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Smart health assist for elder people using IoT

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AIP Conference Proceedings 2022年第1期2405卷

作者： V. Arulkumar I. Chandra D. Subitha A. Raja R. Ganesan Rajasekhar Atla 1Department of Information Technology SSN College of Engineering Kalavakkam TN 603110 India 2Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai TN 600124 India 3Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai TN 600062 India 4Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai TN 602105 India 5Department of Biomedical Engineering G. S. Pillay Engineering College Nagapattinam TN 611002 India 6Department of Electronics and Communication Engineering N.B.K.R.I.S.T Vidyanagar AP 524413 India

Internet of Things is a one which is emerged by advancement in the information and communication technology. And it is a new technology that connects the things from various domains through the internet and gives more convenience to several sectors. In the medical field, the IoT play an important role to monitor the patient’s health from a remote location and healthcare is an essential part of the life. This paper proposes how to design and monitor the elder’s health from a remote location. The parameters that are measured by elder include temperature, glucose level, blood, pulse rate. The glucose level is measured by a non-invasive method. This parameter values are displayed in the LCD screen for elder reference and these values are uploaded to cloud frequently for future reference. Each parameter is assigned to a threshold value if it exceeds the threshold value, a buzzer and LED intimation is given to the elder and text message was sent to the caretaker person and to the nearby hospital in case of emergency.

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Toward high-fidelity quantum information processing and quantum simulation with spin qubits and phonons

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arXiv 2024年

作者： Arrazola, I. Minoguchi, Y. Lemonde, M.-A. Sipahigil, A. Rabl, P. Instituto de Física Teórica UAM-CSIC Universidad Autónoma de Madrid Cantoblanco Madrid28049 Spain Vienna Center for Quantum Science and Technology Atominstitut TU Wien Vienna1040 Austria Institute for Quantum Optics and Quantum Information IQOQI Vienna Austrian Academy of Sciences Boltzmanngasse 3 Vienna1090 Austria Nord Quantique SherbrookeQCJ1J 2E2 Canada Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California Berkeley BerkeleyCA94720 United States Walther-Meißner-Institut Bayerische Akademie der Wissenschaften Garching85748 Germany Technische Universität München TUM School of Natural Sciences Physics Department Garching85748 Germany Schellingstr. 4 Munich80799 Germany

We analyze the implementation of high-fidelity, phonon-mediated gate operations and quantum simulation schemes for spin qubits associated with silicon vacancy centers in diamond. Specifically, we show how the application of continuous dynamical decoupling techniques can substantially boost the coherence of the qubit states while increasing at the same time the variety of effective spin models that can be implemented in this way. Based on realistic models and detailed numerical simulations, we demonstrate that this decoupling technique can suppress gate errors by more than two orders of magnitude and enable gate infidelities below ∼ 10−4 for experimentally relevant noise parameters. Therefore, when generalized to phononic lattices with arrays of implanted defect centers, this approach offers a realistic path toward moderate- and large-scale quantum devices with spins and phonons, at a level of control that is competitive with other leading quantum-technology platforms. © 2024, CC BY-SA.

关键词： Phonons

Physical position based shortcut tree routing in ZigBee WSN’s

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AIP Conference Proceedings 2021年第1期2387卷

作者： Shiju Kumar P. S. A. Ramesh Babu 1Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai- 603103 Tamil Nadu India 2School of Science and Humanities Hindustan Institute of Technology and Science Chennai- 603103 Tamil Nadu India

ZigBee is a worldwide standard wireless personal area network that focuses on reducing power conception and it is also cost efficient, reliable and also contains scalable products and applications. In data communication networks, most significant functions is routing between nodes. ZigBee standard helps a combination of tree routing and vector routing on demand. Shortcut Tree Routing (STR) extensively improves the path effectiveness of ZigBee tree routing by including only the neighbor details of a hop. Also, the routing path is not always optimal in one feature of end-to-end hop distance is STR limitation. Performance of packet delivery ratio and end delay of STR is poor. ZigBee tree routing cannot provide the optimal routing path. To overcome these limitations, enhancement of STR is proposed in this method to improve the delay against STR. It is clearly that preserving two-hop neighbor information generates large protocol overhead on network through large node density. So, this work proposes an enhancement of the STR along with the information on physical position of nodes. That is, STR is used to find the next hop and from which the physical distance among node and destination is used as one of the parameters to get the good route.

