**5.1 Image measurement and computation method at applications**

A digital camera was used for determining dimensions and digital images were evaluated by Myriad image processing software. Measuring and verification process of digital images can be done with the help of this program safely, also digital images can be compared easly (Fig. 12.). This program has two steps for measuring images. First step was calibration of the program. For this purpose software needed a calibration ruler or a calibration plate. Hence a millimetric paper was used for calibration and testing for software (Fig. 13.). For the calibration, a calibration plate with known dimensions was used while taking digital images. Second step was true selection of images (Beyaz, 2009a).

Fig. 11. 3D measurement system that it developed jointly with the National Institute of

between an original 3-D surface and its deformed version. The other important challenge is analysis of 3-D views by using 2-D screens. Image measurements still require identifying the pixels that are connected to each other. In two dimensions, it is necessary to determine

Existing 3D measurement techniques are classified into two major types—active and passive. In general, active measurement employs structure illumination (structure projection, phase shift, moire topography, etc.) or laser scanning, which is not necessarily desirablein many applications. On the other hand, passive 3D measurement techniques based on stereo vision have the advantages of simplicity and applicability, since such

A digital camera was used for determining dimensions and digital images were evaluated by Myriad image processing software. Measuring and verification process of digital images can be done with the help of this program safely, also digital images can be compared easly (Fig. 12.). This program has two steps for measuring images. First step was calibration of the program. For this purpose software needed a calibration ruler or a calibration plate. Hence a millimetric paper was used for calibration and testing for software (Fig. 13.). For the calibration, a calibration plate with known dimensions was used while taking digital

Advanced Industrial Science and Technology (Anonymous, 2011a.).

**5.1 Image measurement and computation method at applications**

images. Second step was true selection of images (Beyaz, 2009a).

differences between touching pixels.

techniquesrequire simple instrumentation.

**5. Agricultural application gallery**

Fig. 12. Interface of digital image analysis software Myriad v8.0

Fig. 13. Milimetric paper test of image processing software.

Colour measurements were determined with Minolta Cr200 model colourmeter which is based on L\*a\*b\* measurement system (Fig. 14.). Avarage of three sample points are used as colour value (Fig. 15.). Minolta Cr200 model colourmeter was calibrated with a white reflective plate.

Machine Vision Measurement Technology and Agricultural Applications 147

Kahramanmaras red peppers and the front projection area values (AF) was pointed as a graph at Fig. 16. Similarly top projection area values were presented at Fig. 17. However left projection areas values and the estimation equation can be seen in Fig. 18 (Beyaz et al.,

Real Volume Value (ml) = - 22,96 + 2,099 (AF)

S 20,7203 R-Sq 49,9% R-Sq(adj) 47,1%

S 16,9114 R-Sq 66,6% R-Sq(adj) 64,8%

**AF (cm2)**

**AT (cm2)**

Fig. 17. The regression equation and graph obtained by using AT projection area

30 35 40 45 50 55

Fig. 16. The regression equation and graph obtained by using AF projection area

40 45 50 55 60 65 70

According to this assessment, the real volume value which was obtained from the front projection area shows the regression coefficient has reached 49.9%. The regression coefficient obtained from estimation equations by using of the top projection area was 66.6% (Fig. 17.).The regression coefficient obtained by using left projection area was 63.6% (Fig.

Real Volume Value (ml) = - 33,61 + 2,733 (AT)

2009d).

**Real Volume Value (ml)**

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

150

125

100

75

50

(Beyaz et al., 2009d).

18.).

150

125

100

75

50

Fig. 14. Sample colour measurement of an apple.

Fig. 15. Selection of L\*a\*b\* sample points at a quice by using colourmeter (Beyaz et al., 2011).

#### **5.1.1 Volume determination of Kahramanmaras red pepper (***Capsicum annuum* **L.) by using image analysis technique**

The size of an agricultural product is an important parameter to determine fruit growth and quality. It can be used to determine the optimum harvest time as a maturity index. In this study, the image analysis method was tested on Kahramanmaras red pepper (*Capsicum annuum* L.) which may have a non uniform shape. For this purpose; the front, top and left side of each pepper was taken into account for evaluations and projection areas. The effect of each image and image combination has been used to determine the volume of peppers. The regression coefficients between the projection areas and volume values have also been assessed for volume estimation. The relationship between the real volumes of

Fig. 15. Selection of L\*a\*b\* sample points at a quice by using colourmeter (Beyaz et al., 2011).

**5.1.1 Volume determination of Kahramanmaras red pepper (***Capsicum annuum* **L.) by**

The size of an agricultural product is an important parameter to determine fruit growth and quality. It can be used to determine the optimum harvest time as a maturity index. In this study, the image analysis method was tested on Kahramanmaras red pepper (*Capsicum annuum* L.) which may have a non uniform shape. For this purpose; the front, top and left side of each pepper was taken into account for evaluations and projection areas. The effect of each image and image combination has been used to determine the volume of peppers. The regression coefficients between the projection areas and volume values have also been assessed for volume estimation. The relationship between the real volumes of

Fig. 14. Sample colour measurement of an apple.

**using image analysis technique**

Kahramanmaras red peppers and the front projection area values (AF) was pointed as a graph at Fig. 16. Similarly top projection area values were presented at Fig. 17. However left projection areas values and the estimation equation can be seen in Fig. 18 (Beyaz et al., 2009d).

Fig. 16. The regression equation and graph obtained by using AF projection area (Beyaz et al., 2009d).

According to this assessment, the real volume value which was obtained from the front projection area shows the regression coefficient has reached 49.9%. The regression coefficient obtained from estimation equations by using of the top projection area was 66.6% (Fig. 17.).The regression coefficient obtained by using left projection area was 63.6% (Fig. 18.).

Fig. 17. The regression equation and graph obtained by using AT projection area (Beyaz et al., 2009d).

Machine Vision Measurement Technology and Agricultural Applications 149

Fig. 20. shows the regression equation graph which was determined from total value of the

Real Volume Value (ml)= - 36,95 + 1,740 (AF+AL)

S 18,5312 R-Sq 59,9% R-Sq(adj) 57,7%

S 15,7518 R-Sq 71,1% R-Sq(adj) 69,4%

**AF+AL (cm2)**

Fig. 20. The regression equation and graph obtained by using AF + AL projection areas

**AF+AT (cm2)**

Fig. 21. The regression equation and graph obtained by using AF + AT projection areas

Fig. 22. shows estimated regression graph obtained from sum of three (front, left and top) projections area values and also describes the regression coefficient 73.9%. This value for the

60 70 80 90 100 110 120

It can be seen in Fig. 20., the regression coefficient was 59.9%. Fig. 21. shows the sum of the regression graph. Estimation coefficient was obtained as 71.1% depending on the regression

Real Volume Value (ml) = - 54,67 + 1,475 (AF+AT)

50 60 70 80 90

front and left projection areas.

**Real Volume Value (ml)**

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

150

125

100

75

50

regression gave the second highest value.

(Beyaz et al., 2009d).

equation.

150

125

100

75

50

Fig. 18. The regression equation and graph obtained by using AL projection area (Beyaz et al., 2009d).

When the volume estimation obtained from one projection area, top projection area values has the highest regression coefficient (66.6%). The volume estimation has been examined by making double groups from these three projection areas. Fig. 19. represents the regression graph which is obtained from the sum of top and left projection areas. Estimation equation has reached a regression coefficient 74.7% depending on this assessment.

The most appropriate estimation formula has been calculated from the top and the left projection area. The following equation is the most appropriate equation (Beyaz et al., 2009d):

Real Volume Value (ml) =-47,29+2,132 (AT+AL)

Fig. 19. The regression equation and graph obtained by using AT + AL projection areas (Beyaz et al., 2009d).

