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SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity Recognition

SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning...

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2025 IEEE international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Wang, Yijun Wang, Yong Xu, Chendong Yao, Shuai Wu, Qisong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350368741

Human Activity Recognition (HAR) such as fall detection has become increasingly critical due to the aging population, necessitating effective monitoring systems to prevent serious injuries and fatalities associated with falls. This study focuses on fine-tuning the Vision Transformer (ViT) model specifically for HAR using radar-based Time-Doppler signatures. Unlike traditional image datasets, these signals present unique challenges due to their non-visual nature and the high degree of similarity among various activities. Directly fine-tuning the ViT with all parameters proves suboptimal for this application. To address this challenge, we propose a novel approach that employs Low-Rank Adaptation (LoRA) fine-tuning in the weight space to facilitate knowledge transfer from pre-trained ViT models. Additionally, to extract fine-grained features, we enhance feature representation through the integration of a serial-parallel adapter in the feature space. Our innovative joint fine-tuning method, tailored for radar-based Time-Doppler signatures, significantly improves HAR accuracy, surpassing existing state-of-the-art methodologies in this domain. Our code is released at https://***/wangyijunlyy/SelaFD. © 2025 IEEE.

关键词： Fine-Tuning Human Activity Recognition Time-Doppler Vision Transformer

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Dark Channel Prior-based Single-image Dehazing Using Type-2 Fuzzy Sets for Edge Enhancement in Dehazed images 8th

Dark Channel Prior-Based Single-Image Dehazing Using Type-2 ...

引用

8th international conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Amin, Nisha Geeta, B. Raibagkar, R.L. Rajput, G.G. Department of Computer Science Karnataka State Akkamahadevi Women’s University Vijayapura India Department of Applied Electronics Gulbarga University Kalaburgi India

ISBN: (纸本)9789819994885

The process of image dehazing is known to have a significant impact in the field of computer vision. It has been found to have practical applications in various real-world scenarios, such as autonomous driving, surveillance, and the enhancement of outdoor images. This study tackles the challenge of image dehazing by introducing an approach that combines the dark channel prior (DCP) technique with type-2 fuzzy set theory. The objective of the proposed methodology is to improve the image quality by utilizing the I-Haze and O-Haze datasets, which encompass a compilation of hazy images captured under diverse environmental circumstances. In order to assess the efficacy of our approach, we employ various quality assessment metrics including Peak signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Lightness Order Error (LOE), and Naturalness image Quality Evaluator (NIQE). Our research contributes to advancing image dehazing techniques and their practical applications, across domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy set theory

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Traffic Lights Recognition based on Position Feature 12th

Traffic Lights Recognition Based on Position Feature

引用

12th international conference on Green Intelligent Transportation systems and Safety, 2021

作者： Yang, Zhi-fa Fan, Xian-jun Yu, Zhuo Li, Shi-wu liu, Ai-min song, Chang-an School of Transportation Jilin University Changchun130022 China School of Mathematics and Statistics Changchun University of Technology Changchun130012 China

ISBN: (纸本)9789811956140

Due to the color and shape characteristics of traffic lights, the color model and shape detection are used to detect traffic lights in the work, the images were processed by the ROI (region of interest) extraction, image enhancement, grayscale binarization processing and morphological processing. Then the contour search and connected domain filtering algorithm were used to extract the traffic signal backlight backplane area, thus detecting and segmenting the traffic signal light backplane. Moreover, taking the traffic signal light backplane as positive sample, the other non-traffic light backplane was used as negative sample to build model library. HOG algorithm was used to extract the feature vectors of samples and exclude the false targets based on SVM classification algorithm. Finally, according to the positions of red and green signal lights on signal light, the pixel value accumulation in the area where the signal light is located was calculated as position feature to recognize the red and green signal lights. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image recognition

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From Blackboards to Byteboards: Adapting to the Digital Age in Undergraduate Education on Campus

From Blackboards to Byteboards: Adapting to the Digital Age ...

