The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this technical paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost appropriate for Internet of things (IoT) communication networks. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the IoT communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm.

The article discusses the Autonomous Connected Electric Shared (ACES) vehicles for the public economy that has been disregarded in the published works. The establishment of ACES will reduce the sources or’ governmental funds and affect the transport expenses unless supplemental policies are presented to match the technology and the taxation requirements. Innovations in tax revenue programs to fit the technology developments may affect governance strategies, such as power shifting, decision and policy-making. The study links the contents of ACES and public funds by joining the interdisciplinary analysis from transport logistics, urban finances, technology, innovations, and environmental development.

Diffuse optical tomography (DOT) leverages near-infrared light propagation through tissue to assess its optical properties and identify abnormalities. DOT image reconstruction from limited-angle data acquisition is severely ill-posed due to the highly scattered photons in the medium and the relatively small number of collected projections. Reconstructions are thus commonly marred by artifacts and, as a result, it is difficult to obtain accurate reconstruction of target objects, e.g., malignant lesions. Reconstruction does not always ensure good localization of small lesions. Furthermore, conventional optimization-based reconstruction methods are computationally expensive, rendering them too slow for real-time imaging applications. Our goal is to develop a fast and accurate image reconstruction method using deep learning, where multitask learning ensures accurate lesion localization in addition to improved reconstruction. We apply spatial-wise attention and a distance transform based loss function in a novel multitask learning formulation to improve localization and reconstruction compared to single-task optimized methods. Given the scarcity of real-world sensor-image pairs required for training supervised deep learning models, we leverage physics-based simulation to generate synthetic datasets and use a transfer learning module to align the sensor domain distribution between in silico and real-world data, while taking advantage of cross-domain learning. Both quantitative and qualitative results on phantom and real data indicate the superiority of our multitask method in the reconstruction and localization of lesions in tissue compared to state-of-the-art methods. The results demonstrate that multitask learning provides sharper and more accurate reconstruction.

We report world record high data transmission over standard optical fiber from a single optical source. We achieve a line rate of 44.2 Terabits per second (Tb/s) employing only the C-band at 1550nm, resulting in a spectral efficiency of 10.4 bits/s/Hz. We use a new and powerful class of micro-comb called soliton crystals that exhibit robust operation and stable generation as well as a high intrinsic efficiency that, together with an extremely low spacing of 48.9 GHz enables a very high coherent data modulation format of 64 QAM. We achieve error free transmission across 75 km of standard optical fiber in the lab and over a field trial with a metropolitan optical fiber network. This work demonstrates the ability of optical micro-combs to exceed other approaches in performance for the most demanding practical optical communications applications.

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.

We theoretically investigate and optimize four-wave mixing (FWM) in silicon nitride (SiN) waveguides integrated with two-dimensional (2D) layered graphene oxide (GO) films. Based on extensive previous measurements of the material parameters of the GO films, we perform detailed analysis for the influence of device parameters including waveguide geometry, GO film thickness, length, and coating position on the FWM conversion efficiency (CE) and conversion bandwidth (CB). The influence of dispersion and photo-thermal changes in the GO films is also discussed. Owing to the strong mode overlap between the SiN waveguides and the highly nonlinear GO films, FWM in the hybrid waveguides can be significantly enhanced. We obtain good agreement with previous experimental results and show that by optimizing the device parameters to balance the trade-off between Kerr nonlinearity and loss, the FWM CE can be improved by as much as ~20.7 dB and the FWM CB can be increased by ~4.4 folds, relative to the uncoated waveguides. These results highlight the significantly enhanced FWM performance that can be achieved in SiN waveguides by integrating 2D layered GO films.

