Observability and estimability of passive radar with unknown illuminator states using different observations

Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.

Beam homogenization structure for a laser illuminator design based on diode laser beam combining technology

With the rapid development of laser technology,laser as the light source of night vision illuminating can realize long-distance and clear imaging,which has been widely used in laser active illuminating field.A high-power diode laser with a wavelength of 808 nm was designed as the laser active illuminating source,and the output power of no less than100 W was obtained by spatial beam multiplexing,polarization multiplexing,and high efficiency fiber coupling techniques.In view of the beam homogenization of illuminating source,a novel beam homogenization system based on waveguide is proposed in this work.A square spot with a horizontal divergence angle of 40°,a vertical divergence angle of 10°,and an illuminating power ratio of 4:1 was obtained by a collimating lens.Comparing with the traditional circular illuminating beam,the square illuminating beam can match the illuminating angle of CCD camera better,and the energy utilization rate is higher.In addition,by optimizing the structure of waveguide and collimating lens,the illuminating angle can be changed to meet the illuminating requirements under different conditions theoretically.

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.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

ED-Ged:Nighttime Image Semantic Segmentation Based on Enhanced Detail and Bidirectional Guidance

Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.

Efficacy and safety of Usights UC100 illuminated microcatheter in microcatheter-assisted trabeculotomy

AIM:To evaluate the efficacy and safety of Usights UC100 illuminated microcatheter in microcatheter-assisted trabeculotomy(MAT).METHODS:Totally 10 eyes of 10 patients with primary open angle glaucoma(POAG)who underwent MAT facilitated by Usights UC100(5 eyes)or iTrack(5 eyes)were reviewed.The success of this surgery was defined as intraocular pressure(IOP)<22 mm Hg with>30%reduction,without oral glaucoma medications,or additional glaucoma surgery.RESULTS:The mean pre-operative IOP was 25.38±10.22 mm Hg in the Usights UC100 group and 19.98±3.87 mm Hg in the iTrack group.MAT was achieved in all eyes in both groups.The success rates for the Usights UC100 group and iTrack groups were in all and 4 eyes,respectively.Both microcatheters produced a statistically significant reduction in IOP,and eyes using Usights UC100 achieved a lower IOP than the iTrack group at 3mo followup(12.58±1.52 and 14.84±1.89 mm Hg,respectively),but no statistical significance was there.No severe side effects were observed in either group.CONCLUSION:MAT using Usights UC100 or iTrack both achieve significant pressure reduction in cases of POAG,and Usights UC100 is as safe as iTrack.

Detour phase Talbot array illuminator

In this Letter, we propose a simple and effective approach for transforming a conventional Talbot array illuminator(TAI) with multilevel phase steps into a binary-phase TAI(BP-TAI) through detour phase encoding.The BP-TAI is a binary(0 π) phase-only diffractive optical element, which can be utilized to generate a large-scale focal spots array with a high compression ratio.As an example, we design a square BP-TAI with the fraction parameter β= 15 for achieving a square multifocal lattice with a high compression ratio β^2.Theoretical analysis and experimental results demonstrate that the detour phase encoding is efficient for designing the BP-TAI, especially with the high compression ratio.Such results may be exploited in practical large-scale optical trapping and X-ray imaging.

The Fold-Illuminator:A low-cost,portable,and disposable incubator-illuminator device

