Adaptive dynamic adjustment star identification threshold estimation for a terrestrial star tracker

Observation angular distance error, as the difference between the actual observation angular distance and the reference angular distance, is an important parameter that affects the identification success rate, attitude measurement accuracy, and real-time performance of a terrestrial star tracker. It is the criterion to determine whether stars are identified in star identification but is still unclarified to date. To resolve the problem, the observation angular error model is presented in this work. This model determines the variation range of the observation angular distance error by analyzing the factors of astrometric transformations. Then, the optimal angular distance matching threshold expression for a terrestrial star tracker is presented on the basis of the proposed model for the optimal efficiency in star identification. Numerical simulations and a night sky experiment demonstrate that the differences between the theoretical model, simulation and actual experiment results are less than 0.5'' and thereby validate the reliability of our conclusions.

In Australia, Poaceae tribe Poeae are represented by 19 genera and 99 species, including economically and environmentally important native and introduced pasture grasses [e.g. Poa (Tussock-grasses) and Lolium (Ryegrasses)]. We used this tribe, which are well characterised in regards to morphological diversity and evolutionary relationships, to test the efficacy of DNA barcoding methods. A reference library was generated that included 93.9% of species in Australia (408 individuals, = 3.7 individuals per species). Molecular data were generated for official plant barcoding markers (rbcL, matK) and the nuclear ribosomal internal transcribed spacer (ITS) region. We investigated accuracy of specimen identifications using distance- (nearest neighbour, best-close match, and threshold identification) and tree-based (maximum likelihood, Bayesian inference) methods and applied species discovery methods (automatic barcode gap discovery, Poisson tree processes) based on molecular data to assess congruence with recognised species. Across all methods, success rate for specimen identification of genera was high (87.5–99.5%) and of species was low (25.6–44.6%). Distance- and tree-based methods were equally ineffective in providing accurate identifications for specimens to species rank (26.1–44.6% and 25.6–31.3%, respectively). The ITS marker achieved the highest success rate for specimen identification at both generic and species ranks across the majority of methods. For distance-based analyses the best-close match method provided the greatest accuracy for identification of individuals with a high percentage of “correct” (97.6%) and a low percentage of “incorrect” (0.3%) generic identifications, based on the ITS marker. For tribe Poeae, and likely for other grass lineages, sequence data in the standard DNA barcode markers are not variable enough for accurate identification of specimens to species rank. For recently diverged grass species similar challenges are encountered in the application of genetic and morphological data to species delimitations, with taxonomic signal limited by extensive infra-specific variation and shared polymorphisms among species in both data types.

Aiming at the problem of plugging and positioning identification in the process of automatic charging of electric vehicles, especially the difficult problem of low efficiency and accuracy of identification in complex operation environment, this article proposes a multi-algorithm fusion of electric vehicle charging port identification method, which can effectively obtain the characteristic information of the round hole of charging port and realize the purpose of automatic identification of charging port by robot. Firstly, an electric vehicle charging port in a complex environment is analyzed and simulated, and camera selection and calibration are explained; on this basis, algorithms based on image smoothing filtering, feature detection segmentation of ROI region, improved Canny edge detection and combined mathematical morphology are proposed to correlate the charging port image respectively, and the features of the target charging port jack are extracted. Finally, the experimental verification of the charging port identification method was conducted for different illumination intensities and different shooting distances. The experimental results show that the identification success rate is 93.3% under the weak illumination and 97.8% under the normal illumination intensity of 4000lx. This shows that the method can effectively improve the robustness and accuracy of the charging port identification.

A novel method for subdivided locating of star image in star identification

In this paper, a laboratory model is designed to evaluate the performance of star tracker algorithms for attitude determination of a satellite. Star tracker is the most accurate attitude sensor that determines satellite direction by applying centroiding algorithm, star identification and attitude determination. To utilize such algorithms, first, high quality of star images are needed which should be provided through the star tracker camera. Then, such images are given to processors and it determines the attitude of camera and satellite in three axes based on mentioned algorithms. First, in preliminary design, we define important star tracker parameters, like accuracy, detector, processor and field of view. In this paper, we have considered the range accuracy in yaw/pitch axis less than 20 arcsec and for roll axis less than 100 arcsec. To improve the attitude determination accuracy, we have applied an adaptive structure in centroiding algorithm, as only brightness stars are selected and identification algorithm is done based on them. Another important parameter is speed of identification algorithm which is handled by an ARM processor and improving pyramid algorithm, we reached less than 25 ms. Duo to this time, updating rate would be desired. Also knowing the coordination of bore sight direction that is distance between focal lens and imager is another important parameter that affects the accuracy of attitude and by ground calibration of camera, this parameter could be estimated carefully. Finally, implementation results on real images that are captured by a star tracker demonstrate optimum performance of algorithms.

