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EAFLoc: A Few-shot Fingerprint Localization Approach with Environmental Awareness

EAFLoc: A Few-shot Fingerprint Localization Approach with En...

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2023 international conference on Intelligent Communication and Networking, ICN 2023

作者： Liao, Minghao Zhu, Zelin TongJi University Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350357677

Localization technology is the foundation of location-based services, which plays a crucial role in various fields such as mobile communication and the internet of Things(IoT). Various approaches have been proposed to achieve precise localization, such as GNSS-based, Time-based, Angle-based, and Proximity-based approaches. However, there are specific drawbacks, including strict time synchronization requirement, susceptibility to signal reflections, and high demands on infrastructure. As a supplement, RSSI-based and image-based localization approaches have been extensively researched and utilized in recent years. Nevertheless, these approaches are often limited by labor-intensive annotations and researches on utilizing RSSI and image modalities to achieve high-precision localization are also limited. Additionally, there are few researches on localization under few-shot and zero-shot conditions. Therefore, in this paper, we propose EAFLoc, an environment-aware few-shot multimodal fingerprint localization approach. EAFLoc addresses the challenge of efficient modality information fusion. Also, data augmentation algorithms are designed for few-shot scenarios, and we further explore localization under zero-shot scenarios with channel prediction methods. Finally, the superiority and effectiveness of EAFLoc are validated on a real dataset. The experimental results demonstrate that EAFLoc achieves better performance compared to the traditional approaches. © 2023 IEEE.

关键词： Location based services

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Embedded-based Touch Screen Driver Design and Implementation

Embedded-Based Touch Screen Driver Design and Implementation

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2023 IEEE international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Gu, Wen Liang, Zhichao Sun, Weijian City Institute Dalian University of Technology Dalian China

ISBN: (纸本)9798350314670

With the development of the internet, embedded systems have become one of the hottest industries at present, and the embedded market still shows a huge demand. People have put forward higher requirements for embedded products, such as stable, intuitive, convenient and easy to operate. Touch screen undoubtedly provides people with great convenience, so in embedded systems, touch screen design with good performance, stable operation and high precision has positive and extensive significance. At the same time, in the embedded field, the Linux operating system's open source code licensing model, easy to customize, easy to cut and port features, making Linux the most popular and most popular operating system in the embedded development platform and application platform. Embedded systems are superior in terms of high specialisation and real-time, with a wide range of application-centric and computer technology-based applications. As today's electronic devices continue to be intelligent, the traditional keyboard, mouse and other human-computer interaction has been unable to meet the needs of people, people want more convenient human-computer interaction tools. The touch screen through the finger touch can achieve the operation of positioning, greatly simplifies the input method of electronic equipment, truly zero distance operation, so the touch screen has been more and more widely used. This paper is based on the Samsung S3C2410 embedded, design and implementation of the touch screen program, to achieve the control and operation of the touch screen, with touch screen sensitivity, positioning touch screen position and other characteristics, to improve the use of touch screen efficiency and user experience. Finally, the feasibility and effectiveness of the design program is verified through experiments. © 2023 IEEE.

关键词： Real time systems

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Hierarchical DNN-based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Cheng-Hsuan Yu Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both large- scaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for over-segmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： Deep learning Training image segmentation Histograms image color analysis Shape Object oriented modeling

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Distributed Gateway-based Security Scheme for Guaranteeing LoRaWAN Networks Availability

Distributed Gateway-based Security Scheme for Guaranteeing L...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Hassan N. Noura Ola Salman Ali Chehab Raphael Couturier FEMTO-ST Institute University of Franche-Comté Belfort France Dept. of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

