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A Miniature Millimeter-Wave Radar Based Contactless Lithium Polymer Battery Capacity Sensing with Edge Artificial Intelligence

A Miniature Millimeter-Wave Radar Based Contactless Lithium ...

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IEEE/ASME international conference on Mechtronic and embedded systems and applications, MESA

作者： Di An YangQuan Chen Electrical Engineering & Computer Science University of California Merced Department of Mechanical Engineering University of California Merced

ISBN: (纸本)9781665455718

It is widely known that the remaining capacity of any lithium polymer (Li-Po) rechargeable battery is hard to know precisely in real time. Battery management systems (BMS) are used to precisely monitor battery health including state of charge (SOC) and the remaining capacity. But, BMS is usually limited by its size, power consumption, and compatibility, which could potentially have a negative impact on the battery powered mission such as long distance drone flights. therefore, in this study, we present a new approach for (Li-Po) battery capacity sensing using a miniature millimeter Wave radar array in real-time. We assessed our contactless battery capacity sensing method with a classifier algorithm using labeled data collected from real battery discharging load circuit experiments. According to the results, our technique achieved 98.8% classification accuracy across eight different battery capacity levels. the machine learning algorithm is computationally light and easily implementable on edge computing platforms such as the Raspberry Pi. this work confirms that it is feasible to sense the real-time remaining capacity of Li-Po batteries that can lead to a capacity-aware cognitive battery management system.

关键词： Machine learning algorithms Millimeter wave radar Lithium real-time systems Batteries Sensors State of charge

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Identification and Validation of Markov Models with Continuous Emission Distributions for Execution times 26

Identification and Validation of Markov Models with Continuo...

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IEEE 26th international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Friebe, Anna Papadopoulos, Alessandro, V Nolte, thomas Malardalen Univ Vasteras Sweden

ISBN: (纸本)9781728144030

It has been shown that in some robotic applications, where the execution times cannot be assumed to be independent and identically distributed, a Markov Chain with discrete emission distributions can be an appropriate model. In this paper we investigate whether execution times can be modeled as a Markov Chain with continuous Gaussian emission distributions. the main advantage of this approach is that the concept of distance is naturally incorporated. We propose a framework based on Hidden Markov Model (HMM) methods that 1) identifies the number of states in the Markov Model from observations and fits the Markov Model to observations, and 2) validates the proposed model with respect to observations. Specifically, we apply a tree-based cross-validation approach to automatically find a suitable number of states in the Markov model. the estimated models are validated against observations, using a data consistency approach based on log likelihood distributions under the proposed model. the framework is evaluated using two test cases executed on a Raspberry Pi Model 3B+ single-board computer running Arch Linux ARM patched with PREEMPT RT. the first is a simple test program where execution times intentionally vary according to a Markov model, and the second is a video decompression using the ffmpeg program. the results show that in these cases the framework identifies Markov Chains with Gaussian emission distributions that are valid models with respect to the observations.

关键词： real-time systems Markov Chain Model Probabilistic Timing Analysis

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Performance Analysis of Solar-Grid Interfaced EV Charging Infrastructure Using STF Controller

Performance Analysis of Solar-Grid Interfaced EV Charging In...

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IEEE Power India conference

作者： Ravi Ranjan Chaudhary Ankita Arora Sikandar Ali Khan Department of Electrical Engineering Delhi Technological University Delhi India

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

In this work, we investigate a new unique way for charging batteries from renewable energy systems and grids with extra power, denoted as excess power. this technique aims to optimise battery charging/discharging and reduce dependent on the power grid. the essence of this technique revolves around self-tuning filters (STF) that offer undistorted fundamental value load current. Using this strategy, excess power charges the battery in an efficient manner. there were probable advantages of this sort of setups (i) decreased dependency on the central grid (ii) increased exploitation of renewable energy sources (iii)) Longer battery life is accomplished by using optimal charging strategies. the report continues by outlining probable possibilities for future research, such as evaluating the integration of this system with energy management platforms and testing its scalability for more large applications.

