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London Imaging Meeting, LIM 2023

作者： Bianconi, Francesco Cusano, Claudio Napoletano, Paolo Schettini, Raimondo Department of Engineering Universitá degli Studi di Perugia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Informatics Systems and Communication University of Milano Bicocca Italy

ISBN: (纸本)9780892083633

In this paper, we present a novel technique that allows for customized Gabor texture features by leveraging deep learning neural networks. Our method involves using a Convolutional Neural Network to refactor traditional, hand-designed filters on specific datasets. The refactored filters can be used in an off-the shelf manner with the same computational cost but significantly improved accuracy for material recognition. We demonstrate the effectiveness of our approach by reporting a gain in discrimina tion accuracy on different material datasets. Our technique is particularly appealing in situations where the use of the entire CNN would be inadequate, such as analyzing non-square im ages or performing segmentation tasks. Overall, our approach provides a powerful tool for improving the accuracy of material recognition tasks while retaining the advantages of handcrafted filters. © 2023 Society for Imaging Science and Technology.

关键词： Convolutional neural networks

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Spatial, Spatiotemporal, and Feature Analysis of Electroencephalogram During Imagined Speech with Visual vs Audio Cues 3

Spatial, Spatiotemporal, and Feature Analysis of Electroence...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Fitriah, Nilam Budikayanti, Astri Zakaria, Hasballah Latifah Erawati Rajab, Tati Universitas Indonesia Faculty of Computer Science Depok Indonesia Universitas Indonesia Department of Neurology Jakarta Indonesia Institut Teknologi Bandung Department of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350327762

People with speech disorders have difficulties in social life and welfare. To overcome this barrier, researchers have conducted studies to recognize imagined speech from biosignals. The essential biosignals in producing speech are brain signals. This study aimed to determine activated brain regions during the imagined speech by spatial and spatiotemporal analysis to assist feature selection in imagined speech recognition. We recorded the normal subjects' brain signals using electroencephalography (EEG) when they imagined saying a word without vocalization. The protocol ran in two different-time sessions to cover the EEG nonstationary. We utilized visual cues in the first sub-session and audio cues in the second one. One word per sub-session had seven trials, each consisting of a rest state and cue presentation, followed by four repetitions of the imagined speech state, each preceded by a preparation state. We calculated power spectral entropy (PSE) values from the acquired dataset and used them as a feature since they captured signals' activity. The normalized PSE above 0.5 (middle value) was considered as high activity. The results showed that repeated-trial protocol induced brain activity during imagined speech. For visual or audio-cued imagined speech, the selected channels were in the central and left frontal regions, with additional right temporal, left parietal, and left occipital regions for visual-cued imagined speech. The highest PSE was found in the central region. Meanwhile, the lowest PSE was shown in the preparation state, confirming that cue presentation was not affecting imagined speech states. © 2023 IEEE.

关键词： Electroencephalography

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Business Intelligence: Security and Privacy-Related Concerns with Cloud Computing

Business Intelligence: Security and Privacy-Related Concerns...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Hamid, Nagham Al-Tahee, Mustafa Al-Hameed, Mazin Riyadh Ahmed, Wasan Saad Fouad, Rami Al-Chilibi, Haider Hasson, Ahmed Rasol Habelalmateen, Mohammed I. College of Business Informatics University of Information Technology and Communications Baghdad Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al Mustaqbal University Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Internet technology is used in Business Intelligence (BI) and continues to provide centralized control assistance with knowledge from many structured and unstructured information sources. Business intelligence's interrelated limits are complicated, pricey, and restrictive. Resources from cloud computing with cost-effective performance and scalability can be used to upgrade existing BI systems and applications to address these issues. Due to rigorous confidentiality, reliability, interoperability requirements, and many dependencies with the other applications, BI systems are often barred from migrating to cloud applications. The paper addresses the obstacles to implementing BI within a cloud infrastructure. It provides the Security and Privacy-related Concerns in Business Intelligence (SPC-BI) framework, which assists decision-makers in considering the impact and privacy, security, costs, and effectiveness of BI structure migrating to network infrastructure. © 2024 IEEE.

