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Enhanced Feedback Residual Attention Network with Slanted Scarce Auto-Encoder and IoT Based Industrial Waste Water Treatment System for Effective Waste Management

Enhanced Feedback Residual Attention Network with Slanted Sc...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prasad, K.V.S. Gavaskar, T. Ravivarman, G. Chinniah, P. Sophia, S. Sathyakala, S. Gmr Institute of Technology Department of Basic Sciences and Humanities Andhra Pradesh Vizianagaram India St. Joseph's College of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India ST. Joseph College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sona College of Technology Department of Management Studies Tamil Nadu Salem India

ISBN: (纸本)9798331509828

In the last few decades, rapid industrialization has increased the problem of wastewater treatment and management because of the increasing complexity and toxicity of wastewater that is difficult to treat by any conventional approach. Commonly used waste water treatment systems are unable to treat a variety of pollutants because of deficiency of good classification and optimization processes which results in wastage of resources and harming the environment. Furthermore, existing systems are inflexible, and an ability to change the nature of treatment in real time is impossible, which is crucial for the timely and efficient delivery and adjustment of therapies. To overcome these challenges, there is a need for smart monitoring systems enabled by IoT and effective treatment processes utilizing machine learning for better waste management. This paper comes up with an IoT and Machine Learning based system for the treatment of industrial wastewater that uses an Enhanced Feedback Residual Attention Network with Slanted Scarce Autoencoder to achieve accuracy in pollutant classification and quantification. Fick's Law Optimization is incorporated for optimization which makes the treatment process more efficient by allowing the treatment to be applied only where necessary and in the right amounts. IoT sensors are also part of the system to ensure that the data is collected in real time and the waste is continuously monitored to allow quick reactions to changes in waste. The introduced approach attains higher accuracy as 99%. © 2025 IEEE.

关键词： Wastewater treatment

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Thermal management challenges and approaches for liquid hydrogen-fueled aircraft

Thermal management challenges and approaches for liquid hydr...

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AIAA Aviation Forum and ASCEND, 2024

作者： Ordonez, Juan C. Sailabada, Chaianan Guo, Wei Cooley, Lance Cheetham, Peter Kim, Chul Zheng, Jim P. Gladin, Jonathan Cattafesta, Louis FAMU-FSU College of Engineering Department of Mechanical Engineering Center for Advanced Power Systems and Energy and Sustainability Center Florida State University TallahasseeFL32310 United States Electrical and Computer Engineering FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Center for Advanced Power Systems FAMU-FSU College of Engineering and Center for Advanced Power Systems Florida State University TallahasseeFL32310 United States Department of Electrical Engineering The State University of New York at Buffalo BuffaloNY14260 United States School of Aerospace Engineering Aerospace Systems Design Laboratory Georgia Institute of Technology AtlantaGA30332 United States Department of Mechanical Materials and Aerospace Engineering Illinois Institute of Technology ChicagoIL60616 United States

ISBN: (数字)9781624107160

ISBN: (纸本)9781624107160

This paper presents an overview of thermal management challenges associated with aircraft that rely on liquid hydrogen as the sole fuel source. The paper discusses strategies that can enhance the overall cooling capacity and enable a larger contribution of fuel cells to the overall power. Design considerations for heat exchangers to integrate cryogenic hydrogen with secondary thermal management loops, outer mold line cooling, and additional thermal management strategies are presented. Cooling requirement estimates for a blended-wing aircraft (112 passengers, 2400 nautical miles) are reported. A finite element simulation using RANS suggests a cooling capacity rate of the order of 10 kW/m2 is available through the Outer Mold Line (OML). The paper also reports on the cooling capacity available by using hydrogen as a heat sink to cool the power train. For the IZEA concept, the hydrogen stream to the fuel cell can handle close to 6% of the fuel cell heat generation. © 2024, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hydrogen fuels

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FedSafe-No KDC Needed: Decentralized Federated Learning with Enhanced Security and Efficiency

FedSafe-No KDC Needed: Decentralized Federated Learning with...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Mohamed I. Ibrahem Mostafa M. Fouda Zubair Md Fadlullah School of Computer and Cyber Sciences Augusta University Augusta GA USA Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy-Studies-(CAES) Idaho Falls ID USA Department of Computer Science Western University London ON Canada

Cloud-based federated learning (FL) services have received increasing attention due to their ability to enable collaborative global model training without the need to collect local data from participants. To generate a global model, local models are trained on participants' local data and only model parameters are sent to an aggregator server. Nonetheless, revealing model parameters can still reveal training data via launching attacks, e.g., inference and membership. Hence, to protect model parameters, a secure global model aggregation scheme is needed to protect these parameters from unauthorized access. Existing solutions to this issue, which are based on homomorphic encryption and secure multi-party computation, tend to have large overheads and slow down training times. Functional encryption (FE) has been proposed as a solution for resolving privacy-preservation issues in FL, but current solutions suffer from high overhead and lack of security such as leaking master private key. To address these issues, this paper proposes a privacy-protecting, efficient, and decentralized FL framework, called FedSafe, based on FE without the need for a trusted key distribution center (KDC). The proposed scheme allows the participants to communicate with an aggregator to construct a global model without disclosing or learning their local models' parameters or the training data, thereby safeguarding their privacy. Through rigorous testing with real-world data, it is demonstrated that FedSafe outperforms the state-of-the-art privacy-protecting FL schemes in terms of security, scalability, and communication and computation overhead. Unlike existing approaches, this is accomplished without depending on any trusted KDC.