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Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography

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arXiv 2023年

作者： Zhang, Linglin Brown-Mulry, Beatrice Nalla, Vineela Hwang, InChan Gichoya, Judy Wawira Gastounioti, Aimilia Banerjee, Imon Seyyed-Kalantari, Laleh Woo, MinJae Trivedi, Hari School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Information Technology Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Computational Imaging Research Center Washington University in St. Louis School of Medicine 4525 Scott Avenue St. LouisMO63110 United States Department of Radiology Mayo Clinic Arizona 13400 E Shea Blvd ScottsdaleAZ85259 United States School of Computing and Augmented Intelligence Arizona State University 699 S Mill Ave TempeAZ85281 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada

Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. This retrospective study uses the Emory BrEast Imaging Dataset (EMBED) containing mammograms from 115,931 patients imaged at Emory Healthcare between 2013-2020, with Breast Imaging Reporting and Data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Multiple deep learning models were trained to distinguish between abnormal tissue patches and randomly selected normal tissue patches from screening mammograms. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics and investigated their associations with false negatives and false positives. We also performed multivariate logistic regression to control for confounding between subgroups. The top-performing model, ResNet152V2, achieved accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operating characteristics curve 0.975 (95%CI=0.972-0.978). Before controlling for confounding, nearly all subgroups showed statistically significant differences in model performance. However, after controlling for confounding, we found lower false negative risk associates with Other race (RR=0.828;p= .050), biopsy-proven benign lesions (RR=0.927;p= .011), and mass (RR=0.921;p= .010) or asymmetry (RR=0.854;p= .040);higher false negative risk associates with architectural distortion (RR=1.037;p Copyright © 2023, The Authors. All rights reserved.

关键词： Mammography

A segmented model based internal model control scheme of electromagnetic micro-mirror systems

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ISA transactions 2025年

作者： Ruili Dong Qingyuan Tan Yonghong Tan Xiaoli Song Tianyu Wang College of Information Science and Technology Donghua University Shanghai 201620 China. Electronic address: ruilidong@***. Department of Electrical and Computer Engineering University of Windsor Windsor N9B 3P4 ON Canada. Electronic address: tan1b@uwindsor.ca. College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai 200234 China. Electronic address: tany@***. Nanjing Institute of Astronomical Optics & Technology Chinese Academy of Sciences Nanjing 210042 China CAS Key Laboratory of Astronomical Optics & Technology Nanjing Institute of Astronomical Optics & Technology Nanjing 210042 China. Electronic address: xlsong@***. School of Mathematical Sciences Henan Institute of Science and Technology Xinxiang 453003 China. Electronic address: wangtianyu125427@***.

In this paper, a segmented internal model control (SIMC) scheme based on the segmented combination model of the electromagnetic micro-mirror system (EMMS) is established. Notice that highly underdamped oscillation and rate-dependent hysteresis exist in the EMMS, so it is a complex nonlinear dynamic system. In order to control the deflection angle of the EMMS using the internal model control strategy, it is necessary to establish the inverse model of the EMMS. Therefore, a new model structure that is convenient for inversion is proposed in this paper to describe the characteristics of the EMMS with underdamped and rate-dependent hysteresis. In the proposed scheme, the model is a combination of a group of weighted sub-models based on the segmentation of the system's operating frequency. The weight of each segmented sub-model is not a constant but a new type of function which is also called the smoothing factor. Its function is to smooth the switching between sub-models, thereby reducing the dynamic error caused by model switching. In addition, the particle swarm optimization (PSO) algorithm is used to determine the optimal frequency segmentation points, which helps to obtain the optimal model for describing the system characteristics. Based on the proposed segmented combination model, the corresponding segmented internal model control with two-degree-of-freedom filters is proposed, and the corresponding filters in the internal model control are designed based on the small gain theorem. Finally, the proposed control strategy is applied to the control of the deflection angle of the electromagnetic micro-mirror to verify the proposed control method. Moreover, the non-smooth internal model control strategy is also used for comparison in the experiments.

