Search Results (18)

Compared to traditional remote sensing technology, video satellites have unique advantages such as real-time continuous imaging and the ability to independently complete staring observation. To achieve effective staring control, the satellite needs to perform attitude maneuvers to ensure that the target’s projection stays within the camera’s visual field and gradually reaches the desired position. The generation of image-based control instructions relies on the calculation of projection coordinates and their rate of change (i.e., visual velocity) of the projection point on the camera’s image plane. However, the visual velocity is usually difficult to obtain directly. Traditional calculation methods of visual velocity using time differentials are limited by video frame rates and the computing power of onboard processors, and is greatly affected by measurement noise, resulting in decreased control accuracy and a higher consumption of maneuvering energy. In order to address the shortcomings of traditional calculations of visual speed by time difference methods, this paper proposes a control method based on the estimation of visual velocity, which achieves real-time calculation of the target’s visual speed through adaptive estimation; then, the stability of the closed-loop system is rigorously demonstrated. Finally, through simulation comparison with the traditional differential method, the results show that the proposed method has an improvement in attitude accuracy for about 74% and a reduction in energy consumption by about 77%. Full article

Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver’s attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the experiment, four visual stimuli were displayed on a laboratory-made head-up display (HUD). This allowed the participants to control the in-car environment by simply staring at a target visual stimulus, i.e., without pressing a button or averting their eyes from the front. The driving performances in two realistic driving tests—obstacle avoidance and car-following tests—were then compared between the manual control condition and SSVEP-BCI control condition using a driving simulator. In the obstacle avoidance driving test, where participants needed to stop the car when obstacles suddenly appeared, the participants showed significantly shorter response time (1.42 ± 0.26 s) in the SSVEP-BCI control condition than in the manual control condition (1.79 ± 0.27 s). No-response rate, defined as the ratio of obstacles that the participants did not react to, was also significantly lower in the SSVEP-BCI control condition (4.6 ± 14.7%) than in the manual control condition (20.5 ± 25.2%). In the car-following driving test, where the participants were instructed to follow a preceding car that runs at a sinusoidally changing speed, the participants showed significantly lower speed difference with the preceding car in the SSVEP-BCI control condition (15.65 ± 7.04 km/h) than in the manual control condition (19.54 ± 11.51 km/h). The in-car environment control system using SSVEP-based BCI showed a possibility that might contribute to safer driving by keeping the driver’s focus on the front and thereby enhancing the overall driving performance. Full article

Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces. Full article

Diabetes is a widespread disease in the world and can lead to diabetic retinopathy, macular edema, and other obvious microvascular complications in the retina of the human eye. This study attempts to detect diabetic retinopathy (DR), which has been the main reason behind the blindness of people in the last decade. Timely or early treatment is necessary to prevent some DR complications and control blood glucose. DR is very difficult to detect in time-consuming manual diagnosis because of its diversity and complexity. This work utilizes a deep learning application, a convolutional neural network (CNN), in fundus photography to distinguish the stages of DR. The images dataset in this study is obtained from Xiangya No. 2 Hospital Ophthalmology (XHO), Changsha, China, which is very large, little and the labels are unbalanced. Thus, this study first solves the problem of the existing dataset by proposing a method that uses preprocessing, regularization, and augmentation steps to increase and prepare the image dataset of XHO for training and improve performance. Then, it takes the advantages of the power of CNN with different residual neural network (ResNet) structures, namely, ResNet-101, ResNet-50, and VggNet-16, to detect DR on XHO datasets. ResNet-101 achieved the maximum level of accuracy, 0.9888, with a training loss of 0.3499 and a testing loss of 0.9882. ResNet-101 is then assessed on 1787 photos from the HRF, STARE, DIARETDB0, and XHO databases, achieving an average accuracy of 0.97, which is greater than prior efforts. Results prove that the CNN model (ResNet-101) has better accuracy than ResNet-50 and VggNet-16 in DR image classification. Full article

