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FPGA-based real-time Spectral Peak Estimator for Radar Altimeter Signal Analysis

FPGA-based Real-Time Spectral Peak Estimator for Radar Altim...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Ritesh Kumar Sharma Jolly Dhar Umang Bhatia B Saravana Kumar Ch V N Rao D R Chowdhury Space Applications Centre Indian Space Research Organisation (ISRO) Ahmedabad India Department of Computer Science and Engineering Indian Institute of Technology (IIT) Kharagpur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This research work presents real-time Spectral Peak Estimator for radar altimeter signal analysis on an FPGA- based platform, focusing on accurate beat frequency estimation for precise above-ground level (AGL) altitude measurements. The proposed design is based on the first-order derivative difference estimator combined with zero crossover point detection to identify spectral maxima efficiently. This estimator is implemented using a fixed-point concurrent FPGA architecture to achieve high computational efficiency, low latency, and optimal hardware utilization, enabling real-time processing of radar return signals. This system has been verified and validated during various lab and field tests for the accurate beat frequency estimation under the dynamic signal conditions. The solution meets the stringent performance requirements of radar altimeter applications, making it suitable for aerospace, autonomous landing, and navigation systems where real-time accuracy is critical.

关键词： Accuracy Fast Fourier transforms Aerodynamics real-time systems Frequency estimation Hardware Robustness Signal analysis Low latency communication Field programmable gate arrays

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A Literature Review: Application of AI for Predictive Maintenance in Digital Twins Across Various Industries

A Literature Review: Application of AI for Predictive Mainte...

引用

ieee SSD international Multi-conference on systems, Signals and Devices

作者： Ilhem Ben Hnaien Eric Gascard Zineb Simeu-Abazi Hedi Dhouibi NOCCS Laboratory Natl. Engr. School of Sousse University of Sousse Sousse Tunisia Univ. Grenoble Alpes CNRS Grenoble INP * G-SCOP * Institute of Engineering Univ. Grenoble Alpes Grenoble France LARATSI Laboratory High. Inst. of Appl. Sci. Technol. of Kairouan University of Kairouan Kairouan Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper provides a comprehensive literature review on the integration of Artificial Intelligence (AI) into Digital Twin (DT) systems for predictive maintenance across various industries. As industries adopt digital transformation, Digital Twins have emerged as powerful tools, linking physical assets with their digital counterparts for real-time monitoring, analysis, and diagnostics. By leveraging AI methods like machine learning and deep learning, predictive maintenance within DTs can identify and mitigate potential failures before they disrupt operations. This review synthesizes recent advancements in AI applications within DTs, examining sector-specific methods, applications, and the impact on operational efficiency. We explore challenges such as real-time data processing, model complexity, and system integration, presenting a comparative analysis across industries including manufacturing, healthcare, energy, and transportation.

关键词： Industries Deep learning Accuracy Transportation System integration Medical services real-time systems Digital twins Predictive maintenance Systematic literature review

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Deep Learning Techniques for Intrusion Detection systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Mahfooz Alam Zubair Ashraf Priya Singh Bishwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services real-time systems Security Long short term memory

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems real-time systems Robots Long short term memory

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real-time Detection of Free-to-Air Television Commercials in Audio Using Feature Extraction and Machine Learning

Real-Time Detection of Free-to-Air Television Commercials in...

引用

ieee international conference on Consumer Electronics (ICCE)

作者： Myke D. M. Valadão Lucas G. M. Castro Kristhian A. O. Aguilar Giselle L. Marinho Matheus S. Uchoa Eddie B. de Lima Filho Orlewilson B. Maia Rômulo Fabrício Waldir S. S. Júnior Celso B. Carvalho Federal University of Amazonas and Center for R&D in Elec. and Inf. Tech. (UFAM/CETELI) AM-Brazil TPV Technology AM-Brazil

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Detecting television commercials is essential for the advertising, broadcasting, and content analysis industries. In that sense, traditional methods, which rely on audio-visual cues or embedded markers, have evolved with machine learning (ML) and artificial intelligence advances. However, while highly accurate, deep neural networks (DNNs) are computationally intensive and often unsuitable for real-time detection. This paper proposes a real-time method for detecting television commercials in audio using feature extraction and ML models. Audio content is segmented through windowing, while features are extracted from each element. Various models are evaluated, and optimization is performed through feature selection and grid search. The proposed method achieved an accuracy of 99.79% with the best-performing model while maintaining low computational complexity. This approach provides a faster and more efficient solution than usual DNN-based methods, making it suitable for real-time applications. It also enhances the ability to replace commercials with alternative content, improving airtime optimization and overall viewer experience.