关键词： Renewable energy sources Total harmonic distortion Filters Scalability Power system management real-time systems Batteries Tuning Power harmonic filters Testing

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Discrete Fourier Transform-Based Point Cloud Compression for Efficient SLAM in Featureless Terrain

Discrete Fourier Transform-Based Point Cloud Compression for...

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international conference on Autonomous Robots and Agents, ICARA

作者： Riku Suzuki Ayumi Umemura Shreya Santra Kentaro Uno Kazuya Yoshida Department of Aerospace Engineering Space Robotics Lab. (SRL) Tohoku University Sendai Japan

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Simultaneous Localization and Mapping (SLAM) is an essential technology for the efficiency and reliability of unmanned robotic exploration missions. While the onboard computational capability and communication bandwidth are critically limited, the point cloud data handled by SLAM is large in size, attracting attention to data compression methods. To address such a problem, in this paper, we propose a new method for compressing point cloud maps by exploiting the Discrete Fourier Transform (DFT). the proposed technique converts the Digital Elevation Model (DEM) to the frequency-domain 2D image and omits its high-frequency components, focusing on the exploration of gradual terrains such as planets and deserts. Unlike terrains with detailed structures such as artificial environments, high-frequency components contribute little to the representation of gradual terrains. thus, this method is effective in compressing data size without significant degradation of the point cloud. We evaluated the method in terms of compression rate and accuracy using camera sequences of two terrains with different elevation profiles.

关键词： Point cloud compression Simultaneous localization and mapping Image coding Discrete Fourier transforms Moon Focusing real-time systems Deserts Reliability Mobile robots

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Cloud computing Methods Based on IoT for Better Patient Data Planning: A Research

Cloud Computing Methods Based on IoT for Better Patient Data...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Arvind Kumar Pandey Sumitra Maneria Department of Computer Science ARKA JAIN University Jamshedpur Jharkhand India Department of CSE PIT Parul University Vadodara Gujarat India

ISBN: (纸本)9781665487351

Clinics all across the world are implementing more recent, extremely sophisticated technologies. Information and communication technology improvements are to blame for the emergence of the Internet of things (IoT). In recent years, new opportunities for data collection and wiser judgments have emerged with the integration of the Internet of things with patient-accessible medical devices, as well as with patients and their surroundings. IoT technologies aid patients and doctors in today's healthcare environment since they are applied in a range of medical sectors (like real-time monitoring, patient information and healthcare management). In the future of IoT for healthcare, various and distributed devices will collect, analyses, and transmit real-time medical data to open clouds, enabling the accumulation, piling, and analysis of crucial data streams in numerous new procedures as well as the activation of context-dependent intimations. IoT paradigm can advance the current development of traditional healthcare model toward engaged medicine by using sensors (environmental, wearable, and implanted) dispersed across local surroundings with the purpose of monitoring patient's health and launching remotely care. the development of new technology for storing vast amounts of clinical data has become increasingly important in recent years. the management of the massive amount of cloud storage is done by fusing edge computing with the Internet of things. Recent studies have shown that the cloud and IoT integration significantly affect remote healthcare. this survey study focuses on the various applications of edge computing IoT devices to improve people's health while ensuring the accuracy of medications and medication delivery. Healthcare practitioners, researchers, and others interested in learning more about IoT with edge computing-based health care applications will be able to use the research's goal as a source and reference for information about smart healthcare.

关键词： Cloud computing Data privacy Smart healthcare Data integration Distributed databases Medical services real-time systems

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AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity 26

AutoSec: Multidimensional Timing-Based Anomaly Detection for...

