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The first circuit is of current-mode type, and second is of transadmittance-mode type. Both the circuits are suitable to modern IC technology and offer the feature of ease of cascadability. The proposed circuits do not need any passive component matching constraints. Moreover, the pole frequency of both the circuits is electronically tunable. The transadmittance-mode type circuit also provides an interesting feature of electronic and independent tuning of amplitudes of output currents without affecting the pole frequency. To check the applicability of the proposed circuit, a dual-mode multiphase oscillator is also derived from the proposed transadmittance-mode type filter. It provides two quadrature voltage outputs and four quadrature current outputs simultaneously. The oscillation frequency and condition of oscillation are orthogonally and electronically controllable. The non-ideal and parasitic analyses of all the proposed circuits are investigated, and HSPICE simulation results are depicted to confirm the proposed theory. Furthermore, transadmittance-mode type first-order universal filter is experimentally verified using commercially available ICs.",{"EN":229},"Electronically Tunable First-Order Filters and Dual-Mode Multiphase Oscillator",{"VOID":231},"[\"9482122899307844497\"]",{"VOID":233},"10.1007\u002Fs00034-018-0849-x","PUBLICATION","VERIFIED","2024-05-02T16:09:41.609+00:00","Auto 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Chaturvedi, Novel CMOS current inverting differential input transconductance amplifier and its application. J. Circuits Syst. Comput. 26(1), 1750010 (2017)",{"doi":337},{"id":399,"text":400,"url":401,"identifiers":402},"4a2cc3a5-d940-46ab-999d-f5a853fd509f","A. Kumar, B. Chaturvedi, Novel electronically controlled current-mode Schmitt trigger based on single active element. AEU Int. J. Electron. Commun. 82, 160–166 (2017)","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1434841117308993",{"doi":403},"10.1016\u002Fj.aeue.2017.08.007",{"id":405,"text":406,"url":407,"identifiers":408},"1dbd7cdf-c0e0-48ac-892f-c80d9b1e66bd","A. Kumar, S.K. Paul, Current mode first order universal filter and multiphase sinusoidal oscillator. AEU Int. J. Electron. Commun. 81, 37–49 (2017)","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1434841117308440",{"doi":409},"10.1016\u002Fj.aeue.2017.07.004",{"id":333,"text":411,"url":335,"identifiers":412},"A. Kumar, B. Chaturvedi, Novel CMOS dual-X current conveyor transconductance amplifier realization with current-mode multifunction filter and quadrature oscillator. Circuits Syst. Signal Process. 37, 2250–2277 (2018)",{"doi":337},{"id":333,"text":414,"url":335,"identifiers":415},"S. Maheshwari, I.A. Khan, Novel first order all-pass sections using a single CCIII. Int. J. Electron. 88, 773–778 (2001)",{"doi":337},{"id":333,"text":417,"url":335,"identifiers":418},"S. Maheshwari, I.A. Khan, Simple first-order translinear-C current-mode all-pass sections. Int. J. Electron. 90, 79–85 (2003)",{"doi":337},{"id":333,"text":420,"url":335,"identifiers":421},"S. Maheshwari, Electronically tunable quadrature oscillator using translinear conveyors and grounded capacitors. Act. Passive Electron. Compon. 26(3), 193–196 (2003)",{"doi":337},{"id":333,"text":423,"url":335,"identifiers":424},"S. Maheshwari, New voltage and current-mode APS using current controlled conveyor. Int. J. Electron. 91, 735–743 (2004)",{"doi":337},{"id":18,"text":426,"url":18,"identifiers":427},"S. Maheshwari, I.A. Khan, J. Mohan, Grounded capacitor first-order filters including canonical forms. J. Circuits Syst. Comput. 15, 289–300 (2006)",{},{"id":18,"text":429,"url":18,"identifiers":430},"S. Maheshwari, A new current-mode current-controlled all-pass section. J. Circuits Syst. Comput. 16, 181–189 (2007)",{},{"id":333,"text":432,"url":335,"identifiers":433},"S. Maheshwari, Novel cascadable current-mode first order all-pass sections. Int. J. Electron. 94, 995–1003 (2007)",{"doi":337},{"id":333,"text":435,"url":335,"identifiers":436},"S. Maheshwari, High input impedance VM-APSs with grounded passive elements. IET Circuits Devices Syst. 1(1), 72–78 (2007)",{"doi":337},{"id":333,"text":438,"url":335,"identifiers":439},"S. Maheshwari, High input impedance voltage-mode first-order all-pass sections. Int. J. Circuit Theory Appl. 36, 511–522 (2008)",{"doi":337},{"id":333,"text":441,"url":335,"identifiers":442},"S. Maheshwari, A canonical voltage-controlled VM-APS with a grounded capacitor. Circuits Syst. Signal Process. 27, 123–132 (2008)",{"doi":337},{"id":333,"text":444,"url":335,"identifiers":445},"S. Maheshwari, High output impedance current-mode all-pass sections with two grounded passive components. IET Circuits Devices Syst. 2(2), 234–242 (2008)",{"doi":337},{"id":333,"text":447,"url":335,"identifiers":448},"S. Maheshwari, J. Mohan, D.S. Chauhan, Voltage-mode cascadable all-pass sections with two grounded passive components and one active element. IET Circuits Devices Syst. 4, 113–122 (2010)",{"doi":337},{"id":333,"text":450,"url":335,"identifiers":451},"S. Maheshwari, B. Chaturvedi, High-input low-output impedance all-pass filters using one active element. IET Circuits Devices Syst. 6, 103–110 (2012)",{"doi":337},{"id":333,"text":453,"url":335,"identifiers":454},"S. Maheshwari, M.S. Ansari, Catalog of realization for DXCCII using commercially available ICs and applications. Radioengineering 21(1), 281–289 (2012)",{"doi":337},{"id":333,"text":456,"url":335,"identifiers":457},"S. Maheshwari, Sinusoidal generator with π\u002F4-shifted four\u002Feight voltage outputs employing four grounded components and two\u002Fsix active elements. Active Passive Electron. Compon. 2014, 480590 (2014)",{"doi":337},{"id":333,"text":459,"url":335,"identifiers":460},"S. Maheshwari, D. Agrawal, High performance voltage-mode tunable all-pass section. J. Circuits Syst. Comput. 24, 1550080 (2015)",{"doi":337},{"id":333,"text":462,"url":335,"identifiers":463},"S. Maheshwari, D. Agrawal, Cascadable and tunable analog building blocks using EX-CCCII. J. Circuits Syst. Comput. 26, 1750093 (2017)",{"doi":337},{"id":333,"text":465,"url":335,"identifiers":466},"S. Maheshwari, Some analog filters of reduced complexity with shelving and multifunctional characteristics. J. Circuits Syst. Comput. 27, 1850150 (2018)",{"doi":337},{"id":18,"text":468,"url":469,"identifiers":470},"S. Maheshwari, Tuning approach for first-order filters and new current-mode circuit example. IET Circuits Devices Syst. (2018). https:\u002F\u002Fdoi.org\u002F10.1049\u002Fiet-cds.2017.0431","https:\u002F\u002Fdoi.org\u002F10.1049\u002Fiet-cds.2017.0431",{"mag":471,"openalex":472,"doi":473},"2789960118","W2789960118","10.1049\u002Fiet-cds.2017.0431",{"id":475,"text":476,"url":477,"identifiers":478},"b9ff484d-b54d-401b-834e-92f9c0dff037","B. Metin, K. Pal, Cascadable allpass filter with a single DO-CCII and a grounded capacitor. Analog Integr. Circuits Signal Process. 61(3), 259–263 (2009)","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10470-009-9301-2",{"doi":479},"10.1007\u002Fs10470-009-9301-2",{"id":333,"text":481,"url":335,"identifiers":482},"S. Minaei, E. Yuce, Novel voltage-mode all-pass filter based on using DVCCs. Circuits Syst. Signal Process. 29(3), 391–402 (2010)",{"doi":337},{"id":333,"text":484,"url":335,"identifiers":485},"J. Mohan, S. Maheshwari, Cascadable current-mode first-order all-pass filter based on minimal components. Sci. World J. 2013, 859784 (2013)",{"doi":337},{"id":333,"text":487,"url":335,"identifiers":488},"J. Mohan, B. Chaturvedi, S. Maheshwari, Novel current-mode all-pass filter with minimum component count. Int. J. Image Graphics Signal Process. 