关键词： Industries Accuracy Computational modeling Atmospheric modeling Machine learning Feature extraction real-time systems Regulation Advertising Optimization

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IRIS: A 8.55mJ/frame Spatial computing SoC for Interactable Rendering and Surface-Aware Modeling with 3D Gaussian Splatting

IRIS: A 8.55mJ/frame Spatial Computing SoC for Interactable ...

引用

ieee international conference on Solid-State Circuits (ISSCC)

作者： Seokchan Song Seryeong Kim Wonhoon Park Jongjun Park Sanghyuk An Gwangtae Park Minseo Kim Hoi-Jun Yoo KAIST Daejeon Korea

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

Interactive Spatial computing (ISC) enables immersive mixed reality (MR) applications, which demand a real-time and user-interactive 3D graphics system [1]–[3]. This requires real-time surface-aware modeling (SAM) to transfer a physical object to the virtual world, and interactive photorealistic rendering (IPR) is required to present the deformed virtual 3D object, as shown in Fig. 2.7.1 [4]–[5]. Neural Radiance Field (NeRF) accelerators [7]–[8] are unable to realize ISC since they cannot support surface extraction and deformation [9].30 Gaussian Splatting (3DGS) with free-deforming algorithms [10]–[11] enables high-quality rendering for deformed objects, but its realization on a high-power-consumption (>5W) edge GPU [12] requires >20minutes for SAM and >400ms for IPR due to its massive external memory access (EMA) and huge computational requirements. In general, there are three hardware challenges to realize a mobile 3DGS accelerator. First, the parameters of 3DGS (>50MB for over 500K Gaussians) far exceed the L2 cache size of an edge GPU (4MB on the Jetson Orin Nano [12]), while being stored in an unordered manner incurs frequent irregular DRAM accesses [14], [19]. Its EMA occupies 81.3% of the system energy consumption, even after parameter pruning schemes are applied [16]. Thus, the Single-Embedding-Multi-MLP (SEMM)-Compressed 3DGS based on a state-of-the-art MLP-based compression approach [15] is adopted, but EMA still constitutes 52.8% of system energy. Second, spatial computing requires to accelerate IPR within a 15ms latency to provide real-time user interaction [23]. However, the three sequential stages of IPR - deform, reflect, and render - require 2.33x more processing time compared to non-interactive rendering on previous 3DGS hardware [13]. Third, backpropagation (BP) consumes -55.8% of the computations in SAM due to FP16 high-precision calculation and limited reusability.

关键词： Solid modeling Three-dimensional displays Computational modeling Graphics processing units Intellectual property Rendering (computer graphics) Neural radiance field real-time systems Hardware Spatial computing

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4.3 A 0.36nW, $\mathbf{820}\boldsymbol{\mu}\mathbf{m}^{\mathbf{2}}$, 32kHz Conduction-Angle-Adaptive Crystal Oscillator in 28nm CMOS for real-time Clock applications

4.3 A 0.36nW, $\mathbf{820}\boldsymbol{\mu}\mathbf{m}^{\math...

引用

ieee international conference on Solid-State Circuits (ISSCC)

作者： Peng Wang Manyu Wang Guangnan Dai Yujia Cao Sining Pan Yihan Zhang Hong Kong University of Science and Technology Hong Kong China Tsinghua University Beijing China

ISBN: (数字)9798331541019

ISBN: (纸本)9798331541026

32.768kHz (32kHz) crystal oscillators (XOs) are widely used in real-time clocks (RTC) embedded in various electronic systems. Their performance is critical in battery-powered Internet-of-Things (loT) sensor nodes when serving as wake-up timers. Extending the battery life requires an ultra-low-power (ULP) XO, as it must stay always-on. It also requires good frequency stability; otherwise, the system has to extend its communication guard times for synchronization, in which power-hungry transceivers need to stay active, leading to extra waste in power [1]. These demands have motivated much recent research to seek alternatives to the classic inverter-based Pierce oscillator, which is simple in design but typically consumes 10 to 100nW of power [2]–[5]. Pulse-injection XOs (PIXOs) are popular due to their sub-nW power consumption. Their power reduction comes from using a tiny conduction angle in the form of short pulses for Class-C-like operations. However, positioning current injections to the optimum timing typically requires generating an accurate delay, making pulse generators area-consuming [6]–[10]. Moreover, these methods cannot initiate the oscillation, as the zero-crossing instants, which are the starting times of the delay, are only available once an oscillation is present. These PIXOs, hence, need to embed separate start-up circuits to build up the initial oscillation, necessitating extra control logic and facing robustness challenges under accidental but strong interferences. A compact and easy-to-use ULP XO remains in demand from the cost-sensitive loT market.

