On Bayesian tracking and data Fusion: A tutorial introduction with examples

In this article is investigated the distribution of magnetic field of linear induction motor. The are two models for simulation: three-phase asynchronous linear motor and single-phase linear capacitor motor. Simulations results of asynchronous LIM at different instants of time and at different widths of air gap are presented. The are given results of simulation, which show the change of magnetic flux at different instants of time of linear capacitor motor how magnetic field varies at different instants of time and at different current shits in phases. Charts of magnetic flux density below ending and middle tooth of inductor, and magnetic flux density in different cross-sections of air gap are compared. Ill. 9, bibl. 5 (in English; summaries in English, Russian and Lithuanian).

This dissertation explores the intersection of data fusion, federated learning, and Bayesian methods, with a focus on their applications in indoor localization, GNSS, and image processing. Data fusion involves integrating data and knowledge from multiple sources. It becomes essential when data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest. Data fusion typically includes raw data fusion, feature fusion, and decision fusion. In this thesis, we will concentrate on feature fusion. Distributed data fusion involves merging sensor data from different sources to estimate an unknown process. Bayesian framework is often used because it can provide an optimal and explainable feature by preserving the full distribution of the unknown given the data, called posterior, over the estimated process at each agent. This allows for easy and recursive merging of sensor data with prior knowledge without the need for storage. However, there are challenges in data fusion, such as the multiple counting problem, which occurs when data is utilized numerous times without the user's knowledge. In a Bayesian setting, a priori information of the unknown quantities is available and can be present among different distributed estimators. When the local estimates are fused, the prior knowledge used to construct several local posteriors might be overused unless the fusion node accounts for this and corrects it. In this thesis, we analyze the effects of shared priors in Bayesian data fusion contexts. Our analysis helps to understand the performance behavior as a function of the number of collaborative agents and different types of priors, depending on different common fusion rules. To perform this analysis, we use two divergences that are common in Bayesian inference. The generality of the results allows us to analyze very generic distributions. These theoretical results are corroborated through experiments in a variety of estimation and classification problems, including linear and nonlinear models, and federated learning schemes in a Bayesian perspective. Federated Learning is a discipline of Distributed Data Fusion that can be used for model training on local data without exchanging the data itself. This technique is useful in scenarios like image classification, indoor positioning, and jammer signal classification, where privacy concerns make data crowdsourcing difficult. In this thesis, we explore FL in two specific applications: Jammer signal and indoor positioning. Jamming signals can disrupt the operation of GNSS receivers, making jamming mitigation and localization techniques essential. Jammer classification can help with these techniques. For indoor positioning, local data points are collected to create a local fingerprinting database. Data-driven machine learning methods are used to predict the location based on this model learned without data sharing. However, Federated Learning faces several challenges, including the non-IID (non-identically distributed) problem. This problem occurs when the data distribution varies across different clients, which can lead to a decrease in performance. In this thesis, we will discuss various FL frameworks and algorithms that can help address this problem. A FL method from Bayesian perspective are proposed to deal with the problem in indoor positioning problem. Additionally, we will delve into the concepts of personalized and clustered Federated Learning. The personalized approach tailors the learning process to individual clients, which enhances the system's performance by leveraging the unique data and characteristics of each client. We will discuss the application of this approach in indoor positioning and jammer signal classification, highlighting its potential improvements and challenges. Expanding on the personalized approach, we introduce and discuss clustered Federated Learning. This learning method groups clients with similar characteristics or data patterns. Each client is typically associated with one data distribution and participates in training a model for that distribution along with their cluster peers. We propose a Bayesian framework for clustered FL, which associates clients to clusters, along with several practical algorithms for efficient learning, considering the trade-offs between performance and computational complexity. In conclusion, this thesis provides a detailed investigation and evaluation of the integration of data fusion, federated learning, and Bayesian methods. The insights and conversations presented in this study are anticipated to enhance the progress and implementation of these technologies in various domains, particularly in indoor positioning, and GNSS.--Author's abstract

The non-stationary linearized Hoff equation is considered in the article. For this equation, a solution is obtained both on the domain and on the geometric graph. For the five-edged graph, the Sturm -- Liouville problem is solved to obtain a numerical solution of the non-stationary linearized Hoff equation on the graph. A numerical method for solving this equation on a graph is described. The graphics for obtained numerical solution are constructed at different instants of time for given values of the equation parameters and functions. The article besides the introduction and the bibliography contains four parts. The first part contains information on abstract non-stationary Sobolev type equations, and solutions for the non-stationary linearized Hoff equation on the domain are constructed. In the second one we consider the Sturm -- Liouville problem on a graph and construct necessary spaces and operators on graphs. In the third one we study the solvability of the non-stationary linearized Hoff equation on the five-edged graph, and finally, in the last part we describe the numerical solution of the equation on the graph and the graphics of these solutions at different instants of time.

This chapter reviews architectures for efficient multi-sensor data fusion (MSDF). The following definitions are used: Data Fusion: This is the process of combining sensed and contextual data through increasing levels of abstraction in order to detect, classify and track objects, and to form a situation assessment and infer its probable impact The chapter focuses only on sensed data fusion and specifically on track fusion. Architecture: This refers to the structural organisation of an MSDF system with respect to its sensing, communications and data processing sub-systems. There are two fundamentally different types of MSDF architecture: centralised and decentralised systems. The chapter emphasizes decentralised systems, as these are generally the more efficient architecture. Efficiency: This is a measure of the performance of an MSDF system as a function of its sensing, computational and communications resource requirements. Information theory is a principled framework for decentralised data fusion and systems resource management.

Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of direct multimodal volume rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the rendering pipeline the data fusion must be realized in order to accomplish the desired visual integration and to provide fast re‐renders when some fusion parameters are modified. In addition, it analyses how existing monomodal visualization algorithms can be extended to multiple datasets and it compares their efficiency and their computational cost. Copyright © 2004 John Wiley & Sons, Ltd.

Thermal/power issues have become increasingly important with more and more transistors being placed on a single chip. Many dynamic thermal/power management techniques have been proposed to address such issues but they all depend heavily on accurate knowledge of the chip's thermal state during runtime. In this paper, we describe a unified statistical framework for designing an on-chip thermal sensing infrastructure that can be used to track the chip's thermal state at runtime. Specifically, we address the following problems in this statistical framework: 1) sensor placement; 2) sensor data compression; 3) sensor data fusion; and 4) overall interplay. Our methods exploit the correlations between temperatures in different parts of the chip to drive sensor placement, data compression, and data fusion in both noiseless and noisy sensor cases. Our framework is also capable of choosing the appropriate degree of compression for each sensor while accounting for their local space constraints during deployment. The experimental results show that the root-mean-square error of the thermal estimates produced by our sensing infrastructure is on average 35% better than an equivalent system that uses a range-based placement scheme and a uniform compression scheme. It took our methods at most about 9 s to decide the overall solution for placement, compression, and data fusion at the design stage. This demonstrates the effectiveness and applicability of our unified statistical design methodology.

We propose a unified framework for sensor management in multi-modal sensor networks, which is inspired by the trading behavior of economic agents in commercial markets. Each sensor node (SN) acts as a seller who wants to sell the data it collects, to the sensor network manager (SM) who acts as a buyer. The resources and the data are priced by looking to balance global supply and demand, with the SN required to purchase resources for producing the data, and the SM required to purchase data to accomplish his tasks. We model this interaction as a double sided market, with both consumers and producers, and propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of sensor selection (SS), resource allocation (RA), and data fusion (DF) problems, which constitute the sensor management. The proposed framework will enable the system to determine the kind and the amount of data that should be produced, and to combine the data that is produced at each SN. To illustrate this framework, we consider the problem of multiple-target tracking as an example. Numerical examples demonstrate the effectiveness of the proposed method, and show that appropriate sensor management will result in an accurate estimate of the number of targets in the scene, higher correct identifications of the targets, and a lower mean-squared error in the estimates of their positions and velocities.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties

In the present work, detailed laser-based diagnostic experiments were conducted to characterise the spray from low pressure 2-hole and 4-hole Port Fuel Injection (PFI) injectors. The main objective of the work included obtaining quantitative information of the spatio-temporal spray structure of such low-pressure gasoline sprays. A novel approach involving a combination of techniques such as Mie scattering, Granulometry, and Laser Sheet Dropsizing (LSD) was used to study the spray structure. The droplet sizes, distributions with time, Sauter Mean Diameters (SMD), droplet velocities, cone angles and spray tip penetrations of the sprays from the injectors were determined. The spray from these injectors is found to be ‘pencil like’ and not dispersed as in high pressure sprays. The application of the above mentioned techniques provides two-dimensional SMD contours of the entire spray at different instants of time, with reasonable accuracy.

The structural response of reinforced concrete slabs in railway bridges is strongly influenced by local dynamic effects and, therefore, detailed calculations of internal forces have to be performed for a realistic fatigue assessment. In this context, this paper discusses the influence of track irregularities and modal damping coefficients in the dynamic response and fatigue behaviour of a railway bridge deck slab. For that purpose, track irregularities were measured (at different instants of time) and damping coefficients were determined based on acceleration records for passing trains in a real bridge. The bridge behaviour was calculated using a train–bridge interaction methodology, considering calibrated numerical models of the viaduct and the train. The fatigue damage was quantified through the linear damage accumulation method. This methodology allowed to understand the way track irregularities and damping coefficients affect the magnitude of applied bending moments and fatigue damage in the slab.

Stress variation due to an impact line load on a four-ply fibre composite plate

A new method of analysis of photoluminescence decay curves

The present paper discusses the modeling and analysis of a diesel–wind generating system capable enough to cater to the electrical power requirements of a small consumer group or society. Due to high variations of the load demand or due to changes in the wind speed, the frequency of the diesel–wind system will be highly disturbed, and hence to regulate the frequency and power deviations of the wind turbine system, an effective controller design is a necessary requirement, and therefore this paper proposes a novel controller design based on PID scheme. The parameters of this controller is effectively optimized through a new snake optimizer (SO) in an offline manner to minimize frequency and power deviations of an isolated diesel–wind system. The performance of SO-PID for the diesel–wind system is evaluated by considering the integral of time multiplied absolute error (ITAE), integral absolute error (IAE), and integral of time multiplied square error (ITSE). The results were calculated for a step change in load, step change in wind speed, load change at different instants of time with diverse magnitude, and for random load patterns, and they were compared with some of the recently published results under similar working conditions. In addition, the effect of an ultracapacitor (UC) and redox flow battery (RFB) on SO-PID was investigated for the considered system, and the application results demonstrated the advantages of our proposal over other studied designs.