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The role of bone fatigue damage at the nanostructural level, and its effect on fatigue properties is an understudied and important subject. In this study, nanoindentation was used to probe the micro-mechanical properties of bovine tibiae subjected to fatigue loading in four-point bending. Indentation tests were conducted in the same 30 μm × 120 μm region before fatigue loading and after loading to fracture. Using an optical microscope and scanning electron microscope, the morphology of the initial residual indentation before fatigue loading appeared the same as that after loading to fracture. The mechanical properties calculated using nanoindentation were reduced modulus and hardness, the time constant based on creep, long-term creep viscosity and the dissipated work. Differences of each parameter before loading and post fracture were examined using paired t-tests. The results show that the reduced modulus decreased significantly (p = 9.47 × 10–3) by 7.62–15.16% after fracture whereas the time constant of creep increased slightly (p = 0.049) by 2.81–5.41%. There was no clear change in hardness or dissipated work with fatigue loading. Fatigue loading has the largest effect on bone’s reduced modulus compared to all other mechanical properties.

With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields.

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Human pose estimation is aimed at locating the anatomical parts or keypoints of the human body and is regarded as a core component in obtaining detailed human understanding in images or videos. However, the occlusion and overlap upon human bodies and complex backgrounds often result in implausible pose predictions. To address the problem, we propose a structure-aware adversarial framework, which combines cues of local joint interconnectivity and priors about the holistic structure of human bodies, achieving high-quality results for multiperson human pose estimation. Effective learning of such cues and priors is typically a challenge. The presented framework uses a nonparametric representation, which is referred to as the Keypoint Biorientation Field (KBOF), to learn orientation cues of joint interinteractivity in the image, just as human vision can explore geometric constraints of joint interconnectivity. Additionally, a module using multiscale feature representation with inflated convolution for joint heatmap detection and Keypoint Biorientation Field detection is applied in our framework to fully explore the local features of joint points and the bidirectional connectivity between them at the microscopic level. Finally, we employ improving generative adversarial networks which use KBOF and multiscale feature extraction that implicitly leverages the cues and priors about the structure of human bodies for global structural inference. The adversarial network enables our framework to combine information about the connections between local body joints at the microscopic level and the structural priors of the human body at the global level, thus enhancing the performance of our framework. The effectiveness and robustness of the network are evaluated on the task of human pose prediction in two widely used benchmark datasets, i.e., MPII and COCO datasets. Our approach outperforms the state-of-the-art methods, especially in the case of complex scenes. Our method achieves an improvement of 2.6% and 1.7% compared to the latest method on the MPII test set and COCO validation set, respectively.

Fatigue can cause cracks to propagate from the micro- to the macroscale, which results in a decrease of Young's modulus of the bone. Non-destructive measurements of bone fatigue damage are of great importance for bone quality assessment and fracture prevention. Unfortunately, there is still a lack of effective nondestructive methods sensitive to the initial deterioration during damage accumulation, particularly in the field of orthopedics and biomechanics. In this study, terahertz spectroscopy was adopted to evaluate microscale bone damage. Specifically, the refractive index and Young's modulus of bone samples subjected to different degrees of fatigue damage were tested at a fixed area. Both parameters are found to decrease in two stages under cycled fatigue loading, which is attributed to the initial onset and subsequent development of microdamage during fatigue loading. The change in refractive index reflects the accumulation of fatigue damage as well as the decrease in Young's modulus.

This article proposes a design of a microstrip patch antenna with reduced radar cross-section (RCS) in a wide frequency band. The RCS of the patch antenna is reduced with the integration of an optimised coding metasurface. The coding metasurface is designed by placing artificial magnetic conductor (AMC) unit cells together to achieve a phase difference between 180 ± 37° within a wide frequency range. The out-of-band RCS for the proposed patch antenna is reduced >7 dB from 12.2 to 19.8 GHz, corresponding to a relative bandwidth of 48%, with a maximum reduction of 20 dB at 18 GHz. The operating frequency of the proposed antenna is 8.7 GHz and the RCS reduction is mainly in the Ku band. Both monostatic and bistatic RCS performance of the proposed patch antenna have been simulated and studied. The radiation performance of the proposed patch antenna is hardly influenced by the employment of the proposed coding metasurface. Details of the design considerations along with the simulation and measurement results are presented and discussed. The measured results are in a good agreement with the simulated ones, verifying the validity of the proposed antenna.

