IEEE time-sensitive networks (TSN) offer redundant paths for automation networks that are essential preconditions for network load balancing (NLB) or distribution. They also provide several traffic shapers and schedulers with different impacts on the data flow control. The selection of the right traffic shaper or scheduler for an automation network is challenging. Their influence depends on various network parameters such as network extension, network cycles, application cycles, and the amount of data per traffic class and network cycle. In this study, the data flow control for network load balancing in an automation TSN using different traffic shapers and schedulers was investigated. The effects of the network parameters on the shapers and schedulers were derived and imported into the data flow control model of the automation network. The sample networks were simulated, and performance comparisons were performed. The results show that enhancements for scheduled traffic (EST), strict priority queuing (SPQ), and the combination of SPQ with frame preemption are better scheduler selections in connection with larger networks, fast network cycles, and fast application cycles. The cyclic queuing and forwarding (CQF) shaper and asynchronous traffic shaper (ATS) are only alternatives for data load control in small networks or in conjunction with slow applications.

This paper analyzes a scenario where two hypothetical Pokémon trainers each have 3 Pokémon and each send out 1 to battle. We form an experiment through repeated battles between each Pokémon matchup to create payoff values for each Pokémon. Game theory and the process of finding a Nash equilibrium are applied to this scenario to determine both the best outcome for both trainers, and how the results state which trainer has more powerful Pokémon sets. Iterated dominance, best responses, and mixed strategies are used routinely to solve for the Nash equilibrium solution. The application of game theory, Nash equilibrium, and data analysis to economics is briefly discussed.

This paper investigates a cooperative non-orthogonal multiple access network where a near user (NU) harvests energy using time-switching (TS) or power-splitting (PS) protocol to assist information transmission to the far user (FU). Considering nonlinear energy harvesting and opportunistic cooperative mode (CM) to non-cooperative mode (NCM) switching, expressions are derived for FU and NU outage probabilities and throughput. We demonstrate that CM/NCM switching can guarantee NU’s performance similar to OMA without any degradation in FU throughput. Also, the choice of optimum PS/TS parameter is essential to maximize FU throughput while ensuring desired NU throughput. CM/NCM switching ensures higher energy efficiency.

With advancements in automated experimental setups, material optimisation and discovery can scale to higher throughput with larger evaluation budgets. Two state-of-the-art algorithms with conceptually different multi-objective optimisation strategies (Bayesian and Evolutionary) are compared on synthetic and real-world datasets. Our results show that the Bayesian optimisation strategy, q-Noisy Expected Hypervolume Improvement (qNEHVI) is superior in finding solutions at the Pareto Front rapidly, and when considering hypervolume improvement as a performance indicator. On the other hand, the Evolutionary optimisation strategy, Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III), can exploit the Pareto Front and propose a larger pool of optimal solutions, given sufficient evaluation budget, and thus may be a better choice for materials discovery problems where knowing the complete Pareto Front provides greater scientific value to understanding materials space. We discuss the limitations of using hypervolume as a performance indicator for optimisation strategies, alongside hypervolume-based strategies such as qNEHVI, which do not adequately explain the number of solutions at or near the Pareto Front. We also performed a comparison of both optimisation strategies at different batch sizes to consider throughput capabilities.

This manuscript addresses the problem of detecting, classifying, and localizing sound sources in an acoustic scene of spatial audio. We propose using bio-inspired Gammatone auditory filters for the acoustic feature extraction stage and a novel deep learning architecture encompassing convolutional, recurrent, and temporal convolutional blocks. Our system exceeded the state-of-the-art metrics on four spatial audio datasets with different levels of acoustical complexity and up to three sound sources overlapping in time. Furthermore, we also performed a comparative analysis of the gap between machine and human hearing, evidencing that our results have already exceeded the human performance in non-reverberant scenarios.

Compared to simple mirrors, meta-mirrors have the ability to conserve the polarization of incident light in reflection. In previous decade, a lot of meta-mirrors designed for various applications. In this paper, a meta-mirror is demonstrated to simultaneously conserve the polarization of incident light and the circular dichroism.  A conventional chiral structure is used to design the meta-mirror in microwave regime. The chiral structure is C2-symmetric (breaking the rotational and mirror symmetries) for chirality inclusion into the meta-mirror. The design methodology uses the principle of febry-perot cavity which includes a sandwiched geometry. The substrate used to design the structure is Roger 5880 (a low-loss material at higher frequency bands) with copper material for chiral structure and back reflector.  Moreover, the meta-mirror shows the insensitivity towards incident angle of light. This meta-mirror can have a lot of applications in optical setup, communication, chiral absorption, spin-dependent reflections etc.

This paper provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly coupled constrained Markov decision processes.

A document by Christine Edwards . Click on the document to view its contents.

Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns.

