**4.9 Reducing fuel use**

Mechanization tools that reduce labor requirements in agriculture generally use fossil fuels. Nowadays, the use of fossil fuel energies directly or indirectly in agriculture has not been economically profitable for producers. In developing countries, large amounts of fossil fuels are used in agricultural production, in particular fertilizer production and machinery use. It is not possible to carry out modern agricultural production processes without using fuel. However, the use of combined agricultural tools and machinery in one pass and the use of renewable energy sources instead of fossil fuels will reduce both the cost of fuel in agriculture and reduce the carbon emissions and make the agriculture sensitive to the environment.

## **4.10 Irrigation**

Effective irrigation is possible by determining the optimum water amount using different parameters such as soil humidity, effective precipitation rate and evapotranspiration and by determining the correct irrigation time with climate, weather forecasts and real-time weather data. In this way, effective and economical irrigation will be provided by protecting the limited water resources and the environmental and agricultural negative effects of leaching, salinity and fungal diseases caused by excess water will be prevented.

#### **4.11 Water storage ponds**

Agricultural ponds are important water sources for irrigated areas. These structures collect water from small sources and allow for efficient storage and use of large flow rates when needed and help to regulate water flow.


*4.5. E*ff*ect of Clozapine and Its Metabolites on Other Viruses*

The clozapine metabolite N-desmethylclozapine was found to inhibit replication of dengue virus (DENV) [41]. The inhibition of N-desmethylclozapine was specific to DENV. It did not have an effect on other flaviviruses (i.e., Japanese encephalitis virus, West Nile virus), or on the RNA viruses respiratory syncytial virus and rotavirus. Inhibition occurred at an early step in the viral life cycle prior to viral replication. Two other metabolites of clozapine, 8-OH-deschloro-clozapine and 8-OH-desmethylclozapine, inhibit human immunodeficiency virus (HIV) type 1 [42]. Neither clozapine, the primary compound, nor desmethylclozapine show any antiviral effects, suggesting that inhibition of HIV is due to the metabolism of clozapine. Clozapine (30 μM) decreased the infection of a human glial cell line by the human polyomavirus JC virus by approximately one-half [43]. The human endogenous retroviruses (HERVs) are associated with schizophrenia and other neurological diseases. Clozapine had no significant effect on the transcription of HERVs in three types of brain cell lines, though VPA upregulated the transcription of many HERVs [44]. Valpromide, an anti-epileptic drug that inhibits EBV lytic reactivation [5], had no effect on vesicular stomatitis virus infection [45]. Valpromide and a structurally-related molecule valnoctamide inhibited infection and replication of the human herpesvirus cytomegalovirus in cell culture, and increased the survival rate of mice infected with mouse cytomegalovirus [45].

In conclusion, the repurposing of antipsychotic drugs as antivirals has the potential for therapeutic use. Understanding how clozapine inhibits EBV may lead to the discovery of cellular pathways that regulate the latent–lytic switch of the virus. Future studies will be aimed at determining the effects of other antipsychotic drugs on EBV lytic reactivation and identifying common molecular targets in cells that may provide information about the mechanisms important in neurological disease and for the viral life cycle.

**Author Contributions:** K.L.G conceived the research and designed the experiments; A.G.A., C.B.G., and J.R.W. performed the experiments; K.L.G., A.G.A., and C.B.G. analyzed the data; K.L.G. did the statistical analysis; K.L.G., A.G.A., and J.R.W. wrote the paper; K.L.G. edited the paper.

**Acknowledgments:** We thank McKenna Theine, Jenna Hayes, and Kayla Feehan for technical assistance. This work was funded by the UWL College of Science and Health and UWL Faculty Research Grants to K.L.G., the UWL Eagle Apprentice Program for J.R.W., and UWL Undergraduate Research and Creativity grants to A.G.A. and C.B.G.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*2.1. Participants*

Fifteen recreational marathon runners (age: 42 ± 7 years, height: 174.9 ± 6.5 cm and body mass: 72.8 ± 6.9 kg) volunteered to take part in the study. All participants were healthy and ran approximately 1–2 years on a systematic basis with a structured program with an average weekly load of 50–60 km. Based on their finishing time at the Athens Marathon 2018, athletes were divided into a moderate-level group, with finishing times < 240 min (*n* = 8), and a low-level group with finishing times > 240 min (*n* = 7). Before the start of the study, the institutional review board committee approved the experimental protocol in accordance with the Helsinki Declaration.

#### *2.2. Maximal Incremental Test*

Three to nine days before participation in the Athens Marathon 2018 race, participants performed a maximal incremental test on a treadmill (Technogym run race 1200, Italy) for the determination of VO2 max, maximum aerobic velocity (MAV), maximum heart rate (HRmax), the relationship between blood lactate concentration and running velocity, oxygen consumption and running velocity, heart rate and running velocity, and the energy cost of running.

The protocol started at 7 km·h−1 and was increased by 1.5 km·h−1 every 3 min until volitional exhaustion. Treadmill grade was set at 1% throughout the protocol. Gas exchange was measured by the open circuit Douglas bag method as described by Cooke (2009). The subject breathed through a low-resistance 2-way Hans-Rudolph 2700 B valve (Shawnee, OK, USA). The concentrations of CO2 and O2 in the expired air were measured by using the Hi-tech (GIR 250) combined Oxygen and Carbon Dioxide Analyzer. The gas analyzers were calibrated continuously against standardized gases (15.35% O2, 5.08% CO2 and 100% N2). Expired volume was measured by means of a dry gas meter (Harvard) previously calibrated against standard air flow with a 3 L syringe. Barometric pressure and gas temperature were recorded and respiratory gas exchange data for each work load (i.e., VO2, VCO2 and VE) were determined based on the computations described by Cooke [23] when VEatps, FECO2 and FEO2 are known. The highest VO2 value obtained during a 30-sec time period during the incremental exercise test was recorded as the subject's .VO2max. HR was continuously measured telemetrically (Polar RS400) and the highest 10 sec value was regarded as maximal. The test was considered as maximal when at least 3 of the following criteria were achieved: (a) visual exhaustion of the participants, (b) a plateau in oxygen consumption (<2 mL kg−1·min−1) despite an increase in running velocity, (c) maximal HR higher than 90% of the predicted maximum (220-age) and (d) maximum respiratory exchange ratio > 1.1. MAV was calculated using the following formula: MAV (km·h−1) = Velocity of the last completed stage + (seconds run at last stage/180).

#### *2.3. LTh and LTP Determination*

At the end of each stage during the incremental test, approximately 0.3 μL of whole blood was collected from the fingertip and immediately analyzed for lactate concentration with a portable analyzer (Lactate Pro 2, Arkray Factory Inc., Koka-Shi, Japan) using an enzymatic-amperometric method. The individual relationships between blood lactate concentrations and running velocities were determined using an exponential model: y = a + b × exp(x/c), where y = lactate concentration, x = running velocity and a, b and c are constants. The LTh and the LTP were identified as the velocities (km·h−1) at which blood lactate concentrations were increased by 0.3 and 1.5 mmol·L−1 from baseline values, respectively. Furthermore, LTh and LTP were expressed relative to MAV (%MAV) units, and based on the relationship between VO2 and running velocity were also expressed in absolute (mL·kg−1·min−1) and relative (%VO2 max) VO2 max values.

#### *2.4. Energy Cost of Running*

The gas exchange data (VO2, VCO2) collected during the final 30 s of every 3-min stage up to the previous stage from the LTP were used for the calculations of the caloric cost of running. Substrate oxidation rate (g·min−1) was estimated using nonprotein respiratory quotient equations [24]:

Fat oxidation (g·min−1) = 1.6947 × VO2 (L·min−1) − 1.7012 × VCO2 (L·min−1)

Carbohydrate oxidation (g·min−1) = 4.5851 × VCO2 (L·min−1) − 3.22259 × VO2 (L·min−1)

The energy produced from each substrate was calculated by assuming an energy equivalent for 1 g of fat and carbohydrate of 9.75 and 4.07 kcal, respectively [25]. Total ECr was quantified from the sum of these values and was expressed in kcal·kg−1·km−1. The energy cost of running at 10 km/h and at the velocities corresponding to LTh and marathon race pace were estimated from the relationship between exergy cost and running velocity derived from the incremental test.

All the physiological data were analyzed after the completion of the marathon race to avoid any pacing strategies from the participants and their coaches based on the results of testing.

