\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"293",leadTitle:null,fullTitle:"Practical Applications and Solutions Using LabVIEW™ Software",title:"Practical Applications and Solutions Using LabVIEW™ Software",subtitle:null,reviewType:"peer-reviewed",abstract:"The book consists of 21 chapters which present interesting applications implemented using the LabVIEW environment, belonging to several distinct fields such as engineering, fault diagnosis, medicine, remote access laboratory, internet communications, chemistry, physics, etc. The virtual instruments designed and implemented in LabVIEW provide the advantages of being more intuitive, of reducing the implementation time and of being portable.\nThe audience for this book includes PhD students, researchers, engineers and professionals who are interested in finding out new tools developed using LabVIEW.\nSome chapters present interesting ideas and very detailed solutions which offer the immediate possibility of making fast innovations and of generating better products for the market. The effort made by all the scientists who contributed to editing this book was significant and as a result new and viable applications were presented.",isbn:null,printIsbn:"978-953-307-650-8",pdfIsbn:"978-953-51-5551-5",doi:"10.5772/819",price:139,priceEur:155,priceUsd:179,slug:"practical-applications-and-solutions-using-labview-software",numberOfPages:488,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"8709b37736bf2d4359e98e5542cae86c",bookSignature:"Folea Silviu",publishedDate:"August 1st 2011",coverURL:"https://cdn.intechopen.com/books/images_new/293.jpg",numberOfDownloads:141439,numberOfWosCitations:45,numberOfCrossrefCitations:33,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:54,numberOfDimensionsCitationsByBook:3,hasAltmetrics:0,numberOfTotalCitations:132,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 3rd 2010",dateEndSecondStepPublish:"December 1st 2010",dateEndThirdStepPublish:"April 7th 2011",dateEndFourthStepPublish:"May 7th 2011",dateEndFifthStepPublish:"July 6th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"6084",title:"Prof.",name:"Silviu",middleName:null,surname:"Folea",slug:"silviu-folea",fullName:"Silviu Folea",profilePictureURL:"https://mts.intechopen.com/storage/users/6084/images/293_n.jpg",biography:"Folea, C., Silviu, PhD, is professor at the Technical University of Cluj-Napoca, Automation Department, Romania. 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Lajunen",authors:[{id:"18856",title:"Prof.",name:"Konstantin",middleName:null,surname:"Popov",fullName:"Konstantin Popov",slug:"konstantin-popov"},{id:"18860",title:"Prof.",name:"Marja",middleName:null,surname:"Lajunen",fullName:"Marja Lajunen",slug:"marja-lajunen"}]},{id:"13931",title:"The Design of Nanoscale Inorganic Materials with Controlled Size and Morphology by Ionic Liquids",slug:"the-design-of-nanoscale-inorganic-materials-with-controlled-size-and-morphology-by-ionic-liquids",signatures:"Elaheh Kowsari",authors:[{id:"16210",title:"Dr.",name:"Elaheh",middleName:null,surname:"Kowsari",fullName:"Elaheh Kowsari",slug:"elaheh-kowsari"}]},{id:"13932",title:"Synthesis of Novel Nanoparticle - Nanocarbon Conjugates Using Plasma in Ionic Liquid",slug:"synthesis-of-novel-nanoparticle-nanocarbon-conjugates-using-plasma-in-ionic-liquid",signatures:"Toshiro Kaneko and Rikizo Hatakeyama",authors:[{id:"19594",title:"Prof.",name:"Toshiro",middleName:null,surname:"Kaneko",fullName:"Toshiro Kaneko",slug:"toshiro-kaneko"},{id:"19595",title:"Prof.",name:"Rikizo",middleName:null,surname:"Hatakeyama",fullName:"Rikizo Hatakeyama",slug:"rikizo-hatakeyama"}]},{id:"13933",title:"Nanoparticle Preparation in Room-Temperature Ionic Liquid under Vacuum Condition",slug:"nanoparticle-preparation-in-room-temperature-ionic-liquid-under-vacuum-condition",signatures:"Tetsuya Tsuda, Akihito Imanishi, Tsukasa Torimoto and Susumu Kuwabata",authors:[{id:"18431",title:"Dr.",name:"Tetsuya",middleName:null,surname:"Tsuda",fullName:"Tetsuya Tsuda",slug:"tetsuya-tsuda"},{id:"20450",title:"Prof.",name:"Tsukasa",middleName:null,surname:"Torimoto",fullName:"Tsukasa Torimoto",slug:"tsukasa-torimoto"},{id:"20451",title:"Prof.",name:"Susumu",middleName:null,surname:"Kuwabata",fullName:"Susumu Kuwabata",slug:"susumu-kuwabata"},{id:"20467",title:"Prof.",name:"Akihito",middleName:null,surname:"Imanishi",fullName:"Akihito Imanishi",slug:"akihito-imanishi"}]},{id:"13934",title:"Perspectives of Ionic Liquids Applications for Clean Oilfield Technologies",slug:"perspectives-of-ionic-liquids-applications-for-clean-oilfield-technologies",signatures:"Rafael Martínez-Palou and Patricia Flores Sánche",authors:[{id:"18064",title:"Dr.",name:"Rafael",middleName:null,surname:"Martínez Palou",fullName:"Rafael Martínez Palou",slug:"rafael-martinez-palou"},{id:"24168",title:"Dr.",name:"Patricia",middleName:null,surname:"Flores Sánchez",fullName:"Patricia Flores Sánchez",slug:"patricia-flores-sanchez"}]},{id:"13935",title:"Ionic