Pengawasan Angkutan Barang di Kota Dumai

All of freight transport in Dumai sometimes make result traffict jum, accident and road damage. And then, author lift this theme for make change, like make safety, comfort and dicipline in driving.In this theme, author use UKAS theory. Dumai transportasion agency and police unit as informant in this study. Author use descriptive qualitative with data collection techniques throught observation, interview with informant and documentation.The result of this study of freight transport in Dumai City signify. Result obtained average but author get kind of deviations as conducted from field supervisors and much drivers. Factors of deviations among human resorces, availabilities of founds and awareness of drivers. The all of factor very important to implementation of better transport control in Dumai City

Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum

We have developed an algorithm called Q5 for probabilistic classification of healthy vs. disease whole serum samples using mass spectrometry. The algorithm employs Principal Components Analysis (PCA) followed by Linear Discriminant Analysis (LDA) on whole spectrum Surface-Enhanced Laser Desorption/Ionization Time of Flight (SELDI-TOF) Mass Spectrometry (MS) data, and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a novel probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is non-iterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques, and can provide clues as to the molecular identities of differentially-expressed proteins and peptides

Modul Bi-lingual Untuk Televisi Dengan Menggunakan SAA7283ZP Yang Diatur Lewat I2C Dari 80C31

At the moment, in Indonesia has been able to enjoy NICAM (Near Instantaneous Companded Audio Multiplex ) technology within the television world. Television without NICAM facility, however, can receive the broadcast of the transmitter with NICAM technology, although it emits mono audio signal only. To receive broadcast with NICAM audio quality, TV audience, therefore has to buy a new set of television equipped with NICAM facility. Dealing with the problem, a module that can externally added to the television was made so that the audience of television with non-NICAM technology can enjoy the system itself. The module used the single chip NICAM decoder SAA7283ZP of Phillips semiconductor that can be adjusted using microcontroller software. The module receives IF signal taken from TV tuner and separates the audio and video signal present. SAA7283ZP controlled by 8031via I2C that is stimulated by 2 pin port 1, then takes and processes the audio 'data' . The output of the process is entered in a separated audio system, the testing result conceals that the occuring error is mostly influenced by the quality of RF signal received by the television set. This result is obviously seen as the testing system employs indoor and outdoor antennas. Besides that, improper grounding the system also affects the module in processing the NICAM signal

System and method to assess signal similarity with applications to diagnostics and prognostics

Signal processing technology for assessing dynamic system similarity for fault detection and other applications is based on time- and frequency-domain time series analysis techniques and compares the entire autocorrelation structure of a test and reference signal series. The test and reference signals are first subjected to similar pre-processing to help guarantee signal stationarity. Pre-processing may include formation of multivariate signal clusters, filtering and sampling. Multivariate periodograms or autocovariance functions are then calculated for each signal series. Test statistics are computed and assessed to determine the equality of the test and reference signals. When the difference between sample autocovariance functions or periodograms of such signals exceeds a preselected threshold value, fault detection signals and/or related diagnostic information are provided as output to a user

Left ventricular outcomes following multivessel PCI vs. infarct artery-only PCI in patients with acute STEMI: the Glasgow PRAMI CMR sub-study

No abstract available