Robust and Automatic Skyline Detection Algorithm Based on MSSDN

Automatic detection of the skyline plays an important role in several applications, such as visual geo-localization, flight control, port security, and mountain peak recognition. Existing skyline detection methods are mostly used under common weather conditions; however, they do not consider bad weather situations, such as rain, which limits their application in real scenes. In this paper, we propose a multi-stream-stage DenseNet to detect skyline automatically under different weather conditions. This model fully considers the adverse factors influencing the skyline and outputs a probability graph of the skyline. Finally, a dynamic programming algorithm is implemented to detect the skyline in images accurately. A comparison with the existing state-of-the-art methods proves that the proposed model shows a good performance under rainy or common weather conditions and exhibits the best detection precision for the public database.

Making radioactivity measurements on building materials accessible to everyone

<p>The CORSAIR (Cloud Oriented Radiation Sensor for Advanced Investigation of Rocks) project was born to meet the EU guidelines 2013/59/EURATOM on safety standards for protection against ionizing radiations. The project designed an automated system capable of providing a real-time measurement of the radioactive activity concentration index for building materials according to regulations of more than 20 different countries. Measurements are conducted through in situ gamma-ray spectroscopy techniques on 3 x 3 x 3 m<sup>3</sup> blocks of rock at quarries and processing centers, and quantify the activities, the abundances and the related effective dose-rates of natural radionuclides (<sup>40</sup>K, <sup>232</sup>Th, <sup>238</sup>U and their progenies) in stone materials for the building industry. The detector comprises a 2” x 2” cylindric CeBr<sub>3</sub> crystal having a 2.5% energy resolution at 1461 keV. A lateral lead shield of 1.3 cm enables a ~60% reduction of the gamma signal coming from above and beside the detector. The system is designed for providing the radiometric index in less than 30 min with an overall uncertainty of the order of 5%.</p><p>The innovative aspects of the detector are in its autonomous operation and the easy fruition of the results of the material characterization. Energy calibration and peak recognition are automatically performed on‑board through an innovative stochastic method based on simulated annealing. The computation of the results is fully-automated and requires no intervention of the operator. The battery-powered detector is equipped with GPS, LoRa, Bluetooth and Wi-Fi connectivity and can be remotely controlled thanks to a dedicated Android app. Acquired data and activity indexes are synced through LoRa connectivity to a cloud database, where they can be easily accessed by sellers and buyers, thus preventing the placing on the market of blocks hazardous to public health.</p>

Efficient Algorithm for SAR Refocusing of Ground Fast-Maneuvering Targets

The synthetic aperture radar (SAR) image of moving targets will defocus due to the unknown motion parameters. For fast-maneuvering targets, the range cell migration (RCM), Doppler frequency migration and Doppler ambiguity are complex problems. As a result, focusing of fast-maneuvering targets is difficult. In this work, an efficient SAR refocusing algorithm is proposed for fast-maneuvering targets. The proposed algorithm mainly contains three steps. Firstly, the RCM is corrected using sequence reversing, matrix complex multiplication and an improved second-order RCM correction function. Secondly, a 1D scaled Fourier transform is introduced to estimate the remaining chirp rate. Thirdly, a matched filter based on the estimated chirp rate is proposed to focus the maneuvering target in the range–azimuth time domain. The proposed method is computationally efficient because it can be implemented by the fast Fourier transform (FFT), inverse FFT and non-uniform FFT. A new deramp function is proposed to further address the serious problem of Doppler ambiguity. A spurious peak recognition procedure is proposed on the basis of the cross-term analysis. Simulated and real data processing results demonstrate the validity of the proposed target focusing algorithm and spurious peak recognition procedure.

A Hybrid Wavelet-Based Method for the Peak Detection of Photoplethysmography Signals

The noninvasive peripheral oxygen saturation (SpO2) and the pulse rate can be extracted from photoplethysmography (PPG) signals. However, the accuracy of the extraction is directly affected by the quality of the signal obtained and the peak of the signal identified; therefore, a hybrid wavelet-based method is proposed in this study. Firstly, we suppressed the partial motion artifacts and corrected the baseline drift by using a wavelet method based on the principle of wavelet multiresolution. And then, we designed a quadratic spline wavelet modulus maximum algorithm to identify the PPG peaks automatically. To evaluate this hybrid method, a reflective pulse oximeter was used to acquire ten subjects’ PPG signals under sitting, raising hand, and gently walking postures, and the peak recognition results on the raw signal and on the corrected signal were compared, respectively. The results showed that the hybrid method not only corrected the morphologies of the signal well but also optimized the peaks identification quality, subsequently elevating the measurement accuracy of SpO2 and the pulse rate. As a result, our hybrid wavelet-based method profoundly optimized the evaluation of respiratory function and heart rate variability analysis.