Diagnostic techniques for the monitoring and control of practical flames

In this article, the theoretical and experimental aspects of three active thermography approaches: pulsed thermography (PT), lock-in thermography (LT), and vibrothermography (VT), are discussed in relation to the nondestructive evaluation (NDE) of honeycomb sandwich structures. For this purpose, two standard specimens with simulated defects (delaminations, core unbonds, excessive adhesive, and crushed core) were tested, and results were processed, examined, and compared. As will be pointed out, the similarities and differences between these active approaches allow conclusions to be made about the most suitable approach for a particular application. In addition, results from NDE inspection by X-rays and c-scan ultrasounds are provided and discussed for reference.

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Comparative Study of Active Thermography Techniques for the Nondestructive Evaluation of Honeycomb Structures

Smart cities contain intelligent things which can intelligently automatically and collaboratively enhance life quality, save people's lives, and act a sustainable resource ecosystem. To achieve these advanced collaborative technologies such as drones, robotics, artificial intelligence, and Internet of Things (IoT) are required to increase the smartness of smart cities by improving the connectivity, energy efficiency, and quality of services (QoS). Therefore, collaborative drones and IoT play a vital role in supporting a lot of smart-city applications such as those involved in communication, transportation, agriculture,safety and security, disaster mitigation, environmental protection, service delivery, energy saving, e-waste reduction, weather monitoring, healthcare, etc. This paper presents a survey of the potential techniques and applications of collaborative drones and IoT which have recently been proposed in order to increase the smartness of smart cities. It provides a comprehensive overview highlighting the recent and ongoing research on collaborative drone and IoT in improving the real-time application of smart cities. This survey is different from previous ones in term of breadth, scope, and focus. In particular, we focus on the new concept of collaborative drones and IoT for improving smart-city applications. This survey attempts to show how collaborative drones and IoT improve the smartness of smart cities based on data collection, privacy and security, public safety, disaster management, energy consumption and quality of life in smart cities. It mainly focuses on the measurement of the smartness of smart cities, i.e., environmental aspects, life quality, public safety, and disaster management.

/pdf/survey-on-collaborative-smart-drones-and-internet-of-things-3xjk9xubzg.pdf

Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities

In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.

https://www.mdpi.com/1424-8220/20/9/2533/pdf

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Thermal imaging (TI) is an emerging, non-invasive process analytical technique suitable for the food industry. While TI was originally developed for military applications, it has recently emerged as a powerful non-destructive measurement technique in other industries. This paper provides an overview of TI theory, equipment, and image processing. Recent advances and potential applications of TI for food safety and quality assessment such as temperature validation, bruise and foreign body detection and grain quality evaluation are reviewed.

Applications of thermal imaging in food quality and safety assessment

http://muscle.ercim.eu/images/DocumentPDF/MP_333_A0-15-2004.pdf

Statistical analysis of IR thermographic sequences by PCA

Intelligent Transportation Systems (ITS) have evolved as a key research topic in recent years, revolutionizing the overall traffic and travel experience by providing a set of advanced services and applications. These data-driven services contribute to mitigate major problems arising from the ever growing need of transport in our daily lives. Despite the progress, there is still need for an enhanced and distributed solution that can exploit the data from the available systems and provide an appropriate and real-time reaction on transportation systems. Therefore, in this paper, we present a new architecture where the intelligence is distributed and the decisions are decentralized. The proposed architecture is scalable since the incremental addition of new peripheral subsystems is supported by the introduction of gateways which requires no reengineering of the communication infrastructure. The proposed architecture is deployed to tackle the problem of traffic management inefficiency in urban areas, where traffic load is substantially increased, by vehicles moving around unnecessarily, to find a free parking space. This can be significantly reduced through the availability and diffusion of local information regarding vacant parking slots to drivers in a given area. Two types of parking systems, magnetic and vision sensor based, have been introduced, deployed, and tested in different scenarios. The effectiveness of the proposed architecture, together with the proposed algorithms, is assessed in field trials.

/pdf/real-time-smart-parking-systems-integration-in-distributed-43f3ivnwcl.pdf

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

The aim of this work is to introduce and design image processing methods for the quantitative analysis of epicardial fat by using cardiac CT imaging.Indeed, epicardial fat has recently been shown to correlate with cardiovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns still remain about the methods for measuring epicardial fat, its regional distribution on the myocardium and the accuracy and reproducibility of the measurements.In this paper, a method is proposed for the analysis of single-frame 3D images obtained by the standard acquisition protocol used for coronary calcium scoring. In the design of the method, much attention has been payed to the minimization of user intervention and to reproducibility issues.In particular, the proposed method features a two step segmentation algorithm suitable for the analysis of epicardial fat. In the first step of the algorithm, an analysis of epicardial fat intensity distribution is carried out in order to define suitable thresholds for a first rough segmentation. In the second step, a variational formulation of level set methods - including a specially-designed region homogeneity energy based on Gaussian mixture models- is used to recover spatial coherence and smoothness of fat depots.Experimental results show that the introduced method may be efficiently used for the quantification of epicardial fat.

/pdf/quantification-of-epicardial-fat-by-cardiac-ct-imaging-2ugem6bylt.pdf

Quantification of Epicardial Fat by Cardiac CT Imaging

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

https://www.mdpi.com/1424-8220/17/11/2588/pdf

Gabriele Pieri

Papers

Statistical analysis of IR thermographic sequences by PCA

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Quantification of Epicardial Fat by Cardiac CT Imaging

An Intelligent Cooperative Visual Sensor Network for Urban Mobility

Object tracking in a stereo and infrared vision system