This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
This project aims to optimize the routing of large-scale Automated Guided Vehicles (AGVs) in a large-scale logistics warehouse in Japan using Quantum Annealing. The project will generate real-world operational problems and use them for performance evaluation. It is designed to be compatible with various solvers, including classical solvers and quantum annealing solvers.
Clean energy technologies, encompassing renewable resources like solar, wind, and hydropower, are essential in the global effort to reduce greenhouse gas emissions and combat climate change. As the globe prepares to transition away from fossil fuels, understanding the factors and parameters influencing the penetration of clean energy into existing energy markets has become a critical step. Controversies surrounding the environmental impacts of renewable technologies, variability in market structures, and economic pressures on clean energy companies can complicate this transition.
This dataset provides electromagnetic spectrum feature data for target recognition in combat formations, supporting both closed and open set scenarios. It includes three subsets: a closed set with known target types, open set 1 with one unknown target type, and open set 2 with multiple unknown target types. Each dataset contains extracted target features, adjacency matrices representing communication links, and ground truth labels. The dataset covers radar and communication attributes, including carrier frequency, pulse characteristics, modulation types, power, and movement parameters.
Recently, pulse transformers have been widely used in pulsed power systems. However, the magnetic saturation phenomenon of the pulse transformer with closed magnetic cores will change its output waveform parameters, which makes it difficult to predict the output waveforms of the pulse transformer accurately, and a more accurate model considering the nonlinear magnetization process of the pulse transformer needs to be established.
Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations.
BitRecover OST to PST Converter is a trustworthy and powerful software tool specifically created to export offline OST files to importable PST format. This sophisticated application allows users to recover and move emails, contacts, calendars, and tasks from inaccessible or broken OST files to Microsoft Outlook while maintaining the original data structure and integrity. With its easy-to-use interface and powerful capabilities, the software is the perfect solution for users and organizations who want to recover access to their precious email data.
Brain tumors are one of the most common diseases threatening human health. Early detection and precise segmentation are of great significance for clinical diagnosis and treatment. This paper presents a Learnable Wavelet Transform and Attention Mechanism network(LWTA-Net2D), based on 2D Convolutional Neural Networks (CNN), integrating Learnable Discrete Wavelet Transform (LDWT), combination of Monte Carlo Attention (MCattn) and Monte Carlo Bottleneck Layer (MCBottleneck), and a U-Net-based encoder-decoder architecture.
Cloud computing has become a relatively new paradigm for the delivery of compute re-
sources, with key management services (KMS) playing a crucial role in securely handling cryptographic
operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, in-
cluding SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryp-
tion/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction
The data are the original data of UAV sound source localization by ODAS system consisting of 12-channel spherical microphone array and circular MEMS microphone array of dual system. The data contains four different types of UAV sound sources, and the UAV sound sources in different frequency bands are used to locate the sound sources of multiple UAVs.
An automatic waste classification system embedded with higher accuracy and precision of convolution neural network (CNN) model can significantly the reduce manual labor involved in recycling. The ConvNeXt architecture has gained remarkable improvements in image recognition. A larger dataset, called TrashNeXt, comprising 23,625 images across nine categories has been introduced in this study by combining and thoroughly analyzing various pre-existing datasets.
This dataset is a comprehensive resource derived from public data, focusing on the relationship between microorganisms and drugs. It includes similarity data between microorganisms and drugs calculated through various methods, supporting diverse analyses of these relationships. Additionally, the dataset provides rich metadata, including the names of drugs, microorganisms, and related diseases, as well as detailed information such as chemical formulas.
This study investigated neural mechanisms underlying working memory by employing a visual n-back task with graded cognitive load (0-back to 3-back). Ten healthy volunteers (6 males, 4 females; mean age 23.3 ± 0.9 years) participated, performing a spatial matching task where they judged whether the current position of a displayed square matched the position presented n trials earlier, responding via keypress ("V" for match, "N" for non-match).
This is a subset of the original GDB-9-Ex_EOM-CCSD dataset at https://doi.org/10.13139/OLCF/2318313. It consists of 100 randomly selected molecules from the original dataset that consists of 80,593 molecules. This dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Equation of Motion Coupled Cluster (EOM-CCSD) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy.
This is a subset of the original GDB-9-Ex_TD-DFT-PBE0 dataset at https://doi.org/10.13139/OLCF/2318314. It consists of 100 randomly selected molecules from the original dataset that consists of 96,766 molecules. The dataset contains data-intensive quantum chemical electronic structure calculations for organic molecules of the GDB-9-Ex dataset. Calculations were performed using the Time Dependent Density Functional Theory (TDDFT) first principles method using the ORCA software. It provides UV-vis spectra calculations of molecules with a high level of accuracy.
On 7 September 2021, a NOAA WP-3D flew a mission through Hurricane Larry with the Imaging Wind and Rain Airborne Profiler (IWRAP) mounted on the aircraft. Horizontal wind vectors and atmospheric wind speeds were retrieved from the Ku-band radar measurements, and are presented with equivalent reflectivity estimates from IWRAP and other measurements and retrievals from collocated instruments (e.g., flight-level wind sensors and the Stepped Frequency Microwave Radiometer [SFMR]). The radar was configured to sample at two incidence angles: 31 degrees and 51 degrees.
The product INSTITUTE MANAGEMENT SYSTEM offers records of students etc. in an
institute. To the users of this project are administrator, staff member. User is a person of
administrative staff at an institute. Therefore “COMPUTER INSTITUTE
MANAGEMENT SYSTEM” has been designed in such a way that it will automate the
manual work of administrative department by maintaining records such as fee records,
payroll records etc. The user can even manipulate the data such as by editing the records
This work introduces a breakthrough two-axis electromagnetic scanning micromirror system that resolves the fundamental aperture-frequency trade-off in MEMS-based LiDAR, achieving 20 mm optical aperture with 610 Hz resonant frequency. The design seamlessly integrates a voice coil motor (VCM) for vertical actuation with a resonant horizontal scanner.
We introduce BS-Breath, the first open dataset for respiration sensing using a cell-free massive MIMO system. Collected from a 64-antenna MIMO testbed, this dataset provides uplink Channel State Information (CSI) at 3.51 GHz, captured from 10 subjects performing controlled breathing. Ground truth respiration data is synchronized using a Motion Capture (MoCap) system, enabling precise validation.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop. To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop. This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.
