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Quantum Dot-Based real-time Disease Detection Using embedded Systems: A Novel Approach for Portable Health Monitoring 8

Quantum Dot-Based Real-Time Disease Detection Using Embedded...

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8th International Conference on Electronics, Materials Engineering and Nano-technology, IEMENTech 2025

作者： Sarna, Soumil Sarna, Mukesh K Vellore Institute of Technology Vellore India Mahatma Gandhi Medical College and Hospital MGUMST Department of General Medicine Jaipur India

ISBN: (纸本)9798331520762

This paper presents an innovative approach to real-time disease diagnostics using a microcontroller integrated quantum dot sensor device. Quantum dots, renowned for their high sensitivity and precise fluorescence properties, are deployed to detect specific biomarkers in biological samples, offering rapid and accurate health diagnostics. This work lays the foundation for future advancements in embedded systems for biomedical applications, offering an efficient and low-cost solution to improve patient outcomes through timely intervention. © 2025 ieee.

关键词： Microcontrollers

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A Point Cloud-Based Non-Intrusive Approach for Human Posture Classification by Utilizing 77 GHz FMCW Radar and Deep Learning Models

A Point Cloud-Based Non-Intrusive Approach for Human Posture...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Mahajan, Pranjal Chaudhary, Devansh Khan, Mujeev Khan, Mohammed Hammad Wajid, Mohd Srivastava, Abhishek IIIT Hyderabad Ctr VLSI & Embedded Syst Technol CVEST Hyderabad India ZHCET Aligarh Muslim Univ Dept Elect Engn Aligarh India

ISBN: (纸本)9798350330991;9798350331004

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures;however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

关键词： Deep Learning (CNN) FMCW Radar mmWave point-cloud posture classification

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Optimization of real-time Signal Processing Algorithm in Electronic Information Systems Integrating Convolutional Neural Networks 24

Optimization of Real-Time Signal Processing Algorithm in Ele...

引用

2024 International symposium on Integrated Circuit Design and Integrated Systems, ICDIS 2024

作者： Ling, Zhicheng School of Micro-Electronics South China University of Technology Guangzhou China

ISBN: (纸本)9798400718229

In recent years, the demand for real-time signal processing in electronic information systems has significantly increased, driven by the growth of applications in areas such as Internet of Things (IoT), mobile devices, healthcare monitoring, and communication systems. However, real-time processing of signals presents a significant challenge due to the complexity of the signals, the need for high classification accuracy, and the limited computational resources available in embedded systems. This paper presents an optimized real-time signal processing algorithm that integrates Convolutional Neural Networks (CNNs) with an advanced pre-processing framework and a multi-scale feature extraction technique. The proposed method aims to address these challenges by enhancing the accuracy of signal classification while minimizing computational overhead, making it suitable for deployment in resource-constrained environments. The methodology incorporates domain-specific prior knowledge during the pre-processing stage, utilizes a multi-scale feature extraction mechanism, and applies a hybrid optimization technique to dynamically balance the trade-off between accuracy and computational efficiency. Experimental results show that the proposed method outperforms both traditional signal processing techniques and baseline CNN models, achieving superior classification accuracy, lower inference times, and better scalability across diverse hardware platforms, including high-performance GPUs and low-power embedded systems. These findings demonstrate the potential of deep learning-based approaches for real-time signal processing in practical, resource-constrained applications. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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DiCE-M: Distributed Code Generation and Execution for Marine applications - An Edge-Cloud Approach 9

DiCE-M: Distributed Code Generation and Execution for Marine...

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9th symposium on Edge Computing

作者： Coviello, Giuseppe Rao, Kunal Mellone, Gennaro De Vita, Ciro Giuseppe Chakradhar, Srimat NEC Labs Amer Inc Princeton NJ 08540 USA

ISBN: (纸本)9798350378290;9798350378283

Edge computing has emerged as a transformative technology that reduces application latency, improves cost efficiency, enhances security, and enables large-scale deployment of applications across various domains. In environmental monitoring, systems such as MegaSense [49], use low-cost sensors to gather and process real-time air quality data through edge-cloud collaboration, highlighting the critical role of edge computing in enabling scalable, efficient solutions. Similarly, marine science increasingly requires real-time processing and analysis of marine data from remote, resource-constrained environments. In this paper, we extend the power of edge computing by integrating it with Generative Artificial Intelligence (GenAI), specifically large language models (LLMs), to address challenges in marine science applications. We propose DiCE-M (Distributed Code generation and Execution for Marine applications), a robust system that uses LLM to generate distributed code for marine applications and then utilizes a runtime to efficiently execute it on an edge+cloud computing infrastructure. Specifically, DiCE-M leverages edge computing to execute lightweight AI models locally on unmanned surface vehicles (USVs) while offloading complex tasks to the cloud, thus balancing computational load and enabling real-time monitoring in marine environments. We use marine litter identification as an example application to demonstrate the utility of DiCE-M. Our results show that DiCE-M reduces latency by more than 2X when marine litter is not detected and cuts cloud computing costs by more than half compared to traditional cloud-based approaches. By selectively cropping and transmitting relevant image portions, DiCE-M further improves bandwidth efficiency, making it a reliable and cost-effective solution for deploying AI-driven applications on resource-constrained USVs in dynamic marine environments.

