New Hyperspectral Procedure to Discriminate Intertidal Macroalgae

The recent development and miniaturization of hyperspectral sensors embedded in drones has allowed the acquisition of hyperspectral images with high spectral and spatial resolution. The characteristics of both the embedded sensors and drones (viewing angle, flying altitude, resolution) create opportunities to consider the use of hyperspectral imagery to map and monitor macroalgae communities. In general, the overflight of the areas to be mapped is conconmittently associated accompanied with measurements carried out in the field to acquire the spectra of previously identified objects. An alternative to these simultaneous acquisitions is to use a hyperspectral library made up of pure spectra of the different species in place, that would spare field acquisition of spectra during each flight. However, the use of such a technique requires developed appropriate procedure for testing the level of species classification that can be achieved, as well as the reproducibility of the classification over time. This study presents a novel classification approach based on the use of reflectance spectra of macroalgae acquired in controlled conditions. This overall approach developed is based on both the use of the spectral angle mapper (SAM) algorithm applied on first derivative hyperspectral data. The efficiency of this approach has been tested on a hyperspectral library composed of 16 macroalgae species, and its temporal reproducibility has been tested on a monthly survey of the spectral response of different macro-algae species. In addition, the classification results obtained with this new approach were also compared to the results obtained through the use of the most recent and robust procedure published. The classification obtained shows that the developed approach allows to perfectly discriminate the different phyla, whatever the period. At the species level, the classification approach is less effective when the individuals studied belong to phylogenetically close species (i.e., Fucus spiralis and Fucus serratus).

Evaluation of Smartphone’s Embedded Sensors Through Applications: A Case Study of Gyroscope and Accelerometer

Smartphones are used for many daily activities like tele-communication, gaming, web browsing, fitness and health monitoring and traditional office working. Smartphones are equipped with built-in sensors to be able to perform these activities. It is well known that the sensors affect the resolution of the smartphone applications which is very vital in life critical applications (LCA). In this paper, two main sensors, the gyroscope and accelerometer have been studied. All commercial smartphones contain these two sensors and support functions related to them. These two sensors have direct link with the physical measurements which feed the fitness and health applications. A fitness application has been selected and ran under Android and iOS operating systems in two different popular smartphones: Samsung Note5 and iPhone7s smartphones. Statistical methodology has been applied to analysis the data and evaluate the performance of the sensors. The results show that commercial smartphones are not reliable devices for motion-related measurements and they can only be used for general purpose monitoring but not in life critical applications.

Privacy-Enhanced MQTT Protocol for Massive IoT

The growing expectations for ubiquitous sensing have led to the integration of countless embedded sensors, actuators, and RFIDs in our surroundings. Combined with rapid developments in high-speed wireless networks, these resource-constrained devices are paving the road for the Internet-of-Things paradigm, a computing model aiming to bring together millions of heterogeneous and pervasive elements. However, it is commonly accepted that the Privacy consideration remains one of its main challenges, a notion that does not only encompasses malicious individuals but can also be extended to honest-but-curious third-parties. In this paper, we study the design of a privacy-enhanced communication protocol for lightweight IoT devices. Applying the proposed approach to MQTT, a highly popular lightweight publish/subscribe communication protocol prevents no valuable information from being extracted from the messages flowing through the broker. In addition, it also prevents partners re-identification. Starting from a privacy-ideal, but unpractical, exact transposition of the Oblivious Transfer (OT) technology to MQTT, this paper follows an iterative process where each previous model’s drawbacks are appropriately mitigated all the while trying to preserve acceptable privacy levels. Our work provides resistance to statistical analysis attacks and dynamically supports new client participation. Additionally the whole proposal is based on the existence of a non-communicating 3rd party during pre-development. This particular contribution reaches a proof-of-concept stage through implementation, and achieves its goals thanks to OT’s indistinguishability property as well as hash-based topic obfuscations.

Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar–aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar–aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar–aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar–aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.

Knowledge-Based Approach for the Perception Enhancement of a Vehicle

An autonomous vehicle relies on sensors in order to perceive its surroundings. However, there are multiple causes that would hinder a sensor’s proper functioning, such as bad weather or lighting conditions. Studies have shown that rainfall and fog lead to a reduced visibility, which is one of the main causes of accidents. This work proposes the use of a drone in order to enhance the vehicle’s perception, making use of both embedded sensors and its advantageous 3D positioning. The environment perception and vehicle/Unmanned Aerial Vehicle (UAV) interactions are managed by a knowledge base in the form of an ontology, and logical rules are used in order to detect and infer the environmental context and UAV management. The model was tested and validated in a simulation made on Unity.

Privacy Preservation, Trust, and Security in Intelligent Embedded Systems

An embedded system is a software and hardware system that is based on a microcontroller or microprocessor designed to accomplish the dedicated functions within a massive electrical or mechanical system. An intelligent embedded system (IES) is a generation or promising evolution of an embedded system (ES). IES has the ability of reasoning about their external atmosphere and acclimates their nature accordingly. The capacity of IES is characterized by the ability of process, service, or product to expose the performance of the environment to enrich the lifetime, quality, and satisfaction of the individual. IES facilitates the processing of information gathered from the embedded sensors. IES rely on numerous multidisciplinary methods for the successive operation.IES is widely employed in consumer electronics, industrial machines, agriculture, medical equipment, and other automated applications. It is programmable and necessary functionalities can be achieved effectively. In this article diversified utilities of embedded intelligence, challenges, issues, privacy, and security metrics of IES are discussed.

Improving Speech Understanding and Monitoring Health with Hearing Aids Using Artificial Intelligence and Embedded Sensors

This article details ways that machine learning and artificial intelligence technologies are being integrated in modern hearing aids to improve speech understanding in background noise and provide a gateway to overall health and wellness. Discussion focuses on how Starkey incorporates automatic and user-driven optimization of speech intelligibility with onboard hearing aid signal processing and machine learning algorithms, smartphone-based deep neural network processing, and wireless hearing aid accessories. The article will conclude with a review of health and wellness tracking capabilities that are enabled by embedded sensors and artificial intelligence.