Nighttime Image Stitching Method Based on Image Decomposition Enhancement.

Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry.

In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost.

Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images.

With the rapid development of mobile Internet technology, localization using visual image information has become a hot problem in the field of indoor localization research, which is not affected by signal multipath and fading and can achieve high accuracy localization in indoor areas with complex electromagnetic environments. However, in practical applications, position estimation using visual images is easily influenced by the user's photo pose. In this paper, we propose a multiple-sensor-assisted visual localization method in which the method constructs a machine learning classifier using multiple smart sensors for pedestrian pose estimation, which improves the retrieval efficiency and localization accuracy. The method mainly combines the advantages of visual image location estimation and pedestrian pose estimation based on multiple smart sensors and considers the effect of pedestrian photographing poses on location estimation. The built-in sensors of smartphones are used as the source of pedestrian pose estimation data, which constitutes a feasible location estimation method based on visual information. Experimental results show that the method proposed in this paper has good localization accuracy and robustness. In addition, the experimental scene in this paper is a common indoor scene and the experimental device is a common smartphone. Therefore, we believe that the proposed method in this paper has the potential to be widely used in future indoor navigation applications in complex scenarios (e.g., mall navigation).

Nighttime Image Stitching Method Based on Guided Filtering Enhancement.

Image stitching refers to stitching two or more images with overlapping areas through feature points matching to generate a panoramic image, which plays an important role in geological survey, military reconnaissance, and other fields. At present, the existing image stitching technologies mostly adopt images with good lighting conditions, but the lack of feature points in scenes with weak light such as morning or night will affect the image stitching effect, making it difficult to meet the needs of practical applications. When there exist concentrated areas of brightness such as lights and large dark areas in the nighttime image, it will further cause the loss of image details making the feature point matching unavailable. The obtained perspective transformation matrix cannot reflect the mapping relationship of the entire image, resulting in poor splicing effect, and it is difficult to meet the actual application requirements. Therefore, an adaptive image enhancement algorithm is proposed based on guided filtering to preprocess the nighttime image, and use the enhanced image for feature registration. The experimental results show that the image obtained by preprocessing the nighttime image with the proposed enhancement algorithm has better detail performance and color restoration, and greatly improves the image quality. By performing feature registration on the enhanced image, the number of matching logarithms of the image increases, so as to achieve high accuracy for images stitching.

A Novel Color Image Encryption Scheme Based on Hyperchaos and Hopfield Chaotic Neural Network.

Problems such as insufficient key space, lack of a one-time pad, and a simple encryption structure may emerge in existing encryption schemes. To solve these problems, and keep sensitive information safe, this paper proposes a plaintext-related color image encryption scheme. Firstly, a new five-dimensional hyperchaotic system is constructed in this paper, and its performance is analyzed. Secondly, this paper applies the Hopfield chaotic neural network together with the novel hyperchaotic system to propose a new encryption algorithm. The plaintext-related keys are generated by image chunking. The pseudo-random sequences iterated by the aforementioned systems are used as key streams. Therefore, the proposed pixel-level scrambling can be completed. Then the chaotic sequences are utilized to dynamically select the rules of DNA operations to complete the diffusion encryption. This paper also presents a series of security analyses of the proposed encryption scheme and compares it with other schemes to evaluate its performance. The results show that the key streams generated by the constructed hyperchaotic system and the Hopfield chaotic neural network improve the key space. The proposed encryption scheme provides a satisfying visual hiding result. Furthermore, it is resistant to a series of attacks and the problem of structural degradation caused by the simplicity of the encryption system's structure.

Infrared assisted extraction of bioactive compounds from plant materials: Current research and future prospect.

The extraction of bioactive compounds from plant materials has attracted much attention due to their potential therapeutic effects. This article reviews the basic principles, characteristics, and recent applications of infrared assisted extraction (IAE) of bioactive compounds from plant materials. The advantages and disadvantages of IAE are considered, and operation mode and technological improvements, processes, solvents used and other future developments are identified. The review indicated that IAE was a simple, rapid, and cost-effective technique with the capacity for industrial scale application. Future research should focus on energy consumption reduction, green chemistry extraction processes, simplified operation steps, intelligent extraction process, and the establishment of kinetic and thermodynamic models. This article provides a comprehensive understanding of the principles and applications of IAE for the preparation of bioactive compounds, which will be of benefit to researchers and users of the technology.

Dendrobium huoshanense stem polysaccharide ameliorates rheumatoid arthritis in mice via inhibition of inflammatory signaling pathways.

The present study explored the beneficial effect of Dendrobium huoshanense stem polysaccharide (cDHPS) after oral administration on rheumatoid arthritis (RA) using type Ⅱ collagen-induced arthritis (CIA) mouse model. It was found that cDHPS effectively alleviated joint swelling, synovial hyperplasia, pannus formation, cartilage erosion and bone destruction in CIA mice. Concurrently, cDHPS remodeled the balance of Th17 and regulatory T cells, reduced the secretion of pro-inflammatory mediators related to fibroblast-like synoviocyte activation, angiogenesis, articular cartilage degradation and osteoclast differentiation, inhibited HIF-1α expression and promoted anti-inflammatory mediator release in the joint tissues and serum of CIA mice. Western blot of joint tissues showed that cDHPS significantly inhibited the phosphorylation of IκB, p65, JNK, p38, ERK1/2, AKT, PI3K, JAK1 and STAT3 in CIA mice. These results suggest that cDHPS possesses the potential of ameliorating RA and its anti-RA effect may be attributed to the inhibition of inflammatory signaling pathways.

