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Investigations on the Use of Ensemble Methods for Specification-Oriented Indirect Test of RF Circuits

JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2020年第2期36卷 189-203页

作者： El Badawi, H. Azais, Florence Bernard, S. Comte, M. Kerzerho, V Lefevre, F. Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Phillips F-14000 Caen France

In order to reduce the costs of industrial testing of analog and Radio Frequency (RF) integrated circuits, a widely studied solution is indirect testing. Indeed, indirect testing is based on learning-machine algorithms to train a regression model that links the space of low-cost indirect measurements to the space of performance parameters guaranteed by datasheets, thus relaxing the constraints on expensive test equipment. This article explores the potential benefit of using ensemble learning in this context. Unlike traditional learning models that use a single model to estimate targeted parameters, ensemble-learning models involve training several individual regression models and combining their outputs to improve the predictive power of the ensemble model. Different ensemble methods based on bagging, boosting or stacking are investigated and compared to classical individual models. Experiments are performed on three RF performances of a LNA for which we have production test data and model quality is discussed in terms of goodness-of-fit, accuracy and reliability. The influence of the training set size is also explored. Finally, the efficiency of classical and ensemble models is compared in the context of a two-tier test flow that permits to tradeoff test cost and test quality.

关键词： Indirect testing RF integrated circuits machine-learning algorithms Ensemble methods Test efficiency

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Evaluation of a Two-Tier Adaptive Indirect Test Flow for a Front-End RF Circuit

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2021年第2期37卷 225-242页

作者： El Badawi, H. Azais, F. Bernard, S. Comte, M. Kerzerho, V. Lefevre, F. Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Phillips F-14000 Caen France

This paper focuses on test cost reduction for RF circuits based on the use of an indirect test strategy. The implementation of a two-tier adaptive test flow is investigated, in which only circuits with a sufficient prediction confidence level are evaluated by the indirect test while others are re-evaluated by specification-based test. A methodology is presented that permits to explore different tradeoffs between test quality and test cost and to make pertinent choices for the efficient implementation of such a test flow. The methodology is applied to a front-end RF circuit designed for WLAN applications and results show that substantial test cost reduction can be achieved without compromising the test quality.

关键词： Indirect test RF integrated circuits machine-learning algorithms Adaptive testing Test efficiency Test confidence

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A machine-learning algorithm for wind gust prediction

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COMPUTERS & GEOSCIENCES 2011年第9期37卷 1337-1344页

作者： Sallis, P. J. Claster, W. Hernandez, S. Auckland Univ Technol Geoinformat Res Ctr Auckland New Zealand Ritsumeiken Asia Pacific Univ Environm Res Lab Beppu Oita Japan Univ Catolica Maule Lab Procesamiento Informac Geospacial Talca Chile

Physical damage to property and crops caused by unanticipated wind gusts is a well understood phenomenon. Predicting its occurrence continues to be a challenge for meteorologists and climatologists. Various approaches to gust occurrence model building have been proposed. The very nature of the event is problematic because of its brief duration following a rapid change of state in wind velocity that immediately precedes it. Events classified as wind gusts have a typical duration of less than 20 s and are often much shorter. The rapidly accelerating wind velocity preceding them is often not apparent until the gust occurs. They come quickly, occur suddenly, and then end as abruptly as they began. Observations of 2000 gust events were made during the research to which this paper refers. These observations indicated a mean interval of 3.2 min between the beginning and end of wind velocity change and a noticeable linear progression in the acceleration pattern. It was also noted that state changes regularly occur, often over only seconds in time. In combination, these factors pose both a sampling and a data interpretation challenge, making reliable prediction difficult. This paper describes some new research undertaken to investigate methods of wind gust measurement and prediction. In particular, a machine-learning approach is taken to determine a satisfactory analytical process and to produce meaningful and useful results. An algorithm for use with real-time climate data collection and analysis is proposed, with a description of its implementation. Real-time data sampling provides input for this study using terrestrial sensor telemetry. Near-ground truth data are recorded independent of geostrophic upper atmosphere conditions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Wind velocity modeling Wind gust prediction machine-learning algorithms Geostatistics

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Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Xie, Yang Li, Jun Tao, Qing Wu, Yonghui Liu, Zide Chen, Youxiang Zeng, Chunyan Nanchang Univ Affiliated Hosp 1 Jiangxi Med Coll Dept GastroenterolDigest Dis Hosp Nanchang Peoples R China

Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the d

关键词： Ulcerative colitis machine-learning algorithms Diagnostic markers Immune cell infiltration

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Spatio-temporal fusion methods for spectral remote sensing: a comprehensive technical review and comparative analysis

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TROPICAL ECOLOGY 2024年第3期65卷 356-375页

作者： Swain, Ratnakar Paul, Ananya Behera, Mukunda Dev Natl Inst Technol NIT Dept Civil Engn Rourkela Odisha India Birla Inst Technol Dept Remote Sensing Ranchi Jharkhand India Indian Inst Technol Kharagpur Dept CORAL Kharagpur W Bengal India

For many years, spectral remote sensing has been essential for research on the Earth's surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion's difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community.

