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Instrumentation Amplifier Input Impedance Calibration With machine learning-Based Optimizations

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第2期72卷 394-398页

作者： Abdelfattah, Safaa Hussein, Hussein M. E. Shrivastava, Aatmesh Onabajo, Marvin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.

关键词： Impedance Optimization Genetic algorithms Capacitors Calibration Capacitance machine learning algorithms Cables Particle swarm optimization Impedance measurement Input impedance calibration instrumentation amplifier genetic algorithm particle swarm optimization algorithm negative capacitance generation feedback (NCGFB)

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UAV Swarm Cooperative Target Search: A Multi-Agent Reinforcement learning Approach

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 568-578页

作者： Hou, Yukai Zhao, Jin Zhang, Rongqing Cheng, Xiang Yang, Liuqing Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China Peking Univ Sch Elect Beijing 100871 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Internet Things Thrust & Intelligent Transporta T Guangzhou 510000 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

The development of machine learning and artificial intelligence algorithms, as well as the progress of unmanned aerial vehicle swarm technology, has significantly enhanced the intelligence and autonomy of unmanned aerial vehicles in search missions, resulting in greater efficiency when searching unknown areas. However, as search scenarios become more complex, the existing unmanned aerial vehicle swarm search method lacks scalability and efficient cooperation. Furthermore, due to the increasing scale of search scenarios, the accuracy and real-time performance of global information are difficult to ensure, necessitating the provision of local information. This paper focuses on the large-scale search scenario and split it to provide both local and global information for running unmanned aerial vehicle swarm search algorithms. Since the search environment is often unknown, dynamic, and complex, it requires adaptive decision-making in a constantly changing environment, which is suitable for modeling as a Markov decision process. Considering the sequential-based scenario, we propose a distributed collaborative search method based on a multi-agent reinforcement learning algorithm, which can operate efficiently in complex and large-scale scenarios. Additionally, the proposed method can utilize a convolutional neural network to process high-dimensional map data with almost no loss of the structure information. Experimental results demonstrate that the proposed method can collaboratively search unknown areas, avoid collisions and repetitions, and find all targets faster compared with the benchmarks.

关键词： Autonomous aerial vehicles Task analysis Search problems Collaboration Scalability Real-time systems machine learning algorithms Unmanned aerial vehicles multi-agent reinforcement learning distributed search algorithm Markov decision process

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Climate warming effects in stratified reservoirs: Thorough assessment for opportunities and limits of machine learning techniques versus process-based models in thermal structure projections

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JOURNAL OF CLEANER PRODUCTION 2024年 454卷

作者： Mi, Chenxi Tilahun, Ammanuel B. Floerke, Martina Duerr, Hans H. Rinke, Karsten Shenyang Agr Univ Coll Water Conservancy Shenyang Peoples R China Helmholtz Ctr Environm Res Dept Lake Res Magdeburg Germany Ruhr Univ Bochum Fac Civil & Environm Engn Bochum Germany

It is nowadays a hot topic to apply machine learning (ML) algorithms to illustrate water temperature dynamics in lentic waters. Due to the limited amount of in-situ temperature measurements from traditional sampling programmes, most of the related studies, however, restricted their analysis within the surface and rarely checked the results for whole depth profiles. Moreover, capability of such methods in projecting thermal dynamics under future climate conditions is even less illustrated. To fill the gap, we collected an unparalleled huge database including 9 million water temperature measurements, from 2013 to 2022, in Rappbode Reservoir, Germany as well as the corresponding climatic and hydrological observations, to train three commonly used ML models (Random Forest, XGBoost, and Long Short-Term Memory) and comprehensively evaluate their performance in reproducing thremal structure within the whole water column. We also systemically compared such simulation results with a well-established process-driven model (CE-QUAL-W2). Going beyond a pure reproduction of observatiosn, we further evaluated the ability of CE-QUAL-W2 and ML models in projecting thermal dynamics under RCP8.5 climate scenario up to 2100. Our results suggested three ML methods yielded high accuracy in capturing water temperature dynamics from the surface (epilimnion) to bottom (hypolimnion), and also satisfactorily reproduced temporal development of the stratification pattern, with the results corresponding well with those from CE-QUAL-W2. By contrast, the projections by ML models were rather insensitive to future climate warming under RCP8.5 comparing with those by CE-QUAL-W2, pointing to an important risk whenever MLbased models are extrapolated beyond their training data range. Supported by millions of in situ measurements, our research clearly illustrated the opportunities (limits) of ML models in projecting thermal dynamics in deep waters, and the conclusion also provides key guidance for scien