5(12), 32–37 (2013)",{"doi":337},{"id":18,"text":490,"url":18,"identifiers":491},"J. Mohan, S. Maheshwari, Additional high-input low-output impedance voltage-mode all-pass sections. J. Circuits Syst. Comput. 23, 1450077 (2014)",{},{"id":333,"text":493,"url":335,"identifiers":494},"J. Mohan, B. Chaturvedi, S. Maheshwari, Low voltage mixed-mode multi phase oscillator using single FDCCII. Electronics 20(1), 36–42 (2016)",{"doi":337},{"id":333,"text":496,"url":335,"identifiers":497},"J. Mohan, B. Chaturvedi, Load insensitive, low voltage quadrature oscillator using single active element. Adv. Electr. Electron. Eng. 15(3), 408–415 (2017)",{"doi":337},{"id":333,"text":499,"url":335,"identifiers":500},"C. Psychalinos, K. Pal, F.A. Khanday, Single MIMO-OTA and single-grounded-capacitor-based first-order allpass filter design. Int. J. Electron. 101(12), 1716–1723 (2014)",{"doi":337},{"id":333,"text":502,"url":335,"identifiers":503},"L. Safari, E. Yuce, S. Minaei, A new ICCII based resistor-less current-mode first-order universal filter with electronic tuning capability. Microelectron. J. 67, 101–110 (2017)",{"doi":337},{"id":333,"text":505,"url":335,"identifiers":506},"A. Toker, O. Cicekoglu, S. Ozcan, K. Kuntman, High-output-impedance transadmittance type continuous-time multifunction filter with minimum active elements. Int. J. Electron. 88, 1085–1091 (2001)",{"doi":337},{"id":18,"text":508,"url":18,"identifiers":509},"E. Yuce, S. Minaei, N. Herencsar, J. Koton, Realization of first-order current-mode filters with low number of MOS transistors. J. Circuits Syst. Comput. 22, 1250071 (2013)",{},{"id":333,"text":511,"url":335,"identifiers":512},"E. Yuce, S. Minaei, A first-order fully cascadable current-mode universal filter composed of dual output CCIIs and a grounded capacitor. J. Circuits Syst. Comput. 25, 1650042 (2016)",{"doi":337},{"id":333,"text":514,"url":335,"identifiers":515},"E. Yuce, DO-CCII\u002FDO-DVCC based electronically fine tunable quadrature oscillators. J. Circuits Syst. Comput. 26(02), 1750025 (2017)",{"doi":337},false,{"id":518,"createTime":519,"updateTime":520,"relativeEntities":521,"slug":522,"properties":523,"entityType":234,"verifyStatus":235,"verifyTime":532,"verifyNote":237,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":533,"fullTextUrl":18,"authors":534,"publicationType":271,"publisherRelationship":567,"citationCount":19,"citationInfo":618,"publishDate":620,"publishYear":324,"citationAnalyzeStatus":329,"lastCitationAnalyze":621,"indexDatabases":622,"openAccess":18,"references":623,"isForceReanalyzing":516},"b3e0f944-58ef-4344-8002-efc0b88c3bfd","2024-02-02T04:50:26.467+00:00","2026-08-19T23:40:06.070+00:00",[],"Directional-Schemes-for-Edge-Detection-Based-on-B-spline-Wavelets",{"abstract":524,"title":526,"gsPaper":528,"doi":530},{"EN":525},"The aim of the present paper is to introduce two efficient robust schemes for edge detection and boundary detection. The main idea is based on the odd-order B-spline wavelets. In the first proposed scheme, high-pass filter of an odd-order B-spline wavelet has been rotated in four directions, and then the best directions for each pixel have been selected through computations. The novelty aspect of this scheme is that unlike to other edge detectors based on wavelets which use wavelet transform modulus value for detecting the edges of the images, each direction information is involved in detecting the singularities of the image independently and then those directions where the singularity in those directions has high absolute value are chosen for detecting the edges. The second scheme, which is a modified active contour model, has been designed for image boundary detection. This model not only is applicable in different scales, but also against the previous active contour models, uses more directional information to guide the motion of the initial contour and is more accurate than previous active contour models for boundary detection or in some cases for segmentation. Moreover, this scheme is not sensitive to the location of initial contour. Experimental results show the accuracy of the proposed schemes in comparison with other state-of-the-art edge detectors like curvelets, shearlets, wavelets and Canny method.",{"EN":527},"Directional Schemes for Edge Detection Based on B-spline Wavelets",{"VOID":529},"[\"16917299176172801344\"]",{"VOID":531},"10.1007\u002Fs00034-018-0753-4","2024-04-30T02:43:24.879+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00034-018-0753-4",[535,552],{"id":536,"sortIndex":19,"researcher":18,"roles":537,"affiliations":538,"properties":547,"displayName":549,"givenName":18,"familyName":18},"528ed2e6-0379-44ad-b3f0-b65892b4261a",[243],[539],{"id":540,"sortIndex":19,"affiliation":541,"properties":18},"00b66341-24c2-4580-9e2e-01dd6e375c95",{"id":540,"createTime":18,"updateTime":18,"relativeEntities":542,"slug":18,"properties":543,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":546,"statistic":18},[],{"title":544},{"VI":545},"Image Processing Laboratory, Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran",[],{"title":548,"gsAuthor":550},{"VI":549},"Parisa Noras",{"VOID":551},"[\"xivz9SUAAAAJ\"]",{"id":553,"sortIndex":180,"researcher":18,"roles":554,"affiliations":555,"properties":562,"displayName":564,"givenName":18,"familyName":18},"337aa741-c168-4c23-aae1-39f882833206",[243],[556],{"id":540,"sortIndex":19,"affiliation":557,"properties":18},{"id":540,"createTime":18,"updateTime":18,"relativeEntities":558,"slug":18,"properties":559,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":561,"statistic":18},[],{"title":560},{"VI":545},[],{"title":563,"gsAuthor":565},{"VI":564},"Nasser Aghazadeh",{"VOID":566},"[\"Tk6RzyAAAAAJ\"]",{"url":533,"publisher":568,"properties":613},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":569,"slug":10,"properties":570,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":573,"manageAffiliations":582,"indexDatabases":593,"url":18,"thumbnailPath":18,"statistic":608,"gsStatistic":18,"type":214,"analyzePriority":18},[],{"issn":571,"title":572},{"VOID":13},{"VOID":15},[574,578],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":575,"label":576,"description":577,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":579,"label":580,"description":581,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},[583,588],{"id":35,"createTime":18,"updateTime":18,"relativeEntities":584,"slug":18,"properties":585,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":587,"statistic":18},[],{"title":586},{"EN":39},[],{"id":42,"createTime":18,"updateTime":18,"relativeEntities":589,"slug":18,"properties":590,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":592,"statistic":18},[],{"title":591},{"EN":46},[48],[594,601],{"id":51,"indexDatabase":595,"url":62,"indexYears":63,"academicFieldIds":600,"indexDatabaseRanking":67},{"id":53,"createTime":18,"updateTime":18,"relativeEntities":596,"label":597,"description":598,"key":59,"publicationTags":599,"standard":18},[],{"EN":56,"VI":56},{"EN":56,"VI":58},[61],[65,66],{"id":69,"indexDatabase":602,"url":82,"indexYears":18,"academicFieldIds":607,"indexDatabaseRanking":18},{"id":71,"createTime":18,"updateTime":18,"relativeEntities":603,"label":604,"description":605,"key":78,"publicationTags":606,"standard":18},[],{"EN":74,"VI":74},{"EN":76,"VI":77},[80,81],[84],{"impactFactor":19,"impactFactorByYear":609,"i10Index":98,"i10IndexLast5Year":99,"totalPublication":100,"totalPublicationByYear":610,"totalCitation":136,"totalCitationByYear":611,"totalCitationPerPublication":174,"totalCitationPerPublicationByYear":612,"hindexLast5Year":117,"hindex":117},{"2012":87,"2013":88,"2014":89,"2015":90,"2016":91,"2017":92,"