Defects in multistage manufacturing processes (MMPs) decrease profitability and product quality. Therefore, MMP parameter optimization within a range is essential to prevent defects, achieve dynamic accuracy, and acco...
详细信息
With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. ...
详细信息
With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.
In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuse...
详细信息
In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile *** study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public *** proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger *** pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of ***,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual *** acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public *** implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.
Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing meth...
详细信息
Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However,most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense *** observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, learning accurate network dynamics with sparse, irregularly-sampled,partial, and noisy observations remains a fundamental challenge. We introduce a new concept of the stochastic skeleton and its neural implementation, i.e., neural ODE processes for network dynamics(NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6% and improving the learning speed for new dynamics by three orders of magnitude.
In this paper, we delve into the investigation of locating broadcast 2-centers of a tree T under the postal model. The problem asks to deploy two broadcast centers so that the maximum communication time from the cente...
详细信息
Mobile Ad hoc Network (MANET) is broadly applicable in various sectors within a short amount of time, which is connected to mobile developments. However, the communication in the MANET faces several issues like synchr...
详细信息
Drones are flying objects that may be controlled remotely or programmed to do a wide range of tasks, including aerial photography, videography, surveys, crop and animal monitoring, search and rescue missions, package ...
详细信息
Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitoni...
详细信息
Agricultural production is critical to the economy. This is one of the reasons why disease detection in plants is so important in agricultural settings, as plant disease is rather common. Farmers are not engaged in in...
详细信息
Agricultural production is critical to the economy. This is one of the reasons why disease detection in plants is so important in agricultural settings, as plant disease is rather common. Farmers are not engaged in increasing their agricultural productivity daily since there are no technologies in the previous system to detect diseases in various crops in an agricultural environment. With the exponential population growth, food scarcity is a huge concern globally. In addition to this, the productivity of agricultural products has been highly impacted by the rapid increase in phytopathological adversities. The main challenges in leaf segmentation and plant disease identification are prior knowledge is required for segmentation, the implementation still lacks the accuracy of results, and more tweaking is required. To reduce the devastating impacts of illnesses on the economy, early detection of illnesses in plants is therefore essential. This paper describes an approach for segmenting and detecting plant leaf diseases based on images acquired via the Internet of Things (IoT) network. Here, a plant leaf area is segmented with a UNet, whose trainable parameters are optimized using the Mayfly Bald Eagle Optimization (MBEO) algorithm. Further, plant type classification is carried out by the Deep batch normalized AlexNet (DbneAlexNet), optimized by the Sine Cosine Algorithm-based Rider Neural Network (SCA-based RideNN). Finally, the DbneAlexNet, with weights adapted by the MBEO algorithm, is used to identify plant disease. The Plant Village dataset is used to evaluate the proposed DbneAlexNet-MBEO for plant-type classification and disease detection. The efficiency of the UNet-MBEO for segmentation is examined based on the Dice coefficient and Intersectin over Union (IOU) and has achieved superior values of 0.927 and 0.907. Moreover, the DbneAlexNet-MBEO is examined considering accuracy, Test Negative Rate (TNR), and Test Positive Rate (TPR) and offered superior values of 0
The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
详细信息
暂无评论