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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Precision Fruit Harvesting: Stem Detection and Cutting Techniques using Image Processing for Robotic systems 6

Precision Fruit Harvesting: Stem Detection and Cutting Techn...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Shankarpure, Madhura Rajesh Patil, Dipti D. STE’s Smt Kashibai Navale College of Engineering Savitribai Phule Pune University Maharashtra Pune India MKSSS’s Cummins College of Engineering for Women Savitribai Phule Pune University Maharashtra Pune India

ISBN: (纸本)9798331522667

To enhance robotic fruit harvesting systems, the proposed methodology uses image processing techniques to recognize orange stems. The method highlights only the stem of an orange fruit in an image by utilizing gray-scale conversion, contour-based filtering, Gaussian blur, and Canny edge recognition. The technique can be incorporated into agricultural robots to improve fruit harvesting accuracy. After detecting the fruit stem, it is crucial to ensure precise cutting of the fruit to prevent damage to both the fruit and the tree. The paper also assesses the performance of the method in terms of robustness in complicated situations and detection accuracy, demonstrating its potential for real-world agricultural applications. © 2025 ieee.

关键词： Citrus fruits

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A Workflow for Automated Testing of a Safety-Critical embedded Subsystem 18

A Workflow for Automated Testing of a Safety-Critical Embedd...

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18th ieee international conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Ignarra, Michele Guarino, Maria Aiello, Andrea Tonziello, Vincenzo De Donato, Giovanni Pascale, Emanuele De Guglielmo, Renato Costa, Antonio Affuso, Cosimo Hitachi Rail Naples Italy Alten Naples Italy Hitachi Rail Genoa Italy

ISBN: (纸本)9798331534677

As embedded systems in safety-critical domains, such as transportation, become increasingly complex, ensuring their reliability thorough testing becomes essential. Manual testing methods are often time-consuming, error-prone, and inadequate to cover all possible failure scenarios, especially in systems that require a high level of functional safety and robustness. To address these challenges, the need for automation in the testing process is evident, particularly for real-time, embedded, and hardware-dependent systems where validation is crucial for ensuring safe operation. This paper proposes a structured workflow for automating the testing of a safety-critical embedded subsystem, utilizing a real hardware-in-the-loop (HIL) environment. The approach covers all major phases of the testing process, including test specification, execution, and results assessment. The inspiring idea is to try to shift the focus of intellectual effort toward the early stages of the validation process, facilitating a clear and shared understanding between testers and developers regarding the system's behavioral and functional requirements. The first phase involves creating a detailed test specification, which includes: developing a behavioral model of the feature to be validated;defining data probes for monitoring the subsystem under test;creating simulation scenarios to provide stimuli to the subsystem's external interfaces. In the second phase, executable test scripts are generated either automatically or semi-automatically using multi-technology scripting languages, followed by the creation of a sequence of test cases. The final phase involves executing the tests on the actual hardware platform, collecting execution logs, and running automated checks to evaluate the results. The workflow and tools are evaluated using a case study of the onboard subsystem hardware and software platform developed by Hitachi Rail. © 2025 ieee.

关键词： Software testing

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 ieee.

关键词： Predictive maintenance

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Energy-Efficient Stochastic computing Architectures for Neural Network Performance in FPGA systems 6

Energy-Efficient Stochastic Computing Architectures for Neur...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Dinesh Babu, A. Gomathy, C. Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9798331522667