2018":88,"2019":93,"2020":94,"2021":95,"2022":96,"2023":97},{"1982":102,"1983":103,"1984":104,"1985":105,"1986":106,"1987":103,"1988":107,"1989":108,"1990":102,"1991":109,"1992":110,"1993":111,"1994":112,"1995":113,"1996":114,"1997":115,"1998":116,"1999":117,"2000":104,"2001":108,"2002":107,"2003":102,"2004":118,"2005":119,"2006":117,"2007":114,"2008":120,"2009":113,"2010":121,"2011":122,"2012":123,"2013":124,"2014":125,"2015":126,"2016":127,"2017":128,"2018":129,"2019":130,"2020":131,"2021":132,"2022":133,"2023":134,"2024":135},{"1982":138,"1983":139,"1984":140,"1985":141,"1986":106,"1987":111,"1988":142,"1989":143,"1990":144,"1991":145,"1992":146,"1993":144,"1994":102,"1995":147,"1996":148,"1997":149,"1998":150,"1999":117,"2000":151,"2001":152,"2002":153,"2003":119,"2004":154,"2005":155,"2006":156,"2007":157,"2008":158,"2009":159,"2010":160,"2011":161,"2012":162,"2013":163,"2014":164,"2015":165,"2016":166,"2017":167,"2018":168,"2019":169,"2020":170,"2021":171,"2022":172,"2023":173,"2024":152},{"1982":176,"1983":177,"1984":178,"1985":179,"1986":180,"1987":181,"1988":182,"1989":89,"1990":87,"1991":183,"1992":184,"1993":185,"1994":186,"1995":187,"1996":188,"1997":189,"1998":190,"1999":180,"2000":191,"2001":192,"2002":193,"2003":194,"2004":195,"2005":196,"2006":197,"2007":152,"2008":198,"2009":199,"2010":200,"2011":201,"2012":202,"2013":203,"2014":204,"2015":205,"2016":206,"2017":207,"2018":208,"2019":204,"2020":209,"2021":210,"2022":211,"2023":212,"2024":213},{"pages":614,"volume":616},{"VOID":615},"3973-3994",{"VOID":617},"37",{"total":19,"publishYear":324,"statisticByYear":619},{},"2018-01-19","2026-08-19T23:40:06.069+00:00",[67,80],[624,627,630,633,636,639,642,645,648,651,654,657,660,663,666,669,672,675,678,681,684,687,690,693,696,699,702],{"id":333,"text":625,"url":335,"identifiers":626},"N. Aghazadeh, Y. Gholizade Atani, Edge detection with hessian matrix property based on wavelet transform. J. Sci. Islam. Repub. Iran 26(2), 163–170 (2015)",{"doi":337},{"id":333,"text":628,"url":335,"identifiers":629},"L. Bin, M.S. yeganeh, Comparison for image edge detection algorithms. IOSR J. Comput. Eng. (IOSRJCE) 2(6), 01–04 (2012)",{"doi":337},{"id":333,"text":631,"url":335,"identifiers":632},"X. Cai, R. Chan, S. Morigi, F. Sgallari, Vessel segmentation in medical imaging using a tight-frame based algorithm. SIAM J. Imaging Sci. 6(1), 464–486 (2013)",{"doi":337},{"id":333,"text":634,"url":335,"identifiers":635},"E.J. Cands, D.L. Donoho, New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Commun. Pure Appl. Math. 57(2), 219–266 (2004)",{"doi":337},{"id":333,"text":637,"url":335,"identifiers":638},"E.J. Cands, D.L. Donoho, Ridgelets: a key to higher-dimensional intermittency? Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 357(1760), 24952509 (1999)",{"doi":337},{"id":333,"text":640,"url":335,"identifiers":641},"J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–697 (1986)",{"doi":337},{"id":18,"text":643,"url":18,"identifiers":644},"K. Guo, G. Kutyniok, D. Labate, Sparse multidimensional representations using anisotropic dilation and shear operators, in International Conference on the Interaction between Wavelets and Splines, At Athens, GA, Volume: Wavelets and Splines: Athens (2005)",{},{"id":333,"text":646,"url":335,"identifiers":647},"W. Guo, M.-J. Lai, Box spline wavelet frames for image edge analysis. SIAM J. Imaging Sci. 6(3), 1553–1578 (2013)",{"doi":337},{"id":18,"text":649,"url":18,"identifiers":650},"P.C. Hansen, J.G. Nagy, D.P. O’Leary, Debluring Images, Matrices, Spectra and Filtering (SIAM, Philadelphia, 2006)",{},{"id":333,"text":652,"url":335,"identifiers":653},"G. Kutyniok, J. Lemvig, W.-Q. Lim, Optimally sparse approximations of 3D functions by compactly supported shearlet frames. SIAM J. Math. Anal. 44, 2962–3017 (2012)",{"doi":337},{"id":333,"text":655,"url":335,"identifiers":656},"C. Li et al., Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Process. 17(10), 1940–1949 (2008)",{"doi":337},{"id":18,"text":658,"url":18,"identifiers":659},"J. Li, A wavelet approach to edge detection. Masters Thesis. Sam Houston State University (2003)",{},{"id":333,"text":661,"url":335,"identifiers":662},"P. Lin et al., Image detection of rice fissures using biorthogonal B-spline wavelets in multi-resolution spaces. Food Bioprocess Technol. 5, 2017–2024 (2012)",{"doi":337},{"id":333,"text":664,"url":335,"identifiers":665},"S. Mallat, W.L. Hwang, Singularity detection and processing with wavelets. IEEE Trans. Inf. Theory 38(2), 617–643 (1992)",{"doi":337},{"id":333,"text":667,"url":335,"identifiers":668},"S. Mallat, S. Zhong, Charactrization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992)",{"doi":337},{"id":333,"text":670,"url":335,"identifiers":671},"J.K. Mandal, A. Ghosh, Edge detection by modified Otsu method. Comput. Sci. Inf. Technol. 3(6), 233–240 (2013)",{"doi":337},{"id":333,"text":673,"url":335,"identifiers":674},"D.-Y. Po, M.N. Do, Directional multiscale modeling of images using the contourlet transform. IEEE Trans. Image Process. 15(6), 16101620 (2006)",{"doi":337},{"id":333,"text":676,"url":335,"identifiers":677},"P.M.K. Prasad et al., Performance analysis of orthogonal and biorthogonal wavelets for edge detection of x-ray images. Procedia Comput. Sci. 87, 116–121 (2016)",{"doi":337},{"id":333,"text":679,"url":335,"identifiers":680},"I.W. Selesnick, R.G. Baraniuk, N.C. Kingsbury, The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22(6), 123151 (2005)",{"doi":337},{"id":333,"text":682,"url":335,"identifiers":683},"D. Selvathi, N. Balagopal, Detection of retinal blood vessels using curvelet transform, in International Conference of Devices, Circuits and Systems (ICDCS). pp. 325–329 (2012)",{"doi":337},{"id":18,"text":685,"url":18,"identifiers":686},"K.P. Soman, K.I. Ramachandran, N.G. Resmi, Insight into Wavelets: From Theory to Practice, 3rd edn. (PHI Learning, New Delhi, 2010)",{},{"id":333,"text":688,"url":335,"identifiers":689},"J.L. Starck, E.J. Candes, D.L. Donoho, The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)",{"doi":337},{"id":18,"text":691,"url":18,"identifiers":692},"R. Szeliski, Computer Vision: Algorithms and Applications (Springer, New York, 2011)",{},{"id":18,"text":694,"url":18,"identifiers":695},"C.L. Tu, W.L. Hwang, J. Ho, Analysis of singularities from modulus complex wavelets. IEEE Trans. Inf. Theory 51(3), 10491062 (2005)",{},{"id":18,"text":697,"url":18,"identifiers":698},"S. Yi, D. Labate, G.R. Easley, H. Karim, A shearlet approach to edge detection. IEEE Trans. Image Process. 18(5), 929–941 (2009)",{},{"id":333,"text":700,"url":335,"identifiers":701},"L. Zhang, P. Bao, A wavelet-based edge detection method by scale multiplication. IEEE 16th Int. Conf. Pattern Recogn. 