关键词： Edge computing Cloud computing Distributed computing Generative artificial intelligence Large language models Code generation Runtime Vision applications Marine science

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POTENTIAL OF AN embedded HYPERSPECTRAL COMPRESSIVE IMAGING SYSTEM FOR REMOTE SENSING applications

POTENTIAL OF AN EMBEDDED HYPERSPECTRAL COMPRESSIVE IMAGING S...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Lim, Olivier Mancini, Stephane Mura, Mauro Dalla Univ Grenoble Alpes CNRS Grenoble INP TIMA 46 Ave Felix Viallet F-38031 Grenoble France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab 11 Rue MathCmatiques F-38402 St Martin Dheres France Inst Univ France IUF 1 Rue Descartes F-75231 Paris 05 France

ISBN: (纸本)9798350320107

The utilization of hyperspectral imaging in remote sensing has seen an increasing trend, as it enables to capture a greater amount of information. In this context, emerging snapshot sensors based on compressed sensing have been employed for various remote sensing applications. This work presents a prospective study by proposing a method to evaluate the performances we can expect when reconstructing data from a compressed sensing imager, the Double-Disperser Coded Aperture Snapshot Spectral Imager on an embedded system, i.e. on either a Graphics Processing Unit or a Field-Programmable Gate Arrays. This is original in the literature since most compressive sensing works focus on reconstruction quality and overlook the requirements for real-time, namely computational cost and data bandwidth. Moreover, works that use an embedded system are even more scarse. The study introduces methods to enhance these restrictions and assesses the resulting improvements. The study's findings support the use of Disperser Coded Aperture Snapshot Spectral Imager for remote sensing applications, potentially enabling a smaller sensor size.

关键词： Compressed sensing CGNE DD CASSI hyperspectral imaging computation complexity embedded systems FPGA GPU

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real-time Classification of Imperfect Gesture Data Utilizing Convolutional Neural Network for Internet of Things (IoT) applications 67

Real-Time Classification of Imperfect Gesture Data Utilizing...

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67th ieee International Midwest symposium on Circuits and Systems (MWSCAS)

作者： Rahnamai, Kourosh Rollins, Jacob Moisan, Luke Kayfus, Ryan Magotra, Neeraj Western New England Univ ECE Dept Springfield MA 01119 USA

ISBN: (纸本)9798350387186;9798350387179

This paper deals with the development and application of an Artificial Intelligence (AI) neural network. The network was designed for IoT edge devices to make them smarter and more efficient in triaging the input information uploaded to the neural network for further analysis and utilization. It presents the development of an image classification Convolutional Neural Network (CNN) that is optimized for implementation in the new generation of powerful, compact, energy efficient embedded systems being developed for IoT edge applications. The network was trained to identify skeleton images in different poses captured using an Xbox 360 Kinect. To accomplish this, various techniques and strategies were employed to train and test the CNN, including the use of various datasets and different configurations of the network's architecture. The results of the study determined the overall accuracy of the CNN to be 99.60%. The results provide valuable insights into the potential of using CNNs for image classification and gesture identification, highlighting the importance of careful design and optimization of the network architecture to achieve optimal performance suitable for IoT edge implementations. Potential applications identified were medical, surveillance, and facial tracking. These are just a few examples of the potential uses of this network for image classification.

关键词： Convolutional neural networks

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A Reactive System-specific Compilation Chain from Synchronous Dataflow Models to FPGA Netlist 10

A Reactive System-specific Compilation Chain from Synchronou...

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10th International Conference on Space Mission Challenges for Information technology

作者： Winandy, Ines Dion, Arnaud Garoche, Pierre-Loic Manni, Florent Univ Toulouse Federat ENAC ISAE SUPAERO ONERA Toulouse France CNES Ctr Natl Etud Spatiales Toulouse France

ISBN: (纸本)9798350384529;9798350384512

Modern Field Programmable Gate Arrays (FPGAs) offer a solution to several issues related to real-time on-board systems, such as guaranteed execution time. They are currently considered as target platforms for space applications. However, the complexity of producing circuits on these components poses a challenge to their widespread adoption. To address this issue, high-level synthesis tools provide another layer of abstraction above the logic circuit design process, for example compiling C code into Hardware Description Languages such as VHDL or Verilog. However, high-level synthesis results are poorly predictable and do not guarantee the efficient use of recent FPGA capabilities provided by new primitives like digital signal processor or random-access memory. In this paper we propose a compilation chain dedicated to reactive systems, ie. controllers, providing a more predictable synthesis process for critical embedded control applications. The implemented solution demonstrates timing performance equivalent to the traditional synthesis process with a more predictable result.