Liquid Based Electrolytes for Dye-Sensitized Solar Cells",slug:"ionic-liquid-based-electrolytes-for-dye-sensitized-solar-cells",signatures:"Chuan-Pei Lee, Po-Yen Chen and Kuo-Chuan Ho",authors:[{id:"5989",title:"Prof.",name:"Kuo-Chuan",middleName:null,surname:"Ho",fullName:"Kuo-Chuan Ho",slug:"kuo-chuan-ho"},{id:"30213",title:"Dr.",name:"Chuan-Pei",middleName:null,surname:"Lee",fullName:"Chuan-Pei Lee",slug:"chuan-pei-lee"},{id:"30223",title:"BSc.",name:"Po-Yen",middleName:null,surname:"Chen",fullName:"Po-Yen Chen",slug:"po-yen-chen"}]},{id:"13936",title:"Quaternary Ammonium and Phosphonium Ionic Liquids in Chemical and Environmental Engineering",slug:"quaternary-ammonium-and-phosphonium-ionic-liquids-in-chemical-and-environmental-engineering",signatures:"Anja Stojanovic, Cornelia Morgenbesser, Daniel Kogelnig, Regina Krachler and Bernhard K. Keppler",authors:[{id:"17516",title:"Dr.",name:"Daniel",middleName:null,surname:"Kogelnig",fullName:"Daniel Kogelnig",slug:"daniel-kogelnig"},{id:"20872",title:"Dr.",name:"Anja",middleName:null,surname:"Stojanovic",fullName:"Anja Stojanovic",slug:"anja-stojanovic"},{id:"20873",title:"Dr.",name:"Regina",middleName:null,surname:"Krachler",fullName:"Regina Krachler",slug:"regina-krachler"},{id:"20874",title:"Dr.",name:"Bernhard K.",middleName:null,surname:"Keppler",fullName:"Bernhard K. Keppler",slug:"bernhard-k.-keppler"},{id:"24420",title:"MSc.",name:"Cornelia",middleName:null,surname:"Morgenbesser",fullName:"Cornelia Morgenbesser",slug:"cornelia-morgenbesser"}]},{id:"13937",title:"Ionic Liquids within Microfluidic Devices",slug:"ionic-liquids-within-microfluidic-devices",signatures:"Marina Cvjetko and Polona Žnidaršič-Plazl",authors:[{id:"18755",title:"Dr.",name:"Polona",middleName:null,surname:"Žnidaršič-Plazl",fullName:"Polona Žnidaršič-Plazl",slug:"polona-znidarsic-plazl"},{id:"23486",title:"Ms.",name:"Marina",middleName:null,surname:"Cvjetko Bubalo",fullName:"Marina Cvjetko Bubalo",slug:"marina-cvjetko-bubalo"}]},{id:"13938",title:"Ionic Liquids: Methods of Degradation and Recovery",slug:"ionic-liquids-methods-of-degradation-and-recovery",signatures:"E.M. Siedlecka, M. Czerwicka, J.Neumann, P. Stepnowski, J.F Fernández and J. Thöming",authors:[{id:"14871",title:"Prof.",name:"Jorg",middleName:null,surname:"Thöming",fullName:"Jorg Thöming",slug:"jorg-thoming"},{id:"20897",title:"Dr.",name:"Ewa Maria",middleName:null,surname:"Siedlecka",fullName:"Ewa Maria Siedlecka",slug:"ewa-maria-siedlecka"},{id:"21083",title:"Dr.",name:"Malgorzata",middleName:null,surname:"Czerwicka",fullName:"Malgorzata Czerwicka",slug:"malgorzata-czerwicka"},{id:"21084",title:"Prof.",name:"Piotr",middleName:null,surname:"Stepnowski",fullName:"Piotr Stepnowski",slug:"piotr-stepnowski"},{id:"24146",title:"Dr.",name:"Jennifer",middleName:null,surname:"Neumann",fullName:"Jennifer Neumann",slug:"jennifer-neumann"}]},{id:"13939",title:"Progress in Paramagnetic Ionic Liquids",slug:"progress-in-paramagnetic-ionic-liquids",signatures:"Yukihiro Yoshida and Gunzi Saito",authors:[{id:"17739",title:"Dr.",name:"Yukihiro",middleName:null,surname:"Yoshida",fullName:"Yukihiro Yoshida",slug:"yukihiro-yoshida"},{id:"20755",title:"Prof.",name:"Gunzi",middleName:null,surname:"Saito",fullName:"Gunzi Saito",slug:"gunzi-saito"}]}]}],publishedBooks:[{type:"book",id:"9386",title:"Direct Numerical Simulations",subtitle:"An Introduction and Applications",isOpenForSubmission:!1,hash:"158a3a0fdba295d21ff23326f5a072d5",slug:"direct-numerical-simulations-an-introduction-and-applications",bookSignature:"Srinivasa Rao",coverURL:"https://cdn.intechopen.com/books/images_new/9386.jpg",editedByType:"Edited by",editors:[{id:"6897",title:"Dr.",name:"Srinivasa",surname:"Rao",slug:"srinivasa-rao",fullName:"Srinivasa Rao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,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"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"327",title:"Ionic Liquids",subtitle:"Classes and Properties",isOpenForSubmission:!1,hash:"659a4b5cbf7f388a9e559b5b558006ca",slug:"ionic-liquids-classes-and-properties",bookSignature:"Scott T. 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Nondeterministic systems are divided into two categories: stochastic and random. A stochastic system has governing physical laws that even if the state at some point in time is known precisely, it is impossible to determine the state of the system at a later time precisely. It is possible to determine the probability of a state, rather than the state itself. A random system is one which has no apparent governing physical laws. Practically, we treat all unpredictable systems, stochastic or random as stochastic systems, since we employ the same methods to study them. While we are unable to predict the state of a random process, we can evolve a strategy to deal with such processes. Such a strategy is based on a branch of mathematics dealing with unpredictable systems, called statistics.