A miniaturized video satellite can observe the ground targets by recording real-time video clips in staring control mode and therefore obtains a unique advantage over traditional remote sensing techniques. To further extend the application of a video satellite, a strategy for simultaneously observing a group of ground targets is to be developed. To cope with the impacts of an uncalibrated camera on the pointing accuracy which can lead to the failure of a multi-target observation task, an adaptive attitude control method is to be exploited. Hence, to observe multiple ground targets using an onboard uncalibrated camera, this paper proposes an image-based adaptive staring attitude controller. First, a target-selection strategy is proposed to realize a more balanced staring observation of the target group. Second, an updating law is proposed to estimate the camera parameters according to the projection equations. At last, an adaptive staring controller based on the estimated parameters is formulated, so that the center of mass of the ground targets on the image can be controlled towards its desired location, which is normally the image center. The stability of the proposed staring controller is proved using Barbalat’s Lemma. The simulation results show that even though the camera parameters are uncertain, the adaptive control method effectively achieves the staring observation for multiple ground targets by keeping their midpoint at the image center. Full article

In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 μrad (σ) to 2.2 μrad (σ), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications. Full article

Previous staring attitude control techniques utilize the geographic location of a ground target to dictate the direction of the camera’s optical axis, while the assembly accuracy and the internal structure of the spaceborne camera are not considered. This paper investigates the image-based staring controller design of a video satellite in the presence of uncertain intrinsic and extrinsic camera parameters. The dynamical projection model of the ground target on the image plane is firstly established, and then we linearly parameterize the defined projection errors. Furthermore, a potential function and a self-updating rule are introduced to estimate the parameters online by minimizing the projection errors. As the parameters are updating constantly, an adaptive control algorithm is developed, so that the errors between the current and the desired projections of the ground target converge to zero. The stability is proved using Barbalat’s lemma. Simulation results show that the designed controller can successfully move the target’s projection to the desired coordinate even though the camera parameters are unknown. Full article

Geosynchronous synthetic aperture radar (Geo-SAR) with a short revisit time can obtain wide-area images. This paper advances a new two-dimensional pitch and roll squint controlling (2D-PRSC) method that can make satellites continuously stare at any scene in the whole orbital period. The maximum attitude steering angle is less than ±7.6 degrees, and the attitude controlling time can be greatly shortened compared with the yaw steering method. Furthermore, a Geo-SAR staring mode model is illustrated and compared with that of low earth orbital SAR (Leo-SAR). Finally, Geo-SAR’s ambiguity property is discussed. The simulation results illuminate that the cross-term ambiguity to signal ratio (CASR) also needs to be considered in addition to the azimuth and range ambiguity to signal ratio (AASR, RASR), and the whole orbital ergodic analysis should be carried out. To ensure that RASR, AASR, and CASR meet the requirement of −20 dB, it is necessary to select an appropriate PRF in the range of a few hundred Hertz. Full article

Synthetic aperture radar (SAR) tomography (TomoSAR) can obtain 3D imaging models of observed urban areas and can also discriminate different scatters in an azimuth–range pixel unit. Recently, compressive sensing (CS) has been applied to TomoSAR imaging with the use of very-high-resolution (VHR) SAR images delivered by modern SAR systems, such as TerraSAR-X and TanDEM-X. Compared with the traditional Fourier transform and spectrum estimation methods, using sparse information for TomoSAR imaging can obtain super-resolution power and robustness and is only minorly impacted by the sidelobe effect. However, due to the tight control of SAR satellite orbit, the number of acquisitions is usually too low to form a synthetic aperture in the elevation direction, and the baseline distribution of acquisitions is also uneven. In addition, artificial outliers may easily be generated in later TomoSAR processing, leading to a poor mapping product. Focusing on these problems, by synthesizing the opinions of various experts and scholarly works, this paper briefly reviews the research status of sparse TomoSAR imaging. Then, a joint sparse imaging algorithm, based on the building points of interest (POIs) and maximum likelihood estimation, is proposed to reduce the number of acquisitions required and reject the scatterer outliers. Moreover, we adopted the proposed novel workflow in the TerraSAR-X datasets in staring spotlight (ST) work mode. The experiments on simulation data and TerraSAR-X data stacks not only indicated the effectiveness of the proposed approach, but also proved the great potential of producing a high-precision dense point cloud from staring spotlight (ST) data. Full article

