Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for pe...
Recently, medical research has revealed that diffusion weighted imaging (DWI) is less sensitive than susceptibility-weighted imaging (SWI) for acute ischemic stroke. Brain vein analysis in SWI is very important for personalised therapy, quantitative diagnosis, and prognosis of acute ischemic stroke. Therefore, the accurate segmentation of veins in SWI images has important significance in supplementary diagnosis and medical research. However, vein segmentation in SWI is a challenging task because different veins show distinct variability and uneven intensity and are difficult to manually split. This paper proposes an improvement method based on U-net, by using dense connection and mixing loss function, the best Dice coefficient and the ASSD were obtained by other comparison methods. The method presented in this article can be used as a potential tool to accurately measure SWI cerebral veins and help physicians in decision making.
While multi-robot systems have been broadly researched and deployed, their success is built chiefly upon the dependency on network infrastructures, whether wired or wireless. Aiming at the first steps toward de-coupli...
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At present, with the development of modern IT technologies and artificial intelligence methods, it is relevant to develop adaptive distance education systems for students of technical specialties capable of providing ...
At present, with the development of modern IT technologies and artificial intelligence methods, it is relevant to develop adaptive distance education systems for students of technical specialties capable of providing a personalized cognitive approach to the process of obtaining knowledge and timely adjustment of the learning trajectory. Particular difficulties arise when teaching the principles of operation of complex industrial equipment based on microprocessor technology, the basics of programming and the development of automatic control systems based on them. The problems associated with the complexity of adapting technical training courses for specific students and the peculiarities of information perception in the absence of direct contact with the teacher can be successfully solved with the introduction of algorithms based on a unified artificial immune system (UAIS). An innovative e-learning technology is proposed for students of engineering specialties in order to obtain high-quality technical professional education in a short time on real industrial equipment. When creating the technology and predicting the learning outcomes of students, immunological homeostasis was used to quickly correct the learning process. UAIS was developed on the basis of modified clonal selection, negative selection and immune network algorithms in order to identify the most effective of them in predicting multivariate dynamic data about students and forming an adequate immune response to maintain immunological homeostasis. The main stages and advantages of the proposed technology are given. The simulation results were obtained with the participation of students of the Faculty of Information Technology on the basis of the Kazakh-British Technical University.
Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a...
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Security and safety are of paramount importance to human-robot interaction, either for autonomous robots or human-robot collaborative manufacturing. The intertwined relationship of security and safety has imposed new ...
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Composite services (CSs) are large-grained services composed of simple ones to satisfy users’ complex requirements. A major issue concerning a CS is that the general quality of service (GQoS) and the domain quality o...
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This paper presents an engine that provides graphics and physics capabilities. It can be used as a powerful library for modern real-time 3D realistic WebGLbased 3D applications. There are two main parts proposed in th...
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In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on t...
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.
Active Object Tracking (AOT) aims to maintain a specific relation between the tracker and object(s) by autonomously controlling the motion system of a tracker given observations. AOT has wide-ranging applications, suc...
In this contribution, we extend the hybridization framework for the Hodge Laplacian [Awanou et al., Hybridization and postprocessing in finite element exterior calculus, 2023] to port-Hamiltonian systems describing li...
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