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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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A video data hiding technique based on pixel sequence, weight interpolation and quorum function

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Multimedia Tools and Applications 2024年第41期83卷 88881-88901页

作者： Roselinkiruba, R. Jothi, C. Saranya Koushik, C.P. Keerthika, A. Sathya, Vanlin School of Computer and Science Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India School of Computer and Science Engineering VIT Bhopal University Bhopal India Electronics and Communication Engineering Aarupadai Veedu Institute of Technology Chennai India Celona Inc CampbellCA United States

The development of multimedia technology has increased the challenge in protecting the information. This paper proposes an effective method for data hiding in videos based on the Pixel Sequence (PS), weight interpolation to protect the secret data from intruders. Initially, the noise is removed from the video with the help of median filter and Moving Objects (MO) inside the frames having different backgrounds are accurately detected using Fast Region-based Convolution Neural Network (FR-CNN). Next, the MO and Non Moving Object (NMO) pixel from each frame are mapped with the adjacent frames to determine the weights applying PS. Then, the cover video is up-scaled with interpolated pixels based on weights of MO and NMO. Finally, the secret data are hidden inside the both interpolated and non-interpolated pixels elevate Embedding Capacity (EC). In interpolated pixels, both Most Significant Bit (MSB) and Least Significant Bit (LSB) of each frames based on their weight are utilized to hide data. Whereas, in non-interpolated pixels, Quorum Function (QF) is used to embed data. Two different data embedding techniques are adopted in each frame to enhance the security. The performance evaluation of the proposed method out-performs the existing methods in terms of precision and recall. Experimental results also show that the stego-video quality is uncompromised and is resistive to security attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

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Multimedia Tools and Applications 2025年第9期84卷 5435-5455页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India School of Electronics and Communication Engineering REVA University Bangalore India

Most real-time computer vision applications heavily rely on Convolutional Neural Network (CNN) based models, for image classification and recognition. Due to the computationally and memory-intensive nature of the CNN model, it’s challenging to deploy on resource-constrained Internet of Things (IoT) devices to enable Edge intelligence for real-time decision-making. Edge intelligence requires minimum inference latency, memory footprint, and energy-efficient model. This work aims to develop an energy-efficient deep learning accelerator using a 3-stage pipeline: Training, Weight-pruning, and Quantization to reduce the model size and optimize the resources. First, we employ YOLOv3, a CNN architecture to detect objects in an image on the trained data. In addition, a sparse network of YOLO has been created by using pruning, which helps to improve the network’s performance and efficiency by reducing the computational requirements. Finally, we utilize 8-bit quantization to reduce the precision of the weights and activations, in a neural network. The evaluation of our proposed model shows that combining pruning and 8-bit quantization improves the efficiency and performance of the model. While pruning shows a decline of 80.39% in model parameters. The combination of 8-bit quantization results in an improvement in inference latency by 22.72% compared to existing SQuantization approach and a reduction of energy consumption by 29.41%. © The Author(s) 2024.

关键词： Convolutional neural networks

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A CNN Approach for Brain Tumor Detection Using Diverse Optimizers 3

A CNN Approach for Brain Tumor Detection Using Diverse Optim...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Revathi, Ar Venkataraman, Rishab Naveen Moola, Maitreya School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350375190

The ability to recognize brain tumors via medical imaging is crucial for brain tumor treatment. This proposed work focused on a technique for detecting brain tumors by employing Convolutional Neural Networks (CNNs) to achieve precise and prompt diagnosis of brain tumors. This frame utilizes a variety of optimizers such as Stochastic Gradient Descent, RMSprop, Adam (Adaptive Moment Estimation), Adadelta, Adagrad, Adamax, and Nadam. The model's performance is evaluated using accuracy, sensitivity, specificity, precision, and recall as the primary metrics. Leveraging a dataset of 6951 MRI images, results reveal varying performance across optimizers, with a peak accuracy of 98.8% emphasizing the need for careful selection based on specific requirements and dataset characteristics. Advancing brain tumor identification, aiming to enhance early intervention and patient outcomes through the integration of cutting-edge technology, particularly Convolutional Neural Networks (CNNs), which offer high precision in analyzing intricate patterns within medical imaging data. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Semantics-Aware Dual Graph Convolutional Networks for Argument Pair Extraction 30

Semantics-Aware Dual Graph Convolutional Networks for Argume...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guan, Minzhao Qiu, Zhixun Li, Fenghuan Xue, Yun School of Electronics and Information Engineering South China Normal University China School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9782493814104

Argument pair extraction (APE) is a task that aims to extract interactive argument pairs from two argument passages. Generally, existing works focus on either simple argument interaction or task form conversion, instead of thorough deep-level feature exploitation of argument pairs. To address this issue, a Semantics-Aware Dual Graph Convolutional Networks (SADGCN) is proposed for APE. Specifically, the co-occurring word graph is designed to tackle the lexical and semantic relevance of arguments with a pre-trained Rouge-guided Transformer (ROT). Considering the topic relevance in argument pairs, a topic graph is constructed by the neural topic model to leverage the topic information of argument passages. The two graphs are fused via a gating mechanism, which contributes to the extraction of argument pairs. Experimental results indicate that our approach achieves the state-of-the-art performance. The performance on F1 score is significantly improved by 6.56% against the existing best alternative. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Water Quality Monitoring and Control in Urban Areas in Real-Time via IoT and Mobile Applications 3

