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HoloStream: a GPU-powered high-speed user interface for holographic microscopy imaging

Optics Express 2025年第7期33卷 15648-15660页

作者： Morales, Johan Obando-Vásquez, Sofia Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering EAFIT University Medellin Colombia Optical Imaging Research Lab Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA United States

This paper presents HoloStream, a GPU-powered high-speed user interface designed for holographic microscopy imaging. The platform reconstructs quantitative phase images rapidly for off-axis digital holographic microscopy (DHM) systems operating in the telecentric regime. By optimizing critical computational procedures, the HoloStream app reconstructs phase maps of 1280 × 960 pixels up to 11 frames per second (FPS) in live visualization and 90 fps for pre-recorded holographic videos. This real-time phase compensation is performed by upgrading the semi-heuristic phase compensation (SHPC) algorithm through GPU acceleration via PyCUDA. The interface also offers a user-friendly experience with flexible control over acquisition modes, visualization formats, and 3D tracking functionalities. Experimental validation demonstrates the platform’s capability to achieve fast and accurate phase imaging for live microscopy applications. The HoloStream app advances DHM technology by combining high-speed processing with ease of use, making it suitable for a wide range of DHM imaging applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer graphics equipment

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Ambient Light Impact on Power Reception in Outdoor VLC: A Distance-Based Analysis

Ambient Light Impact on Power Reception in Outdoor VLC: A Di...

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

作者： Ahmed, Sayeed Yeap, Tet Ahmed, Sabbir Kiringa, Iluju School of Electrical Engineering and Computer Science University of Ottawa ON Canada School of Applied Computer Science and It Conestoga College Ital ON Canada

ISBN: (纸本)9798350364910

Environmental sustainability is crucial for ensuring the long-term health and well-being of our planet and its in-habitants. Precise navigation of autonomous and semi-autonomous vehicles in agricultural usage, therefore, becomes a crucial com-ponent in ensuring such sustainability. It is no surprise that the use of visible light communication (VLC) in navigation is steadily expanding, driven primarily by its remarkable precision in indoor environments. VLC has significant potential as a su-perior alternative to traditional navigation systems, including the global positioning system (GPS), particularly in dynamic outdoor environments. However, despite its indoor success, addressing the challenges posed by ambient light remains critical, particularly in outdoor scenarios where natural lighting conditions vary substan-tially. This article investigates the complex relationship between ambient light and outdoor VLC technology. The study aims to quantify the impact of ambient light on the receiving power level at varying distances in outdoor VLC systems by employing advanced simulation and modelling techniques. It provides a detailed analysis of the effects of sunlight, offering valuable insights for optimizing the positioning of light transmitters and receivers to enhance performance and reliability in real-world applications. The results discussed have significant implications for advancing outdoor VLC technology. They provide a pathway for creating more resilient and efficient navigation systems that function seamlessly across various environmental conditions. © 2024 IEEE.

关键词： Sustainable development

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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Ensemble of Deep Learning with Crested Porcupine Optimizer Based Autism Spectrum Disorder Detection Using Facial Images

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computers, Materials & Continua 2025年第5期83卷 2793-2807页

作者： Jagadesh Balasubramani Surendran Rajendran Mohammad Zakariah Abeer Alnuaim Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105Tamil NaduIndia Department of Computer Science and Engineering College of Applied Studies and Community ServiceKing Saud UniversityP.O.Box 22459Riyadh11495Saudi Arabia

Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several *** all autistic children remain undiagnosed before the age of *** problems affecting face features are often associated with fundamental brain *** facial evolution of newborns with ASD is quite different from that of typically developing *** recognition is very significant to aid families and parents in superstition and *** facial features from typically developing children is an evident manner to detect children analyzed with ***,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic *** study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)*** overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal *** the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed *** the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)*** extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI *** simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.

关键词： Autism spectrum disorder ensemble learning crested porcupine optimizer facial images computeraided diagnosis

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

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Multimedia Tools and Applications 2025年第11期84卷 9591-9665页

作者： Mohanty, Lopamudra Kumar, Ashish Mehta, Vivek Agarwal, Mohit Suri, Jasjit S. School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Computer Science ABES Engineering College UP Ghaziabad India Stroke Diagnostic and Monitoring Division AtheroPoint™ RosevilleCA United States

