Optoelectronic thin films play a critical role across various high-tech industries, including new materials, energy storage sectors, chip manufacturing, and biomedicine. This paper details the enhancement of optoelectronic thin film properties through the innovative use of Sum Frequency Generation (SFG) spectroscopy. This non-linear spectroscopic technique is uniquely suited to studying film surfaces and interfaces without damaging the samples, offering detailed insights into molecular arrangements and chemical states at these critical junctures. Further, this study introduces a novel Python based application developed using the PyQt5 framework, which is designed to efficiently handle and analyze spectroscopic data. The application incorporates advanced data processing functions such as data denoising, Fourier transformation, square wave matrix extraction, inverse Fourier transformation, and data integration, providing a comprehensive tool for researchers. Our results demonstrate significant improvements in the precision and efficiency of data analysis, leading to enhanced performance and quality of optoelectronic films. The integration of interdisciplinary technological approaches with advanced programming techniques and mathematical analysis through SFG spectroscopy underscores its potential to revolutionize the field by providing a more precise characterization of the material's microstructural features and advancing the development and optimization processes of optoelectronic thin film technology.

Designed an elliptical polarization conversion metasurface (PCM) for Ka-band applications, alongside a high-gain substrate integrated waveguide (SIW) antenna. The PCM elements are integrated into the antenna design in a chessboard array configuration, with the goal of achieving effective reduction in the antenna's radar cross section (RCS). Both the PCM elements and antenna structure exhibit a simple design. The top layer of the metasurface (MS) elements employs an elliptical pattern symmetric along the diagonal, enabling efficient conversion of linearly polarized waves. The antenna component, on the other hand, consists of a broadband dipole antenna fed by SIW slot coupling. Verified through simulations, the polarization conversion bandwidth of this PCM unit reaches 80.38% where polarization conversion ratio (PCR) exceeds 90% (25.3-59.3GHz), demonstrating exceptional conversion performance. When the dipole antenna is combined with the PCM, its-10dB impedance bandwidth reaches to 15.09% (33.7-39.2GHz), with a maximum realized gain of 9.1dBi. Notably, the antenna loaded with the chessboard PCM structure effectively disperses the energy of scattered echoes around, significantly reducing the concentration of scattered energy in the direction of the incident wave, thereby achieving an effective reduction in RCS.

This paper presents a novel method for eigenvalue computation using a distributed cooperative neural network framework. Unlike traditional techniques that struggle with scalability in large systems, our decentralized algorithm enables multiple autonomous agents to collaboratively estimate the smallest eigenvalue of large matrices. Each agent uses a localized neural network model, refining its estimates through inter-agent communication. Our approach guarantees convergence to the true eigenvalue, even with communication failures or network disruptions. Theoretical analysis confirms the robustness and accuracy of the method, while empirical results demonstrate its better performance compared to some traditional centralized algorithms

This paper aims to introduce new method for forcing logical thinking to AI by delicate instruction prompting with multiple executions. exhaustive prompting uses general reasoning formation to force the way of thinking to the AI by accomodating of essential factors in prompting. With this new methodology, accuracy in well-known benchmarks has been boosted up at 1.0%p to 9.7%p.

Forward adaptive transform coding of images requires a codebook of transform matrices from which the best transform can be chosen for each macroblock. Codebook construction is a problem of designing a quantizer for Karhunen-Lóeve transform (KLT) matrices estimated from sample image blocks. We present a novel method for KLT matrix quantization based on a finite-lattice non-causal homogeneous Gauss-Markov random field (GMRF) model with asymmetric Neumann boundary conditions for blocks in natural images. The matrix quantization problem is solved in the GMRF parameter space, simplifying the harder problem of quantizing a large matrix subject to an orthonormality constraint to a low-dimensional vector quantization problem. Typically used GMRF parameter estimation methods such as maximum-likelihood (ML) do not necessarily maximize the coding performance of the resulting transform matrices. To this end we propose a method for GMRF parameter estimation from sample image data, which maximizes the high-rate transform coding gain. We also investigate the application of GMRF-based transforms to variable block-size adaptive transform coding.

