From 28 November to 23 December 2009, 24-h PM 2.5 samples were collected simultaneously at six sites in Guangzhou. Concentrations of 18 polycyclic aromatic hydrocarbons (PAHs) together with certain molecular tracers for vehicular emissions (i.e., hopanes and elemental carbon), coal combustion (i.e., picene), and biomass burning (i.e., levoglucosan) were determined. Positive matrix factorization (PMF) receptor model combined with tracer data was applied to explore the source contributions to PAHs. Three sources were identified by both inspecting the dominant tracer(s) in each factor and comparing source profiles derived from PMF with determined profiles in Guangzhou or in the Pearl River Delta region. The three sources identified were vehicular emissions (VE), biomass burning (BB), and coal combustion (CC), accounting for 11±2 %, 31±4 %, and 58± 4 % of the total PAHs, respectively. CC replaced VE to become the most important source of PAHs in Guangzhou, reflecting the effective control of VE in recent years. The three sources had different contributions to PAHs with different ring sizes, with higher BB contributions (75±3 %) to four-ring PAHs such as pyrene and higher CC contributions (57±4 %) to six-ring PAHs such as benzo [ghi]perylene. Temporal variations of VE and CC contributions were probably caused by the change of weather conditions, while temporal variations of BB contributions were additionally influenced by the fluctuation of BB emissions. Source contributions also showed some spatial variations, probably due to the source emission variations near the sampling sites.

This paper presents a new algorithm for the deinterleaving of radar signals, using the direction of arrival (DOA), carrier frequency (RF), and time of arrival (TOA). The algorithm is mainly applied to pulse repetition interval (PRI) signals. This algorithm consists of two steps: In the first step, a PRI transformation is used to the received pulses after pre-deinterleaved of frequency and DOA. In this step, radar signals having the same frequency and DOA are identified as the same class. In the second step, the number of existing emitters and their PRIs is determined by using TOA information. The algorithm for deinterleaving uses the information obtained from the previous analysis to reduce the PRI errors. The simulation results show that the algorithm is successful in high pulse density environments and for the complex signal types.

The presence of noise superimposed on a signal limits the receiver’s ability to correctly identify the intended signal. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper describes the Least Mean Squares (LMS) adaptive filtering algorithm. The algorithm was implemented in Matlab and was tested for noise cancellation in speech signals.

In this paper, first, analytic geometry are used to calculate the normal transversal azimuth on the ellipsoid surface, then modification value is calculated to get a more accurate orientation angle. Second, based on geodesic differential equations, segmented cumulation for distance between two points on the ground is brought forward. Each section is a short distance, so the result is more accurate. Moreover, when applyed to the program, angle is all got by anti cotangent function, thus avoiding to cumbersomely judge the quadrant.