The paper presents how the physical phenomenon, corona discharge, can be evaluated using circuits for measuring partial discharges and radio disturbances. The main features and differences between these circuits are p...
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In wireless sensor networks, post-deployment issues persist despite extensive testing, primarily due to unpredictable environmental factors and limited debugging tools for resource-constrained end nodes. This challeng...
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ISBN:
(纸本)9798350369458;9798350369441
In wireless sensor networks, post-deployment issues persist despite extensive testing, primarily due to unpredictable environmental factors and limited debugging tools for resource-constrained end nodes. This challenge is particularly pronounced in remote applications such as extraterrestrial habitats. To address this, we propose a runtime anomaly detection and diagnosis method for resource-constrained sensor nodes. A key advantage of our approach is its ability to learn expected behavior from historical data, eliminating the need for explicit behavior modeling, unlike other runtime fault detection methods. Our method comprises three main components: logging, detection, and diagnosis. We log event traces on the sensor nodes, enabling activity tracking down to the variable level. For anomaly detection, we explore various methods, including state transition, execution interval analysis, and clustering. Subsequently, diagnosis is performed using the logged event traces.
Real-time monitoring plays a vital role in software testing by facilitating the identification and diagnosis of issues during canary deployments or limited-scale testing phases. This approach serves as a method for te...
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ISBN:
(纸本)9798350365054
Real-time monitoring plays a vital role in software testing by facilitating the identification and diagnosis of issues during canary deployments or limited-scale testing phases. This approach serves as a method for testing and issue identification in the deployment phase. Furthermore, efficiently detecting anomalies in software instrumentation data is a crucial aspect of real-time monitoring. However, due to the volatility of software telemetry data, achieving accurate anomaly detection is quite challenging. In this paper, we present an efficient transformer-based framework designed for real-time anomaly detection. This approach called TEAD deeply explores the temporal correlations in time series data to uncover anomalous patterns, ensuring stability in predictions amidst fluctuating data. Empirical validation conducted at a prominent Internet company highlights the effectiveness of TEAD, emphasizing its ability to improve both efficiency and accuracy in real- time monitoring. Despite its significance, the method is constrained by its business-centric nature, warranting further cross-industry dissemination research. Overall, this study provides a novel solution for the real-time monitoring phase of software testing, validating the applicability and effectiveness of transformer- like models in this field.
We propose SLAMFuse, an open-source SLAM benchmarking framework that provides consistent cross-platform environments for evaluating multi-modal SLAM algorithms, along with tools for data fuzzing, failure detection, an...
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ISBN:
(纸本)9798350377712;9798350377705
We propose SLAMFuse, an open-source SLAM benchmarking framework that provides consistent cross-platform environments for evaluating multi-modal SLAM algorithms, along with tools for data fuzzing, failure detection, and diagnosis across different datasets. Our framework introduces a fuzzing mechanism to test the resilience of SLAM algorithms against dataset perturbations. This enables the assessment of pose estimation accuracy under varying conditions and identifies critical perturbation thresholds. SLAMFuse improves diagnostics with failure detection and analysis tools, examining algorithm behaviour against dataset characteristics. SLAMFuse uses Docker to ensure reproducible testing conditions across diverse datasets and systems by streamlining dependency management. Emphasizing the importance of reproducibility and introducing advanced tools for algorithm evaluation and performance diagnosis, our work sets a new precedent for reliable benchmarking of SLAM systems. We provide ready-to-use docker compatible versions of the algorithms and datasets used in the experiments, together with guidelines for integrating and benchmarking new algorithms. Code is available at https://***/nikolaradulov/slamfuse
Test processes are among the top cost contributors to the production of integrated photonics. This is mainly a result of the absence of standardized, high-throughput systems and solutions, and reliance on highly custo...
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ISBN:
(纸本)9798350377330;9798350377323
Test processes are among the top cost contributors to the production of integrated photonics. This is mainly a result of the absence of standardized, high-throughput systems and solutions, and reliance on highly customized and time-demanding approaches that do not scale well with the production volume demands. One of the main challenges of testing photonics is the probing of optical signals. A comprehensive overview of the progress made in scaling up testing and characterization methods, and structured approach to Test-as-a-Service for photonic integrated circuits at PITC and TU/e laboratories is provided, with the focal point lying in the development and improvement of an automated measurement setup, and optimization of the optical alignment routines. A robust data analysis framework enables rapid processing and visualization of extensive datasets generated during automated tests. Furthermore, the measurement data was used together with machine learning techniques to develop a classification scheme for quantifying the optical alignment quality.
The design flow for a radiation-hardened-bydesign (RHBD) cell library includes the verification of the logic cells therein in terms of soft error rate (SER) under (heavy-ion) irradiation. The RHBD cell library is diff...
