The Laptop Motherboard Health Monitoring Dataset is a synthetically generated dataset designed to aid in the development and testing of machine learning models for predictive maintenance and health monitoring of laptop motherboards. The dataset includes various health metrics such as CPU usage, RAM usage, temperature, voltage, disk usage, and fan speed, along with a label indicating whether a problem was detected and the type of problem.

Dataset Columns ModelName: The name and model of the laptop (e.g., Dell Inspiron 1234, HP Pavilion 5678). This column includes realistic combinations of popular laptop brands and model series, making the dataset relatable and practical.

CPUUsage: The CPU usage percentage, ranging from 0 to 100%. This metric indicates how much of the CPU's capacity is being utilized.

RAMUsage: The RAM usage percentage, ranging from 0 to 100%. This metric shows the proportion of RAM being used out of the total available.

Temperature: The temperature of the motherboard in degrees Celsius, ranging from 20 to 100°C. This metric is crucial for detecting overheating issues.

Voltage: The operating voltage in volts, ranging from 10 to 20V. Voltage measurements help in identifying power-related problems.

DiskUsage: The disk usage percentage, ranging from 0 to 100%. This metric indicates how much of the disk's capacity is being used.

FanSpeed: The speed of the cooling fan in revolutions per minute (RPM), ranging from 1000 to 5000 RPM. Fan speed is an important indicator of cooling performance.

ProblemDetected: The type of problem detected, if any. Possible values are:

No Problem Overheating Power Issue Memory Leak Disk Failure Usage This dataset can be used to train and evaluate machine learning models for the purpose of predictive maintenance. Researchers and practitioners can use the data to classify the type of problem based on the health metrics provided. The dataset is ideal for experimenting with various classification algorithms and techniques in the field of hardware health monitoring.

File Laptop_Motherboard_Health_Monitoring_Dataset.csv: The main dataset file containing 2000 rows of synthetic data. Acknowledgements This dataset is synthetically generated and does not represent real-world data. It is intended for educational and research purposes only.

Dataset Card: Anomaly Detection Metrics Data

  Dataset Summary

This dataset contains system performance metrics collected over time for anomaly detection in time series data. It includes multiple system metrics such as CPU load, memory usage, and other resource utilization statistics, along with timestamps and additional attributes.

  Dataset Details

Size: ~7.3 MB (raw JSON), 345 kB (auto-converted Parquet) Rows: 46,669 Format: JSON Libraries: datasets, pandas… See the full description on the dataset page: https://huggingface.co/datasets/ShreyasP123/anomaly_detection_metrics_data.

CPU hours, institutions, and PI's by year.

Autonomous Underwater Vehicle (AUV) Monterey Bay Time Series from Feb 2016. This data set includes CTD and fluorometer data from the Makai AUV, as context for ecogenomic sampling using an onboard Environmental Sample Processor (ESP).

Average CPU time (s) of all the referenced algorithm on benchmark function.

No description is available. Visit https://dataone.org/datasets/sha256%3A8b9b600f61bbd7d944013b78645ce2bb2494d735129ab86e43ba55f51657d613 for complete metadata about this dataset.

The global neural processor market is projected to grow exponentially in the coming years, driven by the increasing demand for artificial intelligence (AI) in various industries. The market is expected to reach a value of $281.4 million by 2033, expanding at a CAGR of 19.3% from 2025 to 2033. The growth is attributed to the rising adoption of AI in smartphones and tablets, autonomous vehicles, robotics, healthcare, smart home devices, cloud computing, industrial automation, and other applications. The key factors driving the market growth include the increasing demand for AI-powered devices, advancements in AI algorithms and hardware, and government initiatives to promote AI adoption. The rising popularity of smartphones and tablets, the growing adoption of autonomous vehicles, and the increasing use of AI in healthcare and smart home devices are among the major trends influencing the market. However, the market growth is subject to certain restraints, such as high hardware costs, data privacy and security concerns, and the need for skilled AI professionals. The neural processor market is experiencing unprecedented growth, driven by advancements in artificial intelligence and machine learning applications. Valued at [market value] million units in 2023, the market is projected to reach [market value] million units by 2030, exhibiting a CAGR of [growth rate]%. Recent developments include: In September 2024, Intel Corporation has released its Core Ultra 200V processors, which are the company's most power-efficient laptop chips to date. The chips include a neural processing unit optimized for running artificial intelligence models, which is four times faster than the previous generation. This new architecture enhances overall efficiency while maximizing computational power. , In June 2024, Advanced Micro Devices Inc. introduced its artificial intelligence processors, including the MI325X accelerator, at the Computex technology trade show. The company also detailed its new neural processing units (NPUs), designed to handle on-device AI tasks in AI PCs, as part of a broader strategy to enhance its product lineup with significant performance improvements, including the MI350 series expected to achieve 35 times better inference capabilities compared to its predecessors. , In May 2024, Apple Inc. has unveiled the M4 chip for the iPad Pro, utilizing second-generation 3-nanometer technology to enhance power efficiency and enable a thinner design. The chip features a 10-core CPU, a high-performance GPU featuring Dynamic Caching and ray tracing, and the fastest Neural Engine capable of 38 trillion operations per second. , In February 2024, MathWorks, Inc., a developer of mathematical computing software, has launched a hardware support package for the Qualcomm Hexagon Neural Processing Unit. This package enables automated code generation from Simulink and MATLAB models customized for Qualcomm’s architecture, improving data accuracy, ensuring standards compliance, and boosting developer productivity. .

