Wireless communications between vehicles is a focus of research in both the academic research community and automobile industry. Using Unmanned Aerial Vehicles (UAVs) or drones in wireless communications and Vehicular Ad-hoc Networks (VANETs) has started to attract attention. This paper proposes a routing protocol that uses infrastructure drones for boosting VANET communications to achieve a minimum vehicle-to-drone packet delivery delay. This paper proposes a closed-form expression for the probability distribution of the vehicle-to-drone packet delivery delay on a two-way highway. In addition, based on that closed-form expression, we can calculate the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case of the vehicle-todrone packet delivery delay. Moreover, this paper proposes a drones-active service (DAS) that is added to the location service in a VANET. This service dynamically and periodically obtains the required number of active drones based on the current highway connectivity state by obtaining the maximum distance between each two adjacent drones while satisfying a probabilistic constraint for vehicle-to-drone packet delivery delay. Our analysis focuses on two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our analysis and reflect the relation between the drone density, vehicular density and speed, other VANET parameters, and the vehicle-to-drone packet delivery delay.

Color and light are two ambient attributes for interior spaces that can be used in the design and modification of workspaces. The visual and psychological effects of color and light of each have been studied separately and widely. The aim of this study was to investigate the simultaneous effects of warm/cool white light on visual perception and mood in a simulated colored workspace. Thirty-three healthy male participants were recruited. They were asked to judge the visual perception and mood of three types of workspace that were designed by colors of white, red, blue, and lights of a cool and warm white in the random six sessions. The participants have experienced higher levels of tension, anger, depression, anxiety and lower levels of visual comfort, attractiveness, brightness and calmness of environment in the red condition than to white in both state of light. The blue wall reduced brightness and increased attractiveness of environment compared to white wall. Cool white light reduced the warmth of color and increased brightness in all three color environments compared to warm light. The preference for cool or warm light depends on the color of the environment's indoor surface. It seems that the combination of the white color and warm light or the blue color with cool light has a more favorable effect on visual perception and people's mood in workplaces.

Research on electromagnetic (EM) components is essential to enabling the design and optimization of such devices as antennas and filters, leading to improved functionality, reduced costs, and enhanced overall performance. This paper presents an overview of recent developments in optimization and design automation techniques for EM-component design and modeling. Limitations of conventional optimization methods are discussed, while the need for novel machine learning techniques capable of handling multiple objectives and large design spaces is highlighted. In this study, existing methods in the literature are reviewed from four viewpoints: structural view, algorithm view, component view, and application view. Different schemes in distinct design stages or applications are examined with advantages and drawbacks laid out for easier comprehension. Finally, to broaden the scope of optimization in the field of EM design and modeling, some prospective trends are pointed out to shed light on emerging research hotspots.

Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.

The observation has been made by Aujol and Gilboa that the cartoon and texture components of the decomposition of an image should not be correlated, as they are generated from independent processes. They use this observation in order to choose an optimal fidelity parameter lambda for the decomposition process. However, this determination can be quite inefficient since a wide range of parameters lambda must be searched through before an estimated optimal parameter can be found. In the present paper, we take a different approach, in which the cartoon and texture components are explicitly decorrelated by adding a decorrelation term to the energy functional of the decomposition model of Osher, Sole, and Vese (the OSV model). Decomposition results of improved quality over those from the OSV model are obtained, as quantified by a series of new decomposition quality measures, with cartoon and texture information better separated into their respective components. A new derivation of the OSV model is developed which maintains the texture subcomponents g₁ and g₂ so that discrimination results similar to those from other decomposition models (e.g., from the model of Vese and Osher and Improved Edge Segregation) may be obtained. This derivation is extended to the proposed model, for which discrimination results are obtained in a substantially smaller number of iterations.