3, 501504 (2002)",{"doi":337},{"id":333,"text":703,"url":335,"identifiers":704},"Z. Zhang et al., An edge detection approach based on directional wavelet transform. Comput. Math. Appl. 57(8), 1265–1271 (2009)",{"doi":337},{"id":706,"createTime":707,"updateTime":708,"relativeEntities":709,"slug":710,"properties":711,"entityType":234,"verifyStatus":235,"verifyTime":722,"verifyNote":237,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":723,"fullTextUrl":18,"authors":724,"publicationType":271,"publisherRelationship":755,"citationCount":806,"citationInfo":807,"publishDate":810,"publishYear":808,"citationAnalyzeStatus":329,"lastCitationAnalyze":811,"indexDatabases":812,"openAccess":18,"references":18,"isForceReanalyzing":516},"7cdd4d7d-d572-45c0-813b-eb4ddc3a20df","2024-01-17T01:04:09.480+00:00","2026-07-25T20:24:42.022+00:00",[],"A-new-approach-to-speech-segmentation-based-on-the-maximum-likelihood",{"abstract":712,"title":714,"gsPaper":716,"references":718,"doi":720},{"EN":713},"Successful speech recognition is highly dependent on appropriate speech segmentation. The poor efficiency of the sequential detection of abrupt changes in the signals with relatively short stationary intervals, as is the case with speech signals, can be improved by the off-line maximum likelihood segmentation algorithm. In this paper the new segmentation algorithm is presented. For the a priori known number of segments, the algorithm determines such signal partitions for which the sum of segment distortion is minimal. The generalized maximum likelihood distortion measure has been introduced, and has proven to be particularly efficient on short signal segments. In the case of an unknown number of segments, its estimate is obtained comparing the reduction of the distortion. The asymptotic properties of the distortion sequence have been analyzed, which led to the definition of the presented segmentation algorithm. The introduced measure can be applied both to the AR and ARMA models. The segmentation algorithm is verified on test signals as well as on the natural speech signal, for which the pitch synchronous framing scheme is applied. The experimental results also include a comparison of the AR and ARMA model-based segmentations. The first results show that ARMA model-based segmentation gives somewhat better results than the AR model algorithm.",{"EN":715},"A new approach to speech segmentation based on the maximum likelihood",{"VOID":717},"[\"4533041304571871851\"]",{"VOID":719},"R. Andre-Obrecht, A new statistical approach for the automatic segmentation of continuous speech signals,IEEE Trans. Acoust. Speech Signal Process. ASSP-36, no. 1, January 1988, pp. 29–40.\nU. Appel and A. V. Brandt, Adaptive sequential segmentation of piecewise stationary time series,Information Science, vol. 29, no. 1, 1983, pp. 17–56.\nM. Basseville and A. Beneviste, eds.,Detection of Abrupt Changes in Signals and Dynamical Systems, Springer-Verlag, Berlin and New York, 1986.\nB. Friedlander, Lattice filters for adaptive processing,Proc. IEEE, vol. 70, no. 8, August, 1982, pp. 829–867.\nW. Hess,Pitch Determination of Speech Signals, Springer-Verlag, Berlin and New York, 1983.\nF. Itakura and S. Saito, A statistical method for estimation of speech spectral density and formant frequencies,Electron, and Commun., vol. 53-A, 1970, pp. 36–43.\nI. Konvalinka and M. Milosavljević, Sequential detection of the speech signal stationarity boundaries,Proc. XXIX ETAN Conf., Niš, vol. IV, pp. 141–146, June 1985, (in Serbian).\nAshok K. Krishnamurthy and Donald G. Childers, Two-channels speech analysis,IEEE Trans. Acoust. Speech Signal Process. ASSP-34, no. 4, August 1986, pp. 730–742.\nChin-Hui Lee, Frank K. Song, and Biing-Hwang Juang, A segment model based approach to speech recognition,IC ASSP, 1988, pp. 501–504.\nJ. D. Markel and A. H. Gray, Jr.,Linear Prediction of Speech, Springer-Verlag, Berlin and New York, 1976.\nYoshiaki Miyoshi, Kazuharu Yamato, Riichiro Mizoguchi, Masuzo Yanagida, and Osamu Kakusho, Analysis of speech signals of short pitch period by a sample-selective linear prediction,IEEE Trans. Acoust. Speech Signal Process. ASSP-35, no. 9, September 1987, pp. 1233–1239.\nZoran šarić, Reducing the speech signal pitch influence to AR parameters estimation using weighted sum of squares errors,XXXII Yugoslavian Conference ETAN, June 1988, pp. 177–184, Sarajevo (in Serbian).\nZoran šarić and Srbijank R. Turajlić, Estimation and setting starting values in ARMA algorithms,Circuits Systems Signal Process., vol. 12, no. 1, 1993, pp. 85–103.\nT. Svedsen and F. K. Soong, On the automatic segmentation of speech signals,IC ASSP, 1987, pp. 77–80.\nE. Vidal and A. Marzal, A review and new approaches for automatic segmentation of speech signal,Proc. of EUSIPCO-90, Barcelona (Spain), September 1990, vol. 1, pp. 43–54.",{"VOID":721},"10.1007\u002FBF01213958","2024-07-11T01:35:30.275+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF01213958",[725,740],{"id":726,"sortIndex":19,"researcher":18,"roles":727,"affiliations":728,"properties":737,"displayName":739,"givenName":18,"familyName":18},"944b16e6-a4d0-4f9e-8b89-b6e0dbd848f0",[243],[729],{"id":730,"sortIndex":19,"affiliation":731,"properties":18},"0b702813-3655-4697-bc58-9d91ae5e8c69",{"id":730,"createTime":18,"updateTime":18,"relativeEntities":732,"slug":18,"properties":733,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":736,"statistic":18},[],{"title":734},{"VI":735},"Institute for Applied Mathematics and Electronics, Beograd, Yugoslavia",[],{"title":738},{"VI":739},"Z. 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main contribution of this paper is to present a new approach to the analysis of the absolute stability of multiple-input–multiple-output (MIMO) Lurie-type systems using \n                \n                  \n                \n                $$\\mu $$\n                \n              -analysis and linear fractional transformations from the robust control theory. As a consequence, and also as an important contribution, the technique proposed enables the design of controllers via DK-Iteration for Lurie-type systems. For these, one extends the results obtained for Lurie-type systems to a closed-loop version of it. In addition, it is also conjectured that it is possible to make use of this new approach in time-delay MIMO Lurie-type systems. The obtained results allow a generalization of the theory for the analysis and design of controllers that can be useful in complex networks. Examples and comparisons with other results are given to illustrate the effectiveness of the methods of this paper.",{"EN":823},"On the $$\\mu $$ -Analysis and Synthesis of MIMO Lurie-Type Systems with Application in Complex Networks",{"VOID":825},"[\"17286598479427917650\"]",{"VOID":827},"A. Aizerman, On the effect of nonlinear functions of several variables on the stability of automatic control systems (in Russian). Autom. I Telemekh. III 8(1), 2 (1947)\nL. Alvergue, G. Gu, S. Acharya, A generalized sector bound approach to feedback stabilization of nonlinear control systems. Int. J. Robust Nonlinear Control 23(14), 1563–1580 (2012)\nD.S. Bernstein, W.M. Haddad, A.G. Sparks, A Popov criterion for uncertain linear multivariable systems. Automatica 31(7), 1061–1064 (1995)\nS. Boccaletti, V. Latora, Y. Moreno et al., Complex networks: structure and dynamics. Phys Rep. 424(45), 175–308 (2006)\nD. Ding, Z. Wang, Q. Han, A set-membership approach to event-triggered filtering for general nonlinear systems over sensor network. IEEE Trans. Autom. Control 65(4), 1792–1799 (2020)\nJ. Doyle, Analysis of feedback systems with structured uncertainty. IEE Proc. D Control Theory Appl. 129(6), 242–250 (1982)\nJ. Doyle, ONR\u002FHoneywell workshop on advances on multivariable control (Lecture Notes, Minneapolis, 1984)\nJ. Doyle, Structured uncertainty in control system design, in 24th IEEE Conference on Decision and Control, (1985), pp. 260–265\nG.E. Dullerud, F.G. Paganini, A Course in Robust Control Theory: A Convex Approach (Springer, New York, 2013)\nM. Forti, A. Liberatore, S. Manetti, et al., On absolute stability of neural networks, in Proceedings of IEEE International Symposium on Circuits and Systems—ISCAS’94, (London 1994), vol. 6, pp. 241–244\nQ. Gao, J. Du, X. Liu, An improved absolute stability criterion for time-delay Lur’e systems and its frequency domain interpretation. Circuits Syst Signal Process. 