WARPED LINEAR PREDICTION FOR IMPROVED PERCEPTUAL QUALITY IN THE SCELP LOW DELAY AUDIO CODEC (W-SCELP)

Size: px
Start display at page:

Download "WARPED LINEAR PREDICTION FOR IMPROVED PERCEPTUAL QUALITY IN THE SCELP LOW DELAY AUDIO CODEC (W-SCELP)"

Transcription

1 ARPED LINEAR PREDICION FOR IMPROVED PERCEPUAL QUALIY IN HE SCELP LO DELAY AUDIO CODEC (-SCELP) Hauke Krüger and Peter Vary Insttute of Communcaton Systems and Data Processng RH Aachen Unversty, D-5256 Aachen, Germany ABSRAC he SCELP (Sphercal Code Excted Lnear Predcton) audo codec, whch has recently been proposed for low delay audo codng [5], s based on lnear predcton (LP). It apples closed-loop vector quantzaton employng a sphercal code whch s based on the Apple Peelng code constructon rule. Frequency warped sgnal processng s known to be benefcal especally n the context of wdeband audo codng based on warped lnear predcton (LP). In ths contrbuton, LP s ncorporated nto the SCELP low delay audo codec. he overall audo qualty of the resultng -SCELP codec benefts from an mproved perceptual maskng of the quantzaton nose. Compared wth exstng standardzed audo codecs wth an algorthmc delay below 1 ms, the -SCELP codec at a data rate of 48 kbt/ outperforms the IU- G.722 codec at a data rate of 56 kbt/ n terms of the achevable audo qualty. 1. INRODUCION Most of the popular audo codecs, e.g. the Advanced Audo Codec (AAC), [1], are based on perceptual audo codng. In perceptual audo codng n general an audo sgnal s at frst transformed by an analyss flter bank. he resultng representaton n the transform doman s quantzed whereas a perceptual model controls the adaptve bt allocaton. Large transform lengths cause a hgh algorthmc delay. Consderng moble communcatons, the approach of lnear predctve codng (LPC) has been followed for many years n speech codng. In LPC, an all-pole flter models the spectral envelope of an nput sgnal. he sgnal s fltered wth the nverse of that all-pole flter to produce the LP resdual whch s quantzed. In the most recently standardzed speech codecs, vector quantzaton (VQ) based on a sparse codebook s appled, followng the CELP (Code Excted Lnear Predcton) analyss-by-synthess prncple, [2]. A well-known example for ths approach s the adaptve mult rate speech codec (AMR), [3]. Due to the sparseness of the codebook and modelng of the speakers nstantaneous ptch perod, speech coders can not compete wth perceptual audo codng for non-speech nput sgnals. he algorthmc delay s n general lower than that n perceptual codng. he new SCELP audo codec targets applcaton scenaros whch requre hgh audo qualty and a very low algorthmc delay, for example dgtal audo transmsson for a wreless headphone. It employs the prncple of combned lnear predcton and vector quantzaton (LP VQ) as known from speech codng. In order to acheve a better perceptual audo qualty than speech coders, a sphercal codebook s employed. he sphercal codebook s constructed accordng to the Apple Peelng prncple. hs prncple was ntroduced n [4] for the purpose of channel codng. In [5] we have proposed an effcent vector search procedure for the sphercal codebook for lnear predctve quantzaton, and n [6] a representaton of the avalable Apple Peelng code vectors as codng trees has been ntroduced. Both technques enable very effcent encodng and decodng wth respect to computatonal complexty and memory consumpton. In [7] t was shown that warped sgnal processng technques are suted to decrease the requred data rate for wdeband audo codng whle retanng the same subjectve audo qualty. LP s employed n a smulated codng system wth D*PCM n that contrbuton. In contrast to that, n ths contrbuton LP wll be ncorporated nto the closed-loop analyss-by-synthess framework of the SCELP codec whch was ntroduced for conventonal LP prmarly. he prncple of the SCELP audo codec and warped lnear predcton wll be ntroduced n Secton 2 and 3 respectvely. he modfcatons requred for the applcaton of LP n analyss-by-synthess VQ n general and the SCELP framework for hghly effcent encodng n partcular are descrbed n Secton 4. Results are presented n Secton 5, ncludng a comparson of the -SCELP codec wth the IU- G.722 [8] low delay audo codec. 2. PRINCIPLE OF HE SCELP AUDIO CODEC he SCELP low delay audo codec s based on block adaptve combned lnear predcton and vector quantzaton: he correlaton mmanent to an nput sgnal s exploted DAFX-1