Fluorescent reporters have revolutionized modern applications in the fields of molecular and synthetic biology,enabling applications ranging from education to point-of-care diagnostics.Past advancements in these fields have primarily focused on improving reaction conditions,the development of new applications,and the broad dissemination of these technologies.However,field and classroom-based applications have remained limited in part due to the nature of fluorescent signal detection,which often requires the use of costly lab equipment to observe and quantify fluorescence readouts.Users without access to laboratory equipment rely on qualitative assessments of fluorescence,a process that remains highly variable from user-to-user even within the same classroom.To overcome this challenge,we have developed a foldable illuminator and incubator device to support field-applications of synthetic biology-based biosensors for education and diagnostics.The Fold-Illuminator is an affordable,portable,and recyclable device that allows for the visible detection of fluorescent biomolecules.The Fold-Illuminator’s design allows for assembly in under 10 min,a user can then utilize the optional heating element to incubate biochemical reactions and visualize fluorescence outputs in a defined and light-controlled environment.Interchangeable LED strips and light-filtering screens provide modularity to pair with the fluorescence wavelengths of interest.The user can then unfold the device for convenient storage,transport,or even recycling.The cost for the Fold-Illuminator is$5.58 USD and is compatible with an optional heating element for an additional$3.98 cost,with potential for further reductions in cost for larger quantities.Open-source templates for cutting device parts from paper stock are provided for both printing and cutting by hand;cutting can also be achieved with consumer-grade smart cutting machines such as the Cricut®.Combined with the broad applications of fluorescent reporters,the Fold-Illuminator has the potential to improve access to fluorescence visualization and quantification for new users as well as emerging field applications.

Tunable rectangular array illuminator in periodically poled LiNbO_3 crystal

An electro-optic tunable rectangular array illuminator in one-dimensional periodically poled LiNbO3 （PPLN） crystal is presented experimentally which result is in good agreement with results from simu- lation. The illuminator is formed based on the Talbot self-imaging effect by applying an electric field on PPLN. The intensi~.y distribution of rectangular array could be precisely modulated. Compared with other array illuminators, this tunable illuminator uses a lower voltage and could get a more concentrated intensity distribution. The influence of the incident angle to the self-imaging patterns is studied for the first time.

Speckle structured illumination endoscopy with enhanced resolution at wide field of view and depth of field

Structured illumination microscopy(SIM)is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity.The spatial resolution of SIM is typically limited to two times of the diffraction limit and the depth of field is small.In this work,we propose and experimentally demonstrate a low cost,easy to implement,novel technique called speckle structured illumination endoscopy(SSIE)to enhance the resolution of a wide field endoscope with large depth of field.Here,speckle patterns are used to excite objects on the sample which is then followed by a blind-SIM algorithm for super resolution image reconstruction.Our approach is insensitive to the 3D morphology of the specimen,or the deformation of illuminations used.It greatly simplifies the experimental setup as there are no calibration protocols and no stringent control of illumination patterns nor focusing optics.We demonstrate that the SSIE can enhance the resolution 2–4.5 times that of a standard white light endoscopic(WLE)system.The SSIE presents a unique route to super resolution in endoscopic imaging at wide field of view and depth of field,which might be beneficial to the practice of clinical endoscopy.

Deep learning enhanced NIR-Ⅱ volumetric imaging of whole mice vasculature

Fluorescence imaging through the second near-infrared window(NIR-Ⅱ,1000–1700 nm) allows in-depth imaging.However, current imaging systems use wide-field illumination and can only provide low-contrast 2D information, without depth resolution. Here, we systematically apply a light-sheet illumination, a time-gated detection, and a deep-learning algorithm to yield high-contrast high-resolution volumetric images. To achieve a large Fo V(field of view) and minimize the scattering effect, we generate a light sheet as thin as 100.5 μm with a Rayleigh length of 8 mm to yield an axial resolution of 220 μm. To further suppress the background, we time-gate to only detect long lifetime luminescence achieving a high contrast of up to 0.45 Icontrast. To enhance the resolution, we develop an algorithm based on profile protrusions detection and a deep neural network and distinguish vasculature from a low-contrast area of 0.07 Icontrast to resolve the 100μm small vessels. The system can rapidly scan a volume of view of 75 × 55 × 20 mm3and collect 750 images within 6mins. By adding a scattering-based modality to acquire the 3D surface profile of the mice skin, we reveal the whole volumetric vasculature network with clear depth resolution within more than 1 mm from the skin. High-contrast large-scale 3D animal imaging helps us expand a new dimension in NIR-Ⅱ imaging.