The internet of Things (IoT) is interconnecting a large number of devices, some of which are constrained in terms of power and computational capability. Currently, battery-powered IoT devices are deployed in large areas, calling for power-efficient and long-range communication technologies. In this context, the Low Power Wide Area Networks (LPWAN) are considered key enablers for such IoT systems. LoRaWAN, an LPWAN technology, is one of the dominant candidates for major IoT communication protocols. Unfortunately, LoRaWAN suffers from various security and privacy issues, which may lead to several attacks on a LoRaWAN network targeting data availability, authentication, and confidentiality. In this paper, we present an effective solution to prevent data and system availability attacks at the gateway level, which is considered the weakest link in the LoRaWAN communication system. The proposed solution is intended to make the LoRaWAN communication between end devices and network servers more secure, resilient, and efficient. To ensure data availability while also reinforcing data confidentiality and integrity for LoRaWAN networks, the proposed solution utilizes the Information Dispersal Algorithm (IDA) over GF(2 8 ) with a dynamic key derivation scheme. This solution assumes that several gateways (n) are deployed within a LoRaWAN network. The security and efficiency tests show that the proposed solution exhibits a low overhead in terms of computations, resources, and communication while offering a high degree of availability and confidentiality.

关键词： Network servers Protocols Power demand Heuristic algorithms Authentication Logic gates Robustness

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Automated Brain Abnormality Segmentation using Modified JAYA algorithm based Fuzzy C-Means Clustering 1

Automated Brain Abnormality Segmentation using Modified JAYA...

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1st international conference on Software, systems and Information technology, SSITCON 2024

作者： Jeenath Shafana, N. Priya Lakshmi, S. Vijayalakshmi Ramani, U. Ramaraj, Kottaimalai Thilagaraj, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Tiruchirappalli India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350352931

The main risk factors in humans, irrespective of gender or location, are anomalies of the brain, such as tumors and strokes. In healthcare facilities, the degree of disability and underlying cause of brain lesions are typically evaluated by means of regular methods created by trained clinicians. During this procedure, signals and images of the brain are collected in a controlled setting. Selecting an appropriate separation method is essential to getting the desired results. The novel JAYA algorithm, which relies on meta-heuristics, aids in the reclustering of the FCM segmentation results in our suggested techniques, yielding more productive delineation results with clear borders with 86% of Dice Score. However, the exploitation and exploration techniques for searching become more flexible and help identify the optimal solution faster by adjusting the desired function with respect to the accommodating cluster location and population size. Regarding the processing speed, optimal exploitation, and exploratory abilities, the suggested strategy outperforms the other available metaheuristic optimization techniques. © 2024 IEEE.

关键词： image segmentation

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An Optimized Graph Neural Network-based Approach for Intrusion Detection in Smart Vehicles 6th

An Optimized Graph Neural Network-Based Approach for Intrus...

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6th IFIP international conference on internet of Things, IFIP IoT 2023

作者： Zambare, Pallavi Liu, Ying Texas Tech University Lubbock United States

ISBN: (纸本)9783031458774

Due to recent developments in vehicle networks (VNs), more and more people want their electric cars to have access to sophisticated networking features and perform a variety of advanced activities. Several technologies are installed in these autonomous vehicles to aid the drivers. Cars have unquestionably become smarter as we’ve been able to install more and more gadgets and applications on them. As a result, the security of assistant and self-drive systems in automobiles becomes a life-threatening concern, since hostile attacks that cause traffic accidents may infiltrate smart cars. This research provides a technique based on the Graph Neural Network (GNN) deep learning (DL) model for detecting intrusions in VNs. This model can recognize attack patterns and classify threats accordingly. Experimentation utilizes car-hacking datasets. These files include DoS attacks, fuzzy attacks, driving gear spoofing, and RPM gauge spoofing. The car-hacking dataset is converted into image files, and the GNN model provided works with the newly produced image dataset. The findings of comparing our model to DL models indicate that our GNN model is superior. In specific, the test case scenarios may identify abnormalities with respect to detection F1-score, recall, precision, and accuracy to ensure accurate detection and identification of potential false alarm concerns. © 2024, IFIP international Federation for Information Processing.

关键词： internet of things

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Lightweight Deep-Learning based Music Genre Classification: A Study

Lightweight Deep-Learning Based Music Genre Classification: ...