关键词： machine learning algorithms CE-QUAL-W2 Deep-water temperature Stratification structure Future climate warming Hybrid models

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A Refined machine learning Method for Coastal Bathymetry Retrieval Using Minimum Distance From Coastline and Geographical Features

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 20012-20025页

作者： Zhu, Weidong Cao, Tiantian Luan, Kuifeng Liu, Shuai Liu, Zitao Xu, Yuelin Huang, Yanying Shanghai Ocean Univ Sch Oceanog & Ecol Sci Shanghai 201306 Peoples R China Shanghai Engn Res Ctr Estuarine & Oceano g Mapping Shanghai 201306 Peoples R China

Bathymetry is a critical marine parameter for applications, such as coral reef monitoring, erosion forecasting, coastal defense, and other related efforts. The predominant method for obtaining water depth data has been through satellite remote sensing. However, the utilization of multispectral processing in deeper and more complex benthic substrate environments poses significant challenges due to reduced light penetration at greater depths and the influence of benthic substrate on light reflection and absorption. In response to these challenges, a novel inversion method based on machine learning called the minimum distance from the coastline and geographical position-based machine learning bathymetry inversion model (MDCGP-ML) has been introduced in this research. The MDCGP-ML model leverages the spectral data from multispectral remote sensing images, alongside MDCGP features, with 70% of bathymetric data from the South China Sea region used for model training. The machine learning algorithms employed include random forest, support vector regression, backpropagation (BP) neural network, extreme gradient boosting, categorical boosting, and light gradient boosting machine (LightGBM). The 30% of in situ was utilized for validating model accuracy. Experimental results using WorldView-2 imagery indicate that the MDCGP-ML model delivers strong performance [root mean square error: 0.849-1.26]. Furthermore, compared to traditional log-ratio inversion models and the spectral information-based machine learning bathymetry inversion model, the MDCGP-ML approach exhibits distinct advantages. It effectively reduces the overreliance on spectral data in bathymetric inversion. This advancement offers a novel perspective for augmenting the bathymetry inversion capabilities of satellite remote sensing in complex nearshore aquatic regions.

关键词： Remote sensing machine learning Biological system modeling Sea measurements Bathymetry Satellites machine learning algorithms Data models Accuracy Numerical models machine learning multispectral images satellite remote sensing worldview-2

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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and machine learning

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IEEE ACCESS 2024年 12卷 174255-174273页

作者： Al-Kahla, Wafaa Taqieddin, Eyad Shatnawi, Ahmed S. Al-Ouran, Rami Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid 22110 Jordan Al Hussein Tech Univ Dept Cybersecur Amman 11831 Jordan Jordan Univ Sci & Technol Dept Software Engn Irbid 22110 Jordan Al Hussein Tech Univ Dept Data Sci & AI Amman 11831 Jordan

The need to detect and classify malware on Android devices has become crucial due to the widespread use of these devices daily. This paper presents a method for detecting and classifying malware that uses static and dynamic features. Specifically, we extract permissions and intent filters from static analysis files and APK API data from dynamic analysis files. Then, we use Simhash to encode the selected parts of the analysis files to create feature vectors. These vectors are then used to train different machine learning algorithms for detecting and classifying malware. We conducted several experiments to evaluate the effectiveness of our approach on a dataset of 38,355 labeled Android apps and tested various hash functions and tokenizing methods in the Simhash algorithm. Our results showed that using Random Forest as a classifier, SHA-512 the hash function and 2-WORD tokenization led to high accuracy with reduced time and memory requirements for training, validating, and testing. This indicates a highly accurate model for detecting and classifying malware into 24 families.

