a practical approach to digital signal processing k padmanabhan pdf

... Hampshire); Kenneth H. Jacker, (Department of Computer Science, Appalachian State University); Rajiv Kapadia, Ph.D. (Department of Electrical Engineering, Mankato State University); Dan King (Analog ... 11 Mean and Standard Deviation 13 Signal vs. Underlying Process 17 The Histogram, Pmf and Pdf 19 The Normal Distribution 26 Digital Noise Generation 29 Precision and Accuracy 32 Chapter 3. ADC and ... electrical engineering, to name just a few. The goal is to present practical techniques while avoiding the barriers of detailed mathematics and abstract theory. To achieve this goal, three strategies...

... considered a bandlimited signal. It is this signal that is sampled and converted to a discrete-time signal and coded to a digital signal by the analog -to- digital converter (ADC) that was briefly ... data on a few ADCs currently available are given in Table 1.3 [3]. Hence digital signal processing is restricted to approximately one megahertz, and analog signal processors are necessary for processing ... processing signals above that frequency, for example, processing of radar signals. In such applications, analog signal processing is a more attractive and viable choice, and currently a lot of research...

... international normalized ratio (INR), and a liver panel. Although the serum aminotransferase level correlates poorly with liver histology, the ratio of aspartate aminotransferase (AST) to alanine ... HCC, and mortality. In addition, HCV infection has been linked to a variety of extra-hepatic manifestations such as autoimmune diseases, lymphoma, monoclonal gammopathies and cryoglobulinemia. ... International Publisher. All rights reserved Review A Practical Approach to Managing Patients with HCV Infection Richard H. Huang, and Ke-Qin Hu Division of Gastroenterology and Hepatology,...

... quantitation and three approved drugs for HBV treatment, and presents an updated and practical clinical approach to managing CHB. Highly sensitive PCR-based quantitation of HBV DNA makes it ... normal transaminases. ALT Levels For many years, ALT has been used as a standard surrogate for the activity of CHB. Thus, ALT level in combination with HBV DNA level and histological activity ... treatment. Overall, LAM and ADV are probably more favorable due to convenient administration, better tolerance, and less cost. When planning a LAM treatment, medical adherence and regular follow-up...

... fetal monitoring, patient moni- toring, and ECG and EEC mapping. Another example of advanced digital signal processing is found in hearing aids and cardiac pacemakers. 5. Image Processing. Image ... restricted to approximately one megahertz, and analog signal processors are necessary for processing signals above that frequency, for example, processing of radar signals. In such applications, analog signal ... decreases and therefore the accuracy and dynamic range of the input and output data decrease. For example, data on a few ADCs currently available are given in Table 1.3 [3]. Hence digital signal processing...

... Space-TimeAdaptiveProcessing forAirborneSurveillanceRadar HongWang SyracuseUniversity 70.1MainReceiveApertureandAnalogBeamforming 70.2DatatobeProcessed 70.3TheProcessingNeedsandMajorIssues 70.4TemporalDOFReduction 70.5AdaptiveFilteringwithNeededandSample-Supportable DOFandEmbeddedCFARProcessing 70.6Scan -To- ScanTrack-Before-DetectProcessing 70.7Real-TimeNonhomogeneityDetectionandSample ConditioningandSelection 70.8SpaceorSpace-RangeAdaptivePre-SuppressionofJammers 70.9ASTAPExamplewithaRevisittoAnalogBeamforming 70.10Summary References Space-TimeAdaptiveProcessing(STAP)isamulti-dimensionalfilteringtechniquedevelopedfor minimizingtheeffectsofvariouskindsofinterferenceontargetdetectionwithapulsedairborne surveillanceradar.Themostcommondimensions,orfilteringdomains,generallyincludetheaz- imuthangle,elevationangle,polarizationangle,dopplerfrequency,etc.inwhichtherelativelyweak targetsignaltobedetectedandtheinterferencehavecertaindifferences.Inthefollowing,theSTAP principlewillbeillustratedforfilteringinthejointazimuthangle(space)anddopplerfrequency (time)domainonly. STAPhasbeenaveryactiveresearchanddevelopmentareasincethepublicationofReedetal.’s seminalpaper[1].WiththerecentlycompletedMultichannelAirborneRadarMeasurementproject (MCARM)[2]–[5],STAPhasbeenestablishedasavaluablealternativetothetraditionalapproaches, suchasultra-lowsidelobebeamformingandDisplacedPhaseCenterAntenna(DPCA)[6].Muchof STAPresearchanddevelopmenteffortshavebeendrivenbytheneedstomakethesystemaffordable, tosimplifyitsfront-hardwarecalibration,andtominimizethesystem’sperformancelossinseverely nonhomogeneousenvironments.Figure70.1isageneralconfigurationofSTAPfunctionalblocks[5, 7]whoseprincipleswillbediscussedinthefollowingsections. c  1999byCRCPressLLC ... spatial domain. Channel calibration is a problem issue for many STAP approaches. In order to minimize perfor- mancedegradation, thechannelswithsome STAPapproachesmustbematchedacrossthesignalband, and ... Kelly, E.J., An adaptive detection algorithm, IEEE Trans. on Aerospace and Electronic Systems, AES-22(1), 115–127, March 1986. [23] Kazakos, D. and Papantoni-Kazakos, P., Detection and Estimation, Computer...

