Pilot spoofing attack brings new challenges to physical layer secure transmission. However, this method will not work without any knowledge about the pilot sequence and active eavesdropping can be detected by constructing random pilot sequence. Intelligent reflecting surface (IRS), with the real-time programmable features for wireless channels, provides new possibilities for effective pilot spoofing. In this paper, the IRS is deployed near the legitimate users and the legitimate signal can always be passively reflected. Then the control strategy is embedded into the communication process under time-division duplex mode to assist eavesdroppers to conduct pilot spoofing. By setting different phase shifts at the IRS during the uplink phase and downlink phase, the channel reciprocity disappears, and thus secure beamforming vector is biased towards the eavesdropper. Furthermore, in order to overhear more information, the average secrecy rate minimization problem based on statistical channel state information is established by carefully designing the phase shifts, which is non-trivial to solve. With alternating optimization and Charnes-Cooper transformation technique, the original problem is transformed into convex form and a near optimal solution is achieved. Finally, simulation results show that our proposed scheme can pose serious secure threat without any energy footprint. What’s more, if the IRS is not utilized by the internal users properly, it will bring more threat.

Free-space optical (FSO) communications will play an important role in the backhaul of future generation of wireless networks in order to support high data rates. Because of narrow beamwidth inherent to an optical signal, acquisition and tracking form an important component of any FSO communication system. In this study, we have analyzed the optimization of received power allocation between tracking and data channels in an FSO receiver. The objective function that is optimized (minimized) are the probability of error and the probability of outage, and the optimization of power allocation is carried out as a function of parameters such as noise power, pointing error variance, pointing error correlation coefficient, and the threshold of outage. We have analyzed the optimization concerning the lognormal and exponentiated Weibull fading scenarios as well. We learn that the optimal power allocation is a function of the received signal-to-noise ratio: a lower signal-to-noise ratio dictates that a higher fraction of received power should be diverted to the tracking channel and vice versa.

The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people’s mobility. Postprint accepted for publication in the proceedings of the 2020 AEIT International Annual Conference (AEIT). How to cite: P. Scarabaggio, M. La Scala, R. Carli and M. Dotoli, ”Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy,” 2020 AEIT International Annual Conference (AEIT), 2020. DOI: https://doi.org/10.23919/AEIT50178.2020.9241136  © 2020 AEIT 978-8-8872-3747-4. Personal use of this material is permitted. Permission must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper presents a linear complexity iterative rake detector for the recently proposed orthogonal time frequency space (OTFS) modulation scheme. The basic idea is to extract and coherently combine the received multipath components of the transmitted symbols in the delay-Doppler grid using maximal ratio combining (MRC) to improve the SNR of the combined signal. We reformulate the OTFS input-output relation in simple vector form by placing guard null symbols or zero padding (ZP) in the delay-Doppler grid and exploiting the resulting circulant property of the blocks of the channel matrix. Using this vector input-output relation we propose a low complexity iterative decision feedback equalizer (DFE) based on MRC. The performance and complexity of the proposed detector favorably compares with the state of the art message passing detector. An alternative time domain MRC based detector is also proposed for even faster detection. We further propose a Gauss-Seidel based over-relaxation parameter in the rake detector to improve the performance and the convergence speed of the iterative detection. We also show how the MRC detector can be combined with outer error-correcting codes to operate as a turbo DFE scheme to further improve the error performance. All results are compared with a baseline orthogonal frequency division multiplexing (OFDM) scheme employing a single tap minimum mean square error (MMSE) equalizer.

Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudo-labeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging.

A document by Hala Neji . Click on the document to view its contents.

Mutual coupling, or equivalently, the isolation between antennas, is a key parameter in antenna system design. In this work, the previously defined impedance density is generalized, and it is demonstrated how it can be used to obtain spatial information about the mutual coupling. The generalized impedance density is a real-valued scalar and it can be visualized as a three-dimensional density in space. It is shown that there is a strong connection between regions with a positive (negative) generalized impedance density and a decrease (increase) of the coupling when an absorber is placed in that region. This predictive ability is a useful feature, which is tested for three numerical cases. The results are robust to the shape of the platform, and it can be compared across frequencies. By placing absorbers based on the generalized impedance density, it is possible to reduce the required amount of absorbers needed to obtain a certain reduction in mutual coupling. The visualization results and predictions of absorber positions are compared with a Poynting vector based method. Placing absorbers based on the generalized impedance density had a larger impact on the mutual coupling, compared to the predictions with the Poynting vector based method in the investigated cases.

Virtual absorption concept has been recently introduced as a new phenomenon observed in electromagnetics and optics consisting of an undefined energy accumulation within a finite volume of material without dissipation. The anomalous behaviour is achieved by engaging the complex zero scattering eigenmodes of the virtual absorbing system by illuminating it with a proper complex frequency, whose value is strictly determined by the system characteristics. In this paper, we investigate on the position of the zero-pole scattering pairs in the complex frequency plane as a function of the input impedance of the metasurface-based lossless virtual absorber. We analytically derive the conditions under which a properly modulated monochromatic plane wave can be virtually absorbed by the system and stored within its volume. The analysis is developed by modelling the propagation of a normally impinging plane wave though its equivalent transmission line model terminated on an arbitrary reactive load, which in turn models the input impedance of the metasurface-based system under consideration. The study allows to determine a priori whether the metasurface-based system can support the virtual absorption or not by evaluating the time-constant from its equivalent circuit.

This pre-print reports on a significant further development of our previously published work on Linear TDA-MAC: N. Morozs, P. Mitchell, and Y. Zakharov, “Linear TDA-MAC: Unsynchronized scheduling in linear underwater acoustic sensor networks,” IEEE Netw. Lett., vol. 1, no. 3, pp. 120–123, 2019.