Real Volume Value (ml) = - 26,58 + 5,963 (AL)

S 17,6677 R-Sq 63,6% R-Sq(adj) 61,6%

S 14,7205 R-Sq 74,7% R-Sq(adj) 73,3%

**AL (cm2)**

Fig. 18. The regression equation and graph obtained by using AL projection area

has reached a regression coefficient 74.7% depending on this assessment.

15,0 17,5 20,0 22,5 25,0 27,5

When the volume estimation obtained from one projection area, top projection area values has the highest regression coefficient (66.6%). The volume estimation has been examined by making double groups from these three projection areas. Fig. 19. represents the regression graph which is obtained from the sum of top and left projection areas. Estimation equation

The most appropriate estimation formula has been calculated from the top and the left projection area. The following equation is the most appropriate equation (Beyaz et al.,

Real Volume Value (ml) = - 47,29 + 2,132 (AT+AL)

**AT+AL (cm2)**

Fig. 19. The regression equation and graph obtained by using AT + AL projection areas

40 50 60 70 80

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

2009d):

150

125

100

75

50

Real Volume Value (ml) =-47,29+2,132 (AT+AL)

150

125

100

75

50

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

Fig. 20. shows the regression equation graph which was determined from total value of the front and left projection areas.

Fig. 20. The regression equation and graph obtained by using AF + AL projection areas (Beyaz et al., 2009d).

It can be seen in Fig. 20., the regression coefficient was 59.9%. Fig. 21. shows the sum of the regression graph. Estimation coefficient was obtained as 71.1% depending on the regression equation.

Fig. 21. The regression equation and graph obtained by using AF + AT projection areas (Beyaz et al., 2009d).

Fig. 22. shows estimated regression graph obtained from sum of three (front, left and top) projections area values and also describes the regression coefficient 73.9%. This value for the regression gave the second highest value.

Machine Vision Measurement Technology and Agricultural Applications 151

classification and packaging parameter. Overall, at irregular shape products such as pepper is expected to be high regression coefficient relationship between volume and weight. However, classification and packaging system by using image analysis techniques according to the quality of the product is also possible. In this regard research has router features. According to the results of image processing method volume of Kahramanmaras red pepper

**5.1.2 Image analyze technique for measurements of apple and peach tree canopy**

Image analysis measurements and the real manual measurement method had been compared of apple and peach trees canopy volume in this study. The known of the tree canopy volumes are important from the point of views uniform growing, yield estimation, fertilizers and chemical applications. Also this research gives us some information about tree height, skirt height, parallel diameter. And also we can use these parameters for designing harvesting machines. In this research, totally twenty trees which has different canopy heights and volumes have been used. Apple and peach trees randomly have been selected from apple and peach garden belongs to Ankara University Agriculture Faculty. After the taking photographs of all trees by using digital camera, overall canopy height above the ground, canopy diameter parallel to row near ground level, height to point of maximum canopy diameter, height from ground to canopy skirt data have been obtained from these digital images. Apple and peach canopy volume have been calculated by using a formula that is named Albrigo from the prolate spheroid canopy volume values. The real values and image analysis measurements have been provided dimensions for computing the canopy volume where as image analysis measurements gave information that could be used to compute a image analysis canopy volume index. Tables show manual measurements of the 10 trees for each tree varieties and their corresponding canopy volumes, which were computed using prolate spheroid canopy

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,67 1,65 0,67 1,36 2,41 2 2,76 1,48 0,46 2,43 8,68 3 2,65 1,61 0,62 1,73 3,95 4 2,99 1,57 0,70 1,90 5,14 5 2,69 1,58 0,56 2,2 6,68 6 2,66 1,62 0,87 2,49 7,02 7 2,80 1,57 0,64 2,16 6,40 8 2,45 1,58 0,64 2,31 6,36 9 2,45 1,71 0,53 1,6 3,36 10 2,66 1,60 0,56 1,78 4,34 **Min** 2,45 1,48 0,46 1,36 2,41 **Max** 2,99 1,71 0,87 2,49 8,68 **Mean** 2,69 1,60 0,63 2,00 5,43 **S.D.** 0,53 0,06 0,11 0,38 1,92 Table 1. Two dimensions and canopy volumes calculated using prolate spheroid canopy

volume formula from real tree measurements at apple trees (Beyaz et al., 2009c).

seems to be appropriate for volume estimation (Beyaz et al., 2009d).

volume formula (Table 1- 4) (Beyaz et al., 2009c).

**volume**

Fig. 22. The regression equation and graph obtained by using AF + AT + AL projection areas (Beyaz et al., 2009d).

The volume estimation with two projection areas, the highest rate of regression estimation value was 74.7% which obtained from the sum of the top and left projection areas. It can be explained by the front view of Kahramanmaras red peppers is more non uniform than top view.Sum of left-top projection area equation which gave the highest regression coefficient was used for estimate volume values. Then relationship between the real volume of peppers and estimated volume values has been compared. The results have been showed in Fig. 23 (Beyaz et al., 2009d).

Fig. 23. Comparison of the actual volume and regression equation estimation results by using sum of AT + AL projection areas (Beyaz et al., 2009d).

It has been pointed that Fig. 23 the regression coefficient values between the real peppers volume value and the estimated volume values had been found as 89.7%. This volume estimation rate can be used as, before the harvest as a maturity index, after the harvest as a

Real Volume Value (ml) = - 57,34 + 1,256 (AF+AT+AL)

S 14,9621 R-Sq 73,9% R-Sq(adj) 72,4%

S 10,3641 R-Sq 89,7% R-Sq(adj) 89,2%

**AF+AT+AL (cm2)**

**Estimated Volumes (ml)**

50 75 100 125 150

Fig. 23. Comparison of the actual volume and regression equation estimation results by

It has been pointed that Fig. 23 the regression coefficient values between the real peppers volume value and the estimated volume values had been found as 89.7%. This volume estimation rate can be used as, before the harvest as a maturity index, after the harvest as a

80 90 100 110 120 130 140 150

Fig. 22. The regression equation and graph obtained by using AF + AT + AL projection areas

The volume estimation with two projection areas, the highest rate of regression estimation value was 74.7% which obtained from the sum of the top and left projection areas. It can be explained by the front view of Kahramanmaras red peppers is more non uniform than top view.Sum of left-top projection area equation which gave the highest regression coefficient was used for estimate volume values. Then relationship between the real volume of peppers and estimated volume values has been compared. The results have been showed in Fig. 23

Real Volumes (ml)= 0,977 + 1,201 Estimated Volumes

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

(Beyaz et al., 2009d).

**Real Volumes (ml)**

200

175

150 125

100

75 50

using sum of AT + AL projection areas (Beyaz et al., 2009d).

150

125

100

75

50

classification and packaging parameter. Overall, at irregular shape products such as pepper is expected to be high regression coefficient relationship between volume and weight. However, classification and packaging system by using image analysis techniques according to the quality of the product is also possible. In this regard research has router features. According to the results of image processing method volume of Kahramanmaras red pepper seems to be appropriate for volume estimation (Beyaz et al., 2009d).

