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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Murali, K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Department of Mathematics Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350349900

Speaker identification is used for identifying an individual based on their voice. Signal processing and deep neural networks are used for feature extraction. This paper presents a method that combines CNN and LSTM for speaker identification. Multiple models such as GMM, CNN, SVM were compared and CNN+LSTM outperformed with an accuracy of 96.52% and F1 measure of 97%. The CNN+LSTM model combines spatial and temporal information extracted by the CNN and LSTM layers which allows to capture of both local and long-term dependencies in the audio data hence making this model very efficient. The above models were evaluated on the RAVDESS (Ryerson AudioVisual Database of Emotional Speech and Song) dataset. The results highlight their potential for practical applications within speaker identification systems. © 2024 IEEE.

关键词： Deep neural networks

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Design Method for Q-Enhanced Reconfigurable Bandpass Filters With High Selectivity, Large Bandwidth Variable Range, and Large Dynamic Range

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IEEE Transactions on Microwave Theory and Techniques 2024年第11期72卷 6387-6400页

作者： Chen, Ziyuan Li, Kun Liu, Yuanan Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Sichuan Chengdu610054 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A design method of generating multiple transmission zeros (TZs) is proposed by introducing feeding-forward transconductance amplifiers for the cascaded Q-enhanced reconfigurable bandpass filters (BPFs) in this article. With the proposed method, it can generate two TZs at the upper and lower stopbands for every two Q-enhancement resonant units, significantly improving the stopband suppression. Furthermore, by controlling the feeding-forward amplifiers, the position of the zero and the depth of the TZs can be flexibly controlled. Meanwhile, to improve the power consumption, linearity, and noise figure (NF), the guideline of distribution of the transmission poles (TPs) generated by the Q-enhancement units is discussed in detail. To demonstrate the proposed method, a reconfigurable on-chip Q-enhanced BPF was designed and measured based on a 55-nm CMOS process with a core area of 1.36× 0.62mm. Multiple TZs were clearly observed in the filter and achieved a stopband suppression of more than 50 dBc up to 15 GHz. The bandwidth of the proposed filter can be tuned with a range covering from 0.2 to 1 GHz at a center frequency of 1.8 GHz, while the center frequency can be tuned from 1.68 to 1.94 GHz with a constant bandwidth of 600 MHz. The input 1-dB compression point (IP1dB) is better than -1 dBm for all tuning states, and the NF varies from 12.7 to 20.3 dB. The dynamic range (DR) can reach more than 165 dB and the total power consumption is less than 180 mW. © 2024 IEEE.

关键词： Bandpass filters

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A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ravishankar, Joshitha Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350302493

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme's superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec. © 2023 IEEE.

关键词： Dynamic mode decomposition

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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A Deep Learning Based Induced GNSS Spoof Detection Framework

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 457-478页

作者： Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering Cluny Road 117583 Singapore University of Nebraska-Lincoln School of Computing LincolnNE68523 United States

The Global Navigation Satellite System (GNSS) plays a crucial role in critical infrastructure by delivering precise timing and positional data. Nonetheless, the civilian segment of the GNSS remains susceptible to various spoofing attacks, necessitating robust detection mechanisms. The ability to deter such attacks significantly enhances the reliability and security of systems utilizing GNSS technology. Supervised Machine Learning (ML) techniques have shown promise in spoof detection. However, their effectiveness hinges on training data encompassing all possible attack scenarios, rendering them vulnerable to novel attack vectors. To address this limitation, we explore representation learning-based methods. These methods can be trained with a single data class and subsequently applied to classify test samples as either belonging to the training class or not. In this context, we introduce a GNSS spoof detection model comprising a Variational AutoEncoder (VAE) and a Generative Adversarial Network (GAN). The composite model is designed to efficiently learn the class distribution of the training data. The features used for training are extracted from the radio frequency and tracking modules of a standard GNSS receiver. To train our model, we leverage the Texas Spoofing Test Battery (TEXBAT) datasets. Our trained model yields three distinct detectors capable of effectively identifying spoofed signals. The detection performance across simpler to intermediate datasets for these detectors reaches approximately 99%, demonstrating their robustness. In the case of subtle attack scenario represented by DS-7, our approach achieves an approximate detection rate of 95%. In contrast, under supervised learning, the best detection score for DS-7 remains limited to 44.1%. © 2023 CCBY.