Estimation is the process of extracting information from data which can be used to predict the behavior of state variables in a system. The estimation uses statistical criteria to infer the actual value of unknown variables. Estimation models are used to process noisy measurements, filter them, and detect inaccuracies. When random signals are passed through a deterministic system, their statistical properties are modified. A deterministic system to which random signals are input, so that the output is a random signal with desired statistical properties is called a filter. Filters can be linear or nonlinear, time-invariant or time varying. However, for simplicity we will usually consider linear, time-invariant filters. Linear, time-invariant filters are commonly employed in control systems to reduce the effect of measurement noise on the control system. In such systems, the output is usually a superposition of a deterministic signal and a random measurement noise.
The output of a filter not only has a frequency content different from the input signal, but also certain other characteristics of the filter, such as a phase-shift or a change in magnitude. In other words, the signal passing through a filter is also distorted by the filter, which is undesirable. A filter would produce an output signal based upon its characteristics, described by the transfer-function, frequency or impulse response, or a state-space representation of the filter. However, a filter can be designed to achieve a desired set of performance objectives, i.e. the numerator and denominator polynomials of the filter’s transfer function, or coefficient matrices of the filter’s state-space model, can be selected by a design process to achieve the conflicting requirements of maximum noise attenuation and minimum signal distortion.
There are several prediction models to infer the system state, although, it can be shown that of all estimation tools Kalman Filter (KF) is the one that minimizes the variance of the estimation error which enables accurate estimation of the process.
The first application of state estimation was in the aerospace field to solve problems related to the prediction of position in aerospace vehicles. Nowadays, estimation has been applied in several fields of engineering and control systems. One common application is in data acquisition, to solve the problem of predicting the state of a system that cannot be measured directly due to the characteristics and complexity of the environment.
KF is an estimator proposed by Rudolph E. Kalman in 1960. It is an algorithm to estimate the evolution of a dynamic system, especially when data has a lot of noise. The principle of the filter is to find the probability of the hypothesis of predicted state and using the data from the measurement to correct it and improve the future estimation at each time. It is a suitable algorithm to apply in dynamic systems, linking real-time measurements and predicting the state of system parameters through time approaches. KF has been implemented in several fields, such as in navigation systems [1, 2, 3, 4], financial models [5, 6, 7], tracking vehicles [8, 9] and image processing [10, 11, 12]; only to mention some of them. Nevertheless, this statistical tool is useful for two main purposes: estimation and performance analysis of estimators.
In the field of IC technology, it has been implemented for thermal estimation. Multicore processors use a dynamic thermal management mechanism that use embedded thermal sensors for monitoring the real-time thermal behavior of the processor, this kind of sensors are susceptible to a variety of source of noise and this causes the discrepancies between actual temperatures observed by on-chip thermal sensors. Therefore, to fix the discrepancies in sensing, Kalman’s prediction is used to estimate real values from noisy sensor readings [13]. Another novel application of KF is in the electric vehicle industry, the estimation of the charge state of lithium-ion battery is an important parameter in order to guarantee a safe operation of them. The battery performance is influenced by aging; this fact makes difficult to predict the battery state, to overcome this issue the application of KF in combination with other methods is a suitable methodology [14, 15, 16, 17].
Recently, KF has been applied in several industrial applications. With the development of manufacturing process, welding automation emerges as one important tool to speed up the production rate in the assembly line in stronger and high-quality welds. Nevertheless, there are several factors that could influence the welding quality and the most important is the arc length, which could be influenced by the irregular surface of the workpiece and the loss of the tungsten electrode. To enhance the quality during the Gas-Tungsten Arc Welding (GTAW) process, KF is applied in order to keep the arc length stable and minimize the external noise [18]. In the field of sensorless control, KF have been used in intelligence electrical drives. To control induction motor drives without mechanical speed sensor at the motor shaft allows reduced hardware complexity, and low costs. Additionally, the use of induction motors without position sensor is useful for applications with abrasive and hard surface. Thereby, the application of an estimation method it’s necessary in order to predict the position and velocity of the shaft [19, 20, 21].