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

作者： Tasmiah Tamzid Anannya Sharifa Rania Mahmud Shahriar Rahman Khan Syed Rohit Zaman Md. Shah Jalal Sadia Sharmin Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Bangladesh Department of Computer Science American International University Bangladesh

In the current educational landscape, maintaining student engagement is a persistent challenge. This paper addresses this issue and investigates the potential of attention engineering as a solution to enhance engagement among undergraduate students. The main goal is to investigate the use of attention engineering techniques in undergraduate education to find effective methods and measure their impact on student engagement and academic performance. This research conducts a thorough literature review to assess the feasibility and effectiveness of these techniques in undergraduate classrooms, exploring the utilization of personalized content, interactive tools, and data-driven insights to maintain student attention. To improve attention and learning, several technology tools, and strategies such as online videos and tutorials, interactive multimedia content, gamification, flipped learning, social network tools, and Learning Management systems (LMS) are studied. This research addresses three key research questions, the impact of technology integration and interactive activities on student engagement and comprehension, effective technology-based tools for learning environments, and the influence of attention-centric teaching strategies on student learning outcomes. This research identifies focused technologies and approaches, as well as the country attaining and the most utilized technology from current works. Our research has also proposed some findings that offer valuable guidance for educators and institutions seeking to optimize teaching methods and ultimately improve the learning experience in undergraduate education.

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Accurate Parameter Estimation based on the Two-Sided KZ Filter in Low signal-to-Noise Ratio Conditions 6

Accurate Parameter Estimation Based on the Two-Sided KZ Filt...

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6th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Liang Wang, Qing-Guo Hong Kong Baptist University Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Institute of AI and Future Networks Beijing Normal University Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798331541699

System identification is the process of developing models based on data collected from dynamic systems. A significant challenge in this field is accurately estimating model parameters using input-output data. This data, typically gathered through sensors, often contains noise due to inaccuracies in the sensors and errors during communication. The presence of such noise can lead to biased estimates of parameters, which undermines the model’s effectiveness. While traditional methods strive to minimize this bias during parameter estimation, they frequently encounter difficulties when dealing with high levels of noise. An alternative strategy involves employing filtering techniques to clean the data before estimation takes place. Low-pass (LP) filters are designed to reduce frequencies above a specific threshold and are widely used for mitigating noise issues. Among these filters, the Kolmogorov–Zurbenko (KZ) filter operates as multiple iterations of a moving average (MA) filter and has shown strong capabilities in reducing noise;it is commonly applied in high-resolution signal analysis and image processing tasks. In this study, we introduce two KZ filtering approaches aimed at denoising data prior to parameter estimation. Theoretical evaluations and experimental findings indicate that the proposed KZ filtering method surpasses LP filters and wavelet denoising techniques, particularly in environments characterized by low signal-to-Noise Ratios (SNR). © 2024 IEEE.

关键词： Low pass filters

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On the Adoption of Explainable Deep Learning for image-based Network Traffic Classification 14

On the Adoption of Explainable Deep Learning for Image-Based...

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19th international conference on Software Technologies, ICSOFT 2024

作者： Hattak, Amine Martinelli, Fabio Mercaldo, Francesco Santone, Antonella Pisa Italy University of Molise Campobasso Italy La Sapienza University of Rome Rome Italy

ISBN: (纸本)9789897587061

In an era marked by escalating cyber threats, ensuring the security of interconnected devices and networks within the internet of Things (IoT) landscape is imperative. This paper addresses this pressing concern by delving into network security, focusing on the classification of network traffic through the lens of deep learning techniques. Our study presents a deep learning-based approach customized for network traffic classification in the IoT domain, based on image analysis. Crucially, to enhance the interpretability and the transparency in our model’s decisions, we integrate GradCAM (Gradient-weighted Class Activation Mapping), a technique that illuminates the salient regions of input images contributing to the model’s predictions. By leveraging GradCAM, we provide deeper insights into the decision-making process, enabling better understanding and trust in our approach. We evaluate the effectiveness of our methodology using the TON IoT dataset, consisting of 10 network traces categorized into various vulnerability scenarios and trusted applications. Our findings reveal a remarkable accuracy of 99.1%, demonstrating the potential of our approach in fortifying network security within IoT environments. Moreover, the utilization of GradCAM empowers stakeholders with valuable insights into the inner workings of the model, further enhancing its applicability and trustworthiness. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Network intrusion

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An End-to-end Learning based Covolutional Neural Network for Single image Defogging Algorithm 2023

An End-to-end Learning Based Covolutional Neural Network for...

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5th international Symposium on signal Processing systems, SSPS 2023

作者： Li, Qiqing Li, Ru Shen, Xin Lv, Wei School of Aliyun Big Data Applications Zhuhai College of Science and Technology China School of Aliyun Big Data Applications City University of Macau China

ISBN: (纸本)9798400700040

In the era of big data, there are more and more outdoor camera acquisition equipment. Due to the influence of extreme weather, such as fog, camera acquisition equipment is easy to lead to the decline of image quality and destroy the value of image application. Therefore, this paper will propose an advanced dehazing algorithm to make the foggy image clearer. based on the principle of residual neural network, combined with attention mechanism and feature pyramid idea, this paper proposes an end to-end learning single image dehazing algorithm. Let the network learn the relationship between channels and pixels, and use the feature pyramid multi-scale fusion feature to restore the foggy image to a clear image. The SSIM score was 0.9687 and the PSNR score was 29.16. Very good results have been achieved on the RESIDE outdoor dataset. This paper finds the scores obtained by testing DCP, AOD-NET, DeHazeNet, and GFN methods on the same dataset. Compared with these four methods, there is a significant improvement. In particular, it is 15.39% higher than the DCP method on SSIM and 10.03% higher on PSNP. © 2023 ACM.

