Spectral image classification uses the huge amount of information provided by spectral images to identify objects in the scene of interest. In this sense, spectral images typically contain redundant information that is removed in later processing stages. To overcome this drawback, compressive spectral imaging (CSI) has emerged as an alternative acquisition approach that captures the relevant information using a reduced number of measurements. Various methods that classify spectral images from compressive projections have been recently reported whose measurements are captured by non-adaptive, or adaptive schemes discarding any contextual information that may help to reduce the number of captured projections. In this paper, an adaptive compressive acquisition method for spectral image classification is proposed. In particular, we adaptively design coded aperture patterns for a dual-arm CSI acquisition architecture, where the first system obtains compressive multispectral projections and the second arm registers compressive hyperspectral snapshots. The proposed approach exploits the spatial contextual information captured by the multispectral arm to design the coding patterns such that subsequent snapshots acquire the scene’s complementary information improving the classification performance. Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real data set. The proposed approach exhibits superior performance with respect to other methods that classify spectral images from compressive measurements.

5G communications system is on the evolution path with present focus on enabling advanced features and new service capabilities. Multicast and Broadcast Services (MBS) are being considered as one of the most promising use cases of 5G. As a late entrant to 3rd Generation Partnership Project (3GPP) 5G standards, MBS work item is presently being developed over existing 5G framework, and is targeted to enhance 5G New Radio and 5G Core Network capabilities for a reliable, low latency, resource efficient, and massive deployment of a wide array of multicast and broadcast services. MBS development entails enhancements for network and user equipment side on architectural, protocol and transmission aspects in order to address a new set of multicast and broadcast service requirements in 5G. In this article, we present an introduction to MBS standardization and outline newly introduced technical features, and their use cases.

A document by Rocco Sedona . Click on the document to view its contents.

This work presents a device for the ankle kinematics study. It is portable, affordable and some parts can be 3D printed. It is based on the ankle anatomy and biomechanics, also, it can be applied for the characterization of the ankle axis or another models of the ankle.

The complexity of inference using the belief propagation algorithms increases exponentially with the maximum clique size. We describe an approximate inference approach when there are clique size limitations due to memory constraints using incremental construction of clique trees.

Time slotted channel hopping provides reliable and deterministic communication in IEEE 802.15.4 mesh networks. Although slotted access is able to lower energy consumption drastically by reducing the duty cycle of the radio module, it usually leads to significant idle listening experienced by receivers, which makes it a sub-optimal solution when ultra low-power wireless is sought for. In this paper a listening suspension mechanism is described, which operates at the MAC layer and is part of a more general approach aimed at cutting down energy consumption by proactively reducing idle listening. Links can be temporarily disabled, that convey slow-rate data streams whose characteristics, e.g., the generation period, are either known in advance to some extent or can be inferred by traffic inspection.

This paper was accepted for presentation at the IEEE CPE-POWERENG 2021 conference held in Florence, Italy, between the 14th and 16th of July 2021. It investigates the root causes of detrimental oscillations in the dc link voltage of an energy storage system using a dual dq controller, operating at a high-voltage ac grid with high reactance-resistance ratio. Dual dq controllers are recommended in the literature for power converters operating under unbalanced, fault, or reduced voltage conditions. They employ two separated rotating reference frames, one for the positive and one for the negative sequence. The causes of the oscillations are investigated both theoretically and by time-domain computer simulations. As a result of the simulations, the performance of two dual controllers used in the industry is compared. In the presence of exponentially decaying dc currents, the filtering techniques employed by the controllers affect differently the performance of the proportional-integral regulators and disturb the feed forwarding and dq decoupling schemes. Ultimately, this results in undesirable oscillations in the dc-link voltage. This paper sheds light on how a fundamental phenomenon of three-phase ac systems can critically affect the control of power electronic converters. It provides a valuable insight into a possible root cause of oscillations in large electrical system applications with a considerable power converter penetration, such as large industrial plants striving for reducing greenhouse gas emissions.