In applications related with radio astronomy, KF has been applied for the analysis of Very-Long-Baseline Interferometry (VLBI) data, in order to analyze parameters such as base line lengths, earth orientation parameters, radio source coordinates and tropospheric delays. Nowadays, modern antennas are being constructed and equipped with highly accurate broadband receiving systems. Besides the accurate observations gotten by astronomic instruments, it is necessary to implement estimation methods in order to optimize the models applied in data analysis [22, 23]. In power systems, one of the main difficulties is power quality due to total harmonics distortion (THD) that is mainly caused by nonlinear loads. THD effects are strongly correlated with issues as device heating, break down electronic components, network interference, etc. Several filters have been performed to decrease the effect of harmonics; nevertheless, the application of KF has shown an important reduction in the effect of harmonics [24, 25, 26]. In the field of biomedicine KF is widely used over other estimation methodologies to overcome the different sources of noise. Specifically, KF has been used to smooth and predict signals from Electroencephalogram and Electrocardiogram signals [27, 28]. Recently in the literature there are reports on a new methodology to protect the confidentiality of the transmitted data based on a Kalman filter. This strategy proposes the implementation of encrypted algorithm using KF, and is suggested to be used in Industrial cyber–physical systems (ICPSs) to protected data privacy [29, 30].
As it has been mentioned above, KF has been used in diverse fields of science and technology to predict specific parameters of interest according to the application. Temperature evolution is an important parameter to measure and predict, in order to study or control the temperature in an environment [31, 32], device [13, 33, 34] and process [18]. It is well known that RTDs are commercial devices very useful to monitor the temperature due their stability and accuracy. However, RTDs are self-heating causing noisy readings making the RTD a suitable example to implement KF for temperature estimation. Importantly, we searched in the literature and found no evidence of previous work reporting the use of a KF to filter the noise and predict the temperature behavior from RTD readings.
The final objective of this study is to obtain the specification of a linear dynamic system (Wiener filter [35]) which accomplishes the prediction, separation, or detection of a random signal [36]. With the state-transition method, a single derivation covers a large variety of problems: growing and infinite memory filters, stationary and non-stationary statistics, etc. Having guessed the “state” of the estimation (i.e., filtering or prediction) problem correctly, one is led to a nonlinear difference (or differential) equation for the covariance matrix of the optimal estimation error. From the solution of the equation for the covariance matrix we obtain the coefficients characterizing the optimal linear filter [36]. The following is a simplified derivation described previously in the references [37, 38].
The initial state,
Thus
The correlation matrix,
The correlation matrix is the expected value of the matrix
Consider a plant which we cannot model accurately using only a deterministic model, because of the presence of uncertainties called process noise and measurement noise:
In the linear, time-varying state-space representation above,
Since we cannot predict the state-vector,
Since the state-vector,
If T < t, this is a data-smoothing (interpolation) problem. If T = t, this is called filtering. If T > t, we have a prediction problem. Since the original treatment is general enough, the collective term estimation is used [36].
Hence, the best estimate to obtain for
Taking in consideration the deviation of the estimated state- vector,
The best estimate of state-vector happens if
The state-equation of the Kalman filter is that of a time-varying observer, and can be written as follows:
If we substitute Eq. (4) into Eq. (12) we get:
Substituting Eq. (13) into Eq. (7)
Here
Using Eqs. (6) and (7), we obtain:
Eq. (16) is the error covariance update equation, where
The trace of the error covariance matrix is the sum of the mean squared errors. The mean squared error may be reduced by minimizing the trace of
We rewrite Eq. (16);
Taking the trace of this expression gives:
Then, we differentiate with respect to
Setting to zero and solving for
Substitution of Eq. (20) into [17], gives:
Eq. (21) is the update equation for the error covariance matrix with optimal gain.
State projection is derived using;
To project the error covariance matrix into the next time interval, k + 1 we first find an expression for the error based on the prior error;
Eq. (7) in time k + 1 is;
Assuming that
This completes the description of the filter.
An algorithm loop is required to make the program in MATLAB and in C-code for the microprocessor. The loop is summarized in the Figure 1.
Recursive algorithm for the Kalman filter.
The KF assumes that the system model is linear and known, the system and measurement noises are white, and the states have initial conditions with known means and variances. The power spectral densities used can be treated as tuning parameters to design an observer with excellent performance and robustness. The linear Kalman filter can also be used to design observers for nonlinear plants, by treating nonlinearities as process noise with appropriate power spectral density matrix.
Since the Kalman filter is an optimal observer the appearance of matrix Riccati equation is not surprising. We are interested in a steady Kalman filter, i.e. the Kalman filter for which the covariance matrix converges to a constant in the limit
From the projections into
Using Eq. 30 in Eqs. 31 and 32 we get:
Rewriting Eq. 34 we get:
When in steady state:
Then we arrive at the Riccati equation:
The iterative solution of the Riccati equation is not required in real time. The observer gain is calculated off-line for predictive control applications [40]. Riccati equations are mainly used to control large scale systems, estimation, and, detection processes.
In this work the discrete-time algebraic Riccati equation (DARE) was solved to obtain the covariance matrix P of the Kalman gain. The discrete-time algebraic Riccati equation is represented by the next form [41]:
Where
Eq. (38) can be written in the short form:
Where:
The application of the Kalman filter implies solving the DARE, which can be solved by several solution methods. Computational methods to solve Riccati equations can be categorized into three classes: invariant subspace methods, deflating subspace methods, and Newton’s methods. The generalized Schur method that is classified as a deflating subspace method is used to solve DARE. The generalized Schur algorithm is a strong algebraic tool that allows computing classical decompositions of matrices, such as the QR and LU factorizations [42]. The next algorithm was used to solve DARE [43]:
Input arguments:
Output arguments:
Form the pencil
Transform the pencil
Using an orthogonal transformation and reorder the generalized real Schur form. So that all the pencil
Form the matrix:
Resistive temperature detectors (RTD) have attracted attention to be employed as thermal health monitors. As clinical thermometers they are stable and reliable presenting high accuracy and resolution [44]. One of the most widely used RTD is the emerging thin-film resistor which has minimal impact on complex circuits due to its small size and due to their negligible mass.