Alterations to the bone structure from cycle loadings can undermine its damage resistance at multiple scales. The accumulation of fatigue damage in a bone is commonly characterized by the reduction in the elastic modulus. In this study, nano-indentation was used for investigating microscopic damage evolution of bovine tibia samples subjected to fatigue loading. Indentation tests were conducted in the same 60 μm × 120 μm area with different degrees of damage, including fracture, and the evolution of reduced modulus was observed. The results showed that bone’s reduced modulus decreased significantly during the initial 40% of the life fraction, whereas it proceeded slowly during the remaining period. As the size of the residual indentations was about 4 μm in length, the degradation of bone’s reduced modulus reflected the accumulation of fatigue damage at smaller scales.

Background The role of bone fatigue damage at the nanostructural level, and its effect on fatigue properties is an understudied and important subject. An understanding of the subtler aspects of bone’s properties is crucial to understanding bone fragility caused by aging, disease, and fatigue damage. The primary strength of this study was the demonstration of the degradation of bone’s microscopic mechanical properties by nanoindentation following fatigue loading. Methods Nanoindentation was used to probe the micro-mechanical properties of bovine tibiae subjected to fatigue loading in four-point bending. Indentation tests were conducted in the same 30×120 µm region before fatigue loading and after loading to fracture. We analysed the effects of residual indentations on bone’s fatigue resistance using an optical microscope and scanning electron microscope. The mechanical properties calculated using nanoindentation were reduced modulus and hardness, the time constant based on creep, long-term creep viscosity and the dissipated work. Differences of each parameter before loading and post fracture were examined using paired t-tests. Results The morphology of the initial residual indentation before fatigue loading appeared the same as that after loading to fracture. The results show that the reduced modulus decreased significantly (p = 9.47×10− 3) by 7.62%~15.16% after fracture whereas the time constant of creep increased slightly (p = 0.049) by 2.81%~5.41%. The p-value was significantly lower (p ) for reduced modulus compared to all other mechanical properties. Conclusion Residual indents different from fatigue damage created no crack initiators that would weaken bone’s resistance to fatigue loading. Fatigue loading has the most heavy effect on bone’s reduced modulus compared to all other microscopic mechanical properties.

Due to the difficulty of typhoon detection and tracking, a typhoon tracking system based on Meteorological remote sensing is proposed. Firstly, the SSD algorithm is improved to detect typhoon, and then a correlation filter is constructed to track typhoon. In order to enhance the stability of tracking model, a day-night line extraction algorithm is proposed to realize the accurate detection and tracking of typhoon. The tracking accuracy can reach 90%.

Bones are biological composite materials with multiscale structures. Bone fatigue damage is commonly characterized by an increase in strain that is accompanied by microdamage at different scales. This study investigated the damage evolutions of bone specimens under four-point bending fatigue loading using neutral axis migration. Tensile and compressive strains during the fatigue process were simultaneously measured using a digital image correlation technique. The compressive strain of the bone specimen increased rapidly at first and then proceeded slowly while the tensile strain decreased during fatigue loading. Consequently, the neutral axis shifted downward as the damage accumulated. A positive correlation exists between the downward offset of the neutral axis and the number of cycles. The variation in compressive strain is larger than that in tensile strain in this situation.