#### *2.5. Route Characteristics and Race Pace Analysis*

The profile of the Athens Marathon route includes rolling uphills and downhills. More specifically, when calculated in 450 m intervals, the total ascent, total descent, the percent of uphill distance, the percent of downhill distance and the steepest uphill and downhill, respectively, are for: (a) the total route: 317 m, 262 m, 51.2%, 40.5%, 3.8% and −6.2%, (b) the 0–10 km split: 19 m, 36 m, 36.4%, 50%, 1.3% and −2.0%, (c) the 10–21.1 km split: 143 m, 66 m, 66.7%, 25%, 3.6% and −6.2%, (d) the 21.1–30 km split: 122 m, 5 m, 95%, 5%, 3.3% and −1.1% and (e) the 30–42.195 km split: 32 m, 155 m, 15.4%, 76.9%, 3.8% and −5.1% [22].

Finishing time and split times for each participant were exported from the official results posted on the site of the organization [21]. The average running velocity of each runner was calculated by dividing marathon distance to the time needed to complete the race. Race pace was expressed as a percentage of VO2 max (index of fractional utilization of VO2 max), MAV and the velocities at LTh (vLTh) and LTP (vLTP).

To determine the differences between the two groups in pacing during the race, average running velocities for the distances of 0–10, 10–21.1, 21.1–30 and 30–42.195 km were calculated by dividing the distance of the split to the time to complete the split. For the analysis of the data, mean velocity of each split was expressed as a percentage of the vLTh. The velocity at LTh was selected as a reference point because for the whole sample, average marathon running velocity was equal to vLTh.

## *2.6. Statistical Analysis*

All data are presented as means ± SD. Normality of the distribution of the data was examined with the Shapiro–Wilk's W test. A *t*-test was used to examine the differences among the medium-level and the low-level runners in the physiological parameters and race pace characteristics measured. A two-way analysis of variance with repeated measures in the second factor was used to examine the differences between the two groups in the mean velocity of each running split (0–10, 10–21.1, 21.1–30 and 30–42.195 km). Significant differences between means were located with the Newman–Keuls post hoc test. Pearson product moment correlations were used to determine the association between marathon time and the measured parameters. The statistical significance level was set for all tests at *p* < 0.05.


**2.3. Reactions catalyzed by mono-oxygenases**

Mono-oxygenases are known to add single oxygen atom at a time and cause the release of nitro group. Simpson and Evans [62] reported the role of mono-oxygenase in a *Pseudomonas* sp., where 4-nitrophenol was converted to hydroquinone with the concomitant release of nitrite. Subsequently, Spain and Gibson (1991) reported accumulation of hydroquinone and release of nitrite from 4-nitrophenol by partially purified mono-oxygenase from a *Moraxella* sp.


MSE(c0,cw)=2= 12+ 22+...+ k2 (19)

( ( . (Embedding of two patterns with

= k = 10 ( , i.e. embedding of 25 patterns with strength 2 is

( (18)

(i=1,2,...,mn) (17)

 = + +... +

+ +...

 12 k , or

k, of mn1, mn2,..., mnk

(j=1,2,...,k) is

k

j=1 R(i) = (± r (i)) )

R(i) r (i) =

are liable to N(0,1) distribution, and therefore, **rs is reference pattern**.

s 22 2 12 k

 + + ...+

Also, coordinates of vector rs=( rs(1), rs(2),..., rs(mn)), where

and reference pattern influence on resultant pixel value.

combination

takes values from N(0,2), where

Mean square error of this embedding is

= k (

**Example 1:** If k=2, 1=3, 2=4, then 1 2 s

equivalent to embedding of one pattern with strength 10.

k

j=1

 .

r =

We divide image c0 of mn pixels into k disjoint subimages c01, c02, ..., c0

j

embedding strength is

2 2

= 3 +4 =5 ).

pixels, respectively (

strength embedding), then

**3.2.3 Embedding into subimages**

Next we give an estimate of the strength coefficient for watermark, to survive the lossy compression.

In subsection 4.1 we consider the case of one bit, and in subsection 4.2 – of a longer message. For the purpose of simplicity, we only discuss the case of binary one embedding. The contents of this section may be used with small changes for the case of binary zeros.3

#### **4.1 Robustness against compression – one bit message**

#### **4.1.1 Embedding strength parameter after compression ( ')**

With next experiment we try to find which part of watermark will be destroyed by lossy compression.

In Fig. 1, linear correlation values of these images with reference patterns are shown (c0 is the first page scan of the Ru er Boškovi's book *'Elementa geometriae'*, =3, k=20, compression is DjVu Photo, made by program DjVu Solo 3.1 – LizardTech, Inc). Graphic abscissa presents arrays indexes, and ordinate – their values.

The dotted line presents linear correlation values between the **original image** and reference patterns. The dashed line presents linear correlation values between **watermarked images** and corresponding reference patterns. The solid line presents the linear correlation values between **compressed watermarked images** and corresponding reference patterns.

3 We can observe case with binary zero as the opposite pattern embedding. Opposite pattern of rw is -rw, and it also possesses all of reference pattern properties (takes values from distribution N(0,1)).

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 193

If we wish the watermark to be robust against lossy compression, we need to

The procedure of deriving ' from is "natural", because it matches to the events

In order to derive from ', we have to try with different values, by using watermarks

2. In our image we embed watermark for several values; to get several watermarked

4. The detector calculates ' for each compressed watermarked image. Then, we compare obtained ' values with desired value. that corresponds ' value, closest to desired

If we embed k patterns into the image, each with own embedding strength coefficient i

22 2 w 0 1 2 ks 0 s c c + + + ... + r = c + r

11 22 kk s ± r ± r ± ... ± r

The overall strength coefficient remaining after compression is ' (the coefficient that

( , 22 2

, 22 2

12 k

+ ( + ...+ (

12 k

' = p = (p ) +(p ) +... +(p ) (( ( ( (25)

 '= ( ') +( ') +...+( ') (24)

( ( (22)

(23)

r =

wn 0 s c c + 'r \$

corresponds to for our image and compression technique).

p= '/   *setting*) and replace each

**4.1.2 Coefficient setting for a robust watermark**

made from **arbitrary (but only one) reference pattern**.

1. For our image, we set and (desired value of) '

3. We expose these images to expected lossy compression.

'=max(+l0,0) (for binary zero embedding)

chronology: and ' are "cause and effect": 1. We embed watermark with strength ;

2. We compress the image;

one, is appropriate.

After compression, we have:

(i=1, 2... k), we have:

**4.2 Robustness of a longer message**

3. We read '.

Steps for setting:

images.

with ':

modify statements (8) and (9) (subsubsection 3.1.2: *Parameter*

Fig. 1. Linear correlation between the image (original, watermarked and compressed watermarked) and corresponding reference patterns (left), for the first page of the book "*Elementa geometriae*" (right)

We can see from this graphic, that for each reference pattern r(i), (i=1,2,...,k) stands:

(Clearly, binary zero embedding reduces linear correlation value for , and lossy compression raises it by constant – it erases constant part of watermark).

We introduce new concept: *embedding strength coefficient after compression*, '**.**  ' **is part of that survives after lossy compression.**

For all of twenty reference patterns, the effect is the same: for our image and =3, after DjVu Photo compression, it survived '=0.75.

After repeating our experiment for other compression techniques (for various intensities, for JPEG compression and compression used in PDF and DjVu files making), we get the same conclusion:

**For given image and compression intensity, ' is constant for fixed coefficient (it doesn't depend on reference pattern embedded, nor on linear correlation value of original image with it).**

Surely, for given coefficient, value ' is smaller in more intensive compression. For example, ' is bigger for JPEG 70% compression than for JPEG 40%.

#### **4.1.2 Coefficient setting for a robust watermark**

If we wish the watermark to be robust against lossy compression, we need to modify statements (8) and (9) (subsubsection 3.1.2: *Parameter setting*) and replace each with ':

The procedure of deriving ' from is "natural", because it matches to the events chronology: and ' are "cause and effect":

192 Watermarking – Volume 1

Fig. 1. Linear correlation between the image (original, watermarked and compressed watermarked) and corresponding reference patterns (left), for the first page of the book

We can see from this graphic, that for each reference pattern r(i), (i=1,2,...,k) stands:

compression raises it by constant – it erases constant part of watermark).

example, ' is bigger for JPEG 70% compression than for JPEG 40%.

