Being used in for environmental and military Internet of Things (IoT), a low power wake-up system based on frequency analysis is presented in this paper. It aims at detecting continuously the presence of specific very high frequencies in the input acoustic signal of an embedded system. This can be used for detecting specific animal species, and for triggering a recording system or generating alerts. Used for harmful species detection, this helps to save harvests or to protect strict nature reserves. It can also be used for detecting the presence of drones in a specific restricted area. This acoustic low power wake-up system uses a simple 16 bits micro-controller (MCU), with a strong emphasis on the low power management of the system, having a target of continuous detection for at least one year on a single standard 1.2Ah-12V lead battery. For that, it makes the most of mixed analog and digital low power MCU modules. They are including comparators, timers and a special one present on Microchip MCU, called Charge Time Measurement Unit (CTMU). This is a driven constant current source for making time to frequency conversions at a very low power and algorithmic cost. Optimizing low power modes, this low power wake-up system based on frequency analysis has a power consumption of 0.56mW , leading to approximately 3 years of battery life on a single standard 1.2Ah-12V lead cell.

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An ultra low power acoustic wake-up detector based on high frequency signal analysis is presented in this paper. Focused on environmental or military Internet of Things (IoT) applications, it aims at detecting in real time the presence of specific animal species or drones for generating alerts and for triggering power consuming tasks such as high frequency signal recording only when needed. This wake-up detector continuously monitors the presence of specific frequencies in an analog acoustic signal, with a good frequency selectivity and a high frequency detection capability. It is based on an ultra-low analog frequency to voltage converter using a current-mirror, analog timers and comparators. Dedicated to long term stealth environmental or military surveys, a strong emphasis has been put on power consumption reduction in order to limit size and weight of the system. This power consumption has been reduced to 34µW , leading to a full year of autonomy including the microphone when powered by 3 coin cell CR2032 batteries.

(a,b) , M. Ferrari (a,c) , J. Schlüter (a) , R. Marxer (a) , P. Giraudet (a) , V. Barchasz (e) , V. Giès (e) , G. Pavan (d) , H. Glotin (a,e) *

This paper addresses a formation tracking problem of multiple low-cost underwater drones by implementing distributed adaptive neural network control (DANNC). It is based on a leader-follower architecture to operate in hazardous environments. First, unknown parameters of underwater vehicle dynamics, which are important requirements for real-world applications, are approximated by a neural network using a radial basis function. More specifically, those parameters are only calculated by local information, which can be obtained by an on-board camera without using an external positioning system. Secondly, a potential function is employed to ensure there is no collision between the underwater drones. We then propose a desired configuration of a group of unmanned underwater vehicles (UUVs) as a time-variant function so that they can quickly change their shape between them to facilitate the crossing in a narrow area. Finally, three UUVs, based on a robot operating system (ROS) platform, are used to emphasize the realistic low-cost aspect of underwater drones. The proposed approach is validated by evaluating in different experimental scenarios.