technique was tested in phantom experiments and in vivo scans, which showed its capability for suppressing the hyperintensity artifacts without compromising myocardium nulling in the resulting LGE images.

Perspective on the imaging device based on perovskite materials

Large light absorption coefficients,tunable bandgaps,high tolerance to defects,long carrier lifetimes as well as diffusion lengths render lead halide perovskite materials ideal candidates for optoelectronic devices.Except application in solar cell,photodetectors based on perovskite materials have been recognized as another game changer due to the achievements such as high responsivity of 1.9×104 A/W[1],gain factor larger than 5.0×104[1],large detectivity of 1014 J[2],high on/off ratio of 105[3],fastest response time down to 1 ns[4],large linear dynamic range exceeding 170[5]and low detachable light intensity as small as 1 pW/cm2[6],which demonstrate the potential applications of perovskite based photodetector in the areas of weak light detection.

Applications of Secondary Electron Composition Contrast Imaging Method in Microstructure Studies on Heterojunction Semiconductor Devices and Multilayer Materials

The principle, imaging condition and experimental method for obtaining high resolution composition contrast in secondary electron image were described. A new technique of specimen preparation for secondary electron composition contrast observation was introduced and discussed. By using multilayer P+Si1-xGex/pSi heterojunction internal photoemission infrared detector as an example, the applications of secondary electron composition contrast imaging in microstructure studies on heterojunction semiconducting materials and devices were stated. The characteristics of the image were compared with the ordinary transmission electron diffraction contrast image. The prospects of applications of the imaging method in heterojunction semiconductor devices and multilayer materials are also discussed.

Role of radionuclide imaging for diagnosis of device and prosthetic valve infections

Cardiovascular implantable electronic device(CIED) infection and prosthetic valve endocarditis(PVE) remain a diagnostic challenge.Cardiac imaging plays an important role in the diagnosis and management of patients with CIED infection or PVE.Over the past few years,cardiac radionuclide imaging has gained a key role in the diagnosis of these patients,and in assessing the need for surgery,mainly in the most difficult cases.Both ^(18)F-fluorodeoxyglucose positron emission tomography/computed tomography(^(18)F-FDG PET/CT) and radiolabelled white blood cell single-photon emission computed tomography/computed tomography(WBC SPECT/CT) have been studied in these situations.In their 2015 guidelines for the management of infective endocarditis,the European Society of Cardiology incorporated cardiac nuclear imaging as part of their diagnostic algorithm for PVE,but not CIED infection since the data were judged insufficient at the moment.This article reviews the actual knowledge and recent studies on the use of ^(18)F-FDG PET/CT and WBC SPECT/CT in the context of CIED infection and PVE,and describes the technical aspects of cardiac radionuclide imaging.It also discusses their accepted and potential indications for the diagnosis and management of CIED infection and PVE,the limitations of these tests,and potential areas of future research.

Infrastructure of Synchrotronic Biosensor Based on Semiconductor Device Fabrication for Tracking, Monitoring, Imaging, Measuring, Diagnosing and Detecting Cancer Cells

Copper Zinc Antimony Sulfide(CZAS)is derived from Copper Antimony Sulfide(CAS),a famatinite class of compound.In the current paper,the first step for using Copper,Zinc,Antimony and Sulfide as materials in manufacturing synchrotronic biosensor-namely increasing the sensitivity of biosensor through creating Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor and using it instead of Copper Tin Sulfide,CTS(Cu2SnS3)for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells,is evaluated.Further,optimization of tris(2,2'-bipyridyl)ruthenium(II)(Ru(bpy)32+)concentrations and Copper Zinc Antimony Sulfide,CZAS(Cu1.18Zn0.40Sb1.90S7.2)semiconductor as two main and effective materials in the intensity of synchrotron for tracking,monitoring,imaging,measuring,diagnosing and detecting cancer cells are considered so that the highest sensitivity obtains.In this regard,various concentrations of two materials were prepared and photon emission was investigated in the absence of cancer cells.On the other hand,ccancer diagnosis requires the analysis of images and attributes as well as collecting many clinical and mammography variables.In diagnosis of cancer,it is important to determine whether a tumor is benign or malignant.The information about cancer risk prediction along with the type of tumor are crucial for patients and effective medical decision making.An ideal diagnostic system could effectively distinguish between benign and malignant cells;however,such a system has not been created yet.In this study,a model is developed to improve the prediction probability of cancer.It is necessary to have such a prediction model as the survival probability of cancer is high when patients are diagnosed at early stages.

An algorithm supporting fast scheduling and viewing of remote sensing images in embedded devices

In order to resolve the conflict between the limited resources of embedded devices and the growing amount of massive image data to be shown, a solution for fast images rendering in embedded devices is proposed and implemented. First, an improved algorithm of a multi-resolution file-pyramid construction which is used for the organization of massive image data is presented. Then, a strategy, adopting technologies such as view-dependent levels of detail, target-tiles quick search and tiles seamless connection, is presented for fast scheduling and viewing of images. The results show that compared with the solution of multi-scale image representations based on wavelet, the proposed solution can improve the rendering speed, and the rendering speed does not depend on the image size, though it increases some data storage space. And the proposed solution is suitable for embedded devices and friendly experience.