This repository contains the datasets used in Mirzadeh et al., 2023. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.

Dataset 1: Envisat descending track 292

• Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)
• Processor: ISCE/stripmapStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

Dataset 2: ALOS-1 ascending track 569

• Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)
• Processor: ISCE/stripmapStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

Dataset 2: Sentinel-1 ascending track 130 and descending track 137

• Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)
• Processor: ISCE/topsStack + MintPy
• Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5
• Mean LOS Velocity (in HDF-EOS5 format): velocity.h5
• Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5
• Geometry (in HDF-EOS5 format): geometryRadar.h5

The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.

XSEDE Service Provider Resources during 2011–2015.

The rapid development of Digital Twin (DT) technology has underlined challenges in resource-constrained mobile devices, especially in the application of extended realities (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). These challenges lead to computational inefficiencies that negatively impact user experience when dealing with sizeable 3D model assets. This article applies multiple lossless compression algorithms to improve the efficiency of digital twin asset delivery in Unity’s AssetBundle and Addressable asset management frameworks. In this study, an optimal model will be obtained that reduces both bundle size and time required in visualization, simultaneously reducing CPU and RAM usage on mobile devices. This study has assessed compression methods, such as LZ4, LZMA, Brotli, Fast LZ, and 7-Zip, among others, for their influence on AR performance. This study also creates mathematical models for predicting resource utilization, like RAM and CPU time, required by AR mobile applications. Experimental results show a detailed comparison among these compression algorithms, which can give insights and help choose the best method according to the compression ratio, decompression speed, and resource usage. It finally leads to more efficient implementations of AR digital twins on resource-constrained mobile platforms with greater flexibility in development and a better end-user experience. Our results show that LZ4 and Fast LZ perform best in speed and resource efficiency, especially with RAM caching. At the same time, 7-Zip/LZMA achieves the highest compression ratios at the cost of slower loading. Brotli emerged as a strong option for web-based AR/VR content, striking a balance between compression efficiency and decompression speed, outperforming Gzip in WebGL contexts. The Addressable Asset system with LZ4 offers the most efficient balance for real-time AR applications. This study will deliver practical guidance on optimal compression method selection to improve user experience and scalability for AR digital twin implementations.

Summary of multiple linear regression analysis for total time prediction: Effects of vertex count and video size.

Summary of multiple linear regression analysis for maximum RAM prediction: Effects of vertex count and video size.

The rapid development of Digital Twin (DT) technology has underlined challenges in resource-constrained mobile devices, especially in the application of extended realities (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). These challenges lead to computational inefficiencies that negatively impact user experience when dealing with sizeable 3D model assets. This article applies multiple lossless compression algorithms to improve the efficiency of digital twin asset delivery in Unity’s AssetBundle and Addressable asset management frameworks. In this study, an optimal model will be obtained that reduces both bundle size and time required in visualization, simultaneously reducing CPU and RAM usage on mobile devices. This study has assessed compression methods, such as LZ4, LZMA, Brotli, Fast LZ, and 7-Zip, among others, for their influence on AR performance. This study also creates mathematical models for predicting resource utilization, like RAM and CPU time, required by AR mobile applications. Experimental results show a detailed comparison among these compression algorithms, which can give insights and help choose the best method according to the compression ratio, decompression speed, and resource usage. It finally leads to more efficient implementations of AR digital twins on resource-constrained mobile platforms with greater flexibility in development and a better end-user experience. Our results show that LZ4 and Fast LZ perform best in speed and resource efficiency, especially with RAM caching. At the same time, 7-Zip/LZMA achieves the highest compression ratios at the cost of slower loading. Brotli emerged as a strong option for web-based AR/VR content, striking a balance between compression efficiency and decompression speed, outperforming Gzip in WebGL contexts. The Addressable Asset system with LZ4 offers the most efficient balance for real-time AR applications. This study will deliver practical guidance on optimal compression method selection to improve user experience and scalability for AR digital twin implementations.

CPU time for different values of α.

Summary of sample linear regression analysis conducted for compressed bundle size and vertex count.

Allocation plan of CPU.

Model sizes and CPU times needed to solve the model in the computational efficiency test.

Average maximum memory usage for three registration methods and two label fusion methods.