2 n order to acheve a hgh quantzaton sgnal-to-nose-rato (SNR). For ths purpose, a wndowed segment of the nput sgnal of length L LP s analyzed n order to obtan the N tme-varant flter coeffcents a 1 a N. Based on these LP coeffcents the LP analyss flter wth system functon H A (z) = 1 + N =1 a z converts the nput sgnal nto the LP resdual sgnal d(k) whch s segmented nto N V = L LP /L V N non overlappng sgnal vectors d = [ d d 1 d LV 1] of length LV. Each LP resdual vector s quantzed and transmtted to the decoder as placements Buffer uto code vector ndex Q. For sgnal reconstructon, also the LP laton coeffcents must be transmtted to the decoder. In general nson ths can be realzed wth only small addtonal bt rate as rbn shown for example n [9]. In the decoder, the transmtted code vector ndex Q s the bass for the reconstructon of the quantzed LP resdual vector d whch s fltered by the LP synthess flter H S (z) = (H A (z)) 1. he output of the LP synthess flter s the decoded sgnal vector x and hence [ ] the sgnal [1]. a1 a N he prncple of the encoder of a CELP codec s depcted n Fgure 1. he decoder s part of the encoder. Accordng to P Analyss Decoder nthess VQ codebook d MMSE H s (z) e w, (z) Fgure 1: Scheme SCELP Audo Codec, Encoder. the analyss-by-synthess prncple, the LP resdual vector d for each codebook ndex s generated frst. hs exctaton vector s fltered by the LP synthess flter H S (z) to obtan the correspondng decoded sgnal vector canddates x. he error dstance between the nput sgnal and the decoded sgnal, e = x x, s determned for each vector canddate correspondng to ndex. he goal s to fnd the ndex Q for whch the mnmum mean square error s acheved: Q = arg mn {D = e 2 = (x x ) (x x ) }. (1) he error weghtng flter (z) controls the spectral shape of the quantzaton nose nherent to the decoded sgnal for perceptual maskng of the quantzaton nose. he analyss-by-synthess vector search can be exhaustve for a large vector codebook Sphercal Vector Codebook In the SCELP audo codec, vector quantzaton s appled n a gan-shape approach to encode the LP resdual. Each LP x - x e resdual vector d s decomposed nto a radus for the gan and a vector on the surface of a unt sphere for the shape component. hle the radus R s quantzed by means of logarthmc scalar quantzaton, the vald code vectors for the quantzaton of the shape component are based on the Apple Peelng code constructon rule. hs rule was descrbed and demonstrated for the specal case of a 3-dmensonal sphere n [5]. he desgn target of the Apple Peelng code s to place all codebook vectors on the surface of a unt sphere as unformly as possble. he decoder n CELP codng n general s not very complex. For a low computatonal encodng complexty, the analyss-by-synthess approach n Fgure 1 was modfed n the SCELP encoder as descrbed n [5]. he result s a low complexty vector search framework. Addtonally, the technologes called Pre-Selecton and Canddate-Excluson, combned wth an effcent metrc computaton, enable a very effcent code vector search. Furthermore the representaton of the Apple Peelng code vectors as codng trees was explaned n [6] for the sake of hghly effcent encodng and decodng. 3. ARPED LINEAR PREDICION he prncple and propertes of warped lnear predcton are dscussed n [7]. In ths contrbuton only those aspects that are relevant for the analyss-by-synthess vector search of the SCELP wll be brefly presented. In conventonal lnear predcton the approxmaton of the spectral envelope of a sgnal s based on a unform resoluton of the frequency scale. Consderng the perceptual propertes of human hearng, a unform resoluton s known to be nferor compared to a non-unform resoluton of the frequency scale. For ths purpose, a non-unform resoluton of the frequency scale s acheved by applyng LP. Consderng the z-transform of a sgnal, ths can be realzed by replacng all unt delay elements by an allpass flter, z 1 = z 1 1 z 1 < 1; R (2) For postve values of warpng constant, the spectral resoluton s ncreased for lower and decreased for hgher frequences compared to conventonal LP arped LP Analyss In the SCELP codec the LP analyss s based on the auto correlaton method, as for example descrbed n [1]. In [7], t was shown that for the warped LP analyss, n the auto correlaton method all unt delay elements must be replaced by the frst order allpass flter accordng to (2). Hence the warped auto correlaton coeffcents ϕ w x,x () ϕw x,x (N) are determned as demonstrated for the frst three coeffcents n Fgure 2. arped auto correlaton coeffcents can DAFX-2

3 be transformed nto warped LP coeffcents a w 1 a w N by means of the Levnson Durbn algorthm as n conventonal LP. replacements ϕw x,x() ϕ w x,x (1) Fgure 2: arped LP Analyss. ϕ w x,x(2) 4. LP IN HE SCELP CODEC he propertes of the warped lnear predcton prohbt a straght forward ncorporaton nto the SCELP audo codec. herefore the followng modfcatons must be consdered frst Zero-Delay Path n Feedback Loop A zero-delay path n the feedback loop makes the mplementaton of the LP synthess flter accordng to equaton (3) mpractcal. In contrast to the mplementaton of the warped LP synthess flter n [7], n ths contrbuton [12] the substtuton of C(z) = + (6) 3.2. LP Analyss/Synthess flter For the warped LP analyss and synthess flter, all unt delay elements of the conventonal LP analyss/synthess flter are replaced by allpass flters : N HA w (z) = Hw A () = 1+ a w = (HS w (z)) 1. =1 (3) he flter coeffcents a w are calculated accordng to Secton Error eghtng Flter PSfrag replacements he SCELP audo codec employs an error weghtng flter as proposed n [11]. In conventonal lnear predcton ths error weghtng flter can be calculated from the LP analyss flter: 1/ s appled to the frst allpass flter n the allpass chan of the warped LP synthess flter to remove the zero-delay path. he resultng flter structure s employed for warped LP analyss and synthess flter as depcted n Fgure 3, and also for the error weghtng flter (5). (1 2 ) PSfrag replacements d(k) d(k) C(z) 1 2 (a) LP Analyss Flter - C(z) 1 2 1/ (1 2 ) (b) LP Synthess Flter (z) = H A(z/γ 2 ) H A (z/γ 1 ). (4) he coeffcents γ 1 and γ 2 are wthn the range of γ 1 γ 2 1. and control the degree of nose shapng. th the applcaton of the error weghtng flter, the quantzaton nose nherent to the decoded output sgnal s spectrally shaped accordng to the system functon of the nverse of the error weghtng flter, ( (z)) 1. Consderng LP, all unt delay elements n equaton (4) must be replaced by n the warped error weghtng flter: w (z) = Hw A ( γ 2) H w A ( γ 1). (5) Fgure 3: Modfed Structure for arped LP Flters. As a consequence of the appled substtuton, modfed flter coeffcents N are used n the new LP analyss and synthess flter structure. hese can be calculated from the orgnal coeffcents a w aw N recursvely as N = a w N = a w +1 ; = N 1, ; aw = 1.(7) 4.2. Zero-Mean Property Decorrelaton of an nput sgnal wthout any addtonal amplfcaton s connected to the well-known zero-mean property n conventonal LP [13]. LP does not provde ths DAFX-3

