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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]

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV's service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem's definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) and software‐defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. This study is aimed at reviewing the literature on SDN for IoT (SDN‐IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. The study articulates the challenges of managing and orchestrating IoT systems and how SDN can be used to address these challenges by enabling dynamic and flexible network configurations. It delineates not only the function of blockchain (BC) technology in securing and managing IoT networks but also how SDN can be utilized to incorporate BC‐based solutions. Additionally, the potential of SDN for mobile networks is explored, which are increasingly being used to support IoT devices. Finally, this study outlines the issues, challenges, and potential future research directions that may present opportunities for the researchers working in this field, underscoring the demand for more in‐depth investigation and advancement. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensor networks (WRSNs), charging request nodes (RNs) are characterized by several criteria which are contradictory. Recently, on‐demand charging scheduling schemes, which use two or more multicriteria decision‐making (MCDM) methods, have been proposed. However, these on‐demand charging schemes use a pairwise ratio scale which can magnify the actual pairwise difference between multicriteria and do not take into account the trade‐off between performance metrics. In this paper, we propose a novel on‐demand charging scheduling method using a fuzzy cognitive network process (FCNP) which uses a fuzzy pairwise interval scale to solve these issues. The proposed method, coined as an integrated FCNP‐Q‐learning‐based scheduling (iFQS), first uses FCNP to exactly assign the relative weights to five multicriteria for charging prioritization and to three multicriteria for partial charging time (PCT) determination. Then, in charging path planning with Q‐learning, the BS use these five criteria's weights to design the reward function and select the most suitable next charging sojourn point. On the other hand, three criteria's weights are also used to reasonably determine the PCT at charging sojourn points while achieving a desirable trade‐off between charging metrics. The results of the extensive simulation show that the iFQS significantly improves charging performance in comparison with the existing MCDM‐based methods. The network lifetime of the proposed method is 125.1%, 286.7%, and 368.5% longer than FAHP‐VWA‐TOPSIS, fuzzy Q‐charging, and AHP‐TOPSIS, respectively, when the number of nodes is 600. [ABSTRACT FROM AUTHOR]

This paper proposed a model to estimate sensor spacing and a new deployment geometry and evaluates the performance of an ultrasonic sensor array according to the deployment geometry. The proposed spacing estimation model is based on the probability of false detection alarms, the aperture angle, and the sensor range. A design technique has been proposed for elliptical deployment geometry, giving expressions for its parameters to help designers of experimental prototypes to reduce errors related to sensor disposition. A practical method for calibrating ultrasonic sensors has been designed to reduce measurement errors. An experiment was conducted, and the data were analysed using a statistical significance test. An evaluation of the performance of an ultrasonic sensor array with linear and circular deployment geometry with and without our method was done and then compared to our proposed deployment geometry which was combined with the proposed method. The result of the statistical significance test shows that elliptical deployment geometry combined with the suggested strategy works better than circular and linear geometry. This applies to performance such as linear correlation, RMSE, MAPE, and standard deviation. These results show the importance of the choice of deployment geometry for application. [ABSTRACT FROM AUTHOR]

The Dongbao River Xin'an Bridge is the first hybrid continuous girder bridge with corrugated steel webs in China. To assess its service conditions and guarantee its safe operation, a continuous structural health monitoring (SHM) system is designed in this paper. The SHM system consists of a sensor system, a data acquisition and transmission system, and a data processing and control system. The measurement parameters, including vehicle loads, temperature, deformation, strain, and acceleration, are determined, and a detailed sensor layout is given based on the mechanical characteristics of the bridge. The SHM system adopts a bus gateway and an IP modem to collect and transmit sensor data. Monitoring data from October 9, 2022, to March 16, 2023, were analyzed. The probability distributions of the daily maximum temperature differences and vehicle loads are reconstructed. The correlation of girder deflection and strain with ambient temperature has been demonstrated. The identification and tracking of modal parameters are implemented. The results show that the maximum deflection is within the threshold range and that the modal parameters are basically stable. All the results demonstrate the healthy structural condition of the bridge and the excellent working performance of the SHM system. The collected data provide scientific guidance for the performance evaluation of the Dongbao River Xin'an Bridge and the optimized design of similar bridges. [ABSTRACT FROM AUTHOR]