Ecological and habitat ranges of orchids in the northernmost regions of their distribution areas:A case study from Ural Mountains,Russia

The Orchidaceae,which is one of the most interesting families of angiosperms,contains a large number of rare species.Despite their acknowledged importance,little attention has been paid to the study of orchids distributed in northern territories.In this study,we determined the syntaxonomical diversity and ecological parameters of orchid habitats in two of Europe's largest protected areas,the Pechoro-Ilychsky Reserve and the Yugyd Va National Park(northeastern European Russia),and then compared our findings to those in other parts of orchid distribution ranges.For this purpose,we studied 345 descriptions of plant communities(releves) containing species from Orchidaceae and defined habitat parameters using Ellenberg indicator values with the community weight mean approach,nonmetric multidimensional scaling(NMS),and relative niche width.We found that orchids were distributed in eight habitat types and 97 plant associations.The largest number of orchid species is found in forest communities.Half of the orchid species under study occur in the mires and rock habitats with open vegetation.Several orchids consistently occur in areas disturbed by human activity.In addition,our study indicates that the main drivers of orchid distribution across the vegetation types are light and soil nitrogen.Our analysis of the ecological parameters of orchid habitats indicates that some orchid species can be classified as habitat specialists that are confined to a relatively narrow ecological niche in the Urals(e.g.,Goodyera repens,Cypripedium guttatum and Dactylorhiza maculata).Several other species(e.g.Neottia cordata and Dactylorhiza fuchsia) grow under diverse ecological parameters.

Compact and robust dual-color linearly polarized illumination source for three-photon fluorescence imaging

The miniaturized femtosecond laser in near infrared-Ⅱregion is the core equipment of threephoton microscopy.In this paper,we design a compact and robust illumination source that emits dual-color linearly polarized light for three-photon microscopy.Based on an all-polarizationmaintaining passive mode-locked fiber laser,we shift the center wavelength of the pulses to the 1.7m band utilizing cascade Raman effect,thereby generate dual-wavelength pulses.To enhance clarity,the two wavelengths are separated through the graded-index multimode fiber.Then we obtain the dual-pulse sequences with 1639.4 nm and 1683.7 nm wavelengths,920 fs pulse duration,and 23.75 MHz pulse repetition rate.The average power of the signal is 53.64mW,corresponding to a single pulse energy of 2.25 nJ.This illumination source can be further amplified and compressed for three-photon fluorescence imaging,especially dual-color three-photon fluorescence imaging,making it an ideal option for biomedical applications.

Complex-amplitude Fourier single-pixel imaging via coherent structured illumination

We propose a method of complex-amplitude Fourier single-pixel imaging(CFSI)with coherent structured illumination to acquire both the amplitude and phase of an object.In the proposed method,an object is illustrated by a series of coherent structured light fields,which are generated by a phase-only spatial light modulator,the complex Fourier spectrum of the object can be acquired sequentially by a single-pixel photodetector.Then the desired complex-amplitude image can be retrieved directly by applying an inverse Fourier transform.We experimentally implemented this CFSI with several different types of objects.The experimental results show that the proposed method provides a promising complex-amplitude imaging approach with high quality and a stable configuration.Thus,it might find broad applications in optical metrology and biomedical science.

Comparison of point detection and area detection for point-scanning structured illumination microscopy

Structured illumination microscopy(SIM)is suitable for biological samples because of its relatively low-peak illumination intensity requirement and high imaging speed.The system resolution is affected by two typical detection modes:Point detection and area detection.However,a systematic analysis of the imaging performance of the different detection modes of the system has rarely been conducted.In this study,we compared laser point scanning point detection(PS-PD)and point scanning area detection(PS-AD)imaging in nonconfocal microscopy through theoretical analysis and simulated imaging.The results revealed that the imaging resolutions of PSPD and PS-AD depend on excitation and emission point spread functions(PSFs),respectively.Especially,we combined the second harmonic generation(SHG)of point detection(P-SHG)and area detection(A-SHG)with SIM to realize a nonlinear SIM-imaging technique that improves the imaging resolution.Moreover,we analytically and experimentally compared the nonlinear SIM performance of P-SHG with that of A-SHG.

