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Temporal features and relations discovery of activities from sensor data

Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1911-1926页

作者： Nazerfard, Ehsan Department of Computer Engineering Amirkabir University of Technology Tehran Iran

One of the most important problems that arises during the knowledge discovery from data and data mining process in many new emerging technologies is mining data with temporal dependencies. One such application is activity recognition and prediction. Activity recognition is used in many real world settings, such as assisted living systems. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in assisted living settings. We discover temporal relations such as the order of activities, as well as their corresponding start time and duration features. Analysis of real data collected from smart homes was used to validate the proposed method. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Internet of things

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Non-isolated Step-up DC-DC Converter Based on Switched Capacitor Cells

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CSEE Journal of Power and Energy Systems 2023年第3期9卷 1161-1172页

作者： Farid Mohammadi Gevork B.Gharehpetian Hasan Rastegar Mohammad Farhadi-Kangarlu Amirkabir university of technology TehranIran Faculty of Electrical and Computer Engineering Urmia UniversityUrmiaIran

A non-isolated high gain step-up DC-DC converter for low power applications is suggested in this *** the designed transformerless converter,the main switch current and voltage stress is reduced while maintaining high voltage *** instance,with a duty cycle of 0.5 a voltage gain equal to 5 is achieved while the normalized switch voltage stress is ***,it decreases power losses of active and passive *** the proposed converter design,the switched-capacitor(SC)technique is used to obtain maximum voltage transfer gain using only one *** three modes of operation,i.e.,continuous conduction mode(CCM),boundary conduction mode(BCM),and discontinuous conduction mode(DCM),are studied in *** small signal analysis(SSA)of the designed converter is investigated,and its steady-state model is examined under *** of the proposed converter proposed in this study is assessed and tested using a *** of the converter is recorded above 94%in a wide range of output ***,compared to the other converters,the results suggest satisfactory performance of the designed *** issue of the proposed converter is that its input current is not smooth due to using the switched-capacitor cell in its *** issue is alleviated by using input filters.

关键词： DC-DC converter high voltage gain nonisolated switched capacitor cell voltage stress

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Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

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Intelligent Automation & Soft Computing 2023年第4期36卷 633-646页

作者： Hussain Alaaedi Masoud Sabaei Department of Computer Engineering at Computer Networks and Architecture Amirkabir University of TechnologyTehran***Iran

Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented *** narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile *** this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is *** high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic *** a deep reinforcement learning framework is developed for tackling the same opti-mization *** this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated *** investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base *** proposed work outperforms in terms of MOS with multiple antennas.

关键词： Multiple-input and multiple-output quality of experience quality of service(qos) fuzzy-based deep convolutional neural network

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Approximate Convolutional Neural Networks Based on Dynamic Binding Windows 5

Approximate Convolutional Neural Networks Based on Dynamic B...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Adelkhani, Morteza Sedighi, Mehdi Amirkabir University of Technology Computer Engineering Tehran Iran

ISBN: (纸本)9798331529284

The rapid growth of applications focused on handling amounts of data has increased the demand, for computational power especially in areas such as machine learning and graph analysis within big data frameworks. While these applications have traditionally been run on data centers, they now encounter issues concerning security and quick access to data leading to a shift towards embedded systems with processing capacity. To tackle this challenge, we suggest a strategy to improve the efficiency of Convolutional Neural Networks (CNNs) on embedded systems through hardware-driven computing. Our innovative method, "Dynamic Binding Window"adjusts calculation precision dynamically within convolution layers based on feature map windows and layer positions reducing burden and energy usage without compromising accuracy. Our research demonstrates that this approach effectively overcomes the constraints of embedded systems allowing for real-time processing on devices. Comparative evaluations indicate enhancements in processing speed and energy efficiency showcasing the impact of our approach in fields, like autonomous vehicles that rely on swift and precise image analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Breast Cancer Classification with Neighborhood Attention Transformer and Wavelet Scattering Transform 10

