Comparison of computation burden between new algorithm and conventional one
\r\n\tHigher education is on the brink of major reformation. There is little doubt that higher education has contributed to the development of countries and their competitiveness in a global marketplace as well as informing national policies and driving modernization. However, if higher education is to remain relevant and meet the demands of an ever-changing world, institutions and their operations must progress in unison with the changing world in which they function. Innovations create new or significantly improved ways of operating and may be distinguished by their degree of novelty. Innovation can play a critical role in transforming and advancing practice and therein address socio-economic, organizational, operational and social challenges. The complexity and scope of higher education opens up the possibilities and potential for innovations to transpire in diverse settings and contexts or be embedded in a range of products and services. This book provides the reader with a collection of easy-to-follow vignette-based higher education innovations that have transformed or advanced practice and in doing so contributed to ensuring the relevance and value of higher education in a continuously changing world.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"9c8b8a6fe8578fbf2398932ce8c1b717",bookSignature:"Associate Prof. Dominique Parrish and Dr. Joanne Joyce-McCoach",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8283.jpg",keywords:"Curriculum and Pedagogy, Assessment, Work Integrated Learning, Policy, Organizational Development, Gender Equity, Student Experience, Cultural Awareness, Globalization, Student Exchange, Professionalizing Teaching Practice, Learning Analytics, Online Learning Environments, Blended Learning",numberOfDownloads:1604,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 21st 2018",dateEndSecondStepPublish:"June 11th 2018",dateEndThirdStepPublish:"August 10th 2018",dateEndFourthStepPublish:"October 29th 2018",dateEndFifthStepPublish:"December 28th 2018",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,editors:[{id:"197795",title:"Associate Prof.",name:"Dominique",middleName:null,surname:"Parrish",slug:"dominique-parrish",fullName:"Dominique Parrish",profilePictureURL:"https://mts.intechopen.com/storage/users/197795/images/system/197795.jpeg",biography:"Associate Professor Dominique Parrish is currently the Associate Dean (Education) in the Faculty of Science, Medicine and Health at the University of Wollongong, Australia. She has over 30 years experience in education and has held numerous leadership roles including project manager of three national learning and teaching grants, external evaluator for two national learning and teaching grants, Treasurer, Vice-President and now President of The Australasian Society for Computers in Learning in Tertiary Education (ASCILITE). Dominique is a Senior Editor of the Journal of University Teaching and Learning Practice (JUTLP) and a member of the Australasian Journal of Educational Technology (AJET) Management Committee. Dominique is currently co-leading the Technology Enhanced Learning Accreditation Scheme (TELAS) initiative, which is seeking to develop a formal process for assessing and accrediting the quality of online learning.",institutionString:"Macquarie University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Wollongong",institutionURL:null,country:{name:"Australia"}}}],coeditorOne:{id:"258929",title:"Dr.",name:"Joanne",middleName:null,surname:"Joyce-McCoach",slug:"joanne-joyce-mccoach",fullName:"Joanne Joyce-McCoach",profilePictureURL:"https://mts.intechopen.com/storage/users/258929/images/system/258929.jpg",biography:"Dr. Joanne Joyce-McCoach is a proficient clinical nurse, with extensive experience in administration and practice and comprehensive knowledge in the area of primary health, community, and general nursing. She has a broad understanding of higher education, programme leadership and curriculum development. Through her appointments and academic studies Joanne has refined her research and teaching skills and gained an excellent understanding of, and commitment to, the development of a challenging and engaging learning environment in which students become life-long scholars and learners. Joanne is an advocate for practice education, focused on the facilitation of student-centered learning, in a productive supportive environment. She has an adept appreciation of issues pertinent to the changing university learning culture and an understanding of student expectations and requirements for their development as effective healthcare professionals.",institutionString:"LaTrobe University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1316",title:"Higher Education",slug:"higher-education"}],chapters:[{id:"64745",title:"An Integrated Model for Invigorating Innovation and Entrepreneurship in Higher Education",slug:"an-integrated-model-for-invigorating-innovation-and-entrepreneurship-in-higher-education",totalDownloads:216,totalCrossrefCites:0,authors:[null]},{id:"63826",title:"Learning Innovations for Identifying and Developing Talent for University",slug:"learning-innovations-for-identifying-and-developing-talent-for-university",totalDownloads:141,totalCrossrefCites:0,authors:[null]},{id:"64542",title:"Talent Management as a Core Source of Innovation and Social Development in Higher Education",slug:"talent-management-as-a-core-source-of-innovation-and-social-development-in-higher-education",totalDownloads:329,totalCrossrefCites:0,authors:[null]},{id:"65101",title:"Planning for Improvement: Leadership Development among University Administrators",slug:"planning-for-improvement-leadership-development-among-university-administrators",totalDownloads:142,totalCrossrefCites:0,authors:[null]},{id:"65373",title:"Interdisciplinary Engagement in Higher Education: Opportunities Explored",slug:"interdisciplinary-engagement-in-higher-education-opportunities-explored",totalDownloads:159,totalCrossrefCites:0,authors:[null]},{id:"64108",title:"Transformative Teaching of Engineering