Andrei Shkel
Editor-in-Chief
Submission-to-ePublication = 4.8 weeks, median
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(Editor-in-Chief: Chief Name)
Submission-to-ePublication = 4.8 weeks, median
Humidity is one of the most relevant physical parameters to sense and control for a wide range of commercial and industrial applications. Consequently, there is continuing demand for the development of innovative and sustainable humidity sensor solutions. Here, the development and characterization of fully additively manufactured, highly sensitive, resistive Chitosan-based humidity sensors on flexible thermoplastic polyurethane (TPU) foil, as well as on a glass carrier substrate are presented. The sensors unite aspects of sustainability and high performance in a broad humidity range (20–90%rH). The humidity response follows an exponential curve progression with relative changes in the resistance per %rH of 6.9% and 5.7% for the glass carrier sensor and the TPU sensor, respectively. In absolute values, this means that the Chitosan-based sensors are particularly sensitive in the low humidity range with a vast dynamic range (ten times larger compared to commonly used capacitive humidity sensors). The flexible sensor on the TPU substrate shows great stability even after repeated bending. In addition, the combination of flexible and biocompatible materials (TPU and Chitosan) with additive manufacturing technologies makes the sensor particularly sustainable while having great potential for a plethora of biomedical applications.
Today, Internet of Things (IoT)-based sensor devices are ubiquitous. Being cost effective and easy to deploy, they are also considered for many applications outside their original domain, which was consumer electronics. Factory and process automation, smart buildings and homes, and, in general, Industry 4.0 are application fields in which the use of IoT technology is gaining popularity, often in addition to existing, classical communication architectures on the operational technology level. IoT devices, however, typically have a different philosophy for communication and data exchange, which makes them easy to use but poses security challenges by bypassing established security architectures, such as the classical defense-in-depth concept defined, for instance, in the IEC 62443 standard. This letter highlights today's security needs and concepts in industrial environments. Furthermore, it looks at possible new attack surfaces opened by IoT-based applications and shows ways how to bridge the security gap.
This letter introduces a deep learning (DL) framework for the classification of multiple signals in direction finding (DF) scenario via sensor arrays. Previous works in DL context mostly consider a single or two target scenario, which is a strong limitation in practice. Hence, in this letter, we propose a DL framework called DeepMUSIC for multiple signal classification. We design multiple deep convolutional neural networks (CNNs), each of which is dedicated to a subregion of the angular spectrum. Each CNN learns the MUltiple SIgnal Classification (MUSIC) spectra of the corresponding angular subregion. Hence, it constructs a nonlinear relationship between the received sensor data and the angular spectrum. We have shown, through simulations, that the proposed DeepMUSIC framework has superior estimation accuracy and exhibits less computational complexity in comparison with both DL- and non-DL-based techniques.