... and delay estimation (JADE) for multipath signals arriving at an antenna array, IEEE Comm. Lett., Jan. 1997. [50] Van Veen, B.D. and Buckley, K. M., Beamforming: a versatile approach to spatial filtering, IEEE ASSP ... IntroductionandMotivation Thischapterreviewstheapplicationsofantennaarraysignalprocessingtomobilenetworks.Cellular networksarerapidlygrowingaroundtheworldandanumberofemergingtechnologiesareseentobe criticaltotheirimprovedeconomicsandperformance.Amongtheseistheuseofmultipleantennas andspatialsignalprocessingatthebasestation.ThistechnologyisreferredtoasSmartAntennasor, moreaccurately,asSpace-TimeProcessing(STP).STPreferstoprocessingtheantennaoutputsin bothspaceandtimetomaximizesignalquality. Acellulararchitectureisusedinanumberofmobile/portablecommunicationsapplications.Cell sizesmayrangefromlargemacrocells,whichservehighspeedmobiles,tosmallermicrocellsorvery smallpicocells,whicharedesignedforoutdoorandindoorapplications.Eachoftheseoffersdifferent channelcharacteristicsand,therefore,posesdifferentchallengesforSTP.Likewise,differentservice deliverygoalssuchasgradeofserviceandtypeofservice:voice,data,orvideo,alsoneedspecic STPsolutions.STPprovidesthreeprocessingleverages.Therstisarraygain.Multipleantennas capturemoresignalenergy,whichcanbecombinedtoimprovethesignal -to- noiseratio(SNR).Next isspatialdiversitytocombatspace-selectivefading.Finally,STPcanreduceco-channel,adjacent channel,andinter-symbolinterference. Theorganizationofthischapterisasfollows.InSection68.2,wedescribethevectorchannelmodel forabasestationantennaarray.InSection68.3wediscussthealgorithmsforSTP.Section68.4outlines theapplicationsofSTPincellularnetworks.Finally,weconcludewithasummaryinSection68.5. c 1999byCRCPressLLC ... M. and Paulraj, A. , Blind separation of synchronous co-channel digital signals using an antenna array. Part I. Algorithms, IEEE Trans. on Signal Processing, 44(5), 1184–1197, May 1996. [44] Tong,...

... perturbation-based, and neural network-based methods are classified as adaptive because on average they move towards an EVD at each update. On the other hand, rank one, rank k, and sphericalized ... scheme. 66.3.3 Forward-Backward Averaging In manysubspace tracking problems, forward-backward (FB) averaging can improve subspaceas well as DOA (or frequency) estimation performance. Although FB averaging ... projection approximation subspace tracker (PAST) which tracks an arbitrary basis for the signal subspace, and PASTd which uses deflation to track the individual eigencomponents. A multi-vector eigen tracker...

... Regalia, P .A. , Adaptive IIR Filtering in Signal Processing and Control, Marcel-Dekker, New York, 1995. [20] Ren,W. andKumar, P.R., Stochasticparallelmodel adaptation: theoryand applicationstoactive noise ... between these twocategories is the Steiglitz-McBride approach toadaptiveIIR filtering. Each of these approaches has certain advantages but also disadvantages. We have evaluated each approachin termsofconvergence ... of large deviations of thealgorithms fromaminimum point becomeslargewith time with constantà. As a practical matter, however, one can expect the parameter vector to approach and stay near a minimizing...

... 1982–1989, Aug. 1996. [19] Sayed, A. H. and Kailath, T., A state-space approach to adaptive RLS filtering, IEEE Signal Processing Magazine , 11(3), 18–60, July 1994. The time-domain feedback and small gain ... B., Sayed, A. H., and Kailath, T., Linear estimation in Krein spaces — Part II: Applica- tions, IEEE Trans. Automatic Control , 41(1), 34–49, Jan. 1996. The small gain analysis is a standard tool ... in adaptive filtering and H ∞ estimation and control can be found in [14] Hassibi, B., Sayed, A. H., and Kailath, T., LMS and backpropagation are minimax filters, in Neural Computation and Learning ,...

... not guaranteed to be independent from sample to sample. Even so,numerous analyses and simulations have indicatedthat theseassumptions lead to a reasonably accurate characterization of the behavior ... straightforward, simulation has two drawbacks that make it a poor sole choice for charac- terizing the behavior of an adaptive lter: ã Selecting design parameters via simulation alone is an iterative ... systemsassimple as the LMS adaptive lter. ã The amount of data needed to accurately characterize the behavior of the adaptive filter for all cases of interest may be large. If real-world signal measurements...

... ARMA model has an advantage over the AR and MA models because it can better fit spectra with nulls and peaks. Its disadvantage is that it is more difficult to estimate its parameters than the parameters ... from vectors that lie in the signal subspace. The main idea there is to form a reducedrank autocorrelation matrix which is an estimate of the signal autocorrelation matrix. Since this estimate is ... here are wide-sense stationary. The stationarity assumption, however, is often a mathematical abstraction and only an approximation in practice. Many physical processes are actually nonstationary...

... Gains ã Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance ã Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classication Classifying ... Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains ã Signal Detection: Unknown Gains ã Signal Detection: Random Gains ã Signal ... normal with zero mean and p ì p covariance matrix R s the compound signal component s c = Bs is an n- dimensional random Gaussian vector with zero mean and rank p covariance matrix BR s B H .A standard...