Modeling and Designing a Full Beamformer for Acoustic Sensing and Measurement

Abstract

-Acoustic sensing is a viable approach for solving issues related to many applications, namely, biomedical, distance measurements, mechanical, health infrastructure monitoring, etc. It is generally sustainable and of no negative impact on the object under test. The use of acoustic sensing under beamforming technique is an important asset to be exploited, especially for the aforementioned applications. This paper illustrates a generalized approach of modeling and designing a full beamfomer using two specific classes: LCMP (Linear Constrained Minimum Power) beamformers that are used to overcome robustness limitations and MVDR (Minimum Variance Distortionless Response) beamformers. Any aspect of modeling and designing is always related to the DOA (Direction of Arrival). The obtained results are based on assumptions extracted from an actual case of constructed system. Index terms: Acoustic sensing, Beamforming, Ultrasonic shape detection, DOA, Distance measurement, array of sensors, interference-to-noise ratio (INR), signal-to-noise ratio (SNR).

Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System

“…In [7], where an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB is described, it turns out how the need to resort to big data and Internet issues appears as a necessary requirement when dealing with data recovery and data processing related to sensing systems in urban scenarios. This matter becomes especially important because of the exponentially increasing of information to be stored and dynamically recovered to control and manage vehicular traffic (see also [8] and [9] further details). In the emerging field of systems and computing paradigms for big data storage and analytics [10], the traffic monitoring approach proposed in [7] offers a flexible, scalable and inexpensive way to collect sensor data from private vehicles.…”