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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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Challenges and Opportunities in Digital Image Watermarking

Challenges and Opportunities in Digital Image Watermarking

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Aditya Dubey Nitin Dixit Rakhi Arora Jigyasa Mishra Desh Deepak Shrivastava Shreshtha Kinger Centre for Internet of Things Madhav Institute of Technology & Science Gwalior India Computer science & Engineering Institute of Technology and Management Gwalior India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Image tampering is so simple in this era of digital revolution; digital image authentication presents a big difficulty. Digital image watermarking is a pivotal technique for protecting intellectual property rights and ensuring the authenticity and integrity of digital images. This paper explores the myriad challenges and opportunities associated with digital image watermarking. Despite significant advancements, challenges such as robustness against various attacks, maintaining image quality, and developing efficient and secure algorithms persist. The paper delves into these challenges, highlighting the technical hurdles and the ongoing need for innovation in algorithm design and implementation. On the other hand, it also underscores the opportunities that lie ahead with emerging technologies like artificial intelligence, machine learning, and blockchain, which possess the capacity to transform watermarking techniques. Furthermore, the integration of watermarking with evolving multimedia applications and digital rights management systems opens new avenues for research and development. By addressing these challenges and leveraging the opportunities, digital image watermarking can continue to evolve, providing more robust, efficient, and secure solutions for the digital era. This article provides a thorough analysis of the frameworks of popular watermarking systems, diving into their subtleties. It also offers an assortment of common requirements used in the creation of watermarking techniques for various uses. This paper also examines the latest advancements in digital image watermarking methods, including the benefits and drawbacks of each. The paper also examines many traditional attacks on watermarking systems and talks about possible directions for future study.

关键词： Technological innovation Machine learning algorithms Digital images Watermarking Transforms Standardization Robustness Blockchains internet of things Resilience

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Compact Null Steering Antenna with Hemispherical Null Coverage Under Feed Position Controlling

Compact Null Steering Antenna with Hemispherical Null Covera...

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IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications (IMWS-AMP)

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

ISBN: (数字)9798331520717

ISBN: (纸本)9798331520724

This paper proposes an effective strategy to implement pattern null steering by means of controlling the feed position. To demonstrate this strategy, a circular patch antenna with several shorting pins is developed. Under the simultaneous excitation of TM 11 and TM 02 modes, a pattern null is generated and can be reconfigured on the upper hemisphere by switching the feed position. Based on the theoretical analysis of null formation, the mapping relationship between null direction and feed position is revealed. According to the presented mapping relationship, the required feed position for a randomly given null direction can be conveniently determined. The measurement verification is presented. Moreover, a potential application of the proposed antenna in reducing the grating lobes is discussed in the last part of this paper.

关键词： Radio frequency Antenna measurements Patch antennas Conferences Switches Antenna feeds Pins Feeds Gratings

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors 7

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-Assisted Millimeter-Wave Radar

Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-...

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2024 International Conference on Artificial Intelligence of things and Systems, AIoTSys 2024

作者： Zhang, Hanwen Dou, Chenglong Liu, Yucheng Wu, Yuan Qian, Liping Shi, Zhiguo Lu, Rongxing University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798331507886

Non-contact vital sign monitoring with mmWave radar has been regarded as an effective method for identifying lung and heart diseases due to its strong penetration and non-invasive capabilities. Nevertheless, relying solely on millimeter-wave radar for vital sign monitoring is susceptible to interference from multipath reflections, which degrades the measurement accuracy. To address this issue and enhance measurement accuracy, we propose a system design that leverages the depth camera for precise target location and subsequently employs dynamic beamforming techniques to effectively monitor the specified region. The experimental results demonstrate that our proposed system achieves an average error of 2.44 beats per minute (BPM) for heart rate monitoring and 0.22 breaths per minute (BPM) for respiratory rate monitoring. Compared to systems without beamforming, our system reduces the mean error for heart rate monitoring by 4.21 BPM and for respiratory rate monitoring by 0.51 BPM. © 2024 IEEE.

