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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Nithya, S. Palanisamy, Satheesh Kumar Obaid, Ahmed J. Apinaya Prethi, K.N. Alkhafaji, Mohammed Ayad Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore India Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9789819967056

According to reports, 60–70% of the estimated 50 million dementia patients globally have Alzheimer's disease. Forgetting earlier encounters or occurrences is one of the illness’ early warning signals. People with Alzheimer's frequently repeat words and inquiries, forget discussions, appointments, or activities, and then fail to recall them later, as well as routinely misplace their items and become lost in places they are acquainted with. There is no proven medication for Alzheimer's disease that can stop the brain's disease process in its tracks. The goal of this study is to develop a personal assistant that can remind a person with Alzheimer's or Parkinson's disease of important chores. The suggested remedy is a convolutional neural network-based voice-based smart controller. It makes it easier for the user to remember where they put the thing. The suggested solution also contains various extra characteristics that will be beneficial to both the Alzheimer's patient and regular people. The controller also often sends an alert at predetermined intervals so that individuals can pick up their phones, remind themselves of tasks, and see their own to-do list. The controller sends a notification before the meeting if the user has a meeting booked. This smart controller will serve as an assistant to the individual with Alzheimer’s and might also be helpful to ordinary human beings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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Two-Mode Biomedical Sensor Build-up:Characterization of Optical Amplifier

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computers, Materials & Continua 2022年第3期70卷 5487-5501页

作者： Usman Masud Fathe Jeribi Mohammed Alhameed Faraz Akram Ali Tahir Mohammad Yousaf Naudhani Faculty of Electrical and Electronics Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electrical Communication Engineering University of KasselKassel34127Germany College of Computer Science&Information Technology Jazan UniversityJazan45142Saudi Arabia Faculty of Engineering and Applied Sciences Riphah International UniversityIslamabad44000Pakistan Faculty of Information&Communication Technology Balochistan University of Information TechnologyEngineering and Management SciencesQuetta

Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human *** factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said *** the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their ***,the investigation and technical analysis of the amplifier in the system has been explored in *** algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s *** this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its *** this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.

关键词： Biomedical sensor cavity optomechanics spectroscopy relative intensity noise semiconductor laser diode

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Optimizing Optical Attocells Positioning of Indoor Visible Light communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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IoT Based Alcohol Detection and Vehicle Control System 5

IoT Based Alcohol Detection and Vehicle Control System

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sapthami, I. Raju, V. V. Narasimha Vaithianathan, V. Chinnasamy, P. Kumaran, G. Ayyasamy, Ramesh Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad500043 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Kalavakkam Chennai603110 India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering TamilNadu Srivilliputtur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350389609

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates. © 2024 IEEE.

关键词： Highway accidents

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A high available and strong generalization capability data-driven-based wireless fingerprint positioning model 2

A high available and strong generalization capability data-d...

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2nd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2023

作者： Wu, Xiaochong Hu, Minxun Dong, Ganrong Harbin Institute of Technology School of Information Science and Engineering Shandong Weihai China North East University of Qinhuangdao Faculty of Computer and Communication Engineering Hebei China Jiaxing University School of Information Science and Engineering Jiaxing China

ISBN: (纸本)9781665462532

The demand for indoor location-based services is far greater than supply, and many novel indoor localization technologies are emerging. Wi-Fi fingerprint-based method has drawn much attention by virtue of its user-friendliness and no extra device deployment. However, the traditional methods based on signal similarity or received-signal-strength-based (RSS-based) machine learning face their bottlenecks, such as the performance bottleneck or the generalization capability. Thus, a binary-classification model based on sample differences is proposed, which divides the original fingerprint data into positive and negative samples. Then, new relative features are proposed to replace the traditional media-access-control(MAC)-RSS features. Finally, the classification models are trained using decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms, and several sets of controlled experiments are performed to evaluate the proposed method. It is noticeable that the proposed method has better performance in floor detection accuracy and point matching accuracy, and the mean error can reach 2.9m (DT), 3.09m (SVM), and 2.96 m(RF). Besides, the proposed method shows a very excellent model generalization capability, and the model trained by one building's data can still work well in another building. Those advantages have enormous potential in many large indoor localization scenes, like mall, theme park, airports, museums and exhibition halls. © 2023 IEEE.

关键词： Decision trees

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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Performance Analysis of 5G NR Cell Using Classical Scheduling Algorithms and Different MIMO 8th

Performance Analysis of 5G NR Cell Using Classical Schedulin...

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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur303007 India Department of ICT and Science Faculty of Information Technology and Electrical Engineering NTNU–Norwegian University of Science and Technology Ålesund Norway

ISBN: (纸本)9789819949311

5G NR has been deployed in most of the countries and covers three major use case scenarios, viz. enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), and massive machine type of communication (mMTC). To enable the 5G services on the existing 4G infrastructure, the non-standalone architecture (NSA) of 5G NR is equally popular. For NSA deployment, many countries are utilizing the 3.5 GHz band on time division duplex (TDD) transmission. For both uplink and downlink transmissions, TDD is utilizing same frequency bands and scheduling of resources in such environments is quite challenging. Here, the performance of 5G NR is analysed on 20 MHz and 100 MHz channel bandwidth in terms of resource utilization by comparing the performance of different scheduling algorithms, i.e. Professional Fair, Round Robin, and Best CQI on different MIMO (64 × 64, 32 × 32, 16 × 16, 8 × 8) set-up and block error rates. The performance of Best CQI algorithm is found to be better than other scheduling algorithms under considerations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Scheduling algorithms

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The role of data governance in a high-level approach of data migration to open data

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Business Information Review 2024年第3期41卷 94-103页

作者： Yulfitri, Alivia Sensuse, Dana Indra Sumirat Hidayat, Deden Suryono, Ryan Randy Kautsarina Purwaningsih, Erisva Hakiki Prabuwono, Anton Satria Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Esa Unggul West Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Central Jakarta and Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Information and Communications Technology Human Resources Development Agency Ministry of Communication and Information Technology of the Republic of Indonesia Jakarta Central Jakarta Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

Data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One Data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality. © The Author(s) 2024.

关键词： DAMA-DMBOK framework data governance data migration Indonesia’s one data Kazimir et al.’s approach open data

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A Position-Aware Transformer for Image Captioning

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computers, Materials & Continua 2022年第1期70卷 2065-2081页

作者： Zelin Deng Bo Zhou Pei He Jianfeng Huang Osama Alfarraj Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Advanced Forming Research Centre University of StrathclydeRenfrewshirePA49LJGlasgowUnited Kingdom Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia UniversityEgypt

Image captioning aims to generate a corresponding description of an *** recent years,neural encoder-decodermodels have been the dominant approaches,in which the Convolutional Neural Network(CNN)and Long Short TermMemory(LSTM)are used to translate an image into a natural language *** these approaches,the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of ***,most conventional visual attention mechanisms are based on high-level image features,ignoring the effects of other image features,and giving insufficient consideration to the relative positions between image *** this work,we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above *** image-feature attention firstly extracts multi-level features by using Feature Pyramid Network(FPN),then utilizes the scaled-dot-product to fuse these features,which enables our model to detect objects of different scales in the image more effectivelywithout increasing *** the position-aware attentionmechanism,the relative positions between image features are obtained at first,afterwards the relative positions are incorporated into the original image features to generate captions more *** are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4,METEOR,ROUGE-L,CIDEr scores compared with some state-of-the-art approaches,demonstrating the effectiveness of our approach.

关键词： Deep learning image captioning transformer attention position-aware

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