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Plasma Jet Deposition and Self-Sintering of Gold Nanoparticle Ink for Flexible Electronics 5

Plasma Jet Deposition and Self-Sintering of Gold Nanoparticl...

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5th IEEE International Flexible Electronics Technology Conference, IFETC 2023

作者： Manzi, Jacob Varghese, Tony Dhamala, Anupama Prakasan, Lakshmi Eixenberger, Josh Kandadai, Nirmala Estrada, David Subbaraman, Harish School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States Micron School of Material Science and Engineering Boise State University BoiseID83725 United States Boise State University Department of Electrical and Computer Engineering BoiseID83725 United States Center for Advanced Energy Studies Boise State University BoiseID83725 United States Idaho National Laboratory Idaho FallsID83415 United States

ISBN: (纸本)9798350332094

Additive manufacturing has become a promising method for the fabrication of inexpensive, green, flexible electronics. Printed electronics on low-Temperature substrates like paper are very appealing for the flexible hybrid electronics market for their use in disposable and biocompatible electronic applications and in areas like packaging, wearables, and consumer electronics. Plasma-jet printing uses a dielectric barrier discharge plasma to focus aerosolized nanoparticles onto a target substrate. The same plasma can be used to change the properties of the printed material and even sinter in situ. The technology can also be utilized in space and microgravity environments since the plasma-Assisted deposition is independent of gravity. In this work, we show plasma voltage effect on deposition of gold nanoparticles and direct printing of flexible, conductive gold structures onto low-Temperature paper substrates without the need for thermal or photonic post-processing. The effects of plasma parameters on the conductivity and flexible reliability of the printed films are studied, and a paper-based LED electrode is demonstrated. © 2023 IEEE.

关键词： Substrates

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Performance of Gradient-Based Optimizer for Optimum Wind Cube Design

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computers, Materials & Continua 2022年第4期71卷 339-353页

作者： Alaa A.K.Ismaeel Essam H.Houssein Amir Y.Hassan Mokhtar Said Faculty of Computer Studies(FCS) Arab Open University(AOU)Muscat130Sultanate of Oman Faculty of Science Minia UniversityMiniaEgypt Faculty of Computers and Information Minia UniversityMiniaEgypt Electronics Research Institute GizaEgypt Electrical Engineering Department Faculty of EngineeringFayoum UniversityFayoumEgypt

Renewable energy is a safe and limitless energy source that can be utilized for heating,cooling,and other *** energy is one of the most important renewable energy *** fluctuation of wind turbines occurs due to variation of wind velocity.A wind cube is used to decrease power fluctuation and increase the wind turbine’s *** optimum design for a wind cube is the main contribution of this *** decisive design parameters used to optimize the wind cube are its inner and outer radius,the roughness factor,and the height of the wind turbine hub.A Gradient-Based Optimizer(GBO)is used as a new metaheuristic algorithm in this *** objective function of this research includes two parts:the first part is to minimize the probability of generated energy loss,and the second is to minimize the cost of the wind turbine and wind *** Gradient-Based Optimizer(GBO)is applied to optimize the variables of two wind turbine types and the design of the wind *** metrological data of the Red Sea governorate of Egypt is used as a case study for this *** on the results,the optimum design of a wind cube is achieved,and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind *** energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.

关键词： Wind turbine wind cube gradient-based optimizer metaheuristics energy source

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Single-shot quantitative phase gradient microscopy using physics-based untrained neural network phase retrieval 11

Single-shot quantitative phase gradient microscopy using phy...

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Quantitative Phase Imaging XI 2025

作者： Hur, Sun Woong Sengupta, Sourya Kwon, Minsung Manoharaan, Revathi Anastasio, Mark Bhargava, Rohit Department of Bioengineering University of Illinois Urbana-ChampaignIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignIL United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignIL United States Department of Computer Science Carle-Illinois College of Medicine University of Illinois Urbana-ChampaignIL United States Department of Chemical and Biomolecular Engineering Mechanical Science and Engineering and Chemistry Cancer Center at Illinois University of Illinois Urbana-ChampaignIL United States CZ Biohub Chicago LLC IL United States

ISBN: (纸本)9781510684065

Single-shot quantitative phase gradient microscopy (ss-QPGM) enables quantitative phase imaging (QPI) based on differential interference contrast (DIC) microscopy. In ss-QPGM, the analyzer is replaced by a quarter-wave plate and a polarization camera, allowing for phase gradient extraction from a single intensity measurement. The quantitative phase image is reconstructed via numerical integration, but direct integration along a single axis introduces artifacts and phase errors due to missing frequency response along the axis of orthogonal to the shear axis and noise-induced streak lines. In this work, we propose a phase retrieval method for ss-QPGM using an untrained neural network (UNN), which integrates physical priors without requiring paired ground truth and input datasets, overcoming the limitations of traditional regularization methods such as Tikhonov regularization and the alternating direction method of multipliers (ADMM) with total variation (TV) regularization. We demonstrate that the UNN approach not only offers superior phase reconstruction but also alleviates artifacts induced by missing frequency response along the axis and noise-induced streak lines, as shown by phase images of a calibrated target and biological samples. © 2025 SPIE.