The basic function of the sensor is determined by a proportional increment of resistance when temperature is applied. RTDs can be employed on a rigid or flexible substrate [45, 46, 47], the metal combination with a flexible o rigid substrate can cover conformal applications. RTD fabrication can be done by metals like Pt [48, 49, 50], Cu [51], Ag [52], and Ni [53], among other materials. Nickel presents a suitable option for RTD fabrication due to its wide temperature linear range of operation and its relatively low price.
Clinical thermometers require a high definition and reliability because less than 1°C difference can indicate a health problem. The thermometer signal can be amplified by electronic means, but it is desirable to filter such readings. This work is focused to the filtering and prediction of an highly sensitive Nickel based thin film RTD (range, 273–325 K), to be incorporated to complex circuits [54], we present the theoretical analysis about the relation sensibility-resistance that matches with experimental results.
All metals produce an increase in its resistance to an increase in specific temperature, which means that resistance is linearly proportional to temperature change. This dependence between electrical resistance and temperature is the principle of operation used by a resistance temperature detector (RTD). The relation between temperature-resistance for Pt wire (RTD), is described by the equation known as the Calendar-Van Dusen, Eq. 41) [50].
Where R0°C is the resistance at 0°C, α and β are temperature coefficients and T is temperature, the temperature coefficients depend only on material properties. In addition, the RTD resistance depends on its geometrical design, according to Eq. 42.
Where “σ” is the resistivity, “L” length, “A” lateral area, “w” channel width, and “t” channel height. Only by increasing the length “L” or decreasing the area “A” that means reducing the “t” film thickness or the “w” channel wide, the resistance can increase.
The estimation of the thermal system is represented by the linear stochastic state-space description
Generally, the RTD system is modeled as an RLC circuit, which consists of a resistor a capacitor and an inductor in series with an input voltage. The output that we analyzed is the voltage across the resistor which is related to temperature change. The RLC circuit is represented by a second-order differential equation
Also, we may simplify the response of the system to that of a first-order RC circuit. This implies to solve a first-order ordinary differential equation:
In this work, a Kalman Filter is proposed to decrease the time response to improve the speed feedback and filtering of the perturbations by signal noise from physical signals as thermal detectors. In some instances, a reduced model is advisable to use in an embedded system due to easy implementation and low computational complexity [2].
Kalman filter can be embedded in a temperature system made by Resistance Thermal Detectors (RTD).RTD’s are robust elements that require relatively easy measurement, as a consequence are a useful thermal sensor for industry and medical applications. Nevertheless, these devices are exposing to vibration, electrical noise, and measurement errors generated by the thermoelectric effect caused by the temperature difference between electrical contacts, which affects the response time of the sensor. The implementation of the Kalman filter in a temperature system produces an optimal estimative of thermal behavior and decreases the uncertainties about the prediction of the temperature.
In order to describe the system in the state space, it is necessary to apply system identification methods using MATLAB. Then, after obtaining the system’s state space model we are able to use the Kalman filter algorithm to estimate the future output of the system.
To study the dynamics of our system, we used MATLAB functions
Using MATLAB we are able to acquire the Discrete-time identified state-space model:
with:
Estimated using
System ID using MATLAB. Input output model for a step response defined problem.
Bode diagram indicating the system is a second order system as described by the system transfer function.
Systems model and measured evolutions in time. Fit to estimation data: 90.27%.
We modify the MATLAB example for the time-varying case found in [55] and we code our own function to solve the Discrete Algebraic Riccati Equation. MATLAB functions like predict or forecast were found useful to understand the problem at hand, however they were not used in the code we present here.
w(1:n) = sqrt(Q)*randn(n,1);
v(1:n) = sqrt(R)*randn(n,1);
systv = ss(A,B,C,0,Ts);
ytv(1:n) = lsim(systv,U(1:n) + w(1:n)).
yvtv(1:n) = ytv(1:n) + v(1:n);
Ptv(:,:) = B(:,:)*Q*B(:,:)’; % Initial error covariance.
x = zeros(order,1); % Initial condition on the state.
order = 2;
yetv(1:n) = zeros(n,1);
ycov(1:n) = zeros(n,1);
for i = 1:n.
% Measurement update.
Mn(:,:) = Ptv(:,:)*C(:,:)’/(C(:,:)*Ptv(:,:)*C(:,:)’ + R);
x = x + Mn(:,:)*(yvtv(i)-C(:,:)*x); % x[n|n].
Ptv(:,:) = (eye(order)-Mn(:,:)*C(:,:))*Ptv(:,:); % P[n|n].
yetv(i) = C(:,:)*x;
errcov(i) = C(:,:)*Ptv(:,:)*C(:,:)’;
% Time update.
x = A(:,:)*x + B(:,:)*U(i); % x[n + 1|n].
