Sparse array (SA) geometries are proposed to obtain low mutual coupling (MC) and improved degrees-of-freedom (DOF), which are composed of concatenations of multiple uniform linear arrays (ULAs) with certain closed-form expressions and are able to provide long virtual difference coarrays (DCAs) than many existing SAs. Meanwhile, the new proposed SAs have low coupling leakage, hence the proposed SAs have good trade-off between DOF and MC. Numerical experiments are created to give a verify the SA superiority in the presence of heavy MC.

Deployment of Synchronous Condensers (SynCons) has become one popular solution in accommodating large-scale renewable energy resource (RER) for existing power systems. In  this paper, rotor angle instability of SynCon is investigated. To  derive intuitive understandings and quantitative analysis, the  swing equations of the equivalent synchronous generators (ESG)  are derived. The Lyapunov’s direct method is employed to analyze  the attraction domain for a typical benchmark system. Based on  that, a rotor angle stability margin index is developed and an  adaptive low voltage ride through (LVRT) control strategy is  proposed for assessment and enhancement of rotor angle stability.  The criterion and effectiveness are verified with on EMT  simulations, respectively.

This paper investigates the relationship between single and double pendulum synchronization and real-world synchronization. The Kuramoto model was applied to couple single pendulums and the results were analyzed for implementing double pendulum synchronization. A differential equation approach was utilized to model N double pendulums, and an ordinary differential equation solver was implemented in Python. Double pendulum oscillations were modeled using the Lagrangian equations of motion due to the constraint-independent benefits. Investigation outcomes were utilized to explain synchronization phenomena in real-world dynamical systems: lockstep, Galilean moons, Centaurus A, Belousov-Zhabotinsky reaction. Single pendulum synchronization was achieved with sufficient coupling power K. Double pendulum synchronization was achieved with a sufficiently small initial displacement from equilibrium, stable constants for mass and length, and sufficient coupling strength K. The results yield the possibility of phase-shifted synchronization for chaotic systems contingent upon the system’s ability to overcome state-dependent chaos.

The rapid growth of large-scale trajectory data has prompted researchers to develop multiple large-scale trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new index, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency. \end{abstract}

The article presents the results of a study of a modal filter (MF) based on a microstrip line with two side conductors grounded at both ends. New analytical expressions for calculating the time response in such a structure are given. For their validation, an MF prototype was made and the transmission coefficient was measured in the frequency range from 0 to 12 GHz. In addition, the full-wave electrodynamic simulation and the analysis based on the transmission line theory were carried out. The frequency responses of the MF were converted into time domain responses in the ADS system. At the MF output, the response to an ultra-wideband (UWB) pulse was analyzed. The results for the analytical model are in good agreement with the results of measurements and simulations by the two approaches. It was experimentally proven that the structure under study provides the protection of electrical circuits from a UWB pulse by separating it in time and decreasing its effect on the components.

We propose an unsupervised blind fusion network that operates on a single HSI and RGB image pair and requires neither known degradation models nor any training data. Our method takes full advantage of an unrolling network and coordinate encoding to provide a state-of-the-art HSI reconstruction. It can also estimate the degradation parameters relatively accurately through the neural representation and implicit regularization of the degradation model.

Navigational systems, the heart of maritime oper- ations, face escalating cybersecurity risks due to their system of systems nature and reliance on diverse suppliers. Amid concerns about supply chain attacks and insider threats, the potential for malicious radar image injections, including Inverse Synthetic Aperture Radar (ISAR) images, is emerging. Such manipulations can critically undermine navigational integrity through the generation of decoy targets, the strategic relocation of existing ones, and the effective concealment of additional targets. We have identified where an Advanced Persistent Threat (APT) could be concealed within the processing chain of a modern radar system. This study demonstrates the potential of APTs to exploit Generative Adversarial Networks (GANs) for the creation of deceptive ISAR images, thereby spotlighting this previously unexplored threat vector. Our findings offer novel insights into bolstering maritime cybersecurity in an increasingly AI-dominated landscape.

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long Term Evolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia Broadcast Multicast Service over a Single Frequency Network (MBSFN) feature of an LTE network and the Multicast Broadcast Service (MBS) feature of an NR network. The proposed DSS scheme utilizes LTE and NR resource controllers to assign muted MBSFN subframes on the LTE band and muted MBS subframes on the NR band based on traffic needs. In contrast to the state-of-the-art, the proposed DSS scheme does not require a coordination signaling channel between the LTE and NR networks. Instead, a machine learning-based Technology Recognition and Traffic Characterization (TRTC) system is used to identify and characterize traffic patterns. The LTE and NR resource controllers use the TRTC to sense the muted subframes and offload traffic accordingly.

