A time‐frequency method for detecting VHF underdense meteor signals
Underdense meteor echoes observed using very high frequency (VHF) radar can be accurately modeled as a single complex damped sinusoid in additive white Gaussian noise. The normalized damping coefficient is expected to be between 0 and 0.3 for a VHF meteor return based on the modeled ambipolar diffusion rates near 90 km and a radar operating between 30 and 50 MHz. Current meteor echo detection routines operate either in the frequency domain, where it is difficult to detect highly damped signals, or in the time domain, where it is difficult to detect narrow band signals. An added difficulty is that typical approaches require a priori knowledge of the noise variance which can bias the performance of these estimators. In this paper, a time‐frequency waveform detector is proposed to address these problems. By normalizing the signal power and power spectral density in the time and frequency domains, respectively, a detector that is invariant to the noise variance can be implemented. The characteristics of a damped sinusoid in the time and frequency domains are exploited to construct the detector. The threshold for the proposed detector is only a function of of the time series length, and is stable with respect to a range of damping coefficients and noise levels. The time‐frequency waveform detector exhibits superior performance to the conventional energy or power detectors when the signal of interest is highly damped or the noise variance is unknown. A derivation of the time‐frequency waveform detector, comparison with the energy and power detectors and numerical results demonstrating the effectiveness of this detector are presented.
Multilayer detection and classification of specular and nonspecular meteor trails
Meteor radar data are continuously collected by different radar systems that operate throughout the year. Analyzing this fast growing, large data set requires efficient and reliable detection routines. Currently most meteor echo routines search for underdense meteor trails, often discarding overdense and nonspecular meteor trails. This is because their main purpose is the study of mesospheric winds. But the study of meteor flux requires the unique identification of each type of meteor reflections. In this paper, a multilayer radar detection and classification algorithm is proposed to correctly identify multiple types of meteor trail reflections. The process consists of two steps. The first step is based on the time‐frequency waveform detector. In this step, we start by selecting low signal‐to‐noise ratio (SNR) values in order to detect all types of radar echoes; however, a high probability offalse alarm is often produced. In the second step, several features from the detected echoes in step one are extracted and a support vector machine (SVM) classifier is constructed to further classify these echoes. The algorithm was tested using data collected from a 50‐MHz radar stationed near Salinas, Puerto Rico, on April 5, 1998. A total of 270 detected echoes were labeled as underdense, overdense, nonspecular, other ionospheric echoes, and noise. We used 50% of the labeled echoes as training samples and divided the rest 50% testing samples as 10 subsets for testing. This technique successfully classified about 85% of the testing samples. Details concerning implementation, feature extraction, and data visualization are presented and discussed.
Ignition and burning rates of segregated waste combustion in packed beds.
Recent developments in national recycling and re-use programmes for municipal waste have led to segregation of an increasing proportion of waste to enhance material recovery. Several of the segregated streams contain materials that can not viably be re-used or recycled but can be used for energy recovery. In this study, the combustion of cardboard and waste wood was investigated in a small-scale packed bed reactor in order to provide fundamental data for the design/operation of moving bed furnaces. Key parameters of combustion including the ignition and burning rates were evaluated for various air flowrates and compared to the modelling results. Two successive stages of combustion were identified for both samples: the propagation of ignition front into the bed and combustion of the fuel above the ignition front. The burning rate of cardboard reached a peak of about 300 kg/m(2)h at the air flowrate of 936 kg/m(2)h and decreased at higher air flowrates. For waste wood, both the ignition and burning rates increased in the tested range of the air flowrate up to 702 kg/m(2)h, of which the values were very close to those for the cardboard. The model prediction was in good agreement with the test results for waste wood. However, the burning rate for cardboard was under-predicted due to strongly irregular shapes of the fuel.