We introduce new concept: *embedding strength coefficient after compression*, '**.**  ' **is part of**

For all of twenty reference patterns, the effect is the same: for our image and =3, after DjVu

After repeating our experiment for other compression techniques (for various intensities, for JPEG compression and compression used in PDF and DjVu files making), we get the same

**For given image and compression intensity, ' is constant for fixed coefficient (it doesn't depend on reference pattern embedded, nor on linear correlation value of original**

Surely, for given coefficient, value ' is smaller in more intensive compression. For

"*Elementa geometriae*" (right)

image and the reference pattern by

**that survives after lossy compression.**

Photo compression, it survived '=0.75.

conclusion:

**image with it).**

In order to derive from ', we have to try with different values, by using watermarks made from **arbitrary (but only one) reference pattern**.

Steps for setting:

#### **4.2 Robustness of a longer message**

If we embed k patterns into the image, each with own embedding strength coefficient i (i=1, 2... k), we have:

$$\mathbf{c}\_{\rm w} = \mathbf{c}\_0 + \sqrt{\alpha\_1^2 + \alpha\_2^2 + ... + \alpha\_k^2} \cdot \mathbf{r}\_s = \mathbf{c}\_0 + \boldsymbol{\beta} \cdot \mathbf{r}\_s \tag{22}$$

$$\mathbf{r}\_s = \frac{\pm a\_1 \mathbf{r}\_1 \pm a\_2 \mathbf{r}\_2 \pm \dots \pm a\_k \mathbf{r}\_k}{\beta} \tag{23}$$

After compression, we have:

$$\mathbf{c}\_{\rm wn} \approx \mathbf{c}\_0 + \beta^\prime \cdot \mathbf{r}\_{\rm s} \,, \qquad \beta = \sqrt{(a\_1)^2 + (a\_2)^2 + \dots + (a\_k)^2} \tag{24}$$

The overall strength coefficient remaining after compression is ' (the coefficient that corresponds to for our image and compression technique).

$$\mathbf{p} = \boldsymbol{\beta}^\* / \boldsymbol{\beta}, \quad \boldsymbol{\beta}^\* = \mathbf{p} \cdot \boldsymbol{\beta} = \sqrt{(\mathbf{p} \cdot \boldsymbol{\alpha}\_1)^2 + (\mathbf{p} \cdot \boldsymbol{\alpha}\_2)^2 + \dots + (\mathbf{p} \cdot \boldsymbol{\alpha}\_k)^2} \tag{25}$$

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 195

Fig. 2 shows linear correlation values between the image and each of embedded patterns. The dotted line presents linear correlation values for the original image; the dashed line is for the watermarked image. Roundels present linear correlation values between the compressed watermarked image and embedded reference patterns. We can see that their ordinates are near the threshold value (solid line). So, they are the smallest values needed

**Example 5:** The same test is done for 64 bits message. The watermark also survives DjVu Photo compression. Overall embedding strength is =6.94. In Fig. 3, the compressed original

Fig. 3. Compressed original and compressed watermarked image ("*Fishingboat*", 512!512

Many authors suggest watermark embedding in transform, instead of spatial domain. In addition, they propose using transform domain that will be used during lossy compression.

In this subsection we apply the described watermarking algorithm in transform domain (embedding and detection occur in transform, instead of spatial domain). We show that there is no difference from the viewpoint of effective embedding of AWGN watermark, between watermarking in spatial and in transform (DCT, block DCT, Fourier or arbitrary

First of all, most of nowadays used transforms in image processing and in compression are

(c0+(r)=(c0)+((r) (26)

Goal is to achieve greater watermark robustness against expected compression.

pixels, k=64 message bits, DjVu Photo compression)

**5.1 Optimal embedding strength in transform domain**

**5. AWGN watermark in transform domain**

orthogonal (or at least unitary) linear transforms.

Linear transform respects addition and scaling. Therefore,

wavelet) domain.

for the detector to recognize after compression the image as watermarked.

(without watermark) and compressed watermarked image could be seen.

Thus, 12 k 12 k '' ' = = ... = = p

The procedure for coefficients i (i=1,2,...,k) setting is:

1. For our image, we set parameters and i' (i=1,2,...,k);

$$\text{2.}\quad \text{We calculate } \beta^\* = \sqrt{(a\_1)^2 + (a\_2)^2 + \dots + (a\_k)^2}$$

3. We obtain from ' and calculate ' p =

$$\mathbf{4.} \qquad \mathbf{a\_i} = \frac{\mathbf{a\_i'}}{\mathbf{P}} \text{ ( $i=1,2,...,k$ )}$$

**Example 3**: Let 1'=3, 2'=4. Then 2 2 '= 3 +4 =5

If for given image and compression technique, to coefficient '=5 corresponds =10, then

$$\mathbf{p} = \frac{\mathcal{J}}{\mathcal{J}} = \frac{1}{2}\mathbf{\mathcal{I}}\,\qquad\qquad a\_1 = \frac{a\_1\,\mathrm{d}}{\mathbf{p}} = \mathbf{6}\,\,\mathrm{d}\,\qquad\qquad a\_2 = \frac{a\_2\,\mathrm{d}}{\mathbf{p}} = \mathbf{8}\,\,\mathrm{d}\,\,\mathrm{d}\,\,\mathrm{s}$$

**Example 4**: We embed k=16 message bits (binary ones) into the image c0='*Fishingboat*' (512!512 pixels).

$$
\sigma\_{\rm lc} = \frac{\sqrt{\rm E(c\_0)}}{\text{(512 \cdot 512)}} = 0.25 \qquad\qquad\qquad\text{We take }\tau = 0.25
$$

'= [0, 0.18, 0, 0.17, 0.41, 0.54, 0.19, 0.58, 0.42, 0.58, 0.64, 0.66, 0.14, 0.57, 0.31, 0.05]

(using embedding strength adjustment)

Fig. 2. Linear correlation between the image (original, watermarked and compressed watermarked) and k=16 embedded reference patterns

194 Watermarking – Volume 1

Thus, 12 k

' = p

4. i i

(512!512 pixels).

12 k '' ' = = ... = = p

2. We calculate 22 2

3. We obtain from ' and calculate ' p =

**Example 3**: Let 1'=3, 2'=4. Then 2 2

lc

'=1.65 =5.4 p=0.3 i= 'i/p (i=1,2,...,k)

watermarked) and k=16 embedded reference patterns

(using embedding strength adjustment)

(i=1,2,...,k)

The procedure for coefficients i (i=1,2,...,k) setting is:

1. For our image, we set parameters and i' (i=1,2,...,k);

' = ( ') + ( ') + ...+ ( ')

12 k

, 1

' 1 p= = 2

0

E(c ) = 0.25 (512 512)

'= 3 +4 =5

1

'= [0, 0.18, 0, 0.17, 0.41, 0.54, 0.19, 0.58, 0.42, 0.58, 0.64, 0.66, 0.14, 0.57, 0.31, 0.05]

Fig. 2. Linear correlation between the image (original, watermarked and compressed

If for given image and compression technique, to coefficient '=5 corresponds =10, then

' = =6 p

**Example 4**: We embed k=16 message bits (binary ones) into the image c0='*Fishingboat*'

( We take =0.25

, 2

2

' = =8 p

Fig. 2 shows linear correlation values between the image and each of embedded patterns. The dotted line presents linear correlation values for the original image; the dashed line is for the watermarked image. Roundels present linear correlation values between the compressed watermarked image and embedded reference patterns. We can see that their ordinates are near the threshold value (solid line). So, they are the smallest values needed for the detector to recognize after compression the image as watermarked.

**Example 5:** The same test is done for 64 bits message. The watermark also survives DjVu Photo compression. Overall embedding strength is =6.94. In Fig. 3, the compressed original (without watermark) and compressed watermarked image could be seen.

Fig. 3. Compressed original and compressed watermarked image ("*Fishingboat*", 512!512 pixels, k=64 message bits, DjVu Photo compression)

### **5. AWGN watermark in transform domain**

Many authors suggest watermark embedding in transform, instead of spatial domain. In addition, they propose using transform domain that will be used during lossy compression. Goal is to achieve greater watermark robustness against expected compression.

#### **5.1 Optimal embedding strength in transform domain**

In this subsection we apply the described watermarking algorithm in transform domain (embedding and detection occur in transform, instead of spatial domain). We show that there is no difference from the viewpoint of effective embedding of AWGN watermark, between watermarking in spatial and in transform (DCT, block DCT, Fourier or arbitrary wavelet) domain.

First of all, most of nowadays used transforms in image processing and in compression are orthogonal (or at least unitary) linear transforms.