The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and their neighboring stars. However, these methods often fail when insufficient detectable stars or bright noncooperative space objects are in the field of view (FOV). Seldom studies use angular distance matching among all stars for direct identification as it is complex to calculate and store. To solve the problem, we proposed a graph algorithm based on angular distance matching score transfer, which utilizes a graph data structure, where nodes store angular distance matching scores between stars and transfer scores through graph edge weights, overcoming the limitations of incomplete utilization of angular distance matching results. We also established a math model for edge weights based on the probabilities of star pair occurrences. Simulation tests and night sky image experiments demonstrate the robustness of this algorithm against position errors, brightness errors, and fake stars. By equally using information from all sensor stars, even in cases with multiple missing stars, numerous fake stars, and extremely bright interfering stars, the identification rate remains consistently above 99.53%. This algorithm represents a highly robust star identification method and lays the foundation for future research in intelligent sensing of space environmental objects using star sensors.

The concept of software-defined satellite (SDS) enables in-orbit software reconfiguration and payload virtualization, significantly enhancing the configurability and cross-mission reusability of imaging payloads. However, this flexibility also exposes traditional star identification methods-which depend on fixed intrinsic camera parameters-to severe robustness limitations when dealing with multiple cameras and varying operational conditions. Therefore, a universal and adaptive star identification algorithm is essential to ensure stable and precise attitude determination within the SDS architecture. To this end, this paper proposes a parameter-independent star identification algorithm based on topological invariance features. The method integrates a topological invariant feature coordinate system with a self-organizing map (SOM) neural network, enabling consistent recognition across diverse payload configurations and observation scenarios. Extensive experiments under conditions such as stellar position noise, false-star noise, missing-star noise, focal length variation, and dynamic fields of view demonstrate that the proposed algorithm consistently outperforms three benchmark methods and maintains stable accuracy. These results confirm that the proposed method not only aligns with the SDS philosophy of hardware-software decoupling but also provides a necessary algorithmic foundation for its flexible, reconfigurable, and multi-payload attitude determination framework.

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%.

A new type of air-driven multi-fingered dexterous hand, named ZJUT Hand, is proposed. ZJUT Hand has good passive flexibility and can make up for the deficiency of existing dexterous hands in some ways. A new method for grasping model identification based ANFIS is proposed. Simulation experimental results show that the method can easily establish the model of the target objects, and has very good recognition accuracy and convergence. Equivalent rules of target objects are presented and greatly improve the efficiency of identification. A general grasp planning program of multi-fingered dexterous hand is proposed. Finally, the grasp planning experiments for ZJUT Hand is completed. Experimental results show that ZJUT Hand is able to construct the models of two typical target objects.

Adaptive Cruise Control of Vehicle Platoons with Wireless State Information

A description is presented of a neoteric approach to determine the orientation of the spacecraft for any arbitrary attitude by exploiting the offsets in the Earth observational imagery from a pair of canted pushbroom sensors. Due to angular separation one of the imager projections is ahead of the other, which causes a delay in the imaging of the same ground features by the sensors. Thus, the angular separation creates perspective and temporal distortions in the imagery, which give rise to inter-image offsets in the overlapping region. A mathematical model is developed using the sensor configuration of the UK-DMC (disaster monitoring constellation) satellite to demonstrate how the offsets are introduced in the imagery and quantify that model with the aid of synthetic images. To determine the attitude from the image offsets, the attitude model is inverted. The accuracy of attitude estimates is highly dependent on the image texture, angular separation between the sensors, spacecraft attitude, and image registration method. With small along-track separation between the sensors, the registration time is very short, thus, changes in the inter-image offsets are not very rapid at small arbitrary rotations. However, with the increase in along-track separation between the sensors, the registration time increases, which allows changes in inter-image offsets to be discerned due to attitude variation and increases the sensitivity of the technique. But with large along-track separation, the attitude estimates are averaged over the registration time period. At small arbitrary rotations with half-a-degree angular separation between the sensors, this technique gives attitude accuracies of ±0.03°. As attitude estimates come directly from the payload rather than a dedicated sensor, this corresponds to significant savings in cost, mass, and volume, which are critical for small satellites.

