This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this localization technology, but a detailed performance comparison of these solutions is still missing. The aim of this work is to make an experimental analysis combining different solutions for the performance improvement of BLE-based indoor localization, identifying the most effective one. The considered solutions involve different RSSI signals’ conditioning, the use of anchor–tag distance estimation techniques, as well as approaches for estimating the unknown tag position. An experimental campaign was executed in a complex indoor environment, characterized by the continuous presence in the movement of working staff and numerous obstacles. The exploitation of multichannel transmission using RSSI signal aggregation techniques showed the greater performance improvement of the localization system, reducing the positioning error (from 1.5 m to about 1 m). The other examined solutions have shown a lesser impact in the performance improvement with a decrease or an increase in the positioning errors, depending on the considered combination of the adopted solutions.

The paper proposes a new experimental measurement method for State of Charge (SoC) estimation able to optimize between measurement time and target Accuracy adoptable in Battery Management System (BMS) design where both these parameters are key parameters for the overall management performance. The method is applicable when the SoC is estimated in classes through Electrochemical Impedance Spectroscopy (EIS) and is based on two operating stages: i) the experimental characterization of the real behavior of the considered batteries, ii) a time–Accuracy optimization based on suitable feature selection and Machine Learning approaches. In detail, as for the ii) phase, fixed the number of classes in which the SoC is estimated, the proposed measurement method finds the minimum number of impedance spectrum frequency measurements useful for SoC estimation. This is a big issue for BMS designers since in SoC estimation performed by EIS the measurement time is typically greater than some minutes if no optimization is considered. A possible strategy to reduce the required time for SoC estimation could be using Feature Selection (FS) techniques. In our method the FS is implemented using different combinations of search algorithms and fitness functions. Since FS strongly depends on the experimental set–up, the uncertainty of the measurement system, and the classifier adopted for the data–driving evaluation model, the proposed method is flexible and customizable depending on the specific applications. For this reason, the method is divided into steps where the BMS designers define the requirements based on their needs and their hardware. Also the output FS could be adapted to the different exigencies because the selected features could be different if more emphasis is on increasing classification Accuracy or decreasing the measurement time. Hence, we suggest two application scenarios: in the first one, the only requirement is increasing the classification Accuracy rather than the measurement time optimization. The second scenario has both a classification Accuracy target and lowering the measurement time. We also noted that the proposed method is useful also for increasing the overall classification Accuracy because naturally excludes the features that deceive the classifiers. As an application for the proposed method, we developed an automatic measurement system and performed an experimental campaign on seven Lithium Iron Phosphate batteries. The experimental results show that the proposed approach, for all the considered combinations, significantly reduces the measurement time required for EIS while maintaining high SoC estimation Accuracy. Furthermore, for all the obtained solutions, the effectiveness of our approach was confirmed by the significant savings in measurement time. In particular, the best solution reduces from 4 minutes when using all features to about 1.5 s with the selected features.

Thickness measurements of metallic plates are mandatory in many industrial scenarios. Methods based on Eddy Current Testing (ECT) are ideal for fast and accurate online contact–less thickness measurements, making them very attractive in the Industry 4.0 scenario. This contribution is focused on a specific and robust ECT technique proposed in the past by the scientific community. The main limitation is its applicability to thin materials only, where the thickness of the material is much smaller than the overall size of the ECT probe. Extending the range of applicability to thicker materials, introduces a progressive and severe degradation of the measurement accuracy. In this paper, we analyse the theoretical foundation of this method with an entirely original approach based on the celebrated Buckingham’s π theorem. In doing this, we draw the complete theoretical picture of the method, providing a simple, clear and rigorous view of its performance and intrinsic limitations. Moreover, we propose two solutions, one analytical and the other iterative, to accurately estimate the thickness of materials from thin to thick values. Finally, a numerical analysis combined with an experimental campaign confirms the effectiveness of the proposed solutions making the method suitable for industrial and other applications.

Lithium-ion (Li-ion) batteries are crucial in numerous applications, including portable electronics, electric vehicles, and energy storage systems. Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for characterizing batteries, providing valuable insights into charge transfer kinetics like ion diffusion and interfacial reactions. However, obtaining comprehensive and diverse datasets for battery State of Charge (SoC) studies remains challenging due to the complex nature of battery operations and the time-intensive testing process. This paper presents a novel and original EIS dataset specifically designed for 600 mAh capacity Lithium Iron Phosphate (LFP) batteries at various SoC levels. The dataset includes repeated EIS measurements using different battery discharging cycles, allowing researchers to examine the frequency domain properties and develop data-driven algorithms for assessing battery SoC and predicting performance. The data acquisition system employs a battery specific impedance meter and an electronic load, ensuring accurate and controlled measurements. The dataset, comprising EIS measurements from multiple LFP batteries, serves as a valuable resource for researchers in the fields of battery technology, electrochemistry, power sources, and energy storage. Moreover, industries such as consumer electronics, power systems, and electric transportation can benefit from the dataset's insights for effectively managing rechargeable battery devices. The presented dataset expands the scope of impedance spectroscopy measurements and holds significant potential for future applications and advancements in Li-ion battery technologies.

Localizing objects in indoor environments with anchor-based systems poses several challenges to be faced. The main one is the design phase. What is the minimum number of anchors to be adopted? What is the optimal placement of the anchors in the operating domain? These are typical issues that need to be solved in this stage. Such issue is generally addressed by several experimental tests, imposing high costs both in the required large amounts of time and in the use of devices. In addition, sometimes the experiments will not warrant to identify the optimal design solution. In this work, a black-box design tool is proposed, able to manage different environments, in terms of domain size, maximum number of available anchors, device metrological features, and target performance. Such an approach, to be effective, needs a preliminary validation versus experimental results to make the future performance predictions reliable and avoid unexpected localization accuracy degradation. For this reason, the presented work adopts a side-by-side development, by evaluating results’ compatibility, in static and dynamic contexts, before addressing further and more complex analyses relying on the design tool only. Such a comparison proves the goodness of the developed design tool, and the outcomes allow an accurate localization system design by finding anchors’ placement solutions with the minimum computational burden.

Magnetic localization is used in many indoor positioning applications, such as industrial, medical and IoT, for its benefits related to the absence of line of sight needs, multipath and fading, the low cost of transmitters and receivers, the simple development of set-ups made of coils and magnetic sensors. In short ranges applications, this technology could bring some advantages respect to ultrasound, laser or RF ones. Nevertheless, fixed both the desired accuracy and the energy constraints, the optimal design of a localization system based on magnetic measurement depends on several factors: the dimension, the number and the optimal positions of the anchors, the uncertainties due to the sensing elements and the data acquisition systems. To preliminary fix all these parameters, suitable simulation environments allow developers saving time and money in developing localization applications. Many magnetic field simulators are available, but it is rare to find those which, considering the uncertainty due to the receiver and data acquisition systems, are able to provide optimal anchors scenario given a target accuracy. To address this problem, the paper presents a simulation tool providing the user with design requirements for a given target accuracy. The aim of the paper is to perform first steps in providing a read-to-use specification framework that given the localization domain, the mobile sensors and the data acquisition system characteristics and the target accuracy, helps the developer of indoor magnetic positioning systems. The actual validity of the simulation model has been tested on a real set-up.