#### **5.1.2 Image analyze technique for measurements of apple and peach tree canopy volume**

Image analysis measurements and the real manual measurement method had been compared of apple and peach trees canopy volume in this study. The known of the tree canopy volumes are important from the point of views uniform growing, yield estimation, fertilizers and chemical applications. Also this research gives us some information about tree height, skirt height, parallel diameter. And also we can use these parameters for designing harvesting machines. In this research, totally twenty trees which has different canopy heights and volumes have been used. Apple and peach trees randomly have been selected from apple and peach garden belongs to Ankara University Agriculture Faculty. After the taking photographs of all trees by using digital camera, overall canopy height above the ground, canopy diameter parallel to row near ground level, height to point of maximum canopy diameter, height from ground to canopy skirt data have been obtained from these digital images. Apple and peach canopy volume have been calculated by using a formula that is named Albrigo from the prolate spheroid canopy volume values. The real values and image analysis measurements have been provided dimensions for computing the canopy volume where as image analysis measurements gave information that could be used to compute a image analysis canopy volume index. Tables show manual measurements of the 10 trees for each tree varieties and their corresponding canopy volumes, which were computed using prolate spheroid canopy volume formula (Table 1- 4) (Beyaz et al., 2009c).

Table 1. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formula from real tree measurements at apple trees (Beyaz et al., 2009c).

Machine Vision Measurement Technology and Agricultural Applications 153

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,54 1,72 0,33 3,15 15,08 2 2,77 1,51 0,38 3,17 15,54 3 2,77 1,52 0,64 2,68 9,66 4 1,86 1,04 0,49 1,25 1,34 5 2,52 1,56 0,38 2,48 8,78 6 2,49 1,25 0,47 3,38 14,40 7 1,56 1,01 0,31 1,57 2,06 8 2,22 1,04 0,44 1,16 1,46 9 1,41 0,8 0,3 2,07 3,04 10 1,39 0,87 0,3 1,19 1,01 **Min** 1,39 0,8 0,3 1,16 1,01 **Max** 2,77 1,72 0,64 3,38 15,54 **Mean** 2,153 1,23 0,4 2,21 7,24 **S.D.** 0,53 0,32 0,11 0,88 6,15 Table 4. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at peach trees (Beyaz et al., 2009c).

Table 1. and Table 3. shows that skirt height for most trees was about 0,63 m at apple trees and 0,43 m at peach trees respectively. Tables also suggest that the difference in volumes for tree each 10 trees. That difference was because the rest of the trees had almost the same parallel (D) diameters. Possibly the captured image measurements provide the easy canopy

Real tree volumes and tree image volumes regressions have presented in Fig. 24. and 25. Regression coefficient have been calculated as 95,6% for apple tree volume and 96,6% for

Real Apple Trees Volumes = 0,3239 + 0,9316 Apple Tree Image Volumes

S 0,486189 R-Sq 95,6% R-Sq(adj) 95,2%

**Apple Tree Image Volumes**

Fig. 24. Real apple tree volumes and apple tree image volumes regression table

2 3 4 5 6 7 8 9 10

volume estimation because of fast tree dimensions.

peach tree volume.

(Beyaz et al., 2009c).

**Real Apple Tree Volumes**

Table 2. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at apple trees (Beyaz et al., 2009c).

Table 3. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from real tree measurements at peach trees (Beyaz et al., 2009c).

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,75 1,51 0,67 1,36 2,41 2 2,76 1,54 0,58 2,58 9,26 3 2,34 1,60 0,48 1,66 3,48 4 2,88 1,68 0,66 2,05 6,00 5 2,58 1,40 0,46 2,21 6,62 6 2,53 1,68 0,87 2,49 6,70 7 2,57 1,44 0,64 2,16 5,68 8 2,32 1,41 0,64 2,31 5,76 9 2,22 1,70 0,46 1,60 3,18 10 2,87 1,82 0,48 1,78 5,07 **Min** 2,22 1,40 0,46 1,36 2,41 **Max** 2,88 1,82 0,87 2,58 9,26 **Mean** 2,58 1,58 0,59 2,02 5,42 **S.D.** 0,53 0,14 0,13 0,40 2,00 Table 2. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at apple trees (Beyaz et al., 2009c).

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,44 1,76 0,40 2,80 11,16 2 2,66 1,45 0,50 3,00 12,41 3 2,70 1,40 0,58 2,58 8,81 4 1,72 1,20 0,60 1,07 0,85 5 2,59 1,55 0,43 2,60 9,62 6 2,35 1,29 0,60 3,20 11,22 7 1,54 1,04 0,39 1,53 1,80 8 2,36 1,08 0,46 1,19 1,63 9 1,40 1,00 0,22 1,29 1,36 10 1,44 0,92 0,28 1,26 1,22 **Min** 1,40 0,92 0,22 1,07 0,85 **Max** 2,70 1,76 0,60 3,20 12,41 **Mean** 2,12 1,27 0,45 2,05 6,01 **S.D.** 0,53 0,27 0,13 0,85 4,98 Table 3. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from real tree measurements at peach trees (Beyaz et al., 2009c).

Table 4. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at peach trees (Beyaz et al., 2009c).

Table 1. and Table 3. shows that skirt height for most trees was about 0,63 m at apple trees and 0,43 m at peach trees respectively. Tables also suggest that the difference in volumes for tree each 10 trees. That difference was because the rest of the trees had almost the same parallel (D) diameters. Possibly the captured image measurements provide the easy canopy volume estimation because of fast tree dimensions.

Real tree volumes and tree image volumes regressions have presented in Fig. 24. and 25. Regression coefficient have been calculated as 95,6% for apple tree volume and 96,6% for peach tree volume.

Fig. 24. Real apple tree volumes and apple tree image volumes regression table (Beyaz et al., 2009c).

Machine Vision Measurement Technology and Agricultural Applications 155

damage values 0.97. Total yield loss and topping losses differences related to harvest

Table 5. Descriptive statistics of measured surface area values and image processing surface

**Image processing percentage**

Mechanical 2.7 0 0.1 0.27 0 Semi-hyd. 18.6 2.32 0.1 1.86 0.23 Full-hyd. 5.01 2.32 0.1 0.50 0.17

Mechanical 12.1 10.81 0.1 1.21 1.08 Semi-hyd. 27.56 25.58 0.1 2.75 2.55 Full-hyd. 26.59 25.58 0.1 2.65 2.83

Mechanical 31.64 29.73 0.05 1.58 1.48 Semi-hyd. 5 30.23 0.05 0 1.51 Full-hyd. 33.33 30.23 0.05 1.66 0

Mechanical 45.46 44.54 0 0 0 Semi-hyd. 48.84 41.86 0 0 0 Full-hyd. 35.07 41.86 0 0 1.66

Mechanical 8.1 9.51 0.1 0.81 0.95 Semi-hyd. 0 0 0.1 0 0 Full-hyd. 0 0 0.1 0 0

Mechanical 0 5.40 0.05 0 0.27 Semi-hyd. 0 0 0.05 0 0 Full-hyd. 0 0 0.05 0 0

Table 6. Topping yield losses related to harvest machines (Beyaz et al., 2009b).

**Percentage**

**Variable Sugar Beet Harvest Machines Mean SE Mean**

Mechanical 685.0 161.0 Semi-hydraulic 598.0 143.0 Full-hydraulic 542.2 68.5

Mechanical 540.0 130.0 Semi-hydraulic 439.0 106.0 Full-hydraulic 483.3 63.1

> **Loss factor**

**Yield loss in each group**

**Image processing yield loss**

machines are given in Table 6 (Beyaz et al., 2009b).

Measured surface area values (mm2)

Image processing surface area values

**Sugar beet harvest machines**

area values (Beyaz et al., 2009b).

(mm2)

**Topping quality class**

**1**

**2**

**3**

**4**

**5**

**6**

Fig. 25. Real peach tree volumes and peach tree image volumes regression table (Beyaz et al., 2009c).

