Eulalia Balestrieri

This paper presents a method based on Compressed Sensing (CS) and spread-spectrum signals for estimating the frequency response of electrical communication cables, with applications in cable monitoring for diagnostics and physical-layer security. Compared with traditional systems in the literature, the use of CS relaxes the data-acquisition requirements regarding sampling frequency, thereby reducing the cost of electronic components and enabling integration of the method into the network transceivers for real-time, continuous cable monitoring. Moreover, the use of spread-spectrum signals excites the cable’s high-frequency response, allowing for the detection of defects as short as 0.5 cm. The experimental evaluation demonstrates the method’s capability to accurately estimate the cable’s frequency response in the band of interest, up to the 40^th Nyquist band with an undersampling ratio of 10, and to detect defects, specifically the lack of cable insulation.

This article introduces a passive measurement method for diagnosing anomalies in two-wire communication channels using machine learning (ML). The proposed method involves the acquisition of the signal received by a transceiver and the decoded sequence provided by the receiver. In particular, it does not require the acquisition of a particular injected signal and any synchronization of the acquisition with the data transmission, making it suitable for the diagnosis of existing two-wire communication channels without interrupting their operability. An experimental setup has been implemented to generate a dataset of acquired signals through a channel having the following anomalies: air-exposed conductors, water-exposed conductors, and tapping of various lengths. The performance of an ML-based decision tree classifier has been assessed according to features extracted in the time and frequency domains from the acquired signal and an estimated impulse response of the cable obtained from the decoded sequence. The most sensitive features to the anomalies have been analyzed, and the decision tree classifier has been trained according to them by considering several sampling frequencies of the signal acquisition, ranging from 62.5 MHz to 6.25 GHz. The classification accuracy obtained in a set of laboratory experiments carried out on actual anomalies is 99.04% at the sampling frequency of 312.5 MHz. Moreover, an analysis is carried out to assess the sensitivity of the diagnostic tool to the anomaly lengths, thus demonstrating its capability to estimate them.

The measurement of Morphometric parameters of Fish Erythrocytes (MFE) has been recognized as a valuable tool for ecological biomonitoring. Conventional methods for measuring MFE greatly depend on manual measurements, leading to a labor-intensive and time-consuming process that relies on human expertise. To overcome these issues, in this paper, a solution based on image processing and ellipse fitting to provide an automated framework for the measurement of MFE is presented. The performance of the proposed method is assessed quantitatively by comparing its results with manual measurements performed by an expert biologist. Additionally, a comparison of the proposed method with three state-of-the-art methods is presented by providing the statistical analysis of their mean absolute errors. The analysis of results demonstrates the proposed method significantly outperforms existing methods for both cell and nucleus measurement. It notably achieves a lower absolute mean error value of 78 pixels for cell area and 36 pixels for nucleus area, confirming the better performance of the proposed method compared to the others. The experimental results demonstrate that the proposed method can correctly fit the ellipse on both fish blood cells and their nuclei.

Unmanned aerial vehicles’ (UAVs) safety has gained great research interest due to the increase in the number of UAVs in circulation and their applications, which has inevitably also led to an increase in the number of accidents in which these vehicles are involved. The paper presents a classification of UAV safety solutions that can be found in the scientific literature, putting in evidence the fundamental and critical role of sensors and measurements in the field. Proposals from research on each proposed class concerning flight test procedures, in-flight solutions including soft propeller use, fault and damage detection, collision avoidance and safe landing, as well as ground solution including testing and injury and damage quantification measurements are discussed.

The Technical Committee on Biological and Medical Measurements (TC-25) aims at establishing, developing, promoting and supporting cooperation among researchers in the different fields of biological and medical measurements. Sensors for the measurement of clinical and/ or biological parameters, instrument and procedure characterization, instrument interoperability and interconnection, and data processing are some of the topics of interest to the TC-25. To those aims, the technical committee designs and realizes joint activities, like study groups aimed at standardization, such as the Subcommittee on Blood Pressure Measurement (SCOBPM); conferences or special sessions, such as the IEEE International Symposium on Medical Measurements and Applications (MeMeA); and several special sessions during the annual IEEE Instrumentation and Measurement Technical Conferences. TC-25 is also continuously engaged to maintain liaisons with other committees, groups, societies and organizations working on topics related to the TC scope. This paper presents the latest updates about the main activities of the TC-25. Some information about the TC-25 activities and targets is briefly presented, then the objectives and the efforts of the SCOBPM are summarized, and finally, the history and latest updates of our main conference, the MeMeA Symposium, are reported.

Although Unmanned Aerial Vehicles (UAVs) are used as mobile measurement platforms in several applications, no guidelines are provided to include the measurement uncertainty assessment in the design steps of the UAV platform. In this article, the steps required for the design of a UAV-based measurement instrument according to the application measurement target and the uncertainty assessment are delineated. In order to highlight the necessity of the proposed design guidelines, the case study of 3D reconstruction of archaeological sites is discussed.

The advance of technology has enabled the development of unmanned systems/vehicles used in the air, on the ground or on/in the water. The application range for these systems is continuously increasing, and unmanned platforms continue to be the subject of numerous studies and research contributions. This paper deals with the role of sensors and measurements in ensuring that unmanned systems work properly, meet the requirements of the target application, provide and increase their navigation capabilities, and suitably monitor and gain information on several physical quantities in the environment around them. Unmanned system types and the critical environmental factors affecting their performance are discussed. The measurements that these kinds of vehicles can carry out are presented and discussed, while also describing the most frequently used on-board sensor technologies, as well as their advantages and limitations. The paper provides some examples of sensor specifications related to some current applications, as well as describing the recent research contributions in the field.

Today, several applications and technologies require a suitable jitter characterization. For this reason, measurement methods and instruments aimed at quantifying and analyzing jitter continue to be proposed and developed by the research community and manufacturers.

Today a very wide range of different applications relies on the Digital to Analog Converters (DACs). Different DAC architectures have been developed in the years and several different specifications exist to quantify their effective performance, essential information to verify the requirement fulfilling and the DAC suitability for the specific application. As a result, DAC testing has assumed and continues to assume increasing importance. Main testing challenges include the reduction of the test time and cost, the measurement uncertainty computation and facing with the emerging Built-In Self-Test (BIST) solutions. To unambiguously clarify DAC terms, definitions and test methods the IEEE Std. 1658 has been developed and is currently under revision. To highlight the trends and issues also providing useful information for the revision of the standard, the paper presents an overview of the recent research work dealing with the DAC testing.

In this article, an automatic Analog Modulation Classifier based on Empirical mode decomposition and Machine learning approaches (AMC-EM) is proposed. The AMC-EM operates without a priori information and can recognise typical analog modulation schemes: amplitude modulation, phase modulation, frequency modulation, and single sideband modulation. The AMC-EM uses Empirical Mode Decomposition (EMD) to evaluate the features of the signal for the successive classification by using Machine Learning (ML). In the design of the AMC-EM, the selection of the specific ML technique is performed by comparing, with numerical tests, the performance of the (i) Support Vector Machine (SVM), (ii) k-nearest neighbor classifier, and (iii) adaptive boosting, since they are commonly used in the field of signal classification. The tests have highlighted that the SVM, specifically the quadratic SVM, permits the best possible performance concerning classification accuracy, by considering different noise intensities superimposed on the signal. To assess the advantages of the proposal, a comparison with other classifiers available in the literature has been undertaken through numerical tests. Finally, the AMC-EM is experimentally evaluated, and the experimental results agree with those of the simulation.