Indoor Radio Map Construction Based on Position Adjustment and Equipment Calibration.

The crowdsourcing-based wireless local area network (WLAN) indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps. Aiming at the problem of the diverse terminal devices and the inaccurate location annotation of the crowdsourced samples, which will result in the construction of the wrong radio map, an effective indoor radio map construction scheme (RMPAEC) is proposed based on position adjustment and equipment calibration. The RMPAEC consists of three main modules: terminal equipment calibration, pedestrian dead reckoning (PDR) estimated position adjustment, and fingerprint amendment. A position adjustment algorithm based on selective particle filtering is used by RMPAEC to reduce the cumulative error in PDR tracking. Moreover, an inter-device calibration algorithm is put forward based on receiver pattern analysis to obtain a device-independent grid fingerprint. The experimental results demonstrate that the proposed solution achieves higher localization accuracy than the peer schemes, and it possesses good effectiveness at the same time.

Polygonatum cyrtonema Hua Polysaccharide Promotes GLP-1 Secretion from Enteroendocrine L-Cells through Sweet Taste Receptor-Mediated cAMP Signaling.

Glucagon-like peptide-1 (GLP-1) secreted from enteroendocrine L-cells is a pleiotropic hormone with beneficial potential related to islet function, diet control, glucose homeostasis, inflammation relief, and cardiovascular protection. The present study aimed at investigating the effect of Polygonatum cyrtonema polysaccharide (PCP) after structural identification on GLP-1 secretion and the possible mechanism involved in the PCP-stimulated secretion of GLP-1. It was found that GLP-1 secretion was effectively promoted (p < 0.01) by PCP both in rats with oral administration for 5 weeks (13.9 ± 0.3-35.8 ± 0.3 pmol/L) and ileal administration within 2 h (13.6 ± 0.4-34.1 ± 1.1 pmol/L) and in enteroendocrine NCI-H716 cells with direct stimulation within 24 h (2.05 ± 0.3-20.7 ± 0.2 pmol/L). The sweet taste receptor T1R2/T1R3 was identified to be essential for NCI-H716 cells to directly recognize PCP. The intervention experiments showed that PCP-stimulated GLP-1 secretion was significantly depressed (p < 0.01) not only by antibodies, siRNA, and the inhibitor of T1R2/T1R3 but also by an adenylate cyclase inhibitor. These results suggest that PCP stimulates GLP-1 secretion from enteroendocrine cells possibly through activation of the T1R2/T1R3-mediated cAMP signaling pathway.

Unsupervised Indoor Positioning System Based on Environmental Signatures.

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.

Improved Scheduling Mechanisms for Synchronous Information and Energy Transmission.

Wireless energy collecting technology can effectively reduce the network time overhead and prolong the wireless sensor network (WSN) lifetime. However, the traditional energy collecting technology cannot achieve the balance between ergodic channel capacity and average collected energy. In order to solve the problem of the network transmission efficiency and the limited energy of wireless devices, three improved scheduling mechanisms are proposed: improved signal noise ratio (SNR) scheduling mechanism (IS2M), improved N-SNR scheduling mechanism (INS2M) and an improved Equal Throughput scheduling mechanism (IETSM) for different channel conditions to improve the whole network performance. Meanwhile, the average collected energy of single users and the ergodic channel capacity of three scheduling mechanisms can be obtained through the order statistical theory in Rayleig, Ricean, Nakagami-m and Weibull fading channels. It is concluded that the proposed scheduling mechanisms can achieve better balance between energy collection and data transmission, so as to provide a new solution to realize synchronous information and energy transmission for WSNs.

Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks.

To exploit the complementary strengths of WiFi positioning, pedestrian dead reckoning (PDR), and landmarks, we propose a novel fusion approach based on an extended Kalman filter (EKF). For WiFi positioning, unlike previous fusion approaches setting measurement noise parameters empirically, we deploy a kernel density estimation-based model to adaptively measure the related measurement noise statistics. Furthermore, a trusted area of WiFi positioning defined by fusion results of previous step and WiFi signal outlier detection are exploited to reduce computational cost and improve WiFi positioning accuracy. For PDR, we integrate a gyroscope, an accelerometer, and a magnetometer to determine the user heading based on another EKF model. To reduce accumulation error of PDR and enable continuous indoor positioning, not only the positioning results but also the heading estimations are recalibrated by indoor landmarks. Experimental results in a realistic indoor environment show that the proposed fusion approach achieves substantial positioning accuracy improvement than individual positioning approaches including PDR and WiFi positioning.