Filter the results

These filters mean we take in only some information, or keep what is important to know. So. any messages we create are a result of all this information that we receive and filter, from the time we are children to adults.
10p kathy206 30072010 118 66 Download

NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear timeinvariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear leastsquares and minimumvariance theory results obtained so far.
10p khinhkha 30072010 75 12 Download

Digital Signal Processing (DSP) is formally defined as a digital operation performed on an input sequence of numbers (including feedback from the result of the digital operation). The sequence of numbers can represent anything from digitised human speech to stock price data, processed to detect hidden periodicities or pattern
121p aries23 29092012 42 10 Download

In next step impulse signal is shifted by one time unit i.e. ‘sample1’ is assigned a value of one and remaining all sample values are made zero. The corresponding obtained output is same as second coefficient value (13). Similarly impulse signal is moved through all the input samples and corresponding outputs are observed. Simulation results clearly show all the coefficient values coming out as ‘filt_out’ as and when impulse signal traverses through input samples. Thus designed filter is verified by verifying the impulse response of the system....
12p babyuni 17082010 51 8 Download

LINEAR TIMEVARIANT SYSTEM 15.1 INTRODUCTION In this chapter we extend the results of Chapters 4 and 8 to systems having timevariant dynamic models and observation schemes [5, pp. 99–104]. For a timevarying observation system, the observation matrix M of (4.11) and (4.15) could be different at different times, that is, for different n. Thus the observation equation becomes Y n ¼ M nX n þ N n ð15:11Þ For a timevarying dynamics model the transition matrix È would be different at different times. In this case È of (8.
3p khinhkha 30072010 52 7 Download

This excellent book represents the final part of threevolumes regarding MATLABbased applications in almost every branch of science. The book consists of 19 excellent, insightful articles and the readers will find the results very useful to their work. In particular, the book consists of three parts, the first one is devoted to mathematical methods in the applied sciences by using MATLAB, the second is devoted to MATLAB applications of general interest and the third one discusses MATLAB for educational purposes.
0p cucdai_1 16102012 25 7 Download

Inside SCRX2BC the signal is amplified and filtered in two stages between pins 14, 15, 16, 1, and 3. Pin 3 (DI) is the output pulse sequence that was picked up by the receiver; this is used as the input to the decoder. The SCRX2BC scans for the 4 long (synchronization) pulses and then counts the number of short pulses after them to determine which command was sent by the transmitter. The gain of the SCRX2BC stages is high enough to produce a pulse sequence at pin 3 even if no signal from a transmitter is present (it amplifies random noise), but the resulting sequence...
148p nhacnenzingme 23032013 15 3 Download

In 2007, the Tianjin Binhai New Area (TBNA) and one of its administrative zones—the Tianjin EconomicTechnological Development Area (TEDA)—in northeast China asked the RAND Corporation to perform a technologyforesight study to help them develop and implement a strategic vision and plan for economic growth through technological innovation. This book describes the results of that study.
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Báo cáo khoa học: "The Contribution of Stylistic Information to Contentbased Mobile Spam Filtering"
Contentbased approaches to detecting mobile spam to date have focused mainly on analyzing the topical aspect of a SMS message (what it is about) but not on the stylistic aspect (how it is written). In this paper, as a preliminary step, we investigate the utility of commonly used stylistic features based on shallow linguistic analysis for learning mobile spam ﬁlters. Experimental results show that the use of stylistic information is potentially effective for enhancing the performance of the mobile spam ﬁlters.
4p hongphan_1 15042013 19 2 Download

Numerous crosslingual applications, including stateoftheart machine translation systems, require parallel texts aligned at the sentence level. However, collections of such texts are often polluted by pairs of texts that are comparable but not parallel. Bitext maps can help to discriminate between parallel and comparable texts. Bitext mapping algorithms use a larger set of document features than competing approaches to this task, resulting in higher accuracy. In addition, good bitext mapping algorithms are not limited to documents with structural markup such as web pages. ...
4p bunbo_1 17042013 18 2 Download

We describe experiments with a Naive Bayes text classifier in the context of anti spam Email filtering, using two different statistical event models: a multivariate Bernoulli model and a multinomial model. We introduce a family of feature ranking functions for feature selection in the multinomial event model that take account of the word frequency information. We present evaluation results on two publicly available corpora of legitimate and spam Emails.
8p bunthai_1 06052013 19 2 Download

