Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

Water level and discharge measurements

The knowledge of water levels and discharges in urban drainage and stormwater management (UDSM) systems is of key importance to understand their functioning and processes, to evaluate their performance, and to provide data for modelling. In this chapter, devoted mainly to underground combined and separate sewer pipe systems, various methods and technologies are described and discussed. After an introduction to important aspects to deal with when measuring discharges in sewer systems, the following parts are presented successively: (i) measurement of water level with rulers, and pressure, ultrasonic and radar sensors, (ii) measurement of flow velocity with ultrasonic, Doppler, velocity profiler, free surface, and electromagnetic sensors, (iii) direct measurement of discharge with pre-calibrated devices, physical scale models, computational fluid dynamics modelling and use of pumping stations, and (iv) detection and/or measurement of infiltration into and exfiltration from sewers, with flow or pressure measurements, tracer experiments, distributed temperature sensing and geophysical methods.

Mechanical Design and Manufacturing of Signal Interception Antenna in Composite Material for Naval Application

This work shows the mechanical design and the FE analyses performed for an innovative naval Antenna Unit for signal interception application: more than twenty electromagnetic sensors operating from HF up to Ka band and microwave modules are integrated in a unique structure designed for a top mast installation (i.e. for naval platform). The number of constraints in terms of weight and electromagnetic transparency calls for the employment of composite materials such as glass, aramidic and carbon epoxy prepregs. Primary structures was modelled by using FE codes: both orthotropic and isotropic models have been implemented as well as non-linear contacts and bolted joints. The mast-mounted installation requires high mechanical stiffness and strength but the exposure to saline environment needs many manufacturing issues to be respected. In particular, the selection process of suitable materials and the sealing manufacturing procedures to protect them from the external agents was reported. Another key feature of the presented design concerns the electromagnetic compatibility requirement: to avoid electromagnetic emissions (EMC) generated by antenna’s internal units and to protect antenna sensors by external platform’s emitters, an appropriate stacking sequence was chosen for composite laminates with a prepreg copper mesh.

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction

The influence of earthquake disasters on human social life is positively related to the magnitude and intensity of the earthquake, and effectively avoiding casualties and property losses can be attributed to the accurate prediction of earthquakes. In this study, an electromagnetic sensor is investigated to assess earthquakes in advance by collecting earthquake signals. At present, the mainstream earthquake magnitude prediction comprises two methods. On the one hand, most geophysicists or data analysis experts extract a series of basic features from earthquake precursor signals for seismic classification. On the other hand, the obtained data related to earth activities by seismograph or space satellite are directly used in classification networks. This article proposes a CNN and designs a 3D feature-map which can be used to solve the problem of earthquake magnitude classification by combining the advantages of shallow features and high-dimensional information. In addition, noise simulation technology and SMOTE oversampling technology are applied to overcome the problem of seismic data imbalance. The signals collected by electromagnetic sensors are used to evaluate the method proposed in this article. The results show that the method proposed in this paper can classify earthquake magnitudes well.

The Application of Electromagnetic Sensors for Determination of Cherenkov Cone Inside and in the Vicinity of the Detector Volume in Any Environment Known

The neutrinos of cosmic radiation, due to interaction with any known medium in which the Cherenkov detector is used, produce energy radiation phenomena in the form of a Cherenkov cone, in very large frequency spectrum. These neutrinos carry with them the information about the phenomena that produced them and by detecting the electromagnetic energies generated by the Cherenkov cone, we can find information about the phenomena that formed in the universe, at a much greater distance, than possibility of actually detection with current technologies. At present, a very high number of sensors for detection electromagnetic energy is required. Thus, some sensors may detect very low energy levels, which can lead to the erroneous determination of the Cherenkov cone, thus leading to information errors. As a novelty, we propose, to use these sensors for determination of the dielectrically permittivity of any known medium in which the Cherenkov detector is used, by preliminary measurements, the subsequent simulation of the data and the reconstruction of the Cherenkov cone, leading to a significant reduction of problems and minimizing the number of sensors, implicitly the cost reductions. At the same time, we offer the possibility of reconstructing the Cherenkov cone outside the detector volume.

Prevention of Oil Spills During Excavation Work Using Electromagnetic Sensors

This paper presents an analysis of solution to the problems associated with mechanical damage to pipelines. A method of preventing accidental oil spills during excavation using electromagnetic sensors is proposed. The advantages of this method versus alternative options are described herein. A review of existing sensors and methods of their installation on the operating element of excavation equipment is presented. The optimal design solution ensuring accident-free excavation is developed. The economic effect of introducing this technology in the production processes of the oil and gas sector companies is shown.

Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case

Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset.