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Recent AI-based technologies in mobile environments have enabled sign language recognition, allowing deaf individuals to communicate effectively with hearing individuals. However, varying computational performance across different mobile devices can result in differences in the number of image frames extracted in real time during sign language utterances. The number of extracted frames is a critical factor influencing the accuracy of sign language recognition models. If the number of extracted frames is too small, the performance of the sign language recognition model may decline. Additionally, detecting the start and end points of sign language utterances is crucial for improving recognition accuracy, as the period before the start point and after the end point often involves no action being performed. These parts do not capture the unique characteristics of each sign language. Therefore, this paper proposes a technique to dynamically adjust the sampling rate based on the number of frames extracted in real time during sign language utterances in mobile environments, with the aim of accurately detecting the start and end points of the sign language. Experiments were conducted to compare the proposed technique with the fixed sampling rate method and with the no-sampling method as a baseline. Our findings show that the proposed dynamic sampling rate adjustment method improves performance by up to 83.64% in top-5 accuracy and by up to 66.54% in top-1 accuracy compared to the fixed sampling rate method. The performance evaluation results underscore the effectiveness of our dynamic sampling rate adjustment approach in enhancing the accuracy and robustness of sign language recognition systems across different operational conditions.

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.

The interplay between reduced dimensionality and interactions in monolayer transition metal dichalcogenides has been of great research interest. Here the authors report an insulating dimer ground state in 1T-IrTe2, driven by the combined effect of the charge density wave instability and local atomic bond formation.

We investigate the real-space profile of effective Coulomb interactions in correlated kagome materials. By particularizing to KV_{3}Sb_{5}, Co_{3}Sn_{2}S_{2}, FeSn, and Ni_{3}In, we analyze representative cases that exhibit a large span of correlation-mediated phenomena, and contrast them to prototypical perovskite transition metal oxides. From our constrained random phase approximation studies we find that the on-site interaction strength in kagome metals not only depends on the screening processes at high energy, but also on the low-energy hybridization profile of the electronic density of states. Our results indicate that rescaled by the on-site interaction amplitude, all kagome metals exhibit a universal long-range Coulomb behavior.

Numerous services and applications have been developed to monitor anomalies or collect various sensing information in large-scale monitoring areas using drones. Nonetheless, interruptions of drone missions in such areas occasionally occur due to network errors, low battery levels, or physical defects, such as damage to the rotor and propeller. Checkpointing is a technique that periodically saves the system’s state, allowing it to be restored to that point in the event of a failure. In such circumstances, checkpointing techniques can be used to periodically save information related to the drone mission and replace a malfunctioning drone with the saved checkpoint information. In this paper, we propose a dynamic checkpoint interval decision algorithm for a live migration-based drone-recovery system. The proposed scheme minimizes the drone’s energy consumption while efficiently performing checkpointing. According to the basic experimental results, the proposed scheme consumed only about 3.51% more energy, while performing about 25.97% more checkpoint operations compared to the FIC (Fixed Interval Checkpointing) scheme. By using the proposed scheme, it is possible to increase the availability of checkpoint information and quickly resume drone missions, while minimizing the increase in energy consumption of the drone by saving checkpoints more frequently. Therefore, the proposed scheme can improve the reliability and stability of drone-based services.

A heterogeneous computing environment has been widely used with UAVs, edge servers, and cloud servers operating in tandem. Various applications can be allocated and linked to the computing nodes that constitute this heterogeneous computing environment. Efficiently offloading and allocating computational tasks is essential, especially in these heterogeneous computing environments with differentials in processing power, network bandwidth, and latency. In particular, UAVs, such as drones, operate using minimal battery power. Therefore, energy consumption must be considered when offloading and allocating computational tasks. This study proposed an energy consumption fairness-aware computational offloading scheme based on a genetic algorithm (GA). The proposed method minimized the differences in energy consumption by allocating and offloading tasks evenly among drones. Based on performance evaluations, our scheme improved the efficiency of energy consumption fairness, as compared to previous approaches, such as Liu et al.’s scheme. We showed that energy consumption fairness was improved by up to 120%.

As exhaust gas systems of ships become more complicated, it is necessary to calculate an accurate pressure loss at their design stage. If the dynamic loss coefficients of non-standard fittings mainly used in exhaust gas pipe (EGP) are well-documented, it would be possible to calculate precise pressure loss more readily than using the conventional method that analyzes the entire system. In the case of a ship's EGP, the flow rates and temperatures of exhaust gas are determined by engine specifications, and the range of the flow rate and temperature is limited according to operating conditions. In addition, as it is possible to define non-standard fittings frequently used in an EGP, a database can be easily constructed and effective. This paper illustrates effective parameters and analysis cases of several types of non-standard fittings mounted in ship EGPs. The analysis procedure proposed in this paper is verified using existing research results on HVAC fittings. The numerical procedure, which is minimally affected by manpower and grid, is established such that it can be applied at the industry level.

By using drones as mobile IoT gateways in wireless sensor networks, data collection can be possible even in areas where IoT wireless communication is not available. A drone can move near the rendezvous node of the wireless sensor networks and receive the collected data from the node. Then, the drone moves to an area where IoT wireless communication is possible and transfers the data to the IoT server. Because drones operate as a battery, it is important to optimize the energy consumption. Existing research reduces the drone's energy consumption by minimizing flight distance using predetermined information such as the orbit, sensor location and network topology. However, if data collection fails, the additional flying distance of the drone increases dramatically and energy consumption also increases. It is important to increase the success rate of data collection. In this paper, we propose a dynamic rendezvous node estimation scheme, taking into account the average of drone speed and data collection latency without predetermined information. Simulation results show that the proposed scheme is more reliable in terms of the data collection success rate.

Effects of nonmagnetic indium ion substitution on magnetic properties of M-type strontium hexaferrites SrFe12-xInxO19 (x=0, 0.25, 0.5, 0.75, and 1) have been studied by crystallographic and magnetic measurements. Samples were prepared by citric auto-combustion method. Rietveld analysis of X-ray diffraction profiles showed that the samples were single phase with the space group P63/mmc and lattice constants a and c increased linearly with In3+ content. Mössbauer spectra showed that In3+ mainly replaced Fe3+ ions in 4f2 sites and that 12k subspectra split into two distinct components 12k1 and 12k2. Analysis of hysteresis curves showed that while the coercive force and the magnetic anisotropy constant decreased thru the whole doping range x, the saturation magnetization first increased until x=0.5 and decreased thereafter. This behavior was explained by the existence of two competing interactions in terms of nonmagnetic indium substitution.

Unconventional superconductivity: role of magnetic interactions in strontium ruthenate A new framework for analysing the role of magnetic interactions on the unconventional superconductivity in strontium ruthenate. Strontium ruthenate is an unconventional superconductor that used to be touted a potential three-dimensional analogue of Helium-3, as it was thought to have the same type of chiral p-wave pairing. It is now widely accepted that this is not the case, but many questions remain over the exact nature of the pairing, particularly regarding the role of magnetic interactions. An international team of researchers led by Bongjae Kim and Sergii Khmelevskyi from the University of Vienna and Vienna University of Technology now present a framework that can incorporate the leading isotropic and anisotropic magnetic interactions in a different but complimentary way to the widely used Hubbard-model, providing an alternative way of exploring the superconducting pairing symmetry.

Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time.

Recently, various technologies for utilizing unmanned aerial vehicles have been studied. Drones are a kind of unmanned aerial vehicle. Drone-based mobile surveillance systems can be applied for various purposes such as object recognition or object tracking. In this paper, we propose a mobility-aware dynamic computation offloading scheme, which can be used for tracking and recognizing a moving object on the drone. The purpose of the proposed scheme is to reduce the time required for recognizing and tracking a moving target object. Reducing recognition and tracking time is a very important issue because it is a very time critical job. Our dynamic computation offloading scheme considers both the dwell time of the moving target object and the network failure rate to estimate the response time accurately. Based on the simulation results, our dynamic computation offloading scheme can reduce the response time required for tracking the moving target object efficiently.

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency.