This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
The dataset utilized in this study corresponds to the year 2019 (all months) and covers an extensive region within the Mediterranean Basin. This particular year was selected to investigate seasonal variations in column-averaged dry-air mole fraction of methane (XCH₄), providing insights into methane dynamics over different climatic periods.
This paper explores the applications of the 45 MHz U-NII-4 band in vehicle-to-everything (V2X) communication system, a technology adopted (or being adopted) by numerous countries to facilitate safety warning applications and mitigate collision risks. However, the operational efficiency of V2X systems can be undermined by intentional and unintentional interference provoked by the increasing user base in adjacent bands and potential malicious entities in the V2X operating band.
We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,
Synthetic EEG Dataset for CNN-LSTM Training: Clean and Artifact-Contaminated Signals
This dataset consists of synthetically generated EEG and EMG signals designed for training Convolutional Neural Networks (CNNs) in artifact detection and removal. The dataset includes both clean EEG signals and EEG signals contaminated with simulated EMG artifacts from various sources.
This dataset comprises high-resolution 3-axis accelerometer recordings collected from human participants performing distinct hand gestures, intended for training gesture-based assistive interfaces. Each participant’s raw motion signals are individually organized, enabling both user-specific and generalizable model development. The dataset includes time-series accelerometer data, along with a feature-augmented version containing extracted statistical and temporal descriptors such as RMS, Jerk, Entropy, and SMA.
Motion analysis provide important information in rehabilitation, performance evaluation, and movement symmetry assessment, with applications including neurology, biomedicine, surgery, and sports monitoring. The integration of wearable sensors and signal processing forms a robust interdisciplinary platform for such analysis. Specific methods are based on monitoring physiological and motion responses during controlled exercises that simulate real-world motion scenarios.
Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.
Electroplating chemicals are the silent architects behind the shiny, corrosion-resistant surfaces we often take for granted. From the gleaming chrome on car parts to the delicate gold plating on jewelry, these chemicals are the backbone of a process that blends chemistry with craftsmanship. At its core, electroplating is the method of depositing a thin layer of metal onto the surface of another material using electric current, and the chemicals involved make all the difference. But this isn’t just science—it’s also art.
To support research on multimodal speech emotion recognition (SER), we developed a dual-channel emotional speech database featuring synchronized recordings of bone-conducted (BC) and air-conducted (AC) speech. The recordings were conducted in a professionally treated anechoic chamber with 100 gender-balanced volunteers. AC speech was captured via a digital microphone on the left channel, while BC speech was recorded from an in-ear BC microphone on the right channel, both at a 44.1 kHz sampling rate to ensure high-fidelity audio.
