Currently, drone detection is a research hotspot in the security field due to its popularity. This paper proposed a recognition method of the low-altitude drone detection based on the YOLOv4 (You Only Look Once version 4) model and also introduced YOLOv4 algorithm in detection low-altitude UAV object for the first time. Sample set of drone flight attitude images constructed by shooting, downloading from the Internet and expanding the existing data is used to solve the lack of standard data set. The experimental results show that although YOLOv4, YOLOv3 and SSD algorithm all belong to one-stage algorithm, the average accuracy and real-time detection speed of YOLOv4 are better than that of the YOLOv3 and SSD.

In a structured light-based 3D scanning system, the overall 3D information of to-be-measured objects cannot be retrieved at one time automatically. Currently the 3D registration algorithms can be divided into the auxiliary objects-based method and the feature points-based method. The former requires extra calibration objects or positioning platforms, which limits its application in free-form 3D scanning task. The latter can be conducted automatically, however, most of them tried to recover the motion matrix from extracted 2D features, which has been proved to be inaccurate. This paper proposed an automatic and accurate full-view registration method for 3D scanning system. Instead of using the 3D information of detected feature points to estimate the coarse motion matrix, 3D points reconstructed by the 3D scanning system were utilized. Firstly, robust SIFT features were extracted from each image and corresponding matching point pairs are achieved from two adjacent left images. Secondly, re-project all of the 3D point clouds onto the image plane of each left camera and corresponding 2D image points can be obtained. Filter out correct matching points from all 2D reprojection points under the guidance of the extracted SIFT matching points. Then, the covariance method was adopted to estimate the coarse registration matrix of adjacent positions. This procedure was repeated among every adjacent viewing position of the 3D scanning system. Lastly, fast ICP algorithm was performed to conduct fine registration of multi-view point clouds. Experiments conducted on real data have verified the effectiveness and accuracy of the proposed method.

Fast and efficient pedestrian detection technology has become an increasingly important task in the autonomous driving technology. Traditional pedestrian detection algorithms are usually too time-consuming to meet the real-time requirements of autonomous driving. In this paper, we propose a new pedestrian detection algorithm based on the Darknet with optimized feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. In view of the characteristics of pedestrian detection system, we adopt the priori experience about the feature box sizes, instead of K-mean clustering algorithm. We also conduct statistical analysis on the dataset pedestrian label, and design the initial value of the pre-selection box that is more in line with pedestrian characteristics. The proposed algorithm not only improves the detection accuracy, but also enhances the efficiency of pedestrian detection. Experimental results on traffic record benchmark demonstrate that the optimized algorithm satisfies the real-time and accuracy requirements of the low-speed autonomous driving.

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.

Registration of 3D point clouds is an important issue in the field of 3D reconstruction. In this work, we proposed a non-iterative registration method based on the motion screw theory and trackable features. The screw theory is derived from the theory of rigid body mechanics, which holds the idea that the motion of rigid body can be regarded as a kind of spiral motion and can be effectively represented by an angular velocity vector and a linear velocity vector. It has not been utilized in 3D data registration before as far as we know. In this paper, 3D data registration based on the motion screw theory is specifically introduced, and a searching strategy based on the trackable features in image sequences is presented to improve the accuracy of 3D registration. The proposed method has been successfully tested on real multi-view data. Experimental results showed that it could simplify the computational process, accelerate the speed of registration, and achieve higher precision than other methods.

Discrete Cosine Transform (DCT) plays a significant role in digital signal processing, especially in various international compressing standards, such as JPEG (Joint Photographic Experts Group), MPEG4 (Moving Pictures Expert Group), and H26X. Due to its high computational complexity, fast DCT algorithms have been widely studied in recent years. This paper highlights a novel approach to calculate DCT based on first-order moments on FPGA (Field Programmable Gate Array). The proposed method is of high structural regularity, low hardware cost of PE (Processing Element) and low I/O cost. The approach is also applicable to multi-dimensional DCT and inverse DCT.

A novel poly-(3-thiopheneacetic acid) coated Fe3O4@LDHs photocatalyst was prepared and used for the photocatalytic disinfection of bacteria under solar light irradiation. The influencing parameters of the photocatalytic antibacterial activity of the Fe3O4@PTAA-LDHs photocatalyst were optimized against E. coli and S. aureus. Results showed that the Fe3O4@PTAA-LDHs photocatalyst could effectively absorb ultraviolet and visible light. The antibacterial rate could achieve 99.99% when irradiated by solar light with 0.8 mg mL−1 Fe3O4@PTAA-LDHs during 140 min. Moreover, it was confirmed that the ˙OH were generated during the photocatalytic process, which was the major reason for the photocatalytic disinfection. The structural destruction of the bacteria cells that occurred during the bactericidal procedure was observed by TEM images. The novel photocatalyst could have potential applications in the related bactericidal fields.

A method based on electrochemiluminescence resonance energy transfer (ECRET) between luminol as the donor and CdSe/ZnS quantum dot (QD) for evaluation of the interactions between DNAs and measurement of the conformational changes of DNAs was developed. When a positive potential was applied to the conjugates consisting luminol, DNA and QD, ECRET between luminol molecules and QDs could be detected in 0.2mol/L Na2CO3–NaHCO3 buffer (pH 10) containing 1.0×10−2mol/L H2O2. In this case, luminol molecules emitted a light with a maximum emission (λm) of 460nm or transferred energy to proximal ground-state QDs. The excited state QDs relaxed to their ground state by emitting a light with a λm of 655nm. The ECRET between luminol and QD was used to evaluate interactions between DNAs and to measure conformational changes of DNAs.