关键词： time-frequency analysis Crystals real-time systems Transceivers Robustness Delays Solid state circuits Synchronization Oscillators Clocks

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DITHER: Digital Twin for Energy Estimation of Heterogeneous Swarm of Robots

DITHER: Digital Twin for Energy Estimation of Heterogeneous ...

引用

ieee SSD international Multi-conference on systems, Signals and Devices

作者： Omar Hammami Chunyu Zhang U2IS ENSTA Paris Palaiseau France

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The increasing adoption of Unmanned Aerial Vehicles (UAVs) in industrial applications such as construction inspection and infrastructure monitoring necessitates the development of efficient path planning and energy management strategies. This paper presents DITER, a Digital Twin system designed for energy consumption profiling and management in UAV-based construction inspection tasks. The proposed system integrates real-time data acquisition, energy consumption profiling, and path optimization using a hybrid approach combining path planning and energy efficiency models. Experiments conducted with a DJI Tello UAV show that the digital twin framework facilitate UAV operational in complex inspection tasks by balancing energy consumption and path length. The proposed system can be extended to support multi-UA V collaborative operations in future research.

关键词： Energy consumption Collaboration Inspection Autonomous aerial vehicles Path planning real-time systems Digital twins Optimization Robots Monitoring

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SenSync: real-time and Accurate Passive Sensing

SenSync: Real-Time and Accurate Passive Sensing

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ieee international conference on RFID

作者： Ishan Bansal Nagarjun Bhat Agrim Gupta Harine Govindarajan Dinesh Bharadia University of California San Diego La Jolla United States

ISBN: (数字)9798331509057

ISBN: (纸本)9798331509064

SenSync tackles key challenges in RFID-based differential sensing systems, including temporal misalignment, phase ambiguity, and environmental sensitivity. Traditional techniques are limited by sequential data processing, which introduces time shifts, and arbitrary phase jumps injected by commercial RFID readers, which obscure accurate differential measurements. These issues, compounded by multipath effects and dynamic environments, hinder the deployment of robust RFID sensing systems at scale. To address these challenges, we propose innovative algorithms and signal processing techniques to align and interpret time-shifted data from multiple ICs. Our approach mitigates the effects of temporal misalignment and phase ambiguity, ensuring reliable differential sensing in real-world applications. By improving data alignment and robustness, we accelerate the sensory resolution by 5 x. Furthermore, we developed a user interface capable of automatically detecting sensors within the system's field of operation and displaying their readings in real-time, demonstrating the practical applicability and versatility of our proposed solution.

关键词： Wireless communication Weight measurement Wireless sensor networks Accuracy Signal processing algorithms real-time systems Sensors Passive RFID tags Remote sensing Radiofrequency identification

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User-Centric Evaluation Methods for Digital Twin applications in Extended reality

User-Centric Evaluation Methods for Digital Twin Application...

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ieee international conference on Artificial Intelligence and Virtual reality (AIVR)

作者： Francesco Vona Maximilian Warsinke Tanja Kojić Jan-Niklas Voigt-Antons Sebastian Möller Immersive Reality Lab Hochschule Hamm-Lippstadt Lippstadt Germany Quality and Usability Lab Technische Universität Berlin Berlin Germany

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

The integration of Digital Twins (DTs) with Extended reality (XR) technologies, such as Virtual reality (VR) and Augmented reality (AR), is transforming industries by enabling more immersive, interactive experiences and enhancing real-time decision-making. User-centered evaluations are crucial for aligning XR-enhanced DT systems with user expectations, enhancing acceptance and utility in real-world settings. This paper proposes a user-centric evaluation method for XR-enhanced DT applications to assess usability, cognitive load, and user experience. By employing a range of assessment tools, including questionnaires and observational studies across various use cases-such as virtual tourism, city planning, and industrial maintenance-this method provides a structured approach to capturing the user's perspective.

关键词： Extended reality Urban planning Refining Cognitive load User experience real-time systems Digital twins Usability Artificial intelligence Standards

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