关键词： Data fusion techniques Feature-based fusion machine-learning algorithms Pixel-based fusion Spatio-temporal fusion methods Spectral remote sensing

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machine learning-based edge-computing on a multi-level architecture of WSN and IoT for real-time fall detection

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IET WIRELESS SENSOR SYSTEMS 2020年第6期10卷 320-332页

作者： El Attaoui, Amina Largo, Salma Kaissari, Soufiane Benba, Achraf Jilbab, Abdelilah Bourouhou, Abdennaser Mohammed V Univ ENSET STIS Elect Sensors Syst & Nanobiotechnol Rabat Morocco INPT Rabat STRS Lab Smart Embedded Enterprise & Distributed Syst SEED Rabat Morocco

Health telemonitoring systems are constrained by the computational and data transmission load resulting from the large volumes of various measured signals, e.g. in the fall detection application. Nevertheless, the trend of movement and the implementation of computer intelligence in intelligent devices ensure an intelligent and convenient method for continuous real-time telemonitoring of health conditions. In this paper, fall detection is presented while leveraging edge computing integrated on a multi-level architecture combines the Wireless Sensors Network and the Internet of Things. Particularly, we present a complete study and implementation scenarios while investigating the performances of machine learning algorithms to distinguish between different fall patterns and activities of daily living using a set of significant extracted features from measured acceleration and angular velocity signals. For low computational requirements and to improve the classification performances, the Linear Discriminant Analysis is used to reduce the dimensionality of extracted features. The experimental results assess the performances of the proposed approach in fall detection that show the highest accuracy of 99.92% provided using the KNN classifier and accuracy of 97.5% for fall pattern recognition using the SVM classifier. Also, the online classification on the Fog device reached an accuracy of 94.42% using the SVM classifier.

关键词： geriatrics pattern recognition support vector machines feature extraction pattern classification telemedicine patient monitoring wireless sensor networks learning (artificial intelligence) machine learning-based edge-computing multilevel architecture WSN IoT real-time fall detection health telemonitoring systems computational data transmission burden fall detection application computer intelligence implementation smart devices real-time monitoring health conditions human fall detection leveraging edge wireless sensor network implementation scenarios machine-learning algorithms different fall patterns significant extracted features measured acceleration angular velocity signals low computational requirements online computing classification performances supervised learning fall pattern recognition support vector machine classifier run-time classification Fog device

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Predictive Models of Recombination Rate Variation across the Drosophila melanogaster Genome

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GENOME BIOLOGY AND EVOLUTION 2016年第8期8卷 2597-2612页

作者： Adrian, Andrew B. Corchado, Johnny Cruz Comeron, Josep M. Univ Iowa Dept Biol Iowa City IA 52242 USA Univ Iowa Interdisciplinary Grad Program Genet Iowa City IA 52242 USA

In all eukaryotic species examined, meiotic recombination, and crossovers in particular, occurnon-randomly along chromosomes. The cause for this non-random distribution remains poorly understood but some specific DNA sequence motifs have been shown to be enriched near crossover hotspots in a number of species. We present analyses using machine learning algorithms to investigate whether DNA motif distribution across the genome can be used to predict crossover variation in Drosophila melanogaster, a species without hotspots. Our study exposes a combinatorial non-linear influence of motif presence able to account for a significant fraction of the genome-wide variation in crossover rates at all genomic scales investigated, from 20% at 5-kbto almost 70% at 2,500-kb scale. The models are particularly predictive for regions with the highest and lowest crossover rates and remain highly informative after removing sub-telomeric and -centromeric regions known to have strongly reduced crossover rates. Transcriptional activity during early meiosis and differences in motif use between autosomes and the X chromosome add to the predictive power of the models. Moreover, we show that population-specific differences in crossover rates can be partly explained by differences in motif presence. Our results suggest that crossover distribution in Drosophila is influenced by both meiosis-specific chromatin dynamics and very local constitutive open chromatin associated with DNA motifs that prevent nucleosome stabilization. These findings provide new information on the genetic factors influencing variation in recombination rates and a baseline to study epigenetic mechanisms responsible for plastic recombination as response to different biotic and abiotic conditions and stresses.

