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Uneven water-air media distribution or irregular liquid flow can cause changes in light propagation, leading to blurring and distortion of the extracted image, which presents a challenge for object recognition accuracy. To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. Then, an attention correction module for geometric lines is proposed to correct geometric lines in water-air cross-media images by comparing and sensing the marked feature points with large differences and utilizing the line similarity in positive and negative samples. Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. This completes the image restoration of object distortion under water-air cross-media. The proposed method is feasible and effective for restoring aberrated objects in water-air cross-media environments, with numerous experiments conducted on water-air cross-media image datasets. [ABSTRACT FROM AUTHOR]

As network technology advances and more people use devices, data storage has become a significant challenge due to the explosive growth of information and the threat of data leaks. In traditional medical institutions, most medical data is stored centrally through cloud computing technology in the institution's data center. This centralized storage method has many security risks, and once the central server is attacked, it will lead to the loss of medical data, which will lead to the leakage of patients' private data. At the same time, electronic medical records are the most critical data in the current medical field. In the traditional centralized healthcare service system (HSS), there are data leakage problems and tampering with electronic medical records due to human factors. At the same time, each hospital is built independently, resulting in the current centralized healthcare service system having a data silo problem, making it difficult to share medical data between institutions securely. With the increase in the number of users in the system, the electronic medical record data in the system also increases gradually, resulting in the increasing overhead of decryption calculation. Therefore, this paper proposes a blockchain-based access control scheme with multiparty authorization to ensure the security of electronic medical records. The scheme uses an SM encryption algorithm to encrypt the medical data in the system. It adds the patient's signature to ensure the confidentiality and security of the data, and the encrypted electronic medical records (EMRs) are stored in the InterPlanetary File System (IPFS) to realize the distributed storage of EMR. In addition, role-based multiauthorization access control is implemented through smart contract-based to ensure the security of EMR. We have analyzed the security of this paper's solution and compared its performance with the existing schemes based on other cryptographic algorithms. The experimental results show that the proposed solution significantly improves the secure sharing of EMR and provides system performance. [ABSTRACT FROM AUTHOR]

Wireless ad hoc/sensor networks (WASN) have seen increased application in three-dimensional (3D) environments, such as underwater and aerial scenarios. However, WASN may be fragmented or unable to connect continuously due to the harsh surrounding environment or high mobility. Therefore, restoring the network connectivity and transmit data in real time is very important. This paper focuses on the critical task of restoring network connectivity in 3D WASN, a complex issue given that existing connectivity restoration algorithms for two-dimensional environments are not directly applicable or become overly complicated in 3D contexts. We present a comprehensive analysis of the current research landscape, summarizing key findings related to various aspects of 3D WASN connectivity restoration. These aspects include the application environment (underwater, in the sky, and recovery disaster), opportunities for restoration (active, passive, and active/passive), implementation strategies (clustering and sleep scheduling), and resource constraints (node deployment and movement control). Our study also proposes a classification of connectivity restoration solutions for 3D WASN, identifying existing gaps and suggesting potential future research directions. By providing specific insights and a structured overview of the field, we aim to contribute to the ongoing development of robust and resilient 3D WASN. [ABSTRACT FROM AUTHOR]

The low-bit quantization can effectively reduce the deep neural network storage as well as the computation costs. Existing quantization methods have yielded unsatisfactory results when being applied to lightweight networks. Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To address these problems, this paper proposes a custom network quantization approach. Firstly, an improved PArameterized Clipping Activation (PACT) method is employed during the quantization aware training to restrict the value range of neural network parameters and reduce the loss of precision arising from quantization. Secondly, the Consecutive Execution Of Convolution Operators (CEOCO) strategy is utilized to mitigate the resource consumption caused by the frequent environment switching. The proposed approach is validated on Xilinx Zynq Ultrascale+MPSoC 3EG and Virtex UltraScale+XCVU13P platforms. The MobileNetv1, MobileNetv3, PPLCNet, and PPLCNetv2 networks were utilized as testbeds for the validation. Moreover, experimental results are on the miniImageNet, CIFAR-10, and OxFord 102 Flowers public datasets. In comparison to the original model, the proposed optimization methods result in an average decrease of 1.2% in accuracy. Compared to conventional quantization method, the accuracy remains almost unchanged, while the frames per second (FPS) on FPGAs improves by an average of 2.1 times. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO's interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO. [ABSTRACT FROM AUTHOR]

