One of the critical missions for next-generation wireless communication systems is to fulfill the high demand for massive Machine-Type Communications (mMTC). In mMTC systems, a sporadic transmission is performed between machine users and base station...
In this study, we investigate event-triggered distributed fusion estimation for asynchronous Markov jump systems subject to correlated noises and fading measurements. The measurement noises are interrelated, and they are simultaneously coupled with t...
For a mobile robot, navigation in a densely crowded space can be a challenging and sometimes impossible task, especially with traditional techniques. In this paper, we present a framework to train neural controllers for differential drive mobile robo...
We consider the distributed setting of N autonomous mobile robots that operate in Look-Compute-Move (LCM) cycles and use colored lights (the robots with lights model). We assume obstructed visibility where a robot cannot see another robot if a third...
Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data...
In distributed optimization, communication bottlenecks and computational heterogeneity are two critical challenges that undermine system scalability and efficiency. To address these issues, we propose a synchronous algorithm, CC-GT (Conjugate Compres...
With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. However, edge computing ofte...
The acquisition, processing, and monitoring of biomechanical variables in dynamic environments require sensor network architectures capable of handling high concurrency and large data volumes. This study aims to develop, validate, and deploy a robust...
Through a series of projects carried out by the Computer–Human Interaction and COllaboration (CHICO) group of the University of Castilla-La Mancha, some proposals are presented to improve the current e-Learning systems by making use of different para...
In this paper, an asynchronous and decoupled Hardware-In-the-Loop simulation of a DC nanogrid is presented. The DC nanogrid is a recent way to solve problems presented in traditional power generation, such as low efficiency, pollution, and cost incre...
As is well-known, non-proportional fatigue loading, such as asynchronous one, can have significant detrimental effects on the fatigue behavior of metallic materials by reducing the fatigue strength/fatigue limit and by leading to a fatigue damage acc...
Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel processor architectures can include thousands of processing elements on a single chip and using them efficiently remains a big issue. An interesting source o...
In this paper, we apply a Markov decision process to find the optimal asynchronous dynamic policy of an energy-efficient data center with two server groups. Servers in Group 1 always work, while servers in Group 2 may either work or sleep, and a fast...
In time-of-arrival (TOA)-based source localization, accurate positioning can be achieved only when the correct signal propagation time between the source and the sensors is obtained. In practice, a clock error usually exists between the nodes causing...
Addition is the key operation in digital systems, and floating-point adder (FPA) is frequently used for real number addition because floating-point representation provides a large dynamic range. Most of the existing FPA designs are synchronous and th...
The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault t...
The back-projection (BP) algorithm is completely accurate in the imaging principle, but the computational complexity is extremely high. The single-precision arithmetic used in the traditional graphics processing unit (GPU) acceleration scheme has low...
Neuromorphic systems are bio-inspired and have the potential to break through the bottleneck of existing intelligent systems. This paper proposes a neuromorphic high-speed object recognition method based on DVS and SpiNNaker and implements a system i...
This paper investigates the tracking consensus of nonlinear multi-agent systems under undirected topology, considering asynchronous switching caused by delays between communication topology switching and controller switching. First, by using the prop...
Driven by emerging technologies such as edge computing and Internet of Things (IoT), recent years have witnessed the increasing growth of data processing in a distributed way. Federated Learning (FL), a novel decentralized learning paradigm that can...
In the field of robotics, a lot of theoretical models have been settled to formalize multi-agent systems and design distributed algorithms for autonomous robots. Among the most investigated problems for such systems, the study of the Uniform Circle F...
Processes considered rendering information dynamics have been studied, among others in: questions and answers, observations, communication, learning, belief revision, logical inference, game-theoretic interactions and computation. This article will p...
In vehicular networks, inter-vehicle data sharing and collaborative computing improve traffic efficiency and driving experience. However, centralized processing faces challenges with privacy, communication bottlenecks, and real-time performance. This...
In the post-Moore’s Law era, many-core processors integrate diverse on-chip asynchronous accelerators. Existing programming models such as CUDA and OpenCL suffer from coarse-grained scheduling, and fine-grained optimizations only support limite...
The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabiliti...
Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit thei...
The subject of this research is the sensorless control method verification of an asynchronous motor by a simulation in the area of low speed (up to 5 Hz) via the high-frequency analog signal injection method. The implementation of the continuous sign...
With the development of the Internet of Things, edge computing applications are paying more and more attention to privacy and real-time. Federated learning, a promising machine learning method that can protect user privacy, has begun to be widely stu...
Distributed machine learning is primarily motivated by the promise of increased computation power for accelerating training and mitigating privacy concerns. Unlike machine learning on a single device, distributed machine learning requires collaborati...
This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable c...
In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigatio...
With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are red...
The use of artificial intelligence (AI) and machine learning (ML) models has increased significantly in recent years, with rapid advances across domains such as natural language processing, computer vision, code generation, and scientific computing....
We present an open-source code, Xyst, intended for the simulation of complex-geometry 3D compressible flows. The software implementation facilitates the effective use of the largest distributed-memory machines, combining data-, and task-parallelism o...
Multi-radar systems can significantly improve tracking robustness and accuracy, but practical deployments are challenged by asynchronous sensing timestamps across distributed platforms and by limited communication bandwidth. This paper proposes a com...
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce f...
To address the end-edge computation offloading challenge in the multi-terminal and multi-server environment, this paper proposes an intelligent computation offloading algorithm based on Lyapunov optimization and deep reinforcement learning. We formul...
Aiming for simplicity and efficiency in the domain of edge computing, DOORS is a distributed system expected to scale up to hundreds of nodes, which encapsulates application state and behavior into objects and gives them the ability to exchange async...
This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data acros...
The human respiratory Central Pattern Generator (CPG) is a complex and tightly regulated network of neurons responsible for the automatic rhythm of breathing. Among the brain nuclei involved in respiratory control, excitatory neurons within the PreBo...
This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. Th...
In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion o...
The dynamic vision sensor (DVS) measures asynchronously change of brightness per pixel, then outputs an asynchronous and discrete stream of spatiotemporal event information that encodes the time, location, and sign of brightness changes. The dynamic...
In this paper, we propose FDSTCN-EEG, which is a customized federated learning framework for EEG-based seizure detection that leverages deep depthwise separable temporal convolutions and asynchronous model aggregation. The network design tackles majo...
To address the inherent limitations of Future in asynchronous programming frameworks, JDK 1.8 introduced the CompletableFuture class, which features approximately 50 different methods for composing and executing asynchronous computations and handling...
A relevant goal in human–computer interaction is to produce applications that are easy to use and well-adjusted to their users’ needs. To address this problem it is important to know how users interact with the system. This work constitutes a methodo...
Reconfigurable processor-based acceleration of deep convolutional neural network (DCNN) algorithms has emerged as a widely adopted technique, with particular attention on sparse neural network acceleration as an active research area. However, many co...
The biogenetic origin of triterpene dimers from the Celastraceae family has been proposed as assisted hetero-Diels-Alder reaction (HDA). In this work, computational calculation of HDA between natural quinonemethides (tingenone and isopristimerol) and...
The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational model...
Thanks to their event-driven asynchronous computing capabilities and low power consumption advantages, spiking neural networks (SNNs) show significant potential for computer vision tasks, especially in object detection. However, effective training me...
of 8