Enhanced Breast Cancer Classification with Neighborhood Atte...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Firouzbakht, Mahdi Amirmazlaghani, Maryam Computer Engineering Amirkabir University of Technology Tehran Iran

ISBN: (纸本)9798331532543

Breast cancer is one of the most common cancers among women, occurring when abnormal cells in the breast tissue begin to grow and divide uncontrollably. While primarily affecting women, breast cancer can also develop in men, although the likelihood is much lower;less than 1% of all cases occur in men. For both men and women, however, early detection is critical for effective treatment. Early diagnosis through mammography and other imaging methods plays a crucial role in improving outcomes. In recent years, extensive studies have explored using computer systems to assist in diagnosing breast cancer, especially with artificial intelligence and computer vision applications. In this research, we propose a novel model for cancer classification in mammographic images. Our model leverages a framework capable of extracting rich and meaningful features to achieve optimal classification. Mammographic images, generated using low-energy X-rays, are first processed with the Wavelet Scattering Transform to extract precise feature maps, effectively capturing features in both frequency and spatial domains. These feature maps serve as input for the Neighborhood Attention Transformer model, which uses neighborhood attention in place of traditional selfattention. Finally, the extracted feature vectors from the mammographic images are fed into multi-layer perceptron networks to perform the classification. Notably, the proposed model has achieved a Kappa score of 40.3%, an Area Under the Curve of 76.4%, and an F1 score of 71% on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). © 2024 IEEE.

关键词： Diseases

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Visual Self-supervised Learning Scheme for Dense Prediction Tasks on X-ray Images 11

Visual Self-supervised Learning Scheme for Dense Prediction ...

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11th International Symposium on Telecommunication, IST 2024

作者： Halat, Shervin Rahmati, Mohammad Nazerfard, Ehsan Amirkabir University of Technology Computer Engineering Department Tehran Iran

ISBN: (纸本)9798350356250

Recently, significant advancements in artificial intelligence have been attributed to the integration of self-supervised learning (SSL) scheme. While SSL has shown impressive achievements in natural language processing (NLP), its progress in computer vision has comparatively lagged behind. However, the incorporation of contrastive learning into existing visual SSL models has led to considerable progress, often surpassing supervised counterparts. Nonetheless, these improvements have been mostly limited to classification tasks. Moreover, few studies have evaluated visual SSL models in real-world scenarios, as most have focused on datasets with class-wise portrait images, notably ImageNet. Here, we focus on dense prediction tasks using security inspection x-ray images to evaluate our proposed model, Segment Localization (SegLoc). Based upon the Instance Localization (InsLoc) model, SegLoc addresses one of the key challenges of contrastive learning, i.e., false negative pairs of query embeddings. Our pre-training dataset is synthesized by cutting, transforming, and pasting labeled segments from an existing labeled dataset (PIDray) as foregrounds onto instances from an unlabeled dataset (SIXray) as backgrounds. Furthermore, we fully leverage the labeled data by incorporating the concept - one queue per class - into the MoCo-v2 memory bank, thereby avoiding false negative pairs. In our experiments, SegLoc outperformed random initialization by 3% to 6% while underperformed supervised initialization, in terms of AR and AP metrics across different IoU values over 20 to 30 pre-training epochs. © 2024 IEEE.

