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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Jonel R. Macalisang Alvin Sarraga Alon Julie Ann B. Susa Yolanda D. Austria Mark Melegrito Sammy V. Militante College of Industrial Technology Technological University of the Philippines Manila City Philippines Department of Trade and Industry Center for AI Research Makati City Philippines Department of Computer Engineering Southern Luzon State University Quezon Philippines Department of Computer Engineering Adamson University Manila Philippines Department of Electronics Engineering Technological University of the Philippines Manila Philippines College of Engineering and Architecture University of Antique Antique Philippines

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

This work introduces a new method for quantifying and sizing Brassica Oleracea instances via the use of unmanned aerial vehicle (UAV) imagery for instance segmentation. The goal of the project is to create a deep learning-based model that can recognize and classify Brassica Oleracea occurrences in images taken by unmanned aerial vehicles. The study uses the Mask R-CNN architecture and transfer learning strategies to train the model using a dataset of COCO-formatted annotated data and images of cabbage that was obtained from Mendeley Data. To improve resilience and variability, augmentation methods are used in dataset preparation. Evaluation criteria including mean Average Precision (mAP), precision, recall, and confidence levels are used to assess the model's performance. Findings show that the model performed very well, obtaining a remarkable mAP value of 98.6%. Moreover, inferencing test data shows how reliable the model is in identifying Brassica Oleracea occurrences in a variety of settings. The model continuously obtains high identification rates despite obstacles such as item incompleteness and illumination fluctuations, demonstrating its dependability and applicability for practical use in agricultural settings. The suggested strategy has the potential to advance crop management techniques and precision agriculture methods, enhancing agricultural sustainability and production.

关键词： Instance segmentation Accuracy Image recognition Crops Lighting Production Autonomous aerial vehicles Data models Sustainable development Context modeling

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Design of Miniaturized Metamaterial Reflector with Doubly Interdigital Structure for 5G Use Cases

Design of Miniaturized Metamaterial Reflector with Doubly In...

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International Electrical Engineering Congress (iEECON)

作者： Tanee Somwong Pongsathorn Chomtong Jirayut Khamsuthat Nonchanutt Chudpooti Prayoot Akkaraekthalin Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the design of a miniaturized metamaterial reflector composing of an open-loop transmission line unit cell structure. The transmission line ends are connected by using a double interdigital technique, resulting in a large capacitive value compared to the conventional methods. When combined with the open-loop line employing the trisection step-impedance structure with adjusting of the capacitive value, its second and third resonant frequencies will be controllable. As a result, this approach can reduce the unit cell size from a typical value of λ/2 to an approximate value of λ/16, therefore overall size of the reflector is significantly decreased. The designed unit cell is measured to be 1.05 cm × 1.0 cm. We implemented the proposed reflector on an FR-4 PCB with a relative permittivity of about 4.4, a substrate height (h) of 1.6 mm, and a loss tangent of about 0.02. The operating frequency range targets of 5G use cases were set to be at 1.8 GHz, 2.6 GHz and 3.5 GHz, respectively. When used with a dipole antenna, the bandwidths of 390 MHz, 604 MHz, and 1.1 GHz and the antenna gains of about 8.23 dB, 8.14 dB, and 8.19 dB were obtained at the operating frequencies, respectively.

关键词： Wireless communication 5G mobile communication Dipole antennas Simulation Resonant frequency Reflector antennas Transmission line measurements Metamaterials Gain Substrates

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SAMOYE: ZERO-SHOT SINGING VOICE CONVERSION MODEL BASED ON FEATURE DISENTANGLEMENT AND ENHANCEMENT

arXiv

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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangChina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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GöwFed - A novel Federated Network Intrusion Detection System

arXiv

引用

arXiv 2022年

作者： Belenguer, Aitor Pascual, Jose A. Navaridas, Javier Department of Computer Architecture and Technology University of the Basque Country UPV/EHU Manuel Lardizabal 1 Donostia-San Sebastian20018 Spain

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties - violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GöwFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GöwFed have been developed based on state-of the-art knowledge: (1) a vanilla version;and (2) a version instrumented with an attention mechanism. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance - across a set of designed experiments/scenarios. Overall, GöwFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks. © 2022, CC BY-NC-SA.

