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The application fields of modern Unmanned Aerial Vehicles (UAV), such as light Remotely-Piloted Aircraft System (RPAS), are moving from military fields to a wide range of civilian applications. These platforms embed several sensors that enable the use of RPASs as mobile remote measurement systems. This chapter deals with a survey of measurement applications based on RPAS. In the first part, an overview of the architecture of a RPAS is given. Then, a review of sensors used for RPAS flight and for mission tasks is discussed. Attention is paid to measurement uncertainty sources. Therefore, RPAS as a mobile remote measurement system is analyzed and a design guideline is described. Several examples of embedded measurement systems included in RPAS are described. A review of civilian applications using the RPAS as a remote measurement system is presented and discussed. Issues concerning RPAS safety and testing are analyzed. The new trend in the field of insurance for RPAS users is discussed. Finally, RPAS security is introduced. Conclusions and future trends end the chapter.

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.

The IEEE Technical Committee 10 (TC-10), the Waveform Generation, Measurement, and Analysis Committee of the IEEE Instrumentation and Measurement (IM IEEE Std 1057–2017, Standard for Digitizing Waveform Recorders [2]; IEEE Std 1241–2010, Standard for Terminology and Test Methods for Ana-log-to-Digital Converters [3]; IEEE Std 658–2011, Standard for Terminology and Test Methods for Digital-to-Analog Converters [4]; the IEEE Std 1696–2013, Standard for Terminology and Test Methods for Circuit Probes [5]; and the IEEE Std 2414–2020, Standard for Jitter and Phase Noise [6]. Additional information on these standards can be found in [7].

Global trade relies on the ability to reproducibly and accurately communicate the performance of products and to support these attestations. This standardization is essential for accurate, reproducible, reliable, and communicable characterization of the performance of these devices, to support technology and product advancement, product comparison and performance tracking, and device calibration and traceability. Standard terms and definitions, reproducible test methods, and accurate computational procedures are necessary for this communication and facilitate economic growth and technology evolution through the common understanding of technology. The IEEE Technical Committee 10 (TC-10), the Waveform Generation, Measurement, and Analysis Committee of the IEEE Instrumentation and Measurement Society (IMS), fulfills the global need for standardized terms and test and computational methods for describing and/or measuring the parameters that describe the performance of signal generators and waveform recorders and analyzers. The TC-10 has developed and maintains the following documentary standards: IEEE Std 181-2011, “Standard on Transitions, Pulses, and Related Waveforms” [1]; IEEE Std 1057-2017, “Standard for Digitizing Waveform Recorders” [2]; IEEE Std 1241-2010, “Standard for Terminology and Test Methods for Analog-to-Digital Converters” [3]; IEEE Std 1658-2011, “Standard for Terminology and Test Methods for Digital-to-Analog Converters” [4]; and the IEEE Std 1696-2013, “Standard for Terminology and Test Methods for Circuit Probes” [5]. In development is the IEEE Draft Std P2414 “Draft Standard for Jitter and Phase Noise.” The TC-10 comprises an international group of electronics engineers, mathematicians, professors and physicists with representatives from national metrology laboratories, national science laboratories, component manufacturers, the test instrumentation industry, academia, and end users. The status of the TC-10 standards is described herein.

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.

This article proposes a novel method to estimate the frequency response of cables for applications of physical layer testing and network visibility. By exploiting compressive sampling, the method allows using analog-to-digital converters with a sampling frequency much lower than the symbol rate of the transmitted bit stream. Moreover, it does not need a properly generated signal, as it happens in time-domain reflectometry; however, any signal having a pulse-amplitude modulation, orthogonal frequency-division multiplexing (OFDM), or spread spectrum modulation can be used. Therefore, the transceiver actually used for data transmission can also be used to generate test signals for the channel estimation and the measurement instrument can be integrated into the data communication equipment without requiring additional generators. The mathematical formulation of the method is presented, together with its evaluation by numerical simulations and experimental tests. The results show the capability of the method of properly estimating the frequency response of the channel, even with a sampling frequency much lower than the symbol frequency.

The 37th IEEE International Instrumentation and Measurement Conference (I2MTC) was planned to be held in Dubrovnik, Croatia, one of the most prominent destinations in the Mediterranean Sea and UNESCO World Heritage site since 1979. I2MTC is the flagship conference of the IEEE Instrumentation and Measurement Society that is dedicated to advances in measurement methodologies, measurement systems, instrumentation, and sensors in all areas of science and technology. The conference has been held every year since 1986 across the globe bringing together academic and industrial communities in discussions on a wide spectrum of research results and developments with applications in current industrial technology. Strong technical programs, accompanying social events, old and newly acquired friends, and life-long education opportunities provide strong reasons to attend the conference every year.

This paper presents a new approach for the optimization of a dictionary used in ECG signal compression and reconstruction systems, based on Compressed Sensing (CS). Alternatively to fully data driven methods, which learn the dictionary from the training data, the proposed approach uses an over complete wavelet dictionary, which is then reduced by means of a training phase. Moreover, the alignment of the frames according to the position of the R-peak is proposed, such that the dictionary optimization can exploit the different scaling features of the ECG waves. Therefore, at first, a training phase is performed in order to optimize the overcomplete dictionary matrix by reducing its number of columns. Then, the optimized matrix is used in combination with a dynamic sensing matrix to compress and reconstruct the ECG waveform. In this paper, the mathematical formulation of the patient-specific optimization is presented and three optimization algorithms have been evaluated. For each of them, an experimental tuning of the convergence parameter is carried out, in order to ensure that the algorithm can work in its most suitable conditions. The performance of each considered algorithm is evaluated by assessing the Percentage of Root-mean-squared Difference (PRD) and compared with the state of the art techniques. The obtained experimental results demonstrate that: (i) the utilization of an optimized dictionary matrix allows a better performance to be reached in the reconstruction quality of the ECG signals when compared with other methods, (ii) the regularization parameters of the optimization algorithms should be properly tuned to achieve the best reconstruction results, and (iii) the Multiple Orthogonal Matching Pursuit (M-OMP) algorithm is the better suited algorithm among those examined.

This paper presents an experimental analysis aimed at analyzing the performance of several reconstruction algorithms applied to the recovery of ECG signals from compressed sensing, when a dynamic sensing matrix is used. The manuscript briefly recalls the method used to build the sensing matrix and the adopted reconstruction algorithms. The results obtained from the compression and recovery of signals taken from the MIT Physionet database are shown and discussed and they are compared with those obtained with another combination of sensing and dictionary matrices.

This paper proposes the use of the Deterministic Binary Block Diagonal (DBBD) matrix as sensing matrix for compressed sensing of heart sound signals. The use of a deterministic matrix has the advantage of not requiring the generation of random numbers in the acquisition node. Moreover, the DBBD matrix has a very low computational complexity at the compression side, as it only requires a sum of the samples. In the paper, the DBBD sensing matrix is used in combination with the Discrete Cosine Transform and the Mexican Hat wavelet to compress and reconstruct heart sound signal obtained from the PhysioNet database. The results show a lower value of the Percent Root Mean Square Difference compared to that obtained by the random sensing matrix previously used in the literature for heart sound signals.