No abstract

[1] Multiple echo centers of a mesosphere-summer-echo layer (MSE) observed by the six-receiver OSWIN VHF radar (54.1°N, 11.8°E) were examined with the coherent radar imaging (CRI) technique. The data were collected by different observational modes: vertical and oblique radar beams with the receiving configurations of 3 Â 2, 6 Â 1 (meridional alignment) and 1 Â 6 (zonal alignment) antenna groups. The unique receiving configurations of meridional and zonal aligned antenna groups reveal that the echo centers clustered in three distinct groups above the range height of $86 km. The central group of echo centers was around the direction of radar beam; however, the off-zenith angles of the two side groups, ranging between several and 20 degrees, increased with ascendant range height. Two potential causes of the echoes in the two side groups were examined on the basis of simulation calculation, namely, tilted structures in the layer and additionally, the influence of radar beam pattern. It is indicated that some echoes, originating from the lower part (<$86 km) of the layer, can enter from the first and second sidelobes of the radar beam pattern and then be received at higher range gates (>$86 km) at larger off-zenith angles. The tilted structures, which are considered to be related to wave activities, can also produce the features similar to the observations. This is demonstrated by simulation calculation with wavy reflecting layers, in which the waves are supposed to modulate the multiple reflecting layers, with increasing amplitudes, tilted shapes, asynchronous phases, and horizontal travel.

No abstract

In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

No abstract

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

No abstract

No abstract