关键词： Heart

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EcoSense: A GSM-Enabled IoT Solution for Intelligent Waste Management

EcoSense: A GSM-Enabled IoT Solution for Intelligent Waste M...

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Emerging technology (INCET), International Conference for

作者： Nayan Acharya Ram Prasad Kewat Mohamad Kaif Rubel Hossen M. Kumaresan Computer Science and Engineering (Internet of Things) Jain (Deemed-To-Be) University Kanakapura Taluk Karnataka India Faculty of Engineering and Technology Jain (Deemed-To-Be) University Kanakapura Taluk Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Waste management is a major problem on a global scale. We often observe that the trash cans that are placed in public spaces are full. This pollutes people's surroundings. To prevent such things, we have introduced a project called “A GSM-Enabled IoT Solution for Intelligent Waste Management”. These bins come with inexpensive built-in technology that makes it easy to keep track of how much trash is inside. The amount of waste can accumulate in each bin, when it reaches the edge or threshold level, an automatic alarm will be sent to the registered range through the GSM module, indicating that the waste value has reached the threshold value of about 5 cm nearby ultrasonic sensor. Have a unique ID to help identify its location and current status. The relevant authorities will receive this information and with their help, urgent steps can be taken to clean up the rubbish in the bin. The garbage monitoring system is a new invention that uses ultrasonic sensors to detect and show the amount of garbage in a regular trash can and sends a message to the relevant department manager who updates the status of the trash through a GSM modem.

关键词： Waste management GSM Technological innovation Urban areas Inspection Modems Acoustics

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E-Waste Management Using Deep Learning

E-Waste Management Using Deep Learning

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Boddu Manoj Kumar Kaduru Sujitha K Abinaya School of computer science engineering with blockchain technology Sathyabama institute of science and technology Chennai Tamil Nadu School of computer science engineering with Internet of things Sathyabama institute of science and technology Chennai Tamil Nadu School of computer science and engineering Sathyabama institute of science and technology Chennai Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-waste detection, simplifying its incorporation into current waste management systems. Through utilizing deep learning, our goal is to transform the way that electronic waste is categorized, from tiny parts to larger appliances, allowing for more efficient as well as environmentally friendly management techniques. When it comes to aiming for higher performance, privacy comes first. Methods of preservation guarantee the accuracy of sensitive data correlated with pictures of e-waste. By means of execution utilizing data anonymization and encryption methods, we ensure compliance with legal requirements while safeguarding against potential privacy threats and ethical issues. The YOLO v8 model achieved an average precision (Map) of 97.1% in detecting e-waste items, demonstrating its effectiveness and scalability for real-world applications.

关键词： Deep learning YOLO Waste management Data privacy Privacy Accuracy Scalability Transforms Electronic waste Real-time systems

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Segment Anything Model (SAM) Meets Object Detected Box Prompts 25

Segment Anything Model (SAM) Meets Object Detected Box Promp...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Akin, Erdal Caltenco, Héctor Adewole, Kayode S. Malekian, Reza Persson, Jan A. Malmö University Department Of Computer Science And Media Technology Malmö205 06 Sweden Malmö University Internet Of Things And People Centre Malmö205 06 Sweden Bitlis Eren University Computer Engineering Department Bitlis13100 Turkey Ericsson Ab Ericsson Research Lund Sweden

ISBN: (纸本)9798350340266

Segmenting images is an intricate and exceptionally demanding field within computer vision. Instance Segmentation is one of the subfields of image segmentation that segments objects on a given image or video. It categorizes the class labels according to individual instances, ensuring that distinct instance markers are assigned to each occurrence of the same object class, even if multiple instances exist. With the development of computer systems, segmentation studies have increased very rapidly. One of the state-of-the-art algorithms recently published by Meta AI, which segments everything on a given image, is called the Segment Anything Model (SAM). Its impressive zero-shot performance encourages us to use it for diverse tasks. Therefore, we would like to leverage the SAM for an effective instance segmentation model. Accordingly, in this paper, we propose a hybrid instance segmentation method in which Object Detection algorithms extract bounding boxes of detected objects and load SAM to produce segmentation, called Box Prompted SAM (BP-SAM). Experimental evaluation of the COCO2017 Validation dataset provided us with promising performance. © 2024 IEEE.