This paper presents an energy-efficient stochastic computing (SC) architecture tailored for neural network applications on FPGA platforms. The proposed architecture integrates a Bayesian neural network (BNN) computation unit with a novel hybrid stochastic number generator (SNG) that combines Linear Feedback Shift Registers (LFSRs) and Halton sequences. This hybrid approach enhances the quality of generated bit-streams, significantly reducing correlation and improving the accuracy of SC-based computations. Stochastic computing allows complex arithmetic operations to be performed using simple logical gates, reducing hardware complexity and power consumption. Bayesian neural networks, due to their probabilistic framework, align well with the random nature of SC, allowing for resilient and efficient computations in resource-constrained environments. Experimental evaluations were conducted on an FPGA, measuring power consumption, computational accuracy, and resource utilization across varying bit-stream lengths. Results indicate that the proposed architecture achieves approximately 25% lower power consumption compared to conventional SC-based neural network implementations, with an improvement in mean squared error (MSE) accuracy. This energy efficiency and accuracy make the architecture particularly suitable for embedded systems, IoT devices, and other applications requiring low-power, high-performance neural network solutions. The proposed design offers a scalable solution for future neural network implementations in energy-constrained environments, contributing to advancements in sustainable hardware design. © 2025 ieee.

关键词： Mean square error

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Channel Adaptive Decoding in Underwater Optical Camera Communication systems 5

Channel Adaptive Decoding in Underwater Optical Camera Commu...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Aberathna, Thimuthu Kumarage, Miyuni Atthanayake, Nuwan Sri Lanka Technology Campus Faculty of Engineering Padukka Sri Lanka

ISBN: (纸本)9798331530983

Underwater optical camera communication (UOCC) systems that use light-emitting diodes as transmitters and high-frame rate cameras as receivers offer robust and low-cost wireless communication solutions for underwater applications compared to traditional methods. However, its performance is greatly affected by the varying underwater environments such as turbulence, salinity, and oceanic currents. This is mainly due to fixed image processing and data decoding algorithms used in UOCC systems don't provide adaptive methods that change with the underwater environments. To this end, we introduce an adaptive threshold-based algorithm to enhance the system's performance by dynamically adjusting the threshold values used in the decoding algorithm on real-time channel *** this context, adaptive thresholding refers to selecting optimal threshold values for various transmission scenarios, ensuring robust data decoding tailored to specific environmental challenges. This paper presents a comprehensive analysis of the presented algorithm under four distinct channel conditions, such as clear water, muddy water, air bubbles, and chlorophyll water. Our simulations and experiments demonstrate the effectiveness of the adaptive threshold-based algorithm in improving bit error rate (BER), making UOCC more reliable and robust in diverse underwater conditions for short-range applications. The BER performance of the presented algorithm in clear water is nearly zero, while under the other three conditions, it is approximately 1 × 10-3 within a 1-meter communication distance. © 2025 ieee.

关键词： Signal systems

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Designing Energy-Efficient embedded systems with CNNs for real-time Control in Autonomous Robotics 3

Designing Energy-Efficient Embedded Systems with CNNs for Re...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Sanjay Sharma, Priti Kalinga University Department of Cs & It Raipur India

ISBN: (纸本)9798331542375

This research proposes an energy-aware paradigm for the implementation of deep learning using CNN for real-time control in autonomous robotics for embedded systems. The overall power efficiency of the proposed system is achieved through efficient control of power consumption, latency, and runtime while has minor degradation in terms of accuracy through a number of approaches including model pruning, quantization, and adaptive CNN scaling. Studies show that power consumption has been cut down to as low as 47.3% thereby making the optimized system's power consumption in obstacle avoidance tasks as low as 2.8 W, the base system consuming up to 5.0 W power The delay was also lowered to 47.1% in key tasks from 20 ms, thus ensuring more immediate decisions in real time activities. Also, the system up time was enhanced by 40% to perform operations including object identification and sensors integration. Accuracy stayed over 92% in all tasks;the fully optimized CNN design was 0.6% less accurate than an ideal 95.0%, with a 92.4% score. These results endorse that much energy savings and performance improvement are realizable whilst incurring moderate costs on the precision of RTAS, which makes this approach perfect for battery operated, constraint embedded systems in 'smart' robotics. © 2025 ieee.