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IEEE 26th international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Tepic, Milan Abdelaal, Mohamed Weber, Marc Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Vector Informat GmbH Stuttgart Germany

ISBN: (纸本)9781728144030

Nowadays, autonomous driving and driver assistance applications are being developed at an accelerated pace. this rapid growth is primarily driven by the potential of such smart applications to significantly improve safety on public roads and offer new possibilities for modern transportation concepts. Such indispensable applications typically require wireless connectivity between the vehicles and their surroundings, i.e. roadside infrastructure and cloud services. Nevertheless, such connectivity to external networks exposes the internal systems of individual vehicles to threats from remotely-launched attacks. In this realm, it is highly crucial to identify any misbehavior of the software components which might occur owing to either these threats or even software/hardware malfunctioning. In this paper, we introduce AutoSec, a host-based anomaly detection algorithm which relies on observing four timing parameters of the executed software components to accurately detect malicious behavior on the operating system level. To this end, AutoSec formulates the task of detecting anomalistic executions as a clustering problem. Specifically, AutoSec devises a hybrid clustering algorithm for grouping a set of collected timing traces resulted from executing the legitimate code. During the runtime, AutoSec simply classifies a certain execution as an anomaly, if its timing parameters are distant enough from the boundaries of the predefined clusters. To show the effectiveness of AutoSec, we collected timing traces from a testbed composed of a set of real and virtual control units communicating over a CAN bus. We show that using our proposed AutoSec, compared to baseline methods, we can identify up to 21% less false positives and 18% less false negatives.

关键词： Anomaly Detection real-time Operating systems Timing Constraints DBSCAN Classifiers

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applications of Large Language Models in Industrial Internet of things Education

Applications of Large Language Models in Industrial Internet...

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international conference on Web Research (ICWR)

作者： Mehdi Imani Ali Imanifard Babak Majidi Abdolah Shamisa Department of Computer Engineering Khatam University Tehran Iran Department of Electrical Engineering Khatam University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

this paper explores the potential of Large Language Models (LLMs) to enhance educational experiences in the Industrial Internet of things (IIoT), focusing on the domain of smart steel manufacturing. We address the critical shortage of authentic industrial datasets for academic research by presenting an industry-validated IIoT educational dataset derived from three months of operational data from a steel plant, enriched with domain-specific annotations. We propose a comprehensive IIoT framework specifically designed for smart steel manufacturing, integrating LLMs with IIoT infrastructure. this novel architecture unifies real-time process monitoring, predictive analytics, and adaptive learning, simulating a smart factory ecosystem. It facilitates humanmachine collaboration and replicates quality control workflows, thereby bridging the gap between educational theory and industrial practice. A key component of our framework is an interactive decision-making dashboard powered by LLMs. this AI-driven tool contextualizes IIoT data, offering dynamic visualizations of critical parameters such as metallization levels and H2/CO ratios. It further enables scenario-based simulations for energy optimization in a risk-free environment, empowering learners to experiment and understand complex industrial processes. Our study demonstrates the transformative potential of LLMs in HoT education, offering a practical and engaging learning environment that directly aligns with industrial realities.

关键词： Adaptive learning Metallization Large language models real-time systems Steel Predictive analytics Vehicle dynamics Optimization Industrial Internet of things Smart manufacturing

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Microcontroller-Based Low Latency Audio System to Study Cortical Auditory Evoked Potentials: applications with Intraoperative Language Mapping

Microcontroller-Based Low Latency Audio System to Study Cort...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Israt Tasnim Priscella Asman Chandra Prakash Swamy Sudhakar Tummala Sujit Prabhu Nuri Firat Ince Department of Biomedical Engineering University of Houston Houston TX USA Department of Neuro-Oncology UT MD Anderson Cancer Center Houston TX USA