The presence of foreign bodies in packaged food is a serious concern for both final consumers (allergies, injuries, choking) and food manufacturers (reputation and economic losses). In particular, low-density plastics, glass and wood splinters are hard to detect even by the most advanced X-ray imagers. One solution is Machine-Learning-based Microwave Sensing (MLMWS): a non-invasive, contactless, and real-time method which uses a machine-learning (ML) classifier to analyze the scattered microwaves from the irradiated target object. In this paper, we want to extend our previous work about contaminant detection in cocoa-hazelnut spread jars by proposing an enhanced ML flow to increase the accuracy of the ML classifier. For the first time in this case study, we use a multi-class classifier, we train it with scattering parameters measured at multiple microwave frequencies, with a new pre-processing scaler, data augmentation, quantization-aware training and a pruning schedule. The results show a contaminant detection multi-class accuracy of 94.167% with a latency of 26 µs when targeting an AMD/Xilinx Kria K26 FPGA. Finally, we released our datasets publicly to OpenML.1

Vehicular Knowledge Networking (VKN) is a paradigm where vehicles exchange knowledge instead of data. Named Data Networking (NDN) is a resilient architecture for sharing data between vehicles without information about the hosting vehicle. NDN might not be adapted for sharing knowledge, as the peculiar complexity of knowledge requires knowledge-driven NDN functions as well as a specific simulation environment enabling joint knowledge perception, inference and reasoning. In this paper, we present an open-source cosimulation framework connecting NDN, traffic, dynamic control and perception simulators for knowledge perception & inference, and with an AI-as-a-Service (AIaaS) platform for knowledge reasoning. This paper notably describes new interfaces and functions between the NDN daemon and the AIaaS micro-services for handling interest naming, caching and forwarding between knowledge producers and consumers. We demonstrate the benefit of the proposed framework and knowledge-specific extensions through an AI-driven vehicular intersection management.

Reconstruction or imaging of objects, as one type of electromagnetic inverse problem, plays a crucial role in applications such as medical imaging, non-destructive or noninvasive detection and sensing. This paper presents a method for electromagnetic imaging of shapes of scattering objects. Specifically, we employ a metallic enclosure, apply a timereversal imaging method (TRIM) and leverage the equivalent source principle to reconstruct the shapes of the objects (or scatterers). The preliminary results verify the efficacy of the proposed TRIM, demonstrating its ability to solve inverse problems without resorting to intricate mathematical formulations or Green's function. Thus, it offers a practical and efficient alternative solution to inverse imaging problems.

In this era of advanced communication technologies, many remote rural and hard-to-reach areas still lack Internet access due to technological, geographical, and economic challenges. The TV white space (TVWS) technology has proven to be effective and feasible in connecting these areas to Internet service in many parts of the world. The TVWS-based systems operate based on geolocation white space databases (WSDB) to protect the primary systems from harmful interference and thus there is a critical need to know the available and usable channels that can be used by the secondary white space devices (WSDs) in a specific geographic area. In this work, we developed a generalized and flexible graphical user interface (GUI) tool to evaluate the availability and usability of the TVWS channels and their noise levels at each geographic location within the analyzed area. The developed tool has many features and capabilities such as allowing the users to scan the TVWS spectrum for any geographic area in the world and any frequency band in the TVWS spectrum. Moreover, it allows the user to apply widely used terrain-based radio propagation models. It provides the flexibility to import the elevation terrain profile of any region with the desired spatial accuracy and resolution. In addition, various system parameters including those related to regulation rules can be modified in the tool. This tool exports to an external dataset file the output data of the available and usable TVWS channels and their noise levels and it also visualizes these data interactively.

With the widespread adoption of switched power converters within the power grid as both sources and sinks, the necessity arises for establishing verifiable stability conditions. We assume here that power sources can be modelled as synchronous generators-this encompasses virtual synchronous generatorswhile power sinks are operating as constant power loads. We provide a small-signal stability criterion for a model that uses time-varying phasors, characterized by a lossless transmission network and constant power loads distributed throughout the network. A key concept in this study is the matrix H, representing the partial derivatives of the generator powers with respect to their relative angles, computed at an equilibrium point. Our main result is that H > 0 implies the local asymptotic stability of this equilibrium point. Moreover, the equilibrium point is unstable if H is full-rank and not positive definite. To demonstrate the practical significance of our theorem, we conduct a case study, highlighting its applicability in real-world scenarios.