Ptv(:,:) = A(:,:)*Ptv(:,:)*A(:,:)’ + B(:,:)*Q*B(:,:)’; P[n + 1|n].
end
%% DARE. We coded our own dare function [X,L,G] = sdare(A,B,Q,R).
[P_inf,L,M_inf] = sdare(atv,ctv’,Q,R);
for i = 1:p
% Measurement update.
x = x + M_inf’*(yvtv(i)-ctv*x); % x[n|n].
yetv_inf(i) = ctv*x;
errcov_inf(i) = ctv*P_inf*ctv’;
% Time update.
x = atv*x + btv*U(i); % x[n + 1|n].
P_inf = atv*P_inf*atv’ + btv*Q*btv’; % P[n + 1|n].
end
function [SD] = sdare(A,B,Q,R).
At = transpose(A);
Bt = transpose(B);
S1 = size(A);
E = eye(S1);
Z = zeros(S1);
Ri = inv.(R);
S = B*Ri*Bt;
Pdare = [A Z; −Q E];
Ndare = [E S; Z At];
[AA,BB,L,Z] = qz(Pdare,Ndare);
[AAS1,BBS1,QS1,ZS1] = ordqz(AA,BB,L,Z,‘udi’);
O = ZS1(1:2,1:2);
P = ZS1(3:4,1:2);
H = inv.(O);
SD = P*H;
end
Matlab was used to simulate the response of an RTD modelled as a second order system. In Figure 5(A) we show the plot of the true response y (cyan line) and the filtered response (red line). In Figure 5(B) the plot compares the measurement error with the estimation error. As can be seen in Figure 5(C) the time-varying filter also estimates the covariance errcov of the estimation error at each sample which shows when the filter reached steady state. As it can be seen, we have the possibility to predict the state after approximately 8 seconds. Also, we show the evolution of the estimated temperature response showing an error of −0.0948°C in the best of the cases and less than 1°C in the worst of the cases after 45 seconds.
(A) Evolution of the estimated temperature response showing an error of −0.0948°C in the best of the cases and less than 1°C in the worst of the cases. (B) Evolution of the measurement and estimation errors. (C) Evolution of the covariance of the error showing the possibility to predict the state after approximately 8 seconds.
The unit step response depends on the roots of the characteristic equation. If both roots are real-valued, the second-order system behaves like a chain of two first-order systems, and the step response has two exponential components. If the roots are complex, the step response is a harmonic oscillation with an exponentially decaying amplitude [56]. In our case, the roots of the characteristic polynomial:
The state description for an RC system is described above. From there, we know that the dynamics are dependent only on the RC constant. In addition, there is an amplificator in the system electronics that has a gain of 260. To solve for the RC constant of the system we use the least-squares method (Chi square minimization). The system has a solution of the form
Which is a linear equation. Using a linear fitting program:
We obtain
readings[readIndex] = analogRead(inputPin); // read from the sensor.
total = total + readings[readIndex]; // add the reading to the total.
readIndex = readIndex +1; // advance to the next position in the array.
time_equis_readings[time_equis_readIndex] = time_equis_readIndex;
time_equis_readIndex = time_equis_readIndex +1;
if (readIndex > = numReadings) // if we’re at the end of the array.
{
for(i = 0;i < =numReadings-1;i++).
{
Y[i] = log(readings[i]);
time1[i] = time_equis_readings[i];
sumx = (sumx +time_equis_readings[i]);
sumx2 = (sumx2 + time_equis_readings[i]*time_equis_readings[i]);
sumy = (sumy +Y[i]);
sumxy = (sumxy +time_equis_readings[i]*Y[i]);
}
den = (numReadings*sumx2-sumx*sumx);
a = (sumx2*sumy -sumx*sumxy)/den;
Bc = (n*sumxy-sumx*sumy)/den;
// State description.
A = -Bc;B=Bc;C = 260;D = 0;
//wrap around to the beginning:
readIndex = 0;time_equis_readIndex = 0;
}
// KALMAN.
errcov = C*P*C;
for(i = 0;i < =numReadings-1;i++).
{
Mn = P*C/((C*P*C + R)); // initial estimate.
X = X + Mn*(readings[i]-C*X); // update estimate Average_readings[i].
P = (1-Mn*C)*P; // update covariance.
y_e[i] = C*X;
errcov = C*P*C;
X = A*X + B*U; // project into k + 1.
P = A*P*A + B*Q*B; // project into k + 1.
}
timer0_millis = millis();
// Solution to the Riccati equation.
F = -Bc;H = 260;
SQ = sqrt((H*H*Q*R) + (F*F*R*R));
SR = F * R;
P_inf = (SQ + SR)/(H*H);
M_inf = P_inf*C/(C*P_inf*C + R);
for(i = 0;i < =numReadings-1;i++).
{
// Measurement update.
// M_inf;
x_inf = x_inf + M_inf*(readings[i]-C*x_inf); // % x[n|n].
//P_inf; % P[n|n].
y_e_inf = C*x_inf;
errcov_inf = C*P_inf*C;
// Time update.
x_inf = A*x_inf + B*U;
P_inf = A*P_inf*A + B*Q*B;
}
}
The experiments were performed in a thermal bath giving step responses to the desired setup temperature. Figure 6 depicts the upward and downward evolution of the temperature, the Kalman filter and the two predictors (using two different Q and R settings). As can be seen the predictors follow the Temperature of the sensor closely, especially for the upward way, while the Kalman filter lags behind.
