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International Symposium on Antennas and Propagation (ISAP)

作者： Ravi Anand Debabrata Kumar Karmokar Anirban Sarkar School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India UniSA STEM University of South Australia Australia

ISBN: (数字)9798350364774

ISBN: (纸本)9798350364781

Varactor tuned interdigital capacitor (IDC) slot loaded microstrip antenna for fixed-frequency beam scanning is proposed for Ku-band applications. Microstrip patch loaded with an IDC-slot and two shorting vias for forward-backward frequency scanning is designed. A pair of oppositely oriented varactor diode at the gap of IDC slot is studied at fixed-frequency. The main beam scan from +2° to +60° (58°) in the forward quadrant at 13.9GHz, by changing the diode states from 0.9-0.1pF with maximum gain of 9.2dB.

关键词： Varactors Electric potential Slot antennas Microstrip antennas Voltage Leaky wave antennas Ku-band Microstrip Loaded antennas

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Tractogpt: A GPT Architecture for White Matter Tract Segmentation

Tractogpt: A GPT Architecture for White Matter Tract Segment...

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IEEE International Symposium on Biomedical Imaging

作者： Anoushkrit Goel Simroop Singh Ankita Joshi Ranjeet Ranjan Jha Chirag Ahuja Aditya Nigam Arnav Bhavsar School of Computing and Electrical Engineering Indian Institute of Technology Mandi Department of Mathematics Indian Institute of Technology Patna Post Graduate Institute of Medical Education Research Chandigarh

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter bundle segmentation is crucial for studying brain structural connectivity, neurosurgical planning, and neurological disorders. White Matter Segmentation remains challenging due to structural similarity in streamlines, sub-ject variability, symmetry in 2 hemispheres, etc. To address these challenges, we propose TractoGPT, a GPT-based architecture trained on streamline, cluster, and fusion data representations separately. TractoGPT is a fully-automatic method that generalizes across datasets and retains shape information of the white matter bundles. Experiments also show that TractoGPT outperforms state-of-the-art methods on average DICE, Overlap and Overreach scores. We use TractoInferno and 105HCP datasets and validate generalization across datasets.

关键词： Point cloud compression Neurological diseases Image segmentation Shape Magnetic resonance imaging Data visualization Streaming media Planning Neurosurgery White matter

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Construction of Simulation Environment and Design of Path Planning for Maze Robot

Construction of Simulation Environment and Design of Path Pl...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Wei Xie Qian Wang Dengyun Peng School of Information Science and Engineering Harbin Institute of Technology at Weihai Weihai China School of Computing Harbin Institute of Technology at Weihai Weihai China

Aiming at environment modeling and path planning search of the maze robot, a method how to construct simulation environment and design path planning algorithm is presented in this paper. On the basis of WPF framework, Using XAML interface description language and C# to build simulation environment, a multi-functional open maze generation interface and maze solution and verification platform is provided. The user can intuitively design the maze grid map to simulate the maze solving process by animation. They also can easily add new and verify algorithms by inheriting or rewriting the abstract class provided. What’s more, based on the traditional maze algorithm breadth-first searching strategy and depth-first searching strategy, the shortest path planning algorithm using a combination of breadth and depth is brought forward for demonstration. And the final test result has shown the feasibility and effectiveness of this method.

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Selective State Space Model for Monaural Speech Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Moran Zhang, Qiquan Wang, Mingjiang Zhang, Xiangyu Liu, Hexin Ambikairaiah, Eliathamby Chen, Deying Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Nanyang Technological University College of Computing and Data Science Singapore Singapore

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them suffers from the quadratic computational complexity with respect to the sequence length, which hampers their ability to handle long sequences. Recently a novel State Space Model called Mamba, which shows strong capability to handle long sequences with linear complexity, offers a solution to address this challenge. In this paper, we propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for speech enhancement. Our MambaDC marries the benefits of convolutional networks to model the local interactions and Mamba's ability for modeling long-range global dependencies. We conduct comprehensive experiments within both basic and state-of-the-art (SoTA) speech enhancement frameworks, on two commonly used training targets. The results demonstrate that MambaDC outperforms Transformer, Conformer, and the standard Mamba across all training targets. Built upon the current advanced framework, the use of MambaDC backbone showcases superior results compared to existing SoTA systems. This sets the stage for efficient long-range global modeling in speech enhancement. © 2025 IEEE.