Water Quality Monitoring and Control in Urban Areas in Real-...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohanasundaram, R. Sagar, Raghav Kumar Khandelwal, Animan Upadhyay, Diwas Poddar, Harshit Rajagopal, Sivakumar Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The presence of impurities in water resulting from the expansion of industrial activities and the disregard of regulations aimed at protecting the environment presents considerable threats to the well-being of the general population. The establishment of reliable access to uncontaminated drinking water assumes paramount importance in the realm of disease prevention. The continuous monitoring of the quality of water in real-time serves as an indispensable measure for ensuring its safety. The objective of this article is to introduce a system that capitalizes on the Internet of Things (IoT) to automate the distribution and management of water resources within urban areas. By making use of embedded technology, this system allows for the uninterrupted surveillance and regulation of water quality. To further streamline the management process, an application compatible with the Android operating system has been developed to promptly notify users of any irregularities, thereby enabling remote control. Moreover, a web-based application has been designed to cater to the needs of iOS users, thereby broadening accessibility. This comprehensive approach not only heightens the level of user awareness but also provides them with greater control over their water supply, thereby enhancing public health outcomes. © 2024 IEEE.

关键词： Internet of things

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Data Driven Approach for Analysing the Relationship between Weather Patterns and Energy Consumption in Residential Buildings for Improved Energy Management 5

Data Driven Approach for Analysing the Relationship between ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kowshik, S.B. Ramakrishnan, R. Amutha, S. Dhanush, R. Akshay, P. Preetam Kalyaan, P. School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Residential buildings are the most significant providers of global energy consumption. Hence, studying the weather patterns and energy use in residential buildings remains vital for effective energy management strategies. This work explores a data-driven approach to analyse the complex interplay between weather and energy consumption in residential settings. Traditional methods for understanding building energy use often rely on physical models or occupant surveys, which can be time-consuming and expensive. This research addresses the need for a more efficient and comprehensive approach to analyse the effect of weather patterns on energy consumption. Following deep learning sequence models were used to most accurately predict the energy consumption - LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional LSTM) and Attention Based LSTM (ABLSTM) and find the ABLSTM model most accurate and suited for our requirement with RMAE value as 0.3086, R2 as 0.9990. This research provides individuals and communities with the knowledge to make informed decisions about their energy usage, potentially leading to reduced energy costs through optimized consumption and can contribute to improved energy security. © 2024 IEEE.

关键词： Building energy prediction Data driven Deep learning Energy shortage LSTM Sustainability Sustainable energy

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A New HARQ Scheme for 5G Systems via Interleaved Superposition Retransmission

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China Communications 2023年第4期20卷 1-11页

作者： Qianfan Wang Li Chen Xiao Ma School of Computer Science and Engineering Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China College of Computer Science Jiaying UniversityMeizhou 514015China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

Within the framework of the 5G new radio(NR),we propose a new hybrid automatic repeat request(HARQ)scheme to improve the throughput *** difference between the proposed scheme and the conventional one lies in the first retransmission,where the erroneous coded block group is interleaved and superimposed(XORed)onto a fresh coded block *** the receiver,an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group(CBG).Only when the superposed retransmission fails,the conventional incremental redundancy(IR)or repetition redundancy(RR)retransmission is *** any case,since the first retransmission is along with but has negligible effect on the fresh CBG,it costs neither transmitted power nor ***-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10%over block fading channels and up to 50%over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.

关键词： 5G NR HARQ LDPC codes superposition retransmission

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Deep Learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Enhancing Phishing Detection Through Advanced Machine Learning Techniques 5

Enhancing Phishing Detection Through Advanced Machine Learni...

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5th International Conference on Smart electronics and Communication, ICOSEC 2024

作者： Sreenivasan, S. Rout, Amrit Pal, Tarun Kumar Ramachandran, Prakash Vellore University of Technology School of Electronics and Engineering Vellore India Vellore University of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798331504403

With the rapid growth of internet usage, phishing attacks have become increasingly prevalent, presenting a major challenge to cybersecurity. This paper introduces a sophisticated method for detecting phishing websites by leveraging advanced machine learning algorithms. Specifically, Evaluated the performance of Logistic Regression, Linear Regression, and Multinomial Naive Bayes models in distinguishing phishing websites based on their URL features. The study utilizes a robust dataset comprising over 100,000 URLs, each meticulously classified as either legitimate or phishing. Through comprehensive data preprocessing and an innovative optimization pipeline, Refined the Logistic Regression model to achieve a detection accuracy of 98.0369%. Additionally, incorporated the web parameter analysis to enhance detection precision and minimize false positives. This work significantly contributes to the development of more resilient cybersecurity strategies, addressing key issues such as feature selection, handling large datasets, and adapting to the constantly evolving nature of phishing tactics. © 2024 IEEE.

关键词： Phishing

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