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At the local scale, environmental parameters often require monitoring by means of affordable measuring techniques and technologies given they need to be frequently surveyed. Streamflow in riverbeds or in channels is a hydrological variable that needs to be monitored in order to keep the runoff regimes under control and somehow forecast floods, allowing prevention of damage for people and infrastructure. Moreover, measuring such a variable is always extremely important for the knowledge of the environmental status of connected aquatic ecosystems. This paper presents a new approach to assessing hydrodynamic features related to a given channel by means of a beamforming technique that was applied to video sensing. Different features have been estimated, namely the flow velocity, the temperature, and the riverbed movements. The applied beamforming technique works on a modified sum and delay method, also using the Multiple Signal Classification algorithm (MUSIC), by acting as Synthetic Aperture Radar (SAR) post-processing. The results are very interesting, especially compared to the on-site measured data and encourage the use of affordable video sensors located along the channel or river course for monitoring purposes. The paper also illustrates the use of beamforming measurements to be calibrated by means of conventional techniques with more accurate data. Certainly, the results can be improved; however, they indicate some margins of improvements and updates. As metrics of assessment, a histogram of greyscale/pixels was adopted, taking into account the example of layers and curve plots. They show changes according to the locations where the supporting videos were obtained.

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily.

In cognitive radio systems, estimating primary user direction of arrival (DOA) measurement is one of the key issues. In order to increase the probability detection multiple sensor antennas are used and they are analysed by using subspace-based technique. In this work, we considered wideband spectrum with sub channels and here each sub channel facilitated with a sensor for the estimation of DOA. In practical spectrum sensing process interference component also encounters in the sensing process. To avoid this interference level at output of receiver, we used an adaptive learning algorithm known as Normalised Least Absolute Mean Deviation (NLAMD) algorithm. Further to achieve better performance a bias compensator function is applied in weight coefficient updating process. Using this hybrid realization, the vacant spectrum can be sensed based on DOA estimation and number of vacant locations in each channel can be identified using maximum likelihood approach. In order to test at the diversified conditions different threshold parameters 0.1, 0.5, 1 are analysed.

The main idea of this paper is to assess a simpler and faster procedure leading to the evaluation of the fluid flow rate through a pipe. Currently, several methods are available and they involve ad-hoc instruments. All these methods are characterized by high accuracies and dynamic responses, but they are intended to be inserted within the pipe under investigation, bringing to well-known insertion effects, compromising the reliability of the measurements performed. The authors illustrate a newer methodology for the measurement of flow rates by means of the processing of the vibration signals of pipe walls, inferred by the flow turbulence. Previous studies of the same authors showed a linear dependence between the amplitude of the most prominent peak of the vibration spectra and the flow rate. In this work, the authors relate the power content of the processed signals (by introducing the signal Root Mean Square value) to the flow rate.