4 property. It can be shown, however, that the LP flters have zero-mean property f the modfed flter coeffcents (7) are normalzed accordng to a w = / = N,,. (8) Consderng Fgure 3, the flter coeffcents must be replaced by the normalzed coeffcents a w, wth the frst coeffcent resultng to a w = Spectral lt Compensaton Due to the non unform resoluton of the frequency scale n warped sgnal processng, the warped LP resdual sgnal s not perfectly flat but contans a spectral tlt [14]. hs spectral tlt nherent to the LP resdual must be compensated pror to quantzaton to acheve the hghest quantzaton SNR n closed-loop LP VQ. For ths purpose the flters accordng to (3), n the structure as depcted n Fgure 3, employng the normalzed coeffcents a w (8), are operated n the cascade wth the tlt compensaton flter Ht w (z) = 1 z 1 (9) replacements to form the overall LP analyss/synthess flter 1 A (z) = Hw A (z) Hw t (z) = (Hw,t S (z)) 1. (1) 4.4. SCELP Low Complexty Vector Search Consderng the analyss-by-synthess prncple, a low complexty vector search procedure s employed n the SCELP codec. It enables to search the large sphercal vector code- n a very effcent way to acheve the low computa- book tonal complexty of the codec. he prncple was ntro- n [5] and s depcted n Fgure 4. duced h 1 h ecursve flter ulse response (z) S/ S= x A (z) C D d flter rngng h w,t B (z) S A search loop codebook d R d MMSE h w,t h w,t E e w Fgure 4: Modfed Analyss-by-synthess. 1 No tlt compensaton s requred for the error weghtng flter because the same warpng factor s used for numerator and denomnator part. x - x Consderng Fgure 4, those functonal blocks whch must be adapted for the applcaton of LP wll be dentfed n the followng: he bass for an effcent vector search n the SCELP s the determnaton of sgnal d pror to the actual analyss-bysynthess vector search procedure. In order to determne ths sgnal, the flter rngng sgnal, marked by the label A, must be obtaned frst. Latter s the flter output resultng from the hstory related to prevously quantzed sgnal frames. Before a new sgnal nput vector x s quantzed, ths hstory s stored as the flter states S. In order to get the vector related to the flter rngng, the flter (z) s fed wth a zero nput sgnal vector of length L V. For the -SCELP, ths flter s dentfed as the cascade of the warped LP synthess flter accordng to (1) and the error weghtng flter accordng to (5): (z) = Hw,t S (z) w (z). (11) In order to obtan sgnal vector d, the flter rngng sgnal vector must be transformed nto the resdual sgnal doman. hs s done n Fgure 4 by means of convoluton, marked as the block h w,t at poston B. h w,t s dentfed as the truncated mpulse response of the nverse of flter (z) (11): h w,t = [h ] w,t, h w,t,(l V ; h w,t 1),k (H w,t (z)) 1 (12) he convolved flter rngng s added to the warped LP resdual d. he LP resdual d s obtaned by flterng x n the warped LP analyss flter A (z), poston C. he resultng sgnal vector d, poston D, s analyzed to determne the correspondng radus R whch s quantzed as R. In the analyss-by-synthess vector search procedure, code vectors d are generated by multplyng the sphercal shapecomponent vector canddates wth the quantzed radus R. Consderng the metrc (1) to fnd the optmal exctaton vector d Q, the unquantzed and the quantzed resdual vector canddate, d and d, both must be transformed from the LP resdual nto the sgnal doman. For ths purpose, the two blocks h w,t, poston E, represent the transform for both sgnals by means of convoluton wth the truncated mpulse response of flter (z) (11): [ ] h w,t = h w,t, hw,t,(l V 1) ; h w,t,k (z) (13) Now that all functonal blocks, whch were ntroduced for the SCELP codec, have been dentfed also for the -SCELP codec, the prncples of Pre-Selecton, the effcent metrc computaton and the Canddate-Excluson explaned n [5] for hghly effcent encodng can be appled also n the - SCELP. th the determnaton of quantzed LP resdual vector d Q, DAFX-4

