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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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A Method for De-Identification Analysis of Encrypted Video 9

A Method for De-Identification Analysis of Encrypted Video

引用

Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Kim, Deok-Han Kim, Young-Gab Sejong University Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Seoul Korea Republic of

ISBN: (纸本)9798350318241

As the variety of video content increases, so does the likelihood of data breaches of sensitive personal information. Consequently, several video de-identification techniques have been developed to protect personal data, alongside ongoing research into video encryption technology, which offers the advantage of decryption to obtain the original video without storing a separate video copy. While it is possible to visually evaluate whether individual encrypted frames have been correctly de-identified, human interpretation makes this method vulnerable to subjective bias. Many video encryption studies employ traditional image quality evaluation metrics, such as peak signal-to-noise ratio and structural similarity index measure (SSIM), to objectively demonstrate the de-identification of encrypted videos. However, these metrics prove to be insufficiently effective for this purpose. These image metrics can measure the de-identification rate of a single frame in encrypted videos, but not the entire video. This study introduces a quantitative analysis method to confirm de-identification in fully encrypted videos. In this study, we measured the de-identification rates of all frames of encrypted videos using a weighted sum of SSIM and edge differential ratio. We also measured the de-identification rate of a fully encrypted video. Finally, we graphically analyzed these data using line charts. This objective methodology enabled the identification of portions of the encrypted video where de-identification techniques may not have been appropriately applied. © 2024 IEEE.

关键词： Quality control

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A PolInSAR Four Component Target Decomposition Method for Unambiguous Scattering Analysis Between Forests and Oriented Buildings 6

A PolInSAR Four Component Target Decomposition Method for Un...

引用

6th International Conference on information Communication and Signal Processing, ICICSP 2023

作者： Zhuang, Di Zhang, Lamei Zhu, Bing Harbin Institute of Technology Dept. of Information Engineering Harbin China

ISBN: (纸本)9798350339994

Polarimetric SAR target decomposition plays an important role in ground object interpretation because it can obtain scattering mechanism information. In traditional model-based target decomposition, the volume scattering overestimation of the rotated building area has been always addressed from the perspective of polarimetric response ($S$ matrix, $T$ matrix and $C$ matrix). However, the reduction of the volume scattering in the rotated building area comes at the cost of the complex scattering modeling and solution process, and may lead to an underestimation of the volume scattering in other areas. Therefore, the Polarimetric SAR Interferometry (PoIInSAR) coherence was introduced to assist target decomposition and a new decomposition idea and corresponding PolInSAR four component target decomposition method where the scattering ambiguity is removed from the perspective of non-polarimetric response were proposed in this paper. In the proposed method, the PolInSAR coherence was proposed to distinguish natural areas from built-up areas without scattering ambiguity inside. Then, two very simple sets of scattering models with unfixed model quantities and types were applied for target decomposition in two different areas. Three sets of PolInSAR data from were used for experiments. Results shown that compared with the Freeman-Durden decomposition method, the volume scattering obtained by the proposed method can be reduced by 430/0 in oriented building areas. Compared to the six and seven component decomposition methods, both the proposed method and these two methods can obtain a reasonable proportion of volume scattering in rotated building areas, but the proposed method can keep the volume scattering of the forest area from being underestimated. © 2023 IEEE.

关键词： Image processing Polarimetric image interpretation PolInSAR target decomposition scattering characterization analysis Synthetic aperture radar (SAR)

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Smart Toll Booth System Using Smart Contract 8

Smart Toll Booth System Using Smart Contract

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Patil, Sonali Kulkarni, Mrudula Desale, Shivam Adsul, Digvijay Choudhary, Kushal Pimpri Chinchwad of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350334012

The present toll collection system has a number of problems, including varying toll rates on different roadways, heavy traffic, etc. Long wait times, theft from toll plazas, and other issues are problems that the toll collection system, particularly in India, encounters. Only 300 automobiles may be serviced by these systems each hour, and if more cars arrive at that area than that, severe traffic Jams could happen. For toll collection, the present toll methods, including Radio Frequency Identification (RFID), still require a lot of time. To address the above mentioned issues, this study discusses a GPS-based toll collection system. Every time a vehicle switches from a highway to a service road, this system would track the coordinates of the moving object. As a result, the user can pay the toll without being pulled over anywhere along the freeway. This system will leverage blockchain technology to construct a decentralized and intelligent mechanism for toll collection and tracking records because it has recently become one of the most well-liked technologies and has the potential to change the field of intelligent transportation systems. The technology uses blockchain smart contracts for the maintenance of security and integrity of records. Thus, presenting a strategy for putting into place a seamless toll collection system with the most transparency possible. © 2023 IEEE.