关键词： Malware Feature extraction Operating systems Static analysis Accuracy Filters Heuristic algorithms machine learning algorithms machine learning Codes Android malware family classification Simhash feature extraction static analysis dynamic analysis hybrid analysis

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Self-Powered Wireless Devices and machine learning Enable Local Humidity Monitoring

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第8期25卷 10369-10374页

作者： Zhou, Yuankai Cui, Xin Zhang, Yan Univ Elect Sci & Technol China Sch Phys Chengdu 610054 Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 100083 Peoples R China Univ Chinese Acad Sci Coll Nanosci & Technol Beijing 100049 Peoples R China

Self-powered wireless sensors are a promising design paradigm for Internet of Things (IoT) devices that need to enable continuous, real-time environmental monitoring using energy harvesting devices. However, currently available energy harvesting devices such as the triboelectric nanogenerator (TENG), generally have either millimetre-scale environmental monitoring devices or self-powered wireless transmitters, but not both. Typical low energy density of environmental energy has limited self-powered wireless sensors' long-term operation to laboratory settings and prevented their widespread and largescale deployment. Here we have developed a battery-free wireless moisture meter with simple structure and high accuracy. Our device is based on off-the-shelf parts suitable for various sensor and artificial intelligence applications. In the detection process, the light signal from the droplet-powered triboelectric nanogenerator is detected by the machine learning algorithm, and the humidity level detection accuracy is up to 97.3%.

关键词： Humidity Wireless communication Sensors Wireless sensor networks Optical sensors machine learning machine learning algorithms Triboelectric nanogenerator (TENG) environmental monitoring battery-free wireless sensor artificial intelligence

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 1342-1356页

作者： Zois, Elias N. Tsourounis, Dimitrios Kalivas, Dimitrios Univ West Attica TelSip Res Lab Egaleo 12241 Greece Univ Patras Elect Lab Patras 26504 Greece

Identifying the existence or approval of a human in a number of past, recent and present day activities with the use of a handwritten signature is a captivating biometric challenge. Several engineering branches such as computer vision, pattern recognition and quite recently data-driven machine learning algorithms are combined in a multi-disciplined signature verification framework in order to deliver an equivalent and efficient e-assistance to manually executed duties, which usually demand knowledge and skills. In this work, we propose, for the first time, the use of a learnable Symmetric Positive Definite manifold distance framework in offline signature verification literature in order to build a global writer-independent signature verification classifier. The key building block of the framework relies on the use of regional covariance matrices of handwritten signature images as visual descriptors, which maps them into the Symmetric Positive Definite manifold. The learning and verification protocol explores both blind intra and blind inter transfer learning frameworks with the use of four popular signature datasets of Western and Asian origin. Experiments strongly indicate that the learnable SPD manifold similarity distance can be highly efficient for offline writer independent signature verification.

关键词： Measurement Manifolds Covariance matrices Geometry Feature extraction Visualization machine learning algorithms Manifold optimization symmetric positive definite matrices spatial pyramid segmentation writer independent off-line signature verification

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Large-scale prediction of solar irradiation, shading impacts, and energy generation on building Façade through urban morphological indicators: A machine learning approach

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ENERGY AND BUILDINGS 2024年 323卷

作者： Zhao, Hongying Liu, Chengyang Yang, Rebecca Jing Sun, Chayn RMIT Univ Sch Property Construct & Project Management Solar Energy Applicat Lab Melbourne Australia RMIT Univ Sch Sci Melbourne Australia

Incorporating Building-integrated Photovoltaics (BIPVs) into urban environments offers a sustainable approach by transforming building exteriors into renewable energy sources, especially by utilizing the extensive vertical surfaces in cities. While individual building applications have been extensively studied, there is a lack of research on BIPV potential at the urban scale due to the complexity of urban fabrics. Therefore, this study presents a novel method for predicting large-scale solar irradiation and energy generation on building surfaces using urban morphological indicators through machine learning models. Using an Australian city as a case study, a hierarchical two-level machine learning model is developed to predict the solar potential for building fa & ccedil;ade PV systems. The model incorporates 13 urban morphological predictors to determine average shading height and unshaded solar irradiance. By combining these predictions with wall datasets and different fa & ccedil;ade BIPV efficiencies, the proposed method predicts unshaded electricity generation. The results showcase high efficiency and accuracy in the prediction of the shading height, solar irradiance, and electricity generation. This innovative approach aids urban planners in considering large-scale surface BIPVs into urban development.