#### **5.1.3 Determination of sugar beet topping slice thickness by using image analysis technique**

Turkey is one of the important sugar beet producers in the world. Turkey has produced 15 488 332 tones sugar beet from 3 219, 806 ha production area and also produced 2 061 000 tones sugar in 2008. Sugar beet harvesting by machine is general in Turkey. During mechanical harvesting several mechanical loads have caused skin and tissue damages of sugar beet. The damages of skin and tissue at sugar beets results in quantitative and qualitative losses. After harvesting, comparisons of the quality of sugar beets as good and bad are important in factory entrance terms of sugar losses. In this study widely used three types of harvesters have been operated. One of these machines is full hydraulic sugar beet harvest machine with arrangeable depth and row, second one is semihydraulic sugar beet harvest machine and the last one is full mechanic sugar beet harvest machine. Harvesters have been tried at the same field conditions during September-October months of 2009. Performance values have been obtained from three different evaluation methods. These methods are topping quality determination, the determination of sugar beet injury rate and the soil removal rate of the sugar beet. The determinations of these factors are important to obtain the optimum harvest performance. Image process and analysis methods have been used for evalutions. Descriptive statistics of image processing and measured damaged surface area features are given in Table 5 (Beyaz et al., 2009b).

Correlation coefficients between image processing and measured values at sugar beet head diameter have been determined as 0.98, between length values 0.96 and between surface

Real Peach Tree Volumes = 0,2372 + 0,7918 Peach Tree Image Volumes

S 0,913531 R-Sq 96,6% R-Sq(adj) 96,3%

**Peach Tree Image Volumes**

Fig. 25. Real peach tree volumes and peach tree image volumes regression table

**5.1.3 Determination of sugar beet topping slice thickness by using image analysis**

Turkey is one of the important sugar beet producers in the world. Turkey has produced 15 488 332 tones sugar beet from 3 219, 806 ha production area and also produced 2 061 000 tones sugar in 2008. Sugar beet harvesting by machine is general in Turkey. During mechanical harvesting several mechanical loads have caused skin and tissue damages of sugar beet. The damages of skin and tissue at sugar beets results in quantitative and qualitative losses. After harvesting, comparisons of the quality of sugar beets as good and bad are important in factory entrance terms of sugar losses. In this study widely used three types of harvesters have been operated. One of these machines is full hydraulic sugar beet harvest machine with arrangeable depth and row, second one is semihydraulic sugar beet harvest machine and the last one is full mechanic sugar beet harvest machine. Harvesters have been tried at the same field conditions during September-October months of 2009. Performance values have been obtained from three different evaluation methods. These methods are topping quality determination, the determination of sugar beet injury rate and the soil removal rate of the sugar beet. The determinations of these factors are important to obtain the optimum harvest performance. Image process and analysis methods have been used for evalutions. Descriptive statistics of image processing and measured damaged surface area features are given in Table 5 (Beyaz et al.,

Correlation coefficients between image processing and measured values at sugar beet head diameter have been determined as 0.98, between length values 0.96 and between surface

0 2 4 6 8 10 12 14 16

**Real Peach Tree Volumes**

(Beyaz et al., 2009c).

**technique**

2009b).

damage values 0.97. Total yield loss and topping losses differences related to harvest machines are given in Table 6 (Beyaz et al., 2009b).

Table 5. Descriptive statistics of measured surface area values and image processing surface area values (Beyaz et al., 2009b).

Table 6. Topping yield losses related to harvest machines (Beyaz et al., 2009b).

Machine Vision Measurement Technology and Agricultural Applications 157

**Golden Delicious Granny Smith Stark Crimson Days ΔE Days ΔE Days ΔE**  1. Day 23.48 1. Day 12.81 1. Day 7.44 2. Day 31.52 2. Day 14.11 2. Day 9.32 3. Day 35.59 3. Day 15.61 3. Day 11.45 4. Day 37.60 4. Day 16.85 4. Day 14.02 5. Day 38.30 5. Day 16.96 5. Day 14.65

**Golden Delicious Granny Smith Stark Crimson**

**Golden Delicious Granny Smith Stark Crimson**

**Golden Delicious Granny Smith Stark Crimson**

Day 1 57.40+0.68 Aa 53.60+1.92 Ab 45.63+1.23 Ac Day 2 51.21+0.94 Ba 53.36+0.63 ABa 44.63+1.22 ABb Day 3 48.44+1.17 Cb 52.69+0.76 ABa 43.49+1.26 BCc Day 4 46.96+1.17 Cb 51.37+0.93 Ba 41.70+1.19 CDc Day 5 46.49+1.30 Cb 51.37+0.94 Ba 41.31+1.17 Dc

Day 1 -3.02+0.58 Cb -12.66+0.81 Bc 20.72+0.95 Aa Day 2 0.56+0.69 Bb -11.66+0.75 Bc 19.22+0.92 Ba Day 3 1.95+0.69 Ab -10.10+1.15 Ac 17.88+1.01 Ca Day 4 2.90+0.61 Ab -10.02+0.98 Ac 16.61+1.09 Da Day 5 3.00+0.68 Ab -9.96+0.92 Ac 16.37+1.24 Da

Day 1 35.48+0.54 Aa 31.00+0.15 Ab 22.28+1.25 Ac Day 2 31.77+0.84 Ba 29.71+0.62 ABa 21.53+1.24 ABb Day 3 28.87+1.12 Ca 29.25+0.76 Ba 20.33+1.28 BCb Day 4 27.90+1.27 CDa 28.67+0.94 Ba 18.91+1.27 CDb Day 5 27.27+1.44 Da 28.61+0.90 Ba 18.46+1.39 Db

Table 8. Colour value change for each apple varieties (Beyaz et al., 2010).

**Days Variable**

Table 9. The interaction between the apple varieties and days for L\* value

**Days Variable**

Table 10. The interaction between the apple varieties and days for a\* value

**Days Variable**

Table 11. The interaction between the apple varieties and days for b\* value

(Beyaz et al., 2010).

(Beyaz et al., 2010).

(Beyaz et al., 2010).

As shown in Table 6., the total topping yield loss and image processing measurement values have been evaluated. This results respectively, at the mechanical harvesting machine 3.87% and 3.78%, at the semi-hydraulic machine 4.86% and 4.29%, at the fully hydraulic machine 4.81% and 4.66%. According to IIRB ideal topping quality is group is 4. According to the total topping yield loss values, the best machine is mechanical sugar beet harvesting machine (Beyaz et al., 2009b).


The Circular Economy - Recent Advances in Sustainable Waste Management *Edited by Tao Zhang*

Published in London, United Kingdom

The Circular Economy - Recent Advances in Sustainable Waste Management http://dx.doi.org/10.5772/intechopen.98053 Edited by Tao Zhang

### Contributors

Muhammad Ali Musarat, Muhammad Irfan, Wesam Salah Alaloul, Ahsen Maqsoom, Muhammad Jamaluddin Thaheem, Muhammad Babar Ali Rabbani, Lorena Cornejo Ponce, Patricia Vilca-Salinas, María J. Arenas, Hugo Lienqueo-Aburto, Claudia Moraga-Contreras, Noah Gethsemane Akhimien, Ahmed Abdullah Al Tawheed, Eshrar Latif, Shan Shan Hou, Rajib Sinha, Asterios Papageorgiou, Cecilia Sundberg, Anja Enell, Elias Sebastian Azzi, Tao Zhang, Yingyu Zhang, Yingqi Niu

### © The Editor(s) and the Author(s) 2022

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**Cadiele Oliana Reichert 1, Fábio Alessandro de Freitas 1, Juliana Sampaio-Silva 1, Leonardo Rokita-Rosa 1, Priscila de Lima Barros 1, Debora Levy 1 and Sérgio Paulo Bydlowski 1,2,\***

Received: 6 October 2020; Accepted: 28 October 2020; Published: 20 November 2020

**Abstract:** Ferroptosis is a type of cell death that was described less than a decade ago. It is caused by the excess of free intracellular iron that leads to lipid (hydro) peroxidation. Iron is essential as a redox metal in several physiological functions. The brain is one of the organs known to be affected by iron homeostatic balance disruption. Since the 1960s, increased concentration of iron in the central nervous system has been associated with oxidative stress, oxidation of proteins and lipids, and cell death. Here, we review the main mechanisms involved in the process of ferroptosis such as lipid peroxidation, glutathione peroxidase 4 enzyme activity, and iron metabolism. Moreover, the association of ferroptosis with the pathophysiology of some neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's diseases, has also been addressed.