Background: Aptamers have sparked significant interest in cell recognition due to their superior binding specificity and biocompatibility. Cell recognition can be mediated by targeting the major histocompatibility complex (MHC) that presents short peptides derived from intracellular antigens. Although numerous antibodies have demonstrated a specific affinity for the peptide-MHC complex, the number of aptamers that exhibit comparable characteristics is limited. However, the success rate of aptamer identification is low. This is possibly due to the presence of complementary sequences or sequences rich in quinine and cytosine that are less accessible for primers. Here, we use ovalbumin model antigen to explore whether aptamers with specificity for binding to a defined peptide-MHC complex can be identified. We developed DNA aptamers with modified SELEX by employing systemic consecutive selections with minimal PCR amplification. Furthermore, the other tumour antigen, claudin-6, was also used as a target to determine whether our modification for SELEX can be used with any selection technology. Methods: To determine whether our modified SELEX has potential for selecting aptamer capable of cancer-specific targeting, we used ovalbumin presented by H-2Kb (to represent the specific antigen presented by MHC) and claudin-6 (a cancer-associated adhesion molecule) as a target for the selection of aptamers. This modified SELEX consisted of consecutive positive and negative selections with minimal PCR amplification. Meanwhile, the selection of candidate aptamers is based on their appearance in multiple rounds rather than enrichment, and is facilitated by several computational tools. The specificity and affinity of the candidate aptamer are validated by flow cytometry, immunohistochemistry, and immunofluorescence analysis with cells with or without these molecules. Results: After high-throughput sequencing, a total of 211 candidate aptamers were selected for further analysis. The quadruplex forming G-rich sequences (QGRS) mapper, mFold modeling, and NUPACK web servers were used to predict the potential of G-quadruplex formation, resulting in only four potential aptamers. Using MTCQ1-OVA cells with FITC-conjugated aptamers, two aptamers showed selective binding to MTCQ1-OVA cells and had no binding specificity to MTCQ1-WT cells. The binding specificity was further validated by immunohistochemistry in tumour derived from mice inoculated with MTCQ1-WT or MTCQ1-OVA cells. In the pull-down assay, the binding affinity of these two candidate aptamers was 136.2 nM and 155.1 nM affinity. Conclusions: This preliminary study demonstrated that our modification for SELEX is effective in the selection of aptamers with promising potential for specific cancer targeting. The identification of aptamers may facilitate further application in diverse biomedical fields. Citation Format: Yang Lin, Cho-Yi Chen, Zhi-Qian Lin, Bing-Hong Chen, Yi-Chen Sun, Kai-Feng Hung. A modified SELEX enables the efficient identification of DNA aptamers with potential for specific cancer targeting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3761.

The high-accuracy nuclear datum of prompt fission neutron spectrum (PFNS) is not only an important parameter for evaluating nuclear data, but also relevant to a more fundamental understanding of the fission process. However, the PFNS experimental data of main actinides (uranium and plutonium) are very scarce and the existing experimental data are in significant discrepancy. The error of some experimental data is markedly large:the relative uncertainty value reaches above 30%. In order to clarify these discrepancies and reduce the uncertainty, more reliable and accurate fission spectrum measurements are necessary. Most of the traditional measurements of the PFNS rely on time-of-flight technique. In this technique the fission sample needs to be prepared in a fission chamber and the fission neutron is identified by fission fragment. Since the particle range of fission fragment is very short, the thickness of sample is limited. So the number of samples cannot be large and the statistic count of fission neutrons will not be great either. Now a new technique to measure PFNS has appeared based on fission gamma multiplicity, which could be used to discriminate fission neutron from other neutrons. The new technique is based on a physical fact:when a fission event happens, seven-to-eight associated gamma photons are emitted while the inelastic scattering effect emits only one or two associated gamma photons. Accordingly, the fission neutron could be discriminated from other neutrons by fission gamma multiplicity. The principles of the new method and the realizing approach are presented in detail. The experimentally measuring system is established. A spontaneous fission neutron source 252Cf is used to measure the PFNS in order to validate the measuring system. The measured spectra are compared with those of ENDF/B-VⅡ library. The PFNS of 238U induced by D-T neutron is measured by the measuring system. The results show that the new method based on fission gamma identification is be available in the measuring of PFNS. The identification efficiency, the total uncertainty analysis of the new method and the suggestion for improvements in the future are also included in the paper.

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

In order to make the speed adjustment system of the permanent magnet synchronous linear motor more stable and make the convergence performance of the speed control system better, a speed regulator based on the terminal sliding mode algorithm is proposed. In addition, the permanent magnet synchronous linear motor will also be affected by temperature changes during its operation. Aiming to enhance the adaptability of linear motors to parameter changes, a terminal sliding mode speed control idea based on adaptive parameter identification is proposed. The Popov stability theory and the mathematical model of linear motor are used to construct a parameter identification system. The identification flux parameters are used as the control algorithm of the terminal sliding mode speed regulator and the update matching of speed regulator flux parameters and motor system parameters are realized. Through the above algorithm, the performance of the entire speed control system is improved. The results of simulation experiments can illustrate the effectiveness of this control method and its effect on improving the control performance.