in Sub-Saharan Africa",slug:"transformative-teaching-of-engineering-in-sub-saharan-africa",totalDownloads:148,totalCrossrefCites:0,authors:[null]},{id:"63117",title:"A Responsive Higher Education Curriculum: Change and Disruptive Innovation",slug:"a-responsive-higher-education-curriculum-change-and-disruptive-innovation",totalDownloads:472,totalCrossrefCites:0,authors:[{id:"96902",title:"Dr.",name:"Maureen",surname:"Snow Andrade",slug:"maureen-snow-andrade",fullName:"Maureen Snow Andrade"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"192910",firstName:"Romina",lastName:"Skomersic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/192910/images/4743_n.jpg",email:"romina.s@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Renes",coverURL:"https://cdn.intechopen.com/books/images_new/5773.jpg",editedByType:"Edited by",editors:[{id:"158868",title:"Dr.",name:"Susan",surname:"Renes",slug:"susan-renes",fullName:"Susan Renes"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"48276",title:"Efficient FFT-based Algorithms for Multipath Interference Mitigation in GNSS",doi:"10.5772/59940",slug:"efficient-fft-based-algorithms-for-multipath-interference-mitigation-in-gnss",body:'GNSS (Global Navigation Satellite System) has been found application in many areas. In some cases, the performance requirements for GNSS are very high. There are many error sources that would degrade the positioning performance of GNSS, e.g., clock errors, ephemeris errors, tropospheric propagation delay, and multipath. Many positioning errors mentioned above are constant for all GNSS receivers in a given small area and can be removed or reduced by using the popular differential technique. However, due to the geographical position difference between the reference station and the receiver, the multipath environment of receivers, such as amplitudes and number of multipath signals, is totally different with that of the reference station. Thus differential technique can not eliminate the multipath error. Many studies have shown that the multipath interference will lead to a position error around several meters which endangers the reliability and accuracy of GNSS. Therefore, multipath interference mitigation has been a hot topic in the field of satellite navigation receiver design.
Multipath interferences are the signals reflected by the objects around the GNSS receiver. Then the multipath interference and the LOS (line of sight) signal are simultaneously received by antenna which brings a phase distortion in the tracking loops of receivers. Finally, the phase distortion results in the tracking and positioning error.
There have been many studies about the effect of multipath interference. Kalyanaraman et al. in [1] analyzed the multipath effects on code tracking loop. Kos et al. [2] provided a detailed analysis of the multipath effects on the positioning error. Main multipath interference mitigation techniques are based on antenna technique and signal processing algorithms.
By mounting the antenna in a well-designed place based on the multipath environment, Maqsood et al. in [3] compared the performances of multipath interference mitigation for different antennas. Ray et al. in [4] proposed a multipath mitigation algorithm based on an antenna array. A design principle of antenna was proposed by Alfred et al. in [5] for satellite navigation landing system. The multipath mitigation technique based on the special antenna can only suppress the multipath signal coming from the ground below the antenna. However, it is useless for the multipath interference signal reflected by the objects above the antenna.
Popular signal processing techniques are the narrow correlator and MEDLL (Multipath Estimated Delay Locked Loop). Narrow correlator is adopted in many GNSS receivers which suppresses multipath interference by reducing the early-late correlator spacing. The signal model for the narrow correlator is provided by Michael et al. in [6]. Cannon et al. in [7] analyzed the performance of narrow correlator in the satellite navigation system. The performance of narrow correlator can be improved by decreasing the early-late correlator spacing. However, the narrow correlator assumes that the bandwidth of the received signal is infinite which is invalid in the practical applications. Thus, when the early-late correlation spacing is less than the reciprocal of the channel bandwidth, the tracking error cannot be further decreased by reducing the early-late correlator spacing. MEDLL is a multipath interference mitigation algorithm based on the statistical theory as in [8], which estimates the time delays via the maximum likelihood criterion. Therefore, the complexity of MEDLL is much higher than narrow correlator.
This chapter firstly presents a new acquisition algorithm for GNSS. Then two multipath interference mitigation algorithms based on the DPE (Decoupled Parameter Estimation) parameter estimation algorithms are presented. The FFT algorithm is utilized to reduce the computational complexity in all proposed algorithms. Numerical results are provided to demonstrate the performances of the proposed algorithms. The remainder of this chapter is arranged as follows. Section 2 presents the new acquisition algorithm. Two multipath interference mitigation algorithms are separately presented in Section 3. Section 4 concludes the chapter.
GNSS signal is composed of three parts, the carrier, the ranging codes and the data codes. In order to facilitate the presentation, GPS is taken as an example. However, the proposed algorithm is also suitable for other GNSS system. The carrier of GPS (Global Navigation System) consists of the three different frequency bands, i.e., L1 (1575.42 MHz), L2 (1227.6 MHz) and L5 (1176.45MHz), see [9-10]. The ranging codes are pseudo random noise (PRN) codes including C/A code, P code and M code which are known in advance. Data codes or the navigation data are binary codes containing the satellite ephemeris, satellite status, clock system, the orbit perturbation correction of the satellite clock motion state and the atmospheric refraction correction, etc..