5 the dfferental sgnal d d Q must be processed by flter H w (z) to fnally determne the update for the flter states S restored for the quantzaton of the next sgnal frame. codec (below 1 ms) 2. he result of the formal comparson of the new codec wth the G.722 reference codec s lsted n able 1 n the order of descendng perceptual qualty. he 4.5. Complexty he computatonal complexty of the warped LP analyss, synthess and error weghtng flter n the -SCELP codec s hgher than that of the same flters realzed for conventonal LP n the SCELP codec. he bggest part of the overall complexty of the SCELP codec, however, s spent on the analyss-by-synthess vector search. Snce the -SCELP benefts from the same prncples targetng low complexty encodng as the SCELP, the overall complexty s only margnally ncreased. he complexty of the encoder of the conventonal SCELP codec was estmated as 2-25 MOPS n [5], that of the encoder of the -SCELP codec as MOPS. he decoder of the -SCELP codec has an estmated complexty of 2-3 MOPS. 5. RESULS For the comparson of the acheved qualty of the -SCELP and the SCELP codec, both codecs have been confgured dentcally for a sample rate of f s = 16 khz. he resultng overall data rate s approxmately 48 kbt/, and the nose shapng coeffcents have been set to γ 1 =.6 and γ 2 =.94. he order of the lnear predcton n both cases s N = 1 and the algorthmc delay L LP ˆ=9 ms. For the -SCELP codec, the hghest performance has been determned for a warpng factor =.46 n nformal lstenng tests. Comparng the predcton gan n -SCELP and SCELP as a measure of sgnal decorrelaton, LP provdes only an nsgnfcantly hgher value. Consderng perceptual maskng of the quantzaton nose, t was observed n nformal lstenng tests that the hgher spectral resoluton of LP for lower frequences provdes sgnfcant benefts. Especally for audo sgnals wth a sparse spectrum, for example the sound of a flute, LP provdes clearly better perceptual results than conventonal LP. Consderng a formal assessment of the qualty, speech was processed by the -SCELP and the SCELP codec. he decoder output was rated wth the B-PESQ measure [15] whch s wdely used n the speech codng communty. As result, the -SCELP outperformed the SCELP by.2 on the MOS scale. Comparable results may also be obtaned usng the PEAQ qualty measure [16]. For a comparson of the -SCELP codec wth a standardzed audo codec, the same speech sgnal was also processed by the IU- G.722 low delay audo codec at 48, 56 and 64 kbt/. hs reference codec was chosen because of ts algorthmc delay n the magntude of that of the -SCELP Codec Data rate B-PESQ (MOS-LQO) G.722 mode 1 64 kbt -SCELP 48 kbt G.722 mode 2 56 kbt G.722 mode 3 48 kbt able 1: Results Formal Qualty Assessment. performance of the G.722 codecs was rated wth 4.2, 4.39 and 4.47 MOS for the three codec modes respectvely. he -SCELP at a data rate of roughly 48 kbt/ reached a value of 4.4 MOS. Consderng ths result, the qualty of the -SCELP codec at 48 kbt/ can be classfed as slghtly better than that of the G.722 at 56 kbt/. 6. CONCLUSION In ths contrbuton the prncple of warped sgnal processng was ncorporated nto the new SCELP low delay audo codec to form the -SCELP codec. hle the overall complexty of the -SCELP s only nsgnfcantly hgher than that of the SCELP codec, the achevable audo qualty s clearly better. In a comparson wth a standardzed codec that has a smlar algorthmc delay, the -SCELP at a data rate of 48 kbt/ outperforms the IU- G.722 audo codec at a data rate of 56 kbt/. 7. REFERENCES [1] ISO/IEC , Advanced Audo Codng (AAC), [2] M. Schroeder and B. Atal, Code-excted Lnear Predcton (CELP): Hgh-qualty Speech at very low Bt Rates, Proc. ICASSP, [3] Rec. GSM 6.9 ESI, Adaptve Mult-Rate (AMR) Speech ranscodng, [4] E. Gamal, L. Hemachandra, I. Spherlng, and V. e, Usng Smulated Annealng to Desgn Good Codes, IEEE rans. Inform. heory, vol. t-33, [5] H. Krüger and P. Vary, SCELP: Low Delay Audo Codng wth Nose Shapng based on Sphercal Vector Quantzaton, EUSIPCO, Florence, Italy, An alternatve codec wth a comparable algorthmc delay s the ULD codec [17]. hs codec, however, s not freely avalable and was thus not consdered. DAFX-5

6 [6] H. Krüger and P. Vary, An Effcent Codebook for the SCELP Low Delay Audo Codec, MMSP, Vctora, Canada, 26. [7] A. Härmä and U. Lane, A Comparson of arped and Conventonal Lnear Predctve Codng, IEEE rans. Speech and Audo Processng, vol. 9, no.5, 21. [8] IU- Rec. G.722, 7 khz Audo Codng wthn 64 kbt/s, [9] K. Palwal and B. Atal, Effcent Vector Quantzaton of LPC Parameters at 24 Bts/Frame, IEEE rans. Speech and Sgnal Proc., vol. 1, no.1, pp. 3 13, [1] N. Jayant and P. Noll, Dgtal Codng of aveforms, Prentce-Hall, Inc., [11] M. Schroeder, B. Atal, and J. Hall, Optmzng Dgtal Speech Coders by Explotng Maskng Propertes of the Human Ear, Journal of the Acoustcal Socety of Amerca, pp , [12] K. Stegltz, A Note on Varable Recursve Dgtal Flters, IEEE rans. Acc., Speech, and Sgnal Processng, vol. 28, 198. [13] J. Markel and A.Gray, Lnear Predcton of Speech, Sprnger, [14] H.. Strube, Lnear Predcton on a arped Frequency Scale, Journal of the Acoustcal Socety of Amerca, vol. 68, pp , 198. [15] IU- Rec. P.862.2, deband Extenson to Recommendaton P.862 for the Assessment of deband elephone Networks and Speech Codecs, 25. [16] Recommendaton IU-R BS.1387, Method for objectve measurements of perceved audo qualty, 21. [17] Audo Codng wth Ultra Low Encodng/Decodng Delay, 27. DAFX-6

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

3GPP TS V ( )