Linear transform respects addition and scaling. Therefore,

$$f(\mathbf{c}\_0 + \alpha \cdot \mathbf{r}) = f(\mathbf{c}\_0) + \alpha \cdot f(\mathbf{r}) \tag{26}$$

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 197

Fig. 4. Image '*Cameraman*' in spatial and in block DCT domain; one 8!8 block in spatial and

As we can see, in block DCT domain, energy of each block is concentrated in its upper left corner. On the other hand, JPEG compression damages elements in lower right corner much

Block DCT domain, which is the basis of JPEG compression, is used frequently in watermark embedding. At description of such embedding, we use term *image subchannel* (Eggers and Girod introduced it in (Eggers & Girod, 2001)). *Subchannel* is vector that in block DCT domain has for coordinates – elements, with same index in blocks. Subchannels are ordered according to zigzag order (Fig. 5). Thus, subchannel 1 consists of all DC blocks elements; subchannel 10 consists of all elements that are in the position 10 in the block

So, the image of dimension m!n in (8!8) block DCT domain may be presented with 64

sj=(sj(1),sj(2),...,sj(nbl)), (j=1,2,...,64)), where nbl=mn/64 (32)

Fig. 5. Zigzag order; coefficients 1, 10, 22 in zigzag order; region of subchannels 10-22

4 Here, for the purpose of simplicity, we assume that image dimensions are multiples of 8, in order to

fourth objects in Fig. 4), they are presented with logarithmed magnitudes

in block DCT domain

more intensively.

(zigzag order).

subchannels: 4

(colored in gray)

make the partition in 8!8 blocks possible.

Orthogonal transform preserves the inner product and consequently, it preserves vectors lengths and angles between vectors. Therefore,

$$\left\|\mathbf{c}\_{0}\right\| = \left\|f(\mathbf{c}\_{0})\right\|\qquad\text{and}\qquad\left\|\mathbf{r}\right\| = \left\|f(\mathbf{r})\right\|\tag{27}$$

$$\cos(\mathbf{c}\_0 \mathbf{r}) = \cos(f \text{ (co)} \angle f(\mathbf{r})) \tag{28}$$

So, we have

$$\text{lc}(\mathbf{c}\_0 \mathbf{r}) = \text{lc}(f \text{ (c}\_0)\_f f(\mathbf{r})) \tag{29}$$

Also, for orthogonal (and unitary as well) linear transforms applies *Parseval's (energy) identity*:

$$\begin{array}{cccc}\text{If} & \mathbf{b\_{0}} = f(\mathbf{c\_{0}}), & \text{then} & \mathbf{E(b\_{0})} = \sum\_{\mathbf{i=1}}^{\text{min}} (\mathbf{b\_{0}(\mathbf{i})})^{2} = \sum\_{\mathbf{i=1}}^{\text{min}} (\mathbf{c\_{0}(\mathbf{i})})^{2} = \mathbf{E(c\_{0})} \end{array} \tag{30}$$

Orthogonal transform maps reference pattern r=(r(1),r(2),...,r(mn)) (i.e. vector with coordinates taken from distribution N(0,1)), into vector (r) from distribution N(0,1), i.e. **orthogonal transform maps reference pattern to reference pattern**.

Standard deviations from zero of linear correlations between vector c0 and reference patterns in spatial and transform domain are equal:

$$\mathfrak{m}\_{\mathsf{C}}(\mathsf{c}\_{0}) = \mathfrak{m}\_{\mathsf{C}}(f(\mathsf{c}\_{0})) \tag{31}$$

It is all the same if we embed AWGN watermark into an image with strength in spatial or in transform domain. **Correlation values and MSE too, will be the same in both domains.**

Also, there is no difference between spatial and transform domains in AWGN watermark robustness against lossy compression.

#### **5.2 AWGN watermark embedding into subimage in transform domain**

As in spatial domain, it is possible to embed watermark in some **part of coefficients** in transform domain (here also, we call it *embedding into subimage*).

Technically, there is not any difference in optimal strength setting, speaking of subimage and of the whole image. Effective embedding strength we determine by dimension and energy of image (or image part) in that AWGN watermark is to be embedded. Robustness against lossy compression we determine by these coefficients properties in the regard to expected compression.

However, for images in spatial and in transform domain, there is a great difference in **energy distribution**. While in spatial domain, image energy is mainly distributed evenly over entire matrix, in transform domain it is concentrated in some of its elements. Illustration of this fact may be seen in Fig. 4, on example of block DCT (transform used in JPEG compression). Here, for presentation of matrices in DCT domain, we use solution proposed in (Vu kovi, 2008):

196 Watermarking – Volume 1

c0=(c0) and r=(r) (27)

mn mn 2 2 00 0 0

i=1 i=1

cos(c0,r)=cos( (c0),(r)) (28)

lc(c0,r)=lc( (c0),(r)) (29)

E(b ) = (b (i)) = (c (i)) = E(c ) ) ) (30)

lc(c0)=lc((c0)) (31)

Orthogonal transform preserves the inner product and consequently, it preserves vectors

Also, for orthogonal (and unitary as well) linear transforms applies *Parseval's (energy) identity*:

Orthogonal transform maps reference pattern r=(r(1),r(2),...,r(mn)) (i.e. vector with coordinates taken from distribution N(0,1)), into vector (r) from distribution N(0,1), i.e.

Standard deviations from zero of linear correlations between vector c0 and reference

It is all the same if we embed AWGN watermark into an image with strength in spatial or in transform domain. **Correlation values and MSE too, will be the same in both domains.** Also, there is no difference between spatial and transform domains in AWGN watermark

As in spatial domain, it is possible to embed watermark in some **part of coefficients** in

Technically, there is not any difference in optimal strength setting, speaking of subimage and of the whole image. Effective embedding strength we determine by dimension and energy of image (or image part) in that AWGN watermark is to be embedded. Robustness against lossy compression we determine by these coefficients properties in the regard to

However, for images in spatial and in transform domain, there is a great difference in **energy distribution**. While in spatial domain, image energy is mainly distributed evenly over entire matrix, in transform domain it is concentrated in some of its elements. Illustration of this fact may be seen in Fig. 4, on example of block DCT (transform used in JPEG compression). Here, for presentation of matrices in DCT domain, we use solution

lengths and angles between vectors. Therefore,

If b0=(c0), then

patterns in spatial and transform domain are equal:

robustness against lossy compression.

expected compression.

proposed in (Vu kovi, 2008):

**orthogonal transform maps reference pattern to reference pattern**.

**5.2 AWGN watermark embedding into subimage in transform domain**

transform domain (here also, we call it *embedding into subimage*).

So, we have

Fig. 4. Image '*Cameraman*' in spatial and in block DCT domain; one 8!8 block in spatial and in block DCT domain

As we can see, in block DCT domain, energy of each block is concentrated in its upper left corner. On the other hand, JPEG compression damages elements in lower right corner much more intensively.

Block DCT domain, which is the basis of JPEG compression, is used frequently in watermark embedding. At description of such embedding, we use term *image subchannel* (Eggers and Girod introduced it in (Eggers & Girod, 2001)). *Subchannel* is vector that in block DCT domain has for coordinates – elements, with same index in blocks. Subchannels are ordered according to zigzag order (Fig. 5). Thus, subchannel 1 consists of all DC blocks elements; subchannel 10 consists of all elements that are in the position 10 in the block (zigzag order).

So, the image of dimension m!n in (8!8) block DCT domain may be presented with 64 subchannels: 4

$$\mathbf{s} = (\mathbf{s}(1), \mathbf{s}(2), \dots, \mathbf{s}(\text{nbl})), \text{ (}\mathbf{i} = 1, 2, \dots, 64\text{)}\text{, where } \text{nbl} \vdash \text{mn}/64\tag{32}$$

Fig. 5. Zigzag order; coefficients 1, 10, 22 in zigzag order; region of subchannels 10-22 (colored in gray)

 4 Here, for the purpose of simplicity, we assume that image dimensions are multiples of 8, in order to make the partition in 8!8 blocks possible.

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 199

Thus, an additive noise does not affect AWGN watermark (AWGN algorithm is robust

AWGN watermark is also robust against **brightness changing** (cw1=cw+n(J, where J is matrix

cw1=(cw, (35)

After this modification, linear correlation value is equal to starting one, multiplied by factor . Depending on factor , watermark detectability will increase (if >1), or diminish (if <1).

 cw1=cw, (36) where is a filter, and designates convolution. Many common image operations are performed using linear filters. Examples of them are **blurring** and **sharpening** effects.