Suppose that one receiver obtained the signals from P satellites and the received data can be expressed as
where
After A/D conversion, the data can be written as
The conventional acquisition procedure is a two-dimensional searching algorithm, which is time consuming even when using parallel searching algorithm. Moreover, the frequency resolution of the conventional acquisition algorithm cannot satisfy the requirements of the tracking loop. Hence, a more accurate frequency estimation is required before tracking in the conventional receiver.
Therefore, a new GNSS acquisition algorithm is proposed in this section. The Doppler frequency is firstly estimated. After that, the initial code phase is then obtained via NLS (Nonlinear Least Square) fitting. Compared with the conventional acquisition algorithm, the proposed algorithm can obtain a comparative performance with a lower computational complexity.
It can be noted from the data model in section 2.1 that there are three unknown parameters in the acquisition process, which are the PRN index of the
Due to the navigation data and C/A code are
Since the value of navigation data and C/A code is
where,
It can be seen from equation (4) that the spectrum of each satellite only contains a single dominant frequency component corresponding to the Doppler frequency. For the reason that not only the correlation of C/A code of different satellites is very small but also the signal and noise are uncorrelated, it is obviously that in equation (5) the product terms of different C/A codes as well as the product of noise and signal are close to zero. Fourier analysis method can be directly used to estimate the signal frequency.
The Fourier transform of equation (4) can be expressed as
where
Frequency spectrum before and after square operation
The estimated value of
The frequency spectrum of the received data before and after the square operation is shown in Figure 1. It can be noted that before square operation, there are multiple dominant frequency components. Thus the Doppler frequency cannot be extracted directly. However, after being squared, there is only one dominant Doppler frequency present in the spectrum, so an accurate Doppler frequency can be estimated.
After obtaining the Doppler frequency, the initial phases and the PRN index of the C/A code from all satellites can be estimated by using its autocorrelation characteristic. Let
As the Doppler frequency
Suppose the time delay from the
where
where
To estimate
Let
where
where
The time delay estimation can be further obtained by
Equation (16) can be solved by using FFT algorithm with a low computation burden. Since the PRN index of the received signal is unknown, consequently, it is not feasible to reconstruct
It is well-known that the cross correlation coefficients of different C/A codes are very small. In addition, the signal and noise are uncorrelated. Thus Fourier analysis results of the above mentioned component are close to zero. The maximum correlation value can only be obtained in the case that the C/A code of the reconstructed signal is the same as the received one. Therefore, the PRN index can be obtained. Furthermore, the code delay can be estimated by the location of the maximum correlation value. Then, we obtained all the unknown parameters.
Suppose the searching time for Doppler frequency estimation is
Figure 2 compares the searching process of the proposed algorithm and the conventional one. It can be seen that the computation complexity of the proposed algorithm is significantly lower than that of the conventional algorithm in that the typical value of
Comparison of searching process between new algorithm and conventional algorithm
Comparison of computation burden between new acquisition algorithm and conventional acquisition algorithm are shown Table 1. It can be clearly seen from Table 1 that, since
\n\t\t\t | \n\t\t\t\tMultiplication times\n\t\t\t | \n\t\t\t\n\t\t\t\tAddition times\n\t\t\t | \n\t\t\t\n\t\t\t\tFFT times\n\t\t\t | \n\t\t\t\n\t\t\t\tTime Complexity\n\t\t\t | \n\t\t
\n\t\t\t\tconventional method\n\t\t\t | \n\t\t\t7MQ+4M | \n\t\t\t2MQ+3M | \n\t\t\t2MQ | \n\t\t\tO(MQ) | \n\t\t
\n\t\t\t\tnew method\n\t\t\t | \n\t\t\t6MP+4P | \n\t\t\t3MP+7P | \n\t\t\tM (P+1) +1 | \n\t\t\tO(MP) | \n\t\t
Comparison of computation burden between new algorithm and conventional one
To verify the performance of the proposed algorithm, GPS signals from 8 satellites with PRN indexes 1, 2, 13, 20, 22, 24, 25 and 27 are simulated. Considering the IF is 1.25MHz, the sampling rate is 7.5MHz, the pre integration time is 1ms and the signal to noise ratio (SNR) is -20dB. The frequency searching step of the conventional acquisition algorithm is 1kHz. The acquisition results are shown in Figure 3.
Compassion of the acquisition results
In Figure 3, the horizontal axis stands for the PRN index of the satellites and the vertical axis denotes the acquisition metric. Since the proposed algorithm only calculates the correlation of the received satellite signal and the local reference signal, correlation results of the other satellite are not shown in Figure 3(b). It can be noted that acquisition results are identical, and the peak value of the proposed algorithm is higher than the conventional one. Therefore, the proposed algorithm has a comparative performance with the conventional one.
The term multipath is derived from the fact that a signal transmitted from a GNSS satellite can follow a ‘multiple’ number of propagation ‘paths’ to the receiving antenna. This is possible because the signal can be reflected back to the antenna from surrounding objects, including the earth’s surface.
