Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR.

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear-catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor's reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor's reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.

Glucose diagnosis system combining machine learning and NIR photoacoustic multispectral using a low power CW laser.

Non-invasive, portable, economical, dynamic blood glucose monitoring device has become a functional requirement for diabetes in his regulating entire life. In a photoacoustic (PA) multispectral near-infrared diagnosis system, the glucose in aqueous solutions was excited by low power (order of milliwatts) CW laser whose wavelengths were from 1500 to 1630 nm. The glucose in aqueous solutions to be analyzed was contained within the photoacoustic cell (PAC). The PA multispectral signals were measured using a piezoelectric detector, and then the voltage signals from the piezoelectric detector were amplified with a precision Lock-in Amplifier (MFLI500K). The continuously tunable lasers were used to verify the various influencing factors of the PA signal, and the PA spectrum of the glucose solution was examined. Subsequently, six wavelengths with high power were selected at approximately equal intervals from 1500 to 1630 nm, and the gaussian process regression of the quadratic rational kernel was used to collect data through these wavelengths to predict the glucose concentration. The experimental results showed that the near-infrared PA multispectral diagnosis system could be engineered for the prediction of the glucose level (more than 92%, zone A of Clarke Error Grid). Subsequently, the model trained with glucose solution was used to predict serum glucose. With the increase of serum glucose content, the prediction results of the model also showed a high linear relationship, indicating that the photoacoustic method was sensitive to the detection of glucose concentration changes. The results of our study have the potential to not only better develop the PA blood glucose meter but also extend the viability into the detection of otherwise blood components.

Glucose Determination by a Single 1535 nm Pulsed Photoacoustic Technique: A Multiple Calibration for the External Factors.

Photoacoustic spectroscopy has been proved to be a potential method for noninvasive blood glucose detection. We used 1535 nm pulsed laser to excite photoacoustic signal in glucose solution and then explored the influence of different glucose concentration on photoacoustic signal to analyze the sensitivity of photoacoustic signal to glucose at this wavelength. We designed a simple photoacoustic cell structure, which used a focused ultrasonic transducer to receive signals, so as to reduce signal attenuation. In terms of the results, we have found that for high-concentration glucose solutions, the results have strong linearity and discrimination, and when the concentration is close to the human body level, the signal difference is not so obvious. Therefore, we explore the external factors affecting the photoacoustic signal in detail and propose a calibration method. Through calibration, the signal generated by the low-concentration glucose solution also has a good linearity.

Diagnosis of Bone Mineral Density Based on Backscattering Resonance Phenomenon Using Coregistered Functional Laser Photoacoustic and Ultrasonic Probes.

Dual-energy X-ray absorptiometry (DXA) machines based on bone mineral density (BMD) represent the gold standard for osteoporosis diagnosis and assessment of fracture risk, but bone strength and toughness are strongly correlated with bone collagen content (CC). Early detection of osteoporosis combined with BMD and CC will provide improved predictability for avoiding fracture risk. The backscattering resonance (BR) phenomenon is present in both ultrasound (US) and photoacoustic (PA) signal transmissions through bone, and the peak frequencies of BR can be changed with BM and CC. This phenomenon can be explained by the formation of standing waves within the pores. Simulations were then conducted for the same bone µCT images and the resulting resonance frequencies were found to match those predicted using the standing wave hypothesis. Experiments were performed on the same bone sample using an 808 nm wavelength laser as the PA source and 3.5 MHz ultrasonic transducer as the US source. The backscattering resonance effect was observed in the transmitted waves. These results verify our hypothesis that the backscattering resonance phenomenon is present in both US and PA signal transmissions and can be explained using the standing waves model, which will provide a suitable method for the early detection of osteoporosis.