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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Ahmet Sefer Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

An iterative inversion algorithm to reconstruct the shape of two-dimensional dielectric objects from far-field measurements is formulated and implemented. The proposed method uses an integral operator to map the unknown boundary of the object onto the far-field pattern of the scattered field. This mapping is inherently ill-posed and nonlinear. Therefore, Newton iterations are used for linearization, and the resulting linear equation at each iteration is regularized using a Tikhonov scheme. Numerical results validate the accuracy and the applicability of the proposed method.

关键词： Shape Shape measurement Conferences Integral equations Electromagnetic scattering Antennas and propagation Iterative algorithms

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Federated Learning Incorporating Non-Orthogonal Transmission and Unstructured Model Pruning

Federated Learning Incorporating Non-Orthogonal Transmission...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Siyu Zhao, Ming Zhou, Shengli University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications Hefei China University of Connecticut Department of Electrical and Computer Engineering Storrs United States

ISBN: (纸本)9798350351255

In federated learning (FL), the servers are generally seen as omnipotent to distribute the full models to all clients. This is not the case in wireless systems. The broadcast of models inevitably excludes some users with poor channel gain but valuable computation power and diverse local data. In this paper, taking the various computation and communication capabilities of users into account, we propose a federated learning scheme incorporating unstructured model pruning and non-orthogonal transmission. The pruning process divides the model into the core and the extended parts, with the ratio adaptive to the users' achievable working rates. With non-orthogonal transmission, the strong users get both parts of the model while the weak get only the core part. Simulation results show that the proposed scheme involves more users than traditional wireless FL and improves the accuracy of tasks within the given communication rounds. © 2024 IEEE.

关键词： Federated learning

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How Lightweight Deep Learning Enhances Performance in DPU-Accelerated Autonomous Driving on Zynq SoC

How Lightweight Deep Learning Enhances Performance in DPU-Ac...

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Mechatronics and Robotics engineering (ICMRE), International Conference on

作者： Gyu Hyeon Hwang HoBin Oh Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

This study presents a lightweight deep learning model developed for DPU-accelerated systems. It aims to provide real-time autonomous driving on resource-constrained systems such as the Ultra96v2. A customized kids electric car served as the platform. Custom power supply and steering control systems were set up in the car to enable real-world testing. To enhance inference performance, various methods were used. These included input size reduction, channel-pruning, and quantization. As a consequence, the pruned and quantized YOLOv3-Tiny model produced a frame rate of 67.592 FPS. This is roughly a 25x increase over the original YOLOv3's 2.715 FPS on Ultra96v2's PL domain. These results show that real-time deployment is feasible on FPGA-based platforms. The work offers insights for creating efficient and scalable embedded systems for self-driving vehicle system.

关键词： Deep learning YOLO Quantization (signal) Power supplies Real-time systems Automobiles Autonomous vehicles Robots Optimization Testing

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A Cost Effective Sustainable Autonomous Unmanned Surface Vehicle for Automated Water Surface Cleaning

A Cost Effective Sustainable Autonomous Unmanned Surface Veh...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Pathak, Abhinav Haroon, Mohammed Raju, Manogna Shetty, Shivani Rege, Kavvya Shetty, Shanell Kalaichelvi, V. Bits Pilani Department of Computer Science Dubai Campus Dubai United Arab Emirates Bits Pilani Department of Electrical & Electronics Engineering Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798331533311

This paper presents the IFOR CB-24, a fully automated unmanned surface vehicle (USV) designed for waterbody cleaning and quality testing. Utilizing edge computing, computer vision, and lightweight machine learning algorithms like the Random Forest Classifier, the USV navigates water surfaces, performs quality testing, and removes trash using a custom conveyor belt system. The design focuses on cost-effectiveness, computational efficiency, and environmental sustainability, offering significant advancements in automated waterbody management. © 2024 IEEE.

关键词： Unmanned surface vehicles

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Big Data and Machine Learning: Barriers and Methods 4

Big Data and Machine Learning: Barriers and Methods

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Agrawal, Monika Goel, Swati William, P. Agwan, Ritesh Bhangale, Sonal C. Ghige, Onkar Kopargaon Sppu Sanjivani College of Engineering Department of Computer Engineering MH Pune India Inderprastha Engineering College Department of Computer Science and Engineering UP Ghaziabad India Kopargaon Sppu Sanjivani College of Engineering Department of Information Technology MH Pune India Kopargaon Sppu Sanjivani College of Engineering Department of Mechatronics MH Pune India Kopargaon Sppu Sanjivani College of Engineering Department of Electrical Engineering MH Pune India

ISBN: (纸本)9798350307757

In this study, the researchers investigate the mutually beneficial relationship that exists between machine learning (ML) and big data, highlighting both the benefits and the challenges that are brought about by the combination of these two domains. Large datasets provide a difficult environment for machine learning (ML) algorithms to extract meaningful insights from, but ML also provides sophisticated tools to assist in navigating through the complexity of these datasets. Even while there is the potential for synergy, their full-fledged adoption is delayed by significant constraints such as the quality of the data, the need for computing, concerns around privacy, and the skills gap. In addition to highlighting recent advancements in algorithmic efficiency, data preparation, and ethical frameworks, this paper investigates the strategic strategies and techniques that are used to address these issues. An in-depth investigation of machine learning models on a variety of datasets is presented, which offers insights into the efficacy of these models and their applicability for instances that occur in the real world, particularly those that involve the pharmaceutical industry. The research sheds light on the ways in which the environment is undergoing transformations and the ways in which practitioners are actively searching for and putting these challenges into practice in order to fully grasp the revolutionary potential of big data and machine learning. © 2024 IEEE.