关键词： Building PV Solar potential Urban morphology machine learning algorithms Decision making

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Adoption of 3D Holograms in Science Education: Transforming learning Environments

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IEEE ACCESS 2024年 12卷 70984-70998页

作者： Salloum, Said A. Alhumaid, Khadija Alfaisal, Aseel M. Aljanada, Rose A. Alfaisal, Raghad Univ Sharjah Hlth Econ & Financing Grp Sharjah U Arab Emirates Univ Salford Sch Sci Engn & Environm Salford M5 4WT England Rabdan Acad Student Serv Abu Dhabi U Arab Emirates Northern Border Univ Appl Coll English Unit Ar Ar 91431 Saudi Arabia Univ Pendidikan Sultan Idris Fac Comp & Meta Technol Tanjung Malim 35900 Malaysia

This study delves into the deployment of 3D holographic technologies within the realm of science education, with the goal of evaluating their effects on both student engagement and learning achievements. It also seeks to understand the perceptions of consumers in the UAE regarding the adoption of 3D holography in educational environments. The study employs a theoretical framework that incorporates variables such as Perceived Compatibility, Observability, Trialability, Relative Advantage, Ease of Doing Business, and Technology Export. Data were collected through 828 questionnaires distributed across various educational institutions. To analyze the collected data, the study utilized a combination of machine learning (ML) algorithms and Partial Least Squares-Structural Equation Modeling (PLS-SEM), focusing on responses from the student surveys. Additionally, Importance-Performance Matrix Analysis (IPMA) was employed to assess the significance and performance of various factors. What distinguises this study is its theoretical framework, which integrates individual and technological characteristics, offering a novel perspective on the subject matter. Results revealed that factors derived from diffusion theory notably surpassed those concerning the Ease of Doing Business and Technology Export in their influence on technology adoption. Notably, the J48 decision tree classifier demonstrated superior accuracy over other classifiers in predicting the value of the dependent variable. The outcomes of this study highlight the revolutionary capabilities of 3D holograms to enrich science education through creating a dynamic, interactive learning space, thereby significantly boosting learners' comprehension and participation. The implications of this research are profound, providing essential insights for educators, policy makers, and technology developers on the pivotal factors driving 3D holography adoption. This facilitates the development of educational strategies that leverage

关键词： 3D holographic technology importance-performance matrix analysis (IPMA) machine learning algorithms science education technology adoption

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Flight Delay Prediction Using Different Regression algorithms in machine learning

Flight Delay Prediction Using Different Regression Algorithm...

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International Conference on Signal Processing and Communication (ICSPC)

作者： A. Evangeline R. Catherine Joy A. Albert Rajan Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

The two forms of regression algorithms are investigated and compared in this Article Such as LASSO and RIDGE regression. For scheduled airlines to improve customer satisfaction and success, accurate forecasting of delay is essential. There is no way to stop a flight from being delayed, yet they significantly affect the profits and losses of carriers. This research investigates a larger spectrum of potential flight delay difficulties and compares two machine learning algorithms in defined extended flight delay time series forecasting. A dataset for the suggested technique is created by gathering, decoding, and linking automatic dependent surveillance-broadcast (ADS-B) signals with additional data including weather, flight schedules, and airport information. A regression approach is used in conjunction with a number of forecasting tasks as part of the defined prediction challenges. The accuracy of the suggested prediction model was examined and compared to current prediction approaches. The results of LASSO and RIDGE regression with the mean absolute errorMAE (0.2 and 0.1), mean squared error-MSE (0.1 and 0.04), root mean square error-RMSE (0.3 and 0.2) and Accuracy (99.7% and 99.8%) respectively.

关键词： machine learning algorithms Atmospheric modeling Time series analysis Signal processing algorithms machine learning Predictive models Airports

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