**Keywords:** ferroptosis; cell death; iron metabolism; neurodegenerative diseases; glutathione peroxidase 4; GSH; system xc−; Alzheimer's disease; Parkinson's disease; Huntington's disease


*3.1.3 Acoustic emission (AE)*

*Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

Whilst phonoarthrography is based on the sound produced during the flexion or extension of joints, in VAG all vibrations produced during movement are considered [62]. Consequently, it is more common for a single accelerometer to be used as the sensor rather than a microphone [71]. It is also very common for signals in a frequency range below 1000 Hz to be of primary focus [72], with sampling rates of the order 1–4 kHz. A key advantage of the low sampling rate is that it allows for wireless data acquisition and processing using simple microcontrollers or singleboard computers [73, 74]. That said, it has been suggested [71, 75] that single signal processing may be limited and multi-channel recordings may lead to better discrimination of the severity and location of joint injury or disorder. In many cases noise mitigation is achieved through prefiltering (commonly using a bandpass filter from 10 Hz to 1 kHz) and amplification prior to digitization at a specified sampling rate [76, 77]. The digital signal may go through additional filtering, such as that conducted by Andersen et al. [78] who used a Kaiser-windowed finite impulse

There are other rationales for using multiple sensors during VAG as it has been

Therefore, the VAG signal is inherently nonstationary and potentially multicomponent in nature. The nature of the VAG signal means that it is not easily analysed using common signal processing techniques. This coupled with the difficulty in ascertaining the biological origin of the source of the signal is the main barrier to its use as a common diagnostic tool. As a result, much of the recent research activity has been focussed on feature extraction and statistical pattern classification [60]. Adaptive segmentation using least-square, linear prediction and autoregression algorithms is common [81, 82]. A host of statistical measures has been considered to characterise the VAG signal, including the form factors, skewness, kurtosis and entropy [71, 76]. It has also been shown that time-frequency distribution (TFD) [81, 83] and wavelet decomposition [84] are potentially powerful techniques for analysis and may negate the need for segmentation [83] but may be susceptible to noise [85]. These advancements have mostly been driven by developments in digital signal processing technologies that sped up analysis time as well as nonstationary signal analysis techniques developed for other biological signals like EEGs [84]. Using these techniques, spectral features such as frequency, energy and their respective spreads can be classified and linked to joint position, loading and pathology. The commonly used classifiers are neural network-based classifiers and support vector machines (SVM), as well as logistic regression and rule-based techniques [62, 71]. These neural networks and SVMs are supervised learning algorithms which search for a number of independent training data patterns taken from signals measured from participants with known pathologies to characterise new signals. These classification algorithms are increasingly dependable and can perform well with a limited amount of data. A number of different variants of these algorithms and classifiers have been investigated [60, 62]. Wu et al. [73] used an SVM based on the entropy and envelope amplitude features and achieved an overall accuracy of 83.56%. Nalband et al. [86] utilised an a priori algorithm with

observed that VAG may pick up vibrations not necessarily just due to the joint directly or to external interference [79]. For instance, the 10 Hz signal generated by the rectus femoris muscle which activates during the extension of the leg could interfere with the VAG signal recorded from the skin surface over the patella [80]. As this signal may vary in a similar fashion to the VAG signal, simple bandpass filtering may not be sufficient. It may be necessary to record the vibromyogram at the rectus femoris at the same time as the VAG signal and use adaptive filtering and

noise cancellation techniques to isolate the VAG signal [79].

*3.1.2 Vibroarthrography (VAG)*

response (FIR) bandpass filter.

**38**

AE for biomedical applications is derived from non-destructive techniques developed for detecting damage in engineering materials, such as metals and composites [88]. AE occurs when materials locally under stress emit energy in the form of transient elastic waves. This allows for the monitoring of microcrack initiation and propagation in the bones and joints [89]—essential parts of bone remodelling [90], and wear [91, 92]. Other characteristic sounds in joints, such as the bursting of gas bubbles in synovial joints during movement, can also be detected using AE [93]. AE frequencies are usually in the ultrasonic range and so detection often involves the use of ultrasonic sensors.

A number of researchers have proposed AE sensor-based joint monitoring systems using piezoelectric films, electret or MEMS-based microphones.

Toreyin et al. [94, 95] used an off-the-shelf low-noise MEMS microphone in conjunction with gyroscope and accelerometer pairs in order to monitor sounds generated during various complex motions. The microphone used had a sensitivity range of 100 Hz to 10 kHz, and the researchers suggested that the MEMS-based microphone had a similar performance to an electret microphone [94]. The acoustic data were sampled at 100 kHz, and the inertial data (monitoring joint angle and limb movement) at 1 kHz, with the data being collected by a field programmable gate array (FPGA)-based real-time processor. It was noted that air microphones do not exhibit signal losses due to motion artefacts, but they are sensitive to ambient noise.

Teague et al. [96] compared a piezoelectric film-based contact microphone to two air microphones: one electret and one MEMS-based. The air microphones were used with a 15 Hz high-pass filter and a second-order low-pass filter with a cut-off frequency of 21 kHz and sampled at 44.1 kHz using an acoustic recorder. The piezoelectric microphone was used with a 100 Hz high-pass filter followed by a fourth order low-pass filter with a 10 kHz cut-off frequency. It was sampled at 50 kHz using custom circuits. The 100 Hz high-pass filter was chosen to attenuate the motion artefact noise. It was noted that the electret and MEMS microphones performed similarly in detecting joint sounds, although the electret sensor was significantly more expensive. They were both sensitive to ambient and interface noise, including rubbing of the tape securing the sensors. It was noted that the air microphones did not need to be in contact with the skin. Experiments with sensors positioned 5 cm off the skin captured similar acoustic signals, albeit with lower amplitude. The piezoelectric sensor was more sensitive to interface noise but less sensitive to background noise. Importantly, the contact microphone did not pick up higher frequency vibrations as distinctly as the air microphones which provided higher quality recordings as indicated by higher SNIRs.

Jeong et al. [97] used a low-noise electret microphone with a frequency range of 50 Hz to 20 kHz recorded by an audio recorder at a rate of 44.1 kHz. Signals were digitally filtered using a finite impulse response bandpass filter with a bandwidth from 1 to 15 kHz to prioritise short duration joint sounds whilst supressing interface noise.

Feng and Chen [98] developed a piezoelectric sensor comprised of a lead zirconium titanate (PZT) film deposited on titanium cantilever arrays as an acoustic sensing layer. This sensor uses a 1-mm-tall SU8 cylindrical probe on each cantilever to be in direct contact with the skin of the participant and transmit vibrations to the sensor. A thermoresponsive poly(N-isopropylacrylamide) (PNIPA) film was

integrated into the sensor to apply a force to the cantilever and hence improve contact between the probe and the skin when a current is applied across it. The sensor achieved a frequency range of up to 100 kHz, with at least one strong resonant peak at 390 Hz. A sampling rate of 2 MHz was used with a 1 kHz high-pass digital filter to remove low-frequency noise signals. Testing of the sensor on a butchered porcine leg during repeated joint flexure cycles revealed the presence of well-defined peaks located between 30 and 40 kHz, 60 and 70 kHz and 70 and 80 kHz. Similar trends to that observed with commercial AE sensors (the same used in the studies by Mascaro et al. in the JAAS system described later [99]) were noted during overuse of the joint.