3GPP TS V ( ) TS 6.9 V.. (-9) Techncal Specfcaton 3rd Generaton Partnershp Project; Techncal Specfcaton Group Servces and System Aspects; Speech codec speech processng functons; Adaptve Mult-Rate - Wdeband (AMR-WB)

More information

arxiv:cs.cv/ Jun 2000

arxiv:cs.cv/ Jun 2000 Correlaton over Decomposed Sgnals: A Non-Lnear Approach to Fast and Effectve Sequences Comparson Lucano da Fontoura Costa arxv:cs.cv/0006040 28 Jun 2000 Cybernetc Vson Research Group IFSC Unversty of São

More information

CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be

CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be 55 CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER 4.1 Introducton In real envronmental condtons the speech sgnal may be supermposed by the envronmental nterference. In general, the spectrum

More information

SCELP: LOW DELAY AUDIO CODING WITH NOISE SHAPING BASED ON SPHERICAL VECTOR QUANTIZATION

SCELP: LOW DELAY AUDIO CODING WITH NOISE SHAPING BASED ON SPHERICAL VECTOR QUANTIZATION SCELP: LOW DELAY AUDIO CODING WITH NOISE SHAPING BASED ON SPHERICAL VECTOR QUANTIZATION Hauke Krüger and Peter Vary Institute of Communication Systems and Data Processing RWTH Aachen University, Templergraben

More information

Novel Pre-Compression Rate-Distortion Optimization Algorithm for JPEG 2000

Novel Pre-Compression Rate-Distortion Optimization Algorithm for JPEG 2000 Novel Pre-Compresson Rate-Dstorton Optmzaton Algorthm for JPEG 2000 Yu-We Chang, Hung-Ch Fang, Chung-Jr Lan, and Lang-Gee Chen DSP/IC Desgn Laboratory, Graduate Insttute of Electroncs Engneerng Natonal

More information

White Noise Reduction of Audio Signal using Wavelets Transform with Modified Universal Threshold

White Noise Reduction of Audio Signal using Wavelets Transform with Modified Universal Threshold Whte Nose Reducton of Audo Sgnal usng Wavelets Transform wth Modfed Unversal Threshold MATKO SARIC, LUKI BILICIC, HRVOJE DUJMIC Unversty of Splt R.Boskovca b.b, HR 1000 Splt CROATIA Abstract: - Ths paper

More information

Switched Quasi-Logarithmic Quantizer with Golomb Rice Coding

Switched Quasi-Logarithmic Quantizer with Golomb Rice Coding http://dx.do.org/10.5755/j01.ee.3.4.1877 Swtched Quas-Logarthmc Quantzer wth Golomb Rce Codng Nkola Vucc 1, Zoran Perc 1, Mlan Dncc 1 1 Faculty of Electronc Engneerng, Unversty of Ns, Aleksandar Medvedev

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

A NEW DISCRETE WAVELET TRANSFORM

A NEW DISCRETE WAVELET TRANSFORM A NEW DISCRETE WAVELET TRANSFORM ALEXANDRU ISAR, DORINA ISAR Keywords: Dscrete wavelet, Best energy concentraton, Low SNR sgnals The Dscrete Wavelet Transform (DWT) has two parameters: the mother of wavelets

More information

Transform Coding. Transform Coding Principle

Transform Coding. Transform Coding Principle Transform Codng Prncple of block-wse transform codng Propertes of orthonormal transforms Dscrete cosne transform (DCT) Bt allocaton for transform coeffcents Entropy codng of transform coeffcents Typcal

More information

both covariance and autocorrelation methods use two step solutions 1. computation of a matrix of correlation values

both covariance and autocorrelation methods use two step solutions 1. computation of a matrix of correlation values Dgtal Speech Processng Lecture 4 Lnear Predctve Codng (LPC)-Lattce Lattce Methods, Applcatons Lattce Formulatons of LP both covarance and autocorrelaton methods use two step solutons. computaton of a matrx

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

Memory ecient adaptation of vector quantizers to time-varying channels

Memory ecient adaptation of vector quantizers to time-varying channels Sgnal Processng 83 (3) 59 58 www.elsever.com/locate/sgpro Memory ecent adaptaton of vector quantzers to tme-varyng channels orbert Gortz a;,jorg Klewer b a Insttute for Communcatons Engneerng (LT), Munch

More information

Perceptual Postfiltering for Low Bit Rate Speech Coders

Perceptual Postfiltering for Low Bit Rate Speech Coders Perceptual Postflterng for Low Bt Rate Speech Coders We Chen Department of Electrcal & Computer Engneerng McGll Unversty Montreal, Canada November 2007 A thess submtted to McGll Unversty n partal fulfllment

More information

Time-Varying Systems and Computations Lecture 6

Time-Varying Systems and Computations Lecture 6 Tme-Varyng Systems and Computatons Lecture 6 Klaus Depold 14. Januar 2014 The Kalman Flter The Kalman estmaton flter attempts to estmate the actual state of an unknown dscrete dynamcal system, gven nosy

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

NON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS

NON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS NON-LINEAR CONVOLUTION: A NEW APPROAC FOR TE AURALIZATION OF DISTORTING SYSTEMS Angelo Farna, Alberto Belln and Enrco Armellon Industral Engneerng Dept., Unversty of Parma, Va delle Scenze 8/A Parma, 00

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI

Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI Power Allocaton for Dstrbuted BLUE Estmaton wth Full and Lmted Feedback of CSI Mohammad Fanae, Matthew C. Valent, and Natala A. Schmd Lane Department of Computer Scence and Electrcal Engneerng West Vrgna

