Satellite constellation design with genetic algorithms based on system performance

Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.

CHARACTER DETECTION AND RECOGNITION SYSTEM OF BEER BOTTLES

An optical imaging system and a configuration characteristic algorithm are presented to reduce the difficulties in extracting intact characters image with weak contrast, in recognizing characters on fast moving beer bottles. The system consists of a hardware subsystem, including a rotating device, CCD, 16 mm focus lens, a frame grabber card, a penetrating lighting and a computer, and a software subsystem. The software subsystem performs pretreatment, character segmentation and character recognition. In the pretreatment, the original image is filtered with preset threshold to remove isolated spots. Then the horizontal projection and the vertical projection are used respectively to retrieve the character segmentation. Subsequently, the configuration characteristic algorithm is applied to recognize the characters. The experimental results demonstrate that this system can recognize the characters on beer bottles accurately and effectively; the algorithm is proven fast, stable and robust, making it suitable in the industrial environment.

Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms,presenting risks for numerous countries,societies,and individuals,and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection,we propose a detection method based on improved CapsNet and temporal–spatial features(iCapsNet–TSF).First,the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then,the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features.Finally,the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos,and the results are fused.Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%,98.83%,and 98.50%on the Celeb-DF,FaceSwap,and Deepfakes datasets,respectively,displaying a better performance than most existing mainstream algorithms.The iCapsNet–TSF method combines the capsule network and the optical flow algorithm,providing a novel strategy for the deepfake detection,which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.

Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel

Visible light positioning becomes popular recently. However, its performance is degraded by the indoor diffuse optical channel. An artificial neural-network-based visible light positioning algorithm is proposed in this Letter, and a trained neural network is used to achieve positioning with a diffuse channel. Simulations are made to evaluate the proposed positioning algorithm. Results show that the average positioning error is reduced about 13 times, and the positioning time is reduced about two magnitudes. Moreover, the proposed algorithm is robust with a different field-of-view of the receiver and the refiectivity of the wall, which is suitable for various position- ing applications.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.

Modified optical remote sensing algorithms for the Pearl River Estuary

This study aims to develop new algorithms to retrieve sea surface parameters including concentrations of Chlorophyll a （Chl a） and Suspended Particulate Matter （SPM）, and absorbance of Colored Dissolved Organic Matter （aCDOM） by incorporating the contribution of red bands to make them more adaptable to case 2 waters. Optical remote sensing algorithms have demonstrated efficient retrieval of Chl a, SPM, and aCDOM, yet they are not very accurate especially for coastal areas. It has also been found that the default algorithm has overestimated Chl a in the Pearl River Estuary, and shown poor correlation for CDOM absorbance. By incorporating the red band ratios into the algorithm, a correction effect has been shown, which improves the accuracy of quantifying the actual concentration. Modeling and data fitting of the algorithm have been done based on 61 data samples collected in the Pearl River estuary during a cruise from 3 to 11 May 2014. The study also attempts to modify the aerosol correction bands used in SeaDAS to prevent saturation of these bands. The modified algorithms showed an R-Square value of 0.7289 for Chl a fitting, and 0.7338 for CDOM fitting, and corrected overestimation of Chl a concentration in the Pearl River estuary.

A Heuristic Algorithm for P-Cycles Configuration in WDM Optical Networks

Aiming at minimizing spare capacity for optical WDM networks, we propose a new heuristic algorithm for preconfigured protection cycle (p-cycle) design. Numerical results show that the spare capacity obtained by our new algorithm is very close to the optimal solution.

Dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network

Aiming at the problem of dynamic multicast service protection in multi-domain optical network, this paper proposes a dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network. The algorithm uses the minimum cost spanning tree strategy and fuzzy game theory. First, it virtualizes two planes to calculate the multicast tree and the multicast protection tree respectively. Then, it performs a fuzzy game to form a cooperative alliance to optimize the path composition of each multicast tree. Finally, it generates a pair of optimal multicast work tree and multicast protection tree for dynamic multicast services. The time complexity of the algorithm is O(k3 m2 n), where n represents the number of nodes in the networks, k represents the number of dynamic multicast requests, and m represents the number of destination nodes for each multicast request. The experimental results show that the proposed algorithm reduces significantly the blocking rate of dynamic multicast services, and improves the utilization of optical network resources within a certain number of dynamic multicast request ranges.