Adaptive Design of Fluorescence Imaging Systems for Custom Resolution, Fields of View, and Geometries

Objective and Impact Statement:We developed a generalized computational approach to design uniform,high-intensity excitation light for low-cost,quantitative fluorescence imaging of in vitro,ex vivo,and in vivo samples with a single device.Introduction:Fluorescence imaging is a ubiquitous tool for biomedical applications.Researchers extensively modify existing systems for tissue imaging,increasing the time and effort needed for translational research and thick tissue imaging.These modifications are applicationspecific,requiring new designs to scale across sample types.Methods:We implemented a computational model to simulate light propagation from multiple sources.Using a global optimization algorithm and a custom cost function,we determined the spatial positioning of optical fibers to generate 2 illumination profiles.These results were implemented to image core needle biopsies,preclinical mammary tumors,or tumor-derived organoids.Samples were stained with molecular probes and imaged with uniform and nonuniform illumination.Results:Simulation results were faithfully translated to benchtop systems.We demonstrated that uniform illumination increased the reliability of intraimage analysis compared to nonuniform illumination and was concordant with traditional histological findings.The computational approach was used to optimize the illumination geometry for the purposes of imaging 3 different fluorophores through a mammary window chamber model.Illumination specifically designed for intravital tumor imaging generated higher image contrast compared to the case in which illumination originally optimized for biopsy images was used.Conclusion:We demonstrate the significance of using a computationally designed illumination for in vitro,ex vivo,and in vivo fluorescence imaging.Applicationspecific illumination increased the reliability of intraimage analysis and enhanced the local contrast of biological features.This approach is generalizable across light sources,biological applications,and detectors.

Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

An image can be degraded due to many environmental factors like foggy or hazy weather,low light conditions,extra light conditions etc.Image captured under the poor light conditions is generally known as non-uniform illumination image.Non-uniform illumination hides some important information present in an image during the image capture Also,it degrades the visual quality of image which generates the need for enhancement of such images.Various techniques have been present in literature for the enhancement of such type of images.In this paper,a novel architecture has been proposed for enhancement of poor illumination images which uses radial basis approximations based BEMD(Bi-dimensional Empirical Mode Decomposition).The enhancement algorithm is applied on intensity and saturation components of image.Firstly,intensity component has been decomposed into various bi-dimensional intrinsic mode function and residue by using sifting algorithm.Secondly,some linear transformations techniques have been applied on various bidimensional intrinsic modes obtained and residue and further on joining the transformed modes with residue,enhanced intensity component is obtained.Saturation part of an image is then enhanced in accordance to the enhanced intensity component.Final enhanced image can be obtained by joining the hue,enhanced intensity and enhanced saturation parts of the given image.The proposed algorithm will not only give the visual pleasant image but maintains the naturalness of image also.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.

Research and Application of Excitation Method Optimization in Areas with Low Signal-to-Noise Ratio of Seismic Data

In areas with a low signal-to-noise ratio of seismic data,the continuity of the seismic reflection waves in the exploration target layer is very poor,which will reduce the imaging accuracy and make it impossible to solve certain geological tasks.This article suggests an approach to address the issue of seismic acquisition by optimizing excitation parameters.It involves conducting a detailed investigation of the surface structure,enhancing the observation system,increasing the coverage appropriately,and transitioning from combined-well excitation to single-well excitation.Additionally,the use of technical tools like qualitative evaluation of the observation system and forward modeling are employed to determine the final optimized seismic acquisition plan.The effectiveness of this approach is evident from the seismic profile obtained in an exploration area in Inner Mongolia.