The present work discusses the proposal of a spintronic neuromorphic system with spin orbit torque driven domain wall motion-based neuron and synapse. We propose a voltage-controlled magnetic anisotropy domain wall motion based magnetic tunnel junction neuron. We investigate how the electric field at the gate (pinning site), generated by the voltage signals from pre-neurons, modulates the domain wall motion, which reflects in the non-linear switching behaviour of neuron magnetization. For the implementation of synaptic weights, we propose 3-terminal MTJ with stochastic domain wall motion in the free layer. We incorporate intrinsic pinning effects by creating triangular notches on the sides of the free layer. The pinning of domain wall and intrinsic thermal noise of device lead to the stochastic behaviour of domain wall motion. The control of this stochasticity by the spin orbit torque is shown to realize the potentiation and depression of the synaptic weight. The micromagnetics and spin transport studies in synapse and neuron are carried out by developing a coupled micromagnetic Non-Equilibrium Green’s Function (MuMag-NEGF) model. The minimization of the writing current pulse width by leveraging the thermal noise and demagnetization energy is also presented. Finally, we discuss the implementation of digit recognition by the proposed system using a spike time dependent algorithm.

This paper shows how a power converter can generate electromagnetic interference (EMI) through the operation of gate drives only - in the absence of any power conversion. This is due to parasitic capacitances connecting the power semiconductor’s gate to the main circuit. A half bridge converter is used to illustrate this concept. Practical measurements are compared to simulations for an energized and non-energized converter. Even without loading, a converter can exceed regulatory conducted emission EMI limits. This effect is important to consider during the design of converter EMI mitigation - especially for low power converters where the load current is not dominant.

In this paper, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS) antenna is firstly proposed.

The reliable determination of earthquake source parameters is a relevant task of seismological investigations which ground nowadays on high quality seismic waveforms collected by near-source dense arrays of ground motion sensors. Here we propose a parametric modelling technique which analyzes the time-domain P-wave signal recorded in the near-source range of small-to-large size earthquakes. Assuming a triangular moment-rate function and a uniform speed, circular rupture model, we develop the equations to estimate the seismic moment, rupture radius and stress-drop from the corner-time and plateau level of the average logarithm of the P-wave displacement vs time curves (LPDT). The constant-Q, anelastic attenuation effect is accounted by a post-processing procedure that evaluates the Q-unperturbed moment-rate triangular shape. The methodology has been validated through the application to the acceleration records of the 2016-2017 Central Italy and 2007-2019 Japan earthquake sequences covering a wide moment magnitude range (Mw 2.5 - 6.5) and recording distance < 100 km. After correcting for the anelastic attenuation function, the estimated average stress-drop and the confidence interval (〈∆σ〉=0.60 (0.42-0.87) MPa and 〈∆σ〉=1.53 (1.01-2.31) for crustal and subcrustal events of Japan and 〈∆σ〉=0.36(0.30-0.44) MPa for Central Italy) show, for both regions, a self-similar, constant stress-drop scaling of the rupture duration/radius with seismic moment. The smaller sensitivity of the spatially averaged, time-varying peak displacement amplitude to the radiation from localized high slip patch on the fracture surface, could explain the retrieved smaller average stress-drops for sub-crustal earthquakes in Japan and M>5.5 events in Central Italy relative to previous estimates using spectral methods.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. A major revision is done, compared to the previous version. Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.

Aviation is an vital global industry that generates economic growth, creates jobs and facilitates national and international trade and tourism. During the Covid-19 pandemic, the world had seen a sharp decline in commercial air travel due to the worldwide travel restrictions, lock-down orders and widespread fear of infection. However, there is a rapid increase in the use of UAVs for surveillance, communication, delivery and transport. And the aviation industry is believed to be able to provide valid contribution to the post-pandemic economic recovery. In order to reduce the carbon emission from aviation industry, there is a requirement for more electric aircraft. Hydrogen and fuel cell system's capability of creating a clean road transportation have been widely tested. In this paper, the status of hydrogen and fuel cell utilised in aviation is reviewed, and the challenges and issues are identified for a better integration of the hydrogen and fuel cell system in aviation.

