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

引用

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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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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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Designing demand responsive transport services in small-sized cities using an agent-based model

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Transportation Research Procedia 2023年 69卷 759-766页

作者： Giovanni Calabrò Michela Le Pira Nadia Giuffrida Giuseppe Inturri Matteo Ignaccolo Gonçalo H. de A. Correia Department of Civil Engineering and Architecture University of Catania Catania 95123 Italy DICATECH Polytechnic University of Bari Via E. Orabona 4 Bari 70126 Italy Department of Electric Electronic and Computer Engineering University of Catania Catania 95123 Italy Department of Transport & Planning Delft University of Technology Delft Netherlands

This paper presents an agent-based model (ABM) to simulate and compare two different operation strategies of a public transport service in small-sized cities, namely a fixed-route transit (FRT) and a demand-responsive transport (DRT) service, under varying demand rates and supply configurations. The ABM builds upon a previous work by the Authors, where flexible and feeder services of a Mass Rapid Transit system were simulated. In this paper, instead of a many-to-one pattern typical of a feeder service, we considered a many-to-many one. The objective is to investigate the conditions that make a DRT more attractive than a FRT in small-sized cities and guide its design considering the demand fluctuation, land-use pattern, service constraints and passenger preferences. A dispatching algorithm for the DRT allows to assign each new trip request to a vehicle, and a couple of origin and destination stops, updating the vehicle schedule in real time. The service includes fixed and virtual stops, allowing request consolidation and balancing operator-related (cost of the service) and user-related (quality of service) needs. The model is applied to Vittoria (Italy), a small city with 60,000 residents in Southern Italy where most trips are made by car, also due to the absence of an urban public transport service. First results highlight the benefits of providing a flexible service compared to a fixed one to minimize detours, waiting times and walking distances experienced by passengers while allowing for a higher shareability and efficiency of the service.

关键词： Demand Responsive Transport (DRT) Mobility on demand Agent-based simulation Mass rapid transit Low demand areas

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