The ability of social networks to disseminate information across individuals and groups is based on the social influence that people have on each other. In this context, the so-called influence maximization problem consists in identifying the most influential nodes of a given social network. This problem has practical relevance in a wide range of applications and despite the underlying computational complexity, several solution techniques have been presented in the related literature. Nonetheless, bringing together technical feasibility and satisfactory accuracy is still a challenging open research issue. In this work, we address the influence maximization problem with two complementary contributions. First, we analyze two well-known influence diffusion models, namely the independent cascade and the linear threshold models, and provide a new methodology for addressing the problem of computing the influence spread in a given network. Subsequently, we propose a novel approach to select an initial set of nodes that optimizes the influence spread in large-scale scenarios. We apply these techniques to several large-scale experiments in real-world scenarios achieving results comparable with the best-performing state-of-the-art algorithms in a shorter computational time. 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.

This work presents a new framework that utilizes power-domain (PD) nonorthogonal multiple access (NOMA) as a multiplexing scheme to improve the throughput of point-to-point (P2P), or single user, communications. The proposed framework synergizes PD-NOMA and automatic repeat request (ARQ) to enable multiplexing and transmitting multiple packets that belong to the same user simultaneously. To overcome channel estimation and feedback limitations, and to reduce the system complexity, a simple adaptation scheme is proposed select the appropriate number packets to be transmitted within a given transmission slot. Moreover, the number of transmitted packets is limited to a maximum of two to allow the receiver to blindly identify the number of transmitted packets in a particular transmission slot. The obtained results show that the proposed NOM scheme can eventually double the system throughput at high signal-to-noise ratios (SNRs), and hence, reduce the delay by 50%. The system complexity and overhead are generally comparable to conventional ARQ systems.

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in the field of automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an automatic diagnosis of COVID-19 infection from CT scans of the patients using Deep Learning technique is proposed. The proposed model, ReCOV-101 uses full chest CT scans to detect varying degrees of COVID-19 infection, and requires less computational power. Moreover, in order to improve the detection accuracy the CT-scans were preprocessed by employing segmentation and interpolation. The proposed scheme is based on the residual network, taking advantage of skip connection, allowing the model to go deeper. Moreover, the model was trained on a single enterpriselevel GPU such that it can easily be provided on the edge of the network, reducing communication with the cloud often required for processing the data. The objective of this work is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can be combined with medical equipment and help ease the examination procedure. Moreover, with the proposed model an accuracy of 94.9% was achieved.

A novel two-layer path planning method for a cooperated ground vehicle (GV) and drone system is investigated, where the GV acts as the mobile platform of the drone and is used to conduct multiple area covering tasks collaboratively. The GV takes the drone to visit a set of discrete areas, while the drone takes off from the GV at potential nodes around each area and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone’s scanning path for all areas’ coverage and the GV’s travel path for visiting all areas. A 0-1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.

Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss, thanks to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, event-based signals may contain temporal information which is complex to extract when using rate coding. In this work, we present a novel digital implementation of the model, called Time Difference Encoder, for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks which require the encoding of the time difference between events, such as optical flow based obstacle avoidance, sound source localization and gas source localization. This proposed bio-inspired model offers an alternative to the Jeffress model for the Interaural Time Difference estimation, validated with a sound source lateralization proof-of-concept. The model has been simulated and implemented on an FPGA, requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.

In the software version release management process, there is a need, on the part of human specialists, to classify the criticality of each software version However, the subjectivity of this classification may be present according to the experience acquired by specialists over the years. In order to reduce subjectivity in the process, an Artificial Intelligence technique called the Expert System (ES) can be applied to represent the knowledge of human specialists and use it in problem solving. Thus, the aim of this paper was to reduce the subjectivity in the criticality classification of the software version with the support of the Expert System.To this end, a questionnaire was developed with the objective of obtaining the criticality opinions classified as High, Medium and Low in each specialist’s software version to assist in the preparation of the ES production rules. ES generated 17 production rules with a 100% confidence level applied to a production database. The results of the classification carried out by the ES corresponded to the classification carried out by the specialists in the production base, that is, the ES was able to represent their knowledge. Then, another questionnaire was applied to the specialists in order to verify the perception of satisfaction regarding the use of the ES with a result obtained of 4.8, considered satisfactory. It was concluded, then, that the ES supported the reduction of subjectivity in the classification of the criticality of software version.