More information

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic Internatonal Journal of Computer Applcatons Technology and Research A New Desgn of Multpler usng Modfed Booth Algorthm and Reversble Gate Logc K.Nagarjun Department of ECE Vardhaman College of Engneerng,

More information

Microwave Diversity Imaging Compression Using Bioinspired

Microwave Diversity Imaging Compression Using Bioinspired Mcrowave Dversty Imagng Compresson Usng Bonspred Neural Networks Youwe Yuan 1, Yong L 1, Wele Xu 1, Janghong Yu * 1 School of Computer Scence and Technology, Hangzhou Danz Unversty, Hangzhou, Zhejang,

More information

Application of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations

Application of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations Applcaton of Nonbnary LDPC Codes for Communcaton over Fadng Channels Usng Hgher Order Modulatons Rong-Hu Peng and Rong-Rong Chen Department of Electrcal and Computer Engneerng Unversty of Utah Ths work

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider:

Fourier Transform. Additive noise. Fourier Tansform. I = S + N. Noise doesn t depend on signal. We ll consider: Flterng Announcements HW2 wll be posted later today Constructng a mosac by warpng mages. CSE252A Lecture 10a Flterng Exampel: Smoothng by Averagng Kernel: (From Bll Freeman) m=2 I Kernel sze s m+1 by m+1

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Lecture 3: Shannon s Theorem

Lecture 3: Shannon s Theorem CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts

More information

Lossless Compression Performance of a Simple Counter- Based Entropy Coder

Lossless Compression Performance of a Simple Counter- Based Entropy Coder ITB J. ICT, Vol. 5, No. 3, 20, 73-84 73 Lossless Compresson Performance of a Smple Counter- Based Entropy Coder Armen Z. R. Lang,2 ITB Research Center on Informaton and Communcaton Technology 2 Informaton

More information

ECE559VV Project Report

ECE559VV Project Report ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate

More information

MDL-Based Unsupervised Attribute Ranking

MDL-Based Unsupervised Attribute Ranking MDL-Based Unsupervsed Attrbute Rankng Zdravko Markov Computer Scence Department Central Connectcut State Unversty New Brtan, CT 06050, USA http://www.cs.ccsu.edu/~markov/ markovz@ccsu.edu MDL-Based Unsupervsed

More information

TOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION

TOPICS MULTIPLIERLESS FILTER DESIGN ELEMENTARY SCHOOL ALGORITHM MULTIPLICATION 1 2 MULTIPLIERLESS FILTER DESIGN Realzaton of flters wthout full-fledged multplers Some sldes based on support materal by W. Wolf for hs book Modern VLSI Desgn, 3 rd edton. Partly based on followng papers:

More information

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or Sgnal Compresson Sgnal Compresson Entropy Codng Entropy codng s also known as zero-error codng, data compresson or lossless compresson. Entropy codng s wdely used n vrtually all popular nternatonal multmeda

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl RECURSIVE SPLINE INTERPOLATION METHOD FOR REAL TIME ENGINE CONTROL APPLICATIONS A. Stotsky Volvo Car Corporaton Engne Desgn and Development Dept. 97542, HA1N, SE- 405 31 Gothenburg Sweden. Emal: astotsky@volvocars.com

More information

Low Complexity Soft-Input Soft-Output Hamming Decoder

Low Complexity Soft-Input Soft-Output Hamming Decoder Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

Comparison of Wiener Filter solution by SVD with decompositions QR and QLP

Comparison of Wiener Filter solution by SVD with decompositions QR and QLP Proceedngs of the 6th WSEAS Int Conf on Artfcal Intellgence, Knowledge Engneerng and Data Bases, Corfu Island, Greece, February 6-9, 007 7 Comparson of Wener Flter soluton by SVD wth decompostons QR and

More information

Digital Audio Signal Processing DASP. Lecture-2: Noise Reduction-I. Single-Channel Noise Reduction. Marc Moonen

Digital Audio Signal Processing DASP. Lecture-2: Noise Reduction-I. Single-Channel Noise Reduction. Marc Moonen Dgtal Audo Sgnal Processng DASP Lecture-: Nose Reducton-I Sngle-Channel Nose Reducton Marc Moonen Dept. E.E./ESA-SADIUS, KU Leuven marc.moonen@kuleuven.be homes.esat.kuleuven.be/~moonen/ Sngle-Channel

More information

ROBUST ENCODING OF THE FS1016 LSF PARAMETERS : APPLICATION OF THE CHANNEL OPTIMIZED TRELLIS CODED VECTOR QUANTIZATION

ROBUST ENCODING OF THE FS1016 LSF PARAMETERS : APPLICATION OF THE CHANNEL OPTIMIZED TRELLIS CODED VECTOR QUANTIZATION ROBUST ENCODING OF THE FS6 LSF PARAMETERS : APPLICATION OF THE CHANNEL OPTIMIZED TRELLIS CODED VECTOR QUANTIZATION BOUZID Merouane Speech Communcaton and Sgnal Processng Laboratory, Electroncs Faculty,

More information

Mathematical Preparations

Mathematical Preparations 1 Introducton Mathematcal Preparatons The theory of relatvty was developed to explan experments whch studed the propagaton of electromagnetc radaton n movng coordnate systems. Wthn expermental error the

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Suman Shrestha 1, 2 1 Unversty of Massachusetts Medcal School,