Linear filtering and lossy compression are more complex operations than the previous two, because changes that they cause are not strictly localized (pixel change depends on certain

This class of image modifications includes many image distortions (rotation, spatial scaling, translation, skew or shear, cropping, perspective transformation, and changes in aspect

These modifications are more complicated than valumetric, because they displace information about pixels in image matrix. They usually change matrix dimensions as well. Therefore, here is not possible readily to detect the watermark. Yet, with these modifications, information about embedded message is not lost, but only "**masked**". For each geometric distortion, before detection, we need to perform one **correction procedure**. For illustration, we depict one geometric modification – image rotation by an angle . Many

With modifications that erase a part of image data (for example crop operation, or other operation with erasing several columns or rows), after correction procedure, we'll have original image matrix with locally erased data (some elements are erased, but other are untouched). Then, it is possible to calculate accurately parameter lc for untouched image part. For example, if prior to watermark embedding we know that image will be subjected to crop operation with reducing complete image area to its quarter, the watermark needs to

inferences of this analyze could be applied to other modifications.

be embedded twice stronger, to be detectable after cropping.

where >0 is scaling factor. Such operation causes **brightness and contrast changing**.

against this image modification).

of ones and n is integer).

**6.1.3 Linear filtering**

It is given by

ratio).

**6.1.2 Amplitude changing**

It may be presented by the formula

number of surrounding pixels too).

**6.2 Geometric distortions**

In (Vu kovi, 2010a, 2010b) we described in detail our investigations results in connection with embedding into subchannels in block DCT domain. We analyzed case of embedding into group of (first 32) subchannels and embedding into some of individual subchannels (specifically, subchannels 1, 10 and 22). Here, we briefly outline conclusions obtained.

In block DCT domain, image energy is concentrated in upper left corner of the block. For example, in image *'Cameraman'*, even 99.76% of whole image energy is concentrated in its first 32 subchannels. Therefore, with described algorithm, it is needed twice higher strength for effective embedding in first 32 subchannels than into the whole image. In this way we embed in 50% image coefficients, so the mean square error in this case is two times higher comparing with embedding into all of coefficients.

Embedding in lower right block corner is effective even with very small strength. However, even at very low compression, these coefficients will be destroyed. Therefore, embedding into those coefficients is not recommended.

Embedding in vicinity of subchannel 1 is not good solution, because of extremely high energy which requires big embedding strength (and this badly influences image quality).

Our explorations (Vu kovi, 2010a, 2010b) confirmed earlier (Eggers-Girod, 2001), that for effective and robust (against JPEG compression) and imperceptible watermark, it is the best to embed it in several coefficients in upper left block part (somewhere in the region of subchannels 10-22 – see Fig. 5). Such embedding showed also good robustness against compression techniques different from JPEG (for example, against DjVu compression).

#### **6. AWGN watermark and other image modifications**

Image modifications are organized (Cox et al., 2008) in two classes - valumetric and geometric distortions.

#### **6.1 Valumetric distortions**

Valumetric distortions are simpler than geometric ones. They change individual pixels values. These modifications include additive noise, amplitude changes, linear filtering and lossy compression.

#### **6.1.1 Additive noise**

This modification has effect of a random signal adding. For watermarked image, additive noise adding is defined by:

$$\mathbf{c}\_{\mathbf{w}1} = \mathbf{c}\_{\mathbf{w}} + \mathbf{s} = \mathbf{c}\_0 + \mathbf{u} \cdot \mathbf{r} + \mathbf{s},\tag{33}$$

where s is a random vector chosen from some distribution, independently of c0 and r.

Clearly,

$$\text{lc}(\mathbf{c}\_{\text{w1}}\mathbf{r}) = \text{lc}(\mathbf{c}\_{\text{w}}\mathbf{r}) + \text{lc}(\mathbf{s}\_{\text{r}}\mathbf{r}) \text{e}\mathbf{l}\mathbf{c}(\mathbf{c}\_{\text{w}}\mathbf{r}) \tag{34}$$

(because s is uncorrelated with r).

Thus, an additive noise does not affect AWGN watermark (AWGN algorithm is robust against this image modification).

AWGN watermark is also robust against **brightness changing** (cw1=cw+n(J, where J is matrix of ones and n is integer).

#### **6.1.2 Amplitude changing**

It may be presented by the formula

$$\mathbf{c}\_{\text{w1}} = \mathbf{v} \cdot \mathbf{c}\_{\text{w2}} \tag{35}$$

where >0 is scaling factor. Such operation causes **brightness and contrast changing**.

After this modification, linear correlation value is equal to starting one, multiplied by factor . Depending on factor , watermark detectability will increase (if >1), or diminish (if <1).

#### **6.1.3 Linear filtering**

It is given by

198 Watermarking – Volume 1

In (Vu kovi, 2010a, 2010b) we described in detail our investigations results in connection with embedding into subchannels in block DCT domain. We analyzed case of embedding into group of (first 32) subchannels and embedding into some of individual subchannels (specifically, subchannels 1, 10 and 22). Here, we briefly outline conclusions obtained.

In block DCT domain, image energy is concentrated in upper left corner of the block. For example, in image *'Cameraman'*, even 99.76% of whole image energy is concentrated in its first 32 subchannels. Therefore, with described algorithm, it is needed twice higher strength for effective embedding in first 32 subchannels than into the whole image. In this way we embed in 50% image coefficients, so the mean square error in this case is two times higher

Embedding in lower right block corner is effective even with very small strength. However, even at very low compression, these coefficients will be destroyed. Therefore, embedding

Embedding in vicinity of subchannel 1 is not good solution, because of extremely high energy which requires big embedding strength (and this badly influences image

Our explorations (Vu kovi, 2010a, 2010b) confirmed earlier (Eggers-Girod, 2001), that for effective and robust (against JPEG compression) and imperceptible watermark, it is the best to embed it in several coefficients in upper left block part (somewhere in the region of subchannels 10-22 – see Fig. 5). Such embedding showed also good robustness against compression techniques different from JPEG (for example, against DjVu compression).

Image modifications are organized (Cox et al., 2008) in two classes - valumetric and

Valumetric distortions are simpler than geometric ones. They change individual pixels values. These modifications include additive noise, amplitude changes, linear filtering and

This modification has effect of a random signal adding. For watermarked image, additive

cw1 = cw+s = c0+(r+s, (33)

lc(cw1,r)=lc(cw,r)+lc(s,r)lc(cw,r) (34)

where s is a random vector chosen from some distribution, independently of c0 and r.

comparing with embedding into all of coefficients.

**6. AWGN watermark and other image modifications**

into those coefficients is not recommended.

quality).

geometric distortions.

lossy compression.

**6.1.1 Additive noise**

Clearly,

noise adding is defined by:

(because s is uncorrelated with r).

**6.1 Valumetric distortions**

$$\mathsf{C}\_{\mathsf{w}1} \equiv \mathsf{C}\_{\mathsf{w}} \* f\_{\prime} \tag{36}$$

where is a filter, and designates convolution. Many common image operations are performed using linear filters. Examples of them are **blurring** and **sharpening** effects.

Linear filtering and lossy compression are more complex operations than the previous two, because changes that they cause are not strictly localized (pixel change depends on certain number of surrounding pixels too).

#### **6.2 Geometric distortions**

This class of image modifications includes many image distortions (rotation, spatial scaling, translation, skew or shear, cropping, perspective transformation, and changes in aspect ratio).

These modifications are more complicated than valumetric, because they displace information about pixels in image matrix. They usually change matrix dimensions as well. Therefore, here is not possible readily to detect the watermark. Yet, with these modifications, information about embedded message is not lost, but only "**masked**". For each geometric distortion, before detection, we need to perform one **correction procedure**.

For illustration, we depict one geometric modification – image rotation by an angle . Many inferences of this analyze could be applied to other modifications.

With modifications that erase a part of image data (for example crop operation, or other operation with erasing several columns or rows), after correction procedure, we'll have original image matrix with locally erased data (some elements are erased, but other are untouched). Then, it is possible to calculate accurately parameter lc for untouched image part. For example, if prior to watermark embedding we know that image will be subjected to crop operation with reducing complete image area to its quarter, the watermark needs to be embedded twice stronger, to be detectable after cropping.

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 201

lc(cw,rw)= 5.787

lc(cwr,rwr)= 5.815

lc(cwr1,rwr1)= 5.592

lc(cwr1,rw)= 1.953

Fig. 6. Linear correlation values for image and reference pattern (unrotated and rotated)

Each color image can be regarded as three grayscale images combination (one for each of red/ green/ blue components). Therefore, it is not too complicated to generate results for grayscale images to use for color images. Of course, for better results we need to consider

**7. Color images and e-books**

sensitive there).