关键词： Electromagnetic micro-mirror Frequency segmented combination model Hysteresis Internal model control

Multiple intelligent reflecting surfaces assisted cell-free MIMO communications

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arXiv 2020年

作者： Xie, Xie He, Chen Li, Xiaoya Yang, Kun Jane Wang, Z. the School of Information Science and Technology Northwest University Xi’an710069 China the School of Information Science and Technology Northwest University Xi’an710069 China School of Computer Science & Electronic Engineering University of Essex Wivenhoe Park Colchester EssexCO4 3SQ United Kingdom Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate an intelligent reflecting surface (IRS) assisted cell-free multiple input multiple output (MIMO) communication system, where distributed multiple IRSs are dedicated deployed to assist distributed multiple base stations (BSs) for cooperative transmission. Our objective is to maximize the achievable sum-rate of the cell-free system by jointly optimizing the active transmit beamforming matrices at BSs and the passive reflecting beamforming matrices at IRSs, while the constraints on the maximum transmit power of each BS and the phase shift of each IRS element are satisfied. We propose an efficient framework to jointly design the BSs, the IRSs, and the user equipment (UEs). As a compromise approach, we first transform the non-convex problem into an equivalent form based on the fractional programming methods and then decompose the reformulated problem into two subproblems and solve them alternately. Particularly, we propose a Lagrangian dual sub-gradient based algorithm to solve the subproblem of optimizing the active transmit beamforming with nearly closed-form solutions. We reformulate the subproblem of optimizing the passive reflecting beamforming as a constant modulus constrained quadratic programming (CMC-QP) problem. We first provide two feasible solutions by proposing a pair of relaxation-based algorithms. We also develop a low-complexity alternating sequential optimization (ASO) algorithm to obtain closed-form solutions. All three algorithms are guaranteed to converge to locally optimal solutions. Simulation results demonstrate that the proposed algorithms achieve considerable performance improvements compared with the benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Quadratic programming

Enhanced wastewater purification and photocatalytic green energy production via a novel CaIn₂S₄-coupled BiFeO₃ nanocomposite: Characterization and mechanistic insights

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Journal of Water Process engineering 2025年 75卷

作者： Marouani, Ismail Hassan, Waqed H. Soliman, Naglaa F. Singh, Pradeep Kumar Khan, Mohammad I. Mahariq, Ibrahm Atamurotov, Farruh Abdujabbarov, Ahmadjon Diab, M.A. Department of Electronics Engineering Applied College University of Hail Saudi Arabia University of Warith Al-Anbiyaa Kerbala 56001 Iraq Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Mechanical Engineering Institute of Engineering & Technology GLA University UP Mathura 281406 India Department of Chemical Engineering College of Engineering King Khalid University Abha 61411 Saudi Arabia University College Korea University Seoul 02481 South Korea Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Applied Science Research Center Applied Science Private University Amman Jordan Urgench State University Kh. Alimdjan str. 14 Urgench 220100 Uzbekistan New Uzbekistan University Movarounnahr street 1 Tashkent 100000 Uzbekistan School of Physics Harbin Institute of Technology Harbin 150001 China Department of Biotechnology Yeungnam University Gyeongbuk Gyeongsan 38541 South Korea Water Analysis Department Central Public Health Laboratories Ministry of Health and Population Cairo 11613 Egypt

The development of advanced photocatalysts capable of addressing both environmental remediation and sustainable energy conversion remains imperative. This work reports the hydrothermal synthesis of a CaIn₂S₄–BiFeO₃, (CIS/BFO) Z-scheme heterojunction, followed by comprehensive structural, morphological, optical, and electrochemical characterization. Leveraging a Z-scheme charge-transfer pathway improves visible-light harvesting, accelerates charge-carrier separation, and strengthens redox capability, thereby overcoming the inherent shortcomings of conventional type-II heterostructures. The optimized 2:1 CIS/BFO composite demonstrated a pseudo-first-order kinetic rate constant (k = 0.049 min⁻¹), surpassing those of pristine bismuth ferrite (BiFeO₃, BFO;0.006 min⁻¹) and calcium indium sulfide (CaIn₂S₄, CIS;0.013 min⁻¹). Under optimal conditions (32.23 mg/L tetracycline (TC) concentration, 0.655 g L⁻¹ catalyst dosage, 65.93 min irradiation, pH 5.69), 99.15 % TC degradation was achieved, as validated by response surface methodology-central composite design (RSM-CCD) modeling (R² = 0.9994). Electron spin resonance (ESR) and radical quenching experiments confirmed that superoxide (^•O₂⁻) and hydroxyl (^•OH) radicals were the dominant reactive species facilitating TC degradation. The catalyst exhibited ≥85 % degradation efficiency in real-water matrices and retained 91.1 % efficiency after five cycles, demonstrating exceptional stability and reusability. For hydrogen peroxide (H₂O₂) production, the catalyst yielded 1321.06 μM in pure water, which increased to 3104.38 μM with isopropanol, indicating enhanced electron retention. In nitrogen (N₂) fixation, it generated 571.46 μM ammonium (NH₄⁺), further increasing to 813.37 μM with methanol, highlighting its robust reduction capabilities. The catalyst maintained 92.5 % efficiency after fi

