Trustworthy Environmental Monitoring Using Hardware-Assisted Security Mechanisms.

Environmental monitoring is essential for safeguarding the health of our planet and protecting human health and well-being. Without trust, the effectiveness of environmental monitoring and the ability to address environmental challenges are significantly compromised. In this paper, we present a sensor platform capable of performing authenticated and trustworthy measurements, together with a lightweight security protocol for sending the data from the sensor to a central server anonymously.

Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier Detection.

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data.

Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems for Anomaly Detection in Smart Cities.

Environmental Sound Recognition (ESR) plays a crucial role in smart cities by accurately categorizing audio using well-trained Machine Learning (ML) classifiers. This application is particularly valuable for cities that analyzed environmental sounds to gain insight and data. However, deploying deep learning (DL) models on resource-constrained embedded devices, such as Raspberry Pi (RPi) or Tensor Processing Units (TPUs), poses challenges.

XCycles Backprojection Acoustic Super-Resolution.

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary.

A New Method for Acoustic Priority Vehicle Detection Based on a Self-Powering Triboelectric Acoustic Sensor Suitable for Low-Power Wireless Sensor Networks.

Traffic congestion is, on a daily basis, responsible for a significant amount of economic and social costs. One of the critical examples is the obstruction of priority vehicles during fast trajectories, which potentially costs lives and property in case of delay that is too great. By means of visual sensing methods, solutions and schedules have already been proposed for adjusting traffic light sequences depending on a priority vehicle's position.

CABE: A Cloud-Based Acoustic Beamforming Emulator for FPGA-Based Sound Source Localization.

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain.

Using sound-taste correspondences to enhance the subjective value of tasting experiences.

The soundscapes of those places where we eat and drink can influence our perception of taste. Here, we investigated whether contextual sound would enhance the subjective value of a tasting experience. The customers in a chocolate shop were invited to take part in an experiment in which they had to evaluate a chocolate's taste while listening to an auditory stimulus.

An Ultrasonic Multiple-Access Ranging Core Based on Frequency Shift Keying Towards Indoor Localization.

This paper describes a new approach and implementation methodology for indoor ranging based on the time difference of arrival using code division multiple access with ultrasound signals. A novel implementation based on a field programmable gate array using finite impulse response filters and an optimized correlation demodulator implementation for ultrasound orthogonal signals is developed. Orthogonal codes are modulated onto ultrasound signals using frequency shift keying with carrier frequencies of 24.

Ultrasonic multiple-access ranging system using spread spectrum and MEMS technology for indoor localization.

Indoor localization of persons and objects poses a great engineering challenge. Previously developed localization systems demonstrate the use of wideband techniques in ultrasound ranging systems. Direct sequence and frequency hopping spread spectrum ultrasound signals have been proven to achieve a high level of accuracy.

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization.

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors.

Active self-testing noise measurement sensors for large-scale environmental sensor networks.

Large-scale noise pollution sensor networks consist of hundreds of spatially distributed microphones that measure environmental noise. These networks provide historical and real-time environmental data to citizens and decision makers and are therefore a key technology to steer environmental policy. However, the high cost of certified environmental microphone sensors render large-scale environmental networks prohibitively expensive.

Extending the IEEE 802.15.4 security suite with a compact implementation of the NIST P-192/B-163 elliptic curves.

Typically, commercial sensor nodes are equipped with MCUsclocked at a low-frequency (i.e., within the 4-12 MHz range).

On the ability of consumer electronics microphones for environmental noise monitoring.

The massive production of microphones for consumer electronics, and the shift from dedicated processing hardware to PC-based systems, opens the way to build affordable, extensive noise measurement networks. Applications include e.g.