Suppose the signal transmitted by GNSS satellites can be written as
where
where
To obtain the Doppler frequency of the LOS signal, we use the following relation
Assume the radial velocity of the receiver relative to the satellite is
where
As the relative radial velocity is constant, the Doppler frequency can be obtained by
The propagation range of the multipath signal can be written as
where
Further simplify of equation (20) we can get
where
After A/D conversion, the transformed digital signal can be written as
Assume the Doppler frequency has been estimated accurately, then the complex phase
where
The in-phase component of equation (29) can be given by
where
Correlation peak when multipath is present
In classical DLL, the in-phase early and late correlation value is given as
where
In GNSS navigation, the receiver is concerned to maximize the correlation function between the received and locally generated signals. This can be accomplished by determining the locations of the zero output of the discriminator which corresponds to the maximum of the correlation function. Here, the early-late correlator is used to determine the position of this zero. However, the presence of multipath introduces some bias in the position of the first arrival peak and has an impact in the user’s position, which can be clearly seen in Figure 4. Then the classical DLL failed to cope with multipath propagation, see [7-8].
In this subsection, a code delay estimation algorithm based on DPE in correlation domain is proposed. To deploy the proposed algorithm, we combine the complex constant phase
The navigation data cycle is much longer than that of the C/A code, but only one cycle of the C/A code is used in the code delay estimation. Consequently, the navigation data jump can be neglected in the signal reconstruction. Then the navigation data
To simplify the following expression,
Multipath interference mitigation based on code delay estimation focus on the estimation of the LOS delay
To obtain the initial code delay, the correlation function between the received satellite signal and the reference signal is represented as
where
where
where
Define a NLS cost function as
The unknown parameters
Consequently the cost function in equation (42) can be rewritten as
Further, we assume
Substitute equation (47) into equation (46) we have
By minimizing the cost function
and
where
With the above simple preparations, we now proceed to present the correlation domain algorithm for the minimization of the NLS cost function. The proposed algorithm comprises the following steps.
Step 1. Assume
Step 2. Assume
Next, compute
Iterate the previous two substeps until “practical convergence” is achieved then we can obtain
From the previous description one can find that the proposed algorithm can be implemented by simply FFT operation which leads to a less computation load.
The diagram of the novel code delay estimation algorithm is shown in Figure 5.
Diagram of the correlation domain code delay estimation algorithm
Another DPE algorithm is proposed in this subsection. Different from the correlation domain algorithm, the unknown parameters
Again, the signal after down-conversion is given by
To obtain the unknown parameters
where
To obtain
Let
Hence, minimizing the cost function in equation (53) is equivalent to minimizing the following cost function
Assume
Substitute equation (58) into equation (57)
Equation (59) gets the minimum while
From equation (60),
The diagram of the data domain code delay estimation algorithm is shown as in Figure 6.
Diagram of the data domain code delay estimation algorithm
With the above simple preparations, we now proceed to present the relaxation algorithm for the minimization of the nonlinear least-squares cost function. The data domain WRELAX algorithm comprises the following steps.
Step 1. Assume
Step 2. Assume
Next, compute
Iterate the previous two substeps until “practical convergence” is achieved then we can obtain
From the previous description one can find that the proposed algorithm can be implemented by simply FFT operation which deserves a less computation load.
To further analyze the two proposed algorithms, the cost functions of them are further discussed in this subsection. Following the discussion in section 3.2.1 and section 3.2.2, we define
Then
\n\t\t\t\t\tIn the noise free situation we have
Then the correlation domain cost function in equation (49) can be further decomposed as
And in the same situation the data domain cost function in equation (60) can be deployed in the following way
It has been discussed in previous subsection that
Comparison of the correlation domain and the data domain cost function
Correlation of the received data and the locally generated signal and the auto-correlation of the locally generated signal should be calculated in correlation domain WRELAX algorithm. Suppose the iteration times of the two proposed algorithms are the same, the correlation domain WRELAX algorithm is more complex than the data domain WRELAX algorithm.
To investigate the performance of the proposed algorithm in the presence of multipath, a simulation experiment was performed. The code delay estimation error can be represented as the function of
Comparison of the error envelopes
Suppose there is only one reflected multipath signal combined with the LOS signal. The noise and the receiver bandwidth are not considered in the experiment. The relative amplitude of the direct and the reflected signal is
In Figure 8, the curve ‘classical DLL’ denotes the error envelope of the classical DLL, and the curve ‘narrow correlator’ denotes the error envelope of the narrow correlator with spacing
A novel acquisition algorithm is firstly proposed in this chapter. In the proposed algorithm, the Doppler frequency is obtained by utilizing FFT of the squared data. Then, the PRN index and initial code phases of the satellite are obtained based on the NLS criterion. It can be seen that the proposed algorithm can not only reduce the computation complexity but also attain a comparable performance to the conventional acquisition algorithm.
After that, two algorithms are presented to suppress the multipath interference by estimating the code delay. In the proposed algorithms, the code delay was obtained by solving a NLS equation, which can be further realized by FFT operation. Compared with the conventional estimation algorithm, the proposed two algorithms perform better in multipath propagating environments and bear lower computation burden.