关键词： Internet of things

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

arXiv

引用

arXiv 2023年

作者： Wang, Yijun Sun, Changzhi Wu, Yuanbin Li, Lei Yan, Junchi Zhou, Hao WeChat Technical Architecture Department Tencent Inc China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science and Technology East China Normal University China ByteDance AI Lab China University of California Santa Barbara United States

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-byword interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1 ∼ 1.8 F1 points over the prior best unified model. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Göwfeda Novel Federated Network Intrusion Detection System

SSRN

引用

SSRN 2022年

Network intrusion detection systems are evolving into intelligent systems that perform data analysiswhile searching for anomalies in their environment. Indeed, the development of deep learningtechniques paved the way to build more complex and effective threat detection models. However,training those models may be computationally infeasible in most Edge or IoT devices. Currentapproaches rely on powerful centralized servers that receive data from all their parties – violatingbasic privacy constraints and substantially affecting response times and operational costs due to thehuge communication overheads. To mitigate these issues, Federated Learning emerged as a promisingapproach, where different agents collaboratively train a shared model, without exposing training datato others or requiring a compute-intensive centralized infrastructure. This work presents GöwFed,a novel network threat detection system that combines the usage of Gower Dissimilarity matricesand Federated averaging. Different approaches of GöwFed have been developed based on state-ofthe-art knowledge: (1) a vanilla version;and (2) a version instrumented with an attention ***, each variant has been tested using simulation oriented tools provided by TensorFlowFederated framework. In the same way, a centralized analogous development of the Federated systemsis carried out to explore their differences in terms of scalability and performance – across a set ofdesigned experiments/scenarios. Overall, GöwFed intends to be the first stepping stone towards thecombined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats inindustrial-level networks. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Virtual Reality in Tourism Industry within the Framework of Virtual Reality Markup Language

Virtual Reality in Tourism Industry within the Framework of ...

引用

Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Basetty Mallikarjuna Shiju Sebastian Ramesh Chandra Pankaj Dadheech Madhvi Sharma Department of Information Technology Institute of Aeronautical Engineering Dundigal India School of Business and Management CHRIST University India Poonia Department of Computer Science CHRIST University India Swami Keshvanand Institute of Technology Management & Gramothan (SKIT) Jaipur Rajasthan India Amity School of Architecture and Planning (ASAP) Amity University Jaipur Rajasthan

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Virtual Reality (VR) technology has grown and emerged in the tourism industry. It offering immersive and interactive experiences, VR has transformed how people discover and interact with the VRML and people interact with different destinations. This article explores the use of VR in tourism, and focusing on Virtual Reality Markup Language (VRML) and its role in showcasing the evolution of head-mounted displays (HMDs) and the various applications of VR. It emphasizes how VR can improve travel experiences, aid in destination planning, preserve cultural heritage, support adventure tourism, and revolutionize destination marketing. The article also gives the challenges and limitations faced by VR in tourism, as well as future trends and opportunities in the field. The article impact of VR on the tourism industry and discusses the combination of Augmented Reality (AR) and VR to create virtual art exhibitions in physical and online spaces. Additionally, it provides insights into the future of VR, AR, and Mixed Reality (MR), the use of VRML, and the development of 3D modeling for creating virtual environments that help users achieve learning objectives.

关键词： Three-dimensional displays Head-mounted displays Markup languages Tourism industry Virtual environments Mixed reality Focusing Market research Planning Cultural differences

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Unmilled Rice Moisture Content Sensor for Rice Mill Factory Applications

Unmilled Rice Moisture Content Sensor for Rice Mill Factory ...

引用

International Electrical Engineering Congress (iEECON)

作者： Somporn Seewattanapon Nonchanutt Chudpooti Prayoot Akkaraekthalin Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Departmemnt of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This research presents a novel electromagnetic-based sensor for inline measurement of unmilled rice paddy moisture content in the rice mill factory. The proposed sensor has been designed based on the modified planar dipole structure at the operating frequency of 440 MHz. The unmilled rice dielectric constant properties have been studied and applied for characterizing the moisture content. The unmilled rice moisture content sensor system then has been designed consisting of the proposed sensor, a microwave signal generator, a microwave signal detector, and a microcontroller. The moisture content sensor system has been implemented and used for measuring the unmilled rice paddy moisture content in a real rice mill factory in Udon Thani province, Thailand. When compared with the commercial rice moisture content tool based on a capacitance technique, the proposed rice moisture content sensor system gives almost same results, which only about 4% difference has been found.

关键词： Microwave measurement Moisture measurement Transmitting antennas Moisture Receiving antennas Microwave theory and techniques Signal generators

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