More information

Identification of Linear Partial Difference Equations with Constant Coefficients

Identification of Linear Partial Difference Equations with Constant Coefficients J. Basc. Appl. Sc. Res., 3(1)6-66, 213 213, TextRoad Publcaton ISSN 29-434 Journal of Basc and Appled Scentfc Research www.textroad.com Identfcaton of Lnear Partal Dfference Equatons wth Constant Coeffcents

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

DESIGN OF STABLE TWO-DIMENSIONAL IIR NOTCH FILTER USING ROOT MAP

DESIGN OF STABLE TWO-DIMENSIONAL IIR NOTCH FILTER USING ROOT MAP 8th European Sgnal Processng Conference EUSIPCO- Aalborg, Denmar, August -7, DESIGN OF STABLE TWO-DIMENSIONAL IIR NOTC FILTER USING ROOT MAP Soo-Chang Pe and Chen-Cheng Tseng Depart. of Electrcal Engneerng,

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

829. An adaptive method for inertia force identification in cantilever under moving mass

829. An adaptive method for inertia force identification in cantilever under moving mass 89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,

More information

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

Flexible Quantization

Flexible Quantization wb 06/02/21 1 Flexble Quantzaton Bastaan Klejn KTH School of Electrcal Engneerng Stocholm wb 06/02/21 2 Overvew Motvaton for codng technologes Basc quantzaton and codng Hgh-rate quantzaton theory wb 06/02/21

More information

Asymptotic Quantization: A Method for Determining Zador s Constant

Asymptotic Quantization: A Method for Determining Zador s Constant Asymptotc Quantzaton: A Method for Determnng Zador s Constant Joyce Shh Because of the fnte capacty of modern communcaton systems better methods of encodng data are requred. Quantzaton refers to the methods

More information

Tornado and Luby Transform Codes. Ashish Khisti Presentation October 22, 2003

Tornado and Luby Transform Codes. Ashish Khisti Presentation October 22, 2003 Tornado and Luby Transform Codes Ashsh Khst 6.454 Presentaton October 22, 2003 Background: Erasure Channel Elas[956] studed the Erasure Channel β x x β β x 2 m x 2 k? Capacty of Noseless Erasure Channel

More information

DC-Free Turbo Coding Scheme Using MAP/SOVA Algorithms

DC-Free Turbo Coding Scheme Using MAP/SOVA Algorithms Proceedngs of the 5th WSEAS Internatonal Conference on Telecommuncatons and Informatcs, Istanbul, Turkey, May 27-29, 26 (pp192-197 DC-Free Turbo Codng Scheme Usng MAP/SOVA Algorthms Prof. Dr. M. Amr Mokhtar

More information

Regularized Discriminant Analysis for Face Recognition

Regularized Discriminant Analysis for Face Recognition 1 Regularzed Dscrmnant Analyss for Face Recognton Itz Pma, Mayer Aladem Department of Electrcal and Computer Engneerng, Ben-Guron Unversty of the Negev P.O.Box 653, Beer-Sheva, 845, Israel. Abstract Ths

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

Fingerprint Enhancement Based on Discrete Cosine Transform

Fingerprint Enhancement Based on Discrete Cosine Transform Fngerprnt Enhancement Based on Dscrete Cosne Transform Suksan Jrachaweng and Vutpong Areekul Kasetsart Sgnal & Image Processng Laboratory (KSIP Lab), Department of Electrcal Engneerng, Kasetsart Unversty,

More information

Tutorial on Image Reconstruction Based on Weighted Sum (WS) Filter Approach: From Single Image to Multi-Frame Image

Tutorial on Image Reconstruction Based on Weighted Sum (WS) Filter Approach: From Single Image to Multi-Frame Image AU J.. 3(): 75-86 (Oct. 009) utoral on Image Reconstructon Based on Weghted Sum (WS) Flter Approach: From Sngle Image to ult-frame Image Vorapoj Patanavjt Department of Computer and Network Engneerng,

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

CSE4210 Architecture and Hardware for DSP

CSE4210 Architecture and Hardware for DSP 4210 Archtecture and Hardware for DSP Lecture 1 Introducton & Number systems Admnstratve Stuff 4210 Archtecture and Hardware for DSP Text: VLSI Dgtal Sgnal Processng Systems: Desgn and Implementaton. K.

More information

Digital Signal Processing

Digital Signal Processing Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Application of Dynamic Time Warping on Kalman Filtering Framework for Abnormal ECG Filtering

Application of Dynamic Time Warping on Kalman Filtering Framework for Abnormal ECG Filtering Applcaton of Dynamc Tme Warpng on Kalman Flterng Framework for Abnormal ECG Flterng Abstract. Mohammad Nknazar, Bertrand Rvet, and Chrstan Jutten GIPSA-lab (UMR CNRS 5216) - Unversty of Grenoble Grenoble,

More information

COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering,

COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION. Erdem Bala, Dept. of Electrical and Computer Engineering, COMPUTATIONALLY EFFICIENT WAVELET AFFINE INVARIANT FUNCTIONS FOR SHAPE RECOGNITION Erdem Bala, Dept. of Electrcal and Computer Engneerng, Unversty of Delaware, 40 Evans Hall, Newar, DE, 976 A. Ens Cetn,

More information

Second Order Analysis

Second Order Analysis Second Order Analyss In the prevous classes we looked at a method that determnes the load correspondng to a state of bfurcaton equlbrum of a perfect frame by egenvalye analyss The system was assumed to

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

Antenna Combining for the MIMO Downlink Channel

Antenna Combining for the MIMO Downlink Channel Antenna Combnng for the IO Downlnk Channel arxv:0704.308v [cs.it] 0 Apr 2007 Nhar Jndal Department of Electrcal and Computer Engneerng Unversty of nnesota nneapols, N 55455, USA Emal: nhar@umn.edu Abstract

