Search

Modern ultrasonic phased-array controllers are electronic systems capable of delaying the transmitted or received signals of multiple transducers. Configurable transmit–receive array systems, capable of electronic steering and shaping of the beam in near real-time, are available commercially, for example, for medical imaging. However, emerging applications, such as ultrasonic haptics, parametric audio, or ultrasonic levitation, require only a small subset of the capabilities provided by the existing controllers. To meet this need, we present Ultraino, a modular, inexpensive, and open platform that provides hardware, software, and example applications specifically aimed at controlling the transmission of narrowband airborne ultrasound. Our system is composed of software, driver boards, and arrays that enable users to quickly and efficiently perform research in various emerging applications. The software can be used to define array geometries, simulate the acoustic field in real time, and control the connected driver boards. The driver board design is based on an Arduino Mega and can control 64 channels with a square wave of up to 17 Vpp and $\pi $ /5 phase resolution. Multiple boards can be chained together to increase the number of channels. The 40-kHz arrays with flat and spherical geometries are demonstrated for parametric audio generation, acoustic levitation, and haptic feedback. [ABSTRACT FROM PUBLISHER]

Motivation is fundamental for students to achieve successful and complete learning. Motivation can be extrinsic, i.e., driven by external rewards, or intrinsic, i.e., driven by internal factors. Intrinsic motivation is the most effective and must be inspired by the task at hand. Here, a novel strategy is presented to increase intrinsic motivation toward activities requiring the use of sample data sets, by automatically extracting the student's social data from a social media network, to create a personalized sample data set. A tool was developed to access Facebook and create a database to be used in practical assignments on database queries. A user study $(N=74)$ measured intrinsic motivation before and after the assignment in three different groups of first-year university students. Each group had to create a set of queries of similar difficulty, with each group using a different database. Intrinsic motivation was found to be significantly higher for the group using their own social media data than for the groups using social media data from an unknown person or a business. The approach presented here can be applied to other subjects, such as data mining, image processing, or statistics. [ABSTRACT FROM PUBLISHER]

Traditionally, e-Health solutions were located at the point of care (PoC), while the new ubiquitous user-centered paradigm draws on standard-based personal health devices (PHDs). Such devices place strict constraints on computation and battery efficiency that encouraged the International Organization for Standardization/IEEE11073 (X73) standard for medical devices to evolve from X73PoC to X73PHD. In this context, low-voltage low-power (LV-LP) technologies meet the restrictions of X73PHD-compliant devices. Since X73PHD does not approach the software architecture, the accomplishment of an efficient design falls directly on the software developer. Therefore, computational and battery performance of such LV-LP-constrained devices can even be outperformed through an efficient X73PHD implementation design. In this context, this paper proposes a new methodology to implement X73PHD into microcontroller-based platforms with LV-LP constraints. Such implementation methodology has been developed through a patterns-based approach and applied to a number of X73PHD-compliant agents (including weighing scale, blood pressure monitor, and thermometer specializations) and microprocessor architectures (8, 16, and 32 bits) as a proof of concept. As a reference, the results obtained in the weighing scale guarantee all features of X73PHD running over a microcontroller architecture based on ARM7TDMI requiring only 168 B of RAM and 2546 B of flash memory. [ABSTRACT FROM AUTHOR]