36, 916–930 (2017)\nP.B. Gapski, J.C. Geromel, A convex approach to the absolute stability problem. IEEE Trans. Autom. Control 25, 613–617 (1994)\nD.W. Gu, P.H. Petkov, M.M. Konstantinov, Robust control design with MATLAB, in Series Advanced Textbooks in Control and Signal Processing (Springer, London, 2006)\nM. Guzman, Ecuaciones Diferenciales Ordinarias-Teoria de estabilidad y control (Alhambra, Madrid, 1980)\nE. Gyurkovics, D. Eszes, Sufficient conditions for stability and stabilization of networked control systems with uncertainties and nonlinearities. Int. J. Robust Nonlinear Control 21(14), 3004–3022 (2014)\nW.M. Haddad, D.S. Bernstein, Explicit construction of quadratic Lyapunov functions for the small gain, positivity, circle, and Popov theorems and their application to robust stability. Part I: Continuous–time theory. Int. J. Robust Nonlinear Control 3, 313–339 (1993)\nF. Hao, X. Zhao, Absolute stability of Lurie networked control systems. Int. J. Robust Nonlinear Control 20(12), 1326–1337 (2009)\nY. He, M. Wu, J. She et al., Robust stability for delay Lure control systems with multiple nonlinearities. J. Comput. Appl. Math. 176, 371–380 (2005)\nJ.J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. 81, 3088–3092 (1984)\nA. Imani, M. Montazeri-Gh, A multi-loop switching controller for aircraft gas turbine engine with stability proof. Int. J. Control Autom. Syst. 17, 1359–1368 (2019)\nR.E. Kalman, Lyapunov functions for the problem of Lurie in automatic control. Proc. Natl. Acad. Sci. 49(8), 201–205 (1963)\nE. Kaskurewicz, A. Bhaya, Comments on necessary and sufficient condition for absolute stability of neural network. IEEE Trans. Circuits Syst. 42, 497–499 (1995)\nN.N. Krasovskii, On the stability of the solutions of a system of two differential equations (in Russian). Prikl. Mat. i Mekh. XVII, 6 (1953)\nC.M. Lee, J.C. Juang, A novel approach to stability analysis of multivariable Lurie systems. in IEEE International Conference on Mechatronics and Automation, (2005), pp 199–203\nX. Liao, Absolute Stability of Nonlinear Control Systems (Kluwer Academic China Science Press, Beijing, 1993)\nX. Liao, L. Wang, P. Yu, Stability of Dynamical Systems (Elsevier, Amsterdam, 2007)\nX. Liao, P. Yu, Absolute Stability of Nonlinear Control Systems, 2nd edn. (Springer, Berlin, 2008)\nX. Liao, C. Zhen, X. Fei et al., Robust absolute stability of Lurie interval control systems. Int. J. Robust Nonlinear Control 17(18), 1669–1689 (2007)\nX. Liu, J.Z. Wang, Z.D. Duan et al., New absolute stability criteria for time-delay Lur’e systems with sector-bounded nonlinearity. Int. J. Robust Nonlinear Control 20, 659–672 (2010)\nA.I. Lurie, V.N. Postnikov, On the theory of stability of control systems (in Russian). Prikl. Mat. i Mekh. VII I(3), 246–248 (1944)\nT. Matsumoto, A chaotic attractor from Chua’s circuit. IEEE Trans. Circuits Syst. 31(12), 1055–1058 (1984)\nB.G. Morton, R.M. McAfoos, A mu-test for robustness analysis of a real-parameter variation problem, in American Control Conference (1985), pp. 135–138\nT. Naderi, D. Materassi, G. Innocenti, Revisiting Kalman and Aizerman conjectures via a graphical interpretation. IEEE Trans. Autom. Control 64(2), 670–682 (2018)\nL. Peiran, B. Zhejing, Y. Qiang et al., \\(\\cal{H}_{\\infty }\\) control synthesis for Lurie networked control systems with multiple delays based on the non-uniform characteristic. Asian J. Control 15(4), 1112–1123 (2012)\nR.F. Pinheiro, The Lurie problem and applications to neural networks (in Portuguese). Master’s thesis, USP, São Paulo (2015)\nR.F. Pinheiro, D. Colón, An application of the Lurie problem in Hopfield neural networks, in Proceedings of DINAME 2017 (Springer, New York, 2019), pp. 371–382\nR.F. Pinheiro, D. Colón, Controller by\\(\\cal{H}_{\\infty }\\)mixed-sensitivity design (S\u002FKS\u002FT) for Lurie type systems, in 24th ABCM International Congress of Mechanical Engineering (ABCM, Curitiba, 2017)\nV.M. Popov, Absolute stability of nonlinear systems of automatic control. Remote Control XXI I(8), 857–875 (1961)\nX. Qi, J. Li, Y. Xia et al., On the robust stability of active disturbance rejection control for SISO systems. Circuits Syst Signal Process. 36, 65–81 (2017)\nM. Seron, J. Dona, On invariant sets and closed-loop boundedness of Lurie-type nonlinear systems by LPV-embedding. Int. J. Robust Nonlinear Control 26(5), 1092–1111 (2015)\nS. Skogestad, I. Postlethwaite, Multivariable Feedback Control: Analysis and Design, 2nd edn. (Wiley, New York, 2005)\nA. Townley, M.G. Ilchmann, W. Weib et al., Existence and learning of oscillations in recurrent neural networks. IEEE Trans. Neural Netw. 11, 205–214 (2000)\nM. Vidyasagar, Nonlinear Systems Analysis, 2nd edn. (Englewood Cliffs, Bergen, 1993)\nY. Wang, Y. Xue, X. Zhang, Less conservative robust absolute stability criteria for uncertain neutral-type Lur’e systems with time-varying delays. J. Frankl. Inst. 353(4), 816–833 (2016)\nZ. Yi, P.A. Heng, P. Vadakkepat, Absolute periodicity and absolute stability of delayed neural networks. IEEE Trans. Circuits Syst. 49, 256–261 (2002)\nS.H. Zak, Systems and Control (Oxford University Press, New York, 2003)\nH. Zeng, L. Ding, S.P. 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Glover, Robust Optimal Control (Pearson, London, 1995)",{"VOID":829},"10.1007\u002Fs00034-020-01464-0","2024-06-25T03:42:00.392+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00034-020-01464-0",[833,850],{"id":834,"sortIndex":19,"researcher":18,"roles":835,"affiliations":836,"properties":845,"displayName":847,"givenName":18,"familyName":18},"862a4511-ed38-4d50-a64f-7109bcef9de6",[243],[837],{"id":838,"sortIndex":19,"affiliation":839,"properties":18},"3eb9f607-3c64-4cf9-9007-51fb52862abd",{"id":838,"createTime":18,"updateTime":18,"relativeEntities":840,"slug":18,"properties":841,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":844,"statistic":18},[],{"title":842},{"VI":843},"Telecommunications and Control Department, University of São Paulo, São Paulo, Brazil",[],{"title":846,"gsAuthor":848},{"VI":847},"Rafael Fernandes 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paper presents a high imperceptible and robust audio watermarking algorithm by optimizing the scaling parameter as per the imperceptible requirements with minimum bit error rate. The spread spectrum-based audio watermarking is used in this paper, where the scaling parameter is optimized to achieve trade-off between imperceptibility and robustness of watermarked audio. Primarily, the effect of scaling parameter on the perceptual quality of watermarked audio is investigated and the total error introduced in the host audio due to embedding the watermark is computed. The scaling parameter is optimized for maximizing the robustness by considering objective difference grade (ODG) score (for music signals), perceptual evaluation of speech quality (PESQ) score (for speech signals) as a constraint to meet the imperceptibility requirements. To solve this proposed optimization problem, two search algorithms are developed. The embedding is performed in low-pass framelet transform coefficients through SVD with the optimized scaling parameter. The experimental results show that the proposed algorithm achieves good imperceptibility with an average ODG score of −0.32 and PESQ score of 3.86 for music and speech signals, respectively, under various payload conditions. The proposed algorithm shows better robustness to the common signal processing attacks such as noise addition, filtering, resampling, MP3 compression, amplitude scaling, cropping, and requantization.