Extracellular vesicles (EVs) that contain human epidermal growth factor receptor 2 (HER2) biomarkers are released by both healthy and cancerous cells, presenting substantial potential for the precise detection of numerous disorders, including cancer. To accurately quantify proteins, EVs must initially be separated from serum and then lysed to extract their protein content. Although ultracentrifugation is the predominant isolation technique, it has constraints regarding scalability and repeatability. Furthermore, traditional detergent-based lysis techniques endanger protein stability. This study introduces an innovative method for EV isolation utilizing colloidal gold nanoparticles, succeeded by lysis through sinusoidal electrical stimulation. A nonFaradaic electrochemical impedance spectroscopy (EIS) system has been developed utilizing screen-printed electrodes for determining HER2 protein levels. EV isolation was confirmed via western blotting for the EV-associated markers CD63 and HSP70. To promote the lysis of EVs, the EV sample was exposed to sine wave signals of differing amplitudes, with optimal disruption noted between 100 mV and 500 mV. The lysate was examined via EIS, producing a linear behavior from 5 μg/mL to 0.05 ng/mL with a limit of quantification of 0.109 μg/mL in human serum. The developed platform thus proves suitable for quantifying the HER2 protein from breast cancer patients.

Multi-light detection and ranging (LiDAR) fusion is widely used to increase scene coverage and improve 3-D reconstruction quality, but jointly registering point sets from scanners with different resolutions, scales, fields of view, and noise characteristics remains difficult and directly impacts sensing accuracy. This letter presents a sensor-centric multi-LiDAR joint registration framework, which includes the following. First, it lifts iterative closest points (ICP) to a high-dimensional formulation to jointly align multiple scans with block-structured rotation couplings. Second, it introduces data-driven weighting and a two-stage outlier diagnostics procedure tailored to cross-sensor inconsistencies. Lastly, it performs uncertainty-aware regularization using closed-form covariances for both rotation and translation. The method preserves simple singular value decomposition (SVD)-based updates while explicitly addressing heterogeneous sensor characteristics. Validation on two hardware platforms—a dual 2-D spinning setup (SICK TIM520 and Hokuyo UST10LX) and a dual Ouster OS1128 suite—demonstrates sensor-system-level accuracy gains, reducing accumulated pose error by 26.9%--40.8% relative to representative ICP variants, with run times ≈3× faster than point-to-plane ICP and ≈38× faster than Go-ICP (slightly slower than efficient sparse ICP). These results substantiate a direct contribution to sensor systems by improving multi-LiDAR integration robustness, accuracy, and deployment practicality.

A thermal anemometer was industrially fabricated without any preprocessing, coprocessing, or postprocessing for the first time. The total sensor footprint is only 0.245mm2 using TSMC’s 65-nm CMOS process. The dual-mode anemometer measures wind speeds up to 1.2m/s with a precision of 3.1% (or 0.037m/s) calorimetrically. Alternatively, using the second hot-element mode, the sensor measures in the range of 0–7.5 ms−1 with a 0.1m/s accuracy and an average 1.34% precision. This is the first industrially fabricated thermopile-based CMOS anemometer, paving the way for a compact foundry SoC with inbuilt underlying computing for low-cost air speed monitoring.