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In understanding human behaviour, computers sometimes condense their analysis of human behaviour into an analysis of the state of movement of keypoints in the human body. Thus, the technique of human pose estimation provides a convenient means for machines to recognise people's behaviour. The lightweight pose estimation network enables computers to detect human poses in real-time. This paper proposes Lightweight HRNet, a bottom-up lightweight network for multi-person human pose estimation. The network is proposed based on the HRNet architecture and includes four network branches with different resolutions and two network stages. The network backbone uses the ShuffleNet model, which allows the network to be better used on smaller devices. Notably, Lightweight HRNet focuses on a problem with multi-resolution, multi-branch parallel networks: not all stages of the network contain information about the feature maps of all its branches. Therefore, we proposed the Channel Exchange Module (CEM), which improves the exchange of information between each stage of the network and each of its branches. Among the tasks of human pose estimation, the addition of CEM improved the network accuracy of Lightweight HRNet by 0.9% in the COCO2017 test-dev. Finally, the network was able to achieve 46.6% accuracy on the COCO2017 test-dev. This accuracy is superior in the study of bottom-up lightweight human pose estimation networks. [ABSTRACT FROM AUTHOR]

Deep learning is proliferating within the field of computer vision. Road damage detection technology, as its offshoot, already plays vital role in road maintenance and traffic safety. With road damage such as potholes and cracks, accurate and efficient detection results are essential for timely road safety repair and maintenance. Therefore, road damage detection algorithms based on deep learning have attracted wide attention. YOLOv5 is an advanced target detection algorithm known for its efficient detection speed and good accuracy. However, there is still room for further improvement in its performance for road damage detection. The ability of multi-scale damage detection and spatial structure capture is not perfect. Therefore, this paper proposes three improvement points to improve the accuracy of road damage detection based on YOLOv5. The first introduced module is the Non-linear Spatial Pyramidal Pooling-Fast (NSPPF) module. This module allows for better capture of detailed features of road damage areas. Non-linear transformation and fast pyramid operation improve the sensing ability and multi-scale damage detection ability. Secondly, a combination of the CoordConv and SK attention modules is constructed. The CoordConv module fuses coordinate information with features to provide a more spatially informed representation. The SK attention module also learns correlations between global and local features, enhancing the model's ability to detect damages at different scales. This paper can better capture road injuries' spatial structure and context information by combining these two modules. Finally, experimental results on the RDD2020 dataset demonstrate the effectiveness of our model. Compared to the baseline model, the proposed improvement algorithm increases the accuracy by 2.2%, resulting in a mAP of 58.2%. This demonstrates its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Addressing the problem that the human body is difficult to be correctly identified in the process of falling, this study proposes an Attention Mechanism and Time Series MLP (AT-MLP) model. To realize the capture of body features during the state and process of the fall of the human body, the time series is combined in the submodule. Thus, the subspace Angle Time Series algorithm (ATS) and the subspace Angle Difference-value Time Series algorithm (ADTS) are designed. The AT-MLP stage composed of Convolutional Block Attention Module (CBAM) and MLP blocks is constructed in the model to strengthen the relation between the time and space dimensions. Comparing with other methods on the Le2i Fall detection Dataset (LFDD) to verify the effectiveness of the AT-MLP model, the experimental results show that the AT-MLP model effectively improves the detection efficiency of human falls and has strong generalization ability. [ABSTRACT FROM AUTHOR]

In this paper, the historical process of chaotic image encryption algorithm is introduced. Classical Logistic chaotic system and piecewise linear chaotic map (PWLCM) are fused to form a novel chaotic system LPW. Based on LPW chaotic system, a snake encryption algorithm for digital image with multiple chaotic fusion is proposed. The algorithm carries out three rounds of image encryption, including forward crawling, reverse crawling and hibernation of snakes. The encryption of the algorithm uses the diffusion algorithm associated with the plain and the single row non-repetitive scrambling algorithm, and performs the scrambling and diffusion processes simultaneously. Experimental results show that the algorithm has high security, fast encryption and decryption speed, larger key space, and can resist common attacks such as differential attack and separate attack of scrambling and diffusion. [ABSTRACT FROM AUTHOR]

Entropy is one of many important mathematical tools for measuring uncertain/fuzzy information. As a subclass of neutrosophic sets (NSs), simplified NSs (including single-valued and interval-valued NSs) can describe incomplete, indeterminate, and inconsistent information. Based on the concept of fuzzy exponential entropy for fuzzy sets, this work proposes exponential entropy measures of simplified NSs (named simplified neutrosophic exponential entropy (SNEE) measures), including single-valued and interval-valued neutrosophic exponential entropy measures, and investigates their properties. Then, the proposed exponential entropy measures of simplified NSs are compared with existing related entropy measures of interval-valued NSs to illustrate the rationality and effectiveness of the proposed SNEE measures through a numerical example. Finally, the developed exponential entropy measures for simplified NSs are applied to a multi-attribute decision-making example in an interval-valued NS setting to demonstrate the application of the proposed SNEE measures. However, the SNEE measures not only enrich the theory of simplified neutrosophic entropy, but also provide a novel way of measuring uncertain information in a simplified NS setting. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is proposed for multi-information acquisition from the electromagnetic flow meter, using magnetic excitation to measure the fluid velocity and electrochemistry impedance spectroscopy (EIS) for both the fluid quality and the contamination level of the transducer. The impedance spectra of the transducer are measured with an additional electrical stimulus in series with the electrode measurement loop. The series connection mode instead of the parallel one improves the signal-to-noise ratio (SNR) of the fluid velocity measurement and offers a wide range of impedance measurements by using a sample capacitance. In addition, a multi-frequency synchronous excitation source is synthesized based on the method of dual-base power sequences for fast EIS measurement. The conductivity measurements in the range of 1.7 µS/cm-2 mS/cm showed a relatively high accuracy with a measurement error of 5%, and the electrode adhesion detection on both with coating and no coating showed the ability of the qualitative determination of the electrode adhesion, which validated the feasibility of the multi-information acquisition method for the electromagnetic flow meter (EMFM). [ABSTRACT FROM AUTHOR]

Aiming at the problems of low recognition accuracy and large calculation amount of age estimation, this paper proposes a staged single aggregation backbone network VoVNetv4. In order to improve the feature extraction ability of the network, the Regional One-Shot Aggregation (ROSA) module is proposed in the network for regional encapsulation of the feature module. The classifier adopts the COnsistent RAnk Logits (CORAL) framework, which can solve the problem of inconsistency of classifiers in ordered regression CNNs for multiple binary classification tasks. This paper conducts experimental verification on the Asian Face Age Dataset (AFAD) and compares it with other research methods. The experimental results show that VoVNetv4 can effectively improve the feature extraction and detection ability of the network and improve the detection efficiency of age estimation. [ABSTRACT FROM AUTHOR]

Aiming at the problem that human behavior is difficult to identify, a bottom-up and horizontal connections Cross-dimensional Feature Fusion MLP model (CFF-MLP) was proposed in this paper. To improve the detection performance of human behavior and realize the effective capture of continuous behavior features, a human key point Vector tracking (VT) algorithm based on optical flow was designed in the CFF-MLP model. This model can obtain the behavioral features and local dependencies of different layers hierarchically. Furthermore, it enhances the generalization ability of the model. The validity of the proposed model is verified and analyzed using KTH dataset and compared with other research methods. The experimental results show that the CFF-MLP model can effectively improve the detection ability of human micro-motion, and its average accuracy rate reaches 94.23%. It improves the detection efficiency of human behavior. [ABSTRACT FROM AUTHOR]