关键词： State space methods

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Immediate-Access Indexing Using Space-Efficient Extensible Arrays 22

Immediate-Access Indexing Using Space-Efficient Extensible A...

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Proceedings of the 26th Australasian Document computing Symposium

作者： Alistair Moffat Joel Mackenzie School of Computing and Information Systems The University of Melbourne Australia School of Information Technology and Electrical Engineering The University of Queensland Australia

ISBN: (纸本)9798400700217

The array is a fundamental data object in most programs. Its key functionality – storage of and access to a set of same-type elements in O(1) time per operation – is also widely employed in other more sophisticated data structures. In an extensible array the number of elements in the set is unknown at the time the program is initiated, and the array might continue to grow right through the program’s execution. In this paper we explore the use of extensible arrays in connection with the task of inverted index construction. We develop and test a space-efficient extensible array arrangement that has been previously described but not to our knowledge employed in practice, and show that it adds considerable flexibility to the index construction process while incurring only modest run-time overheads as a result of access indirections.

关键词： dynamic arrays text querying Extensible arrays text indexing

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Novel hybrid classifier synergizing 3D_2D CNN with Support Vector Machine to enhance Hyperspectral image classification 14

Novel hybrid classifier synergizing 3D_2D CNN with Support V...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sridevi, S. Bhagya Sree, V.V.V. Sudarshan, R. PrasannaKumar, R. Shanmugakumar, M. Balasubramanian, E. Surendar, G. Balachandran, S. Kanimozhi, T. Choi, Jae Sung Rithani, M. Vellore Institute of Technology Scope Chennai India Amrita School of Computing Computer Science and Engg. Chennai India Amrita School of Computing Cse Chennai India Matic Iiitdm Kancheepuram India Mechanical Engineering Nittr Taramani Chennai India Institute of Science and Technology Aeronautical Engg VelTech Rangarajan Dr. Sagunthala R&d Chennai India Amrita School of Computing Central Library Chennai India Institute of Science and Technology ECE VelTech Rangarajan Dr.Sagunthala R&d Chennai India Sun Moon University Department of Cse Asan-si Korea Republic of

ISBN: (纸本)9798350335095

Hyperspectral imaging (HSI) is a potent tool that offers accurate identification and analysis of materials and objects in various domains ranging from remote sensing, agriculture, chemo-metric analysis for agricultural products and bio-medical applications, and much more. However, accurately categorizing HSI images remains challenging due to high dimensionality pertaining to spectral resolution in multiple bands, inadequate training samples, and pixel mixing. Deep learning Convolutional Neural Networks (CNN) have undergone development over time to process data of varied dimensions as 1D CNNs were developed to handle time series data, while 2D CNNs operate on spatial image data, and 3D CNNs evolved for processing 3D volumetric data. This paper proposes a synergistic hybrid classifier model that generalizes the 3D_2D CNN deep learning model to extract features from HSI images automatically and a robust Support Vector Machine (SVM) classifier to classify them accurately with less computational efficiency. The prominent intention of this proposed classifier is to be robust in terms of efficiency and less computationally complex. The proposed method involves HSI image dimensionality reduction using principal component analysis (PCA) and 3D_2D convolutional layers to extract significant spectral-spatial features. These feature maps are flattened, which is then used to train the SVM classifier. On the Kennedy Space Centre (KSC) HSI dataset, experimental results demonstrate that the proposed hybrid technique provides excellent accuracy and outperforms the modern techniques regarding classification precision and computational effectiveness. The proposed hybrid 3D 2D CNN synergized with the SVM approach achieved 99.8% accuracy, which is a 1.31% improvement compared to the existing Cube CNN with an SVM similar kind of hybrid classifier. © 2023 IEEE.

关键词： Convolution

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Symmetrical Voltages Dips Analysis in a Wind Turbine Based on DFIG for High Power Conversion 6th

Symmetrical Voltages Dips Analysis in a Wind Turbine Based o...