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The lift-off spacing distance between the eddy current sensor and test piece will influence the detected signals and accuracy of the measurement. Various techniques including novel sensor designs, features (lift-off point of intersection, lift-off invariance phenomenon), and algorithms have been proposed for the compensation of error caused by the lift-off effect using the eddy current sensor. However, few of these have directly measured the lift-off spacing distance, particularly for the distance up to 15 mm.In this paper, a lift-off tolerant pancake sensor has been designed. By analysing the sensitive region of the magnetic vector potential change (due to the test piece), the receiver of the sensor is designed as a circular spiral pancake coil with a large mean radius and span length (the difference between inner and outer radius). Experiments on the inductance measurement of three different non-magnetic samples have been carried out using both the designed pancake sensor and the previous triple-helix sensor. From the experiment result, the detected signal of the designed sensor has been proved much larger than that of the triple-helix sensor. Besides, simplified algorithms have been proposed for the measurement of the lift-off spacing and thickness of non-magnetic samples when using the proposed pancake sensor. Results show that the lift-off spacing and thickness can be measured with a small error of 0.14 mm (absolute error under 209.66 kHz), and 1.35 % (relative error, under low working frequencies of 142, 238, and 338 Hz) for the lift-off spacing from 1 to 15 mm.

The AI, deep learning and machine learning algorithms are gaining the ground in every application domain of information technology including information security. In formation security domain knows for traditional password management systems, auto-provisioning systems and user information management systems. There is another raising concern on the application and system level security with ransomware. On the existing systems cyber-attacks of Ransomware asking for ransom increasing every day. Ransomware is the class of malware where the goal is to gain the data through encryption mechanism and render back with the ransom. The ransomware attacks are mainly on the vulnerable systems which are exposed to the network with weak security measures. With the help of machine learning algorithms, the pattern of the attacks can be analyzed. Create or discuss a workaround solution of a machine learning model with combination of cryptographic algorithm which will enhance the effectiveness of the system response to the possible attacks. The other part of the problem, which is hard part to create an intelligence for the organizations for preventing the ransomware attacks with the help of intelligent system password management and intelligent account provisioning. In this paper I elaborate on the machine learning algorithms analysis for the intelligent ransomware detection problem, later part of this paper would be design of the algorithm.

Physical layer secret key generation is a promising candidate to achieve one-time-pad encryption approach for wireless communication system. The main issue of this method is the insufficient secret key rate. Especially in a static environment, the entropy of the channel complex coefficient is limited due to the lack of channel time-variation. To solve the problem, this paper proposes a novel secret key generation scheme assisted by intelligent reflecting surface (IRS) with discrete phase. We construct a dynamic time-varying channel by switching the phase of the reflection elements rapidly and randomly, and use channel estimation samples to update the secret key. Based on the common IRS channel model, we derive the expression of secret key rate. Monte Carlo simulation and numerical results show that the scheme can update secret key even if the channel coefficient is constant, and the secret key rate is higher than schemes based on artificial random signal. In addition, we comprehensively analyze the influence of various factors, such as the number of reflection elements and the IRS switching frequency on the secret key rate.

An AC-AC converter with high-frequency link employing LLC resonant converter operating in the vicinity of the resonance frequency is studied, in which the output stage is unique and formed by a high-frequency AC-AC converter employing four quadrant switches. The topology, its operation and the modulation strategy are presented. The high-frequency stage switches located on the primary side of the transformer operate with soft switching of the ZVS type, while the four quadrant switches that form the output stage operate with soft switching of the ZCS type. Experimental data on a 1.5 kW experimental prototype that was designed, built and tested in the laboratory, with 220 VRMS input, 220 VRMS output and 40 kHz switching frequency are given in the paper. The studied converter can be considered a candidate for the building block of medium voltage solid-state transformers (SST) for power distribution systems.