196,552 research outputs found

    Signal De-noising method based on particle swarm algorithm and Wavelet transform

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    Wavelet analiza je novi alat za analizu odnosa vrijeme-frekvencija, razvijen na temelju Fourierove analize s dobrim svojstvom lokaliziranja vremena i frekvencije i mogućnosti donošenja višestrukih rješenja. Koristi se u cijelom nizu primjena u području obrade signala. U ovom se radu analizira primjena wavelet transforma u filtriranju signala korištenjem poboljšane optimalizacije roja čestica i predlaže inteligentna metoda uklanjanja šuma iz signala zasnovana na wavelet analizi. Metoda koristi Center Based Particle Swarm Algorithm (CBPSO) za izbor optimalnog praga za svaki pod-pojas u različitim mjerilima, inteligentno razaznavajući vrstu šuma iz samog signala, što ne zahtijeva nikakvo prethodno poznavanje šuma. Poboljšani algoritam roja čestica koristi se da potakne optimalni izbor različitih mjerila praga wavelet domena, što je dovelo do uklanjanja šuma iz signala kod različitih tipova pozadinskog šuma, i povećane brzine wavelet transforma i wavelet konstrukcije te ima veću fleksibilnost. Eksperimentalni rezultati su pokazali da se CBPSO algoritmom može postići bolji učinak uklanjanja šuma.Wavelet analysis is a new time-frequency analysis tool developed on the basis of Fourier analysis with good time-frequency localization property and multi-resolution characteristics, which is in a wide range of applications in the field of signal processing. This paper studies the application of wavelet transform in signal filtering, by using an improved particle swarm optimization, proposes an intelligent signal de-noising method based on wavelet analysis. The method uses a Center Based Particle Swarm Algorithm (CBPSO) to select the optimal threshold for each sub-band in different scales, learning the type of noise from the signal itself intelligently, which does not require any prior knowledge of the noise. The improved particle swarm algorithm is used to enhance the optimal choice of the different scales of the wavelet domain threshold, which realized the signal De-noising under different types of noise background, and improved the speed of wavelet transform and wavelet construction, and has greater flexibility. The experimental results showed that CBPSO algorithm can get better De-noising effect

    Timeline analysis and wavelet multiscale analysis of the AKARI All-Sky Survey at 90 micron

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    We present a careful analysis of the point source detection limit of the AKARI All-Sky Survey in the WIDE-S 90 μ\mum band near the North Ecliptic Pole (NEP). Timeline Analysis is used to detect IRAS sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90 μ\mum flux of a source. Combined with a robust noise measurement, the point source flux detection limit at S/N >5>5 for a single detector row is 1.1±0.11.1\pm0.1 Jy which corresponds to a point source detection limit of the survey of \sim0.4 Jy. Wavelet transform offers a multiscale representation of the Time Series Data (TSD). We calculate the continuous wavelet transform of the TSD and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our multiscale analysis, IRAS sources selected above 4σ4\sigma can be identified as the only real sources at the Point Source Scales. We also investigate the correlation between the non-IRAS sources detected in Timeline Analysis and cirrus emission using wavelet transform and contour plots of wavelet power spectrum. It is shown that the non-IRAS sources are most likely to be caused by excessive noise over a large range of spatial scales rather than real extended structures such as cirrus clouds.Comment: 16 pages, 19 figures, 5 tables, accepted for publication in MNRA

    Kompetensi guru dalam pengajaran amali reka bentuk dan teknologi di Sekolah Rendah Daerah Batu Pahat

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    Kompetensi bermaksud kemampuan atau kecekapan seseorang individu dalam melakukan sesuatu tugasan. Kompetensi juga merujuk kepada kemampuan seseorang dalam melaksanakan sesuatu yang diperolehi melalui pendidikan dan juga merujuk kepada prestasi dan perbuatan yang rasional untuk memenuhi spesifikasi tertentu di dalam pelaksanaan tugas-tugas pendidikan. Objektif kajian ini dijalankan adalah untuk mengenalpasti tahap kompetensi guru terhadap pengajaran amali Reka Bentuk dan Teknologi di Sekolah Rendah Daerah Batu Pahat. Kajian ini berbentuk tinjauan deskriptif yang menggunakan borang soal selidik sebagai instrumen kajian. Borang soal selidik yang dibina adalah berdasarkan kepada tiga elemen iaitu elemen pengetahuan, kemahiran dan sikap. Seramai 118 orang guru yang mengajar mata pelajaran ini telah terlibat sebagai responden. Data yang dikumpulkan telah dianalisis dengan menggunakan perisian Statistical Package for Social Science (SPSS) versi 19 yang melibatkan statistik skor min dan ujian-T tidak bersandar. Hasil dapatan kajian yang diperolehi menunjukkan guru-guru Reka Bentuk dan Teknoogi mempunyai tahap kompetensi yang tinggi terhadap proses pengajaran amali iaitu skor min yang diperolehi pada elemen pengetahuan adalah 4.23, elemen kemahiran adalah 4.30, dan elemen sikap adalah 4.47. Dapatan kajian juga menunjukkan tidak terdapat perbezaan yang signifikan terhadap tahap kompetensi berdasarkan jantina guru lelaki dan guru perempuan dengan nilai sigifikan melebihi 0.05 iaitu sebanyak 0.059. Beberapa cadangan untuk penambahbaikan juga dikemukan dalam kajian ini. Hasil dari dapatan kajian ini dapat digunakan sebagai cadangan garis panduan kepada guru-guru Reka Bentuk dan Teknologi untuk mencapai Standard Kompetensi Guru

    Optimal design of linear phase FIR digital filters with very flat passbands and equiripple stopbands

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    A new technique is presented for the design of digital FIR filters, with a prescribed degree of flatness in the passband, and a prescribed (equiripple) attenuation in the stopband. The design is based entirely on an appropriate use of the well-known Reméz-exchange algorithm for the design of weighted Chebyshev FIR filters. The extreme versatility of this algorithm is combined with certain "maximally flat" FIR filter building blocks, in order to generate a wide family of filters. The design technique directly leads to structures that have low passband sensitivity properties

    Index to NASA Tech Briefs, 1975

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    This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

    Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

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    A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

    Machine learning techniques applied to multiband spectrum sensing in cognitive radios

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    This research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signalsPeer ReviewedPostprint (published version
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