The work of this chapter is supported by the Project of the National Natural Science Foundation of China (Grant No. 61179064, 61172112, 61271404 and 61471363), and the Fundamental Research Funds for the Central Universities (Grant 3122014D008 and 3122014B001).
Nowadays, the demand for huge buildings, long-span structures, and skyscrapers has increased. As a result, (1) sectional performance of compressive members, and (2) fast and safe construction method become important. Figure 1(a) shows a conventional concrete-encased steel (CES) composite column using a wide-flange steel section at the center of the cross section. Generally, the wide-flange steel is placed at the center of the cross section, and then longitudinal bars and tie bars are placed in construction site. Thus, the contribution of the steel section to the overall flexural capacity of the column could be limited. Further, the need for rebar and formwork construction requires considerable construction time. Particularly, in mega structures such as semiconductor factory and warehouse using long and large sized columns, fast and safe construction methods are necessary.
\nComparison of CES column and PSRC columns. (a) CES composite column, (b) PSRC composite column (weld connection), (c) PSRC composite column (bolt connection).
In order to improve structural capacity and cost efficiency, a prefabricated steel-reinforced concrete (PSRC) composite column has been used. As shown in Figure 1(b) and (c), the prefabricated steel angles at the four corners replace the conventional wide-flange steel, and the steel angles are weld connected or bolt connected with transverse bars or plates [1, 2, 3, 4, 5]. The weld connection should follow the details prescribed in welding standards [6, 7]. The steel angles resist axial load and flexural moment. The transverse bars and plates provide shear resistance, concrete confinement, and bond resistance between the steel angles and concrete. Because the steel cage of angles and transverse reinforcement are prefabricated off site, field rebar work is unnecessary. Further, the self-erectable steel cage can provide sufficient strength and rigidity to support the construction loads of beams and slabs that are superimposed on the PSRC composite column.
\nFigures 2 and 3 show field application of weld-connected and bolt-connected PSRC composite columns, respectively. Generally, the PSRC composite column with 20 m height and 1.5 m × 1.5 m to 2.0 m × 2.0 m sectional area is used for two- or three-story construction at the same time. In the case of the weld-connected PSRC composite column, the steel cage of angles and transverse reinforcement is moved to construction site, and then concrete form is installed (Figure 2). On the other hand, in the case of the bolt-connected PSRC composite column, concrete form is preattached to the steel cage and it can be permanently used after concrete pouring (Figure 3). Thus, field work related to reinforcing bar placement and concrete form work is excluded, which improves the construction safety and saves the construction time at working in high place.
\nSemiconductor FAB (weld-connected PSRC composite column).
Semiconductor FAB (bolt-connected PSRC composite column).
The prefabricated steel angle composite columns strongly depend on the transverse reinforcement that connects the corner steel angles. The transverse reinforcement provides (1) shear transfer between the steel angles, (2) bond between the steel angles and concrete, (3) buckling resistance for the steel angles, and (4) lateral confinement for the core concrete. To satisfy the requirements of (3) and (4), close spacing as well as sufficient strength are required for the transverse reinforcement. However, the transverse bars welded to steel angles or the transverse plates bolt-connected to steel angles may cause premature tensile fracture of the connection, which should be considered in design.
\nCover concrete provides local buckling resistance and fire resistance for the steel angles. However, when the transverse bars are not closely spaced, the PSRC composite columns are vulnerable to premature spalling of the cover concrete due to smooth surface of steel angles. Particularly, when the columns are subjected to high axial compression force, the load-carrying capacity and deformation capacity of the PSRC composite columns can be degraded by early spalling of the cover concrete. Further, under cyclic lateral loading, the PSRC composite columns are expected to be more susceptible to such damages as the ductility demand increases.
\nThe nominal compressive strengths Pn of a PSRC composite column under concentric axial loading can be evaluated by current design code equations for a concrete-encased steel composite column. For example, AISC 360–10 [8] specifies the nominal compressive strength based on plastic stress and column length effect.
\nwhere Pno = maximum compressive strength of the composite column; Pe = elastic buckling strength of the column; fc’ = concrete strength; Fy and As = yield strength and area of the steel section, respectively; fyr and Asr = yield strength and area of the longitudinal bars, respectively; EIeff = effective flexural stiffness of the composite column; KL = effective buckling length; Es = elastic modulus of steel; Ec = elastic modulus of concrete (= 4700√fc’); Is, Isr, and Ic = second-order moments of inertia of the steel section, reinforcing bar, and concrete, respectively, with respect to the centroid of the column section; and Ac = concrete area.
\nFor better evaluation of the load-carrying capacity of a PSRC composite column, numerical analysis using the stress–strain relationship of confined concrete, unconfined concrete, and steel can be used as shown in Figure 4 [1, 3]. The numerical analysis can be performed using strain compatibility method [9]. The stress–strain relationship of confined and unconfined concrete can be determined from various existing models [10, 11, 12].
\nStress–strain relationships of concrete and steel for numerical analysis.