Data fusion plays a crucial part in performance evaluation processes in multisensor systems; thus, it is important to use an effective technique to cut down on errors. By improving the sensors' location and their capacity to adjust to the deployment geometry, the paper's technique for reducing data fusion errors is proposed. The preprocessing stage of data is also included in the suggested technique, which starts with the design of the data‐collecting device and ends with a hybrid model algorithm. Particle swarm optimization and artificial neural network methods are combined in the hybrid algorithm. Preprocessing is based on the compensation and adjustment factors to ensure data gathering synergy. A true experimental apparatus was designed under three different deployment geometries, comprising five ultrasonic sensors constituting a multisensor system and a hardwood target (Triplochiton scleroxylon and Milicia excelsa). The results obtained show that the suggested deployment geometry has mean absolute percentage errors of 3.97, mean square errors of 0.182, and mean absolute errors of 0.045 which are lower than those associated with the deployment configurations found in the literature. The results of the proposed method show an overall detection accuracy in linear deployment geometry of 97.308%. Then, the circular deployment geometry obtained a detection accuracy of 97.005%. Finally, elliptical deployment geometry obtained a detection accuracy of 98.745%. These results show that the proposed method better predicts the range of a target with a lower error rate than conventional methods. [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]

Wireless sensor networks (WSNs) require optimized energy consumption and an improved packet delivery ratio (PDR) for optimal performance. Clustering and routing strategies are frequently used to reduce energy usage, while multiradio (MR) multichannel (MC) systems improve PDR levels. Transmission power control (TPC) features in both energy consumption reduction and PDR improvement. While the mentioned techniques are used in previous studies, they were investigated separately and without a holistic approach. This study discusses the challenge of obtaining high‐PDR, energy‐efficient, and cost‐effective clustering and routing in WSNs. As a means of reducing deployment costs, a heterogeneous setting with energy‐constraint normal sensors for environmental monitoring as well as some high‐energy, TPC‐enabled, MR super nodes is assumed. The super nodes are considered as CHs and are responsible for collecting the sensed data from the normal sensors. Installing additional radios on super nodes enables static channel assignment (CA), which yields high PDR with low imposed overhead on the network. The mentioned TPC‐based MC heterogeneous setting, which yields cost‐effectiveness, high PDR, and energy efficiency, was not investigated in previous studies. The considered problem is decoupled into two phases of configuring the super nodes and normal sensors, which are solved using the grey wolf optimization (GWO) algorithm. The performed simulations show an average improvement of our proposed algorithm in PDR, energy consumption, consumed energy per delivered bit, and network lifetime by 9%, 30%, 65%, and 44%, respectively. [ABSTRACT FROM AUTHOR]

In this work, we focus on the issue of secure communication in energy harvesting untrusted relay networks taking into account the impact of hardware impairments, where an energy‐constrained relay, powered by received radio frequency signals, attempts to decode sensitive data from the source without authorization. To achieve secure communication, we employ destination‐assisted jamming to prevent the untrusted relay from decoding sensitive data while serving as a useful energy source. We derive the interception probability (IP), connection outage probability (COP), and effective secrecy throughput (EST) in closed form for time switching relaying (TSR) strategy. Subsequently, we conduct an asymptotic analysis in the high signal‐to‐noise ratio (SNR) regime to provide practical insights into how various system parameters affect security, reliability, and efficiency, including time switching factor and the level of hardware impairments. Our findings demonstrate that the presence of hardware impairments improves security performance but compromises reliability performance, which does more harm than good. [ABSTRACT FROM AUTHOR]

Detecting and classifying microparticles in water and other liquid substances is crucial due to their detrimental impact on ecosystems and human health. This is because particles such as microplastics, micropollutants, or heavy metals in water have demonstrated a high impact on the health of ecosystems and a high risk when this water is used for human consumption. Water quality is a critical factor when it comes to human consumption. Currently, some of these pollutants are not correctly detected during water treatment processes or directly in ecosystems, which can carry health risks for humans and animals. From this point of view, the development of tools for detecting these particles is still needed, and research for new strategies for detecting and classifying these microparticles with in situ methods is required. As a contribution to the solution of this problem, this work presents a microplastic detection and classification methodology that uses an electronic tongue system, impedance spectroscopy, and machine learning algorithms for detecting and classifying microplastics. Validation is performed using various sizes of PET (polyethylene terephthalate) microparticles in water to validate the possibility of classification. Results show the advantages of using the methodology, obtaining high accuracy in the classification process. [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]