Implemented system. Step response for the upwards and downwards evolution. Two different Kalman filters were used to predict (by solving the DARE equation) the evolution of the future state with different Q and R to calibrate the desired response. In blue the evolution of the RTD sensor analog input, in Yellow and red the two Kalman predictors and the Kalman estimation in cyan color.
Sliding control [57] is an additional tool to predict the behavior of a second order system basically smoothing the system by boundary layers. The prediction of the system state trajectory is given using an uncertain model of the system. The subspace which represents the quantity of uncertainties in the prediction process, forces the estimate state trajectory to switching gain to converge the estimates to within a boundary of the real state values. To predict the state trajectory of our RLC system it’s possible to switch its gain by the subspace represented by a first-order RC model. The estimated state trajectory is forced to keep a switch back and forth within the boundary layer represented in our case by a RC model. By creating a boundary layer, the system is further constrained to have a solution existing in between two RC model solutions.
In Figure 7 it can be clearly seen that the use of two estimators may help predict the behavior of the RTD in a much better way. The system needs to be calibrated first in order to have the two Kalman filters enveloping the required solution. As can be seen in the upward direction, both predictors (yellow and red) envelope the desired response (blue), that of the RTD sensor improving the response of the Kalman filter without boundaries (cyan). Unfortunately, this is not the case in the downward evolution. From the nonlinear control systems point of view these two evolutions demark a region where the RTD stands thus making possible to program a better estimator. It is left as an outlook to program a third estimator using this boundary layer in order to have a better predictor, especially for the downward evolution.
Ascending and descending step responses of the Kalman filter and two Predictors which function in real time. In blue the RTD sensor response, in cyan the estimator response, in yellow and in red the two differently calibrated Kalman predictors.
As it can be shown the implementation of the Kalman filter brings the opportunity to estimate the forecast in real time of a second order system using first, MATLAB and second that of two first order systems using a simple RC system coded in C-language for a microprocessor. It has been shown that the program is able to predict the evolution of temperature for a RTD system. Even if the system is implemented using a first order system we can find evolving solutions for our estimation and prediction to be good enough. We predict the state after approximately 8 seconds showing an error of −0.0948°C in the best of the cases. In addition, a boundary layer may be programmed using two first order Kalman predictors which may be tuned by setting Q and R properly. We believe this is the first report on the use of a Kalman filter to predict the evolution of temperature from a RTD.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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Women are diagnosed with PTSD approximately twice as often as men. In this review, we outline the evidence of gender differences related to PTSD, and the factors of resilience and susceptibility differ between men and women.",book:{id:"5472",slug:"gender-differences-in-different-contexts",title:"Gender Differences in Different Contexts",fullTitle:"Gender Differences in Different Contexts"},signatures:"Jingchu Hu, Biao Feng, Yonghui Zhu, Wenqing Wang, Jiawei Xie\nand Xifu Zheng",authors:[{id:"190985",title:"Dr.",name:"Xifu",middleName:null,surname:"Zheng",slug:"xifu-zheng",fullName:"Xifu Zheng"},{id:"194981",title:"BSc.",name:"Yonghui",middleName:null,surname:"Zhu",slug:"yonghui-zhu",fullName:"Yonghui Zhu"},{id:"194982",title:"MSc.",name:"Wenqing",middleName:null,surname:"Wang",slug:"wenqing-wang",fullName:"Wenqing Wang"},{id:"194985",title:"Dr.",name:"Jingchu",middleName:null,surname:"Hu",slug:"jingchu-hu",fullName:"Jingchu Hu"},{id:"194986",title:"MSc.",name:"Biao",middleName:null,surname:"Feng",slug:"biao-feng",fullName:"Biao Feng"},{id:"194987",title:"Ph.D. Student",name:"Jiawei",middleName:null,surname:"Xie",slug:"jiawei-xie",fullName:"Jiawei Xie"}]},{id:"52472",doi:"10.5772/65410",title:"Gender and Health",slug:"gender-and-health",totalDownloads:3432,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"Research has found differences between women and men in some health indicators. 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Gender is one such variable that must be examined with regard to optimizing leadership effectiveness. The topic of gender and leadership deserves serious and thoughtful consideration and discussion because of professional, political, cultural, and personal realities of the twenty‐first century. Women and men have been, are, and should be leaders. Gender must be considered to determine how each leader can reach maximum potential and effectiveness. The FourCe‐PITO conceptual framework of leadership is designed to help guide leadership development and education. The present chapter uses this conceptual framework of leadership to discuss how consideration of gender may affect and optimize leadership development and effectiveness. 