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ISBN:
(纸本)9798350387186;9798350387179
The design flow for a radiation-hardened-bydesign (RHBD) cell library includes the verification of the logic cells therein in terms of soft error rate (SER) under (heavy-ion) irradiation. The RHBD cell library is different from the standard (non-RHBD) cell library in terms of the need to provide test structures to ascertain its radiation hardness. We propose a novel test structure design strategy. This involves a unique Multi-Error-Lock-Trace (MELT) mechanism, which features the ability to temporarily lock the single-event-transient (SET) or single-event-upset (SEU) error into registers, and thereafter the ability to trace back to the location of the error occurrence. We implement the aforementioned methodology with three test structures in 55nm CMOS node to verify 15 RHBD cells. The proposed strategy is very worthwhile. Specifically, when compared to the reported approach of assigning individual I/O pads for each device-under-test (DUT), our method reduces the number of I/O pads by 80%, translating to >95% test die area reduction. Further, when compared to the other reported approach of multiplexing I/O pads for each DUT, our method reduces the number of test runs at each linear-energy-transfer (LET) level by 80%.
The prevalence of battery-based applications has necessitated rapid improvement in batteries and allied technologies. The Battery Management system is a critical component of the battery energy storage systems (BESS) ...
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ISBN:
(纸本)9798350300246
The prevalence of battery-based applications has necessitated rapid improvement in batteries and allied technologies. The Battery Management system is a critical component of the battery energy storage systems (BESS) which is needed to maintain operational safety as well as improve the reliability and Quality of Service of the system. In this work, we propose a next-generation battery management system for Li-ion batteries consisting of a battery state monitoring unit (BMU), active cell balancing, and fault localization and diagnosis methodology. The Battery monitoring unit estimates the critical battery states with high accuracy and reliability by considering the degradation of cells due to aging. The BMU is supplemented by an energy-efficient flyback converter-based Active Cell Balancing methodology. Finally, a fault diagnosis methodology is proposed using fault localization in the battery pack for identifying the cells which do not perform optimally in the system in real-time. The results have been presented for state monitoring and cell balancing system which shows improved system efficiency, greater accuracy, and reliability compared to the state-of-the-art methodologies.
In the case of a single pressure-gradient vector hydrophone, if the ratio of its diameter to wavelength fails to satisfy the engineering approximation conditions, the direct calculation of the angle through the arc ta...
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ISBN:
(纸本)9798350386288;9798350386271
In the case of a single pressure-gradient vector hydrophone, if the ratio of its diameter to wavelength fails to satisfy the engineering approximation conditions, the direct calculation of the angle through the arc tangent method for directional of arrival (DOA) estimation will result in a significant estimation error. While using the modified sound intensity method to correct its array flow pattern, a higher signal-to-noise ratio is required. This paper presents the upper limit for the diameter-to-wavelength ratio and introduces a refined approach for estimating the direction of arrival (DOA) by employing a mapping function replacement the modified sound intensity method. The proposed method improves the performance of the estimation of DOA based on a single pressure-gradient vector hydrophone, particularly in scenarios with low signal-to-noise ratios. Simultaneously, this method serves to broaden the applicable frequency range for azimuth angle estimation. The simulation findings provide empirical evidence supporting the efficacy and superiority of the proposed algorithm.
Robotic systems are highly complex, and debugging failures in them can prove challenging. We propose a technique for using multivariate decision trees to create human interpretable descriptions of the input conditions...
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ISBN:
(纸本)9798350395716;9798350395709
Robotic systems are highly complex, and debugging failures in them can prove challenging. We propose a technique for using multivariate decision trees to create human interpretable descriptions of the input conditions that cause these failures in robotics systems. This approach uses active learning to efficiently create tests, and uses a multivariate decision tree that captures common boundary conditions in the software fault space. We provide an evaluation of this technique on a small set of faults from several robotics systems, and compare it against a previous technique in this space, Hierarchical Product Set Learning. Our proposed technique requires fewer tests, provides more accurate estimates of the fault conditions, and is more interpretable than the prior approach.
Software testing is essential for the reliable and robust development of complex software systems. This is particularly critical for cyber-physical systems (CPS), which require rigorous testing prior to deployment. Th...
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ISBN:
(纸本)9798350378030;9798350378023
Software testing is essential for the reliable and robust development of complex software systems. This is particularly critical for cyber-physical systems (CPS), which require rigorous testing prior to deployment. The complexity of these systems limits the use of formal verification methods. Furthermore, testing and fault localization can be very costly. To mitigate this cost, we outline in this work a holistic machine-learning-guided test case design and fault localization (MaLT) framework, which leverages recent probabilistic machine learning methods to accelerate the testing of complex software systems. MaLT consists of three steps: (i) the construction of a suite of test cases using a covering array for initial testing, (ii) the investigation of posterior root cause probabilities via a Bayesian fault localization procedure, then (iii) the use of such Bayesian analysis to guide selection of subsequent test cases via active learning. The proposed MaLT framework can thus facilitate efficient identification and subsequent diagnosis of software faults with limited test runs. This framework has potential for integration with an assertion-based test oracle approach, which may prove to be an efficient and cost-effective way of integrating light-weight formal methods with testing.
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