Objective: The purpose of this research was  to predict the tonsillitis using machine learning algorithms. Increasing utilization of smartphones with sensor systems and machine learning capability promise better M-Healthcare services. Tonsillitis is an inflammation of your tonsils.  Tonsillitis analysis requires contemporary technology. Method: Different machine learning algorithms and frameworks used for evaluating the accuracy and performance.Artificial Neural Networks combined with picture processing and RGB color coding used for identify tonsillitis early and monitor prognosis at home. This study describes an innovative machine learning and  smartphone-based optimization approach with a linked camera. Results:Patients in remote locations, poor and impoverished countries may check, assess, and frequently do tonsillitis exams anywhere, anytime, and any place.  This research proposes an unique method and machine learning approach to evaluate tonsillitis photos and diagnose infections with 90\% accuracy for Random Forest and Decision Tree, . Conclusion:In this research paper, we have introduced an advanced MHealth application on human health and monitoring systems. The use and technological advancement of smartphones has skyrocketed in the last decade. Now embedded sensors in smartphone devices help to assess physiological indicators and evaluate the health status. We have demonstrated that M-health can be effectively applied in the detection of tonsillitis by using smartphone devices and machine learning

The conventional information leakage metrics assume that an adversary has complete knowledge of the distribution of the mechanism used to disclose information correlated with the sensitive attributes of a system. The only uncertainty arises from the specific realizations that are drawn from this distribution. This assumption does not hold in various practical scenarios where an adversary usually lacks complete information about the joint statistics of the private, utility, and the disclosed data. As a result, the typical information leakage metrics fail to measure the leakage appropriately. In this paper, we introduce multiple new versions of the traditional information-theoretic leakage metrics, that aptly represent information leakage for an adversary who lacks complete knowledge of the joint data statistics, and we provide insights into the potential uses of each. We experiment on a real-world dataset to further demonstrate how the introduced leakage metrics compare with the conventional notions of leakage. Finally, we show how privacy-utility optimization problems can be formulated in this context, such that their solutions result in the optimal information disclosure mechanisms, for various applications.

This work presents the design techniques to realize quasi-elliptic-type inline waveguide bandpass filters based on mutually-coupled rotated-dual-post (MCRDP) nonlinear frequency-variant coupling structures. A pair of closely-spaced partial-height posts placed inside the waveguide allows to pro- duce a first-order bandpass filtering transfer function with two transmission zeros (TZs). The pole can be moved either below or above the TZ pair by separating the posts laterally along the length of the waveguide and by controlling the sign of the mutual coupling between the posts. The mutual-coupling sign can be reversed by placing both posts either at the same or at opposite broad walls of the waveguide. EM simulated results for two third-order filters with a pair of TZs located above and below the passband are provided to validate the proposed design theory.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles (CAVs) at unsignalized intersections. The constraint-coupled bi-level optimization is operated with a limited view of incoming traffic using the rolling horizon procedure to reduce computational complexity. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established while demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios with high penetration market rate of CAVs based on the assumption of tracking errors of Human Driven Vehicles (HDVs) trajectories. Numerical experiments are conducted on extensive scenarios with different traffic densities, where the Constraint Programming (CP) technique is used to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

The design of two elements MIMO Patch antenna with polarized diversity reduces the mutual coupling between the antenna elements.

To fully address the problem for surface defect detection in real product line, we proposed an abnormal detection method based on Distillated Spatial-Attention Reconstruction (DSAR) module and apply it to the industrial magnetic tile surface quality inspection line. The proposed structure is a reconstruction-based algorithm, which applies distillation module and spatial-attention module to reduce the generalization of reconstruction network. Compared with existing methods, DSAR achieves the best results on our magnetic tile dataset and solved the critical long crack defect that the AUROC is improved from 88.6 to 98.6. On the public dataset MVTec, DSAR gets the best results of the reconstruction-based method and the third of all methods. We experimentally verify the role of distillation module and spatial attention module. We believe that our method also can be applied to the inspection of many types of industrial products in real product lines.

Lifetime of motes in wireless sensor networks can be enlarged by decreasing the energy spent for communication. Approaches like time slotted channel hopping pursue this goal by performing frame exchanges according to a predefined schedule, which helps reducing the duty cycle. Unfortunately, whenever the receiving radio interface is active but nobody in the network is transmitting, idle listening occurs. If the traffic pattern is known in advance, as in the relevant case of periodic sensing, proactive reduction of idle listening (PRIL) noticeably lowers energy waste by disabling receivers when no frames are expected for them. Optimal PRIL operation demands that, at any time, the transmitter and receiver sides of a link have a coherent view of its state (either enabled or disabled). However, this is not ensured in the presence of acknowledgment frame losses. This paper presents and analyzes some strategies to cope with such events. An extensive experimental campaign has been carried out through discrete event simulation to determine what consequences above errors may have from both a functional and performance viewpoint. Results show that, although no strategy is optimal in all circumstances, different solutions can be profitably adopted depending on the specific operating conditions.

The magnetic field surface integral equation for perfect electrically conducting scatterers suffers from accuracy problems when discretized with lowest-order Rao-Wilton-Glisson (RWG) functions. For high-frequency scattering scenarios, one of the various reported countermeasures are hierarchical higher-order (HO) functions. We demonstrate that the accuracy of these HO methods of up to 1.5th order may be further improved by employing a weak-form discretization scheme for the identity operator inside the magnetic field integral equation (MFIE), in particular for scatterers with sharp edges. As expected, the presented numerical results indicate that this approach becomes less effective for increasing order. Moreover, since the weak-form discretization overcomes only the anisotropy problems of the standard discretizations, parts of the accuracy problems of the MFIE persist for HO discretizations if the testing is performed with non dual-space conforming functions.

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.