A QoS-Guanranteed Scheduling Algorithm with High Throughput for Edge Nodes of Optical Burst Switching Networks

A scheduling algorithm for the edge nodes of optical burst switching (OBS) networks is proposed to guarantee the delay requirement of services with different CoS (Class of Service) and provide lower burst loss ratio at the same time. The performance of edge nodes based on the proposed algorithm is presented.

Validation of MODIS and Deep Blue aerosol optical depth retrievals in an arid/semi-arid region of northwest China

The global aerosol optical depth （AOD or r） has been retrieved using the Dark Target algorithm （the C004 and C005 products） and the Deep Blue algorithm （DB product）. Few validations have thus far been performed in arid/semi-arid regions, especially in northwest China. The ground-based remote sensing of AOD from sun photometers at four sites in Xinjiang during the years 2002-2003 is used to validate aerosol products, including C004, C005 and DB of the Moderate Resolution Imaging Spectroradiometer （MODIS）. The results show substantial improvement in the C005 aerosol product over the C004 product. The average correlation coefficient of regression with ground measurements increased from 0.59 to 0.69, and the average offset decreased from 0.28 to 0.13. The slopes of the linear regressions tended to be close to unity. The percentage of AODs falling within the retrieval errors of 30% （or Δτ = ±0.1 ± 0.2τ） increased from 16.1% to 45.6%. The best retrievals are obtained over an oasis region, whereas the worst are obtained over urban areas. Both the MODIS C004 and C005 products overestimate AOD, which is likely related to improper assumptions of the aerosol model and of the estimation of surface reflectance. An encouraging result has been derived with regard to validation of the DB AOD. Overall, the average offset, slope and correlation coefficient of regression with sun-photometer measurements are -0.04, 0.88 and 0.85, respectively. Approximately 73% of the DB AOD retrievals fall within the expected error of 30%. Underestimation of the AOD by the DB products is observed. The aerosol model and estimations of surface reflectance in this region require further improvements.

Diffuse optical tomography in the human brain: A briefly review from the neurophysiology to its applications

The present work describes the use of noninvasive diffuse optical tomography(DOT)technology to measure hemodynamic changes,providing relevant information which helps to understand the basis of neurophysiology in the human brain.Advantages such as portability,direct measurements of hemoglobin state,temporal resolution,non-restricted movements as occurs in magnetic resonance imaging(MRI)devices mean that DOT technology can be used in research and clinical fields.In this review we covered the neurophysiology,physical principles underlying optical imaging during tissue-light interactions,and technology commonly used during the construction of a DOT device including the source-detector requirements to improve the image quality.DOT provides 3 D cerebral activation images due to complex mathematical models which describe the light propagation inside the tissue head.Moreover,we describe briefly the use of Bayesian methods for raw DOT data filtering as an alternative to linear filters widely used in signal processing,avoiding common problems such as the filter selection or a false interpretation of the results which is sometimes due to the interference of background physiological noise with neural activity.

Design of temperature insensitive in vivo strain sensor using multilayer single mode optical fiber

Bone strain measurement is a case of interest and demanding task for osteogenic adaption responses. In this paper, a novel biocompatible optical sensor for the bone axial strain measurement was proposed. In case modern multilayer single mode WII type optical fibers are well designed, they exhibit superior characteristics compared to conventional metal strain gauges （SGs）. Furthermore, they could be strong competitors for SGs based on fiber Bragg grating （FBG） devices. In this study, mode field diameter （MFD） was selected as the indirect parameter for sensing task, which was totally a new approach. The strain sensitivity of 70.7733 pm/με was obtained. Moreover, temperature sensitivity was -3.0031 x 10-6 pm/℃, which was negligible and removed the temperature compensation complexity for the sensor structure presented. The satisfactory property achieved for the designed sensor is as a result of multilayer fiber＇s complicated structure as well as the design procedure based on evolutionary genetic algorithm （GA）. In addition, the sensor demonstrated a reliable performance as its sensitivity was independent of the magnitude of the applied load.