Modulation recognition (MR) plays a pivotal role due to its application in the spectrum sensing of 5G industrial cognitive communications and radio interference detection at the physical layer of the Internet of Things (IoT). Previous works have mainly focused on simulated fourth-generation (4G) multicarrier systems and ideal radio frequency (RF) scenarios. To bridge the gap between practice and theory, we propose a viable MR algorithm on all-physical testbeds, with nonlinear impairments of 28 GHz active phased arrays (APA). Specifically, our testbed is built on the Rohde&Schwarz (R&S) vector signal generation R&S-SMBV100B and spectrum analyzer R&S-FSW 67 GHz. To extract salient modulation patterns, we develop a physical-informed scattering transform (SCT) MR network (SCTMR-Net). With SCT modules, SCTMR-Net produces the translation-invariant and deformation-stable representations of 5-G signals by wavelet convolution, nonlinear modulus and low-pass filters. Extensive experiments on real-world measurement verify the viability of SCTMR-Net for high robustness to APA impairments.

An industrial network connects devices, sensors, and other physical devices through wireless networks to collect, share, and analyze data to increase productivity, reduce costs, and improve product quality. Due to the accessibility of wireless channels to everyone, the number of malicious exploits of these services is increasing. Thus, confirming the validity of smart devices and servers and guaranteeing communication security are essential. Mutual authentication has become an essential security concern in wireless communication to meet this tendency. However, available techniques rely on prime factorization and discrete logarithm problems that cannot effectively resist quantum attacks. This study presents an anonymous authentication system based on edge computing for the Industrial Internet of Things (IIoT). Specifically, we devised an RLWE-based authentication method to withstand diverse attacks while improving operating efficiency. The proposed scheme achieves mutual authentication in a single-message exchange round, while ensuring user anonymity and perfect forward security. Security analysis and verification with ProVerif demonstrate that our scheme enhances privacy and reliability. Furthermore, experimental results show that our scheme outperforming existing schemes in terms of communication and computing costs.

This paper describes the optimization of a neuromorphic architecture for printable organic neurons as part of an ongoing project to develop Form + Function 4-D Printing. The previously proposed architecture prioritizes simplicity and massive redundancy for a printable analog neural network consisting of only one transistor plus memristors for synapses, per neuron, but sacrifices negative synaptic weights. This paper demonstrates an optimization technique to minimize the insertion of inverting amplifiers to realize a minimal approximating set of negative weights. This helps to develop a compact, printable and accurate neuromorphic computer to bring new function to the 3-D printing of form in multi-functional robotic materials. An example robotic skin is developed with the ability to compute the centroid of touch "compiled into the skin" to an average accuracy of 9.19% in comparison to an unconstrained Artificial Neural Network (ANN). The presented soft robotic skin is fabricated by hand using conventional silicon components, but serves as a proof-of-concept for radical new capabilities in Form + Function 4-D Printing.

Capacitance sensors are widely used for sensing physical parameters. Conventional capacitance to digital methods use complex analog ADC techniques which are power hungry. Recently a fully digital solution was proposed with improved power consumption. This paper describes a number of problems in that solution, analyzes these problems, and proposes a new design free of these problems. A voltage senor as an example was designed based on the proposed capacitance to digital conversion in this paper. The new method achieves the same accuracy with less than half the circuit size, and 25% and 33% savings on power and energy consumption.