\n",{"EN":934},"An Adaptive Embedding Approach for High Imperceptible and Robust Audio Watermarking Using Framelet Transform and SVD",{"VOID":936},"[\"16368906372297142713\"]",{"VOID":938},"A first hands-on lab on Speech Processing. https:\u002F\u002Fwww.csd.uoc.gr\u002F~hy578\u002F2018\u002FProject0_Part1.pdf (2018)\nA. Al-Haj, An imperceptible and robust audio watermarking algorithm. EURASIP J. Audio Speech Music Process 2014(1), 1–12 (2014). https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13636-014-0037-2\nP. Bassia, I. Pitas, N. Nikolaidis, Robust audio watermarking in the time domain. IEEE Trans. Multimedia 3(2), 232–241 (2001). https:\u002F\u002Fdoi.org\u002F10.1109\u002F6046.923822\nC.S. Burrus, R. Gopinath, H. Guo, Introduction to Wavelets and Wavelet Transforms-A Primer (Prentice-Hall, New Jersey, 1998)\nS.T. Chen, H.N. Huang, Optimization-based audio watermarking with integrated quantization embedding. Multimed. Tools Appl. 75, 4735–4751 (2016). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-015-2500-1\nO. T. C. Chen, W. C. Wu, Highly robust, secure, and perceptual-quality echo hiding scheme. IEEE Trans. Audio Speech Lang. Process. 16(3), 629–638 (2008). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTASL.2007.913022\nS.T. Chen, T.W. Huang, C.T. Yang, High-SNR steganography for digital audio signal in the wavelet domain. Multimed. Tools Appl. 80(6), 9597–9614 (2021). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-020-09980-6\nF. Djebbar, B. Ayad, K.A. Meraim, H. Hamam, Comparative study of digital audio steganography techniques. EURASIP J. Audio Speech Music Process. 2012(1), 25 (2012). https:\u002F\u002Fdoi.org\u002F10.1186\u002F1687-4722-2012-25\nS. Erkucuk, S. Krishnan, M. Zeytinoglu, A robust audio watermark representation based on linear chirps. IEEE Trans. Multimedia 8(5), 925–936 (2006). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2006.879879\nM. Fallahpour, D. Megías, Audio watermarking based on fibonacci numbers. IEEE\u002FACM Trans. Audio Speech Lang. Process. 23(8), 1273–1282 (2015). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTASLP.2015.2430818\nJ. Garofolo, L. Lamel, W. Fisher, J. Fiscus, D. Pallett, N. Dahlgren, V. Zue, TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1 (1993). https:\u002F\u002Fdoi.org\u002F10.35111\u002F17gk-bn40\nB. Han, Properties of discrete framelet transforms. Math. Model. Nat. Phenom. 8(1), 18–47 (2013). https:\u002F\u002Fdoi.org\u002F10.1051\u002Fmmnp\u002F20138102\nB. Han, Framelets and Wavelets: Algorithms, Analysis, and Applications (Springer, Berlin, 2018)\nR.A. Horn, C.R. Johnson, Matrix Analysis (Cambridge University Press, Cambridge, 1985)\nH.T. Hu, T.T. Lee, High-performance self-synchronous blind audio watermarking in a unified FFT framework. IEEE Access 7, 19063–19076 (2019). https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2893646\nY. Hu, P.C. Loizou, Subjective comparison and evaluation of speech enhancement algorithms. Speech Commun. 49(7), 588–601 (2007). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.specom.2006.12.006\nH.T. Hu, H.H. Chou, T.T. Lee, Robust blind speech watermarking via FFT-based perceptual vector norm modulation with frame self-synchronization. IEEE Access 9, 9916–9925 (2021). https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2021.3049525\nG. Hua, J. Goh, V.L.L. Thing, Time-spread echo-based audio watermarking with optimized imperceptibility and robustness. IEEE\u002FACM Trans. Audio Speech Lang. Process. 23(2), 227–239 (2015). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTASLP.2014.2387385\nG. Hua, J. Huang, Y.Q. Shi, J. Goh, V.L.L. Thing, Twenty years of digital audio watermarking-A comprehensive review. Signal Process. 128, 222–242 (2016). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sigpro.2016.04.005\nM.J. Hwang, J. Lee, M. Lee, H.G. Kang, SVD-based adaptive QIM watermarking on stereo audio signals. IEEE Trans. Multimedia 20(1), 45–54 (2018). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2017.2721642\nR. ITU-R, Recommendation ITU-R BS. 1387-1 method for objective measurements of perceived audio quality, BS. 1387-1 International Telecommunications Union-Recommendation, Geneva (1998)\nW. Jiang, X. Huang, Y. Quan, Audio watermarking algorithm against synchronization attacks using global characteristics and adaptive frame division. Signal Process. 162, 153–160 (2019). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sigpro.2019.04.017\nR. Jiao, S. Ma, B. Li, Framelet image watermarking considering dynamic visual masking. Optik 126(21), 3197–3202 (2015). https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ijleo.2015.07.084\nP. Kabal, An examination and interpretation of ITU-R BS. 1387: perceptual evaluation of audio quality. TSP Lab Technical Report, Dept. Electrical & Computer Engineering, McGill University, pp. 1–89 (2002)\nX. Kang, R. Yang, J. Huang, Geometric invariant audio watermarking based on an LCM feature. IEEE Trans. Multimedia 13(2), 181–190 (2011). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2010.2098850\nA. Kanhe, A. Gnanasekaran, A DCT-SVD based speech steganography in voiced frames. Circuits Syst. Signal Process. 37, 5049–5068 (2018). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00034-018-0805-9\nA. Kaur, M.K. Dutta, An optimized high payload audio watermarking algorithm based on LU-factorization. Multimedia Syst. 24(3), 341–353 (2018). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00530-017-0545-x\nB.S. Ko, R. Nishimura, Y. Suzuki, Time-spread echo method for digital audio watermarking. IEEE Trans. Multimedia 7(2), 212–221 (2005). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2005.843366\nA. Lang, StirMark benchmark for audio (2008). http:\u002F\u002Fsourceforge.net\u002Fprojects\u002Fstirmark. Accessed on Jan 2022\nA.N. Lemma, J. Aprea, W. Oomen, L. van de Kerkhof, A temporal domain audio watermarking technique. IEEE Trans. Signal Process. 51(4), 1088–1097 (2003). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTSP.2003.809372\nW.N. Lie, L.C. Chang, Robust and high-quality time-domain audio watermarking based on low-frequency amplitude modification. IEEE Trans. Multimedia 8(1), 46–59 (2006). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2005.861292\nZ. Liu, Y. Huang, J. Huang, Patchwork-based audio watermarking robust against de-synchronization and recapturing attacks. IEEE Trans. Inf. 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Natgunanathan, D. Peng, W. Zhou, S. Yu, A dual-channel time-spread echo method for audio watermarking. IEEE Trans. Inf. Forensics Secur. 7(2), 383–392 (2012). https:\u002F\u002Fdoi.org\u002F10.1109\u002FTIFS.2011.2173678\nM. Xiao, Z. He, T. Quan, A robust digital watermarking algorithm based on framelet and SVD. In: Proceedings of SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 981119, vol. 9811, pp. 295–300. SPIE (2015). https:\u002F\u002Fdoi.org\u002F10.1117\u002F12.2209570\nY. Xue, K. Mu, Y. Wang, Y. Chen, P. Zhong, J. Wen, Robust speech steganography using differential SVD. IEEE Access 7, 153,724-153,733 (2019). https:\u002F\u002Fdoi.org\u002F10.1109\u002Faccess.2019.2948946\nJ. Zhao, T. Zong, Y. Xiang, L. Gao, W. Zhou, G. Beliakov, Desynchronization attacks resilient watermarking method based on frequency singular value coefficient modification. IEEE\u002FACM Trans. Audio Speech Lang. 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Signal Process. 