关键词： Smart contract

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Deepfake Detection of Singing Voices With Whisper Encodings

Deepfake Detection of Singing Voices With Whisper Encodings

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharma, Falguni Gupta, Priyanka Centre for Artificial Intelligence Banasthali Vidyapith Jaipur India Dept. of Communication and Computer Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9798350368741

The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI's Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be noise-robust, the encodings are rich in non-speech information, and are noise-variant. This leads us to evaluate Whisper encodings as feature representations for the SVDD task. Therefore, in this work, the SVDD task is performed on vocals and mixtures, and the performance is evaluated in %EER over varying Whisper model sizes and two classifiers-CNN and ResNet34, under different testing conditions. © 2025 IEEE.

关键词： anti-spoofing Deepfake Detection Singing voice deepfake detection Transfer Learning Whisper

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A Face Recognition-Based Smart Student Attendance and Activeness Monitoring System 4

A Face Recognition-Based Smart Student Attendance and Active...

引用

4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Ghodekar, Atharva Gunjal, Tejas Landage, Abhijeet Phapale, Abhijit Thorat, Dinesh Nikam, Rohit Sanjivani College of Engineering Dept. of Information Technology Kopargaon India

ISBN: (纸本)9798350393248

A Face Recognition-Based Smart Student Attendance and Activeness Monitoring System represents a transformative solution to revolutionize attendance management within educational institutions. This project introduces a new way of tracking student attendance in schools, moving away from traditional methods like calling names or using cards. We're using advanced facial recognition technology, specifically the OpenFace model, to make attendance more accurate and easier. This system corrects mistakes intelligently and refines its data for better performance. A distinctive feature of the system involves the incorporation of the Haar Cascade model to monitor student engagement. Which does more than just track attendance. It watches students' facial expressions and head movements in real-time. This gives teachers insights into how engaged students are in class, helping them improve participation. Teachers get immediate feedback, allowing them to adjust their teaching methods for a more interactive and enjoyable learning experience. Our system doesn't just focus on attendance, it takes a broader approach by monitoring student engagement. By simplifying tasks for school staff and giving helpful information, this system has the potential to change how schools operate. It uses cutting-edge technology to make attendance and engagement monitoring easy, promising to create better classroom experiences for everyone involved. © 2023 IEEE.

关键词： Face recognition

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Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach 3

Dynamic Landslide Prediction, Monitoring, and Early Warning ...

引用

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

作者： Binu, Kristin Elizabeth Anoopkumar, Lakshmi Thampi Sunil, Maria Jose, Merin Preetha, K.G. Rajagiri School of Engineering & Technology Dept. of Computer Science and Engineering Rajagiri Valley PO Kochi India

ISBN: (纸本)9798350375190

Landslides are a major threat to infrastructure, human lives, and the environment. Therefore, early warning systems and precise forecasting are essential for reducing their effects. This study investigates how to enhance landslide prediction models using Explainable Artificial Intelligence (XAI) methodologies. Intending to improve complex machine learning models' interpretability and transparency, XAI seeks to improve comprehension and confidence in the model's predictions. XAI methodologies used are SHapley Additive exPlanations (SHAP), LIME (Local Interpretable Model-agnostic Explanations), and feature importance analysis. These techniques produce comprehensible explanations for each forecast and assist in determining the most significant variables in landslide prediction. The proposed XAI-enhanced landslide prediction model demonstrates promising results in terms of accuracy and interpretability. The research reported here helps to design more potent early warning systems for landslides by advancing explainable AI applications in geoscience and disaster risk reduction. © 2024 IEEE.

关键词： Landslides

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Integrating Artificial Intelligence and Educational technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket

Developing and Training a Mathematical Model for Optimizing ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Alahakon, Shalitha Siriwardana, Tharindu Udupihilla, Deshan Wickramasinghe, Tharukshi Rajapaksha, Samantha Sri Lanka Institute of Information Technology Dept. Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers. © 2023 IEEE.

关键词： Python

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