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31st European Signal Processing Conference, EUSIPCO 2023

作者： Zhao, Huaibo Higuchi, Yosuke Kida, Yusuke Ogawa, Tetsuji Kobayashi, Tetsunori Department of Communications and Computer Engineering Waseda University Tokyo Japan LINE Corporation Tokyo Japan

ISBN: (纸本)9789464593600

Achieving high accuracy with low latency has always been a challenge in streaming end-to-end automatic speech recognition (ASR) systems. By attending to more future contexts, a streaming ASR model achieves higher accuracy but results in larger latency, which hurts the streaming performance. In the Mask-CTC framework, an encoder network is trained to learn the feature representation that anticipates long-term contexts, which is desirable for streaming ASR. Mask-CTC-based encoder pre-training has been shown beneficial in achieving low latency and high accuracy for triggered attention-based ASR. However, the effectiveness of this method has not been demonstrated for various model architectures, nor has it been verified that the encoder has the expected look-ahead capability to reduce latency. This study, therefore, examines the effectiveness of Mask-CTC-based pre-training for models with different architectures, such as Transformer-Transducer and contextual block streaming ASR. We also discuss the effect of the proposed pre-training method on obtaining accurate output spike timings, which contributes to the latency reduction in streaming ASR. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Network architecture

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A Hybrid Security Framework for Medical Image Communication

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computers, Materials & Continua 2022年第11期73卷 2713-2730页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Ashraf A.M.Khalaf Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Authentication of the digital image has much attention for the digital *** image authentication can be verified with image watermarking and image encryption *** schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful *** on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this *** framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using *** the encrypted image is watermarking based on *** combination between watermarking and encryption increases the security and robustness of transmitting an *** performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

关键词： Walsh hadamard transform watermarking encryption double random phase encoding structural similarity index

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Implementation of an IoT-Based Car Park Availability Monitoring System

Implementation of an IoT-Based Car Park Availability Monitor...

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2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023

作者： Navarro, Jerome Daniel P. Punla, Radyn Matthew H. Agustin, Luisito L. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350319040

To assist drivers, the researchers propose a car park occupancy monitoring system that uses a Raspberry Pi to obtain photos needed by YOLOv7 to determine the presence of vehicles in a parking area. OpenCV is used to count the number of available parking spaces from the analysis of YOLOv7. The information is uploaded to the internet in real-time for access by users of the parking area. The data is uploaded and processed in Google Sheets and is accessed by a chatbot, using FlowXO. The chatbot is then deployed in Facebook Messenger, available to the targeted end users. © 2023 IEEE.

关键词： Internet of things

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Wet and Dry Cough Classification System Using Support Vector Machine and Logistic Regression 9

Wet and Dry Cough Classification System Using Support Vector...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Sean Andrei, P.Co Madamba, Claudine Anne J. Guico, Maria Leonora C. Galicia, Jan Kevin A. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350325218

Dry cough has been recognized as a common symptom of coronavirus respiratory diseases, emphasizing the importance of accurately identifying and classifying cough types to mitigate the spread of the disease. The study employs various acoustic features and a Python-based data processing algorithm to extract and analyze the Energy Envelope Peaks, Crest Factors, Zero-Crossings, and Formant Frequencies 1-4 from a dataset of 870 cough samples. The analysis of 347 wet cough sound samples and 523 dry cough samples reveals distinctive characteristics. Wet coughs exhibit a higher number of peaks and zero-crossings, while dry coughs display a slightly higher crest factor on average. Moreover, the F1 and F2 formant frequencies are higher in wet coughs, whereas the F3 and F4 formant frequencies are higher in dry coughs. To classify the cough types, both Support Vector Machine (SVM) and Logistic Regression Method (LRM) classifiers are trained using the identified features. The SVM classifier achieves an average accuracy of 71.26%, sensitivity of 72.73%, specificity of 70.87%, and F1-score of 67.94% during testing. Similarly, the LRM classifier achieves an accuracy of 71.26%, sensitivity of 70.59%, specificity of 71.55%, and F1-score of 68.45%. Such automated classification systems have the potential to aid in the early detection and monitoring of respiratory diseases in enclosed spaces. © 2023 IEEE.

关键词： Support vector machines

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Employing CNN and black widow optimization for sustainable wastewater management in an environmental engineering context

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Asian Journal of Civil engineering 2024年第5期25卷 3973-3988页

作者： Ismail, Rabah Alsadi, Jamal Hatamleh, Randa Telfah, Dua’a Jaradat, Aiman Aljamal, Marwa Trrad, Issam Al-Mattarneh, Hashem Civil Engineering Department Jadara University Irbid Jordan Renewable Energy Engineering Department Jadara University Irbid Jordan Civil Engineering Department Yarmouk University Irbid Jordan Communications and Computer Engineering Department Jadara University Irbid Jordan