Choi et al. [93] developed the bone joint acoustic sensor (BJAS). This has a pin-type probe on a disk-shaped piezoceramic supported by a damped metal plate. The structure is in a metal case with the probe in direct contact with the skin. The system used in conjunction with IMUs seems to have a frequency range of 100 Hz to 25 kHz and is sampled at 50 kHz.

Shark and Goodacre developed the joint acoustic analysis system (JAAS) [99, 100]. This system uses commercial piezoelectric contact ultrasonic acoustic sensors (with high sensitivity in the range 50–200 kHz but monitored over 20–400 kHz at a 1–5 MSPS sampling rate) [100] and electro-goniometers to provide joint anglebased AE during knee joint movement (see **Figure 1**).  The housing is fixed to the skin with surgical tape to maintain a rigid contact. The AE data acquisition operates in a non-continuous recording mode to minimise data volume. When the AE PCI data acquisition board is triggered by a signal value above a pre-set threshold, a 'hit' is recorded corresponding to an acoustic event. Each AE hit is recorded with a set of characteristic waveform features (i.e. dominant frequency, maximum amplitude and duration), and in addition the full waveforms were also stored, digitalized at a 1 MHz sampling frequency over a maximum duration of 15 ms [99]. The number of hits during each joint motion was used to determine a correlation with OA severity defined by KL scores determined using MRI data. It was noted that the frequency response of the acoustic sensor data is characterised by two peaks with a high probability of occurrence during

#### **Figure 1.**

*Output from the joint acoustic analysis system (JAAS). Recording is made as the participant performs five sit-stand-sit movements. A: Acoustic 'hits' from a single knee recorded using a piezoelectric contact ultrasonic acoustic sensors. Each square indicates one acoustic emission captured by the system. For each 'hit' a waveform is also captured [D] from which waveform characteristics are calculated by the software. Alongside the acoustic emissions, joint angle [B] and weight through the leg [C] are also recorded.*

**41**

*Acoustic Monitoring of Joint Health*

**4. Conclusion**

*DOI: http://dx.doi.org/10.5772/intechopen.92868*

to a peak of sensitivity of the sensor used [99].

robust and statistically significant way [78].

knee measurements using a sit-stand-sit protocol, one in the low-frequency range (20–50 kHz) and the other one around 150 kHz. The latter frequency is mainly due

Using radiographic techniques to monitor variations in joint structure and morphology is the classic method of quantifying OA. However, this technique is ionising, often requires multiple measurements as only the plane perpendicular to the radiation is observed and cannot monitor soft tissue directly. MRI can measure the thickness and volume of cartilage, but there are limitations with respect to time and cost. Ultrasound can monitor joint effusion and the thickness of cartilage, but it is not possible for ultrasound to penetrate thick bone tissue and observe the whole joint. There is the additional issue of subjectivity and the large difference in reproducibility based on the skill of those analysing the image. The use of invasive cameras in arthroscopy and joint endoscopy necessitate recovery after diagnosis. These techniques also do not facilitate measurements using dynamic movements. The use of acoustic sensors has the potential to quantify and classify joint pathology whilst removing the subjectivity of classic imaging techniques. Despite progress in detecting differences between type and severity of joint disorders, questions remain about the true origin and form of acoustic signals generated by joint structural changes. Thus, a significant part of the challenge linked to acoustic signal analysis resides in the retrieval of pertinent parameters from irrelevant information in a

As yet, whilst several protocols, sensor types and data analysis techniques have been developed, to date there is no consensus on the most adequate way to record and process vibration data [60]. The methodological aspects of acoustic assessments, such as sensor placement and outcomes measures have not been thoroughly investigated allowing doubt in the technique to remain. For instance, for knee investigations, many studies [73, 81, 101] favour what may be called an open kinematic chain configuration [102] whereby participants sit in a chair and lift their legs in a repetitive fashion, perhaps with weights attached. This has the advantage of being able to vary the load on the joint and allow for the inclusion of participants with advanced degenerative conditions or injuries affecting the limitation of the range of motion in the joint. A common alternative protocol involves repeated sit-stand-sit movements [103–105], creating a closed kinematic chain. This latter configuration perhaps has the advantage of forming a more natural loading of the knee joint. It potentially has the consequence of being inconsistent over time, as people can have the tendency of adjusting their movement to compensate for restricted or painful movement, thus changing the distribution of forces and moments acting on the knee [106]. Data comparing the protocols is limited, and there is no strong evidence for favouring one protocol over the other or indeed over alternatives, such as squatting [94, 102]. Given the protocol affects the loading of the joint and the frequency response of the vibration data generated, it also affects the potential consistency of the statistics derived therefrom and their subsequent interpretation for diagnostic and prognostic purposes. This suggests the necessity of a standard protocol if such techniques are to be used for monitoring the development of OA in an individual over time for clinical or research purposes. Similarly, it is unclear what sort of vibrations and which frequency range is the most pertinent range to measure. In phonoarthrography acoustic waves in the audible range are of most interest. In VAG, focus is on low-frequency (<1000 Hz) vibrations, the cause and nature of which is more general. In AE, acoustic signals are of primary focus, albeit generally of a higher frequency than that used

knee measurements using a sit-stand-sit protocol, one in the low-frequency range (20–50 kHz) and the other one around 150 kHz. The latter frequency is mainly due to a peak of sensitivity of the sensor used [99].


**3. Indoor airflow**

#### **3.1 Airflow temperature measurement test room**

An airflow temperature measurement test room was used to develop the indoor airflow control model. This test room is constructed with wooden walls, windows, and doors that mimic a room in a Japanese wooden house. The outside air temperature in this room can be controlled by an air conditioning system. This room is equipped with thermocouples, which can measure indoor airflow temperature distribution. **Figure 11** shows the layout of this room and the arrangement of the thermocouples. The air temperature was acquired using a test room with thermocouples taken down from the ceiling. As shown on the right of **Figure 11**, the temperature distribution of the airflow can be visualized by interpolating the temperature in the space without thermocouples through heat conduction calculations.

#### **3.2 Numerical model**

#### *3.2.1 Model development*

**Figure 12** shows how the mesh of the computational model is created. We simplified indoor unit cross-flow fans and vanes to an inlet boundary condition and indoor

*Application of CFD to Prediction of Heat Exchanger Temperature and Indoor Airflow Control… DOI: http://dx.doi.org/10.5772/intechopen.110076*

#### **Figure 11.**

*Schematic diagram of the thermocouple arrangement. The test room is 3.6 (m) deep by 7.2 (m) high by 2.4 (m) wide. Thermocouples are suspended from the ceiling of the test chamber, and thermocouples are placed at intervals of 0.05 to 0.4 (m). A total of 1134 thermocouples were installed in a layout of nine thermocouples in the x direction, 18 in the y direction, and seven pointing in the z direction (height).*

#### **Figure 12.**

*CFD mesh. This mesh consists of a room space, walls, windows, and a door. The room space is the indoor airflow environment, and wall indicates the floor, ceiling, and front, back, left side, and right side walls. The room air conditioner was installed on a back side wall. Outer surfaces of walls can set outside temperature as the boundary condition.*

unit inlet to outlet boundary condition. The boundary conditions for the inlet surface are (1) airflow volume rate, (2) airflow angle, and (3) airflow temperature. The walls, windows, and doors of the test room have small gaps through which drafts can enter and heat can leak out. It is difficult to model heat leakage from a draft numerically because air gap locations are unknown. Therefore, the initial temperature of the wall was set lower than the room temperature to model the heat leakage by the air gaps. The outer surface of the walls was set to the boundary condition of the outside air temperature in the experiment.

