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Efficient multiple target tracking (MTT) is the key to achieving green, precision, and large-scale aquaculture, marine exploration, and marine farming. The traditional MTT methods based on Bayes estimation have some pending problems such as an unknown detection probability, random target newborn, complex data associations, and so on, which lead to an inefficient tracking performance. In this work, an efficient two-stage MTT method based on a YOLOv8 detector and SMC-PHD tracker, named EMTT-YOLO, is proposed to enhance the detection probability and then improve the tracking performance. Firstly, the first detection stage, the YOLOv8 model, which adopts several improved modules to improve the detection behaviors, is introduced to detect multiple targets and derive the extracted features such as the bounding box coordination, confidence, and detection probability. Secondly, the particles are built based on the previous detection results, and then the SMC-PHD filter, the second tracking stage, is proposed to track multiple targets. Thirdly, the lightweight data association Hungarian method is introduced to set up the data relevance to derive the trajectories of multiple targets. Moreover, comprehensive experiments are presented to verify the effectiveness of this two-stage tracking method of the EMTT-YOLO. Comparisons with other multiple target detection methods and tracking methods also demonstrate that the detection and tracking behaviors are improved greatly. [ABSTRACT FROM AUTHOR]

Accurate target identification of UAV (Unmanned Aerial Vehicle)-captured images is a prerequisite for maritime rescue and maritime surveillance. However, UAV-captured images pose several challenges, such as complex maritime backgrounds, tiny targets, and crowded scenes. To reduce the impact of these challenges on target recognition, we propose an efficient maritime rescue network (EMR-YOLO) for recognizing images captured by UAVs. In the proposed network, the DRC2f (Dilated Reparam-based Channel-to-Pixel) module is first designed by the Dilated Reparam Block to effectively increase the receptive field, reduce the number of parameters, and improve feature extraction capability. Then, the ADOWN downsampling module is used to mitigate fine-grained information loss, thereby improving the efficiency and performance of the model. Finally, CASPPF (Coordinate Attention-based Spatial Pyramid Pooling Fast) is designed by fusing CA (Coordinate Attention) and SPPF (Spatial Pyramid Pooling Fast), which effectively enhances the feature representation and spatial information integration ability, making the model more accurate and robust when dealing with complex scenes. Experimental results on the AFO dataset show that, compared with the YOLOv8s network, the EMR-YOLO network improves the mAP (mean average precision) and mAP50 by 4.7% and 9.2%, respectively, while reducing the number of parameters and computation by 22.5% and 18.7%, respectively. Overall, the use of UAVs to capture images and deep learning for maritime target recognition for maritime rescue and surveillance improves rescue efficiency and safety. [ABSTRACT FROM AUTHOR]

The Poisson multi-Bernoulli Mixture (PMBM) filter, as well as its variants, is a popular and practical multitarget tracking algorithm. There are some pending problems for the standard PMBM filter, such as unknown detection probability, random target newborn distribution, and high energy consumption for limited computational and processing capacity in sensor networks. For the sake of accommodating these existing problems, an improved multitarget tracking method based on a PMBM filter with adaptive detection probability and adaptive newborn distribution is proposed, accompanied by an associated distributed fusion strategy to reduce the computational complexities. Firstly, gamma (GAM) distribution is introduced to present the augmented state of unknown and changing target detection probability. Secondly, the intensity of newborn targets is adaptively derived from the inverse gamma (IG) distribution based on this augmented state. Then, the measurement likelihood is presented as a gamma distribution for the augmented state. On these bases, the detailed recursion and closed-form solutions to the proposed filter are derived by means of approximating the intensity of target birth and potential targets to an inverse gamma Gaussian mixture (IGGM) form and the density of existing Bernoulli components to a single IGGM form. Moreover, the associated distributed fusion strategy generalized covariance intersection (GCI), whose target states are measured by multiple sensors according to their respective fusion weights, is applied to a large-scale aquaculture tracking network. Comprehensive experiments are presented to verify the effectiveness of this IGGM–PMBM method, and comparisons with other multitarget tracking filters also demonstrate that tracking behaviors are largely improved; in particular, tracking energy consumption is reduced sharply, and tracking accuracy is relatively enhanced. [ABSTRACT FROM AUTHOR]

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%. [ABSTRACT FROM AUTHOR]

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes-OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)-are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results. [ABSTRACT FROM AUTHOR]

This paper presents recent development on magnetostrictive linear position sensors (MLPS). A new compensation coil structure improves the EMI suppression and accuracy considerably. Furthermore, experimental results indicate that the new structure can improve the accuracy to ± 0.13 mm nearly double the ± 0.2 mm obtained with traditional structures. As another design continuation after the differential waveguide structure, this new structure is a practical and reliable implementation technique for the commercialization of MLPS.