The method proposed in this paper provides theoretical and practical support for the intelligent recognition management of beef cattle. Accurate identification tracking cattle behaviors are essential components production management. Traditional methods time-consuming labor-intensive, which hinders precise farming. This utilizes deep learning algorithms to achieve multi-object cattle, as follows: (1) behavior detection module is based on YOLOv8n algorithm. Initially, a dynamic snake...

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal access points for connection. A request, sent automatically the active scanning mode, consisting of MAC address device expresses an advertisement its presence. real-time wireless sniffing system is able sense packets and analyse traffic. This provides opportunity obtain insights into interaction between humans carrying mobile environment. Susceptibility loss data transmission important limitation...

Location-based services (LBS) such as LoRa geolocation are important aspects of IoT applications. In this article, we propose a hierarchical clustering-based technique for urban vehicle localization using received signal strength indicator (RSSI) measurements in public LoRaWan network. The solution relies on two-layer hierarchy: the first layer consists <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$...

We consider multitarget detection and tracking problem for a class of multipath system where one target may generate multiple measurements via propagation paths, the association relationship among targets, measurements, paths is unknown. In order to effectively utilize from improve performance, tracker has handle high-dimensional estimation latent variables including existence state, kinematic data association. Based on variational Bayesian inference, we propose novel joint algorithm that...

Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on origins and destinations units relationships between them. These measure gravity or positional directional autocorrelations that are treated as objects. However, intrinsic complexity actual data necessitates consideration not only gravity, positional, but also variables interest. This study proposes a global method to interest consists three steps....

The extensive deployment of wireless infrastructure provides a low-cost approach to tracking mobile phone users in indoor environments using received signal strength (RSS). Crowdsourcing has been promoted as an efficient way reduce the labor-intensive site survey process conventional fingerprint-based localization systems. Despite its stated advantages, use crowdwourcing for issues accuracy and reliability applications, large part because multipath propagation. This paper discusses evaluates...

Federated learning (FL) is an emerging collaborative machine method to train models on distributed datasets with privacy concerns. To properly incentivize data owners contribute their efforts, Shapley Value (SV) often adopted fairly assess contribution. However, the calculation of SV time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL enable feasible based profit distribution. blockchain consensus entities...

The focus of this letter is the estimation a delay between two signals. Such problem common in signal processing and particularly challenging when non-stationary nature. Our proposed solution based on an all-pass filter framework comprising elements: time equivalent to filtering can be represented terms ratio finite impulse response (FIR) its reversal. Using these elements, we propose adaptive algorithm with LMS style update that estimates FIR coefficients delay. Specifically, at each step,...

Future spacecraft systems will require careful co-design over both physical and cyber elements to provide a better performance for holistic system. Furthermore, the event triggered control is resource-aware strategy which allows reduction of computation communication demand without sacrificing performance. In this paper, we discuss that nonlinear approach applied attitude problem. Actuation only performed when needed quaternion based feedback proposed in ensuring system exponential stability...

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by set known network nodes in harsh indoor environment. While RSS wireless can be conveniently accessed, it to estimate location is non-trivial presence multipath propagation, shadowing and radio interference. Auxiliary information, such as building map user's orientation potentially help improve localization performance. As layout usually priori, moving direction or given may contain...

Although the formation control of multi-agent systems has been widely investigated from various aspects, problem is still not well resolved, especially for case distributed output-feedback controller design without input information exchange among neighboring agents. Using relative output information, this paper presents a novel reduced-order estimation error at predefined time. Based on proposed observer, neural-network-based then designed with connected graphs. The results are verified by...

In unmanned surface vehicle (USV) platform based marine radar target detection, as a result of low installation height, the presence occlusion by islands, nearby vessels well near-shore infrastructure is very common. This makes it difficult for ship to be consistently detected and tracked, leading potential track loss collision risks. However, efficient solutions identifying maintaining sound tracking performance in detection occluded maritime environment are not explored literature. this...

The deep convolutional neural network (CNN) has made remarkable progress in image classification. However, this performs poorly and even cannot converge many actual applications, where the training test samples contain lots of noises. To solve problems, paper puts forward a strategy based on stochastic max pooling. Unlike traditional pooling, proposed first ranks all values each receptive field, then selects random value from top-n as pooling result. Compared with common methods, can limit...

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness mobile phone users (MPUs) in indoor environments by processing the received signal strength (RSS) phone. In such a signal-processing framework, hidden Markov models (HMMs) are often used to model uncertainties RSS data and incorporate environmental information into localization. Since semi-Markov (HsMMs) outperform HMMs their ability state duration more flexibly, employing HsMMs...

We consider the problem of localizing a radio emitter in wireless network using RSS measured by set known nodes multipath environment. While signal can be conveniently accessed, it to estimate location is nontrivial presence multipath. propose HMM model within Bayesian learning framework for processing data localization process deal with fluctuations induced interference. To address uncertainty dynamics, semi-Markov also adopted duration time sojourn state. compare performance methods, HsMM...