52(7), 1983–1996 (2004)",{"doi":1332},"10.1109\u002FTSP.2004.828923",{"id":1334,"createTime":1335,"updateTime":1336,"relativeEntities":1337,"slug":1338,"properties":1339,"entityType":234,"verifyStatus":235,"verifyTime":1352,"verifyNote":237,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1353,"fullTextUrl":18,"authors":1354,"publicationType":271,"publisherRelationship":1370,"citationCount":18,"citationInfo":18,"publishDate":1416,"publishYear":1417,"citationAnalyzeStatus":1418,"lastCitationAnalyze":1419,"indexDatabases":1420,"openAccess":18,"references":18,"isForceReanalyzing":516},"0253ad0d-b7b4-434a-815f-f3f54ed1b3b2","2024-04-08T16:08:21.244+00:00","2026-07-20T16:51:50.546+00:00",[],"Global-Synchronization-for-Coupled-Lur-e-Dynamical-Networks",{"abstract":1340,"title":1342,"gsPaper":1344,"keywords":1346,"references":1348,"doi":1350},{"EN":1341},"This paper studies the global exponential synchronization for coupled Lur’e dynamical networks. Based on Lyapunov stability theory and novel matrix techniques, several sufficient conditions are established to ensure the global synchronization of the proposed network model. These conditions are expressed in terms of matrix and algebraic inequalities, which can be verified and solved easily. It is noted that, unlike existing approaches which may involve heavy computation when the number of subsystems is large, the method developed in this paper can be applied to tackle this case efficiently. Finally, the coupled chaotic Chua system is used to show the effectiveness of the obtained results.",{"EN":1343},"Global Synchronization for Coupled Lur’e Dynamical Networks",{"VOID":1345},"[]",{"EN":1347},"",{"VOID":1349},"S. Boyd, L. Vandenberghe, Convex Optimization (Cambridge University Press, Cambridge, 2004)\nS. Boyd, L. El Ghaoui, E. Feron, U. Balakrishnana, Linear Matrix Inequalities in System and Control Theory (SIAM, Philadelphia, 1994)\nJ. Cao, H. Li, D.W.C. 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Su, J. Chu, Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data. IEEE Trans. Syst. Man Cybern., Part B, Cybern. (2012). doi:10.1109\u002FTSMCB.2012.2230441\nM. Yalcin, J. Suykens, J. Vandewalle, Master-slave synchronization of Lur’e systems with time-delay. Int. J. Bifurc. Chaos 11(6), 1707–1722 (2001)\nX. Zhang, G. Lu, Y. Zheng, Synchronization for time-delay Lur’e systems with sector and slope restricted nonlinearities under communication constraints. Circuits Syst. Signal Process. 30(6), 1573–1593 (2011)\nY. Zhang, S. Xu, Y. Chu, Global synchronization of complex networks with interval time-varying delays and stochastic disturbances. Int. J. Comput. Math. 88(2), 249–264 (2011)\nJ. Zhou, T. Chen, Synchronization in general complex delayed dynamical networks. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 53(3), 733–744 (2006)",{"VOID":1351},"10.1007\u002Fs00034-013-9609-0","2024-06-26T07:10:10.617+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00034-013-9609-0",[1355],{"id":1356,"sortIndex":19,"researcher":18,"roles":1357,"affiliations":1358,"properties":1367,"displayName":1369,"givenName":18,"familyName":18},"ef333758-082c-424a-9f3b-20aaa3dbd502",[243],[1359],{"id":1360,"sortIndex":19,"affiliation":1361,"properties":18},"8c4f7efc-1ad1-4ddf-9cc6-9000e6116881",{"id":1360,"createTime":18,"updateTime":18,"relativeEntities":1362,"slug":18,"properties":1363,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1366,"statistic":18},[],{"title":1364},{"VI":1365},"School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China",[],{"title":1368},{"VI":1369},"Guangzhen 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conventional frequency response masking (FRM) approach is one of the most well-known techniques for the design of sharp transition band finite impulse response (FIR) digital filters. The resulting FRM digital filters permit efficient hardware implementations due to an inherently large number of zero-valued multiplier coefficients. The hardware complexity of these digital filters can further be reduced by representing the remaining (non-zero) multiplier coefficient values by using their canonical signed-digit (CSD) representations. This paper presents a novel diversity-controlled (DC) genetic algorithm (GA) for the discrete optimization of bandpass FRM FIR digital filters over the CSD multiplier coefficient space. The resulting bandpass FIR digital filters are permitted to have equal or unequal lower and upper transition bandwidths. The proposed DCGA is based on an indexed look-up table of permissible CSD multiplier coefficients such that their indices form a closed set under the genetic operations of crossover and mutation. The salient advantage of DCGA over the conventional GA lies in the external control over population diversity and parent selection, giving rise to a rapid convergence to an optimal solution. The external control is achieved through the judicious choice of a pair of DCGA optimization parameters. An empirical investigation is undertaken for choosing appropriate values for these control parameters. The convergence speed advantages of the DCGA are demonstrated through its application to the design and optimization of a pair of bandpass FRM FIR digital filters with equal or arbitrary lower and upper transition bandwidths. In both cases, an increase of about an order of magnitude in the speed of convergence is achieved as compared to the conventional GAs.",{"EN":1431},"A Novel Diversity-Controlled Genetic Algorithm for Rapid Optimization of Bandpass FRM FIR Digital Filters Over CSD Multiplier Coefficient Space",{"VOID":1433},"[\"15033876894005269149\"]",{"VOID":1435},"L. Cen, Y. Lian, Hybrid genetic algorithm for the design of modified frequency-resposne masking filters in a discrete space. Circuits Syst. Signal Process. 25(2), 153–174 (2006)\nV.S. Dimitrov, G.A. Jullien, Loading the bases: A new number representation with applications, Technical report, IEEE Circuits and Systems Magazine, 2003\nA.T.G. Fuller, B. Nowrouzian, F. Ashrafzadeh, Optimization of fir digital filters over the canonical signed-digit coefficient space using genetic algorithms, in Midwest Symposium on Circuits and Systems (1998), pp. 456–469\nD.E. Goldberg, Genetic Algorithms in Search, Optimization, and Maching Learning (Addison-Wesley, Reading, 1989)\nO. Hermann, L.R. Rabiner, D.S.K. Chan, Practical design rules for optimum finite impulse response lowpass digital filters. Bell Syst. Tech. J. 52, 769–799 (1981)\nW.R. Lee, V. Rehbock, K.L. Teo, Frequency-response masking based fir filter design with power-of-two coefficients and suboptimum pwr. J. Circuits Syst. Comput. 12(5), 591–600 (2003)\nY.C. Lim, A digital filter bank for digital audio systems. IEEE Trans. Circuits Syst. 33(8), 848–849 (1986)\nY.C. Lim, Frequency-response masking approach for the synthesis of sharp linear phase digital filters. IEEE Trans. Circuits Syst. CAS-33, 357–364 (1986)\nY.C. Lim, Y. Lian, The optimum design of one- and two-dimensional fir filters using the frequency response masking technique. IEEE Trans. Circuits Syst. II CAS-40, 88–95 (1993)\nY.C. Lim, S.R. Parker, A.G. Constantinides, Finite word length fir filter design using integer programming over a discrete coefficient space. IEEE Trans. Acoust. Speech Signal Process ASSP-30, 661–664 (1982)\nY.C. Lim, R. Yang, D. Li, J. Song, Signed power-of-two term allocation scheme for the design of digital filters. IEEE Trans. Circuits Syst. Analog Digit. Signal Process. II, 577–584 (1999)\nW. Lu, T. Hinamoto, Optimal design of frequency-response-masking filters using semidefinite programming. IEEE Trans. Circuits Syst. 5, 557–568 (2003)\nW. Lu, T. Hinamoto, Improved design of frequency-response-masking filters using enhanced sequential quadratic programming. IEEE Int. Symp. Circuits Syst. 5, 528–531 (2004)\nP. Mercier, S. Mohan-Kilambi, B. Nowrouzian, Optimization of frm fir digital filters over csd and dbns multiplier coefficient spaces employing a novel genetic algorithm. J. Comput. 2(7), 20–31 (2007)\nS.K. Mitra, Digital Signal Processing—A Computer-Based Approach (McGraw Hill, New York, 2006)\nY. Neuvo, C.Y. Dong, S.K. Mitra, Interpolated finite impulse response filters. IEEE. Trans. Acoust. Speech Signal Process. ASSP-32, 563–570 (1984)\nT. Saramaki, J. Yli-Kaakinen, H. Johansson, Optimization of frequency-response making based fir filters. J. Circuits Syst. Comput. 