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2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007

作者： Lopez, Miguel Angel Pomares, Hector Damas, Miguel Prieto, Alberto De La Hernandez, Eva Maria Plaza Department of Computer Architecture and Computer Technology University of Granada

ISBN: (纸本)3540730524

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based braincomputer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of Kohonen Maps as feature selector for a selective attention to auditory stimuli based BCI system. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Feature extraction

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Signal processing and perceptrons in an auditory based brain-computer interface

Signal processing and perceptrons in an auditory based brain...

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8th International Conference on Hybrid Intelligent Systems, HIS 2008

作者： Lopez, M.A. Pomares, H. Prieto, A. Pelayo, F. Department of Computer Architecture and Computer Technology University of Granada

ISBN: (纸本)9780769533261

Selective attention to visual or auditory stimuli that elicits steady-state visual or auditory responses (SSVEP or ASSR respectively) amplifies the power of those flickering frequencies of the stimuli measured in the electroencephalography (EEG). The design of brain-computer interfaces (BCI) based on selective attention to auditory stimuli that elicits ASSRs has two major advantages: First, no much training is needed as selective attention is an inherent skill of human beings. Second, as the human hearing system behaves as an amplitude modulated (AM) envelope detector, if the auditory stimuli are previously AM modulated, a steady-state response signal can be easily elicited. As EEG signals are considered to be non stationary, this is an interesting approach to EEG analysis as the feature of our interest can be considered stationary, hence Fourier analysis applied. In this paper we study the behavior of a Perceptron as a classifier of a BCI system where the features for classification are extracted from EEG as responses to AM modulated auditory stimuli and how we can use it in a BCI system. © 2008 IEEE.

关键词： Brain computer interface

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Integrating Simulation and AI for Optimal Electric Vehicle Charging Infrastructure: Achievements and Future Directions 16th

Integrating Simulation and AI for Optimal Electric Vehicle...

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16th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Guisado-Lizar, José-Luis Ragel-Díaz-Jara, David Navas-Orozco, Antonio Morera-Figueroa, José Diaz-del-Rio, Fernando Morón-Fernández, María José Cerezuela-Escudero, E. Cardenas-Montes, Miguel Jiménez-Moreno, Gabriel Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain I3US Research Institute Universidad de Sevilla Seville Spain SCORE Lab Universidad de Sevilla Seville Spain CIEMAT Centro Inv. Energéticas Medioambientales y Tecnológicas Madrid Spain

ISBN: (纸本)9783031873447

Electric vehicles (EVs) are the most prominent technology for reducing road transport emissions. Their rapid adoption necessitates efficient deployment of charging infrastructure. This study presents works in progress in an innovative approach integrating simulation and artificial intelligence (AI) to optimize EV charging station placement. We develop a microscopic traffic simulation model combining cellular automata and agent-based techniques to capture the dynamic interactions between vehicles, charging stations, and urban environments. Genetic algorithms and neural networks are employed to navigate the complex solution space and identify optimal configurations. The model considers multiple factors including traffic patterns, air quality, grid impacts, and urban mobility. The objective of this holistic approach is to improve charging infrastructure planning, leading to reduced congestion, lower emissions, and enhanced urban mobility. We discuss achievements to date and outline future research directions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Genetic algorithms

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IEEE 8th VR Workshop on Sonic Interactions for Virtual Environments

IEEE 8th VR Workshop on Sonic Interactions for Virtual Envir...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Stefania Serafin Ali Adjorlu Rolf Nordahl Federico Fontana Michele Geronazzo Tifanie Bouchara Florent Berthaut Romain Michon Dorte Hammershøj Lorenzo Picinali Department of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Department of Mathematics Computer Science and Physics University of Udine Udine Italy Department of Engineering and Management University of Padova Vicenza Italy LISN CNRS Université Paris-Saclay France CRIStAL CNRS Univ. Lille Lille France INRIA Lyon France Department of Electronic Systems Aalborg University Denmark Dyson School of Design Engineering Imperial College London London UK

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Sonic interaction design is defined as the study and exploitation of sound as one of the principal channels conveying information, meaning, and aesthetic/emotional qualities in interactive contexts. This field lies at the intersection of interaction design, and sound and music *** the virtual reality community, the focus of research on topics related to auditory feedback has been rather limited when compared, for example, to the focus placed on visual feedback or even on haptic feedback. However, in such communities as film production or product sound design it is well known that sound is a powerful way to communicate meaning and emotion to a scene or a *** 2025 is the 8th of a series of workshops, whose main goal is to increase awareness among the virtual reality community of the importance of sonic elements when designing virtual/augmented/mixed reality (XR hereafter) environments. Participants to the workshop will also discuss how research in other related fields such as film sound theory, product sound design, sound and music computing, game sound design, and accessibility can inform designers of XR environments. Moreover, the workshop will feature state-of-the-art research in the field of sound for XR environments.