**7.1 Digital watermark and color image**

human visual system (HVS) characteristics:

watermark algorithm, this fact should be considered.

(=10)

cw(256,256) watermarked image ('*Cameraman*')

cwr(297,297) – watermarked image, rotated by

rwr(297,297) – reference pattern, rotated by

after cropping to original dimensions

cwr1 (256,256) – watermarked image, rotated by and

rwr1 (256,256) – reference pattern, rotated by and -,

cwr1 (256,256) – watermarked image, rotated by and -, after cropping to original dimensions

lc(cwr1,rwr1)= each

rw(256,256) – (embedded) reference pattern

rw(256,256) – embedded reference pattern

#### **6.2.1 Robustness against rotation**

In the case of image rotation, one of several different correction procedures may be used prior to detection. If watermarked image is rotated by angle , to make watermark detection possible, we may do one of the following:

Linear correlation values for image and reference pattern (unrotated and rotated) are presented in Fig. 6, for image *"Cameraman".* Reference pattern rw is embedded with strength =5. Then, image is subjected to rotation by angle =10.

In each row we present image and pattern for which the correlation is calculated. So, for each example it is specified which image and pattern (and their dimensions) we deal with, and also the linear correlation value for them. In the first figure row, watermarked image and embedded reference pattern are presented. Second, third and fourth row contain information about three previously stated solutions of correction and detection.

The rotation practically does not reduce linear correlation value, if it is calculated for image and reference pattern that are rotated in the same manner. However, if we compare the rotated image (after restoration to original position by rotation in opposite direction and cropping to original dimension) with the original reference pattern, certain watermark amount will be lost ( ' value will be considerably less than embedding strength value ).

It should be noted that in cases where not all details of distortion occurrence are known, the latest solution is usually only possible.

Experiments have confirmed (Vu kovi, 2010a) that stated inference for lossy compression (subsubsection 4.1.1: *Embedding strength parameter after compression (* '*))* stays also for rotation (and other distortions too). For our image and embedding strength , after given modification, remaining strength ' **doesn't depend on the reference pattern nor on its correlation with the original image**. Hence, to set embedding strength needed that after expected modification remains '**, it is sufficient to experiment with only one reference pattern**.

The procedure of determining the necessary embedding strength may be performed in the next steps:

200 Watermarking – Volume 1

In the case of image rotation, one of several different correction procedures may be used prior to detection. If watermarked image is rotated by angle , to make watermark detection

Linear correlation values for image and reference pattern (unrotated and rotated) are presented in Fig. 6, for image *"Cameraman".* Reference pattern rw is embedded with strength

In each row we present image and pattern for which the correlation is calculated. So, for each example it is specified which image and pattern (and their dimensions) we deal with, and also the linear correlation value for them. In the first figure row, watermarked image and embedded reference pattern are presented. Second, third and fourth row contain information about three previously stated solutions of correction

The rotation practically does not reduce linear correlation value, if it is calculated for image and reference pattern that are rotated in the same manner. However, if we compare the rotated image (after restoration to original position by rotation in opposite direction and cropping to original dimension) with the original reference pattern, certain watermark amount will be lost ( ' value will be considerably less than embedding strength value ).

It should be noted that in cases where not all details of distortion occurrence are known, the

Experiments have confirmed (Vu kovi, 2010a) that stated inference for lossy compression (subsubsection 4.1.1: *Embedding strength parameter after compression (* '*))* stays also for rotation (and other distortions too). For our image and embedding strength , after given modification, remaining strength ' **doesn't depend on the reference pattern nor on its correlation with the original image**. Hence, to set embedding strength needed that after expected modification remains '**, it is sufficient to experiment with only one reference**

The procedure of determining the necessary embedding strength may be performed in the

2. By experimenting with only one reference pattern, we determine the necessary embedding strength , that after expected modification, remaining strength is at

 *setting for a*

1. We determine ' using procedure given in subsubsection 4.1.2 (*Coefficient*

**6.2.1 Robustness against rotation**

rotated by

and detection.

**pattern**.

next steps:

least '.

*robust watermark*)

possible, we may do one of the following:

latest solution is usually only possible.

and - and cropped to original dimension

(we may) compare with the original reference pattern.

=5. Then, image is subjected to rotation by angle =10.

Fig. 6. Linear correlation values for image and reference pattern (unrotated and rotated)

#### **7. Color images and e-books**

#### **7.1 Digital watermark and color image**

Each color image can be regarded as three grayscale images combination (one for each of red/ green/ blue components). Therefore, it is not too complicated to generate results for grayscale images to use for color images. Of course, for better results we need to consider human visual system (HVS) characteristics: (one

AWGN Watermark in Images and E-Books – Optimal Embedding Strength 203

With black-white text pages there exists another problem, which makes them especially inconvenient for watermarking: watermark can be removed simply by text retyping from

For given grayscale image and AWGN watermark, we described procedure for optimal embedding strength setting. We analyzed optimal strength for effective embedding, and

and

We analyzed AWGN watermark embedding cases, into the whole image and into images, in spatial and transform domains and robustness of such embedding against expected

For other image modifications, procedures for optimal strength setting are similar as in the case of compression. For each geometric modification, however, we need prior detection to

Obtained results we applied to color images and e-books. AWGN algorithm is not applicable on black-white e-books that originate from DOC or TeX files, because watermark

The work presented here was supported by the Serbian Ministry of Education and Science

Cox, I.J., Kilian, J., Leighton, F.T. & Shamoon, T. (1997): Secure spread spectrum

Cox, I . & Miller, M., Bloom, J., Fridrich, J. & Kalker, T. (2008): *Digital Watermarking and Steganography*, Morgan Kaufmann Publishers, ISBN 978-0-12-372585-1 Eggers, J. & Girod, B. (2001): Quantization effects on digital watermarks, *Signal Processing* 81

Feng, G., Jiang, L., Wang, D. & He, C. (2005): Quickly tracing detection for spread spectrum

Feng, G., Jiang, L., He, C. & Xue, Y. (2006): Chaotic spread spectrum watermark of optimal

Mijajlovi, Ž. & Vasiljevi, D. (2008): Web page *Virtual library eLibrary*, Available from

Mora-Jimenez, I. & Navia-Vazquez, A. (2000); A new spread spectrum watermarking

Volume 38 Issue 12, December, 2005, pp. 2530 – 2536

space-filling curves, *Chaos, Solitons and Fractals* 27, pp. 580–587

watermarking for multimedia, *IEEE Transactions on Image Processing,* 6(12), pp.

watermark based on effect estimation of the affine transform, *Pattern Recognition,*

method with self-synchronization capabilities,*, Proceedings 2000 International Conference on Image Processing*, pp. 415 - 418 vol. 1 , Print ISBN: 0-7803-6297-7

This text contains integrated results of several earlier papers.

also watermark robust against expected image modification.

may be removed from them by simply retyping of text.

them.

**8. Conclusion**

compression.

perform one correction procedure.

**9. Acknowledgment**

1673-1687

(2001) pp. 239–263

http://elibrary.matf.bg.ac.rs/

(project III44006).

**10. References**

#### **7.2 Watermark for e-books**

Electronic book (e-book) is the digital equivalent of a printed book. In our *Virtual library* (Mijajlovi & Vasiljevi, 2008), e-books are in PDF format. On Internet, DjVu format is used often as well.

PDF books in Virtual library, from the viewpoint of digital watermark embedding, may be classified as:

#### **7.2.1 PDF e-books produced by scanning**

Embedding procedure for PDF e-books:

#### Detection:

#### **7.2.2 PDF e-books originated from DOC and TeX files**

These PDF pages need at first to be converted to bitmaps; this can be done by conversion of PDF pages into (e.g.) TIFF images. Then we may proceed with just described procedure (7.2.1).

However, by converting pages into images, we lose possibility of text retrieving in the file. This problem can be overcome by using some OCR procedure.

#### **7.2.3 Problem with black-white books**

Proposed algorithm is not suitable for simple black-white images (as book pages usually are). In big white image regions (for example, page margins) a large part of embedded data will be destroyed by "squeezing" operation: values that, after adding of white noise to image matrix become greater than 255, after "squeezing" return to 255. Also, it is very easy to remove watermark data from these image regions (for example, with eraser in Photoshop).