The paper considers a multi-source relaying status update system with stochastic arrivals and develops three scheduling policies to minimize the sum average age of information subject to transmission capacity and long-run average resource constraints. Namely, we provide these policies under two different scenarios regarding the knowledge of system statistics: known environment and unknown environment. For the known environment, a constrained Markov decision process approach and a drift-plus-penalty method are proposed, and for the unknown environment, a deep learning policy is developed. The structure of an optimal policy is analyzed, and simulation results are provided.

An innovative and efficient algorithm was established for generating a G1 parametric bicubic spline interpolation surface (PACSIS) for any polygonal mesh. The resulting G1 surface interpolates all vertices of the quads; and the G1 smoothness was guaranteed by the special creation of new control points for each quad so that all tangent planes at any vertex match. The algorithm was demonstrated by a variety of PLY files representing different polygonal meshes. There will be more applications of PACSIS to areas such as signal and image processing, animation, data science, feature representation, mesh rendering, visualization, as well as computer graphics.

“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 focuses on the design and simulation of a novel low-profile ultra-wideband antenna that combines the feature of vertical stacking with wing structure similar to bow-tie antennas. Additionally, it investigates the effectiveness of vertical stacking and discusses its optimization. To gauge the effectiveness of stacking, we have compared the non-stacked, single-stack, double-stack, and triple-stack models against an optimizer function that covers parameters such as normalized cost, normalized return loss, and normalized -10dB bandwidth. This optimizer function concludes that the single-stack model performs best compared to other models. The antenna geometry is simulated and optimized using Altair FEKO. The proposed design of the single-stack model achieves a -10dB return loss over 248-752 MHz with VSWR < 2 and a relatively constant omnidirectional radiation pattern.

Reinforcement learning holds the promise of allowing autonomous vehicles to learn complex decision making behaviors through interacting with other traffic participants. However, many real-world driving tasks involve unpredictable perception errors or measurement noises which may mislead an autonomous vehicle into making unsafe decisions, even cause catastrophic failures. In light of these risks, to ensure safety under perception uncertainty, autonomous vehicles are required to be able to cope with the worst case observation perturbations. Therefore, this paper proposes a novel observation adversarial reinforcement learning approach for robust lane change decision making of autonomous vehicles. A constrained observation-robust Markov decision process is presented to model lane change decision making behaviors of autonomous vehicles under policy constraints and observation uncertainties.  Meanwhile, a black-box attack technique based on Bayesian optimization is implemented to approximate the optimal adversarial observation perturbations efficiently. Furthermore, a constrained observation-robust actor-critic algorithm is advanced to optimize autonomous driving lane change policies while keeping the variations of the policies attacked by the optimal adversarial observation perturbations within bounds. Finally, the robust lane change decision making approach is evaluated in three stochastic mixed traffic flows based on different densities. The results demonstrate that the proposed method can not only enhance the performance of an autonomous vehicle but also improve the robustness of lane change policies against adversarial observation perturbations.

Cell localization is an important area of medical image analysis, which is dedicated to predicting the precise location of cells in an image. The existing localization paradigm is to predict the density map using a Convolutional Neural Networks (CNN) model based on vanilla convolution and then process the density map using a local maximum search strategy to obtain the cell location and number information. However, there are three main problems in this paradigm: 1) CNN models based on vanilla convolution have difficulty in handling large variations in cell color; 2) The density map is difficult to provide accurate cell location information and ideal gradient information, and the information loss is more obvious in dense regions; 3) The post-processing strategy of density maps is susceptible to background noise and mutual interference between negative and positive cells. To tackle the above issues, we have made a comprehensive update of the existing paradigm, which consists of three parts: 1) A multi-scale gradient aggregation module based on difference convolution to effectively mitigate the challenge of large variations in cell color; 2) A new exponential distance transform map that provides accurate cell location information along with ideal gradient information for model learning; 3) A post-processing strategy named cell center localization strategy based on location maps that can significantly improve the localization performance. Extensive experiments on multiple datasets show that our approach can substantially improve cell localization and counting performance, establishing a new baseline for the cell localization task, and thereby increasing the efficiency of computer-aided diagnosis.

The likelihood of cyberattacks that pose a threat to the safety of wireless sensor networks (WSNs) and Android devices increases with their use. A comprehensive literature review on how to protect WSNs and Android devices from cyberattacks is presented in this paper. The survey centers around the utilization of AI and different methods to recognize and forestall malware, as well as the utilization of encryption and other safety efforts to safeguard against unapproved access. According to the findings, although there is no one solution that can completely secure WSNs and Android devices, employing a proactive strategy and a variety of security measures can significantly lower the risk of cyberattacks.

