Leakage current analysis on polymeric surface condition using time-frequency distribution

This paper presents results and analysis of different leakage current waveforms on porcelain and polymeric insulator material during the clean and pollution contamination under laboratory conditions. The artificial pollution is a combination kaolin and kieselguhr. The resulting leakage current waveforms recorded with different time lapses after contamination are comparing both in, time and frequency domains. The evaluation of insulator surface condition is compared leakage current behavior with hydrophobic degree (contact angle of water drop) on the insulator surface. The experiment result showed that the ceramics insulator has leakage current of surface too different than polymer insulator in dry clean, wet, and polluted conditions. This in caused by polymer isolation had hydrophobic, meanwhile ceramics isolator has hydrophilic.

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

The recommended power quality (PQ) indices in electric power systems are defined based on the discrete Fourier transform (DFT). DFT is mostly evaluated applying fast Fourier transform (FFT) algorithm which requires signals to be periodic in nature. Hence, it evaluates PQ indices accurately for stationary PQ disturbances only. However, in case of nonstationary PQ disturbances signal is aperiodic and FFT results in erroneous assessment due to spectral leakage phenomena. Furthermore, FFT cannot provide time information of the PQ indices as computation is performed in frequency domain only. Which is significant shortcoming of the FFT when dealing with time-varying PQ disturbances to extract dynamic signature of the signal. Thereby, this paper presents a framework of redefining PQ indices for stationary and nonstationary PQ disturbances employing time-frequency distribution (TFD) method. Among all TFDs, reduced interference distribution (RID) represents the most suitable properties for analyzing time-varying PQ disturbances. Hence, in this work, it is employed to reformulate PQ indices typically used in electric power systems. The results of two synthetic examples considering stationary and nonstationary PQ disturbances imply that the RID-based redefined PQ indices are closer to the actual values. Also, they provide more accurate results than the existing transient PQ indices and traditional FFT-based method.

When considering wafer level packaging (WLP) applications, the use of dielectric polymer materials becomes more relevant for reliability performance. Indeed, polymer materials can have excellent dielectric performances with a processability at lower thermal balance. Their mechanical properties also offer a better compliance between the silicon 3D stack and the organic substrate underneath, improving the overall Front-End / Back End compatibility. This study reports the thermo-mechanical behavior and properties evaluation of the most advanced photo-sensitive dielectric polymers with higher resolution available in the market. It will allow direct comparisons between different dielectric polymer materials with the focus of stress control and thermal stability. First part of the paper will present thermal and mechanical characterization of these polymers performed with the same experimental conditions. Second part will present the advantages and limitations of each measurement characterization technique. And finally, the most relevant characterizations will be extracted in order to compare such dielectric polymers for 3D packaging applications.

This paper presents the calibration of pulse generator according to International Special Committee on Radio Interference (CISPR) 16-1-1 standard using Fast Fourier Transform (FFT) technique for Electromagnetic Compatibility (EMC) Testing. The pulse generator is configured to source a pulse signal for each CISPR frequency band A, B and C respectively and the signal is then split into two paths using a splitter. A delay line is inserted into one path of them as a trigger signal to allow the sampling head of digital sampling oscilloscope to capture the signal from the other path. After that, the data is recorded and transferred to the computer using Labview software via a GPIB interface. This signal waveform is now analyzed using FFT and calculated on Matlab to compare with the standard. Finally, the calibration results are shown with uncertainty budget.

3D packaging using through silicon via (TSV) technology is becoming important in IC packaging industry. The polymer material as insulation material was fabricated by the spin-coating or spray-coating process. And the polymer insulation can relax some of the stress induced and makes the process more convenient compared with CVD. Therefore, it is the most cost effective technology for the wafer level packaging. In this study, some polymer insulation materials with different silanes are investigated comprehensively, including viscosity thixotropic index, adhesion strength, thermogravimetry analysis, differential scanning calorimetry and conformal coating. All the results show that the silane has significant effect on the adhesion strength and conformal coating. The adhesion strength is improved when the silane with nitrogen atom, and the insulation layer can be spin-coated uniformly or most filled vias when the silane with different polarity are used.

The discrete Fourier transform is an important tool for processing digital images. Efficient algorithms for computing the Fourier transform are known as fast Fourier transforms (FFTs). One of the most common of these is the Cooley-Tukey radix-2 decimation algorithm that efficiently transforms one-dimensional data into its frequency domain representation. The orthogonality of rectangular sampling allows the separability of the Fourier kernel which enables the use of the Cooley-Tukey algorithm on two-dimensional digital images that have been sampled rectangularly. Hexagonal sampling provides many benefits over rectangular sampling, but it does not result in the orthogonal rows and columns that can be transformed independently as is done with rectangular samples. Use of the Array Set Addressing (ASA) coordinate system for hexagonally sampled images has been shown to provide a separable Fourier kernel, leading to an efficient FFT, however its implementation is composed of nonstandard transforms that require custom routines to evaluate. This work develops a hexagonal FFT in ASA coordinates that uses only the standard Fourier transform, allowing the user to implement the hexagonal FFT using standard FFT routines.

