This paper presents a new method for automatic radio spectrum segmentation. Spectrum segmentation serves as a first step to identify the subbands that are in use at a certain time, when a wide portion of the spectrum is observed. The proposed method, based on the evaluation of the histogram of the power spectral density of the received signal, is designed for obtaining a low processing complexity and to be implemented in a measurement instrument or a cognitive radio receiver. The proposed method is validated on emulated signals, and the results show good accuracy of the subband boundary estimation and a processing complexity much lower than the other segmentation methods.

The phase measurement of sinewave signals is important in several applications, such as electric and electronic instrumentation; telecommunications; and optical interferometry. The uncertainty of the phase measurement has an essential role in ensuring the suitable performance of the devices and systems used by the relevant application. Some highly accurate phase measurement methods have been developed and implemented in different instrument types that are currently available on the market or have been proposed in the scientific literature, each capable of covering very different frequency ranges. This article presents an overview of these methods and instruments in order to highlight the characteristics in terms of the measurement uncertainty of the main methods and instruments that are used, by taking into account a varying operative frequency range. The standard deviations considered in the surveyed literature are used to identify a phase measurement method that is capable of covering a large high-frequency range, simultaneously maintaining a low value of measurement uncertainty, as requested by some applications (like waveform recorder frequency response testing).

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.

Nowadays, the development of a new open architecture for Analog-to-Information Conversion (AIC) represents an open field of research. This paper presents the preliminary exploration of two AIC architectures concerning Non-Uniform Sampling (NUS), and a derivation of it, called Non-Uniform Wavelet Band-pass Sampling (NUWBS). To this goal, based of the existing mathematical models of these AIC architectures, a numerical investigation has been carried out in order to assess their capabilities in terms of Compressed Sensing (CS) framework for spectrum analysis. By considering non-idealities such as random jitter and noise affecting the AIC architectures, an evaluation of two Figures-of-Merit (FoM) has been accomplished. The obtained results highlight that from a hardware implementation point of view of both AIC architectures, the NUWBS is not restricted to use control signals for the NUS process at the Nyquist rates, as in case of NUS AIC architecture. Moreover, the reported results indicate that the NUWBS AIC architecture is advantageous for the future Wideband Spectrum Sensing (WSS) applications, such as those required in case of Cognitive Radios (CR) for 5G telecommunications and for Internet-of-Things (IoT) devices.