Epilepsy is a debilitating and potentially life-threatening neurological condition which affects approximately 65 million people worldwide. There is currently no reliable and simple early warning seizure-onset device available, which means many people with unstable epilepsy live in fear of injury or sudden death and the negative impact of social stigmatization. If anecdotal claims that untrained dogs anticipate seizures are found to be true, they could offer a simple and readily available early warning system. We hypothesized that, given the extraordinary olfactory ability of dogs, a volatile organic compound exhaled by the dog’s epileptic owner may constitute an early warning trigger mechanism to which make dogs react by owner-directed affiliative responses in the pre-seizure period. Using 19 pet dogs with no experience of epilepsy, we exposed them to odours that were deemed to be characteristic of three seizure phases, by using sweat harvested from people with epilepsy. The odours were delivered to a point immediately under a non-epileptic and seated pet dog owner’s thighs. By altering the alternating odours emerging from sweat samples, captured before seizure, during a seizure and after a seizure, and two nonseizure controls, we were able to record the response of the 19 pet dogs. Our findings suggest that seizures are associated with an odour and that dogs detect this odour and demonstrate a marked increase in affiliative behaviour directed at their owners. A characteristic response of all 19 dogs to seizure odour presentation was an intense stare which was statistically significant, (p < 0.0029), across the pre-seizure, seizure and post-seizure phases when compared to control odours of nonseizure origin. Full article

Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR are the hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from the retinal fundus images. First, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a new convolutional neural network (CNN) architecture is proposed to detect hemorrhages. A modified pre-trained CNN model is used to extract features from the detected hemorrhages. In the third stage, all extracted feature vectors are fused using the convolutional sparse image decomposition method, and finally, the best features are selected by using the multi-logistic regression controlled entropy variance approach. The proposed method is evaluated on 1509 images from HRF, DRIVE, STARE, MESSIDOR, DIARETDB0, and DIARETDB1 databases and achieves the average accuracy of 97.71%, which is superior to the previous works. Moreover, the proposed hemorrhage detection system attains better performance, in terms of visual quality and quantitative analysis with high accuracy, in comparison with the state-of-the-art methods. Full article

Microwave Staring Correlated Imaging (MSCI) is a newly proposed computational high-resolution imaging technique. The imaging performance of MSCI with the existence of modeling errors depends on the properties of the imaging matrix and the relative perturbation error resulted from existing errors. In conventional transient-radiation-fields-based MSCI, which is commonly accomplished by utilizing random frequency-hopping (FH) waveforms, the multiple transmitters should be controlled individually and simultaneously. System complexity and control difficulty are hence increased, and various types of modeling errors are introduced as well. The computation accuracy of radiation fields is heavily worsened by the modeling errors, and the transient effect makes it hard to take direct and high-precision measurements of the radiation fields and calibrate the modeling errors with the measuring result. To simplify the system complexity and reduce error sources, in this paper, steady-radiation-fields-sequence-based MSCI (SRFS-MSCI) method is proposed. The multiple transmitters are excited with coherent signals at the same observation moment, with the signal frequency varying in the whole frequency band during the imaging process. By elaborately designing the array configuration and the amplitude and phase sequences of the coherent transmitters, the SRFS-MSCI is thus implemented. Comparing the system architecture of the proposed SRFS-MSCI with the conventional random FH-based MSCI, it can be found that the proposed method significantly reduces the number of baseband modules and simplifies the system architecture and control logic, which contributes to reducing error sources such as baseband synchronization errors and decreasing deterioration caused by error cascade. To further optimize the design parameters in the proposed SRFS-MSCI system, the Simulated Annealing (SA) algorithm is utilized to optimize the amplitude sequences, the phase sequences, and the antenna positions individually and jointly. Numerical imaging experiments and real-world imaging experiment demonstrate the effectiveness of the proposed SRFS-MSCI method that recognizable high-resolution recovery results are obtained with simplified system structure and optimized system parameters. Full article