The nominal compressive strengths Pn of a PSRC composite column under eccentric axial loading can be evaluated by assuming plastic stress distribution of the composite section. In Figure 5(a), performance points A and B indicate pure compressive strength Pno and moment strength Mn, respectively. Performance points C indicates the compressive strength Pnc corresponding to the moment Mn. Performance points D indicates the maximum moment strength corresponding to Pnc/2. Considering the slenderness effect of columns, each performance point should be decreased by multiplying the slenderness reduction factor λ to the compressive strength (where λ = ratio of Pn to Pno). Further, using the strain-compatibility method, P-M interaction diagram can be estimated (Figure 5(b)).
\nSection analysis methods of PSRC column under eccentric axial loading: (a) P-M interaction diagram and (b) strain-compatibility method.
Figure 6 shows the test setup for the concentric axial loading and eccentric axial loading tests. The eccentricity can be controlled using the distance between the column center and loading center. During the test, the load-carrying capacity, axial shortening, and horizontal expansion were measured to evaluate the structural performance of the PSRC composite columns.
\nTest setup of compressive loading tests: (a) concentric axial loading and (b) eccentric axial loading.
Figure 7(a) compares the axial load-strain relationships of a CES and PSRC composite columns using steel ratio of 2.0% under concentric axial compression force [3, 4, 5]. The axial load behavior of the PSRC composite column was similar to that of the CES composite column. The contribution of the steel angles to the lateral confinement increased the peak strength Pu and the deformation capacity in the PSRC composite column. Under concentric axial force, local buckling of the steel angles was not observed. After the peak strengths, significant cracking and subsequent concrete spalling occurred. Particularly, the early spalling can be initiated at the corners of the cross section because of the smooth surface of the angle. Thus, to secure robust axial load behavior of the PSRC composite columns under high axial load, it is recommended that the spacing of transverse reinforcement be decreased to half of the requirement of CES composite columns.
\nAxial load-carrying capacities of CES and PSRC columns: (a) concentric axial loading and (b) eccentric axial loading.
Figure 7(b) compares the P-M interaction curves of design codes [8, 13, 14] with the test results of CES and PSRC composite columns under eccentric load with a low eccentricity ratio of e/h = 0.1 and 0.3. As the eccentricity ratio increased, the structural behavior changed from compression to flexure. The peak strength of the CES composite column was less than that of the PSRC composite column with e/h = 0.3. This is because the effective compressive area of the steel and concrete sections was increased in the PSRC composite column with large eccentricity. In the PSRC composite columns, the P-M interaction curves of design codes were similarly predicted at the load level of 0.2Pn to 0.5Pn, regardless of the assumption for the stress distribution. Further, the maximum flexural strength predictions of the PSRC composite columns were greater than those of the CES composite columns, because of the steel angles placed at the four corners of the column. This result indicates that when the same amount of the steel is used for composite columns, the flexural strength of PSRC composite columns can be greater than that of CES composite columns under general design compression load (= 0.2Pn ~ 0.5Pn).
\nThe ultimate flexural strength of a PSRC composite column can be evaluated by performing a section analysis based on either plastic stresses (i.e., plastic stress method) or strain-induced stresses (i.e., strain-compatibility method); the stresses of the steel angles, reinforcing bars, and concrete are determined by linear strain distribution and the stress–strain relationships of the materials [1]. Figure 8 shows the stress distributions at the PSRC cross section by two methods. For the plastic stress method (Figure 8(a)), plastic stresses of concrete, steel angles, and reinforcing bars can be used as 0.85fc′, Fy, and Fyr, respectively. For the strain-compatibility method (Figure 8(b)), strain-induced stresses of angles, reinforcing bars, and concrete can be determined assuming linear distribution of strain over the entire cross section.
\nSection analysis methods of PSRC column: (a) plastic stress method and (b) strain-compatibility method.
where \n
The shear resistance of a PSRC composite column is provided by concrete, transverse reinforcement, and steel angles. Since the dimensions of the angle cross section are significantly less than that of the entire cross section, the contribution of the angles to the shear strength is neglected. Thus, the nominal shear strength Vn of a PSRC composite column can be calculated, on the basis of current RC design code [13].
\nwhere b = width of the cross section; d = effective depth of the cross section; and Ast, Fyt, and s = area, yield stress, and spacing of the transverse reinforcement, respectively.
\nFigure 9 shows the details of the weld- or bolt-connection between the steel angles and transverse reinforcement. Before spalling of concrete cover, the shear transfer between the steel angles and concrete is provided by friction of the angle surface and concrete bearing of the transverse reinforcement projected from the angle surface. Conservatively neglecting the frictional resistance, the nominal bond strength Bn of a steel angle is provided by the concrete bearing of the transverse reinforcement [1].
\nwhere dbt = diameter or thickness of a transverse reinforcement projected from the angle surface; lwt = weld length of a transverse bar or projected length of a transverse plate from the angle surface; ls = shear span length of the PSRC composite column; and α = a factor addressing the confinement effect on the bearing area (≤2.0). After spalling of concrete cover at large inelastic flexural deformation, concrete bearing disappears and is replaced by dowel action of the transverse reinforcement.