关键词： High Level Synthesis (HLS) embedded systems Guidance Navigation and Control (GNC) Dataflow Programming Computer-aided Design (CAD)

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Run time and Accuracy of Machine Learning Algorithms for Speaker Identification 13

Run Time and Accuracy of Machine Learning Algorithms for Spe...

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13th International symposium on Computer Science and Educational technology, ISCSET 2024

作者： Lede, Stephan Schmidt, Rene Hardt, Wolfram Chemnitz University of Technology Department of Computer Engineering Chemnitz Germany

ISBN: (纸本)9798350390803

The efficiency and accuracy of machine learning algorithms in embedded systems are paramount for real-time processing tasks such as identifying an intended speaker out of potential sound sources. This paper presents an evaluation of support vector machines (SVMs) and feed-forward neural networks (FFNNs) for the regression problem of speaker identification, with a focus on constant calculation time and the influence of the use of different features. Utilizing a dataset comprising different speaker configurations and combinations of features were examined to assess their influence on accuracy and runtime. Results indicate that SVMs with linear, polynomial, and RBF kernels, as well as FFNNs, demonstrate effectiveness in solving the identification problem by providing real-time capabilities. © 2024 ieee.

关键词： Adversarial machine learning

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Comparative Study for Monocular Depth Detection Models on embedded Systems 13

Comparative Study for Monocular Depth Detection Models on Em...

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13th International symposium on Computer Science and Educational technology, ISCSET 2024

作者： Moussa, Abeer Aly Saleh, Shadi Hardt, Wolfram Technische Universität Chemnitz Department of Computer Engineering Chemnitz Germany

ISBN: (纸本)9798350390803

Monocular depth detection plays a pivotal role in numerous computer vision applications, particularly on edge devices such as the NVIDIA Jetson Nano, which pose unique constraints in terms of computational resources. This paper presents a thorough comparative study focusing on the deployment of two leading monocular depth detection models on the Jetson N ano hardware platform, leveraging a pruning algorithm to enhance efficiency. The study investigates the application of pruning algorithms to reduce the model size and computational demands of monocular depth detection models, specifically tailored for the Jetson N ano. Two representative models are selected for the comparative analysis, and their performance is evaluated in terms of accuracy, inference speed, and resource utilization before and after the application of pruning. The hardware-centric analysis explores the implications of pruning on the computational efficiency of the models, emphasizing their suitability for real-time applications on the NVIDIA Jetson Nano. The comparative study provides insights into the trade-offs between model complexity and accuracy, highlighting the impact of pruning on the models 'adaptability to the Jetson N ano' s hardware constraints. By understanding the implications of pruning algorithms on both model performance and hardware efficiency, this research aims to facilitate informed decision-making in selecting and implementing monocular depth detection models for real-world applications in resource-constrained environments. © 2024 ieee.

关键词： Program compilers

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Development of an Electronic Monitoring System for a Savonius Wind Turbine 30

Development of an Electronic Monitoring System for a Savoniu...

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30th ieee International symposium for Design and technology in Electronic Packaging, SIITME 2024

作者： Matusa, Gheorghe-Alexandru Mares, Marius-Cristian Svasta, Paul National University of Science and Technology POLITEHNICA Bucharest Centre for Technological Electronics and Interconnection Technologies Bucharest Romania

ISBN: (纸本)9798331539511

Wind energy stands as a significant renewable resource, particularly in the European Union, where it holds the primary position among renewable energy sources, followed by hydropower and solar photovoltaics [1]. Forecasts indicate that by 2028, wind and solar energy together will constitute a significant 96% share of renewable electricity capacity [2]. This paper investigates the development of an electronic monitoring system for a Savonius vertical-Axis wind turbine (VAWT) designed for low-wind energy areas, based on our previous research [3]. The design and implementation of an embedded electronic monitoring system specifically for this type of wind turbine are discussed. The integration of the wind turbine with the monitoring system enables real-time tracking and analysis of key performance parameters, such as output power, turbine speed, and generator voltage, which are systematically recorded in an online database. This approach not only improves the operational efficiency and reliability of the wind turbine but also offers a scalable solution for remote monitoring and data management in renewable energy applications. © 2024 ieee.

关键词： Windmill

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