关键词： recombination double strand break DNA motif analysis machine-learning algorithms

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MAPCAST: an Adaptive Control Approach using Predictive Analytics for Energy Balance in Micro-Grid Systems

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第2期10卷 945-954页

作者： Elmouatamid, Abdellatif Ouladsine, Radouane Bakhouya, Mohamed El Kamoun, Najib Zine-Dine, Khalid Khaidar, Mohammed Chouaib Doukkali Univ Fac Sci STIC Lab CUR EnR&SIE El Jadida Morocco Int Univ Rabat Coll Engn & Architecture LERMA Lab Sala El Jadida Morocco Mohammed V Univ Fac Sci Rabat Morocco

Control approaches for micro-grid (MG) systems are recently developed for efficient energy management in distributed systems. The aim is to increase the integration of renewable energy sources (RES) in buildings while keeping optimal operational conditions of storage devices. However, the variability and the unpredictable behavior of the power produced by RESs require the use of energy management systems and adaptive control strategies for their seamless integration within the traditional electric grid. In this paper, a model predictive control (MPC) strategy is developed, named MAPCAST, for measuring, analyzing, predicting, and forecasting actions in order to ensure efficient and optimal operation of MG systems. The control strategy is based on machine-learning algorithms to predict main parameter inputs, which are used for forecasting suitable actions. Its main objective is to manage the batteries' charge/discharge (C/D) currents, and consequently, the battery state of charge (SoC), taking into consideration the variable nature of RES generation and loads demand satisfaction. A real data-set was gathered from our actual MG system using an IoT/Big-Data platform, which was deployed to measure the different input control parameters. Simulation results are presented to show the utility of the proposed control strategy for efficient operation and optimal energy balance in MG systems.

关键词： Micro-grid systems Energy management system IoT and Big-Data platform machine-learning algorithms Model predictive control

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Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems

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MOLECULAR ECOLOGY 2023年第22期32卷 5944-5958页

作者： Cambon, Marine C. Trillat, Marine Lesur-Kupin, Isabelle Burlett, Regis Chancerel, Emilie Guichoux, Erwan Piouceau, Lucie Castagneyrol, Bastien Le Provost, Gregoire Robin, Stephane Ritter, Yves Van Halder, Inge Delzon, Sylvain Bohan, David A. Vacher, Corinne Univ Bordeaux INRAE BIOGECO Pessac France Bangor Univ Sch Nat Sci Bangor Wales HelixVenture Merignac France Sorbonne Univ CNRS LPSM Paris France Univ Bourgogne Franche Comte Agroecol INRAE Dijon France Bangor Univ Bangor Wales

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

关键词： environmental DNA data machine-learning algorithms molecular biomarkers next-generation biomonitoring phyllosphere microbial communities water stress

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Predicting the habitat suitability of the invasive white mango scale, Aulacaspis tubercularis;Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables

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PEST MANAGEMENT SCIENCE 2022年第10期78卷 4114-4126页

作者： Azrag, Abdelmutalab Ga Mohamed, Samira A. Ndlela, Shepard Ekesi, Sunday Int Ctr Insect Physiol & Ecol ICIPE POB 30772 Nairobi 00100 Kenya Univ Gezira Fac Agr Sci Dept Crop Protect Wad Madani Sudan

BACKGROUND The white mango scale, Aulacaspis tubercularis (Hemiptera: Diaspididae), is an invasive pest that threatens the production of several crops of commercial value including mango. Though it is an important pest, little is known about its biology and ecology. Specifically, information on habitat suitability of A. tubercularis occurrence and potential distribution under climate change is largely unknown. In this study, we used four ecological niche models, namely maximum entropy, random forest, generalized additive models, and classification and regression trees to predict the habitat suitability of A. tubercularis under current and future [representative concentration pathways (RCPs): RCP4.5 and RCP8.5 of the year 2070] climatic scenarios, using bioclimatic variables. Models' performance was evaluated using the true skill statistic (TSS), the area under the curve (AUC), correlation (COR), and the deviance. RESULTS All models sufficiently predicted the occurrence of A. tubercularis with high accuracy (AUC >= 0.93, TSS >= 0.81 and COR >= 0.77). The random forest algorithm had the highest accuracy among the four models (AUC = 0.99, TSS = 0.93, COR = 0.90, deviance = 0.26). Temperature seasonality (Bio4), mean temperature of the driest quarter (Bio9), and precipitation seasonality (Bio15) were the most important variables influencing A. tubercularis occurrence. Models' predictions showed that countries in east, south, and west Africa are highly suitable for A. tubercularis establishment under current conditions. Similarly, Mexico, Brazil, India, Myanmar, Bangladesh, Thailand, Laos, Vietnam, and Cambodia are also highly suitable for the pest to thrive. Under future conditions, the suitable areas might slightly decrease in many countries of sub-Saharan Africa under both RCPs. However, the range of expansion of A. tubercularis is projected to be higher in Australia, Brazil, Spain, Italy, and Portugal under the future climatic scenarios. CONCLUSION The results report

关键词： Mangifera indica L scale insects ecological niche modelling climate change habitat suitability machine-learning algorithms

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