Implantable LED light sources have received a lot of attention in the field of optogenetic neuromodulation. This type of light modulation enables effective stimulation of neurons. However, as optogenetics moves towards clinical trials, combinatorial photostimulation based on arrays of LED light sources has emerged. This approach inevitably brings about a large increase in transient temperature, resulting in the inability to achieve precise stimulation of cells. We designed a wireless optogenetic hardware system to realize the control of the stimulation mode and temperature of the light source array. At the same time, a set of combined photothermal physical model was established to simulate the photothermal response of the whole experiment. The physical model can effectively guide the wireless optogenetic hardware circuits to perform effective stimulation within a controlled temperature range. Our model provides a new technical approach for photothermal studies in optogenetic clinical trials. [ABSTRACT FROM AUTHOR]

During sonar detection, the fuzzy function of hyperbolic frequency modulation (HFM) in the waveform of active signal is in the shape of a "oblique blade," which leads to the coupling characteristics of its fuzzy function in the time dimension and frequency dimension. Doppler causes a frequency shift in the frequency dimension and a time delay in the time dimension, which makes it impossible for a single HFM signal to measure speed accurately. However, the time delay caused by the same moving target to HFM signals with different frequency bands and different pulse widths is also different. A velocity measurement method combining HFM signals (VCH) is proposed, which employs the time delay correlation between the combined HFM signals to achieve the target distance and speed. In the VCH method, the echo signal is no longer matched and filtered as a whole but is divided into two channels and matched and filtered separately. And then, the combined HFM signals are used to obtain the distance and speed of the target. Extensive simulation results show that the proposed method can estimate the distance and speed of moving targets accurately, and it has reference value for engineering application. [ABSTRACT FROM AUTHOR]

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate. [ABSTRACT FROM AUTHOR]

This study presents frequency-based substructuring (FBS) techniques and an identification method for predicting joint parameters. Two FBS techniques, FBS-1 and FBS-2, were derived by assuming pseudomasses at the joint nodes between adjacent substructures. It is estimated that the main reason for the discrepancy with the analytical FRFs is the difficulty in describing the low-frequency responses owing to the assumed pseudomasses of the substructures. Although the FRF curve based on the FBS-2 technique is very close to the analytical FRF curve up to the first resonance frequency, some inconsistencies occur thereafter. It is analyzed that the FRFs up to the first resonance frequency can be utilized for data expansion methods and system identification techniques. Paying attention to this result, this study also provides an identification method to estimate the joint parameters based on the FRF variation. Its validity is illustrated using a numerical example. [ABSTRACT FROM AUTHOR]

The channel in the marine environment is a time-varying and space-varying channel. Pulse-truncated continuous wave (PCW) speed measurement is often used in sonar, but the instability effect of PCW signal in the channel limits the effectiveness of speed measurement. Hyperbolic frequency modulation (HFM) signal is insensitive to Doppler; therefore, HFM signals are widely used in ranging and velocity measurement of sonar and radar. However, due to the filtering effect of the marine environment, the HFM signal of a single frequency band may cause excessive transmission loss, and the echo energy may be too weak to detect the target. Based on the analysis of the influence of speed on the distance measurement of HFM signal, a pulse sequence method based on HFM for speed measurement (PHS) is proposed, which uses HFM signals of different frequency bands and pulse widths in the pulse sequence to perform speed measurement. Extensive simulation results show that PHS method not only guarantees the speed measurement but also makes full use of the energy of the HFM sequence to improve the accuracy of the distance measurement. And PHS method is valuable to the practical application of engineering. [ABSTRACT FROM AUTHOR]

This review was conducted to demonstrate how artificial intelligence (AI) has affected livestock farming. Livestock is essential for maintaining ecological integrity and providing food security. In this review, the history of artificial intelligence (AI), its impact on current and future livestock farming, and its drawbacks have been highlighted. The term "artificial intelligence," was first coined by John McCarthy in 1956, and currently, the technology is widely applied in the management of many livestock farms like poultry, dairy, and pigs. Although it has been studied for decades and widely applied, AI is still one of the least understood subfields. Artificial intelligence provides farmers with unrivalled support, enabling them to minimize resource use, improve the sustainability of their feeding patterns, and increase farm productivity in general, especially when it comes to reducing the carbon footprint. AI is a blessing for boosting efficiency and productivity while also lowering the possibility of human error. Producers can use artificial intelligence to simulate human decision-making and provide interpretations and solutions for the data gathered by sensors and other hardware technologies. Through AI tools, it is possible to easily trace animal activities and locations and collect data about behaviors, habitats, and health conditions. Animal identification, animal welfare monitoring, sex determination, vaccine delivery, and pasture evaluation are some areas where AI has been widely applied so far. Drones, robots, and blockchains are some forms of automation that have been widely used in dairy farms. However, the expense of development, which needs more infrastructure on the farm, and the potential for automation to replace them are the limitations of the technology. [ABSTRACT FROM AUTHOR]