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arXiv 2024年

作者： Wang, Lu Abanto-Leon, Luis F. Department of Computer Science Technical University of Darmstadt Darmstadt Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany

Future 6G networks are expected to empower communication systems by integrating sensing capabilities, resulting in integrated sensing and communication (ISAC) systems. However, this integration may exacerbate the data traffic congestion in existing communication systems due to limited resources. Therefore, the resources of ISAC systems must be carefully allocated to ensure high performance. Given the increasing demands for both sensing and communication services, current methods are inadequate for tracking targets frequently in every frame while simultaneously communicating with users. To address this gap, this work formulates an optimization problem that jointly allocates resources in the time, frequency, power, and spatial domains for targets and users, accounting for the movement of targets and time-varying communication channels. Specifically, we minimize the trace of posterior Cramér-Rao bound for target tracking subject to communication throughput and resource allocation constraints. To solve this non-convex problem, we develop a block coordinate descent (BCD) algorithm based on the penalty method, successive convex approximation (SCA), and one-dimensional search. Simulation results demonstrate the validity of the proposed algorithm and the performance trade-off between sensing and communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic congestion

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Fog Computing in IoT: Architecture, Applications, and Research Challenges

Fog Computing in IoT: Architecture, Applications, and Resear...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Habelalmateen, Mohammed I. Khare, Neha Joshi, Abhishek Aravind, Ar Sapaev, B. Badhoutiya, Arti Siri, Dharmapuri The Islamic University Najaf Iraq IES University IES Insititute of Technology and Management Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Uttaranchal University School of Liberal Arts Department of Liberal Arts Dehradun248007 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Alfraganus University Faculty of Medicine Department of Pharmaceuticals and Chemistry Tashkent Uzbekistan GLA University Department of Electrical Engineering Mathura India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The union of Internet of Things (IoT) and Mist Figuring envoys a change in perspective in processing foundation. This paper gives an extensive investigation of Mist Figuring with regards to IoT, zeroing in on its design, applications, and the examination challenges that underlie this extraordinary innovation. Starting with an inside and out assessment of Haze Processing's calculated structure and its distinctive highlights, we clarify its basic differentiations from conventional Distributed computing models. Through a thorough examination of true applications, going from brilliant urban communities to medical care and modern IoT, this paper grandstands how Haze Figuring expands the capacities of IoT gadgets, giving restricted handling power and decreasing idleness. The conversation stretches out to the diverse design of Haze Registering, clarifying its progressive construction and hidden correspondence conventions. Also, the paper digs into safety efforts, asset allotment methodologies, and adaptability concerns, tending to the complicated difficulties that go with Haze Figuring executions. Through illustrative contextual investigations, we epitomize the common sense and viability of Mist Figuring, clarifying its effect across assorted areas. Besides, this paper offers bits of knowledge into arising patterns like Edge artificial intelligence joining and investigates expected cooperative energies with Blockchain innovation. The paper finishes up by summing up key discoveries and imagining future headings for Haze Registering in the IoT scene, accentuating its urgent job in molding the advanced biological system of tomorrow. © 2023 IEEE.

关键词： Internet of things

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A Multimode Null Reconfigurable Antenna for Grating Lobe Suppression

A Multimode Null Reconfigurable Antenna for Grating Lobe Sup...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Shi-Tong Wang Lei Zhu Department of Electrical and Computer Engineering Faculty of Science and Technology State Key Laboratory of Internet of Things for Smart City University of Macau Macau China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This paper introduces a novel multimode null reconfigurable antenna (NRA) and its attractive application in grating lobe suppression. The proposed NRA generates a pattern null by combining far fields of TM 01 and TM 02 modes of a rectangular patch antenna. The theoretical predictions of null angles versus feeding locations are presented based on electric field distributions under the patch. The proposed NRA is realized with a switchable feeding network. Simulated and measured results are presented demonstrating that the pattern null can be switched from -64° to 54° at 2.45 GHz. In final, the proposed NRA is utilized as antenna elements of a sparse array with the element spacing of one wavelength. Simulated array patterns strongly verify the grating lobe suppression capacity of the proposed NRA array compared to the conventional patch array.