关键词： Object detection

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Multi-Source Shortest Paths Mining Algorithm for Unweighted Directed Graphs Based on Rough Sets Theory

Multi-Source Shortest Paths Mining Algorithm for Unweighted ...

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computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Wen Zhou Taihua Xu Lisi Dai Jianjun Chen Jingjing Song School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer and Internet of Things Chongqing Institute of Engineering Chongqing China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Shortest path mining is one of the classic problems in graph theory. How to design an efficient multi-source shortest paths mining algorithm has practical significance. In this article, an efficient multi-source shortest paths algorithm is designed based on rough sets theory. Firstly, within the framework of rough sets theory, investigation has been conducted on the multisource shortest paths, providing a theoretical basis for calculating multi-source shortest paths. Secondly, the R-related set operator is used to search all vertices reachable from each source vertex. Meanwhile, the k-step upper approximation operator is used to search for all vertices that can reach the target vertex. Then, a marking strategy is designed to control the construction of multi-source shortest paths, which can improve the mining efficiency. Finally, a multi-source shortest paths algorithm for unweighted directed graphs (RMSSP) based on rough sets theory is proposed. Experimental results show that the RMSSP algorithm can accurately discover multi-source shortest paths for unweighted directed graphs. Also, it performs better than similar algorithms in terms of computational efficiency. With the expansion of the dataset size, the efficiency advantage of RMSSP algorithm is more obvious.

关键词： computers Directed graphs Rough sets Approximation algorithms Computational efficiency Artificial intelligence

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Privacy-aware Hydra (PA-Hydra) for 3D Scene Graph Construction 10

Privacy-aware Hydra (PA-Hydra) for 3D Scene Graph Constructi...

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10th IEEE World Forum on internet of things, WF-IoT 2024

作者： Akin, Erdal Adewole, Kayode S. Caltenco, Hector Malekian, Reza Persson, Jan A. Malmö University Department of Computer Science and Media Technology Malmö205 06 Sweden Malmö University Internet of Things and People Centre Malmö205 06 Sweden Bitlis Eren University Computer Engineering Department Bitlis13100 Turkey Ericsson Research Ericsson AB Lund Sweden

ISBN: (纸本)9798350373011

A 3D Scene Graph (3DSG) is a hierarchical 3D representation of a physical environment. Hydra is a promising real-time spatial perception framework for 3DSG developed by MIT Spark Lab. It uses sensor and camera data from an agent to construct a graph of the environment with five layers and continuously updates it. Albeit its utility and efficiency in generating and updating real-time 3DSG using visual-inertial sensors, Hydra and many other 3DSG frameworks ignore violating to identify private sensitive objects by segmenting, identifying, and reconstructing everything in data. Therefore, in this paper, we enhance Hydra to preserve sensitive data and increase the privacy-awareness of the framework. Accordingly, we propose a Privacy-aware Hydra (Pa-Hydra) framework, which integrates a state-of-the-art Object Detection (OD) algorithm that utilizes a proposed Filter Algorithm to cover sensitive objects with black boxes and prevent them from being constructed by Hydra. We implemented the framework with two popular OD algorithms with two different pre-trained models: You Only Look Once version 9 (YOLOv9) and Real-time Detection Transformer (RTDETR). We evaluated the algorithms using the COCO2017 validation dataset and observed the efficiency of the proposed framework on the uHuman2 (uH2) dataset. © 2024 IEEE.

关键词： Differential privacy

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