Active LowPass Filter Design focuses on active low pass filter design using operational amplifiers. Low pass filters are commonly used to implement antialias filters in data acquisition systems. Design of second order filters is the main topic of consideration.
24p hanhphucgiandon803 23062016 25 2 Download

We describe reﬁnements to hierarchical translation search procedures intended to reduce both search errors and memory usage through modiﬁcations to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in translation. Rules are put into syntactic classes based on the number of nonterminals and the pattern, and various ﬁltering strategies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST ArabictoEnglish evaluation task. ...
9p bunthai_1 06052013 15 1 Download

This study aimed to scalingup Chaetoceros sp., which has been isolated from Vinh chau saltfield prior inoculation as a stock for fertilizer pond in Artemia culture system. The cultulre system included of 100 L, 500 L in plastic baskets, while 2 m3 and 15 m3 were the earthen ponds with plastic lining. Before starting the new culture, natural brackish water was filtered and treated with chlorine within 48 hours. Culture medium was enriched with Walne, Silicate salts and vitamins and the culture were maintained during 7–day period. The result indicated that scalingup of Chaetoceros sp....
10p doncongtu 22012010 399 151 Download

Theoretically the Kalman Filter is an estimator for what is called the linearquadratic problem, which is the problem of estimating the instantaneous ``state'' (a concept that will be made more precise in the next chapter) of a linear dynamic system perturbed by white noiseÐby using measurements linearly related to the state but corrupted by white noise. The resulting estimator is statistically optimal with respect to any quadratic function of estimation error
24p huggoo 20082010 181 93 Download

Purpose: –Using the Process Editor to create a modified version of the sink process model. –Adding a new statistic to compute ETE delay. Overview: 1. Create modified sink process model to compute ETE delay. 2. When there is a packet arrival, get the packet, obtain the creation time, write out its ETE delay as a global statistic and destroy the packet. 3. Incorporate new sink process model into existing node model. 4. Create ETE delay statistic probe. 4. Run simulation for a duration of 2,000 seconds to ensure convergence. 5. Filter the “View Results” graphs to answer questions....
7p g16bit 11072010 119 59 Download

4.2.3 MTMF MTMF combines the best parts of the Linear Spectral Mixing model and the statistical Matched Filter model while avoiding the drawbacks of each parent method (Boardman, 1998). It is a useful Matched Filter method without knowing all the possible endmembers in a landscape especially in case of subtle, subpixel occurrences. Firstly, pixel spectra and endmember spectra require a minimum noise fraction (MNF) (Green et al., 1988, Boardman, 1993) transformation. MNF reduces and separates an image into its most dimensional and nonnoisy components.
464p lulanphuong 22032012 118 39 Download

A threestage method for compressing bilevel linedrawing images is proposed. In the first stage, the raster image is vectorized using a combination of skeletonizing and line tracing algorithm. A feature image is then reconstructed from the extracted vector elements. In the second stage, the original image is processed by a featurebased filter for removing noise near the borders of the extracted line elements. This improves the image quality and results in more compressible raster image. In the final stage, the filtered raster image is compressed using the baseline JBIG algorithm....
6p kienk6e 31032011 54 14 Download

LEASTSQUARES AND MINIMUM– VARIANCE ESTIMATES FOR LINEAR TIMEINVARIANT SYSTEMS 4.1 GENERAL LEASTSQUARES ESTIMATION RESULTS In Section 2.4 we developed (2.43), relating the 1 Â 1 measurement matrix Y n to the 2 Â 1 state vector X n through the 1 Â 2 observation matrix M as given by Y n ¼ MX n þ N n ð4:11Þ It was also pointed out in Sections 2.4 and 2.10 that this linear timeinvariant equation (i.e., M is independent of time or equivalently n) applies to more general cases that we generalize further here. Speciﬁcally we assume Y n is a 1 Â ðr...
50p khinhkha 30072010 63 13 Download

FIXEDMEMORY POLYNOMIAL FILTER 5.1 INTRODUCTION In Section 1.2.10 we presented the growingmemory g–h ﬁlter. For n ﬁxed this ﬁlter becomes a ﬁxedmemory ﬁlter with the n most recent samples of data being processed by the ﬁlter, slidingwindow fashion. In this chapter we derive a higher order form of this ﬁlter. We develop this higher order ﬁxedmemory polynomial ﬁlter by applying the leastsquares results given by (4.132). As in Section 1.2.
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