Pages in e-books produced by scanning usually aren't black-white, but grayscale (or color) images; even their margins are not completely white, and watermarking is possible for them. Problem is emphasized with second mentioned e-books class. Watermarking makes sense only if these books aren't purely textual files but they contain some amount of pictures, in which watermark could be embedded.

With black-white text pages there exists another problem, which makes them especially inconvenient for watermarking: watermark can be removed simply by text retyping from them.

#### **8. Conclusion**

202 Watermarking – Volume 1

Electronic book (e-book) is the digital equivalent of a printed book. In our *Virtual library* (Mijajlovi & Vasiljevi, 2008), e-books are in PDF format. On Internet, DjVu format is used

PDF books in Virtual library, from the viewpoint of digital watermark embedding, may be

2. In each book page (TIFF file) we embed watermark – result is watermarked (TIFF)

1. Book page in which we search for watermark we extract from PDF file and save as

These PDF pages need at first to be converted to bitmaps; this can be done by conversion of PDF pages into (e.g.) TIFF images. Then we may proceed with just described procedure

However, by converting pages into images, we lose possibility of text retrieving in the file.

Proposed algorithm is not suitable for simple black-white images (as book pages usually are). In big white image regions (for example, page margins) a large part of embedded data will be destroyed by "squeezing" operation: values that, after adding of white noise to image matrix become greater than 255, after "squeezing" return to 255. Also, it is very easy to remove watermark data from these image regions (for example, with eraser in Photoshop). Pages in e-books produced by scanning usually aren't black-white, but grayscale (or color) images; even their margins are not completely white, and watermarking is possible for them. Problem is emphasized with second mentioned e-books class. Watermarking makes sense only if these books aren't purely textual files but they contain some amount of

3. We combine watermarked (TIFF) pages into one PDF file – watermarked e-book.

may be embedded into them in the same manner as in other images.

1. We scan book pages – results are bitmapped (e.g., TIFF) images.

images. Hence before embedding, they need to be converted to bitmaps.

**7.2 Watermark for e-books**

**7.2.1 PDF e-books produced by scanning**

2. We detect the watermark message in this image.

**7.2.2 PDF e-books originated from DOC and TeX files**

This problem can be overcome by using some OCR procedure.

Embedding procedure for PDF e-books:

**7.2.3 Problem with black-white books**

pictures, in which watermark could be embedded.

often as well.

classified as:

page.

(e.g.) TIFF image.

Detection:

(7.2.1).

This text contains integrated results of several earlier papers.

For given grayscale image and AWGN watermark, we described procedure for optimal embedding strength setting. We analyzed optimal strength for effective embedding, and also watermark robust against expected image modification.

We analyzed AWGN watermark embedding cases, into the whole image and into images, in spatial and transform domains and robustness of such embedding against expected compression.

For other image modifications, procedures for optimal strength setting are similar as in the case of compression. For each geometric modification, however, we need prior detection to perform one correction procedure.

Obtained results we applied to color images and e-books. AWGN algorithm is not applicable on black-white e-books that originate from DOC or TeX files, because watermark may be removed from them by simply retyping of text.

#### **9. Acknowledgment**

The work presented here was supported by the Serbian Ministry of Education and Science (project III44006).

#### **10. References**

http://www.ncd.matf.bg.ac.yu/casopis/12/NCD12017.pdf

204 Watermarking – Volume 1

Perez-Freire, L. & Perez-Gonzalez, F. (2009): Spread-Spectrum Watermarking Security,

Ruanaidh, J.J.K.O. & Pun, T. (1998): Rotation, scale and translation invariant spread

Ruanaidh, J.J.K.O. & Csurka, G. (1999): A Bayesian approach to spread spectrum watermark

Vu kovi, V. (2008): Image and its matrix, matrix and its image, *Review of the National Center*

Vu kovi, V. (2010a): Embedding strength criteria for AWGN watermark, robust against

Vu kovi, V. (2010b): *AWGN watermark optimal strength (Optimalna snaga žiga belog Gausovog šuma)* – doctoral dissertation, Faculty of Mathematics, Belgrade, 2010, http://elibrary.matf.bg.ac.rs/bitstream/handle/123456789/1073/PhDVVuckovic.

Vu kovi, V. (2011): Digital watermark in digital images and e-books, *Review of the National*

pp. 2 – 24, ISSN: 1556-6013

303-317, ISBN:<PHONE_NUMBER>

ISSN 1335-9150

pdf

1, 1999, pp. 1207-1212, Print ISBN: 0-7695-0149-4

*for Digitization*, Issue: 12, pp. 17-31, ISSN: 1820-0109, http://www.ncd.matf.bg.ac.yu/casopis/12/NCD12017.pdf

*Center for Digitization* 19, pp. 1–6, ISSN: 1820-0109, http://elib.mi.sanu.ac.rs/files/journals/ncd/19/ncd19001.pdf

*IEEE Transactions on Information Forensics and Security,* March 2009, Vol. 4, Issue: 1,

spectrum digital image watermarking, *Signal Processing* 66 No. 3, May 1998, pp.

detection and secure copyright protection for digital image libraries, *IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99)* - Volume

expected distortion*, Computing and Informatics*, Vol. 29, no. 3 (2010), pp. 357–387,

*Edited by Mithun Das Gupta*

This collection of books brings some of the latest developments in the field of watermarking. Researchers from varied background and expertise propose a remarkable collection of chapters to render this work an important piece of scientific research. The chapters deal with a gamut of fields where watermarking can be used to encode copyright information. The work also presents a wide array of algorithms ranging from intelligent bit replacement to more traditional methods like ICA. The current work is split into two books. Book one is more traditional in its approach dealing mostly with image watermarking applications. Book two deals with audio watermarking and describes an array of chapters on performance analysis of algorithms.

Photo by NicoElNino / iStock

Watermarking - Volume 1

Watermarking

Volume 1

*Edited by Mithun Das Gupta*


*2.2. Descriptive Statistics*

Table 1 reports the summary statistics of the variables used in this paper for the sample under analysis. We separated the variables into 4 panels by mother, child, household, and community characteristics. Columns 1–3 presented mean, standard deviation, and the number of observations for the sample in the 2006/2007 round. Similarly, columns 4–6 showed the same statistics for the 3rd YL round (2009/2010).

**Table 1.** Descriptive Statistics by Survey Round.

Table 1 present summary statistics (mean and standard deviation) of the variables used in the analysis. These variables are available in the first three rounds of the Peruvian Young Lives Survey. The sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009.

As presented in Panel A, mothers were 31–35 years old on average between the 2 rounds. On average, 16% of these mothers reported being of indigenous origin, and although 79% of them reported they were literate, 57% had not completed primary school. Finally, statistics showed that 30% of mothers had mental health issues in 2002, and 94% of them reported that they attended antenatal care while they were pregnant from the index child. In terms of children's characteristics (Panel B), half of index children were boys and 16% of them were the eldest. Cognitive outcomes, as measured by PPVT Z-scores, were practically unchanged between the 2 rounds, even if, as expected, the mean score increased as the children age, reflecting a larger vocabulary. The average child in the sample scored 0.06 standard deviations above the mean PPVT score of a reference child in both 2006/2007 and 2009/2010. Children's height-for-age Z-scores, on the other hand, showed an improving trend.

To summarize information at the household level, we created some indexes that captured information on wealth, housing quality, and consumption of durable goods (see Panel C). Each of these indexes took values between 0 to 1. A household with an index level close to 0 (1) indicated that the family was worse (better) in the particular dimension that the index was measuring. In 2007, the average household in the sample under analysis was below the median of the distribution in all indexes. The wealth and housing quality indexes of the average household from our sample remained similar between the 2 rounds. Only the consumption of durable goods index increased between 2006–2009, which can be related to an increase in the number of older household members that consumed more expensive durable goods. Moreover, 58% of households under analysis lived in urban areas and had 5.5 members on average, 1.3 of them were school-aged children in 2006/2007. Three years after, 18% of households were more likely to live in urban areas.

Appendix A compared the sample under analysis with the observations excluded from the study. There are only 2 differences in maternal characteristics between these 2 sub-samples, and the difference remained statistically significant at 10%: Mothers in the sample were less likely to have completed primary school and were less likely to live in urban areas.


**8. Conclusion**

The restoration of adequate mechanical axes is critical for implant survival. Preoperative planning anticipates surgical difficulties and gives a chance for creating resolutive strategies.

