Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation

Extraction optimization of Eucommia ulmoides Oliver and its effect on bone quality in OVX rats

Purpose: To maximize the yield of extract from Eucommia ulmoides Oliver and its effect on bone quality. Methods: Different extraction indices were optimized with response surface methodology (RSM) for maximization of extract yield from Eucommia ulmoides Oliver. Box–Behnken design (BBD) was used to identify the effects of temperature, time, and liquid to solid ratio on extract yield from Eucommia ulmoides Oliver. After 4-week acclimatization, thiry-two rats were randomly assigned to 4 groups (n = 8): group 1 (sham) given vehicle only; group 2 (OVX rats given Eucommia ulmoides Oliver extract at a dose of 4 g/kg; group 3 (OVX + vehicle); group 4 (OVX + EUOE), i.e., OVX rats given Eucommia ulmoides Oliver extract (4 g/kg). Sham rats had intact ovaries. After surgery, the rats received gentamicin intramuscularly for 3 successive days. Two months after surgery, blood and trabecular bones was taken for analysis. Results: Temperature and liquid-to-solid ratio had marked impact on extract yield from Eucommia ulmoides Oliver, with the best conditions being temperature of 88 °C, time of 137 min, and liquid to solid ratio 16:1. Using these optimized conditions, the maximum yield of extract obtained experimentally (2.53%) was very close to the predicted value of 2.49 %. There was a good fit between the mathematical model evolved and the data on extract yield. The extract significantly (p < 0.01) increased the Ca and P and Cr levels in OVX + EUOE group compared to those in OVX control. Moreover, the extract significantly (p < 0.01) increased macro-mechanical indices of trabecular bone in OVX+EUOE group, relative to those in OVX control. Conclusion: The yield of Eucommia ulmoides Oliver extract has been successfully optimized using RSM. The extract exhibited strong effects on bone quality. Keywords: Optimization, Eucommia ulmoides, Box–Behnken design, Response surface methodology, Bone loss, Gen

Circular RNAs as potential biomarkers and therapeutics for cardiovascular disease

Circular RNAs (circRNAs) are genetic regulators that were earlier considered as “junk”. In contrast to linear RNAs, they have covalently linked ends with no polyadenylated tails. CircRNAs can act as RNA-binding proteins, sequestering agents, transcriptional regulators, as well as microRNA sponges. In addition, it is reported that some selected circRNAs are transformed into functional proteins. These RNA molecules always circularize through covalent bonds, and their presence has been demonstrated across species. They are usually abundant and stable as well as evolutionarily conserved in tissues (liver, lung, stomach), saliva, exosomes, and blood. Therefore, they have been proposed as the “next big thing” in molecular biomarkers for several diseases, particularly in cancer. Recently, circRNAs have been investigated in cardiovascular diseases (CVD) and reported to play important roles in heart failure, coronary artery disease, and myocardial infarction. Here, we review the recent literature and discuss the impact and the diagnostic and prognostic values of circRNAs in CVD

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method

Model predictive trajectory tracking control of automated guided vehicle in complex environments

Autonomous navigation in a real-world industrial environment is in many ways a challenging task. One of the key challenges is rapid collision-free planning and execution of trajectories to reach any target position and orientation with high accuracy, taking into account the limitations of imperfectness of the vehicle. The model prediction-based motion planners have been successfully used in recent years to generate feasible motions for imperfect vehicles. This paper develops and implements a Model Predictive Control (MPC)-based trajectory controller for path tracking problem in narrow corridors. To evaluate the performance of the proposed method, we designed comparative simulations and experiments. We confirm that the proposed MPC-based controller can track the trajectory precisely and smoothly in specific complex environments. In addition, the proposed methodology can also be a suitable solution to other way-point tracking situations for an industrial mobile robot.Accepted versionThe research is partially supported by the Delta - NTU Corporate Lab through the NRF corporate lab@university scheme. The authors would like to thank Mr. Yongjun Wee and Dr. Jeffrey Soon for providing us use cases and many discussions during the course of this work

An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions

The clinical success of vascular interventional surgery relies heavily on a surgeon’s catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon’s technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon’s hands or to interventional devices for data collection, which constrains the surgeon’s operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon’s manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon’s catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery

Design and Performance Evaluation of a Force/Torque Sensor for Tele-Operated Catheterization Procedures

10.1109/JSEN.2016.2522657IEEE SENSORS JOURNAL1693208-321

Learning to Pan-sharpening with Memories of Spatial Details

Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multi-spectral images to obtain high-resolution MS images. Since deep learning has received widespread attention because of its powerful fitting ability and efficient feature extraction, a variety of pan-sharpening methods have been proposed to achieve remarkable performance. However, current pan-sharpening methods usually require the paired PAN and MS images as the input, which limits their usage in some scenarios. To address this issue, in this paper, we observe that the spatial details from PAN images are mainly high-frequency cues, i.e., the edges reflect the contour of input PAN images. This motivates us to develop a PAN-agnostic representation to store some base edges, so as to compose the contour for the corresponding PAN image via them. As a result, we can perform the pan-sharpening task with only the MS image when inference. To this end, a memory-based network is adapted to extract and memorize the spatial details during the training phase and is used to replace the process of obtaining spatial information from PAN images when inference, which is called Memory-based Spatial Details Network (MSDN). We finally integrate the proposed MSDN module into the existing DL-based pan-sharpening methods to achieve an end-to-end pan-sharpening network. With extensive experiments on the Gaofen1 and WorldView-4 satellites, we verify that our method constructs good spatial details without PAN images and achieves the best performance. The code is available at https://github.com/Zhao-Tian-yi/Learning-to-Pan-sharpening-with-Memories-of-Spatial-Details.git

Identification and analysis of serum samples by surface-enhanced Raman spectroscopy combined with characteristic ratio method and PCA for gastric cancer detection

This study aimed to explore the application of surface-enhanced Raman scattering (SERS) in the rapid diagnosis of gastric cancer. The SERS spectra of 68 serum samples from gastric cancer patients and healthy volunteers were acquired. The characteristic ratio method (CRM) and principal component analysis (PCA) were used to differentiate gastric cancer serum from normal serum. Compared with healthy volunteers, the serum SERS intensity of gastric cancer patients was relatively high at 722cm−1, while it was relatively low at 588, 644, 861, 1008, 1235, 1397, 1445 and 1586cm−1. These results indicated that the relative content of nucleic acids in the serum of gastric cancer patients rises while the relative content of amino acids and carbohydrates decreases. In PCA, the sensitivity and specificity of discriminating gastric cancer were 94.1% and 94.1%, respectively, with the accuracy of 94.1%. Based on the intensity ratios of four characteristic peaks at 722, 861, 1008 and 1397cm−1, CRM presented the diagnostic sensitivity and specificity of 100% and 97.4%, respectively, and the accuracy of 98.5%. Therefore, the three peak intensity ratios of I722/I861, I722/I1008 and I722/I1397 can be considered as biological fingerprint information for gastric cancer diagnosis and can rapidly and directly reflect the physiological and pathological changes associated with gastric cancer development. This study provides an important basis and standards for the early diagnosis of gastric cancer