关键词： Statistical tests

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Vedic Multiplier based Medical image Encryption - A VLSI Approach 7

Vedic Multiplier based Medical Image Encryption - A VLSI App...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Vinoth Raj, R. Reddy S, Meharnath Kishore, H. Karthick, V. Yazhini, K. Kamesh, M. Velammal Institute of Technology Electronics & Communication Engineering Chennai India Kongunadu College of Engineering and Technology Electronics & Communication Engineering Thottiyam India

ISBN: (纸本)9798350341485

The protection of medical image data is more crucial as telemedicine continues to advance. The use of encryption techniques can serve as an approach to safeguard sensitive information pertaining to medical images. Software-based encryption solutions exhibit reduced computational efficiency and are susceptible to security vulnerabilities. The efficacy of flexible platform-based approaches surpasses that of alternative methods. The integration of prevailing image encryption systems with typical scrambling diffusion algorithms presents a challenge due to the inherent trade-off between real-time speed and security, since the number of scrambling diffusion cycles directly impacts both aspects. The encryption method employed in this study involves the utilization of a Linear Feedback Shift Register (LFSR) as the confusion unit and a Vedic multiplier for the diffusion process. This technique demonstrates a reduction in resource utilization and power dissipation due to the utilization of hardware-friendly designs for the diffusion and confusion modules. To ensure the efficacy of the proposed medical image encryption technique, several statistical measures such as entropy, peak signal-to-noise ratio (PSNR), histograms, and correlation factors were computed from a security perspective. Furthermore, the unpredictability of the key was evaluated by the use of the NIST SP 800-22 test. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Spatiotemporal Modeling of Cellular-Connected UAV Networks with NOMA scheme 16

Spatiotemporal Modeling of Cellular-Connected UAV Networks w...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Ti, Yueyu Sun, Hongguang Hou, Jingkai Zhang, Hongming Li, Shuqin College of Information Engineering Northwest A&F University Yangling Shaanxi China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Yangling Shaanxi China

ISBN: (纸本)9798350390643

The integration of Unmanned Aerial Vehicles (UAVs), a new type of Aerial User (AU), into terrestrial cellular networks to form a cellular-connected UAV network has emerged as a significant research topic in the field of communications. However, the introduction of AUs competes for channel resources with Terrestrial Users (TUs), which can lead to a deterioration in the performance of TUs. Taking into account the channel differences between AUs and TUs, this paper utilizes a Non-Orthogonal Multiple Access (NOMA) scheme to enhance the spectral efficiency of the network. Utilizing tools from stochastic geometry and queueing theory, we propose a spatiotemporal analytical model to analyze the network performance from both spatial and temporal perspectives. based on this framework, we first calculate the average queue length and average waiting time to quantify the system's temporal performance. Following this, we derive the successful transmission probability for both AUs and TUs, and subsequently obtain the average ergodic rate for the entire network. This comprehensive approach enables a thorough understanding of the network's performance across both spatial and temporal domains. © 2024 IEEE.

关键词： Stochastic systems

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Fine-Grained Voice Discrimination for Low-Resource Datasets Using Scalogram images 1

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international conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2022

作者： Moirangthem, Gourashyam Nongmeikapam, Kishorjit Indian Institute of Information Technology Manipur Imphal India

ISBN: (数字)9789819932504

ISBN: (纸本)9789819932498

Deep learning is known to be data-intensive. In this light, low-resource voice datasets face a great disadvantage. The current study proposes a novel method of fine-grained voice discrimination. The available voice signal dataset is increased multifold by splitting uniformly into shorter samples and converting them into continuous wavelet rransform wavelet scalogram images. These images are then used to train deep convolutional neural network-based image classification models. The study uses a small voice dataset to demonstrate the efficacy of the proposed approaches in the classification of five Manipuri vowel phonemes represented in IPA as [ ], [e], [i], [o] and [u]. The paper also explores the efficacy of three different state-of-the-art pre-trained image classification models in such a scenario, with the best overall accuracy of 99.66%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image classification

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