The ATJ® application for mobile phones has proven useful and comes to optimize the surgical planning in TKAs. As it establishes a rational step-by-step process, based on literature, it directs the user to a possible reliable form of surgical planning.


**Mental Health Matters: From Cross-Cultural Perspectives**

Editors

**Yasuhiro Kotera Elaina Taylor**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Yasuhiro Kotera Faculty of Medicine and Health Sciences University of Nottingham Nottingham United Kingdom

Elaina Taylor College of Health, Psychology and Social Care University of Derby Derby United Kingdom

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Healthcare* (ISSN 2227-9032) (available at: www.mdpi.com/journal/healthcare/special issues/mentalhealth matters).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-8481-2 (Hbk) ISBN 978-3-0365-8480-5 (PDF)**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**5. FTIR spectra**

A schematic diagram of FTIR spectroscopy is reported in **Figure 15**. There is a broadband infrared source, which gives radiation. This radiation is split in the beam splitter. The split beam gets deflected onto the sample through a parabolic mirror.

**Figure 15.** *Schematic of FTIR spectroscope.*

#### **Figure 16.**

*Spectra for (a) soft kaolin and (b) metakaolin [15].*

#### **Figure 17.**

*FTIR spectra of unsoaked RFFG sample and RFFG samples soaked in (a) sulfuric acid (pH = 3.0) and (b) deionized water (pH = 7.0) for 1, 56 and 120 days [20].*

**33**

**Figure 19.**

*A schematic of a synchrotron.*

*Summary of Some Selected Characterization Methods of Geopolymers*

The sample is kept in an atomic force microscope. The radiation from the reflection

**Figure 16** gives the difference between the spectra for soft kaolin and metakaolin. There is a distinct change in the spectral lines. Comparison of spectra for red

The vertical dotted lines in **Figure 17** both indicate the position of the asymmetric stretching vibration band of Si-O-T for geopolymer gels. The dip or movement of

Generally, as shown in **Figure 18**, stretching, bending and twisting are clearly

A synchrotron is a high energy device in which particles are accelerated to a very

*DOI: http://dx.doi.org/10.5772/intechopen.82208*

from the sample is detected using a detector.

*Stretching, bending and twisting as seen in FTIR spectra.*

seen as dips in the FTIR.

**6. Synchrotron**

**Figure 18.**

mud fly ash-based geopolymers is given in **Figure 15**.

the Si-O-T bond has to be carefully noticed while interpreting results.

high voltage. **Figure 19** is a schematic diagram of a synchrotron.

*Summary of Some Selected Characterization Methods of Geopolymers DOI: http://dx.doi.org/10.5772/intechopen.82208*

#### **Figure 18.**

*Geopolymers and Other Geosynthetics*

**32**

**Figure 17.**

**Figure 16.**

**Figure 15.**

*Schematic of FTIR spectroscope.*

*Spectra for (a) soft kaolin and (b) metakaolin [15].*

*deionized water (pH = 7.0) for 1, 56 and 120 days [20].*

*FTIR spectra of unsoaked RFFG sample and RFFG samples soaked in (a) sulfuric acid (pH = 3.0) and (b)*

*Stretching, bending and twisting as seen in FTIR spectra.*

The sample is kept in an atomic force microscope. The radiation from the reflection from the sample is detected using a detector.

**Figure 16** gives the difference between the spectra for soft kaolin and metakaolin. There is a distinct change in the spectral lines. Comparison of spectra for red mud fly ash-based geopolymers is given in **Figure 15**.

The vertical dotted lines in **Figure 17** both indicate the position of the asymmetric stretching vibration band of Si-O-T for geopolymer gels. The dip or movement of the Si-O-T bond has to be carefully noticed while interpreting results.

Generally, as shown in **Figure 18**, stretching, bending and twisting are clearly seen as dips in the FTIR.

#### **6. Synchrotron**

A synchrotron is a high energy device in which particles are accelerated to a very high voltage. **Figure 19** is a schematic diagram of a synchrotron.

**Figure 19.** *A schematic of a synchrotron.*

**Figure 20.** *Synchrotron infrared microscopy of metakaolin-based geopolymer [21].*

**35**

times.

*Summary of Some Selected Characterization Methods of Geopolymers*

A **synchrotron** is a special type of cyclic particle accelerator. It is a modified form of cyclotron, in which the accelerating particle beam travels around a fixed closed-loop path. The magnetic field bends the particle beam into a closed path. This magnetic field increases with time during the accelerating process. The increased magnetic field is *synchronized* to the increasing kinetic energy of the particles. The concept of synchrotron facilitated and enabled the building of large scale research facilities to study particles in greater detail. Bending, beam focusing and acceleration can be separated into different components. The most powerful modern particle accelerators use versions of the basic synchrotron design. The largest synchrotrontype accelerator, also the largest particle accelerator in the world, is the 27-kilometer-circumference (17 mi) Large Hadron Collider (LHC) near Geneva, Switzerland, built in 2008 by the European Organization for Nuclear Research (CERN). It can

accelerate beams of protons to an energy of 6.5 teraelectronvolts (TeV).

tion, injection, bending and focusing and final ejection.

have been practically applied in research have been explained.

structure and stability.

seeding (b).

**7. Conclusions**

**Acknowledgements**

The block diagram shown in **Figure 19** shows the particles subjected to accelera-

**Figure 20** reports a typical synchrotron infrared spectrum of a metakaolinbased geopolymer. Information on the Si/Al-O bonds can be deduced from this spectrum. Homogeneity of distribution can be determined using this data. This is an important result with consequences in geopolymer mix design for optimal gel

Synchrotron infrared microscopy data for geothermal silica-sodium aluminate geopolymer binders have been generated by John L. Provis et al. **Figure 21** shown above gives the synchrotron peaks without (a) and with 0.5 wt.% nano-Al2O3

A summary of selected characterization techniques that have been used to study geopolymers has been given in this chapter. Some concrete examples of research work on geopolymers have been reviewed, and characterization techniques that

I thank Intech Publishers for motivating me to write this chapter. I thank the management, SSN College of Engineering, for the academic freedom given. I thank all members of my family for their encouragement and help offered at various

*DOI: http://dx.doi.org/10.5772/intechopen.82208*

*Summary of Some Selected Characterization Methods of Geopolymers DOI: http://dx.doi.org/10.5772/intechopen.82208*

A **synchrotron** is a special type of cyclic particle accelerator. It is a modified form of cyclotron, in which the accelerating particle beam travels around a fixed closed-loop path. The magnetic field bends the particle beam into a closed path. This magnetic field increases with time during the accelerating process. The increased magnetic field is *synchronized* to the increasing kinetic energy of the particles. The concept of synchrotron facilitated and enabled the building of large scale research facilities to study particles in greater detail. Bending, beam focusing and acceleration can be separated into different components. The most powerful modern particle accelerators use versions of the basic synchrotron design. The largest synchrotrontype accelerator, also the largest particle accelerator in the world, is the 27-kilometer-circumference (17 mi) Large Hadron Collider (LHC) near Geneva, Switzerland, built in 2008 by the European Organization for Nuclear Research (CERN). It can accelerate beams of protons to an energy of 6.5 teraelectronvolts (TeV).

The block diagram shown in **Figure 19** shows the particles subjected to acceleration, injection, bending and focusing and final ejection.

**Figure 20** reports a typical synchrotron infrared spectrum of a metakaolinbased geopolymer. Information on the Si/Al-O bonds can be deduced from this spectrum. Homogeneity of distribution can be determined using this data. This is an important result with consequences in geopolymer mix design for optimal gel structure and stability.

Synchrotron infrared microscopy data for geothermal silica-sodium aluminate geopolymer binders have been generated by John L. Provis et al. **Figure 21** shown above gives the synchrotron peaks without (a) and with 0.5 wt.% nano-Al2O3 seeding (b).

#### **7. Conclusions**

*Geopolymers and Other Geosynthetics*

**34**

**Figure 21.**

**Figure 20.**

*Synchrotron infrared microscopy of metakaolin-based geopolymer [21].*

*Synchrotron peaks (a) without and (b) with seeding [21] already given as [21].*

A summary of selected characterization techniques that have been used to study geopolymers has been given in this chapter. Some concrete examples of research work on geopolymers have been reviewed, and characterization techniques that have been practically applied in research have been explained.

#### **Acknowledgements**

I thank Intech Publishers for motivating me to write this chapter. I thank the management, SSN College of Engineering, for the academic freedom given. I thank all members of my family for their encouragement and help offered at various times.

*Geopolymers and Other Geosynthetics*