关键词： Microwave measurement Microwave technology Wavelength measurement Patch antennas Millimeter wave technology Millimeter wave measurements Switches

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Optimizing Cloud Computing Performance using Adaptive and Evolutionary Algorithms for Large Scale Applications

Optimizing Cloud Computing Performance using Adaptive and Ev...

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Distributed Computing and electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Parashiva Murthy B M Vidhyadar Unni Krishnan M K Pushpa Madhu B K Falguni Tlajiya Aparajita Mohanty Department of Computer Science and Engineering SJCE JSS Science and Technology University Mysuru India Visa Commercial Solutions Division Visa Inc Foster City USA Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Mysore India Department of Electrical Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

An increasing number of applications are straining cloud computing structures which require novel optimization strategies to boost performance measurements as well as resource utilization. The research methodology incorporates adaptive algorithms together with evolutionary algorithms which provide real-time workload adaptation and distributed system resource optimization. The project implemented machine learning models which predicted resource needs to trigger automatic parameter modification for computing systems. The three major cloud platforms provided results which showed a 37.8% decrease in response latency and a 42.3% lower resource wastage rate and a 29.5% enhanced throughput capacity than static allocation techniques. The cost efficiency rose 31.7% while service reliability maintained 99.4% during the period. A proposed adaptable-evolutionary framework provides an effective solution that leads to improved performance measurements of large-scale cloud programs while reducing operational expenses for transitioning between dynamic computing settings.

关键词： Performance evaluation Cloud computing Machine learning Evolutionary computation Predictive models Throughput Real-time systems Resource management Reliability Optimization

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The Impact of Varying Batch Sizes on the Deep Neural Network Algorithms Performance

The Impact of Varying Batch Sizes on the Deep Neural Network...

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International Conference on electrical, Telecommunication and computer engineering (ELTICOM)

作者： Naemah Muharakah Poltak Sihombing Syahril Efendi Fahmi Fahmi Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

Batch size is one of the determining factors in the quantity of sample data used in one iterative model training in deep learning. One of the key factors influencing the effectiveness of hyperparameters and model training results. Group size is very important in selection because the right group size will influence the effectiveness and performance of the training. Choosing a batch size that is too small or large can affect accuracy and performance. Deep Neural Network (DNN) algorithm is a form of artificial neural network consisting of several deep layers. There are several batch sizes used in data processing. Optimal performance for batch size is obtained when using a batch size of 64, as it yields the highest average AUC value of 0.815. Furthermore, the optimal configuration was achieved with a batch size of 64 and 6 hidden layers, utilizing a combination of Leaky-ReLV and Sigmoid activation functions, resulting in an AUC value of 0.83.

关键词： Training Deep learning Accuracy Artificial neural networks Data processing Data models Communications technology Iterative methods

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Ramifications of the Greenhouse Effect: A Closed Vehicle Example

Ramifications of the Greenhouse Effect: A Closed Vehicle Exa...

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International Conference on Smart Systems and Technologies (SST)

作者： Dina Jukić Hrvoje Glavaš Jana Dukić Lukrecia Vulić Department of Core Courses Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482165

In this contribution, ramifications of the greenhouse effect on a closed vehicle exposed to solar radiation are discussed. The Earth's atmosphere-the vehicle is considered a system which physical background of energy transfer and transformations are analyzed. Based on the physics presented, a product is proposed: barium sulphate window covers that would minimize the effect. Such covers are easy to assemble, can be DIYed, preserve vehicle interior, and lover fuel consumption due to air-conditioning temperatures, which have the second most negative effect on the fuel economy.

