Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure

A novel dynamical filter based on multi-epochs least-squares to integrate the carrier phase and pseudorange observation for GNSS measurement

© 2020 by the authors. The high noise of pseudorange and the ambiguity of carrier phase observation restrain the GNSS (Global Navigation Satellite System) application in military, industrial, and agricultural, to name a few. Thus, it is crucial for GNSS technology to integrate the pseudorange and carrier phase observations. However, the traditional method proposed by Hatch has obtained only a low convergence speed and precision. For higher convergence speed and precision of the smoothed pseudorange, aiming to improve positioning accuracy and expand the application of GNSS, we introduced a new method named MELS (Multi-Epochs Least-Squares) that considered the cross-correlation of the estimating parameters inspired by DELS (Double-Epochs Least-Square). In this study, the ionospheric delay was compensated, and so its impact was limited to the performance of the filters, and then exploited the various filters to integrate carrier phase observation and pseudorange. We compared the various types of Hatch's filter and LS (Least-Square) methods using simulation datasets, which confirmed that the types of LS method provided a smaller residual error and a faster convergence speed than Hatch's method under various precisions of raw pseudorange. The experimental results from the measured GNSS data showed that LS methods provided better performance than Hatch's methods at E and U directions and a lower accuracy at N direction. Nevertheless, the types of LS method and Hatch's methods improved about 12% and 9-10% at the 3D direction, respectively, which illustrated the accumulating improvement at the enhanced directions was more than the decreased direction, proving that the types of LS method resulted to better performance than the Hatch's filters. Additionally, the curve of residual and precision based on various LS methods illustrated that the MELS only provided a millimeter accuracy difference compared with DELS, which was proved by the simulated and measured GNSS datasets

Effect of pectin on properties of potato starch after dry heat treatment

Purpose: To evaluate the effect of pectin on the properties of potato starch after dry heat treatment. Methods: Rapid visco analyzer (RVA), differential scanning calorimetry (DSC), texture profile analyzer (TPA), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and x-ray diffractometry (XRD) were used to determine the properties of modified potato starch and pectin blends after dry heat treatment. Results: Results from RVA showed that the peak viscosity of modified potato starch decreased gradually with increase in pectin concentration, dry heat time and dry heat temperature, while starch breakdown decreased and setback was increased to varying degrees. The lowest breakdown was 792 cP at dry heat temperature of 140 °C. Modified potato starch had broader ranges of gelatinization temperatures and lower gelatinization enthalpy than raw potato starch. Dry heat treatment improved the hardness, gumminess and chewiness of the gels of modified potato starch and pectin blends SEM micrographs showed some cluster shapes in microstructure after dry heat treatment of starch-pectin blends. Infrared spectra revealed that pectin addition and dry heat treatment did not cause changes in starch structure. However, x-ray diffractograms indicated that dry heat treatment weakened the third peak of potato starch. Conclusion: These results indicate that dry heat treatment effectively alters the properties of potato starch and pectin blends. This finding broadens the applications of modified potato starch in food and pharmaceutical industries

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters

Selenium level and depressive symptoms in a rural elderly Chinese cohort

BACKGROUND: Selenium is considered a protective agent against free radicals through the maintenance of better enzyme activity. The few studies examining the relationship between selenium and depression have yielded inconsistent results and none of these studies considered the role of cognitive function in this context. METHODS: A cross-sectional evaluation of 1737 rural Chinese age 65 and over from two provinces in China was conducted. Depressive symptoms were assessed using the Geriatric Depression Scale (GDS). Cognitive function was assessed using various cognitive instruments. Selenium measures were obtained from nail samples. Other information collected included demographic characteristics and medical history. Analysis of covariance models were used to identify factors associated with GDS score. RESULTS: Higher selenium levels were associated with lower GDS scores adjusting for demographic and medical conditions (p = 0.0321). However, the association between selenium and depressive symptoms was no longer significant when cognitive function score was adjusted in the model (p = 0.2143). CONCLUSIONS: Higher selenium level was associated with lower depressive symptoms without adjusting for cognition in this cohort. However, after cognition was adjusted in the model the association between selenium and depressive symptoms was no longer significant, suggesting that selenium’s association with depressive symptoms may be primarily through its association with cognitive function

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead