DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although YOLOv2 approach is extremely fast on object detection; its backbone network has the low ability on feature extraction and fails to make full use of multi-scale local region features, which restricts the improvement of object detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating the object detection accuracy of YOLOv2. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale local region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of mean square error and cross entropy, and the object detection is realized. Experiments demonstrate that the mAP (mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table

Multi-Grained Angle Representation for Remote Sensing Object Detection

Arbitrary-oriented object detection (AOOD) plays a significant role for image understanding in remote sensing scenarios. The existing AOOD methods face the challenges of ambiguity and high costs in angle representation. To this end, a multi-grained angle representation (MGAR) method, consisting of coarse-grained angle classification (CAC) and fine-grained angle regression (FAR), is proposed. Specifically, the designed CAC avoids the ambiguity of angle prediction by discrete angular encoding (DAE) and reduces complexity by coarsening the granularity of DAE. Based on CAC, FAR is developed to refine the angle prediction with much lower costs than narrowing the granularity of DAE. Furthermore, an Intersection over Union (IoU) aware FAR-Loss (IFL) is designed to improve accuracy of angle prediction using an adaptive re-weighting mechanism guided by IoU. Extensive experiments are performed on several public remote sensing datasets, which demonstrate the effectiveness of the proposed MGAR. Moreover, experiments on embedded devices demonstrate that the proposed MGAR is also friendly for lightweight deployments.Comment: 13 pages, 9 figures, 14 table

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public datasets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page

High-Altitude Balloon-Based Sensor System Design and Implementation

As a kind of large-scale unmanned aerial vehicle, a high-altitude balloon can carry a large load up to tens of kilometers in the near space for a long time, which brings a new way for the stratosphere atmospheric detection. In order to provide a suitable working environment for the near-space detection load, it is necessary to design a sensor system based on a high-altitude balloon, which is used to provide environmental temperature, height position, and attitude information, current working, and video surveillance. The high-altitude balloon-based sensor system designed in this paper had participated in the near-space flight experiment, whose total flight time was 30 h and 53 min, and the horizontal flight time was 28 h and 58 min crossing the day and night. The high-altitude balloon-based sensor system had withstood the severe environment of the near-space during the day and night, providing accurate temperature measurement, real-time altitude position and attitude data acquisition, reliable current monitoring, and comprehensive video surveillance. In the next three years, the high-altitude balloon-based sensor system developed in this paper will continue to participate in the experiment and provide support for more detection loads

Identification of MiRNA–Disease Associations Based on Information of Multi-Module and Meta-Path

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA–disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision–recall curve (AUPR) of 0.9379 and an area under the receiver–operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets

The Impact of the Geometric Characteristics on the Hemodynamics in the Stenotic Coronary Artery.

The alterations of the hemodynamics in the coronary arteries, which result from patient-specific geometric significances are complex. The effect of the stenosis on the blood flow alteration had been wildly reported, but the combinational contribution from geometric factors required a comprehensive investigation to provide patient-specific information for diagnosis and assisting in the decision on the further treatment strategies. In the present study, we investigated the correlation between hemodynamic parameters and individual geometric factors in the patient-specific coronary arteries. Computational fluid dynamic simulations were performed on 22 patient-specific 3-dimensional coronary artery models that were reconstructed based on computed tomography angiography images. Our results showed that the increasing severity of the stenosis is associated with the increased maximum wall shear stress at the stenosis region (r = 0.752, P < 0.001). In contrast, the length of the recirculation zone has a moderate association with the curvature of the lesion segment (r = 0.505, P = 0.019) and the length of the lesions (r = 0.527, P = 0.064). Moreover, bifurcation in the coronary arteries is significantly correlated with the occurrence of recirculation, whereas the severity of distal stenosis demonstrated an effect on the alteration of the flow in the upstream bifurcation. These findings could serve as an indication for treatment planning and assist in prognosis evaluation

Improved Active Disturbance Rejection Double Closed-Loop Control of a Rotary-Type VCM in a Moving Mirror Control System

Aiming to address the problem of moving mirror speed fluctuations in moving mirror control systems, an improved active disturbance rejection double closed-loop controller (IADR-DCLC) is proposed and verified by simulation to realize the high-performance control of a moving mirror control system. First, the mathematical model of a rotary-type voice coil motor (RT VCM) is established, and the relationship between the angular velocity of the RT VCM and the optical path scanning velocity is analyzed. Second, in order to suppress the model uncertainty and external disturbance of the system, an improved active disturbance rejection controller (IADRC) is proposed. Compared with a conventional ADRC, the tracking differentiator of the proposed IADRC is replaced with desired signal optimization (DSO), and the actual speed is introduced to the extended state observer (ESO). The IADRC is used in the position&ndash;speed double closed-loop control model. Finally, the simulation results show that the IADR-DCLC has not only a good tracking effect but also a good anti-interference ability and can meet the requirements of the moving mirror control system for the uniformity of optical-path scanning speed and accurate control of the position of the moving mirror

Cr(VI) anion-imprinted polymer synthesized on mesoporous silicon via synergistic action of bifunctional monomers for precise identification and separation of Cr(VI) from aqueous solution by fixed-bed adsorption

The novel Cr(VI) anion-imprinted polymer (Cr(VI)-IIP) was prepared by a surface imprinting technique with bifunctional monomers pre-assembly system based on mesoporous silicon (SBA-15). The synthesized Cr(VI)-IIP was characterized by Fourier transmission infrared spectra (FT-IR), energy dispersive spectrometer (EDS), scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray powder diffractometer, N2 adsorption–desorption and thermogravimetric analysis (TGA), proving to be with a highly ordered mesoporous structure, as well as favorable thermal stability. The saturated adsorption amount was 96.32 mg/g, which was 2.7 times higher than that of non-imprinted polymer (NIP). Kinetic experiments showed that the adsorption equilibrium state was obtained within 70 min. In addition, in the selectivity experiments, Cr(VI)-IIP exhibited strong specific recognition ability for Cr(VI) and could realize the separation of Cr(VI) and Cr(III) from an aqueous solution. The dynamic adsorption experiments exhibited that the dynamic adsorption efficiency of Cr(VI)-IIP was as high as 71.57%. Meanwhile, the dynamic regeneration experiments showed that the adsorption amount of Cr(VI)-IIP did not decrease significantly after repeating for five times. All of the findings suggested that Cr(VI)-IIP could achieve precise identification as well as efficient separation of Cr(VI) from aqueous solution. HIGHLIGHTS Preparation of Cr(VI) anion-imprinted polymer (Cr(VI)-IIP) by bifunctional monomers pre-assembly system.; The Cr(VI) imprinted sites formed by the coordination of bifunctional monomers and template ions could realize the separation of Cr(VI) and Cr(III).; Cr(VI)-IIP exhibited superior selectivity compared to other common adsorbents and Cr(VI) imprinted polymers prepared by a single functional monomer.

The geometric characteristics include severity of stenosis, tortuosity of the lesion segment, curvature of the lesion segment, angle of the lesion and length of the lesion.

<p>A: Tortuosity (T) was taken as the ratio of the length from the ostium of the coronary segment to 1 cm distal to the stenosis (L) to the distance between the ends of the segment (C). B: The severity of the stenosis is the ratio of the cross-section area in the remain lumen at the stenosis and the healthy proximal segment. C: Curvature (unit: 1/m) of the stenotic artery segment was measured, of which the ends of the segment were 1cm proximal to the stenosis and 1cm distal to the stenosis. D: The angle of the lesion was measured at the immediate distal edge of the lesion (unit: degree). E: The length of the lesion was also measured (unit: mm).</p