关键词： cover energy transfer energy transformation greenhouse effect vehicle

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OpenAI-Powered NLP for SQL Generation: Enhancing Database Access for Non-Technical Users 10

OpenAI-Powered NLP for SQL Generation: Enhancing Database Ac...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Nilsiam, Yuenyong Khammaw, Piyaphiphat Nuchitprasitchai, Siranee Faculty of Engineering King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok Thailand Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Department of Information Technology Bangkok Thailand

ISBN: (纸本)9798331543273

Database accessibility can be challenging for users without technical expertise, particularly when generating SQL queries to retrieve information. This study addresses this gap by developing an OpenAI-powered system designed to automate SQL command generation, enabling non-expert users to interact with databases using natural language. Implemented as a web application, the system was evaluated by expert users, assessing both performance in query accuracy and user satisfaction. Results indicate high usability and satisfaction scores, with the system effectively enhancing accessibility to database functions, thus providing a user-friendly solution for database management. © 2025 IEEE.

关键词： Expert systems

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FRaC: FMCW-Based Joint Radar-Communications System Via Index Modulation

FRaC: FMCW-Based Joint Radar-Communications System Via Index...

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作者： Ma, DIngyou Shlezinger, Nir Huang, Tianyao Liu, Yimin Eldar, Yonina Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Faculty of Mathematics and Computer Science Weizmann Institute Rehovot Israel

Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while simultaneously communicating with neighbouring devices. An emerging approach to implementing DFRC systems is to embed information in radar waveforms via index modulation (IM). Implementation of DFRC schemes in vehicular systems gives rise to strict constraints in terms of cost, power efficiency, and hardware complexity. In this paper, we extend IM-based DFRC systems to utilizing sparse arrays and frequency modulated continuous waveforms (FMCWs), which are popular in automotive radar for their simplicity and low hardware complexity. The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling. This is achieved via array sparsification in transmission, formulating a virtual multiple-input multiple-output array by combining the signals in one coherent processing interval, in which the narrowband waveforms are transmitted in a randomized manner. Performance analysis and numerical results show that the proposed radar scheme achieves similar resolution performance compared with a wideband radar system operating with a large receive aperture, while requiring less hardware overhead. For the communications subsystem, FRaC achieves higher rates and improved error rates compared to dual-function signalling based on conventional phase modulation. © 2007-2012 IEEE.

关键词： MIMO radar

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Automated Diagnosis of Breast Cancer Using Deep Learning-Based Whole Slide Image Analysis of Molecular Biomarkers

Automated Diagnosis of Breast Cancer Using Deep Learning-Bas...

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IEEE International Conference on Image Processing

作者： A. Aboudessouki Kh. M. Ali M. Elsharkawy A. Alksas A. Mahmoud F. Khalifa M. Ghazal J. Yousaf H. Abu Khalifeh A. El-Baz Bioengineering Department BioImaging Laboratory University of Louisville USA Pathology Department Faculty of Medicine Mansoura University Egypt Electrical and Computer Engineering Department Morgan State University USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE

Breast cancer is a prevalent and diverse type of cancer that exhibits unique clinicopathologic characteristics, making the correct identification of its subtype critical to providing targeted treatment and increasing survival rates. This identification process involves testing for the presence of four key molecular biomarkers, namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67. For accurate diagnosis ,the expertise of a pathologist and immunohistochemistry is required. To overcome this diagnostic challenge, we present a novel approach based on a deep learning pipeline for automated classification. Our approach can detect tumor and non-tumoral regions of the HER2 biomarker. Our deep learning framework comprises a Dense Convolutional Network (DenseNet), which process whole slide images (WSIs) of breast tissues, dividing them into patches for input into the network. Moreover, our approach provides both patchwise and pixelwise classification and analyzes ten WSIs of breast cancer histology. Our proposed approach generates an image map that classifies slide images on the pixel-level, detecting the status of hormone HER2 receptor as either positive or negative. The obtained results show that our deep learning-based approach has the potential to enhance the pathologist’s capabilities in diagnosing histopathological images with automated classification.

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Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Ahmed Shafee Tasneem A. Awaad Ahmed Moro Department of Engineering and Computer Science Adams State University Alamosa CO USA Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Siemens EDA Cairo Egypt Integrated Electrical Systems Segment Siemens Digital Industries Software Cairo Egypt

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable; attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks.

关键词： Radio frequency Training Computational modeling Intrusion detection computer architecture Robustness Automobiles Random forests Vehicles Resilience

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