\nBond strength between steel angle and concrete.
Figure 10 shows the test setup for the flexural loading tests to investigate the flexural and shear strengths, shear transfer between steel angle and concrete, and strength at the joint between steel angle and transverse reinforcement. The columns are simply supported, and two-point loading is applied at the center of the columns. To estimate the curvature, deflections at least three points need to be measured.
\nTest setup of flexural loading tests.
Figure 11(a) shows the moment-curvature relationships at the center span of a CES column and a PSRC column with a cross section of 500 mm × 500 mm (i.e., 2.0% steel ratio) [1]. In the PSRC column using the same steel ratio of the CES column, the peak strength was 56.7% greater than that of the CES column for the following reasons: (1) the yield strength of the steel angles used for the PSRC column was 15.9% greater than that of the wide-flange steel section used for the CES column and (2) the angles placed at the four corners of the cross section can develop 35.2% higher nominal flexural capacity than the center wide-flange steel section. In the PSRC column, the load-carrying capacity was suddenly decreased when bond failure (i.e., concrete bearing failure) occurred in the bottom angles in the shear span (Figure 12(b)). After the bond failure, dowel action of the transverse bars developed, causing significant bond-slip deformation. Ultimately, it failed due to the fracture of the transverse bars and spalling of web concrete, which was the typical bond-shear failure mode of the PSRC composite column.
\nMoment-curvature relationships at the center span of CES and PSRC columns: (a) CES and PSRC columns; (b) PSRC columns according to transverse bar spacing.
Failure modes of CES and PSRC columns. (a) CES column, (b) PSRC column, (c) PSRC column @ 100 mm tie, (d) PSRC column @ 200 mm tie, (e) PSRC column @ 300 mm tie.
Figure 11(b) compares the effect of transverse reinforcement spacing on the moment-curvature relationship of PSRC composite columns with a smaller cross section of 400 mm × 400 mm (i.e., 3.1% steel ratio). When the transverse reinforcement at a spacing of 100 mm was used, the load-carrying capacity was degraded due to spalling of cover concrete in the uniform moment span, but was slightly recovered due to the strain hardening of the steel angles. Ultimately, it failed due to the tensile fracture of the bottom angle in the uniform moment span (Figure 12(c)). The moment-curvature relationship and failure mode of the PSRC composite column with greater transverse reinforcement spacing of 200 mm were similar to those of the PSRC composite column with transverse reinforcement spacing of 100 mm. It failed due to tensile fracture of the angles in the uniform moment span (Figure 12(d)). In the PSRC composite column with the greatest transverse reinforcement spacing of 300 mm, bond failure occurred in the bottom angles. After the bond failure, the load-carrying capacity was significantly decreased due to spalling of web concrete caused by dowel action of the transverse reinforcement. Ultimately, it failed due to fracture of the transverse reinforcement (Figure 12(e)).
\nFigure 13 shows the test setup of cyclic loading tests for CES and PSRC composite columns. Using two oil jacks, a uniform axial load corresponding to about 22% of compressive capacity are applied, and a cyclic lateral load is applied using an actuator to the column.
\nTest setup of cyclic loading tests.
Figure 14 compares the cyclic behaviors of the CES and PSRC composite columns using steel ratio of 2.2%. In the CES composite column, the load-carrying capacity gradually decreased after the peak strength. Although spalling of the cover concrete occurred at the lateral drift of 3.0–4.0% in the plastic hinge region, the load-carrying capacity was not significantly decreased. However, after the spalling of the cover concrete, post-yield buckling occurred in the longitudinal bars. Ultimately, it failed at the drift ratio of 7.0% because of a low cycle fatigue fracture of the longitudinal bars. In the PSRC composite column, the peak strength was 20% greater than that of the CES composite column because of the higher yield strength and the location of the steel angles. Spalling of the cover concrete occurred at the drift ratio of 3.0%, and significant shear cracks occurred in the plastic hinge region because of the increased shear demand. The angles and transverse reinforcement were exposed because of the spalling of cover concrete at the drift ratio of 5.0%. The exposed angles and longitudinal bars were subjected to local buckling during repeated cyclic loading. As a result, the load-carrying capacity was degraded. However, despite the local buckling of angles and longitudinal bars, tensile fracture did not occur at the joint between the angles and transverse reinforcement.
\nLateral load-drift relationships of CES and PSRC columns: (a) CES column and (b) PSRC column.
Figure 15 shows a U-shaped composite beam-PSRC composite column connection. In order to minimize the workability problem during concrete pouring, only the web plate of the U-section is passed through the joint, and the top and bottom flanges are connected to the top and bottom band plates in the joint. Under high axial compressive force and cyclic lateral loading, premature cover concrete spalling of the joint may deteriorate the connection’s strength and deformation capacity.
\nBeam-column joint details: (a) interior beam-column joint and (b) exterior beam-column joint.