Non-orthogonal multiple access (NOMA) is a promising candidate for future mobile networks as it enables improved spectral-efficiency, massive connectivity and low latency. This paper derives exact and asymptotic bit error rate (BER) expressions under Rayleigh fading channels for NOMA systems with arbitrary number of users and arbitrary number of receiving antennas and modulation orders, including binary phase-shift keying and rectangular/square quadrature amplitude modulation. Furthermore, the power coefficients’ bounds, which ensure users’ fairness, and solve the constellation ambiguity problem, are derived for N=2 and 3 users cases with any modulation orders. In addition, this paper determines the optimal power assignment that minimizes the system’s average BER. These results provide valuable insight into the system’s BER performance and power assignment granularity. For instance, it is shown that the feasible power coefficients range becomes significantly small as the modulation order, or N, increases, where the BER performance degrades due to the increased inter-user interference. Hence, the derived expressions can be crucial for the system scheduler in allowing it to make accurate decisions of selecting appropriate N, modulation orders, and power coefficients to satisfy the users’ requirements. The presented expressions are corroborated via Monte Carlo simulations.

Unmanned aerial vehicles (UAVs) have recently received a significant interest to assist terrestrial wireless networks thanks to their strong line-of-sight links and flexible/instant deployment. However, UAVs’ assistance is limited by their battery lifetime and wireless backhaul link capacity. At the expense of limited mobility, tethered UAVs (T-UAVs) can be a viable alternative to provide seamless service over a cable that simultaneously supplies power and data from a ground station (GS). Accordingly, this paper presents a comparative performance analysis of T-UAV and regular/untethered UAV (U-UAV)-assisted cellular traffic offloading from a geographical area that undergoes heavy traffic conditions. By using stochastic geometry tools, we first derive joint distance distributions between the hot-spot users, the terrestrial base station (TBS), and the UAV. To maximize the endto- end signal-to-noise ratio, a user association policy is developed, and corresponding association regions are analytically identified. Then, the overall coverage probability of the U-UAV/T-UAV-assisted system is derived for given locations of the TBS and the U-UAV/T-UAV. Moreover, we analytically prove that optimal UAV location falls within a partial surface of the spherical cone centered at the GS. Numerical results show that T-UAV outperforms U-UAV given that sufficient GS locations accessibility and tether length are provided.

This paper reviews, discusses, and highlights some of the standard cloud computing vulnerabilities primarily related to virtualization platforms and their implementations while outsourcing services and resources to different end-users and business enterprises. Furthermore, we provide Blockchain-enabled solutions for virtualized cloud computing platform involving both the end-users as well as cloud service providers (CSP) to address and solve different security and privacy-related vulnerabilities using blockchain-enabled solutions to improve their cloud services and resource provisioning facilities.

High cross-polarization isolation (CPI) is crucial to the accurate polarization measurement using polarimetric phased array radar (PPAR). In this paper, we propose an adaptive direction-dependent polarization state configuration (AD2PSC) method to improve the polarization isolation. Compared with conventional fixed polarization state of radiated wave whether it is linear, circular or elliptical polarization state, our AD2PSC approach configures the polarization state on basis of beam steering. To achieve the adaptive configuration of magnitude and phase of the dual-polarization antenna, an improved steepest descent algorithm is put forward. To facilitate the uniform representation for the polarization measurement application of PPAR, the universal expressions of intrinsic and measured backscatter matrices are derived for arbitrary polarization state. The dualpolarization dipole array is used to assess the priority of our proposed method. Compared with the conventional approaches, our approach could obtain higher CPI while being available for a larger scanning range. The configured CPI meets the specific polarization requirement for PPAR.

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

We propose a CNN based visual servoing scheme for precise positioning of an eye-to-hand manipulator in which the control input of a robot is calculated directly from images by a neural network. In this paper, we propose Difference of Encoded Features driven Interaction matrix Network (DEFINet), a new convolutional neural network (CNN), for eye-to-hand visual servoing. DEFINet estimates a relative pose between desired and current end-effector from desired and current images captured by an eye-to-hand camera. DEFINet includes two branches of the same CNN that share weights and encode target and current images, which is inspired by the architecture of Siamese network. Regression of the relative pose from the difference of the encoded target and current image features leads to a high positioning accuracy of visual servoing using DEFINet. The training dataset is generated from sample data collected by operating a manipulator randomly in task space. The performance of the proposed visual servoing is evaluated through numerical simulation and experiments using a six-DOF industrial manipulator in a real environment. Both simulation and experimental results show the effectiveness of the proposed method. *The complete version of this preprint paper is published in IEEE Access. Please refer to “https://ieeexplore.ieee.org/abstract/document/9464907”.

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers’ personal data and carry out interest-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using the internet services. In this paper, we address users’ privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns, and interest-based ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework’s applicability by implementing its critical components using POC (Proof Of Concept) ‘System App’. We compare the proposed framework with other privacy-protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.

We propose a new general framework attempting a unification of circuit and quantum theories. The theory is based on our discovery of a common Lie-group-theoretic structure shared by continuous circuits and quantum systems. Applications are proposed by applying topology to selected problems in microwave network theory.

In this work, the Piecewise Variable-strain (PVS) approach is applied to the case of Concentric Tube Robots (CTRs) and extended to include the tubes’ sliding motion. In particular, the currently accepted continuous Cosserat rod model is discretized onto a finite set of strain basis functions. At the same time, the insertion and rotation motions of the tubes are included as generalized coordinates instead of boundary kinematic conditions. Doing so, we obtain a minimum set of closed-form algebraic equations that can be solved not only for the shape variables but also for the actuation forces and torques for the first time. This new approach opens the way to torque-controlled CTRs, which is poised to enhance elastic stability and improve interaction forces’ control at the end-effector.