In this paper, we present a novel photometric compensation network named CASEformer, which is built upon the Swin module. For the first time, we combine coordinate attention and channel attention mechanisms to extract rich features from input images. Employing a multi-level encoder-decoder architecture with skip connections, we establish multiscale interactions between projection surfaces and projection images, achieving precise inference and compensation. Furthermore, through an attention fusion module, which simultaneously leverages both coordinate and channel information, we enhance the global context of feature maps while preserving enhanced texture coordinate details. The experimental results demonstrate the superior compensation effectiveness of our approach compared to the current state-of-the-art methods. Additionally, we propose a method for multi-surface projection compensation, further enriching our contributions.

Human action recognition (HAR) exhibits limited accuracy in video surveillance due to the 2D information captured with monocular cameras. To address the problem, a depth estimation-based human skeleton action recognition method (SARDE) is proposed in this study, with the aim of transforming 2D human action data into 3D format to dig hidden action clues in the 2D data. SARDE comprises two tasks, i.e., human skeleton action recognition and monocular depth estimation. The two tasks are integrated in a multi-task manner in end-to-end training to comprehensively utilize the correlation between action recognition and depth estimation by sharing parameters to learn the depth features effectively for human action recognition. In this study, graph-structured networks with inception blocks and skip connections are investigated for depth estimation. The experimental results verify the effectiveness and superiority of the proposed method in skeleton action recognition that the method reaches state-of-the-art on the datasets.

Two-ray (TR) fading model is one of the fading models to represent a worst-case fading scenario. We derive the exact closed-form expressions of the generalized moment generating function (G-MGF) for the TR fading model, which enables us to analyze the numerous types of wireless communication applications. Among them, we carry out several analytical results for the TR fading model, including the exact ergodic capacity along with asymptotic expressions and energy detection performance. Finally, we provide numerical results to validate our evaluations.

According to the match-degree between lightpaths, an HC-sharing approach is proposed to assign wavelength for an arriving transmission request for dynamic traffic in LOBS-based datacenter networks. The simulation results demonstrate that the proposed approach can provide lower block probability than other approaches for both unicast and multicast transmissions.

In networked control systems, uncontrollable events may unexpectedly occur in a plant before a proper control action is applied to the plant due to communication delays. In the area of supervisory control of discrete event systems, Park and Cho [5] proposed the notion of delay-nonconflictingness for the existence of a supervisor achieving a given language specification under communication delays. In this paper, we present the algebraic properties of delay-nonconflicting languages which are necessary for solving supervisor synthesis problems under communication delays. Specifically, we show that the class of prefix-closed and delay-nonconflicting languages is closed under intersection, which leads to the existence of a unique infimal prefix-closed and delay-nonconflicting superlanguage of a given language specification.

We propose a novel epidemic routing policy, named energy optimal epidemic routing, for delay tolerant networks (DTNs). By investigating the tradeoff between delay and energy, we found the optimal transmission range as well as the optimal number of infected nodes for the minimal energy consumption, given a delivery requirement, specifically delay bound and delivery probability to the destination. We derive an analytic model of the Binary Spraying routing to find the optimal values, describing the delay distributions with respect to the number of infected nodes.

A method to construct Boolean functions with maximum algebraic immunity have been proposed in . Based on that method, we propose a different method to construct Boolean functions on even variables with maximum algebraic immunity in this letter. By counting on our construction, a lower bound of the number of such Boolean functions is derived, which is the best among all the existing lower bounds.

This paper presents algorithms for extracting the values of relevant parameters from field measurements and 3-dimensional geographical data to be used in neural network modeling of wave propagation loss in microcells. The algorithms extract the feature values from 3-dimensional elevation maps and vector maps based on the theory in Computational Geometry. The neural networks trained on these parameters as their input approximate the function of wave propagation loss and can produce predictions with high accuracy. Some experimental results which show the superior performance of our approach over COST-231 method in actual PCS cell sites operating in the city of Seoul are presented.

In a real-time system, when the execution of a task is preempted by another task, the interrupted task falls into a blocked state. Since its re-execution begins from the interrupted point generally, the task's timer containing the remaining time until its completion should be maintained in the blocked state. This is the reason for introducing the notion of memorable events in this paper. We present a new timed discrete event model (TDEM) that adds the memorable events to the TDEM framework of Brandin and Wonham (1994). Using supervisory control theory upon the proposed TDEM, we analyze the schedulability of preemptable periodic and sporadic tasks executing on a uniprocessor.

In this paper, a new parameter estimator for coherently distributed (CD) noncircular (NC) signals is proposed, and can estimate both the central direction-of-arrivals (DOAs) and the angular spreads. It can also be considered as an extended version of the generalized Capon method by using both covariance matrix and an elliptic covariance matrix. The central DOAs and angular spreads are obtained by two-dimensional spectrum-peak searching. Numerical examples illustrate that the proposed method can estimate the central DOAs and the angular spreads when the number of signals is greater than the number of sensors. The proposed method also offers better performance than the methods against which it is compared.

In this study, a wideband 3/4 elliptical ring patch operating millimeter wave band is proposed. Using this structure, the patch antenna is designed for circular polarization and wide-band operation at about 32.1-40 GHz for millimeter wave communication. Simulated and measured results for main parameters such as voltage standing wave ratio (VSWR), impedance bandwidth, axial ratio, radiation patterns and gains are also discussed. The study shows that modeling of such antennas, with simplicity in designing and feeding, can well meet the requirements of millimeter-wave wireless communication systems.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.