In recent years, applications such as marine search and rescue, border patrol, etc. require electro-optical equipment to have both high resolution and precise geographic positioning abilities. The step and stare working based on a composite control system is a preferred solution. This paper proposed a step and stare system composed of two single-axis fast steering mirrors and a two-axis gimbal. The fast steering mirrors (FSMs) realize image motion compensation and the gimbal completes pointing control. The working principle and the working mode of the system are described first. According to the imaging optical path, the algorithm and control flow of the line of sight (LOS) and image motion compensation are developed. The proposed method is verified through ground imaging and flight tests. Under the condition of flight, the pointing accuracy of the target can be controlled within 15 m. The proposed algorithm can achieve effective motion compensation and get high-resolution images. This achieves high resolution and accurate LOS simultaneously. Full article

Step/stare imaging with focal plane arrays (FPAs) has become the main approach to achieve wide area coverage and high resolution imaging for long range targets. A fast steering mirror (FSM) is usually utilized to provide back-scanned motion to compensate for the image motion. However, the traditional optical design can just hold one field point relatively stable, typically the central or on-axis field point, on the FPA during back-scanning; all other field points may wander during the exposure due to imaging distortion characteristics of the optical system, which reduces the signal to noise ratio (SNR) of the target. Aiming toward this problem, this paper proposes a non-rotationally symmetric field mapping method for the back-scanned step/stare imaging system, which can make all field points stable on the FPA during back-scanning. First of all, the mathematical model of non-rotationally symmetric field mapping between object space and image space is established. Then, a back-scanned step/stare imaging system based on the model is designed, in which this non-rotationally symmetric mapping can be implemented with an afocal telescope including freeform lenses. Freeform lenses can produce an anamorphic aberration to adjust distortion characteristics of the optical system to control image wander on an FPA. Furthermore, the simulations verify the effectiveness of the method. Full article

Background. Dialysis treatment is improving, but several long-term problems remain unsolved, including metabolic bone disease linked to chronic kidney disease (CKD-MBD). The availability of new, efficacious but expensive drugs (intravenous calcimimetic agents) poses ethical problems, especially in the setting of budget limitations. Methods. Reasons of choice, side effects, biochemical trends were discussed in a cohort of 15 patients (13% of the dialysis population) who stared treatment with intravenous calcimimetics in a single center. All patients had previously been treated with oral calcimimetic agents; dialysis efficacy was at target in 14/15; hemodiafiltration was employed in 10/15. Median Charlson Comorbidity Index was 8. The indications were discussed according to the principlist ethics (beneficience, non maleficience, justice and autonomy). Biochemical results were analyzed to support the clinical-ethical choices. Results. In the context of a strict clinical and biochemical surveillance, the lack of side effects ensured “non-maleficence”; efficacy was at least similar to oral calcimimetic agents, but tolerance was better. Autonomy was respected through a shared decision-making model; all patients appreciated the reduction of the drug burden, and most acknowledged better control of their biochemical data. The ethical conflict resides in the balance between the clinical “beneficience, non-maleficience” advantage and “justice” (economic impact of treatment, potentially in attrition with other resources, since the drug is expensive and included in the dialysis bundle). The dilemma is more relevant when a patient’s life expectancy is short (economic impact without clear clinical advantages), or when non-compliance is an issue (unclear advantage if the whole treatment is not correctly taken). Conclusions. In a context of person-centered medicine, autonomy, beneficence and non-maleficence should weight more than economic justice. While ethical discussions are not aimed at finding “the right answer” but asking “the right questions”, this example can raise awareness of the importance of including an ethical analysis in the choice of “economically relevant” drugs. Full article