12(5), 563–591 (2003)\nH. Shimodaira, Dcga: a diversity control oriented genetic algorithm, in Proceedings on Ninth IEEE International Conference on Tools with Artificial Intelligence (1997), pp. 367–374\nH. Shimodaira, A diversity-control-oriented genetic algorithm (dcga): Performance in function optimization, in Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1 (2001), pp. 44–51\nD. Suckley, Genetic algorithm in the design of fir filters. IEEE Proc. G 138, 234–238 (1991)\nR. Yang, Y.C. Lim, S.R. Parker, Design of sharp linear-phase fir bandstop filters using the frequency response-masking technique. Circuits Syst. Signal Process. 17, 1–27 (1998)\nJ.H. Yu, Y. Lian, Design equations for jointly optimized frequency-resposne masking filters. Circuits Syst. Signal Process. 26(1), 27–42 (2007)\nY.J. Yu, Y.C. Lim, Frm based fir filter design—the wls approach. IEEE Int. Symp. Circuits Syst. III, 221–224 (2002)\nY.J. Yu, Y.C. Lim, Genetic algorithm approach for the optimization of multiplierless sub-filters generated by the frequency response masking technique, in IEEE International Conference on Electronics, Circuits and Systems, vol. 3 (2002), pp. 1163–1166\nY.J. Yu, Y.C. Lim, A novel genetic algorithm for the design of a signed power-of-two coefficient quadrature mirror filter lattice filter bank. Circuits Syst. Signal Process. 21, 263–276 (2002)",{"VOID":1437},"10.1007\u002Fs00034-008-9045-8","2024-05-15T19:24:42.456+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00034-008-9045-8",[1441,1458,1473],{"id":1442,"sortIndex":19,"researcher":18,"roles":1443,"affiliations":1444,"properties":1453,"displayName":1455,"givenName":18,"familyName":18},"11ae5d77-4af9-423b-baa0-2bfe5b776a9c",[243],[1445],{"id":1446,"sortIndex":19,"affiliation":1447,"properties":18},"751a56c7-20c7-48fd-9307-b70af11a78fb",{"id":1446,"createTime":18,"updateTime":18,"relativeEntities":1448,"slug":18,"properties":1449,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1452,"statistic":18},[],{"title":1450},{"VI":1451},"Nortel Networks Ltd, Ottawa, Canada",[],{"title":1454,"gsAuthor":1456},{"VI":1455},"Sai Mohan 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perception is a transformative technology that can recognize patterns from environments through visual inputs. Automatic surveillance of human activities has gained significant importance in both public and private spaces. It is often difficult to understand the complex dynamics of events in real-time scenarios due to camera movements, cluttered backgrounds, and occlusion. Existing anomaly detection systems are not efficient because of high intra-class variations and inter-class similarities existing among activities. Hence, there is a demand to explore different kinds of information extracted from surveillance videos to improve overall performance. This can be achieved by learning features from multiple forms (views) of the given raw input data. We propose two novel methods based on the multi-view representation learning framework. The first approach is a hybrid multi-view representation learning that combines deep features extracted from 3D spatiotemporal autoencoder (3D-STAE) and robust handcrafted features based on spatiotemporal autocorrelation of gradients. The second approach is a deep multi-view representation learning that combines deep features extracted from two-stream STAEs to detect anomalies. Results on three standard benchmark datasets, namely Avenue, Live Videos, and BEHAVE, show that the proposed multi-view representations modeled with one-class SVM perform significantly better than most of the recent state-of-the-art methods.",{"EN":1553},"Deep Multi-view Representation Learning for Video Anomaly Detection Using Spatiotemporal Autoencoders",{"VOID":1555},"[\"16437930093210308706\"]",{"VOID":1557},"10.1007\u002Fs00034-020-01522-7","2024-04-29T19:28:15.225+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs00034-020-01522-7",[1561,1578,1593,1606],{"id":1562,"sortIndex":19,"researcher":18,"roles":1563,"affiliations":1564,"properties":1573,"displayName":1575,"givenName":18,"familyName":18},"d6dcd395-a8e6-4395-964e-847225d9ca99",[243],[1565],{"id":1566,"sortIndex":19,"affiliation":1567,"properties":18},"24cc5a79-50a3-4106-a090-1de8199eeb22",{"id":1566,"createTime":18,"updateTime":18,"relativeEntities":1568,"slug":18,"properties":1569,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1572,"statistic":18},[],{"title":1570},{"VI":1571},"Intelligent Systems Group, School of Computing, SASTRA University, Thanjavur, India",[],{"title":1574,"gsAuthor":1576},{"VI":1575},"K. 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Pattern Recogn. Lett. 33(9), 1188–1195 (2012)",{"doi":337},{"id":333,"text":1745,"url":335,"identifiers":1746},"S.K. Kumaran, D.P. Dogra, P.P. Roy, A. Mitra, Video trajectory classification and anomaly detection using hybrid CNN-VAE. ArXiv preprint arXiv:1812.07203 (2018)",{"doi":337},{"id":333,"text":1748,"url":335,"identifiers":1749},"R. Leyva, V. Sanchez, C.T. Li, Abnormal event detection in videos using binary features, in 2017 40th International Conference on Telecommunications and Signal Processing (TSP) (IEEE, 2017), pp. 621–625",{"doi":337},{"id":333,"text":1751,"url":335,"identifiers":1752},"R. Leyva, V. Sanchez, C.T. Li, The LV dataset: a realistic surveillance video dataset for abnormal event detection, in 2017 5th International Workshop on Biometrics and Forensics (IWBF) (IEEE, 2017), pp. 1–6",{"doi":337},{"id":333,"text":1754,"url":335,"identifiers":1755},"Q. Li, W. 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Forensics Secur. 14(10), 2537–2550 (2019)",{"doi":337},{"id":1841,"createTime":1842,"updateTime":1843,"relativeEntities":1844,"slug":1845,"properties":1846,"entityType":234,"verifyStatus":235,"verifyTime":1857,"verifyNote":237,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1858,"fullTextUrl":18,"authors":1859,"publicationType":271,"publisherRelationship":1915,"citationCount":18,"citationInfo":18,"publishDate":1965,"publishYear":324,"citationAnalyzeStatus":17,"lastCitationAnalyze":1966,"indexDatabases":1967,"openAccess":18,"references":18,"isForceReanalyzing":516},"b0caa078-6e42-4504-8144-efe075e263b7","2023-12-19T12:40:12.532+00:00","2026-07-17T12:08:01.933+00:00",[],"A-Frequency-Demodulator-Based-on-Adaptive-Sampling-Frequency-Phase-Locking-Scheme-for-Large-Deviation-FM-Signals",{"abstract":1847,"title":1849,"gsPaper":1851,"references":1853,"doi":1855},{"EN":1848},"A frequency demodulation scheme based on adaptive sampling frequency phase-locking loop (PLL) is proposed for extracting large deviation message signal from sinusoidal frequency-modulated signals. The proposed scheme has been designed to track the frequency-modulated input signal using a look-up-table and adaptively changing the sampling period. While tracking the carrier signal, the numerically controlled oscillator involved in the PLL structure produces the sampling frequency according to the variation of input frequency. In the PLL, the message signal has been extracted at the output of proportional integral controller. Simulation results prove that the PLL exhibits quick acquisition behavior, wide operating range, negligible steady-state error. Experimental investigation validates the efficacy of the proposed PLL performance in message signal extraction.",{"EN":1850},"A Frequency Demodulator Based on Adaptive Sampling Frequency Phase-Locking Scheme for Large Deviation FM Signals",{"VOID":1852},"[\"14602610862766191236\"]",{"VOID":1854},"T. Addabbo, A. Fort, R. Biondi, S. Cioncolini, M. Mugnaini, S. Rocchi, V. 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