Sensing and imaging are fundamental components at the heart of all devices that capture data. In conventional sensing systems, semantic information is obtained by processing already-recorded data. In such a sequential setting, the Heisenberg uncertainty principle is interpreted as follows: it is impossible to simultaneously possess both "data" and "information". In this paper, we pose the challenge as a "sampleinformation uncertainty dilemma". To address this challenge, we propose an evolutionary sensing, sampling, or sensor-placement solution derived from a modelling of the universe's evolution function, in which samples and underlying information are acquired cyclically. Our method features an adaptive closedloop architecture, consisting of two main parts: first, a plant system for sensing and reconstruction; and second, a feedback mechanism, as an information predictor, that identifies informative samples for acquisition at the next sensing iteration. We explore versatile practical applications of our proposed theory, including: sensing and imaging; sampling; sensor-placement; sixth-generation communications; Industry 5.0; and, the Internet of Things. Specifically, we investigate in detail the application of digital imaging. Simulation results demonstrate that our imaging technique significantly improves the Peak Signal-to-Noise Ratio 6.24 dB on average after reconstructing images of the Microsoft object recognition database by the average adaptive sampling rate 60.15 %, compared to the best state-of-the-art competing method in compressed sensing with the same fixed rate. This achievement makes our theoretical and implementation framework attractive for real-world applications, towards smarter, faster, greener, and cheaper sensing and recovery of fine information in signals and images. Our codes are available for development.

Over the course of the last five decades, there has been substantial growth in conventional network routing design, transitioning from small static node networks to vast systems connecting billions of devices. This evolution has been facilitated by the separation of concerns principle, which involves integrating network functionalities into graph or random network designs and utilizing specific protocols to enable diverse communication capabilities. The landscape of quantum networks is rapidly evolving, presenting new paradigms in information transmission and processing. Routing protocols play a pivotal role in establishing efficient communication pathways within quantum networks, enabling the transmission of quantum states and entanglement across interconnected nodes. The objective of this paper is to shed light on existing entanglement routing design techniques within quantum networks. The design of quantum entanglement routing necessitates a departure from conventional network protocols due to its unique considerations of quantum entanglement and entanglement swapping. However, the implementation of these techniques is fraught with significant challenges, including quantum system decoherence and noise, limitations on communication ranges, and the need for specialized hardware. The paper initiates by surveying critical research in quantum routing design techniques, then delves into essential aspects of the routing concept, associated quantum operations, and the step-wise process for establishing efficient and resilient quantum networks. In summary, this paper provides an overview of the current landscape of quantum entanglement routing techniques. It outlines their core principles, protocols, and challenges, while also highlighting potential applications and charting future directions for research and development.

Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive Chronic Kidney Disease (CKD) is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. With the progression of time, chronic kidney disease has moved from a life-threatening disease affecting few people to a common disorder of varying severity. The goal of this research is to visualize dominating features, feature scores, and values exhibited for early prognosis and detection of CKD using ensemble learning and explainable AI. For that, an AIdriven predictive analytics approach is proposed to aid clinical practitioners in prescribing lifestyle modifications for individual patients to reduce the rate of progression of this disease. Our dataset is collected on body vitals from individuals with CKD and healthy subjects to develop our proposed AI-driven solution accurately. In this regard, blood and urine test results are provided, and ensemble tree-based machine-learning models are applied to predict unseen cases of CKD. Our research findings are validated after lengthy consultations with nephrologists. Our experiments and interpretation results are compared with existing explainable AI applications in various healthcare domains, including CKD. The comparison shows that our developed AI models, particularly the Random Forest model, have identified more features as significant contributors than XgBoost. Interpretability (I), which measures the ratio of important to masked features, indicates that our XgBoost model achieved a higher score, specifically a Fidelity of 98%, in this metric and naturally in the FII index compared to competing models.