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Handheld ultrasound devices are known for their portability and affordability,making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening.However,the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size,which hinders accurate diagnoses by doctors.At the same time,paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated.Therefore,we propose a modified cycle generative adversarial network(cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training.We introduce an ultrasound image pre-training method that does not require paired images,alleviating the requirement for large-scale paired datasets.We also propose an enhanced block with different structures in the pre-training and fine-tuning phases,which can help achieve the goals of different training phases.To improve the robustness of the model,we add Gaussian noise to the training images as data augmentation.Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,InceptionV3,and MobileNetV2 accuracy are observed to be 92.59%,83.38%,and 90.16%,respectively.The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices.

Evaluation of the systematic set-up errors using electronic portal image device in the radiotherapy procedures

Objective： The aim of this work was to quantify the extent of set-up errors to conduct a quality assurance （QA） aspect of treatment delivery, verification of the treatment field＇s position on different days using electronic portal. Methods： This study was carried out on 12 patients, treated for pelvis tumor; and total of 240 images obtained by electronic portal image device （EPID） were analyzed. The EPIs acquire using EPID attached to the Siemens linear accelerator. The anatomy match- ing software （Theraview） was used and displacement in two dimensions were noted for each treatment field to study patient setup errors. Results： The percentages of mean deviations less than 5 mm in X direction were 65% ＆ 92%, from 5-10 mm were 31% ＆ 19% and more than 10 mm were 11% ＆ 9% forNP and lateral direction respectively. The percentages of mean deviations less than 5 mm in Y direction were 65% ＆ 63%, from 5-10 mm were 33% ＆ 28% and more than 10 mm were 22% ＆ 29%. The mean deviations in 2D-vector errors were 〈 5 mm in 47% and 46%, 5-10 mm in 36% and 37% and 〉 10 mm in 37% and 37% of images in the NP and lateral direction respectively. Conclusion： The results revealed that the ranges of set up errors are immobilization method to improve reproducibility. The observed variations were not within the limits..

Research on Detection Technology of Micro-Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.

Retinal axial focusing and multi-layer imaging with a liquid crystal adaptive optics camera

With the help of adaptive optics （AO） technology, cellular level imaging of living human retina can be achieved. Aiming to reduce distressing feelings and to avoid potential drug induced diseases, we attempted to image retina with dilated pupil and froze accommodation without drugs. An optimized liquid crystal adaptive optics camera was adopted for retinal imaging. A novel eye stared system was used for stimulating accommodation and fixating imaging area. Illumination sources and imaging camera kept linkage for focusing and imaging different layers. Four subjects with diverse degree of myopia were imaged. Based on the optical properties of the human eye, the eye stared system reduced the defocus to less than the typical ocular depth of focus. In this way, the illumination light can be projected on certain retina layer precisely. Since that the defocus had been compensated by the eye stared system, the adopted 512 × 512 liquid crystal spatial light modulator （LC-SLM） corrector provided the crucial spatial fidelity to fully compensate high-order aberrations. The Strehl ratio of a subject with -8 diopter myopia was improved to 0.78, which was nearly close to diffraction-limited imaging. By finely adjusting the axial displacement of illumination sources and imaging camera, cone photoreceptors, blood vessels and nerve fiber layer were clearly imaged successfully.

Progress of Terahertz Devices Based on Graphene

Graphene is a one-atom-thick planar sheet of sp2-hybridized orbital bonded honeycomb carbon crystal. Its gapless and linear energy spectra of electrons and holes lead to the unique carrier transport and optical properties, such as giant carrier mobility and broadband flat optical response. As a novel material, graphene has been regarded to be extremely suitable and competent for the development of terahertz （THz） optical devices. In this paper, the fundamental electronic and optic properties of graphene are described. Based on the energy band structure and light transmittance properties of graphene, many novel graphene based THz devices have been proposed, including modulator, generator, detector, and imaging device. This progress has been reviewed. Future research directions of the graphene devices for THz applications are also proposed.

Proof-of-principle experimental demonstration of quantum secure imaging based on quantum key distribution

We present a quantum secure imaging(QSI) scheme based on the phase encoding and weak+vacuum decoy-state BB84 protocol of quantum key distribution(QKD). It allows us to implement a computational ghost imaging(CGI) system with more simplified equipment and reconstructed algorithm by using a digital micro-mirror device(DMD) to preset the specific spatial distribution of the light intensity. What is more, the quantum bit error rate(QBER) and the secure key rate analytical functions of QKD are used to see through the intercept-resend jamming attacks and ensure the authenticity of the imaging information. In the experiment, we obtained the image of the object quickly and efficiently by measuring the signal photon counts with a single-photon detector(SPD), and achieved a secure key rate of 571.0 bps and a secure QBER of 3.99%, which is well below the lower bound of QBER of 14.51%. Besides, our imaging system uses a laser with invisible wavelength of 1550 nm, whose intensity is as low as single-photon, that can realize weak-light imaging and is immune to the stray light or air turbulence, thus it will become a better choice for quantum security radar against intercept-resend jamming attacks.

