Sensors

A novel and ultrasensitive strategy to detect protein CREPT (Cell Regulated and Expression-evaluated Protein in Tumor) in cancer cells using quartz crystal microbalance (QCM) sensor is developed in this study. CREPT is an oncoprotein and plays vital roles in cancer initiation, growth and metastasis via mediating oncogene transcription, and the content of CREPT can reflect the degree of carcinogenesis of tissues and organs. However, there is no rapid, low-cost and ultrasensitive procedure for the quantitative detection of CREPT content in cancer tissues.

The quartz crystal microbalance (QCM) sensor with asymmetric ring electrodes has received widespread attention for its uniform sensitivity distribution. At the same time, its 5-overtone mass sensitivity also greatly improves the fundamental frequency mass sensitivity. The 5-overtone quality (Q) factor is a technical indicator to measure the stable operation of the quartz crystal resonator with asymmetric ring electrodes in the fifth overtone mode. However, there are few papers to report the 5-overtone quality factor (Q5) of QCM sensor with asymmetric ring electrodes.

This dataset is for the paper “FMOBA: Frequency-Domain Multi-Objective Black-Box Adversarial Attacks for SAR Image”

This dataset provides experimental validation data supporting the proposed finite element method (FEM) for optimizing a directional eddy current testing (ECT) probe designed to detect in-plane waviness on the surface of carbon fiber prepreg. The study includes the design and evaluation of six ECT probe configurations, varying the angle and aspect ratio of the receiver coils. Sensitivity measurements were conducted to assess the probes' ability to detect fiber-related features.

This dataset provides high-grade Received Signal Strength Indicator (RSSI) data collected from a set of experiments meant to estimate the number of drones present in a closed indoor space. The experiments are conducted varying the number of drones from one to seven, where all the variations in RSSI signal data are captured using a 5G transceiver setup established using Ettus E312 software-defined radio. There are seven files in the database, with a minimum of about 270 million samples.

Ultrasonic gas flowmeters are widely adopted in industrial applications due to their advantageous features, such as zero pressure loss and easy installation. However, the conventional threshold detection method, despite its real-time performance, suffers from degraded accuracy caused by circuit and pipeline noise, which introduces fluctuations in echo signal amplitude and phase. This paper theoretically demonstrates that doubling the ultrasonic signal frequency steepens the rising edge slope of the echo signal, reducing the time interval during which random noise affects comparator output.

Comparison of sensor outputs before and after waveform reconstruction with the source signal

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. 

This dataset contains 60,000 annotated records modeling UAV-based and IoT sensor-driven agriculture environments. Each record includes UAV imaging data (NDVI, NDRE, RGB damage score), IoT sensor values (NPK, pH, moisture, temperature, humidity), semantic labels (NDI, PDI), and metadata for energy consumption, latency, and service migration. It is designed for validating Digital Twin frameworks, semantic communication models, and Federated Deep Reinforcement Learning (FDRL) in precision farming.

Real-time tracking of electricians in distribution rooms is essential for ensuring operational safety. Traditional GPS-based methods, however, are ineffective in such environments due to complex non-line-of-sight (NLOS) conditions caused by dense cabinets and thick walls that obstruct satellite signals. Existing solutions, such as video-based systems, are prone to inaccuracies due to NLOS effects, while wearable devices often prove inconvenient for workers.