#### *3.2.2 Buyoyant modeling*

We use unsteady RANS model for indoor airflow control model. The governing equations were discretized by the finite volume method and solved using the SIMPLEC method. We used commercial CFD Code SCRYU/Tetra [12] for the calculation. To realize a rotating fan turbulence flow, the inlet condition was also given turbulence kinetic energy of 10 (m2 /s2 ) and turbulence dissipation of turbulent kinetic energy of 0.1 (m2 /s2 ). These values were obtained from the result of previous air-side

**Figure 13.**

*Temperature distribution. We measured 54 points of air temperature at 0.4 (m) in front of the air conditioner and compared two RANS models.*

CFD calculations. The blowing airflow from air outlet is acted as buoyant force, so that the Boussinesq approximation was used for buoyancy force as a momentum conservation equation.

To evaluate the influence of the turbulence model difference, we performed an experiment and two RANS model calculations. One model is linear RANS (standard kepsilon model) and the other is used nonlinear RANS [14]. **Figure 13** shows the results of an experiment and two calculations. The experimental result and calculation of the nonlinear turbulence model for temperature distribution show the long warm air region in a horizontal direction. Otherwise, linear turbulence model shows a vertical long hot air region. The standard k-epsilon model is isotropic turbulence and the Reynolds stresses act equally in all three directions in three-dimensional space. Since buoyancy forces act only in the vertical direction, they cannot be reproduced by an isotropic model. Therefore, we consider that the anisotropic model is closer to the experimental temperature distribution than the isotropic model. We selected a nonlinear turbulence model for investigating the influence of the buoyancy force.

**Table 3** is specification of CFD for our indoor air-control model.

**Table 3.** *Specification of CFD.*


**3. Anatomy of FAI**

The morphology of the proximal femur and acetabulum can vary significantly, which can have an important clinical relevance and impact on hip joint biomechanics. The detailed anatomy of the hip is well described in the manuals of anatomy. In FAI, two types of hip morphology have been described, namely, the cam type and the pincer type. These two types may exist separately or be associated within the same patient.

Thus, the isolated presence of either cam or pincer morphology is insufficient for a diagnosis of FAI syndrome. It is important to note that these morphologies are thought to be fairly common (around 30% of the general population) including in people without hip symptoms [9, 10].

#### **3.1 Cam morphology**

It describes a flattening or convexity of the femoral head neck junction [6] (**Figures 1**, **2**).

This morphology is more common in men [10].

**27**

**Figure 4.**

**Figure 2.**

**Figure 3.**

*Femoroacetabular Impingement: Anatomy and Pathogenesis*

more generally across the acetabular rim [6] (**Figure 3**). This morphology is more common in women [10].

associated within the same patient (**Figure 4**).

*CAM morphology in a young man with a childhood hip disease.*

*Normal aspect of femoroacetabular junction versus Pincer morphology.*

*Mixed morphology with both aspects of CAM and Pincer morphology.*

It describes the overcoverage of the femoral head by the acetabulum in which the acetabular rim is extended beyond the typical amount, either in one focal area or

These two types (CAM and Pincer Morphology) may exist separately or be

*DOI: http://dx.doi.org/10.5772/intechopen.90296*

**3.2 Pincer morphology**

**Figure 1.** *Normal aspect of femoral head-neck junction versus cam morphology.*


**5.2 PGE2 synthesis inhibition and the control over endometriosis development**

There have been conducted several studies in which AIs were used and the progression of endometriosis was evaluated. When letrozole or anastrozole were added to endometrial epithelial cells from endometriosis patients in culture, cell proliferation was inhibited and apoptosis augmented (Meresman et al., 2005). When the same two compounds were used for the treatment of surgically induced endometriosis in mice, not only cell proliferation was diminished and apoptosis increased within the endometriotic like lesion, but PGE was also

Involvement of Prostaglandins in the Pathophysiology of Endometriosis 125

overexpressed in several pathological circumstances as different types of cancers and other

There is no doubt that PGE2 is implicated in the aetiopathogenesis of endometriosis and contributes to the development and maintenance of the disease. The elevated levels of PGs found in the peritoneal fluid from patients with endometriosis are mainly produced by peritoneal macrophages and endometriotic tissues. As well, the peritoneal estrogens, proinflammatory and pro-angiogenic molecules contribute to elevate the expression of COX-2 and consequently the levels of PGE2 in endometriosis patients. The pelvic pain associated to endometriosis has also been demonstrated to be mediated through the action of PGE2 and

Given these data, further investigations should focus on how to inhibit the production of

Attar, E., Tokunaga, H., Imir, G., Yilmaz, M.B., Redwine, D., Putman, M., Gurates, B., Attar,

Baird, D.T., Cameron, S.T., Critchley, H.O., Drudy, T.A., Howe, A., Jones, R.L., Lea, R.G., &

Ballard, K.D., Seaman, H.E., de Vries, C.S., & Wright, J.T. Can symptomatology help in the

Banu, S.K., Lee, J., Speights, V.O., Jr., Starzinski-Powitz, A., & Arosh, J.A. Cyclooxygenase-2

Basu, G.D., Pathangey, L.B., Tinder, T.L., Gendler, S.J., & Mukherjee, P. Mechanisms

Basu, G.D., Tinder, T.L., Bradley, J.M., Tu, T., Hattrup, C.L., Pockaj, B.A., & Mukherjee, P.

Bilotas, M., Meresman, G., Stella, I., Sueldo, C., & Baranao, R.I. Effect of aromatase inhibitors

R., Yaegashi, N., Hales, D.B., & Bulun, S.E. Prostaglandin E2 via steroidogenic factor-1 coordinately regulates transcription of steroidogenic genes necessary for estrogen synthesis in endometriosis. *Journal of Clinical Endocrinology and Metabolism*,

Kelly, R.W. Prostaglandins and menstruation. *European Journal of Obstetrics & Gynecology and Reproductive Biology.*, Vol.70, No.1, (December 1996), pp. (15-17),

diagnosis of endometriosis? Findings from a national case-control study--Part 1. *BJOG: An International Journal of Obstetrics and Gynaecology*, Vol.115, No.11, (October

regulates survival, migration, and invasion of human endometriotic cells through multiple mechanisms. *Endocrinology*, Vol.149, No.3, (March 2008), pp. (1180-1189),

underlying the growth inhibitory effects of the cyclo-oxygenase-2 inhibitor celecoxib in human breast cancer cells. *Breast Cancer Research*, Vol.7, No.4, (April

Cyclooxygenase-2 inhibitor enhances the efficacy of a breast cancer vaccine: role of IDO. *Journal of Immunology*, Vol.177, No.4, (August 2006), pp. (2391-2402), 0022-1767

on ectopic endometrial growth and peritoneal environment in a mouse model of

PGs in endometriosis, to control the pain and the development of the pathology.

We thank Dr. Gustavo Leirós for providing technical assistance in designing figures.

Vol.94, No.2, (February 2009), pp. (623-631), 0021-972X

inflammation related diseases.

**7. Acknowledgement**

0301-2115

0013-7227

2008), pp. (1382-1391), 1470-032

2005), pp. (R422-R435), 1465-5411

**8. References**

inhibition of PG production usually resolves the pain.

decreased in the peritoneal fluid of mice treated with letrozole but not with anastrozole (Bilotas et al., 2010).

