Possible choices of flight paths on spherical surfaces - for example on hemispheres - for goniometric mapping with UAVs are compared and explicit parametrizations are given. The quantities measured during mapping should be easily integrable numerically in order to determine physical quantities like source strength, flux, etc. Some brief considerations regarding the UAVs energy budget are given also.

In recent years, Indoor Positioning Systems (IPS) have gained significance across various applications, including asset tracking, monitoring, interior navigation, and location-based services. The use of Wi-Fi-based technology is a popular choice for IPS due to its cost-effectiveness and widespread accessibility. The Wi-Fi signal’s Round Trip Time (RTT) measurement, using the Fine Time Measurement (FTM) protocol, offers fewer ranging errors in Line of Sight (LOS) conditions. However, Wi-Fi RTT ranging measurements encounter higher-ranging errors in NonLine of Sight (NLOS), multipath, and interference scenarios. This study examines the error in ranging measurements for different scenarios such as LOS, Glass, Metal, and Wall blocking scenarios. To address these challenges, we propose a method that combines calibrated RTT range and processed Received Signal Strength (RSS) feature values in constructing a fingerprinting map to enhance positioning accuracy. The methodology includes the development of a range compensation model for RTT range calibration, utilizing a Gaussian filter for RSS measurement values processing, and creating a classifier model to distinguish between LOS and Wall scenarios. This integrated approach reduces noise in measurement values, and the Gaussian Process Regression (GPR) algorithm is utilized to predict the final location of the user. Our proposed method achieved a positioning error of 0.79 m, surpassing the performance of RTT fingerprinting by 17.71 %, RSS fingerprinting by 49.68 % and trilateration methods by 29.46 %.

Generative AI has achieved remarkable progress in producing human-like text, yet challenges remain in improving the predictability, reliability, and overall performance of language models. The novel approach of decomposing the GPT-2 model into specialized modules-memory, reasoning, and perception-significantly enhances its capabilities by compartmentalizing different cognitive functions. Comprehensive experiments demonstrate that the modular architecture results in substantial improvements in perplexity, accuracy, and F1 scores compared to baseline models. The findings demonstrate the potential of modular designs in achieving superior performance and adaptability, contributing to the field of factored cognition models. Addressing computational challenges and optimizing integration strategies will further enhance the practicality of deploying modular LLMs in diverse applications. The study provides a robust foundation for future research, exploring more efficient training algorithms, alternative modular configurations, and ethical considerations, ultimately advancing the development of sophisticated, reliable, and adaptable AI systems.

A vision transformer (ViT) is developed to perform image classification on beam profiles coupled out from silicon photonics (SiPh) gratings. The image classification task is aimed to distinguish 'converged' and 'diverged' beam profiles, and the regions where the corresponding beam profiles are located above the SiPh gratings. Upon training with 1247 beam profile images, the ViT model is able to perform 6-category image classification task on 832 beam profile images with classification accuracy of 0.989. Since the training of ViT is probabilistic in nature, the ViT training is repeated for 100 times to test its robustness. Classification accuracy ranges from 0.83 to 0.99, where 82/100 runs with testing accuracy values of >0.95, are obtained.

In today's data-driven world, seamless data exchange between different systems, teams, and organizations is crucial for operational efficiency and informed decision-making. Data contracts have emerged as a vital tool to ensure that data is exchanged reliably, consistently, and with high quality. This article thoroughly examines data contracts - formal agreements that specify how data is structured, formatted, and shared between different systems or parties. Data contracts define the schema, semantics, quality, and terms of use for data exchange, ensuring a common understanding between data providers and consumers.

This paper explores the practical implementation of data warehouse technologies in organizational setups through an applied examination of data warehouse architectures. It begins with an initial focus on database paradigms OLAP and OLTP and their analytical capabilities, followed by data warehouse fundamentals and various modern data buzzwords; it further details the data warehouse design components, data modeling methods, and data warehouse implementation strategies, and also discusses the multi-hop approach to managing data storage. The study outlines approaches for designing a modern data warehouse and demonstrates how these systems significantly enhance data management and decision-making processes. Methodologically, the paper employs an analysis of data warehouse implementations, providing insights into the architectural choices and design considerations that lead to successful deployment. The findings underscore the critical role of tailored data warehouse solutions in achieving analytical efficiency and operational agility. This study contributes to the existing literature by detailing the architectural nuances and strategic planning necessary for optimizing data warehouse functionality.