The study explores the application of convolutional neural networks (CNN) trained with black widow optimization (BWO) algorithms to enhance eco-friendly wastewater treatment processes. This study aims to address the pressing need for improved predictive models in environmental engineering by utilizing advanced AI techniques to explore the complexities of wastewater treatment. This work aims to enhance models for hyperparameter tuning using BWO and to utilize CNNs for pattern identification and prediction. The objective is to enhance the accuracy of forecasts regarding wastewater treatment outcomes. The technique entails constructing and refining a convolutional neural network (CNN) model, focusing on adjusting the model’s hyperparameters using Bayesian optimization to get optimal outcomes. The study evaluates the model’s effectiveness by comparing it to traditional models and other AI-based methodologies. Optimization led to a considerable enhancement in predictive performance, with the CNN-BWO model outperforming earlier models in efficiency and accuracy. This result has significant implications, indicating a novel approach to utilizing AI for managing wastewater treatment systems. This work demonstrates that enhanced CNN models can provide precise and reliable predictions, paving the way for improved and more sustainable wastewater management processes. The research highlights the significance of continuously enhancing AI and optimization algorithms to encourage their broader application in environmental engineering. Future research will explore the scalability of an AI model for enhancing wastewater management globally across various treatment facilities and conditions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence (AI) Black widow optimization (BWO) Convolutional neural networks (CNN) Environmental engineering Hyperparameter tuning Predictive modeling Sustainable practices Wastewater management

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Development of Android-Based Pulmonary Monitoring System for Automated Lung Auscultation Using Long Short-Term Memory (LSTM) Network with Post-Processing from Edge Impulse 5

Development of Android-Based Pulmonary Monitoring System for...

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5th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Avila, Kaye Antoinette V. Cabrera, Beatrice Corine R. Reyes, Rosula S. J. Oppus, Carlos M. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350320978

Chronic pulmonary diseases remain a prevalent threat globally. With the emergence of COVID-19 and its transmission, there has been a rapid increase in the number of deaths due to respiratory illnesses. In this study, lung sound classifications were performed using a Thinklabs One digital stethoscope and through the utilization of Long Short-Term Memory (LSTM) in the classification of a person's lung auscultation record into either the normal, crackle, wheeze, or stridor categories with a 92.50% accuracy. Performance evaluation of this system was also done to cross-check for the validity of the algorithm modeled through Edge Impulse, which provided a 92.77% accuracy. The integration of the system adopted an Android-based mobile application as the pulmonary monitoring platform that records a person's general respiratory health data. The inputs from the mobile application were anonymously stored in a centralized database system correspondingly for post-processing and analysis. © 2023 IEEE.

关键词： Long short-term memory

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Sustainability Analysis of Residential Photovoltaic Systems: A Life Cycle Assessment Approach 15

Sustainability Analysis of Residential Photovoltaic Systems:...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Caballa, Lorenzo Gabriel C. Recto, King Harold A. Engineering Ateneo de Manila University Department of Electronics Computer and Communications Quezon City Philippines

ISBN: (纸本)9798350381177

Renewable energy has emerged as a prominent topic in dialogues about energy production sustainability in the Philippines. This is mostly attributed to the excessive dependence of the country on imported petroleum and fossil fuels, which are well recognized for their detrimental effects on the environment. Solar energy is often acknowledged as a very accessible kind of renewable energy, especially in tropical nations like the Philippines, owing to the country's abundant sun irradiation levels throughout the year. Despite the significant progress made in solar technology and its effective implementation in residential environments, some limits continue because of constraints associated with photovoltaic technology, economic feasibility, and environmental implications. The use of the life-cycle assessment (LCA) methodology offers a structured framework for the examination and evaluation of the sustainability aspects pertaining to residential PV systems. This research does this by investigating PV systems throughout their lifecycle, starting with the selection of materials, manufacturing process, operation, and maintenance, and concluding with the environmental impacts at the end-of-life phase. Subsequently, this paper will also discuss the policy implications arising from the study results. The research aims to provide valuable insights into PV residential systems in the Philippines and aspires to contribute to policy-relevant literature in this field. © 2023 IEEE.

关键词： Life cycle

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Atre, Anshul Singh, Kamlesh Kumar Chaurasia, Brijesh Kumar Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering UP Lucknow India Amity University Amity School of Engineering & Technology Department of Electronics Communications and Engineering UP Lucknow India Pranveer Singh Institute of Technology Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350385199

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes. © 2024 IEEE.

关键词： Cloud platforms

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Automated People Counter for Restricted Capacity Monitoring on Philippine Public Utility Bus

Automated People Counter for Restricted Capacity Monitoring ...

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2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： Chan, John Neil M Guico, Maria Leonora C De Guzman, Carlos Gabriel P Galicia, Jan Kevin A Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350321302

The ability to inform transportation businesses and regulatory bodies about the demand for transportation services and how resources may be best deployed to meet this need is strategic to resource allocation and planning of national transportation system. Case in point, the pandemic has forced the government to reevaluate the number of utility buses and its ridership and implement various restrictions and regulations within society. One of the restrictions that is often affected is the seating capacity for Public Utility Vehicles. This study provides a proof of concept to an easier and more efficient way to be able to monitor capacity in a Public Utility Bus through the use of a people counter. Photoelectric sensors will be used to monitor the number of people entering and exiting the bus with information being uploaded to ThingSpeak while also being displayed onboard using an LCD. The system is capable of bi-directional counting through the use of two pairs of sensors and will work most accurately when the sensor pairs are at a distance of at least 12 cm from each other and passengers will board and depart in intervals of one second or greater. © 2023 IEEE.

关键词： Buses

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