Hardware design for radio frequency devices often demands the use of full-wave electromagnetic (EM) field-solvers to extract scattering parameters (S-parameters) for systems that contain discontinuities throughout their printed circuit boards (PCBs) and substrate packages. Using EM field-solvers not only requires extensive EM knowledge during the setup phase, but also demands considerable computational resources. Previously, machine learning (ML) and neural network (NN) models have been used to solve EM fields; however, they tend to use design parameters as input rather than the structure's geometry. Parametric analysis is beneficial when the solution space is well bounded, but it is more desirable to develop the capability of feeding generic and arbitrary geometry into a machine learning model for rapid S-parameter extraction. This work presents an algorithm for converting a subsection of hardware geometry designed with an electronic design automation (EDA) tool into a dataset suitable for training and validating machine learning models including fully connected NNs and convolutional NNs (CNNs).

This letter proposes a new control scheme for the provision of adaptive inertial response by virtual synchronous generators. In contrast to state-of-the-art methods, the proposed strategy proactively allocates higher inertia values at the onset of frequency disturbances while respecting the energy limits of the associated energy storage system. The performance of the developed technique is assessed by means of rms simulations on the IEEE 39-bus system. The results demonstrate that the proposed scheme enhances the dynamic performance of the power system by reducing the rate of change of frequency and delaying the occurrence of the frequency extrema (nadir/zenith).

This paper presents a novel guidance and control strategy for the efficient capture and stabilization of fast-spinning target satellites, such as spin-stabilized models, utilizing a spinning-base servicing satellite equipped with a robotic manipulator, joint locks, and reaction wheels (RWs). The proposed method leverages the RWs of the servicing satellite to replicate the target satellite's spin dynamics, while locking the manipulator joints to achieve precise spin-matching. This technique effectively neutralizes the target's relative motion, simplifying the capture trajectory planning and eliminating the need for post-capture adjustments. Following spin-matching, the manipulator's joints are unlocked, enabling a coordinated control system to execute the capture maneuver while maintaining zero relative rotation between the servicing and target satellites. Upon successful capture, the system transitions into the spin stabilization phase. Here, the joints are re-engaged to create a unified tumbling rigid body composed of the interconnected satellites. An optimized control system then applies negative torques through the RWs to rapidly dampen the tumbling motion, constrained by the actuation limits of the RWs and the manipulator's end-effector torque capabilities.

The integration of artificial intelligence (AI) in next generation cloud-native mobile networks has transformed network and service orchestration, driving the evolution towards fully automated zero-touch operations. As the AI in telecommunications market rapidly expands, the demand for transparency in AI decision-making, known as eXplainable AI (XAI), becomes of paramount importance. XAI mitigates the "black box" nature of AI models, ensuring transparency, crucial for regulatory compliance, user acceptance and system improvement. In this article, motivated by the diversity of data types in cloudnative orchestration scenarios (i.e., tabular, time-series and raw text), we explore the impact that the different data types may have on the selection of the appropriate XAI technique. In addition, we classify and discuss XAI techniques suitable for each data type, outlining a series of use cases in edge/cloud orchestration. Finally, we present two real-world proof-of-concept use cases, demonstrating the benefits that XAI can bring to cloudnative networks. Our contributions aim to facilitate the effective integration of AI in dynamic environments, bridging the gap between AI model interpretability and practical deployment in next generation cloud-native networks.

The problem of robust quickest change detection (QCD) in non-stationary processes under a multi-stream setting is studied. In classical QCD theory, optimal solutions are developed to detect a sudden change in the distribution of stationary data. Most studies have focused on single-stream data. In non-stationary processes, the data distribution both before and after change varies with time and is not precisely known. The multi-dimension data even complicates such issues. It is shown that if the non-stationary family for each dimension or stream has a least favorable law (LFL) or distribution in a well-defined sense, then the algorithm designed using the LFLs is robust optimal. The notion of LFL defined in this work differs from the classical definitions due to the dependence of the post-change model on the change point. Examples of multi-stream non-stationary processes encountered in public health monitoring and aviation applications are provided. Our robust algorithm is applied to simulated and real data to show its effectiveness.