11 - Dielectric Polymer Materials with High Thermal Stability

To study the change rules of leakage current of long string insulators under high altitude condition, and using the current to characterize the insulators' running state and pollution level can be highly useful to prevent flashovers from happening. In this paper, waveforms the leakage current of long string insulators under high altitude condition are measured by artificial pollution tests and the amplitude-frequency characteristics of the leakage current are analyzed. The results show that with the increase of leakage current amplitude, the third harmonic content decreases, the current waveform became more sinusoidal, the phase difference between current and voltage reduces. Through the analysis of maximum leakage current of insulator strings in different salt deposit density (SDD), a linear relationship is found between the maximum leakage current and SDD, thus the insulator pollution level can be characterized with leakage current peak value. Through the analysis of leakage peak currents under different altitudes, it is found that the effects of altitude on the leakage current is the same with the influence of altitude on U50, leakage current peak becomes smaller with the increase of altitude, therefore the leakage current of different altitudes can be corrected by an index n which stands for the effect level of altitude drops on the flashover voltage.

Spectrum analysis can quickly extract and analyze frequency domain features of signal, and it has been widely applied in fields of image processing, noise processing and signal processing. Fast Fourier transform (FFT) is fast and efficient, because it can efficiently decrease complexity of discrete Fourier transform. As a consequence, FFT is a very good method for image frequency spectrum analysis. In this paper, we propose to consolidate frequency domain representation by sparse representation (SR) and collaborative representation classification (CRC) which has excellent performance in comparison with general sparse representation-associated classification algorithms. Our proposed novel method has three main phases. The first phase utilizes FFT to extract frequency domain features of original images, which are complementary with representations of the original images. The second phase of our proposed novel method exploits CRC or SR to obtain scores of original images and obtained features, respectively. The third phase integrates the scores of original images and obtained features and uses them to classify images. The major contribution of the proposed method is that it is usually more robust than methods using only FFT, CRC or state-of-art method CIRLRC for image classification. The experiments of image classification demonstrate that the simultaneous use of FFT and CRC or sparse representation classification has high accuracy on image recognition.

An efficient implementation of the Volterra filter is presented which uses a frequency domain representation to reduce the number of computations. The multidimensional convolution of the Volterra filter is transformed to the frequency domain giving a transformed input matrix which is sparse and obtained directly from a one-dimensional Fourier transform. In addition to the sparse nature of the transformed input matrix, symmetries in both the Volterra filter and the frequency domain representation are exploited to increase the efficiency of the algorithm. The computational saving is demonstrated by comparing it with the direct implementation of the time domain representation and another technique which uses a frequency domain representation but does not utilise symmetry.

This paper presents a fast and robust framework integrating local features for the matching of multimodal geospatial data (e.g., optical, LiDAR, SAR and map). In the proposed framework, local feature descriptors, such as Histogram of Oriented Gradient (HOG) and Local Self Similarity (LSS), are first extracted for every pixel to form a pixel-wise structural feature representation of an image. Then we define a similarity metric based on the feature representation in frequency domain using the 3 Dimensional Fast Fourier Transform (3DFFT) technique, followed by a template matching scheme to detect control points between multimodal data. The proposed framework is based on the hypothesis that structural similarity between images is preserved across different modalities. The major advantages of this framework include (1) structural similarity representation using pixel-wise feature description and (2) high computational efficiency due to the use of 3DFFT. Experimental results on different types of multimodal geospatial data show more accurate matching performance of the proposed framework than the state-of-the-art methods.

In the present work, Heart Rate Variability (HRV) is described by time-frequency representation (TFR), in order to stratify hypertrophic cardiomyopathy (HCM) patients with increasing risk of suffering sudden cardiac death (SCD). TFR and Fast Fourier Transform (FFT) analysis are also compared. The analysis is based on three frequency bands: VLF, 0-0.04 Hz; LF, 0.04-0.15 Hz; and KF, 0.15-0.45 Hz. New variables based on the instantaneous frequency and energy functions using TFR and time-domain analysis allow to discriminate HCM patients with high risk and low risk of SCD (p<0.05). Results shown that TFR analysis of the HRV seems to present more robustness than FFT analysis in order to characterize HRV.

To improve the numerical efficiency, the Fast Fourier Transform (FFT) technique was facilitated in Parker–Oldenburg’s method for a regional gravimetric Moho recovery, which assumes the Earth’s planar approximation. In this study, we extend this definition for global applications while assuming a spherical approximation of the Earth. In particular, we utilize the FFT technique for a global Moho recovery, which is practically realized in two numerical steps. The gravimetric forward modeling is first applied, based on methods for a spherical harmonic analysis and synthesis of the global gravity and lithospheric structure models, to compute the refined gravity field, which comprises mainly the gravitational signature of the Moho geometry. The gravimetric inverse problem is then solved iteratively in order to determine the Moho depth. The application of FFT technique to both numerical steps reduces the computation time to a fraction of that required without applying this fast algorithm. The developed numerical producers are used to estimate the Moho depth globally, and the gravimetric result is validated using the global (CRUST1.0) and regional (ESC) seismic Moho models. The comparison reveals a relatively good agreement between the gravimetric and seismic models, with the RMS of differences (of 4–5 km) at the level of expected uncertainties of used input datasets, while without the presence of significant systematic bias.

Charge profiles in LDPE containing both DCP and sulfur-containing antioxidants, which were heat-pressed under different temperatures, were examined to study the heterocharge generation mechanism in polymeric dielectrics. Negative hetero-charges near the positive electrode were detected in samples containing both DCP and the antioxidants when those samples were soaked in acetophenone. The negative heterocharges were not detected in samples containing either DCP or the antioxidant, even when the sample was soaked in acetophenone. These results demonstrate that the heterocharge is generated by the combined effect between acetophenone and substance produced by interaction between DCP and a sulfur-containing antioxidant Considering results of experiments measuring the amount of heterocharges in samples that had been heat-pressed at different temperatures, the mechanism of negative heterocharge generation in polymeric dielectric material is inferred.