Figure 16 shows the three mechanisms that contribute to the joint shear strength: web shear yielding of the U-shaped steel section (Figure 16(a)), direct strut action of the in-filled concrete inside the U-shaped section (Figure 16(b)), and the strut-and-tie action between the concrete (outside of the U-shaped section) and the band plates (Figure 16(c)) [2]. The shear strength Vws of the two web plates is defined as follows:
\nwhere tw = web thickness; and hwj = effective horizontal length of the web in the joint in the direction of the shear.
\nJoint shear strength mechanisms. (a) Web shear yielding (Vws), (b) Concrete diagonal strut (Vcs), (c) Strut and tie action (Vst), (d) Effective width bo for interior joint, (e) Joint shear demand.
The shear strength Vcs of the direct concrete strut is defined as follows:
\nwhere bic = width of the in-filled concrete, hc = depth of the PSRC composite column; and hb = overall depth of the composite beam including the concrete slab.
\nThe shear strength Vst of the strut-and-tie action is defined as follows:
\nwhere bo = effective width of the joint concrete; ho = effective length of the joint concrete in the direction of shear; baf = distance between the corner angles in the direction orthogonal to the shear; and bic and tw = width of the in-filled concrete and web of the U-shaped section, respectively.
\nFor the strut-and-tie mechanism in Eq. (14), the tension force caused by the concrete strut should be resisted by the top and bottom band plates. Thus, the required cross-sectional area Abp of each band plate is calculated as follows:
\nwhere 2Fybp = yield strength of the top and bottom band plates.
\nThe joint shear capacity and demand can be expressed as moments, from the static moment equilibrium at the joint (Figure 16(e)).
\nwhere (ΣMuc - ΣVb ho/2) and Mnc = joint shear demand and capacity, respectively; ΣMpb and ΣVb = sums of the plastic moments and shear demands, respectively, of the composite beams framing into the joint; Vc = column shear demand; dw = center-to-center distance between the top and bottom flanges; 0.75hb = approximate moment arm for the composite beam section; and db = center-to-center distance between the top and bottom band plates.
\nFigure 17 shows the cyclic lateral load-story drift ratio relationships of U-shaped steel composite beam-PSRC composite column joints. The composite beam has 330 mm width and 700 mm height including slab. The PSRC composite column has 800 mm × 800 mm cross-sectional area. In the interior beam-column joint, cover concrete spalling in the PSRC composite column occurred in the vicinity of the U-section, whose web plate buckled, and severe diagonal cracking occurred in the joint face. The fracture was initiated at the weld joint between the web and the bottom flange and then propagated vertically. Because the band plates were not connected to the bottom flange of the U-section, the band plates were not damaged even after cover concrete spalling. However, a gap occurred between the end of the U-section and the column face under positive moments, which was attributed to an anchorage slip of the web plates showing plastic strains inside the joint. As the anchorage slip increased, development of the beam’s flexural capacities was delayed, which caused significant pinching in the cyclic behavior. The top band plates welded to the top flange of the U-section were pulled out under a negative moment. In the exterior beam-column joint, after the peak strengths, the load-carrying capacity was significantly degraded because of cover concrete spalling in the PSRC composite column and because of local buckling and fracture of the web in the composite beam. The steel angles of the column were completely exposed.
\nLateral load-drift relationships: (a) interior beam-column joint and (b) exterior beam-column joint.
In this chapter, a prefabricated steel-reinforced concrete (PSRC) composite column using steel angles was introduced. Using current design codes, the structural capacities of PSRC composite columns can be evaluated, and it shows better performance than those of a conventional concrete-encased steel (CES) composite column.
The axial load-carrying capacity and deformation capacity of PSRC composite columns are comparable to, or even better than, those of conventional CES composite columns. In the PSRC composite column under axial compression, the corner angles and the transverse reinforcement provide adequate lateral confinement to the concrete.
The corner angles of the PSRC composite column increase the flexural strength and stiffness up to about 30%, compared to those of the conventional CES composite columns.
The bond strength between steel angles and concrete is generated by the bearing strength of the transverse reinforcement projected from the steel angle surface. When the bond strength is greater than the demand (i.e., when close spacing of tie bars is used), the PSRC composite columns show ductile behavior after flexural yielding without bond failure, and ultimately fail due to fracture of the steel angles. Otherwise, significant bond slip occurs in the steel angles, and ultimately the columns fail due to the bond failure of steel angles or the fracture of transverse bars.
Under cyclic lateral loading, the PSRC composite columns show about 20% lower deformation capacities than that of the conventional CES composite column. The lower deformation capacity of the PSRC composite columns is attributed to the following reasons: (1) relatively large plastic strains occur in the corner steel angles; (2) after spalling of the cover concrete, the exposed steel angles are susceptible to local buckling; and (3) the shear demand of the transverse reinforcement increases.
In composite beam-PSRC composite column joints, the load-carrying capacity decreases because of the web local buckling of the U-section at the column face. Ultimately, the beam-column joints fail because of a tensile fracture of the buckled web plates initiated at the weld joint between the web and the bottom flange of the U-section. As yielding occurs in the web plate of the beam, significant web plate anchorage slip occurs inside the joint. However, the anchorage slip mitigates possible stress concentration that may be caused by the discontinuity of the flange plate, which contributes to the increase in deformation capacity.
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I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. 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