关键词： Self-supervised learning

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An Adaptive Task Scheduling Approach for Cloud Computing Using Deep Reinforcement Learning 3

An Adaptive Task Scheduling Approach for Cloud Computing Usi...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Amini, Pedram Kalbasi, Amir Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350381580

Today, many important internet services are provided on cloud computing platforms. The ever-increasing expansion of services and user requirements has necessitated the optimal use of resources. Therefore, several algorithms for optimal resource usage have been proposed to increase cloud performance and provide more satisfactory services to internet users. Typically, each algorithm is suitable for a specific environment, and their performance is affected by changes of the execution environment. Meanwhile, the assignment of tasks to resources in cloud computing is a difficult problem that NP-Complete. This paper proposes a novel deep reinforcement learning-based approach for task scheduling in cloud computing environments, aiming to minimize the makespan, which is the total time required to complete all tasks. The proposed approach utilizes a deep Q-learning algorithm to learn the near-optimal task allocation strategies based on the current state of the system. The task scheduling performance of the proposed approach is compared with the Min-Min, Min-Max, FCFS and GA algorithms based on three criteria: makespan, algorithm execution time, and computational complexity. Simulation results demonstrate that the proposed approach results in excellent makespan, while demonstrating very small algorithm execution time. © 2024 IEEE.

关键词： Reinforcement learning

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IoT-Based Intelligent Transportation System for Passenger-Shuttle Taxi Matchmaking 3

IoT-Based Intelligent Transportation System for Passenger-Sh...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Zamani, Mohammadreza Kalbasi, Amir Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350381580

As cities continue to grow in population, the demand for public transportation, particularly shuttle taxis, has increased. Shuttle taxis typically follow specific routes within or between cities. However, this transportation mode is not currently optimized, as taxi drivers often have to stop frequently to find passengers and inquire about their destination to see if it aligns with their route. With more stops, the average speed of taxis and other vehicles on the road decreases, leading to longer travel times for both shuttle taxis as well as other vehicles. Furthermore, frequent stop-start cycles increase fuel consumption, increase CO2 emissions, increase vehicles' wear and tear and, therefore, reduce vehicles' life. To address these issues, a matchmaking system between passengers and shuttle taxis called Shuttle Taxi with Targeted Stops (STTS) is proposed. Passengers can choose their taxi lines and get matched with a shuttle taxi with free capacity based on their source and destination locations. The STTS system aims to reduce travel times and enhance the overall quality of shuttle taxi services. Implementation of this system is expected to reduce urban traffic congestion, decrease the number of car stops, particularly taxis, and minimize fuel consumption and environmental impact caused by shuttle taxis and other vehicles on the road. Based on simulation results, the STTS method increases average speed of non-taxi cars and taxis up to 9.3% and 51.7%, respectively, while decreasing the average travel time of passengers by up to 20%. © 2024 IEEE.

关键词： Travel time

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Hybrid Vision Transformer for Detection of Dentigerous Cysts in Dental Radiography Images 14

Hybrid Vision Transformer for Detection of Dentigerous Cysts...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Tavasoli, Reza VarastehNezhad, Arya Farbeh, Hamed Department of Computer Engineering Amirkabir University of Technology Tehran Iran Department of Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

Dentigerous cysts, a prevalent form of odontogenic lesions, present a significant challenge in dental diagnostics due to their often asymptomatic nature and potential for complications if left untreated. Early and accurate detection of these cysts is crucial for optimal patient care and treatment planning. This study introduces a novel approach to automate the detection of dentigerous cysts in dental radiographic images using advanced deep learning techniques. We present a Hybrid Adaptive Vision Transformer (HA-ViT) architecture that uniquely combines convolutional neural networks (CNNs) with Vision Transformers (ViTs) to process both full panoramic and focused dental radiographs. This dual-path approach allows for comprehensive analysis of dental structures at multiple scales, addressing the varied imaging scenarios encountered in clinical practice. Our model was trained and evaluated on a large dataset of dental radiographs, labeled by a dentist. The HA-ViT architecture demonstrated an excellent performance, achieving an accuracy of 94.44%, sensitivity of 90.64%, and specificity of 96.74% in dentigerous cyst detection. The model’s robustness is further evidenced by an Area Under the Receiver Operating Characteristic curve (AUC-ROC) of 0.9829. These results underscore the potential of our approach to significantly enhance the efficiency and accuracy of dentigerous cyst detection in clinical settings. ©2024 IEEE.

关键词： Patient treatment

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