Determining the imaging plane of a retinal capillary layer in adaptive optical imaging

Even in the early stage,endocrine metabolism disease may lead to micro aneurysms in retinal capillaries whose diameters are less than 10 μm.However,the fundus cameras used in clinic diagnosis can only obtain images of vessels larger than 20 μm in diameter.The human retina is a thin and multiple layer tissue,and the layer of capillaries less than10 μm in diameter only exists in the inner nuclear layer.The layer thickness of capillaries less than 10 μm in diameter is about 40 μm and the distance range to rod＆cone cell surface is tens of micrometers,which varies from person to person.Therefore,determining reasonable capillary layer（CL） position in different human eyes is very difficult.In this paper,we propose a method to determine the position of retinal CL based on the rod＆cone cell layer.The public positions of CL are recognized with 15 subjects from 40 to 59 years old,and the imaging planes of CL are calculated by the effective focal length of the human eye.High resolution retinal capillary imaging results obtained from 17 subjects with a liquid crystal adaptive optics system（LCAOS） validate our method.All of the subjects＇ CLs have public positions from 127 μm to 147 μm from the rod＆cone cell layer,which is influenced by the depth of focus.

Ocular surface heat effects on ocular hemodynamics detected by real-time measuring device

AIM: To investigate the ocular hemodynamic effects of applying a hot compress to the eye.METHODS: The right eyes of five New Zealand white rabbits, both male and female, were hot-compressed for 18 min. An independently designed novel ocular contacttype temperature measuring device was used to measure the ocular surface temperature before and after the heating. Relevant retrobulbar hemodynamic parameters such as peak systolic velocity(PSV), end diastolic velocity(EDV), and resistance index(RI) of each of the central retinal artery(CRA), long posterior ciliary artery(LPCA), and ophthalmic artery(OA), as well as the mean velocity(V_m) of the central retinal vein(CRV), were measured using a color Doppler flow imaging(CDFI) technique and expressed as mean values with standard deviation(mean±SD). A statistical analysis was conducted based on a paired t-test and the Wilcoxon signed-rank test. RESULTS: The employed real-time temperature measuring device was able to accurately measure ocular surface temperature during the hot-compress process. The temperature increased after the hot compress was applied. Analysis showed that the PSV and EDV values of the CRA and LPCA significantly increased after the application of the hot compress, as did the V_m of the CRV. There were no significant changes in the EDV of the OA nor the RI of each artery. CONCLUSION: This experiment, which is the first of its kind, confirms that the retrobulbar blood flow velocities can increase upon heating the ocular surface. This simple method may be useful in the future.

A smartphone-based calibration-free portable urinalysis device

As one of the most common medical diagnosis methods, urinalysis is a highly demanded technique for screening tests or daily monitoring of various diseases. With the rapid development of POC(point-of-care) systems, a convenient house-using urinalysis device is widely needed. However, considering the difference of onboard systems and multiple test indicators in urinalysis, the design of such an intelligent device is still challenging. In this paper, a smartphone-based portable urinalysis system has been developed and applied for the colorimetric analysis of routine urine examination indices using an Android app. By integrating the test paper sensor in the portable device for urinalysis,our system significantly improves the instability of conventional dipstick-based manual colorimetry, and the smartphone application used for color discrimination enhances the accuracy of the visual assessment of sample strips. Using a simple operation approach that takes ~ 2 min per test, our system can be applied as rapid urinalysis for routine check-ups.

Performance study of aluminum shielded room for ultra-low-field magnetic resonance imaging based on SQUID： Simulations and experiments

The aluminum shielded room has been an important part of ultra-low-field magnetic resonance imaging （ULF MRI） based on the superconducting quantum interference device （SQUID）. The shielded room is effective to attenuate the external radio-frequency field and keep the extremely sensitive detector, SQUID, working properly. A high-performance shielded room can increase the signal-to-noise ratio （SNR） and improve image quality. In this study, a circular coil with a diameter of 50 cm and a square coil with a side length of 2.0 m was used to simulate the magnetic fields from the nearby electric apparatuses and the distant environmental noise sources. The shielding effectivenesses （SE） of the shielded room with different thicknesses of aluminum sheets were calculated and simulated. A room using 6-mm-thick aluminum plates with a dimension of 1.5 m x 1.5 m x 2.0 m was then constructed. The SE was experimentally measured by using three-axis SQUID magnetometers, with tranisent magnetic field induced in the aluminum plates by the strong pre-polarization pulses. The results of the measured SE agreed with that from the simulation. In addition, the introduction of a 0.5-mm gap caused the obvious reduction of SE indicating the importance of door design. The nuclear magnetic resonance （NMR） signals of water at 5.9 kHz were measured in free space and in a shielded room, and the SNR was improved from 3 to 15. The simulation and experimental results will help us design an aluminum shielded room which satisfies the requirements for future ULF human brain imaging. Finally, the cancellation technique of the transient eddy current was tried, the simulation of the cancellation technique will lead us to finding an appropriate way to suppress the eddy current fields.