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2023 international conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Mythili, N. Rajakumar, M.P. Arunmozhi, S. Mohammed, Mazin Abed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India St. Joseph's Institute of Technology Department of Computer Science and Engineering TN Chennai600119 India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science TN Chennai600119 India Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of CSE TN Chennai602105 India

ISBN: (纸本)9798350315127

Deep-learning (DL) applications that are used real-time across various industries have gained a lot of traction and have become increasingly popular, especially when it comes to data-driven recommendation systems. This work aims to develop a DL scheme to support the music-recommendation system (MS) based on the music data. The various phases of this scheme includes;(i) data collection and signal-image conversion to get the necessary RGB scale images from the data, (ii) pre-trained DL based feature extraction, and (iii) deep-features based detection to recommend the appropriate music. This research considered the classic- (CL) and pop-music (PO) for the examination and the achieved results are evaluated to substantiate the performance of this arrangement. In this work, the signal-image conversion procedure is implemented to convert 1D signal to 2D image and then it is examined using proposed technique. The experimental outcome is separately presented for (i) spectrogram and (ii) synchro-extracting-transform and obtained results are presented. The experimental investigation is presented with MobileNet variants and this study authorizes that the implemented scheme achieved a better detection MobileNetV2 (>99%) compared to other schemes in this study. © 2023 IEEE.

关键词： Real time systems

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Baker’s Map based Chaotic image Encryption in Military Surveillance systems 1

引用

international conference on internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Velocity and Distance Estimation of Multiple Objects Using YOLO-based OFDM Radar System 15

Velocity and Distance Estimation of Multiple Objects Using Y...

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15th international conference on Information and Communication technology Convergence, ICTC 2024

作者： Jeon, So-Yeon Hyun, In-Young Jeong, Eui-Rim Hanbat National University Dept. of Artificial Intelligence Daejeon Korea Republic of Hanbat National University Dept. of Artificial Intelligence Software Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In this study, we propose a method for simultaneously estimating the speed and distance of multiple objects using a YOLO model within an OFDM radar system. The proposed technique inputs a 2D spectrogram image generated from OFDM radar signal processing into the YOLO model to predict the speed and distance of objects. Simulation results demonstrate that the proposed method performs well in recognizing multiple objects. Notably, the speed and distance estimation accuracy improves as the number of OFDM symbols increases and the number of objects decreases. The proposed technology is applicable not only to 5G but also to 6G OFDM signals. © 2024 IEEE.

关键词： 5G mobile communication systems

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Performance Evaluation of Micro-Doppler based UAV Identification Using Different 5G Frame Structures 2

Performance Evaluation of Micro-Doppler Based UAV Identifica...

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2nd international conference on Mobile internet, Cloud Computing and Information Security (MICCIS)

作者： Ma, Dingyou Wei, Jiachen Zhang, Qixun Wei, Zhiqing Jiang, Weiwei Guo, Ningyan Huang, Sai Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China

ISBN: (纸本)9798350389913;9798350389906

The integrated sensing and communication (ISAC) technology is considered as a key feature in the upcoming B5G/6G networks. Currently, most ISAC research focuses on target detection and localization, with limited attention given to target identification. The lack of focus on target identification results in challenges in distinguishing small unmanned aerial vehicles (UAVs) from other birds or insects in urban low-altitude scenarios. In this study, we propose a method for extracting micro-motion feature spectrograms of UAVs using 5G signals. Unlike traditional radar systems, ISAC base stations utilize communication waveforms and time-division duplex (TDD) types with downlink and uplink slots for environmental sensing. To assess the performance of UAV identification using different TDD types, we evaluate the mean squared error in estimating rotor blade parameters for each TDD type. Simulation results demonstrate the feasibility of utilizing 5G signals to extract micro-motion features of UAVs. While all evaluated 5G TDD types show promising results in recovering rotor UAV blade parameters at high signal-to-noise ratios (SNRs), their estimation performance varies significantly at low SNRs. These findings provide a solid foundation for future research on micro-Doppler recognition in ISAC systems.

关键词： integrated sensing and communication micro-Doppler signature micro-motion parameters extraction

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