关键词： Cloud security

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EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2021年 2卷 1页

作者： Kaselimi, Maria Doulamis, Nikolaos Voulodimos, Athanasios Doulamis, Anastasios Protopapadakis, Eftychios Department of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece

Energy disaggregation, namely the separation of the aggregated household energy consumption signal into its additive sub-components, bears resemblance to the signal (source) separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose EnerGAN++, a model based on Generative Adversarial Networks (GAN) for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method's superiority compared to the current state of the art. © 2020 IEEE.

关键词： Generative adversarial networks

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG

Deep-Source EEG Generation with Deep Learning from Different...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiadao Zou Qingxue Zhang Luddy School of Informatics Computing and Engineering Indiana University Department of Computer Science and Department of Biomedical Engineering Indiana University School of Informatics Computing and Engineering Indiana University Luddy Indianapolis

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.

关键词： Deep learning Heuristic algorithms Smart healthcare Epilepsy Big Data Electroencephalography Streams Consumer electronics

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality 46

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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

作者： Momeni, Mahdi Radomski, Adrian Guler, Ulkuhan Teichmann, Daniel University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Denmark Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350371499

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness. © 2024 IEEE.

关键词： Contact sensors

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Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency 44

Comparing Impact of Time Lag and Item Lag in Relative Judgme...

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44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022

作者： Maini, Sahaj Singh Labuzienski, Louis Gulati, Saurabh Tiganj, Zoran Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Judgment of Recency Time versus items Variable presentation time

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DsetGenS: An Automated Technique for Building Dataset From Speech with respect to Gujarati-English 11

DsetGenS: An Automated Technique for Building Dataset From S...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Patel, Margi Joshi, Brijendra Kumar Indore Institute of Science and Technology Department of Computer Science and Engineering Indore India Military College of Telecommunication Engineering Department of Informatics Mhow India

ISBN: (纸本)9781665480383

The computer has seen significant evolution in recent years, with applications in variety of disciplines like Machine Learning, Deep Learning etc. Machine Translation (MT) technology has advanced significantly as a subfield, with many methodologies and techniques. The number of individuals using the internet has risen tremendously. Most documents are written in English since that is the most extensively used language on the internet. If a user's first dialect is Gujarati, he or she will naturally prefer to access the information in Gujarati whenever possible. Even though there are already various MT systems and tools that support Indian languages;however, the translation's quality is mediocre and might be improved. As observed, when models are trained on limited quantities of parallel data, their performance declines. Learned models often have limited performance (inaccurate translations and feature scores) along with low coverage (high out-of-vocabulary rates). Furthermore, the researchers were driven to present novel methodologies and solutions that would automatically construct Datasets for MT due to the increased demand for effective technologies to process and translate information from/to Gujarati Language. Our objective of generating a Gujarati-English dataset has been met in two ways. We have already introduced GEDset, that is automatic Dataset Builder for Machine Translation System with Specific Reference to Gujarati-English [1]. Here, in this paper we are proposing a model to automatically build Gujarati-English dataset from audio that is available in Gujarati Language through Speech Processing. © 2022 IEEE.

关键词： Deep learning

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Improving Power Transformer Lifetime Prediction using Hyperparameter Optimization 24

Improving Power Transformer Lifetime Prediction using Hyperp...

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24th International Electronics Symposium, IES 2022

作者： Ningrum, Ayu Ahadi Maulida, Ihdalhubbi Ansari, Rudy Syarif, Iwan Universitas Muhammadiyah Banjarmasin Department of Informatics Banjarmasin Indonesia Electronic Engineering Polytechnic Institute of Surabaya Department of Informatic and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Since the global pandemic has significantly impacted human life, technology has become a vital role in various sectors. The more technology used, the more we need the electricity supply. The stability of the electricity supply is an absolute thing to customers. Power Transformer is essential equipment for delivering electricity to customers. So the condition of the power transformers should be an essential thing that must be considered. Deep Learning is part of artificial intelligence that is widely applied to facilitate the human need. Based on its role, accuracy in the prediction results will be absolute. Hyperparameter optimization is the essential methods in the machine learning process. Errors in assigning hypermeter values can harm producing predictive values. This study discusses how to optimize the prediction results of the lifetime prediction on a power transformer. With optimal prediction results, it can help electricity management companies monitor conditions. Thereby minimizing the risk of disruption of electricity supply to customers. Models are tested and verified using a real dataset from a power transformer in several locations. The best hyper-parameter for this dataset is Bayesian Search, producing 0,001343 for Mean Absolute Error (MAE). © 2022 IEEE.

关键词： Power transformers

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