This article derives and implements a computational physics model for model-based image reconstruction in magnetic particle imaging (MPI) applications. To our knowledge, this is the first ever computationally tractable model-based image reconstruction in MPI, which is neither constructed from calibration or simulation experiments or limited to specific scan acquisition geometries. The derived model results in a system constructed from a series of fast linear transforms, each of which incorporate the individual components from the paramagnetic model. These include the transmit velocity, field free point location, gradient strength, receive coil sensitivity, and receive chain filtering, and each of these modeling components are amendable to any changes in the acquisition parameters. This allows us to adopt a computationally tractable system matrix modeling approach to MPI for any scan specific parameters at very high pixel resolutions. The model is derived from first principles, and it results from taking the fundamental MPI signal theory and decomposing these modeling equations into the series of linear transforms acting on a pixelated image. Each transform is formally defined in matrix form but implemented in a matrix-free fashion with fast and/or sparse operations. For these reasons, our new model should be a fundamental tool in the future of computational imaging in MPI. We demonstrate our new method on a variety of pre-clinical and simulated data sets, and these results confirm that our method is both efficient and accurate.

This work presents the design and analysis of an enhanced phase-locked loop (EPLL) for single-phase power systems. This paper examines two methods for developing an EPLL: gradient-flow optimization and Lyapunov theory. The derived nonlinear differential equations of EPLL are analyzed for stability using dynamic system theory. Computer simulations show that the proposed EPLL structure is highly efficient at extracting amplitudes, phases, and frequencies that match actual values, even in the presence of harmonics.

The inpainting problem is addressed in this work through a very simplified version of the approach based on low-dimensional manifold model (LDMM), in which the actual working principle of the LDMM is put into evidence, namely, the simulated diffusion of image pixels that takes place in a manifold from which patches are drawn to form a given image. The simplicity of this principle is translated into a straightforward algorithm that borrows ideas from the Locally Linear Embedding (LLE) method. The equivalence between this much simpler algorithm and the LDMM is illustrated through visual inspection and experimental measurements of peak signal-to-noise ratios. Additionally, a (U-shaped) multi-scale use of the proposed algorithm is presented as a significantly better initializer for missing pixels, thus reducing the number of algorithmic iterations.

A document by Roos M. Bakker. Click on the document to view its contents.

Accurate reference tracking is essential in control tasks in a variety of applications, and, in repetitive systems, model-based Iterative Learning Control (ILC) is a standard solution that is underpinned by formal stability and convergence guarantees. To overcome the limitations of model-based ILC design, data-driven ILC methods have been introduced which lead to a patchwork of several, very different approaches that do not preserve the modularity and theoretical guarantees of the many, well-established model-based ILC methods. We, for the first time, propose a unifying framework for data-driven ILC that preserves the modularity and formally proven properties of model-based ILC. Specifically, we propose Iterative Model Learning (IML) and Dual Iterative Learning Control (DILC). The IML framework enables iterative learning of unknown dynamics in repetitive systems using input/output trajectory pairs. We formally prove the duality between IML and ILC, i.e., an IML system is equivalent to an ILC system with a trial-varying reference and trial-varying but known dynamics. We further provide generic conditions to guarantee the convergence of the model and prove the duality of monotonic convergence in ILC and IML. This duality allows arbitrary, established, model-based ILC methods to be effectively applied within the IML framework to learn models of unknown dynamics. Unlike existing data-driven ILC approaches which only combine specific model learning schemes with specific model-based ILC approaches, the proposed DILC framework combines IML with model-based ILC such that established, model-based ILC methods can be arbitrarily combined for simultaneous model and control learning with a formally proven separation principle. The proposed DILC is a unified framework for data-driven ILC that not only relieves model-based ILC of a priori model requirement but also preserves the modularity and theoretical guarantees of model-based ILC. Both IML and DILC are validated by extensive simulations and real-world experiments.

Recently, there has been a growing interest in analyzing resting-state fMRI (rs-fMRI) signals using Time-Varying Phase Synchronization (TVPS) measures. TVPS serves as a functional connectivity metric, allowing for the quantification of phase synchronization between different brain regions. However, extracting the phase from fMRI signals poses challenges due to inherent noise and insufficient bandlimitation. Traditional filtering methods struggle to effectively eliminate noise and necessitate prior knowledge of cutoff frequencies. In this context, data-driven multivariate decomposition techniques present promising solutions for extracting narrow-band components suitable for TVPS analyses. Previous studies have identified Multivariate Variational Mode Decomposition (MVMD) as particularly suitable for fMRI decomposition. However, MVMD's requirement for predefining the number of extracted modes K limits its analytical capabilities. To address this limitation, we employ an enhanced MVMD scheme called Noise-Assisted MVMD (NA-MVMD), designed to reduce sensitivity to parameters and enhance decomposition quality. We apply NA-MVMD to synthetic signals, showcasing improved decomposition quality, noise robustness, and reduced sensitivity in setting the K parameter.