Language processing in the brain comprises complex neurophysiology involving multiple regions and multimodal functions like cognition, memory, speech, etc. Recent studies show language processing is not restricted within anatomically established cortical language regions, and there is considerable inter-person variability. Clinically intraoperative functional language mapping is critical for studying patient-specific language processing and identifying the eloquent cortex to retain language functionalities during resection surgeries. Cortical auditory evoked potential (CAEP) induced by different auditory stimuli can provide a profound understanding of language processing. In this study, we present a low-cost, low-latency microcontroller-based audio system to induce and analyze CAEP. the system is established using off the shelf components; a 32bit Teensy (3.2) microcontroller, Teensy (3) audio shield etc. which makes it easily replicable. the Teensy can generate digital trigger locked to auditory stimulus onset to interface the system with biosignal amplifiers to record and study neural data simultaneously. the presented hardware tools enable the analysis of neural signals aligned with the audio stimuli with sub-millisecond temporal precision. Implementation of the system can provide a flexible platform to observe and analyze real-time cortical CAEP in the clinical settings. the system also provides great customization opportunities to study not only CAEP but also complex language processing tasks.

关键词： Audio systems Source coding Surgery Neural engineering real-time systems Hardware Low latency communication

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Smart City Transition Pillars with Layered applications Architecture 18

Smart City Transition Pillars with Layered Applications Arch...

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18th international conference on Mobile systems and Pervasive computing, MobiSPC 2021, the 16th international conference on Future Networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information Technology, SEIT 2021

作者： Embarak, Ossama Higher Colleges of Technology Dept. of Computer Sciences Abu Dhabi United Arab Emirates

Smart cities provide smart services that rely on flexible infrastructure that responds to challenges posed by the rapid growth of urbanization. Nonetheless, the smart city concept is still emerging and not mainstreamed worldwide due to technological, economic, and governance obstacles. this paper reveals the essence of smart cities. It demonstrated the requirements for implementations and discussed four smart applications considering the required input data and produced services. We proposed and illustrate smart city main pillars: transition entities (Governmental Entities, Private Sector Investors, and Ad-Hoc Research Units), list of policies standards and protocols (Holistic, Flexible, Applicable, Sustainable, Integration, Highly Obliged), the components that should govern by regulations. the paper also demonstrated the proposed smart city architecture layers, including the infrastructure layer, Application Layer, Services Layer, the big data clouds, and the end-users. Moreover, it provides a pure exploration of expected challenges and responsibilities for real breakthroughs in smart city implementations. © 2021 Elsevier B.V.. All rights reserved.

关键词： Smart city

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Advanced Skin Disease Classification with VGG19, YOLOv3, and MobileNet

Advanced Skin Disease Classification with VGG19, YOLOv3, and...

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international conference on computing and Networking Technology (ICCNT)

作者： Himani Bhatia Satvik Vats Rishabh Sharma Lovely Professional University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Skin diseases are classified into various types hence proper classification of the disease is very important because its determination of the type of treatment to offer the patients will highly determine the results of the patients. this research paper presents an advanced approach to skin disease classification by employing three state-of-the-art deep learning models: VGG19 – a convolutional model that is 19 layers deep, YOLOv3 – an improved version of the YOLO algorithm, MobileNet – a lightweight neural network designed for mobile devices. the performance of these models will be assessed and compared to identify the most effective way of categorizing numerous skin diseases using a large data set. ResNet and VGG19 are employed based on their resilience in feature extraction, and despite the model’s complexity, it is rather accurate. When it comes to the detection of skin abnormalities, object localization is critical, for which YOLOv3 is used because of its high accuracy. MobileNet is evaluated for its applicability in real-time classification for mobile and integrated embedded device applications. the dataset is composed of skin lesion images and has high resolutions and dermatoscopic images, which means that the models had a diverse set of skin conditions on which to train. Metrics including accuracy, precision, recall, and F1-score are used to assess and rank the efficiency of one specific model with another. In highlighting the strengths and weaknesses of each model, the findings help in informing studies applying such frameworks in real-world practice contexts. this present comparative study intends to Provide an Improved understanding in the field of dermatology With Better Machine Learning Algorithms to Automate skin Disease Identification, with an impressive patient care impact.

关键词： YOLO Deep learning Accuracy Machine learning algorithms Computational modeling Skin real-time systems Lesions Diseases Load modeling

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