关键词： Energy conversion Environmental remediation H₂O₂ production N₂ fixation Photocatalysis RSM–CCD modeling

Smart Agriculture Using IoT for Automated Irrigation, Water and Energy Efficiency

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Smart Agricultural Technology

Smart Agricultural technology 2025年

作者： Subir Gupta Subrata Chowdhury Ramya Govindaraj Kassian T.T. Amesho Sumarlin Shangdiar Timoteus Kadhila Sioni Iikela Dept of AIML Haldia Institute of Technology West Bengal 721657 India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India School of Computer Science Engineering and Information Systems VIT Vellore Tamil Nadu India Department of Higher Education and Lifelong Learning School of Education Faculty of Education and Human Sciences University of Namibia Private Bag 13301 340 Mandume Ndemufayo Avenue Windhoek Namibia Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung 804 Taiwan The International University of Management Centre for Environmental Studies Faculty of Environment Management and Sustainability Science Main Campus Dorado Park Ext 1 Windhoek Namibia

This study presents an innovative smart agriculture system that integrates Internet of Things (IoT) technologies, predictive algorithms, and automated control mechanisms to optimize irrigation and enhance resource efficiency. The proposed solution leverages soil moisture, temperature, and humidity sensors connected to an Arduino-based microcontroller to automate irrigation based on real-time data. Farmers can remotely monitor and manage irrigation schedules through mobile devices, ensuring precision and convenience. The predictive algorithm combines historical and real-time data to forecast irrigation requirements, ensuring water is applied only when necessary. Field trials demonstrated a 30% reduction in water usage compared to traditional irrigation methods, while maintaining optimal soil moisture levels. Additionally, the system significantly reduced labor and energy costs, contributing to operational efficiency. By addressing water scarcity and promoting sustainable resource utilization, this smart system has the potential to improve crop yields, reduce greenhouse gas emissions, and advance ecological farming practices. In regions with limited water resources, the implementation of this technology represents a significant step toward sustainable agriculture, showcasing immense potential for broader adoption.

关键词： Smart agriculture IoT-enabled irrigation Predictive irrigation algorithms Water and energy efficiency Automated farming systems Sustainable agriculture technologies Resource optimization in agriculture

Better, Not Just More: Data-Centric Machine Learning for Earth Observation

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arXiv 2023年

作者： Roscher, Ribana Rußwurm, Marc Gevaert, Caroline Kampffmeyer, Michael dos Santos, Jefersson A. Vakalopoulou, Maria Hänsch, Ronny Hansen, Stine Nogueira, Keiller Prexl, Jonathan Tuia, Devis Data Science for Crop Systems Group Forschungszentrum Jülich GmbH Wilhelm-Johnen-Straße Jülich52428 Germany Remote Sensing Group University of Bonn Niebuhrstr. 1a Bonn53113 Germany Laboratory of Geo-information Science and Remote Sensing Wageningen University Droevendaalsesteeg 3 Wageningen Gelderland6708 PB Netherlands Department of Earth Observation Science Faculty ITC University of Twente Drienerlolaan 5 Enschede Overijssel7522 NB Netherlands Department of Physics and Technology UiT The Arctic University of Norway Klokkargårdsbakken 35 Tromsø9019 Norway Department of Computer Science University of Sheffield Sheffield City Centre 211 Portobello SheffieldS1 4DP United Kingdom Münchner Str. 20 Oberpfaffenhofen-Wessling82234 Germany Computing Science and Mathematics University of Stirling StirlingFK9 4LA United Kingdom Department of Aerospace Engineering University of the Bundeswehr Munich Werner-Heisenberg-Weg 39 Neubiberg Bavaria85579 Germany Ronquos 86 Sion1951 Switzerland

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many methods has already saturated on these datasets. We argue that a shift from a model-centric view to a complementary data-centric perspective is necessary for further improvements in accuracy, generalization ability, and real impact on end-user applications. Furthermore, considering the entire machine learning cycle—from problem definition to model deployment with feedback—is crucial for enhancing machine learning models that can be reliable in unforeseen situations. This work presents a definition as well as a precise categorization and overview of automated data-centric learning approaches for geospatial data. It highlights the complementary role of data-centric learning with respect to model-centric in the larger machine learning deployment cycle. We review papers across the entire geospatial field and categorize them into different groups. A set of representative experiments shows concrete implementation examples. These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches. © 2023, CC BY.

关键词： Deep learning