More information

A New Adaptive Filter Approach for Acoustic Echo Canceller in Teleconference Systems

A New Adaptive Filter Approach for Acoustic Echo Canceller in Teleconference Systems European Scentfc Journal September 28 edton Vol4 No27 ISSN: 857 788 (rnt) e - ISSN 857-743 A New Adaptve Flter Approach for Acoustc Echo Canceller n eleconference Systems Hamze Hadar Alaeddne Al Beydoun

More information

Dr. Ing. J. H. (Jo) Walling Consultant Cables Standards Machinery

Dr. Ing. J. H. (Jo) Walling Consultant Cables Standards Machinery The common mode crcut resstance unbalance (CMCU) calculaton based on mn. / max. conductor resstance values and par to par resstance unbalance measurements ncludng loop resstance evsed and extended verson

More information

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k) ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

Non-linear Canonical Correlation Analysis Using a RBF Network

Non-linear Canonical Correlation Analysis Using a RBF Network ESANN' proceedngs - European Smposum on Artfcal Neural Networks Bruges (Belgum), 4-6 Aprl, d-sde publ., ISBN -97--, pp. 57-5 Non-lnear Canoncal Correlaton Analss Usng a RBF Network Sukhbnder Kumar, Elane

More information

Basic Business Statistics, 10/e

Basic Business Statistics, 10/e Chapter 13 13-1 Basc Busness Statstcs 11 th Edton Chapter 13 Smple Lnear Regresson Basc Busness Statstcs, 11e 009 Prentce-Hall, Inc. Chap 13-1 Learnng Objectves In ths chapter, you learn: How to use regresson

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques

More information

A narrowband low bit rate sinusoidal audio and speech coder

A narrowband low bit rate sinusoidal audio and speech coder at.lab. Unclassfed Report /84 Date of ssue: /3 Unclassfed Report A narrowband low bt rate snusodal audo and speech coder G.H. Hotho and R.J. Sluter Phlps Electroncs ederland B.V. 3 /84 Phlps Electroncs

More information

Error Probability for M Signals

Error Probability for M Signals Chapter 3 rror Probablty for M Sgnals In ths chapter we dscuss the error probablty n decdng whch of M sgnals was transmtted over an arbtrary channel. We assume the sgnals are represented by a set of orthonormal

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models

Statistics for Managers Using Microsoft Excel/SPSS Chapter 14 Multiple Regression Models Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 14 Multple Regresson Models 1999 Prentce-Hall, Inc. Chap. 14-1 Chapter Topcs The Multple Regresson Model Contrbuton of Indvdual Independent Varables

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3 Transmtted by the expert from France Informal Document No. GRB-51-14 (67 th GRB, 15 17 February 2010, agenda tem 7) Regulaton No. 117 (Tyres rollng nose and wet grp adheson) Proposal for amendments to

More information

DPCM Compression for Real-Time Logging While Drilling Data

DPCM Compression for Real-Time Logging While Drilling Data 28 JOURAL OF SOFTWARE, VOL. 5, O. 3, MARCH 21 DPCM Compresson for Real-Tme Loggng Whle Drllng Data Yu Zhang Modern Sgnal Processng & Communcaton Group, Insttute of Informaton Scence, Bejng Jaotong Unversty,

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

An Interactive Optimisation Tool for Allocation Problems

An Interactive Optimisation Tool for Allocation Problems An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents

More information

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL The Synchronous 8th-Order Dfferental Attack on 12 Rounds of the Block Cpher HyRAL Yasutaka Igarash, Sej Fukushma, and Tomohro Hachno Kagoshma Unversty, Kagoshma, Japan Emal: {garash, fukushma, hachno}@eee.kagoshma-u.ac.jp

More information

Lecture 5 Decoding Binary BCH Codes

Lecture 5 Decoding Binary BCH Codes Lecture 5 Decodng Bnary BCH Codes In ths class, we wll ntroduce dfferent methods for decodng BCH codes 51 Decodng the [15, 7, 5] 2 -BCH Code Consder the [15, 7, 5] 2 -code C we ntroduced n the last lecture

More information

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder. PASSBAND DIGITAL MODULATION TECHNIQUES Consder the followng passband dgtal communcaton system model. cos( ω + φ ) c t message source m sgnal encoder s modulator s () t communcaton xt () channel t r a n

More information

Communication with AWGN Interference

Communication with AWGN Interference Communcaton wth AWG Interference m {m } {p(m } Modulator s {s } r=s+n Recever ˆm AWG n m s a dscrete random varable(rv whch takes m wth probablty p(m. Modulator maps each m nto a waveform sgnal s m=m

More information

SYNTHETIC aperture radar (SAR) uses the relative motion

SYNTHETIC aperture radar (SAR) uses the relative motion 512 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 4, OCTOBER 2006 SAR Mnmum-Entropy Autofocus Usng an Adaptve-Order Polynomal Model Junfeng Wang and Xngzhao Lu Abstract A new algorthm s presented

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

Chapter 15 Student Lecture Notes 15-1

Chapter 15 Student Lecture Notes 15-1 Chapter 15 Student Lecture Notes 15-1 Basc Busness Statstcs (9 th Edton) Chapter 15 Multple Regresson Model Buldng 004 Prentce-Hall, Inc. Chap 15-1 Chapter Topcs The Quadratc Regresson Model Usng Transformatons

More information