Hitting a high frame rate is the holy grail of virtual reality (VR). Indeed, a high frame rate offers an amazing experience, but also pushes current technology to its limits. This is particularly true for networked VR gaming systems which rely on split rendering between a client and a server. In this paper, we investigate the impact of frame rate on such systems in terms of responsiveness and timeliness, using both experiments and simulations. Responsiveness refers to how quickly the information is exchanged, while timeliness refers to the frequency of updates. In terms of responsiveness, we show that 90 fps and 120 fps may tolerate up to 6 ms network delay. In terms of timeliness, we show that the best choices are 120 fps when network delay is low and 60 fps when network delay is high.

Dynamic Causal Modeling (DCM) is a Bayesian framework to investigate effective connectivity between brain regions using neuroimaging data. High noise levels can significantly affect the efficiency and reliability of DCM. Here, we propose a new multivariate method, called Multivariate Variational Mode Decomposition (MVMD) for enhanced DCM (MVMD-DCM). This method works on the extracted time-series of the regions of interest by reducing the contribution of noise, which ultimately ensures that only relevant task-related information in the time-series are fed to DCM. We demonstrate the effectiveness of MVMD-DCM in mitigating the impact of noise using simulated task-based fMRI data. Simulated data was generated at two Signal-to-Noise Ratio (SNR) levels of 0.5 (-3dB) and 1 (0dB). A comparative analysis with respect to model evidence, true model selection frequency and model parameters estimation demonstrated the superiority of MVMD-DCM over the default DCM for both SNR levels. Our study paves the way for the development of robust methods for inferring effective connectivity with DCM from noisy fMRI data.

A document by Jonas Noeland . Click on the document to view its contents.

Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review evaluating systems, which let users evaluate whether reviews are helpful or not; in turn, these evaluations assist review readers and encourage review contributors. Although review helpfulness scores have been studied extensively in the literature, our knowledge regarding their counterpart, review unhelpfulness scores, is lacking. Addressing this gap in the literature is important because researchers and practitioners have assumed that unhelpfulness scores are driven by intrinsic review characteristics and that such scores are associated with low-quality reviews. This study validates this conventional wisdom by examining factors that influence unhelpfulness scores. We find that, unlike review helpfulness scores, unhelpfulness scores are generally not driven by intrinsic review characteristics, as almost none of them are statistically significant predictors of an unhelpfulness score. We also find that users who receive review unhelpfulness votes are more likely to cast unhelpfulness votes for other reviews. Finally, unhelpfulness voters engage much less with the platform than helpfulness voters do. In summary, our findings suggest that review unhelpfulness scores are not driven by intrinsic review characteristics. Therefore, helpfulness and unhelpfulness scores should not be considered as two sides of the same coin.

In the evolving landscape of 5G network, network slicing has been considered as a key technology for the realization of multiple virtual networks running on a shared physical infrastructure, each designed to fulfill a specific service or application. However, with such networks, the dynamic and real-time allocation of these resources remains a prime concern, particularly with respect to highly variable conditions of traffic. In this paper, an adaptive novel real-time resource allocation algorithm under 5G network slicing is proposed, constructed by a hybrid optimization framework with AI-based traffic prediction. The proposed approach combines the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) with a heuristic adaptive adjustment mechanism to make it possible for robust and high-efficiency solutions to the problem of resource allocation optimization. An AI-based traffic prediction model is proposed, which utilizes ARIMA and LSTM techniques to accurately predict traffic demand in the future in order to proactively allocate resources. Wide simulation results indicate that the proposed methodology brings significant performance improvement in terms of resource utilization, latency, throughput, QoS, energy efficiency, and security level. The proposed approach ensures a consistent level of resource use, low latency, stable throughput, a high level of QoS, and efficient energy and security. Such results indicate the potential of our approach in improving the adaptability and efficiency of the 5G network slicing. Future work is aimed at energy efficiency improvement, followed by the model being fine-tuned through real-world datasets and adapted to a large scale of complex network environments.

This paper presents a new proof of the Goldbach conjecture, which is a well-known problem originating from number theory that was proposed by Christian Goldbach back in 1742. Our way gives a simple but deep understanding of the even integers can be written as the sum of two prime numbers Through examining fully we show that every other even integer larger than two will essentially represent itself in form adding up two prime numbers. The revelation of a straightforward and elegant line to this enduring conjecture comes from the use of basic number theory concepts such as; by going a step further and coming up with creative strategies. There is more evidence and sound payments she makes for her assertion as we continue. The centuries-old mathematical puzzle has been solved paving way for the exploration of new possibilities in number theory and we are grateful for the perspective and the persistence accorded us by God, which enabled us to reach this milestone.