Trained Artificial Intelligence (AI) models are challenging to install on edge devices as they are low in memory and computational power. Pruned AI (PAI) models are therefore needed with minimal degradation in performance. This is the first study of its kind where we have characterized three types of PAI paradigms, namely, weight pruning (WP), channel pruning (CP), and hybrid pruning (HP). Further, the study also analyses the risk of bias (RoB) in PAI models, and explainable PAI (XPAI) networks. PRISMA model was adapted for the selection of the best 145 studies. The review had two hypotheses: (i) PAI had bias and (ii) the order of performance for the three PAI is: HP > CP > WP in terms of performance. Thirty-two AI attributes using each of two experts were categorized into four clusters, namely, architecture, optimization, dataset design, and scientific validation. The RoB was analyzed in these PAI studies using AP(ai)Bias 2.0 (AtheroPoint™, CA, USA), which was based on a scoring-and-ranking strategy. This was then compared against the Butterfly model, composed of radial and regional biases, and analyzed using a Venn diagram in three independent bins based on low-moderate and moderate-high cut-offs. Recommendations were laid out for low-bias studies. PAI studies that lack adequate AI attributes were more inclined to the RoB. It has been observed that 19.64%, 21.42%, and 58.92% studies were in low-bias, moderate-bias, and high-bias bins, respectively. Pruning made inference faster and reduced sparsity. We have observed low participation of the PAI studies in the XPAI framework. The study presented the challenges and five-step recommendations for mitigating bias in PAI models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Risk assessment

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Blockchain privacy: Fundamental aspects and challenges for the future Internet data sharing

IET Blockchain

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IET Blockchain 2024年第2期4卷 152-168页

作者： Kapsoulis, Nikolaos Psychas, Alexandros Litke, Antonios Varvarigou, Theodora School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Division of Mathematics and Engineering Science Hellenic Army Academy Athens Greece

Privacy concerns the majority of individuals, governments, and organizations that share data over dissimilar networks of nodes. Every kind of participant requires awareness of the data journey that unfolds inside a blockchain network with its own trustworthy rules of data management and accessibility. This paper provides a methodological approach on privacy for data sharing within blockchain environments. Specific technological aspects of blockchains that relate to on-chain privacy such as network nature, party join, smart contracts, blockchain states, transactions, and ledger flows, are analysed with respect to the involvement and impact of privacy in distributed ledgers. A high-level architectural approach is suggested that is intended to address significant privacy concerns on data sharing among different kinds of users in the context of blockchain networks deployment and the broader Web 3.0 ecosystem building. Simultaneously, many pertinent challenges are discussed regarding network nature, configurable privacy, ownership, confidentiality, secured computation, and data monetization that appear in the field and ought to be carefully tackled for the future mass adoption of privacy-matured distributed ledgers that interconnect delivering the future Internet. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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Classification using a Spiking Neural Network: A Review

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IEIE Transactions on Smart Processing and Computing 2023年第1期12卷 64-71页

作者： Park, Yuntae Sunarya, Unang Yang, Geunbo Lee, Choongseop Baek, Jaewoo Baek, Suwhan Park, Cheolsoo Department of Computer Engineering KwangWoon University Korea Republic of School of Applied Science Telkom University Bandung Indonesia

In this paper, different types of spiking neural network (SNN) and the methods to teach them are addressed. The spiking neural network is especially designed in a biologically plausible way. Like other neural networks, SNNs trained with certain learning methods have been used for image processing, data mining, and classification, etc. Unlike other neural networks, the SNN uses one-bit spike information, and one of its main advantages is reducing energy consumption. Copyrights © 2023 The Institute of Electronics and Information Engineers.

关键词： Neural networks

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Developing Smart Skin: A Force Sensor-Based Touch and Pressure Detector 12

Developing Smart Skin: A Force Sensor-Based Touch and Pressu...

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12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Zain, Muhammad Aulia Rifqi Mutiara, Giva Andriana Periyadi Alfarisi, Muhammad Rizqy Sitorus, Angelica Adhira, Raka Duta Applied Science School Computer Technology Telkom University Bandung Indonesia Applied Science School Multimedia Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

This research discusses the advancements in developing smart skin by employing Force Sensitive Resistor (FSR). The objective of this research is to advance research on the fundamental concept of artificial skin that relies on the human sense of touch sensors especially in pressure to imitate the sensory capabilities of human skin, since humans are incapable of accurately quantifying the pain induced by external pressure. Therefore, by employing FSR sensors, the acquired data will be stabilized, thereby serving as a dependable reference. A prototyping method is implemented in this research to construct and observe the smart skin design. This research is utilized RP-S40-ST and FS7548 sensors for gathering data pressure and touch. The smart skin is coated with an elastomeric substance that closely resembles human skin in terms of its flexibility, softness, and mechanical properties. The FSR sensor is linked to a microcontroller board called TTGO LoRa OLED, which enables the integration of data transfer for further development through the Long Range (LoRa) protocol. The AMF-500 force gauge functions as a standard for converting data obtained from analog sensors into precise force (N) and pressure measurements (kPa). This research represents a significant step towards developing smart skin using FSR sensors that aim to mimic human touch by detecting external pressure up to a maximum of under 60 Newtons and a maximum bend of under 90 degrees. © 2024 IEEE.

关键词： Contact sensors

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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