Power Spectral Density Signals Research Articles

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. AmDrs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbour (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

Effects of Long-Term and Acute Hindlimb Unloading Model on Neuroelectrophysiological Signals of Hippocampal Interneurons and Pyramidal Cells Using Microelectrode Arrays

Microgravity is the key environment of weightlessness experienced by all astronauts during spaceflights that cause severe physiological alterations in the human body. The effect of microgravity on the nervous system has not been elucidated. In this study, hindlimb unloading rats were used as the simulated microgravity model, and self-designed novel microelectrode arrays were used as the essential tools. The special arrangement of detection sites and platinum nanoparticles modification made the electrodes have excellent performance at the neuron level. By comparing the changes of the neuroelectrophysiological signals in the CA1 regions of rats in different periods of long-term and short-term modeling, the following results were obtained. In the long-term model, the firing rates reached a peak after about seven days of modeling, and then kept decreasing till 28 days. The average firing rates and individual single cell firing rates of the acute model rose immediately after modeling. Analysis of power spectral density signals showed that the signals shifted to the low frequency band after both long-term and acute modeling. The neurons in the CA1 were classified into pyramidal cells and interneurons. The continuous observation of neurons under the acute modeling showed that the firing rates and amplitude of pyramidal cells have a greater increase than interneurons. The self-designed novel implantable microelectrode arrays provide an advanced tool for the detection of neurons in hindlimb unloading rats. The hippocampal nerve cells were impaired after modulation, and that pyramidal cells were more susceptible than interneurons.

Basics of the Corrective - Resonant Spectral Method of Gas Flow Identification from the Damaged Pipeline and its Experimental Verification

Abstract An important problem in the operation of gas pipelines is the identification of accidental gas leakage from the damaged pipeline. It is the hardest when the leakage is small. A disadvantage of prior methods is that with their use is only possible to identify and locate significant outflow with only a low accuracy. In proposed method of identifying the gas flow will be used a new signal from the corrective element “r” attached to the tested pipeline and calculated from it and mass flow rate signals auto and cross power spectral density Sxx, Syy, Srr, Sxy, Sxr, Syr. Using the power spectral density signals allows for transition from the signal diagnostic to the parameter diagnostic which is more effective. Assumptions methods will be verified at the experimental station.

Analysis of the influence of fixed-point computing on quantization noise in software DDC

Introduction. With the development of digital technology more attention is paid to making radio-receiving tracks based on SDR technology (Software Defined Radio), which is based on the processing of digitalized radio-frequency signals exclusively by means of software in real time. Moreover the construction ofdata-acquisition systems on one integral chip (SoC) is widely used right now. The main fundamental basis of SoC systems is the FieldProgrammable Gate Array (FPGA). One important step of receiving of radio-frequency signals is a digital down conversion, followed by filtration and repeated decimation. Since processing in DDC is made in a fixed calculation format, than before the immediate implementation of this structure on FPGA, it is necessary to analyze the effect offixed calculation bits on the quality of signal processing. Later it can be used to choose DDC internal calculation bits without sacrificing the set quality of signal processing and hardware resources on its implementation. In the well-known papers, analysis of the effect of signal quantization is reduced to two methods: deterministic and probability methods. Deterministic method gives the top-ofthe-line evaluation of quantizing noise at the output of digital system. Probability method is based on evaluation of quantizing noise dispersion at the output of the device. Evaluation of the signal and the quantization noise in the SDDC based on their PSD. In this section, mathematical expressions were obtained for the construction of PSD signal and the quantizing noise at the output of each of the subsystems of the digital DDC. Criterion for selecting SDDC bit is the relative level of PSD quantizing noise at the output of the digital system. Since the ADC is fixed, the choice of bit computing in SDDC should be such that the total quantizing noise in SDDC would not exceed the ADC quantizing noise at the output of the digital system. Example calculation and construction of PSD signal and quantization noise in Matlab for reception QPSK-signal. In this part of the article an example of the analysis of DDC structure for receiving QPSK- signal was considered. Mathematical Matlab's model has been developed which allows to calculate the PSD of the signal and the quantization noise depending on the internal structure of the SDDC bit based on the formulas of section 2 this paper. These expressions make it possible to construct a PSD noise separately from each SDDC source output, and separately estimate the contribution of each source of quantizing noise to the total noise at the output of the digital system. From these results a decision on the choice of a particular quantization step that depends on the selected calculating bit at each stage of processing was made. Conclusions. Quantizing noise dispersion is not an adequate quality evaluation of processing in multi-stage and multi-rate systems, which include software DDC under review. In such systems the sequential changes of the line, as well as the nature of the spectrum of interfering components, including quantization noise, happen. Therefore in this paper I propose a different approach based on the calculation of the power spectral density (PSD) of the quantizing noise at the output of the device, taking into account the filtering effect of all of its stages and repeated decimation.