Remote ice detection has emerged as an application of Radio Frequency (RF) sensors. While antenna-based “RFID” sensing can detect various measurands, antenna-based sensors are not currently designed based on a systematic methodology, and in most cases may have a low sensitivity requiring specialist hardware or broadband interrogation signals, incompatible with spectrum regulations. Here, we develop a systematic methodology for designing an antenna-based sensor, applicable to measurands inducing a dielectric change in the near-field of the antenna. The proposed methodology is applied to designing printable antennas as highly-sensitive sensors for detecting and measuring the thickness of ice, demonstrating best-in-class sensory response compared to more complex antenna designs. Antenna design is investigated systematically for wireless interrogation in the 2.4 GHz band, where it is found that a loop antenna outperforms a dipole owing to its more distributed capacitance. The antenna’s realized gain was identified as the optimum parameter-under-test, with “positive” sensing proposed as a method of improving linearity and immunity to interference. The developed loop antenna sensor exhibits resilience to interference and applicability to different real-world deployment environments, demonstrated through over 80% average ice thickness measurement accuracy and at least 5 dB real-time sensitivity to ice deposition.

This research proposes a realistic mutual authentication protocol using non-ideal PUF in an RFID environment. Initially, we evaluate and show the weaknesses of two popular schemes, Gope et al.’s and Feng Zhu et al.’s schemes; based on our investigation, their schemes fail to achieve several security features and cannot withstand various attacks. Later, we propose a new scheme to resolve the problem their schemes and provide the solution for the RFID security problem generically. Analyzes informal and formal are used to ensure that our proposal fulfills security properties and withstands various attacks.

In this letter, we propose a novel joint channel estimation and data detection scheme for massive multiple-input multiple-output (MIMO) uplink, which operates in a time-varying fading channel. More specifically, at the receiver of the proposed scheme, a data frame is divided into multiple blocks, and in each block, a demodulated data block is used for updating channel state information (CSI) in an iterative manner. Furthermore, the initial CSI in the block of interest is given by the estimated CSI in the previous block, hence allowing accurate tracking of CSI in a time-varying channel without imposing additional pilot insertion inside the data frame. Since the length of the divided blocks affects both the achievable channel tracking and data detection performances, it is optimized so as to maximize the discrete-input continuous-output memoryless channel’s (DCMC) capacity derived in this letter. It is demonstrated that the DCMC capacity of the proposed scheme is capable of nearly achieving those of the perfect CSI counterpart without imposing any substantial pilot overhead.

We describe a fast three-round mutual authentication protocol for parties A and B belonging to the same coalition group. Parties A and B keep their own independent long-term private keys that are used in the process of authentication and can be used for other purposes. The scheme assumes an initial setup with a trusted third party T. This party initiates another secret information that includes factors of a large RSA modulus. For authentication, both parties must demonstrate each other the knowledge of their private keys without revealing them and the ability to factorize a large RSA modulus. Thus, the protocol based on the suggested scheme provides reciprocal authentication. The scheme possesses all desirable properties of an interactive proof, i.e., completeness, soundness, and zero-knowledge. The security of the protocol relies on assumptions of difficulty of the RSA factorization and existence of a cryptographic hash function.

In this work, non-coherent massive MIMO differential phase-shift keying modulation (DPSK) detection is considered to get rid of the complexity of channel estimation. However, most of the well-performing DPSK detection techniques require high computational complexity at the receiver. The use of deep-learning is proposed for detecting the transmitted DPSK symbols over a single-user massive MIMO system. We provide a multiple-symbol differential detection implementation using deep-learning. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with differential detection (DD), decision-feedback differential detection (DFDD), and multiple-symbol differential detection (MSDD) for the same system parameters. Where multiple-symbol differential sphere detection (MSDSD) is used to implement MSDD. The results show that the proposed deep-learning-based classification neural networks outperform decision-feedback differential detection and achieve an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection.