In this paper, we propose an uplink multiuser-multiple input multiple output (MU-MIMO) cooperative retransmission control scheme for the reliable and efficient communication of autonomous unmanned aerial vehicles (i.e., flight communication and drone-to-drone communication). This scheme is a key element in the design of a drone-based ad-hoc network. Because drone relay transmission operates in the air, flexible wireless relay networks over large areas can be achieved without interference from multipath signals caused by buildings and obstacles in ground-based communication. However, two main challenges exist. First, the relay transmission system occupies bandwidth every time the drone relays information. Therefore, because the radio channel is shared by multiple relay stations, the throughput is greatly reduced. To improve throughput, it is better to transmit using the minimum number of relay stations. Ultimately, long-distance direct transmission is the best. The other issue is a direct result of the first issues solution: the propagation loss increase when relaying information directly over a long distance. Moreover, because the frequency of interference waves from the ground increases, transmission signal errors are an important issue. To solve this issue, a transmission scheme to reduce errors is required. The scheme proposed in this study achieves high transmission reliability and efficiency even under high transmission error conditions. The results of a computer simulation reveal that the proposed method achieves a direct transmission throughput that is 1.5 times better than that of a conventional scheme. It is also shown that the proposed scheme requires only about three stations to achieve acceptable results.

Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is a simple and powerful algorithm. However, the performance of PSO is degraded in the case of non-separable and ill-conditioned problems. In this article, we discuss the relation between the Hessian matrix of a function and the covariance matrix of the search distribution. The covariance matrix adaptation mechanism is required to solve non-separable and ill-conditioned problems. Therefore, in order to solve such problems, we propose a simple covariance matrix adaptation mechanism that uses the difference vector of the personal best positions. In addition, we propose a selection rule to improve the local search ability. Finally, we clarify the effectiveness of the proposed method in solving non-separable and ill-conditioned problems by using test functions.

Firefly algorithm is one of meta-heuristic algorithms. The fundamental principle of the firefly algorithm is based on the mimic of the characteristic of the blinking of natural fireflies. In this paper, we analyze the dynamics of the firefly algorithm. In order to analyze the dynamics rigorously, we apply a deterministic system that is removed the stochastic factors from the state update equation. The behavior of the individual deterministic firefly exhibits a chaotic behavior depending on the parameters. Therefore, we investigate relationship between the behavior of the firefly and the parameters. Also, we investigate the effect that the chaotic behavior influences the searching ability.

Cucumber farmers classify harvested cucumbers based on specific criteria before they are introduced to the market. During peak harvesting periods, farmers must process a large volume of cucumbers; however, the classification task requires specialized knowledge and experience. This expertise-dependent process poses a significant challenge, as it prevents untrained individuals, including hired workers, from effectively assisting in classification, thereby necessitating that farmers perform the task themselves. To address this issue, this study aims to develop a classification system that enables individuals, regardless of their level of expertise, to accurately classify cucumbers. The proposed system employs a convolutional neural network (CNN) to process cucumber images and generate classification results. The CNN used in this study consists of a total of 11 layers: 2 convolution layers, 2 pooling layers, 3 dense layers, and 4 dropout layers. To facilitate the widespread adoption of this system, improving classification accuracy is imperative. In this paper, we propose a method for embedding information related to cucumber length, bend, and thickness into the background space of cucumber images when creating training data. Specifically, this method encodes these attributes into the RGB color space, allowing the background color to vary based on the cucumber’s length, bend, and thickness. The effectiveness of the proposed method is validated through an evaluation of multi-class classification metrics, including accuracy, recall, precision, and F-measure, using cucumbers classified based on the criteria established by an actual agricultural cooperative. The experimental results demonstrate that the proposed method improves these evaluation metrics, thereby enhancing the overall performance of the system.Specifically, the proposed method achieved 79.1% accuracy, while the method without RGB color space achieved 70.1% accuracy. This indicates that the proposed method achieves 1.1 times better performance than the conventional method.

The firefly algorithm is a meta-heuristic algorithm, the fundamental principle of which mimic the characteristics associated with the blinking of natural fireflies. This paper, presents a rigorous analysis of the dynamics of the firefly algorithm, which the authors performed by applying a deterministic system that removes the stochastic factors from the state update equation. Depending on its parameters, the individual deterministic firefly algorithm exhibits chaotic behavior. This prompted us to investigate the relationship between the behavior of the algorithm and its parameters as well as the extent to which the chaotic behavior influences the searching ability of the algorithm.

In recent years, with the widespread use of the Internet, the number of malicious denial-of-service (DoS) attacks on network servers has increased. Malicious traffic reaches the server through multiple routers, which results in heavy load on many links; thus, load suppression of malicious traffic is important. Therefore, we propose a throughput suppression control scheme using multiple communication routes obtained by ant colony optimization (ACO). The ACO algorithm is used to distribute the routing path. The effectiveness of this proposed scheme is confirmed through computer simulations, which shows that the throughput can be adjusted by the selection probability of the longest route and the number of available routes.