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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava842 16 Slovakia Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava842 16 Slovakia

ISBN: (纸本)9798350377569

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles). © 2024 IEEE.

关键词： Failure rate

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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Predictive Modeling for Early Identification of Disease Severity in Acute Respiratory Infections: A Case Study with COVID-19 5

Predictive Modeling for Early Identification of Disease Seve...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kalapuge, Vindhya Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The COVID-19 pandemic, declared a global emergency by WHO in March 2020, has had a profound impact on global health. With millions affected, the pandemic's magnitude became evident. Even advanced healthcare systems grappled with resource shortages, from protective gear to ventilators. Late diagnosis and the inability to adapt to new SARS-CoV-2 variants led to misdiagnoses and high casualties. Overburdened healthcare facilities and the absence of a centralized patient data system hindered swift decision-making. The lack of real-time monitoring meant many didn't receive timely, personalized care, underscoring the need for an integrated healthcare response during global emergencies. To tackle these challenges and improve hospital resource management during pandemics, this study presents a web approach harnessing big data analytics to develop a machine learning predictive model. This model has been developed to enable hospitals to assess patients' severity in real-time, taking into account a wide range of factors such as symptoms, medical history, and demographics. To acquire data regularly, a streaming data pipeline was established using Apache Kafka and Apache Streaming. In predicting the severity levels of patients, both Multinomial Logistic Regression and Multilayer Perceptron (MLP) models were employed. One noteworthy finding was that using datasets with balanced classes yielded significantly higher accuracy for the MLP model compared to unbalanced datasets. Specifically, MLP achieved the highest accuracy of 60.4% when trained on balanced classes. This model's real-time severity insights have the potential to revolutionize decision-making and resource allocation in healthcare. It represents a significant step towards a more effective response to future pandemics and the strengthening of the global healthcare system. © 2023 IEEE.

关键词： COVID-19

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A New Technique of Blackboard Architecture in Decision-Making for Timber Harvesting 17

A New Technique of Blackboard Architecture in Decision-Makin...

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17th International Conference on Ubiquitous information Management and Communication, IMCOM 2023

作者： Muhd Mukhtar, Hana Munira Ismail, Roslan Software Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9781665453486

A new technique of Blackboard architecture is presented herein, the implementation of a system that dynamically decides its action based on certain reasons that correspond to the problem-solving plan and strategies. We describe the principal elements of the blackboard;the design of domain and control knowledge sources (KSs). The mechanism of problem-solving uses a blackboard on how domain and control KSs integrate to solve the problem. The concept being used and the problem that they applied is supported by the trigger and feasible conditions as well as an adaptive scheduler that decides its own actions corresponding to the current interest of problem-solving at that particular time. The implementation of these new concepts on the blackboard is presented in timber harvesting decision-making for sustainable forest management. © 2023 IEEE.

关键词： Decision making

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Smart System to Optimize Organic Crop Rotation Using Precision Agriculture Data 7

Smart System to Optimize Organic Crop Rotation Using Precisi...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Ransinghe, M.R.A.D. Samarakoon, D.S.M.A. Waththaladeniya, M.N. Ihalagedara, B.I.H.U. Samaratunge Arachchilllage, Udara Srimath S. Kodagoda, N. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350342154

Optimizing organic crop rotation and effectively managing pests and diseases remain a significant challenge for farmers. In this research, we propose a Smart System to Optimize Organic Crop Rotation using Precision Agriculture Data. The system leverages Internet of Things (IoT) technology, machine learning (ML) algorithms, and cloud computing to enhance decision-making and improve productivity in organic farming. The proposed system integrates multiple components to provide farmers with informed decisions for optimized crop rotation. Soil sensors collect data on soil health, which is then analyzed using a multi-objective optimization technique to determine the best crop for a given soil sample. Real-time weather data is incorporated to enable climate-resilient farming practices and help farmers make educated choices in selecting crops and implementing rotation plans. Additionally, a cloud computing-based model is developed for pest and disease identification, providing farmers with effective solutions to combat these challenges in organic methodologies. By leveraging IoT, ML, and cloud computing, our system proposes farmers a more efficient and effective approach to managing their crops. The system provides real-time data-driven recommendations on fertilizer selection and crop rotation, leading to improved crop growth, increased yields, and reduced environmental impact. Moreover, by adopting organic farming practices, the system contributes to the sustainable development of the agricultural sector. © 2023 IEEE.

关键词： Crop rotation

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U-net of joint spatial domains with multi-scale atrous convolution for rectal image segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Rao, Yunbo Gao, Li Zeng, Shaoning Shao, Tingting Sun, Jihong School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu611731 China Yangtze Delta Region Institute University of Electronic Science and Technology of China Huzhou313000 China Zhejiang University School of Medicine Zhejiang Hangzhou310058 China

Medical image segmentation is very important for the diagnosis of related diseases. To reduce the labeling work of related medical images, numerous models based on U-Net have been proposed to achieve automatic segmentation of target regions. However, most of these models are only trained in one coordinate system, ignoring the joint effects of different spatial coordinate systems. In addition, most of the encoding modules in these models do not pay attention to multi-scale spatial information. Our proposed solution for the aforementioned challenges involves using U-Net model with joint spatial domains and multi-scale encoding module, which enables us to segment rectal image better. The model includes a self-designed multi-layer dilated convolution encoding module named AIR (Atrous Inception Residual Block), to achieve a multi-scale content fusion. Besides this, it utilizes the center point and polar coordinates to realize attention mechanism and rotation invariance. Furthermore, retraining the output of polar coordinate network with Cartesian coordinate system realizes the translation invariance of segmentation. Compared with the commonly used medical segmentation models, the dice coefficient of our model is improved by about 2% on our in-house rectal dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image segmentation

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Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning 5

Sinhala Sign Language Translation through Immersive 3D Avata...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Gamage, B.G.J. Paranagama, R.P.S.D. Ranaweera, R.M.S.H. Dilshan, A.V.R. Weerathunga, Ishara Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. The focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. These include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. The outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. The research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. The proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users. © 2023 IEEE.

关键词： Emotion Recognition

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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KidSwatch: Children Development Observation System 5

KidSwatch: Children Development Observation System

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Oshadhini, P.A.L.N. Siriwardana, K.A.D.H.O. Peshala Ranmini, H.K. Sandeepa, S.R. Weerasinghe, Lokesha Kelegama, Thamali Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The kidSwatch is a mobile application designed specifically for grade 1 children aged six years. It serves as a comprehensive framework for observing and evaluating children's developmental progress. The application focuses on the main components of children's development: Cognitive development, Emotional development, Language development, and Learning development. By analyzing the results obtained from observing these four components, parents can gain valuable insights into their children's development within a matter of minutes. The kidSwatch employs a range of observational techniques, including direct observation, structured interviews, and standardized tests, to gather information on a child's talents, skills, and behaviors. Trained observers proficient in the kidSwatch framework carefully record and analyze the child's interactions, activities, problem-solving abilities, emotional expressions, and social interactions. Ultimately, the kidSwatch empowers parents with a quick and effective means of understanding and assessing their child's development. To satisfy the above features there are a Hand number detection algorithm, letter recognition algorithm, Emotion recognition algorithm, and voice-based answering algorithm They perform an accuracy between 75%-90% on CNN. © 2023 IEEE.

关键词： Emotion Recognition

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