关键词： Visualization Three-dimensional displays Conferences Spatial audio Music Virtual environments Production Games User interfaces Haptic interfaces

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Cuddapah Anitha Dalia Al-Tarazi GD. Vignesh A. Vinotha Vasuki Jayant Giri M. Dinesh Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati India Department of Architecture Engineering Faculty of Engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering St. Joseph's College of Engineering Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Background: Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security.

关键词： Vehicular ad hoc networks Routing Throughput Synchronization Resource management Dedicated short range communication Particle swarm optimization Artificial intelligence Vehicle dynamics Testing

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IEEE 8th VR Workshop on Sonic Interactions for Virtual Environments

IEEE 8th VR Workshop on Sonic Interactions for Virtual Envir...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Serafin, Stefania Adjorlu, Ali Nordahl, Rolf Fontana, Federico Geronazzo, Michele Bouchara, Tifanie Berthaut, Florent Michon, Romain Hammershøj, Dorte Picinali, Lorenzo Aalborg University Department of Architecture Design and Media Technology Copenhagen Denmark University of Udine Department of Mathematics Computer Science and Physics Udine Italy University of Padova Department of Engineering and Management Vicenza Italy Cnrs Université Paris-Saclay Lisn Orsay France Univ. Lille CRIStAL Cnrs Lille France Inria Lyon France Aalborg University Department of Electronic Systems Denmark Imperial College London Dyson School of Design Engineering London United Kingdom

ISBN: (纸本)9798331514846

关键词： 3D audio auditory display augmented reality eXtended reality H.5.5 [Information interfaces and presentation (e.g., HCI)]: Miscellaneous H.5.5 [Information interfaces and presentation (e.g., HCI)]: Sound and Music Computing virtual reality

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Emerging methodologies in multiple sequence alignment using high throughput data

Emerging methodologies in multiple sequence alignment using ...

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作者： Guzman, Francisco M. Ortuño Rojas, I. Pomares, H. Urquiza, J.M. Florido, J.P. Department of Computer Architecture and Computer Technology University of Granada Spain

ISBN: (纸本)9783642199134

New computational methodologies are increasingly being demanded in Bioinformatics due to the amount of data provided by high-throughput experiments. One of these approaches is multiple sequence alignment since feature integration is necessary to obtain more accurate and faster alignments. Alignments of nucleotide and protein sequences can help us to understand tasks like biological functions or structures in these molecules. Recent applications tend to use more available data that represent similarity among sequences: homologies, structures, functions, domains, motifs, etc. Thus, we present a review of current methods in multiple sequence alignments and their improvements integrating accurately and efficiently these heterogeneous data. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Throughput

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Short-term prediction of chaotic time series by using RBF network with regression weights.

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International journal of neural systems 2000年第5期10卷 353-364页

作者： Rojas, I. Gonzalez, J. Cañas, A. Diaz, A.F. Rojas, F.J. Rodriguez, M. Department of Architecture and Computer Technology University of Granada Spain

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

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Scalable selective re-execution for EDGE architectures

Scalable selective re-execution for EDGE architectures

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作者： Desikan, Rajagopalan Sethumadhavan, Simha Burger, Doug Keckler, Stephen W. Computer Architecture and Technology Laboratory Department of Computer Sciences University of Texas Austin Computer Architecture and Technology Laboratory Department of Electrical and Computer Engineering University of Texas Austin

Pipeline flushes are becoming increasingly expensive in modern microprocessors with large instruction windows and deep pipelines. Selective re-execution is a technique that can reduce the penalty of mis-speculations by re-executing only instructions affected by the mis-speculation, instead of all instructions. In this paper we introduce a new selective re-execution mechanism that exploits the properties of a dataflow-like Explicit Data Graph Execution (EDGE) architecture to support efficient mis-speculation recovery, while scaling to window sizes of thousands of instructions with high performance. This distributed selective re-execution (DSRE) protocol permits multiple speculative waves of computation to be traversing a dataflow graph simultaneously, with a commit wave propagating behind them to ensure correct execution. We evaluate one application of this protocol to provide efficient recovery for load-store dependence speculation. Unlike traditional dataflow architectures which resorted to single-assignment memory semantics, the DSRE protocol combines dataflow execution with speculation to enable high performance and conventional sequential memory semantics. Our experiments show that the DSRE protocol results in an average 17% speedup over the best dependence predictor proposed to date, and obtains 82% of the performance possible with a perfect oracle directing the issue of loads. Copyright 2004 ACM.

关键词： Pipeline processing systems

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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