Novel electrochemiluminescence resonance energy transfer (ECRET) between an emitter electrochemically generated by luminol as the donor and luminescent quantum dots as the acceptor is investigated. The ECRET technique can be used to study the interactions and conformational changes of proteins.

We developed an ultrasensitive electrochemiluminescence (ECL) method for DNA determination using magnetic submicrobeads (SMBs) as the carrier of Ru(bpy)32+ (bpy=2,2′-bipyridy) and carbon nanotubes (CNTs) as accessorial electrode material. The SMBs with Ru(bpy)32+ were wrapped with CNTs and then immobilized on an Au electrode. In the presence of tri-n-propylamine, ECL of the Ru(bpy)32+ on the SMBs was detected. Since one target DNA (t-DNA) molecule corresponded to one SMB with a large number of Ru(bpy)32+, the ECL signal was amplified. In addition, the Ru(bpy)32+-loaded SMBs were wrapped with CNTs that contacted the electrode. The ECL of Ru(bpy)32+ was greatly increased. Using this method, t-DNA of 3×10−16mol/L could be detected. The method could be used to quantify mRNA in cells.

In this paper, we reported an ultrasensitive ECL spectrometry for determination of DNA using magnetic streptavidin-coated nanobeads MNBs (SA-MNBs) as the carrier of Ru(bpy)(3)(2+)-NHS, where bpy = 2,2'-bipyridyl and NHS = N-hydroxysuccinimide ester, to amplify signal. The SA-MNBs were conjugated to the hybrids consisting of capture DNA, target DNA (t-DNA) and probe DNA immobilized on a substrate, followed by releasing the SA-MNBs and binding a huge number of Ru(bpy)(3)(2+)-NHS to the SA-MNBs. The SA-MNBs with Ru(bpy)(3)(2+)-NHS were immobilized on an Au film electrode by means of a magnet. In the presence of tri-n-propylamine. the ECL spectrum of the Ru(bpy)(3)(2+)-NHS at 1.35V was acquired by using an optical multi-channel analyzer. The maximum emission intensity on the ECL spectrum was used to quantify DNA. Using this method, not only the limit of detection for DNA determination was as low as 1.2 x 10(-15) mol/L. but also the ECL spectrum of Ru(bpy)(3)(2+)-NHS on the surface of the SA-MNBs was obtained. The ultrasensitive ECL spectrometry could be used to measure gene expression level in cells. (C) 2011 Elsevier B.V. All rights reserved.

In the present paper, in-situ preparation of silver nanoparticles have been conducted in 3D network structure of BC membrane through liquid phase chemical deoxidization method. The characterization of products was investigated using scanning electron microscopy (SEM), infrared spectroscopy (IR), energy dispersion spectrometry (SEM-EDS). The absorbing water capacity and preserving water capacity of substitutes and the antibacterial capacities of antibacterial agent-loaded artificial skin were tested. The results showed the silver nanoparticles were approximately spherical particles with an average diameter of 45nm, and were noted to have excellent sterilizing efficacy the efficiency of against Escherichia coli, yeast and Candida albicans.

The electrochemistry of mercaptopropionic acid capped CdTe quantum dots (QDs) was studied by differential pulse voltammetry, three processes were obtained at 0.36 (A1), 0.68 (A2) and 0.84V (A3), respectively. A1 process could be selectively suppressed by magnesium ion, an electrochemical sensor for magnesium ion was developed with a linear response from 4×10−5 to 1×10−2mol/L and good reproducibility. This electrochemical sensing application of QDs may provide a new strategy for QDs-based detection.

A novel synthesis method of biocompatible polymers-coated magnetic nanogels, was developed. Poly(PEGMA) modified superparamagnetic nanogels were synthesized by in-situ polymerization using poly(ethylene glycol) methacrylate (PEGMA) as monomer and N,N'-methylene-bis-(acrylamide) (MBA) as cross-linking agent in magnetite aqueous suspension under UV irradiation. The surface functional groups, structure, particle size, Zeta potential and magnetic property were characterized, respectively. The Fourier transform infrared spectroscopy (FTIR) results indicated that the poly(PEGMA) modified magnetic nanogels were synthesized successfully under UV irradiation. The analyses of transmission electron microscopy (TEM) and photon correlation spectroscopy (PCS) indicated that the poly(PEGMA) modified magnetic nanogels were essentially monodisperse, nearly spherical in shape, the mean diameter in aqueous solution was 68.4 nm, and the size distribution was narrow. The thermogravimetric analyzer (TGA) result indicated that there was a high Fe3O4 content of 53.4% in the magnetic nanogels, which endowed the magnetic nanogels with a strong magnetic response under external magnetic field. Magnetic measurement revealed that the saturated magnetization of the poly(PEGMA) magnetic nanogels reached 58.6 emu/g and the nanogels showed the characteristics of superparamagnetic. The Zeta potential was -17.3 mV at physiological pH (pH 7.4) which helped maintain stability in blood. The transverse relaxation time (T-2) was also performed for potential use of magnetic resonance imaging (MRI) contrast agents. The preliminary experiment verified the feasibility of the magnetic nanogels for potential MRI contrast agents.