关键词： Large datasets

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A Deep Learning Based Approach to Perform Fingerprint Matching

A Deep Learning Based Approach to Perform Fingerprint Matchi...

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Baghel, Vivek Singh Patel, Smit Prakash, Surya Srivastava, Akhilesh Mohan Department of Computer Science and Engineering Indian Institute of Technology Indore Indore453552 India Department of Electrical Engineering Indian Institute of Technology Indore Indore453552 India

ISBN: (纸本)9783031220173

As we move towards a technological-driven era, the traditional methods of authenticating an individual are becoming redundant and easier to crack. Biometric authentication has emerged as a very promising authentication method as it uses physiological and behavioral characteristics of the human body, which are unique to every individual. To adopt recent developments in deep learning and apply them to biometric authentication, we propose a novel Vision Transformer (ViT) based Siamese network framework for fingerprint matching in a fingerprint authentication system. Our primary focus is holistic, and an end-to-end pipeline has been constructed and implemented using an ensemble of task-specific algorithms to procure the best possible result from the model. We also endeavor to identify specific problems in the application of ViT to fingerprint matching and used two existing approaches, which are Shifted Patch Tokenization (SPT) and Localized Self Attention (LSA), to tackle those shortcomings effectively. We have considered two variations for the model, namely Intermediate-Merge (I-M) Siamese network and Late-Merge (L-M) Siamese network, and tested the performances on the IIT Kanpur fingerprint database. The obtained results in terms of accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) clearly show the significant performance of the proposed technique for fingerprint matching by means of an approach based on deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biometrics

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Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (Information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 IEEE.

关键词： Dataset Condensation Deep Hashing Retrieval

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PharmaChain 3.0: Blockchain Integrated Efficient QR Code Mechanism for Pharmaceutical Supply Chain 20

PharmaChain 3.0: Blockchain Integrated Efficient QR Code Mec...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Bapatla, Anand Kumar Mohanty, Saraju P. Kougianos, Elias Puthal, Deepak University of North Texas Department of Computer Science and Engineering United States University of North Texas Department of Electrical Engineering United States Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665493482

Because of globalization, many different entities distributed across the locations were able to work together and achieve the availability of services even at remote locations. Supply Chains helped in leveraging such businesses globally with reduced costs and increased efficiency. Pharmaceutical Supply Chain (PSC) is one in which the prescription drugs are moved from the manufacturer to the patient. Providing the right medicine at the right time to the right patient in the right doses coming from the right route is called the five rights of medication. Due to the increased number of participating entities, and interactions between entities and adversaries trying to profit by introducing counterfeit drugs into the supply chain, efficient tracking and tracing mechanism is very much needed in PSC. The current paper proposes an architecture that is integrated with Blockchain, Inter Planetary File System (IPFS) along with QR code technologies to provide a secure QR code mechanism for addressing such tracking and tracing issues in PSC. The proposed model is evaluated for security and efficiency using different metrics. © 2022 IEEE.

关键词： Blockchain

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An Experimental Evaluation of an Effective QR Code Based Duplicate Product Detection Using Blockchain Technology

An Experimental Evaluation of an Effective QR Code Based Dup...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Geetha, R. Jayakumar, D. Prabakaran, P. Nivedha, S. Sindhu, M. Thivyarathi, R.C. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Science and Humanities Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Over the span of the last few years, innovations in blockchain technology have received a great deal of attention. Currency exchange, which has a variety of applications in addition to digital money, is the subject that receives the most attention. Because of this, it may have an effect on multiple industries. Large-scale financial transactions have become more transparent and straightforward as a result of the implementation of blockchain technology. Through the utilization of blockchain technology, we are able to recognize counterfeit merchandise. When it comes to making a purchase of any kind in this day and age, the question of authenticity frequently makes an appearance. In addition, studies have shown that these aspects are essential for the expansion of the economy. In light of this, it is of the utmost importance to educate consumers about the transparency of the items in order to decrease the number of counterfeit goods. With blockchain technology, we have taken a step closer to eliminating the ever-increasing presence of false and hazardous items from the global market. This is a cause for concern. Each and every person ought to be made aware of the fact that the utilization of technology will result in a reduction in the production of merchandise that is counterfeit. Every item must be given a one-of-a-kind digital code in order to guarantee that the production and packaging processes are carried out correctly. In this research, a novel method for detecting counterfeit goods is presented. This method is referred to as the Blockchain and QR based Product Analyzer (BQRPA). It undergoes cross-validation with the traditional Classical Fake Product Identifier (CFPI) in order to determine whether or not it is effective. Within the context of the software installation process, this programme performs a scan of the product code in order to ascertain whether or not the product in question is a counterfeit. © 2024 IEEE.

关键词： Blockchain

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A Hybrid Feature Selection Framework for Predicting Students Performance

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computers, Materials & Continua 2022年第1期70卷 1893-1920页

作者： Maryam Zaffar Manzoor Ahmed Hashmani Raja Habib KS Quraishi Muhammad Irfan Samar Alqhtani Mohammed Hamdi High Performance Cloud Computing(HPC3) Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information Technology University of LahorePakistan Department of Process Engineering Pakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information Systems Najran University61441NajranSaudi Arabia

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha

关键词： Educational data mining feature selection hybrid feature selection

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