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I am giving examples from my developmental psychology research where the split-sample analysis by gender showed amazing and often unexpected effects.",book:{id:"5472",slug:"gender-differences-in-different-contexts",title:"Gender Differences in Different Contexts",fullTitle:"Gender Differences in Different Contexts"},signatures:"Chris Lange-Küttner",authors:[{id:"190245",title:"Prof.",name:"Chris",middleName:null,surname:"Lange-Küttner",slug:"chris-lange-kuttner",fullName:"Chris Lange-Küttner"}]},{id:"53721",doi:"10.5772/66093",title:"Professional Women's Experience of Autonomy and Independence in Sindh-Pakistan",slug:"professional-women-s-experience-of-autonomy-and-independence-in-sindh-pakistan",totalDownloads:1644,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"This chapter summarises the part of findings of my doctoral studies at the University of Sussex, Brighton, UK. In this case study, there are elements of both qualitative and quantitative approaches; the former is the principal approach to this research while the latter works as complementary. Participants of the research were divided into two categories: academic and non‐academic. Forty semi‐structured interviews (20 from each category) and 100 survey questionnaire (50 from each category) were collected. This research argues that existing concepts of ‘autonomy’ and ‘independence’ may not be useful indices/indicators for measuring the social status or position of women in Sindhi society, due to variations in understanding or the meanings attributed to these concepts across the globe. Findings argue that these professional women perceived concepts of ‘autonomy’, ‘independence’ and ‘individuality’ categorically different than those of Westernised understandings. This research asserts that Sindhi society, similarly to that of Tamil society, emphasises social groups rather than individuals. Hence, ‘collective identities’ are the essence of Sindhi society; however, individuals find their autonomy, independence and individuality in the context of ‘others’, which means to be more responsible for group's interests.",book:{id:"5472",slug:"gender-differences-in-different-contexts",title:"Gender Differences in Different Contexts",fullTitle:"Gender Differences in Different Contexts"},signatures:"Mukesh Kumar Khatwani",authors:[{id:"196384",title:"Dr.",name:"Mukesh",middleName:"Kumar",surname:"Khatwani",slug:"mukesh-khatwani",fullName:"Mukesh Khatwani"}]}],mostDownloadedChaptersLast30Days:[{id:"52503",title:"Gender and Leadership",slug:"gender-and-leadership",totalDownloads:4125,totalCrossrefCites:5,totalDimensionsCites:8,abstract:"The topic of leadership has been addressed and applied for millennia. Yet, it is only within the past 80 years that leadership has been a topic of serious discussion. It is important to understand variables relevant to effective leadership. 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In this review, we outline the evidence of gender differences related to PTSD, and the factors of resilience and susceptibility differ between men and women.",book:{id:"5472",slug:"gender-differences-in-different-contexts",title:"Gender Differences in Different Contexts",fullTitle:"Gender Differences in Different Contexts"},signatures:"Jingchu Hu, Biao Feng, Yonghui Zhu, Wenqing Wang, Jiawei Xie\nand Xifu Zheng",authors:[{id:"190985",title:"Dr.",name:"Xifu",middleName:null,surname:"Zheng",slug:"xifu-zheng",fullName:"Xifu Zheng"},{id:"194981",title:"BSc.",name:"Yonghui",middleName:null,surname:"Zhu",slug:"yonghui-zhu",fullName:"Yonghui Zhu"},{id:"194982",title:"MSc.",name:"Wenqing",middleName:null,surname:"Wang",slug:"wenqing-wang",fullName:"Wenqing Wang"},{id:"194985",title:"Dr.",name:"Jingchu",middleName:null,surname:"Hu",slug:"jingchu-hu",fullName:"Jingchu Hu"},{id:"194986",title:"MSc.",name:"Biao",middleName:null,surname:"Feng",slug:"biao-feng",fullName:"Biao Feng"},{id:"194987",title:"Ph.D. Student",name:"Jiawei",middleName:null,surname:"Xie",slug:"jiawei-xie",fullName:"Jiawei Xie"}]},{id:"52472",title:"Gender and Health",slug:"gender-and-health",totalDownloads:3430,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"Research has found differences between women and men in some health indicators. Women’s life expectancy is higher than men’s, but research on differences in morbidity has proved less consistent than on the differences in mortality. These differences vary in terms of the type of health indicator used, the life cycle period analyzed, and even the country where research is conducted. Generally, men have more life-threatening chronic diseases at younger ages, including coronary heart disease, as well as more externalizing mental health problems and substance use disorders. Women present higher rates of chronic debilitating conditions such as arthritis, frequent or severe headaches, gallbladder conditions, and also more internalizing mental problems such as affective and anxiety disorders. Results of research on the differences between women and men in self-rated health have also highlighted the complexity of gender differences in health. Although several studies have shown that women have poorer self-rated health than men, this is not the case in all countries. Also, differences in self-rated health vary depending on other psychosocial and demographic variables. The present study reviews the main differences in women’s and men’s health as well as the most relevant factors that may account for them.",book:{id:"5472",slug:"gender-differences-in-different-contexts",title:"Gender Differences in Different Contexts",fullTitle:"Gender Differences in Different Contexts"},signatures:"María Pilar Matud",authors:[{id:"189729",title:"Prof.",name:"M. Pilar",middleName:null,surname:"Matud",slug:"m.-pilar-matud",fullName:"M. Pilar Matud"}]},{id:"53212",title:"Broken Dreams—Balancing Self and Family Well-Being: The Experiences of Women Immigrants to Hamilton, ON",slug:"broken-dreams-balancing-self-and-family-well-being-the-experiences-of-women-immigrants-to-hamilton-o",totalDownloads:1512,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter presents the preliminary analysis of a study conducted in Hamilton, ON. It explores the intersection of women’s immigration, integration and mental health. Their perceptions of what is needed from them in relation to the various challenges/changes that moving to a new country entails is a particular focus of this research. To begin with, the term “women immigrant” (WI) is used, rather than immigrant women as commonly used—as the participants were women long before they became immigrants. 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She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. 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