Much research has been done evaluating the effects of inhibiting COXs activity regarding the development of endometriosis with *in vivo* and *in vitro* approaches. Celecoxib has been shown to have anti-proliferative and pro-apoptotic effects over endometrial epithelial cells in culture obtained from biopsies of women with and without endometriosis; it was also effective in diminishing COX-2 expression, reducing the synthesis of VEGF and PGE2 (Olivares et al., 2008). Similar results had previously been achieved in various cancer models (Basu et al., 2005; Chun & Surh, 2004). Given that the endometrial cells at the ectopic site have a very similar behaviour to neoplasic cells; it is not surprising that treatments aim at the same targets.

There is one very complete work from Efstathiou and coworkers where they compared seven different selective (rofecoxib and celecoxib) and non-selective (aspirin, ibuprofen, indomethacin, naproxen and sulindac) NSAIDs on the establishment and development of endometriosis in a mouse model (Efstathiou et al., 2005). In this work, celecoxib given orally twice daily and indomethacin administered subcutaneously daily for four weeks, were both effective in significantly diminishing the percentage of established lesions compared to the control group. All the NSAIDs evaluated in the same study, except for aspirin, significantly inhibited the growth of the established lesions compared to the control group (Efstathiou et al., 2005). A study in a rat model of endometriosis, evaluated the effect of parecoxib, another selective COX-2 inhibitor, and showed not only a significant reduction in lesion size, but also, a significant inhibition on the expression of VEGF, its receptor Flk-1 and COX-2 compared to the untreated group with endometriosis (Machado et al., 2010). Machado and coworkers also observed a significant reduction of the levels of PGE2 in endometriotic homogeneized tissue treated with parecoxib compared to the untreated group (Machado et al., 2010).

More recently a new approach targeting more than one molecule to prevent the development of the disease has gained importance. Promising results were achieved firstly in cancer models, which targeted COX-2 and PPARγ. This combinational therapy resulted in the inhibition of cell proliferation and apoptosis enhancement *in vitro* and increased overall survival rate *in vivo* (Mustafa & Kruger, 2008; Sun et al., 2009). When celecoxib was combined with rosiglitazone, a PPARγ agonist, for the treatment of surgically induced endometriosis in a mouse model, a reduced number of established lesions was observed as well as the volume of established ones; also induction of apoptosis and reduction of the cell proliferation rate and vascularization was achieved (Olivares et al., 2011).

Reducing PGE2 concentration in the peritoneal environment would not only be relieving the pain caused by the disease but would also be affecting its development. This is one of the main goals of endometriosis treatments; now it is time to decide whether the drugs used to achieve these results are the appropriate ones to manage with this disease.


**4. Novel applications**

SDON is a promising solution for next‐generation high‐speed optical transport network, which embraces a wide application perspective. Here are some sample applications, which can make optical network being used in a more flexible manner. The sample applications include bandwidth on demand [29], Virtual Optical Network (VON) services [30], and some emerging applications [31, 32].

#### **4.1. Bandwidth on demand**

Based on the centralized control plane of SDON, providers are able to predict resource occupation status in time dimension, and accordingly arrange and accommodate users' requests to avoid spectrum conflicts and fragmentations. One application, which is called time‐aware bandwidth on demand, has already been implemented and demonstrated to do such things. Bandwidth on demand can improve the utilization of spectrum resource in SDON. The overall feasibility and efficiency of this time‐aware Bandwidth on demand architecture is experimentally verified on a testbed with real OpenFlow‐enabled tunable optical modules in terms of blocking probability and resource occupation rate, as shown in **Figure 7**. First, the users' requests are sent to the controller, and then the controller will schedule these requests to make the spectrum resource utilization optimized, finally the controller will send messages to optical nodes to set up lightpaths. As one of the basic types of services in intelligent optical network, bandwidth on demand can prove the integrity and availability of networking by offering bandwidth resource directly. Also, bandwidth on demand can be a basic measurement index of network automation.

### **4.2. Virtual optical network**

**4. Novel applications**

12 Optical Fiber and Wireless Communications

emerging applications [31, 32].

**4.1. Bandwidth on demand**

SDON is a promising solution for next‐generation high‐speed optical transport network, which embraces a wide application perspective. Here are some sample applications, which can make optical network being used in a more flexible manner. The sample applications include bandwidth on demand [29], Virtual Optical Network (VON) services [30], and some

**Figure 6.** Architecture and procedure of OaaS: (a) architecture, (b) CSO procedures, (c) module diagram.

Based on the centralized control plane of SDON, providers are able to predict resource occupation status in time dimension, and accordingly arrange and accommodate users' requests to avoid spectrum conflicts and fragmentations. One application, which is called time‐aware bandwidth on demand, has already been implemented and demonstrated to do such things. Bandwidth on demand can improve the utilization of spectrum resource Virtual optical network (VON), which aims to provide multiple dedicated virtual network over shared network infrastructure, has gained a lot of attention by network providers. By VON, service providers can request a dedicated network for each application on a per‐need basis and have full control ability over that network. Network virtualization technologies can partition/composite of network infrastructure (i.e., the physical optical nodes and links) into independent virtual resource, adopting the same functionality as the physical resource. Also, the composition of these virtual resource (i.e., virtual optical nodes and links) allows deploying multiple VON. A VON must be composed of not only a virtual transport plane but also a virtual control plane, with the purpose of providing the required independent, and full control functionalities. In datacenter optical network, since the computing resources are highly distributed, it is impossible to build a dedicated optical network for a special ser‐ vice. So joint considering computing and networking resource is very important if they are under the control of same provider. Network virtualization can eradicate the ossification of the network, stimulate innovation of new network architectures and applications, and enable network operators to operate different VON that share a common physical infrastructure. **Figure 8** shows the schematic of SDN‐based VON provisioning.

There are many challenges in VON provisioning, such as VON design and VON resource management. VON design refers to the procedures to find a subset of the underlying physical

**Figure 7.** Time‐aware bandwidth on demand: (a) without SS, three slots occupied; (b) with SS, two slots occupied, one slot resource is saved.

**Figure 8.** VON provisioning.

network topology connecting the computing resources with virtual nodes hosted on the physi‐ cal nodes and virtual links spanning over the physical links. The VON design problem is related to the traffic demand model and the entire physical network topology information. VON resource management includes two schemes, that is, dedicated scheme and shared scheme. The dedicated VON assumes that the link resources are exclusively assigned to a VON user, so each VON topology runs independently, and its reserved link resources cannot be occupied by other VON. The dedicated VON is relatively easier to achieve, but it has a drawback that it increas‐ ingly decreases the efficiency of resource utilization. Note that the sharing VON scheme can be fully or partially shared, and partially shared VON manner is more profitable. On each physical link belonging to a VON, a minimum guaranteed capacity must be assigned to it, while residual capacity is shared by all the VON on that link. The challenge in employing the partially shared VON approach is that the service provider should treat the dedicated and shared resources with different policies, which inevitably increases implementation complexity and difficulty.

## **5. Conclusions**

In this chapter, we summarize the basic principles and key technologies of SDON. First, the architecture of SDON is described with southbound and northbound interfaces and proto‐ cols. Then, several key technologies of SDON are introduced, including sliceable transponder, WSS, BV‐OXC, routing and spectrum assignment, virtual optical network mapping, and CSO algorithms. Finally, two novel applications are prospected, such as bandwidth on demand and VON. More studies need to be conducted to promote the evolution of SDON in the future.

## **Author details**

Yongli Zhao\*, Yuqiao Wang, Wei Wang and Xiaosong Yu

\*Address all correspondence to<EMAIL_ADDRESS>
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China