关键词： Global positioning system

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An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

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SMARTGOV: Strategic Management of Assets for Resilient and Transparent Government Operations 9

SMARTGOV: Strategic Management of Assets for Resilient and T...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Chauhan, Naman Atri, Pranshul Tomar, Riya Kumar, Aman Chauhan, Chhavi Kumar, Kaitala Charan Sophiya, E. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350350845

In this age, we introduce an application that aims to revolutionize the management of government buildings. With a commitment, to supporting Digital India initiative, proposed project addresses the changing needs of governance and public service delivery. This application serves a purpose;it provides authorities with an easy-To-use platform for efficient building management while also empowering the public with real-Time information and a streamlined channel for expressing concerns. We leverage technologies like GPS camera Lite, Google Maps, Automatic fire detection using CNN with a mAP of 98.61% and Cloud Firestore to seamlessly integrate our application with Android Studio and Kotlin ensuring an optimal user experience. Our motivation behind this initiative stems from the need to contribute to our nation's progress by promoting governance and aligning with India's aspirations for Digital India. By fostering communication, transparency and accountability our application becomes a tool, in building an empowered and prosperous India. Proposed Work outlines the features, technological framework and societal benefits of our solution that resonates with both government requirements and public aspirations. © 2024 IEEE.

关键词： Cameras

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A broad learning-based comprehensive defence against SSDP reflection attacks in IoTs

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Digital Communications and Networks 2023年第5期9卷 1180-1189页

作者： Xin Liu Liang Zheng Sumi Helal Weishan Zhang Chunfu Jia Jiehan Zhou College of Computer Science and Technology China University of Petroleum(East China)QingdaoChina School of Computing Lancaster University UK Nankai University TianjinChina Information Technology and Electrical Engineering University of OuluFinland

The proliferation of Internet of Things(IoT)rapidly increases the possiblities of Simple Service Discovery Protocol(SSDP)reflection *** DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance,and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing *** paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks,called Broad Learning based Comprehensive Defence(BLCD).The defence strategies work along the attack chain,starting from attack sources to *** defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection *** also detects suspicious traffic at bots,service providers and victims by using broad learning,and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS *** evaluations,we thoroughly analyze attack traffic when deploying BLCD to different defence *** show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service,and detect malicious packets with an accuracy of 99.99%.

关键词： Denial-of-service DRDoS SSDP reflection Attack Broad learning Traffic detection

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Blockchain-based crowdsourcing for human intelligence tasks with dual fairness

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Blockchain(Research and Applications) 2024年第4期5卷 1-13页

作者： Yihuai Liang Yan Li Byeong-Seok Shin School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea

Human intelligence tasks(HITs),such as labeling images for machine learning,are widely utilized for crowdsourcing human *** crowdsourcing platforms face challenges of a single point of failure and a lack of service *** blockchain-based crowdsourcing approaches overlook the low scalability problem of permissionless blockchains or inconveniently rely on existing ground-truth data as the root of trust in evaluating the quality of workers’*** propose a blockchain-based crowdsourcing scheme for ensuring dual fairness(i.e.,preventing false reporting and free riding)and improving on-chain efficiency concerning on-chain storage and smart contract *** proposed scheme does not rely on trusted authorities but rather depends on a public blockchain to guarantee dual *** efficient and publicly verifiable truth discovery scheme is designed based on majority voting and cryptographic *** truth discovery scheme aims at inferring ground truth from workers’*** ground truth is further utilized to estimate the quality of workers’***,a novel blockchain-based protocol is designed to further reduce on-chain costs while ensuring *** scheme has O(n)complexity for both on-chain storage and smart contract computation,regardless of the number of questions,where𝑛denotes the number of *** security analysis is provided,and extensive experiments are conducted to evaluate its effectiveness and performance.

关键词： Crowdsourcing Dual fairness Blockchain Human intelligence task Truth discovery

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Transferable and Robust Dynamic Adversarial Attack Against Object Detection Models

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IEEE Internet of Things Journal 2025年第11期12卷 16171-16180页

作者： Wang, Xinxin Chen, Jing Zhang, Zijun He, Kun Wu, Zongru Du, Ruiying Li, Qiao Liu, Gongshen Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Shanghai Jiao Tong University Department of Electronic Information and Electrical Engineering Shanghai201100 China

Object detection models have been widely deployed in physical world applications, and they are vulnerable to adversarial attacks. However, most adversarial attacks are implemented in a glass box setting, and under ideal shooting conditions, such as fixed distances and angles, and thus have limited attack success rate (ASR) in practice. In this article, we present a transferable and robust dynamic adversarial attack where the adversarial patches can be printed on or attached to nonrigid objects, such as clothes. We develop a cascade module with a momentum-based technique to optimize adversarial patches against various object detection models, achieving better transferability of the patches in a closed box setting. We also develop a strategy of distance-adaptive patch generation and employ perspective transformation to enhance the robustness of patches. To evaluate the attack performance, we conduct extensive experiments on seven mainstream object detection models at different distances and angles. The results show that our method can achieve an average ASR of 69.85%, which is 3.27 times that of the baseline method at 3 m. © 2014 IEEE.

关键词： Object detection Computational modeling Closed box Robustness Adaptation models Trusted computing Information security Aerospace engineering Proposals Aerodynamics

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