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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gulia, Rahul Ganguly, Amlan Kwasinski, Andres Kuhl, Michael E. Rashedi, Ehsan Hochgraf, Clark Rochester Institute of Technology Department of Computer Engineering RochesterNY United States Rochester Institute of Technology Department of Industrial and Systems Engineering RochesterNY United States Rochester Institute of Technology Department of Electrical and Computer Engineering Technology RochesterNY United States

ISBN: (纸本)9798350364910

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments. © 2024 IEEE.

关键词： Signal to noise ratio

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Foundry Manufactured 6-bit Resolution, 150μm Long Slow-Light Electro-Optic Modulator for On-Chip Photonic Tensor Computing

Foundry Manufactured 6-bit Resolution, 150μm Long Slow-Ligh...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Zhang, Meng Begović, Amir Yin, Dennis Gangi, Nicholas Gu, Jiaqi Huang, Rena Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering 110 8th Street TroyNY12180 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85281 United States

ISBN: (纸本)9781957171395

We demonstrate 6-bit DAC resolution with an ultra-compact slow-light electro-optic modulator. The 10× modulation length reduction enables 31× compute density, 1.17× energy efficiency, and 36.1× energy efficiency per unit area for photonic tensor computing. © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Light modulation

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Energy-Aware Trajectory Design for UAV-mounted Full-duplex Relays 35

Energy-Aware Trajectory Design for UAV-mounted Full-duplex R...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Tyrovolas, Dimitrios Mitsiou, Nikos Boufikos, Thomas Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Karagiannidis, George Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Hilti Corporation Feldkircher Strasse 100 Schaan9494 Liechtenstein University of Ioannina Computer Science Engineering Department Ioannina Greece Artificial Intelligence & Cyber Systems Research Center Lebanon

ISBN: (纸本)9798350362244

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV's energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs' performance in future networks. © 2024 IEEE.

关键词： Time division multiple access

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Dynamics of self-hybridized exciton–polaritons in 2D halide perovskites

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Light(Science & Applications) 2024年第1期13卷 13-22页

作者： Surendra B.Anantharaman Jason Lynch Christopher E.Stevens Christopher Munley Chentao Li Jin Hou Hao Zhang Andrew Torma Thomas Darlington Francis Coen Kevin Li Arka Majumdar PJames Schuck Aditya Mohite Hayk Harutyunyan Joshua R.Hendrickson Deep Jariwala Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA19104USA KBR Inc. BeavercreekOH45431USA Air Force Research Laboratory Sensors DirectorateWright-Patterson Air Force BaseOH45433USA Department of Physics University of WashingtonSeattleWA98195USA Department of Physics Emory UniversityAtlantaGA30322USA Department of Chemical and Biomolecular Engineering Rice UniversityHoustonTX77005USA Department of Materials Science and Nanoengineering Rice UniversityHoustonTX77005USA Applied Physics Program Smalley‐Curl InstituteRice UniversityHoustonTX77005USA Department of Mechanical Engineering Columbia UniversityNew YorkNY10027USA Department of Electrical and Computer Engineering University of WashingtonSeattleWA98195USA

Excitons,bound electron–hole pairs,in two-dimensional hybrid organic inorganic perovskites(2D HOIPs)are capable of forming hybrid light-matter states known as exciton-polaritons(E–Ps)when the excitonic medium is confined in an optical *** the case of 2D HOIPs,they can self-hybridize into E–Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the ***,the fundamental properties of these self-hybridized E–Ps in 2D HOIPs,including their role in ultrafast energy and/or charge transfer at interfaces,remain ***,we demonstrate that>0.5µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E–P *** E–Ps have high Q factors(>100)and modulate the optical dispersion for the crystal to enhance sub-gap absorption and *** varying excitation energy and ultrafast measurements,we also confirm energy transfer from higher energy E–Ps to lower energy E–***,we also demonstrate that E–Ps are capable of charge transport and transfer at *** findings provide new insights into charge and energy transfer in E–Ps opening new opportunities towards their manipulation for polaritonic devices.

关键词： charge polar excitation

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Enhanced DFT-Based Chaotic Image Block Cipher with Several Modes of Operation 12

Enhanced DFT-Based Chaotic Image Block Cipher with Several M...

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12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Yassin, Mahmoud Y. M. Nasir, Ali A. Taha, Mostafa Iqbal, Naveed Department of Electrical Engineering Dhahran Saudi Arabia Center for Communication Systems and Sensing Kfupm Dhahran Saudi Arabia Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350325416

Chaotic image encryption has been investigated extensively in the literature in different domains, including the frequency domain using Discrete Fourier Transform (DFT). Despite DFT-based chaotic image cryptosystems exhibiting good confusion properties, they suffer from poor diffusion characteristics and non-uniform histograms. Therefore, they are vulnerable to many attacks, such as differential attacks, and statistical cryptanalytic attacks. This paper presents an enhanced block cipher image cryptosystem with several modes of operation based on the concept of bit reversal using the 2D chaotic Baker map and DFT. The proposed enhancement develops an improved image encryption scheme that involves properly diffusion-confusion enhanced operations within modified versions of the known block cipher modes of operation, namely Cipher Block Chaining Mode (CBC), Cipher Feedback Mode (CFB), and Output Feedback Mode (OFB). These enhanced operations are based on a 2D chaotic baker map in the Fourier domain. The proposed enhancement is validated using a MATLAB simulation. Results show that the proposed work manages to overcome the abovementioned problems outstandingly and outperforms the existing results in the literature in many encryption quality metrics. © 2023 IEEE.

关键词： Discrete Fourier transforms

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Personalized Data-Driven State Models of the Circadian Dynamics in a Biometric Signal 46

Personalized Data-Driven State Models of the Circadian Dynam...

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

作者： Ike, Chukwuemeka O. Wen, Yunshi Wen, John T. Oishi, Meeko M.K. Brown, Lee K. Julius, A. Agung Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States Engineering Research Center TroyNY United States University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States University of New Mexico Department of Internal Medicine AlbuquerqueNM United States

ISBN: (纸本)9798350371499

Circadian rhythms are endogenous 24-hour oscillations that are vital for maintaining our overall well-being. They are driven at a high level by a core circadian clock located in the brain, making their dynamics difficult to track. Various modeling approaches exist to predict the dynamics, but as the models are typically designed on population-level data, their performance is diminished on the individual level. This paper proposes a method for learning personalized latent state models, i.e., dynamical models that explicitly use latent state variables, that relate circadian input(s) to observable biometric signals. Our models combine an autoencoder with a recurrent neural network and use the pair to model the salient dynamics present in the data. We validate our method using experimental data, where the circadian input is light and the biometric data are actigraphy signals. We demonstrate that our method results in models with low-dimensional latent state that can accurately reconstruct and predict the observable biometric signals. Further, we show that the oscillation of the learned latent state agrees with the subjects' circadian clock oscillation as estimated with melatonin *** relevance - This proposes a technique for personal-ized modeling of the circadian system with potential applications in feedback control and individualized circadian studies. © 2024 IEEE.

关键词： Feedback control

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