Taking into account of the illumination characteristics of nighttime imaging, a new method for nighttime image dehazing is proposed in this paper. In the first place, based on the color transfer theory, the illumination level of nighttime hazy image can be artificially enhanced through flexibly selecting the reference image. In contrast to the classical model of color transfer with the strategy of overall to overall transfer, the modified model focuses on the different characteristics of various regions in the original image, and it works well even though the nighttime image is interfered by various artificial light sources. In the second place, the enhancement dehazing method based on the theory of guided image filtering is adopted since the key parameters of dehazing method using the atmospheric degradation model are difficult to obtain in the conditions of nighttime imaging. In addition, the key model parameters of guided image filter are selected according to the boundary information of original image rather than the original image itself, which makes it more advantageous for dehazing image taken on the hazy night. The experimental results show that the proposed method has better performance than the classical daytime dehazing methods. Additionally, our method exhibits superior effect compared to the well-known nighttime dehazing method in the aspects of suppressing color distortion and background illumination controlling. The evaluations of the experimental results are established on both the subjective and objective aspects, so the conclusion in this paper is more convincing.

Video surveillance is an effective way to record current events. In view of the difficulty of efficient transmission of massive surveillance video and the risk of leakage in the transmission process, a new data encryption and fast transmission algorithm is proposed in this paper. From the perspective of events, the constraints of time and space dimension is broken. First, a background and moving object extraction model is built based on video composition. Then, a strong correlation data encryption and fast transmission model is constructed to achieve efficient data compression. Finally, a data mapping mechanism is established to realize the decoding of surveillance video. Our experimental results show that the compression ratio of the proposed algorithm is more than 60% under the premise of image confidentiality.

Traffic estimation is a popular approach to acquire traffic conditions in urban areas. At present, using the traffic data to realize the low-cost traffic estimation has already been widely favored. Although those data include various sensitive element, people ignore the harm caused by information leakage while the data are used. Additionally, the transmission of vehicle data also requires a very large communication bandwidth. To address those problems, we focus on the privacy-preserving vehicle data and reducing the amount of ciphertext data to achieve a city-scale traffic estimation. Meanwhile, we present a novel framework that integrates compressive sensing (CS) technology into privacy- preserving vehicle data. Furthermore, outsourcing vehicle data to the cloud is adopted to overcome the limitations of the in-vehicle sensors. In particular, we present a feasible computational scheme for traffic estimation, further improve the capacity of privacy- preserving and decrease system energy consumption. Finally, we validate the effectiveness of the scheme proposed through the real-world dataset.

Sign language recognition is emerging as a vital component of our smart life. In addition, commercial RFID shall become a popular technology for sign language recognition. As we all know, there are 70 million deaf people using sign language as their first language and sign language can facilitate communication with deaf people. However, most of the researches are isolated word recognition. There is few researches about sentence-level sign language recognition. More importantly, they are limited and it is difficult to achieve the desired results of realworld applications. So this paper introduces the first sentence-level sign language recognition system based on RFID. It mainly collects the phase sequence of signals received by commercial RFID device. We obtain relatively pure phase characteristics and present a method to carry out sign language segmentation. Effective feature extraction and classifier selection are crucial to sign language recognition. By evaluating our system in real-word environment, we fill in the gaps between corresponding low-cost sentence-level sign language recognition. We implement and evaluate through extensive experiments and the average accuracy of the method are 96% and 98.11% in different multipath scenarios. The results show that our method has high recognition accuracy and robustness.

Network coding is emerging as a promising technique that can provide significant improvements in the throughput of Internet of Things (IoT). Previous network coding schemes focus on several nodes, regardless of the topology and communication range in the whole network. Consequently, these schemes are greedy. Namely, all opportunities of combinations of packets in these nodes are exploited. We demonstrate that there is still room for whole network throughput improvement for these greedy design principles. Thus, in this paper, we propose a novel network coding scheme, ECS (Efficient Coding Scheme), which is designed to achieve a higher throughput improvement with lower computational complexity and buffer occupancy compared to current greedy schemes for wireless mesh networks. ECS utilizes the knowledge of the topologies to minimize interference and obtain more throughput. We also prove that the widely used expected transmission count metric (ETX) in opportunistic listening has an inherent error ratio that would lead to decoding failure. ECS therefore exploits a more reliable broadcast protocol to decrease the impact of this inherent error ratio in ETX. Simulation results show that ECS can greatly improve the performance of network coding and decrease buffer occupancy.