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6th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2022

作者： Bouregba, H. Hachemi, M. Mekhilef, S. Ratni, A. Laboratoire Energétique Mécanique & Ingénierie Faculté de Technologie Université M’Hamed Bougara Boumerdes Algeria School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia

ISBN: (纸本)9783031212154

The most common source of renewable energy generation worldwide is wind turbines (WT) based on doubly-fed induction generators (DFIGs). To keep such sources connected to the grid during outages, low voltage ride-through (LVRT) capacity must be increased. Due to its vulnerability to grid disruptions, it is essential to follow stringent methods to keep DFIG connected to the grid. As a result, this study employs the crowbar resistance protection approach on a practical 2MW to improve WT’s LVRT. Under steady-state and symmetrical grid disturbances, the effectiveness of the DFIG-based WT with crowbar resistance is investigated and compared. The dynamic performance of the proposed scheme is analyzed to that of the existing capacitor bank system using a variety of metrics in order to demonstrate its efficacity. The modeling and simulation tools MATLAB and Simulink are used in this study. The findings reveal that the suggested LVRT capability outperforms the competition in practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wind turbines

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MAIDS - A Framework for the Development of Multi-Agent Intentional Dialogue Systems 22

MAIDS - A Framework for the Development of Multi-Agent Inten...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Engelmann, Débora C. Panisson, Alison R. Vieira, Renata Hübner, Jomi Fred Mascardi, Viviana Bordini, Rafael H. School of Technology PUCRS Porto Alegre Brazil DIBRIS University of Genoa Genoa Italy Department of Computing UFSC Araranguá Brazil CIDEHUS University of Évora Évora Portugal Automation and Systems Engineering UFSC Florianópolis Brazil

This paper introduces a framework for programming highly sophisticated multi-agent dialogue systems. The framework is based on a multi-part agent belief base consisting of three components: (i) the main component is an extension of an agent-oriented programming belief base for representing defeasible knowledge and, in particular, argumentation schemes;(ii) an ontology component where existing OWL ontologies can be instantiated;and (iii) a theory of mind component where agents keep track of mental attitudes they ascribe to other agents. The paper formalises a structured argumentation-based dialogue game where agents can "digress" from the main dialogue into subdialogues to discuss ontological or theory of mind issues. We provide an example of a dialogue with an ontological digression involving humans and agents, including a chatbot that we developed to support bed allocation in a hospital. The example is used to show that our framework supports all features of recent desiderata for future dialogue systems. We also report an initial evaluation of the chatbot carried out by domain experts. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Ontology

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： M Ranjith Kumar G Jeevan Sendur S Praveen Kumar R Annamalai School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 %

关键词： Computer vision Accuracy Transfer learning Transportation Signal processing Real-time systems Character recognition Security License plate recognition Testing

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Enhancing Ensemble Model Accuracy and Interpretability: A Framework Integrating Rough Set Theory and Recursive Feature Elimination for Feature Selection and Interpretability with Association Rule Analysis in Ensemble Models

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SSRN 2024年

作者： Mwangi, Isaac Kega Nderu, Lawrence Mwangi, Ronald Waweru Njagi, Dennis Gitari School of ICT Media and Engineering Zetech University Nairobi2768 – 00200 Kenya School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi62000-00200 Kenya

In machine learning, feature selection is of utmost importance for augmenting the predictive capabilities of ensemble models. This paper presents an innovative hybrid framework for selecting features in ensemble models, which combines Rough Set Theory (RST) with Recursive Feature Elimination (RFE), complemented by Association Rule Mining, to enhance interpretability. The suggested method considerably improves ensemble models' prognostic accuracy and comprehensibility, particularly Random Forests and Gradient Boosting Machines. The framework starts with the RFE process, meticulously eliminating less influential features, and then applies RST to refine the feature set further by eliminating redundancies. This two-phase approach results in a feature set that is optimally reduced yet highly influential. By implementing this hybrid method on ensemble models, significant improvements in predictive accuracy are demonstrated across three diverse datasets: cancer, Pima Indians Diabetes, and a weather dataset from Underground. The accomplished accuracies for these datasets were 0.9663, 0.8793, and 0.8427, respectively, highlighting the proposed approach's effectiveness. This article also proposes the incorporation of association rule mining to analyze the outcomes of the models. This technique improves the understandability of the models, offering more profound insights into the connections and patterns, thus tackling the difficulty of interpretability in intricate ensemble models. Our empirical analysis confirms the effectiveness of the proposed hybrid feature selection model, representing a significant advancement in the field. The integration of RFE and RST optimizes the feature selection process and bridges the gap in interpretability, offering robust solutions for applications where accuracy and understanding of model decisions are crucial. © 2024, The Authors. All rights reserved.

关键词： Rough set theory

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