In today's world, the railway has emerged as a shining example of an environmentally friendly and well-linked mode of transportation, particularly in significant metropolises worldwide. Its popularity stems from its widespread use and its inherent comfort to commuters. A key aspect that bolsters this appeal is the railway network's well-earned reputation for being the safest and most efficient transportation system. However, railway crossings remain perilous, challenging traffic control and safety. To address this concern, we propose an innovative and automated railway crossing system that promises to revolutionize how we approach railway safety. Our automated railway crossing system encompasses multiple essential features to ensure unparalleled safety and efficiency. First and foremost, the heart of this system lies in its automatic control of the railway crossing gates. By removing the need for manual operation, the potential for human errors is significantly reduced, providing commuters with an added layer of assurance during their journeys. In addition, our system boasts an advanced warning mechanism designed to alert approaching traffic well before the gate closure. This crucial feature enhances the safety of vehicular traffic and pedestrians by giving them ample time to prepare for the crossing. A clear and user-friendly LCD display serves as the medium for this alert system, making it intuitive and visually accessible to all users. Understanding the value of commuters' time, we have also integrated a real-time counter into the system. This counter keeps track of the estimated waiting time, empowering commuters to know when they can expect the gates to open again. With this feature, we strive to minimize inconvenience and optimize the efficiency of railway crossings. In our relentless pursuit of safety, we have taken it further by incorporating innovative anti-collision and line-breaking technology. By actively detecting potential collisions and disruptions, our system acts as a vigilant guardian, thwarting accidents and safeguarding lives.

Utilizing current controlled voltage source (CCVS), we obtain negative effective resistance, design wireless power transfer circuit of parity-time (PT) symmetry and obtain the conditions of the PT symmetry circuits. According to PT symmetry theory, we obtain PT symmetry broken point (PTSBP). We experimentally investigate wireless power transfer at various frequency including the frequency of PTSBP and magnetic resonance. The experimental results show that the transfer distance of maximum power increase 0.1 mm at the frequency of PTSBP than that at the frequency of magnetic resonance.

Renewable energy generation is non-dispatchable and notably unpredictable. Furthermore, a volatile electricity spot market means vulnerability to both price and volumetric risk for generation firms. As such, project financiers may be hesitant to invest in renewable energy projects. Electrical offtakers purchase from the same spot market, and thus also suffer its uncertainty. Ideas from the world of blockchain and decentralised finance may present solutions for these cases. This paper proposes tokenised revenue streams \textit{(RevToks)} as a novel mechanism for the electricity industry. A RevTok allows the owning party to directly claim a share of a specific generator’s revenue stream. This is combined with tranching, a technique from finance where priority on a revenue flow is divided based on seniority. Project financiers hold this senior RevTok that entitles them to precedence on generator incomes, ensuring loan repayments. A case study using real-world generation, pricing, and consumption data is formulated. A market simulation is performed in the form of optimisation problem to establish an idea market equilibrium. Junior tranch RevToks can be purchased by electrical offtakers to decrease their budget variance by offsetting fluctuations in their monthly energy bill. The tranched revenue profiles of generators are demonstrated visually. For offtakers, monthly variance is universally decreased. The case study market simulations shows evidence that tranched revenue sharing arrangements show benefits for generators, financiers, and offtakers.

Whilst Deep Neural Networks have been developing swiftly, most of the research has been focused on RGB image. This type of image has been traditionally optimised for human vision. However, RGB data is a highly re-elaborated and interpolated version of the collected raw data (i.e. the sensor collects one value per pixel), but an RGB image for human viewing contains 3 values, for red, green, and blue. This processing through the ISP (Image Signal Processing) requires computational resource, time, power and obviously increases by a factor of three the amount of output data. This work investigates Deep Neural Network based detection using (for training and evaluation) Bayer data, generated in different ways, from a benchmarking automotive dataset (i.e. KITTI dataset). A Deep Neural Network (DNN) is deployed in unmodified form, and also modified to accept only single field images, such as Bayer frames. Eleven different re-trained version of the DNN are produced, and cross-evaluated across different data formats. The results demonstrate that the selected DNN has the same accuracy when evaluating RGB or Bayer data, without significant degradation in the perception (the variation of the Average Precision is <1%). Moreover, the colour filter array position and the colour correction matrix do not seem to contribute significantly to the DNN performance. This work demonstrates that Bayer data can be used for object detection in automotive without significant performance loss, and that the processing currently used in ISP can be avoided, allowing for more efficient sensing-perception systems.