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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Wang, Ruotong Sun, Jian Zhang, Jianxin Ding, Xueyan Wang, Guangpeng Wu, Yanming School of Computer Science and Engineering Dalian Minzu University Dalian116000 China Research Center of Multimodal Information Perception and Intelligent Processing Dalian116000 China School of Automation Shenyang Aerospace University Shenyang110136 China

ISBN: (纸本)9789819622115

This article explores the consensus control of multi-agent systems, focusing on intermittent communication in dependent regions. Based on this, a novel region-dependent intermittent scheme is introduced, which employs system dynamics for its implementation on agents. The communication scheme divides a nonnegative real region into three subregions, and among them, the operation intervals and the nonoperating intervals represent the functions of the predefined sub-region and the error state, respectively. Based on the above scheme, a new region-dependent intermittent control protocol is designed, and the conditions are provided to prove the consensus. This region-dependent method can accept more nonoperating time than the existing time-dependent methods, and the simulation experiments are also carried out. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus Intermittent Communication Multi-Agent Systems

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Experimental demonstration of a flexible-grid 1×(2×3)mode-and wavelength-selective switch using silicon microring resonators and counter-tapered couplers

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Chinese Optics Letters 2024年第1期22卷 52-58页

作者：孔德军鲁昊汪鹏君符强戴世勋陈伟伟王栎沣张波豪马凌霄李军戴庭舸杨建义 Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Electrical and Electronic Engineering Wenzhou UniversityWenzhou 325035China College of Science and Technology Ningbo UniversityNingbo 315300China Laboratory of Infrared Materials and Devices The Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211China Department of Information Science and Electronics Engineering and Cyrus Tang Center for Sensor Materials and Applications Zhejiang UniversityHangzhou 310027China

A flexible-grid 1×(2×3)mode-and wavelength-selective switch which comprises counter-tapered couplers and silicon microring resonators has been proposed,optimized,and demonstrated experimentally in this *** carefully thermally tuning phase shifters and silicon microring resonators,mode and wavelength signals can be independently and flexibly conveyed to any one of the output ports,and different bandwidths can be generated as *** particle swarm optimization algorithm and finite difference time-domain method are employed to optimize structural parameters of the twomode(de)multiplexer and crossing *** bandwidth-tunable wavelength-selective optical router composed of12 microring resonators is studied by taking advantage of the transfer matrix *** results show that,for the fabricated module,cross talk less than-10.18 dB,an extinction ratio larger than 17.41 d B,an in-band ripple lower than0.79 dB,and a 3-dB bandwidth changing from 0.38 to 1.05 nm are obtained,as the wavelength-channel spacing is 0.40 *** corresponding response time is measured to be 13.64μs.

关键词： integrated optics optical waveguide mode-and wavelength-selective switch

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Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3265-3275页

作者： Kaur, Inderjeet Nassa, Vinay Kumar Kavitha, T. Mohan, Prakash Velmurugan, S. Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Artificial Intelligence & Data Science Sharad Institute of Technology College of Engineering Maharashtra Yadrav-Kohiapur India Department Computer Applications Kongu Engineering College Perundurai Tamil Nadu Erode India Data Science and Analytics Center Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Vel Tech Multi Tech Dr. RR & Dr.SR Engineering College Tamil Nadu Chennai India

In recent times, speech enhancement (SE) becomes a significant process in the field of speech signal processing. Since the speech signal in real time gets affected by background noise, the efficacy of the speech-based applications gets severely affected. The SE techniques have gained considerable attention in the usage of speaker recognition, video conferences, speech broadcast, speech-enabled biometric systems, smartphones, hearing aid, and so on. Numerous conventional SE and machine learning (ML) models have been employed for processing and removing the additive noise from the speech signal. This paper presents a novel Maximum Likelihood Based Estimation with Quasi Oppositional Chemical Reaction Optimization (MLE-QOCRO) algorithm for speech signal enhancement. The presented MLE-QOCRO algorithm follows three-stage processes namely preprocessing, mask generation, and spectral filtering. Besides, the mask generation process involves QOCRO algorithm to classify the speech signal into noise or speech frames. In addition, MLE based spectral filtering technique is employed at the final stage to get enhanced speech signals. A set of experiments were performed to highlight the improved performance of the proposed MLE-QOCRO algorithm, and the results are investigated in terms of several performance measures. Speech signal augmentation using the unique MLE-QOCRO algorithm, which combines maximum likelihood estimation with quasi-opposing chemical reaction optimization, is the subject of this paper. The QOCRO algorithm is used in the mask generation process in the model to distinguish between noise and speech frames in the input speech signal. A spectral filtering method based on MLE is also utilized to improve the voice signal at the very end. The resultant experimental values pointed out the enhanced outcome of the proposed MLE-QOCRO algorithm over the other compared techniques. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Appl

关键词： Chemical reaction optimization Maximum likelihood Spectral filtering Speech signal enhancement

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Leveraging AI and NLP in Chatbot Development: An Experimental Study 20

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Paracha, Abdul Wahab Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model's performance indicates a considerable dependence on the dataset's size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems. © 2023 IEEE.

关键词： Natural language processing systems

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

Performance Evaluation of Popular Deep Neural Networks for N...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Naeem, Muhammad Siddique, Abu Bakar Ali, Raja Hashim Arshad, Usama Abideen, Zain Ul Ali Khan, Talha Shah, Muhammad Huzaifa Zeeshan Ijaz, Ali Ali, Nisar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels. © 2023 IEEE.

关键词： Long short-term memory

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Granular Network Traffic Classification on Mobile Application Service for Better Parental Control

Granular Network Traffic Classification on Mobile Applicatio...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dhivvya J P Guru Aswini Dath Mahesh Kumar Chinta Ankit Karki Department of Computer Science and Engineering Amrita School of Computing Center for Wireless Networks and Applications Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The widespread use of mobile applications among children had its limitations in excessive screen usage in entertainment. Traditional parental control techniques mostly depend on blocking applications based on their name or categories and are ineffective at identifying potentially new dangerous applications. This study addresses this issue by introducing a paradigm shift for protecting kids' digital experiences through granular network traffic classifications. This system classifies mobile app network traffic into various groups by implementing machine learning or deep learning models. Machine learning models like linear regression, Adaboost Classifier, Support Vector Classification (SVC), Ridge Classifier and deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) were used to identify and classify mobile applications. Initially, the deep learning RNN model results had 80% accuracy in the classification of mobile apps, and the machine learning logistic SVC and Adaboost Classifier had 72% accuracy. After adding new applications to the existing dataset, the accuracy of the RNN model has remained the same 80% accuracy. Meanwhile, the machine learning model accuracy reduced to 70%, proving the flexibility of the system. This work can assist parents in monitoring their kids to use mobile applications more for education. This work has a huge scope for information technology management of social change using hybrid learning models in software-defined networking in local networks for the administrators to empower their users to wisely use the internet.

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Adversarial AI threats on digitization of Nuclear Power Plants

Adversarial AI threats on digitization of Nuclear Power Plan...

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2023 Transactions of the American Nuclear Society Winter Conference and Expo, ANS 2023

作者： Gupta, Kishor Datta Talukder, Sajedul Alam, Syed Bahauddin Clark Atlanta University Computer Science Cyber Physical System Atlanta United States Southern Illinois University Computer Science CarbondaleIL United States University of Illinois at Urb Ana-Champaign Nuclear Plasma & Radiological Engineering IL United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign ChampaignIL United States

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Multi-agent robotics system with whale optimizer as a multi-objective problem

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9637-9649页

作者： Gul, Faiza Mir, Imran Mir, Suleman Abualigah, Laith Electrical Department Air University Aerospace and Aviation Campus Kamra Pakistan School of Avionics and Electrical Engineering College of Aeronautical Engineering National University of Sciences and Technology Risalpur Pakistan Electrical Department Fast-National University of Computer and Emerging Sciences Peshawar Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

A multi-objective optimization method for exploring uncharted space is presented in the study. Robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Multiobjective optimization

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A Voicebot with Pronunciation Feedback Mechanism for Rural Indian English Learners

A Voicebot with Pronunciation Feedback Mechanism for Rural I...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Kasa Amareswar Reddy Pitti Abhiram Reddy Chandolu Shanmuka Srinivas Sruthy Anand Sethuraman N Rao Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

In this current digital world, the proficiency in the English language can bring in more employment and economic opportunities. Vocabulary retention capacity is the major factor in developing the ability to speak and write for an individual. In addition, fluency in any language is also influenced by the pronunciation of words while speaking. The rural Indian population struggles to speak good English, and the lack of vocabulary prevents their opportunity in this digital world. In this work, we have introduced a digital voice bot that helps users learn English words and sentences with a special focus on improving pronunciation. The paper focuses on how NLP techniques, along with Whisper an Automatic Speech Recognition model (ASR), can be utilized to help students provide feedback on their learning and pronunciation. The work also discusses how the software is designed to introduce new vocabulary to learners in a hierarchical manner. The multi-level hierarchy presented in this work ensures that the user will be able to retain good vocabulary along with right pronunciation once he/she completes the different levels of bot software.

关键词： Training Pervasive computing Vocabulary Systematics Social networking (online) Heuristic algorithms Chatbots Software Real-time systems Automatic speech recognition

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