关键词： Image reconstruction

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Drilling Simulation of AA7075 T351 Graded Aluminium Alloy Using Deform-3D 2nd

Drilling Simulation of AA7075 T351 Graded Aluminium Alloy Us...

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2nd International Conference on Functional Material, Manufacturing and Performances, ICFMMP 2021

作者： Bagal, Dilip Kumar Jeet, Siddharth Barua, Abhishek Pradhan, Swastik Rath, Arati Panda, Surya Narayan Department of Mechanical Engineering Government College of Engineering Odisha Kalahandi Bhawanipatna India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Odisha Rourkela India School of Mechanical Engineering Lovely Professional University Punjab Phagwara India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Production Engineering Birsa Institute of Technology Jharkhand Sindri Dhanbad India

ISBN: (纸本)9789811941467

The article develops a three-dimensional drilling model using DEFORM-3D software based on the Usui Tool Wear model, the Johnson and Cook model and the Lagrangian approach. Thermal, structural and chip morphology analyses were performed in this section. The effect of output responses is examined through the lens of their simulation results. The workpiece is made of AA7570 T351 Aluminium alloy with machining type and the drill bit is made of tungsten carbide (WC) with a diameter of 10 mm. Due to its high strength and resistance to corrosion, this alloy is widely used in the aerospace and biomedical equipment manufacturing industries. Thermal analysis reveals that the tool's maximum temperature is 254.8 °C, while the highest temperature is 273.1 °C in the case of AA7075 T351 graded alloy workpiece. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Corrosion resistance

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

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computers, Materials & Continua 2021年第7期68卷 431-446页

作者： Ahmed S.Salama Mohamed Amr Mokhtar Mazhar B.Tayel Esraa Eldesouky Ahmed Ali Future University in Egypt CairoEgypt Cairo Higher Institute for Engineering Computer Science and ManagementCairoEgypt Department of Electrical Engineering Faculty of EngineeringAlexandria UniversityAlexandria21544Egypt Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia CityEgypt Higher Future Institute for Specialized Technological Studies CairoEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Assuring medical images protection and robustness is a compulsory necessity *** this paper,a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform(DWT)with the energy compaction of the Discrete Wavelet Transform(DCT).The multi-level Encryption-based Hybrid Fusion Technique(EbhFT)aims to achieve great advances in terms of imperceptibility and security of medical images.A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT ***,a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the ***,a PN-sequence key is formed along with a supplementary key in the third layer of the ***,the watermarked image is generated by enclosing both keys into DWT and DCT *** fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT *** order to validate the proposed technique,a standard dataset of medical images is *** results show higher performance of the visual quality(i.e.,57.65)for the watermarked forms of all types of medical *** addition,EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation(NC).Finally,extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.

关键词： Medical image processing digital image watermarking discrete wavelet transforms discrete cosine transform encryption image fusion hybrid fusion technique

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Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation

Motor Focus: Fast Ego-Motion Prediction for Assistive Visual...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Hao Wang Jiayou Qin Xiwen Chen Ashish Bastola John Suchanek Zihao Gong Abolfazl Razi School of Computing Clemson University Department of Electrical and Computer Engineering Stevens Institute of Technology School of Cultural and Social Studies Tokai University

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by translating visual information into voice commands. In complex scenarios where multiple objects are present, it is imperative to prioritize object detection and provide immediate notifications for key entities in specific directions. This brings the need for identifying the observer's motion direction (ego-motion) by merely processing visual information, which is the key contribution of this paper. Specifically, we introduce Motor Focus, a lightweight image-based framework that predicts the ego-motion - the humans' (and humanoid machines') movement intentions based on their visual feeds, while filtering out camera motion without any camera calibration. To this end, we implement an optical flow-based pixel-wise temporal analysis method to compensate for the camera motion with a Gaussian aggregation to smooth out the movement prediction area. Subsequently, to evaluate the performance, we collect a dataset including 50 clips of pedestrian scenes in 5 different scenarios. We tested this framework with classical feature detectors such as SIFT and ORB to show the comparison. Our framework demonstrates its superiority in speed $(> 40\text{FPS})$ , accuracy (MAE $=60\text{pixels})$ , and robustness (SNR $=23\text{dB})$ , confirming its potential to enhance the usability of vision-based assistive navigation tools in complex environments. The code is publicly available at https://***/***/project/VisionGPT.

关键词： Visualization Accuracy Pedestrians Navigation Detectors Cameras Motors Feature extraction Stability analysis Usability

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