关键词： Energy utilization

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A Multi-Level Parallel Algorithm for Detection of Single Scatterers in SAR Tomography 33

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Russo, Massimiliano Nisar, Mehwish Pauciullo, Antonio Imperatore, Pasquale Lapegna, Marco Romano, Diego University of Naples Federico II Dept. of Mathematics and Applications Naples Italy National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (纸本)9798331524937

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth's surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring. © 2025 ieee.

关键词： Light scattering

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TrustMesh: A Blockchain-Enabled Trusted Distributed computing Framework for Open Heterogeneous IoT Environments 22

TrustMesh: A Blockchain-Enabled Trusted Distributed Computin...

引用

22nd ieee international conference on Software Architecture, ICSA 2025

作者： Rangwala, Murtaza Buyya, Rajkumar Laboratory School of Computing and Information Systems Australia

ISBN: (纸本)9798331520908

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trust-worthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these challenges through a unique three-layer architecture combining permissioned blockchain technology with a novel multi-phase Practical Byzantine Fault Tolerance (PBFT) consensus protocol. The key innovation lies in TrustMesh's ability to support non-deterministic scheduling algorithms while maintaining Byzantine fault tolerance, features traditionally considered mutually exclusive in blockchain systems. The framework supports a sophisticated resource management approach that enables flexible scheduling decisions while preserving the security guarantees of blockchain-based verification. Our experimental evaluation using a real-world cold chain monitoring scenario demonstrates that TrustMesh successfully maintains Byzantine fault tolerance with fault detection latencies under 150 milliseconds, while maintaining consistent framework overhead across varying computational workloads even with network scaling. These results establish TrustMesh's effectiveness in balancing security, performance, and flexibility requirements in trustless IoT environments, advancing the state-of-the-art in secure distributed computing frameworks. © 2025 ieee.

关键词： Edge computing

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Cyber-Physical systems: Design Standards, applications, Limitations, Challenges, and Future Perspectives 5th

Cyber-Physical Systems: Design Standards, Applications, Limi...

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5th EAI international conference on Cognitive computing and Cyber Physical systems, IC4S 2024

作者： Palaniappan, Rajkumar Department of Mechatronics Engineering College of Engineering University of Technology Bahrain Salmabad Bahrain

ISBN: (纸本)9783031770777

A revolutionary technology that merges the real and virtual worlds is called a "cyber-physical system" (CPS). This article provides a comprehensive examination of CPS with an emphasis on its design principles, uses, constraints, difficulties, and potential. We examine the foundational ideas behind CPS design, talk about current design guidelines, and emphasize the value of these guidelines in guaranteeing safe and dependable operation. Additionally, we look at several fields where CPS is used and evaluate the restrictions and difficulties deployments of CPS encounter. Our discussion of probable future advancements and research trajectories in the area of cyber-physical systems concludes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： embedded systems

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CNN Approach for Live Speech Emotion Detection 3

CNN Approach for Live Speech Emotion Detection

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Subramani, Nevedha Sivasankar, Srividhya Saravanan, K. Sakthivel, K. K. S. Rangasamy College of Technology Tiruchengode India Computer Science and Business Systems K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798331509675

The application of convolutional neural networks (CNNs) in real-time speech recognition in real-time speech improves the real-time processing capability and the ability to accurately and effectively control speech in many places. CNNs are useful for this task because they can learn complex features from noise sources that represent the frequency and amplitude of sounds over time. Using multiple convolutional techniques, these networks can capture spatial and temporal patterns in audio data, making them very good at distinguishing loud speech. This approach is particularly useful for applications in smart assistants, fast change services, and hearing aids, where immediate detection and response are important. The main advances include the development of well-designed models and training methods to increase model accuracy and speed. These models can be further developed to recognize specific sounds, tones, or words to provide personalized experiences for users. As commercial performance improves, CNNs for instant speech recognition have become easier to use, paving the way for widespread use of equipment, security, and technology among humans. © 2025 ieee.

关键词： Convolutional neural networks

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