$$\mathbf{x}\_t = \begin{bmatrix} \nu\_{o,t}, \nu\_{\phi,t}, \nu\_{\psi,t}, \boldsymbol{b}\_t^{p1}, \boldsymbol{b}\_t^{p2}, \dots, \boldsymbol{b}\_t^{pm} \end{bmatrix}^T \tag{52}$$

Thus the state-vector xt contains three TEC parameters νo,t, νϕ,t, νψ,t and ð Þ m � 1 betweensatellite DCBs b ps t (s 6¼ p). The dynamic model is assumed to be given by (cf. 2, 4 and 21)

Would one, for instance, make use of the very precise carrier-phase measurements and/or formulate a multi-GNSS measurement setup, solutions of higher precision levels are therefore

Figure 8. A network of 13 GNSS receivers simulated over Perth, Western Australia. Each node (white circle) represents a receiver tracking GNSS satellites. The receivers have data links to their neighbors with inter-station distances up to 4 km.

from satellite s at time instance t. The corresponding measurement model, formed by the

i,t;ϕ νϕ,t þ a

between-satellite differential code biases (DCBs). We use a regional single-layer model [45, 46] to represent the slant ionospheric delays in terms of 1) νo,t as the vertical total electron content (TEC), 2) νϕ,t and 3) νψ,t as the south-to-north and west-to-east spatial gradient of νo,t, respec-

> i,t � ϕo,t � �

the ionospheric piercing points (IPPs) corresponding to the receiver-to-satellite line-of-sight

n o

ps

where the term within f g: refers to the first-order slant ionospheric delays, and b

i,t and ϕs i,t

i,t denote the pseudo-range ionospheric observable that the receiver i collects

i,t ð Þ s 6¼ p , reads (cf. 5)

ps i,t;ψ νψ,t

, as

� b ps t þ ε ps

i,t;ψ ¼ 1

cos zs i,t � � cos ϕs

, respectively, denote the longitude and latitude of

i,t (49)

ps

i,t � �

ψs i,t � ψo,t � �

t denotes the

(50)

expected.

as

i,t; o ¼ 1

cos zs i,t � � , as

with ð Þ: ps≔ð Þ: s � ð Þ: p

Let the scalar ys

between-satellite differences y

The data links are shown by red lines.

294 Kalman Filters - Theory for Advanced Applications

ps i,t ≔ys i,t � y p

tively. The corresponding known coefficients follow from Ref. [47]

cos zs i,t � � ϕs

i,t;ϕ ¼ 1

. The angles ψs

y ps i,t ¼ a ps i,t; o νo,t þ a

$$
\begin{bmatrix} \boldsymbol{\nu}\_{\boldsymbol{\phi},t} \\ \boldsymbol{\nu}\_{\boldsymbol{\phi},t} \\ \boldsymbol{\nu}\_{\boldsymbol{\psi},t} \end{bmatrix} = \begin{bmatrix} \boldsymbol{\nu}\_{\boldsymbol{\phi},t-1} \\ \boldsymbol{\nu}\_{\boldsymbol{\phi},t-1} \\ \boldsymbol{\nu}\_{\boldsymbol{\psi},t-1} \end{bmatrix} + \begin{bmatrix} \boldsymbol{d}\_{\boldsymbol{\phi}} \\ \boldsymbol{d}\_{\boldsymbol{\phi}} \\ \boldsymbol{d}\_{\boldsymbol{\psi}} \end{bmatrix}, \quad \text{and} \quad \boldsymbol{b}\_{t}^{\rm ps} = \boldsymbol{b}\_{t-1}^{\rm ps} \quad s \neq p \tag{53}
$$

Thus the DCBs b ps t are assumed constant in time, while the temporal behavior of the TEC parameters νo,t, νϕ,t, νψ,t is captured by a random-walk process. The corresponding zero-mean

Figure 9. Longitude (ψ) and latitude (ϕ) of an ionospheric piercing point (IPP) corresponding to a receiver-to-satellite line-of-sight. The distance scales are exaggerated.

process noises are assumed to be mutually uncorrelated, having the standard-deviations σdo ¼ 1 mm/ ffiffiffiffiffiffi sec p and σdϕ ¼ σdpsi ¼ 5 mm/rad/ ffiffiffiffiffiffi sec p [49].

5.3. Central (network-based) versus local (single-receiver) solutions

of the local TEC solutions are ffiffiffiffiffi

5.4. Role of CKF in improving local solutions

Before discussing the precision performance of the CKF solutions, we first compare the networkbased (central) TEC solutions with the solutions that are obtained by the data of one singlereceiver only (referred to as the local solutions). At the filter initialization, the standard-deviations

square-root of the number of nodes). This is because of the fact that each of the 13 network receivers independently provides equally precise solutions. In that case, the central solution follows then by averaging all the 13 local solutions. Due to the common dynamic model Eq. (53) however, the local solutions become correlated over time. After the filter initialization, the central solution would therefore not follow the average of its local versions. The standard-deviation results, after one hour of the filter initialization, are presented in Figure 11. Only the results of the receiver 1 are shown as local solutions (in red). As shown, the standard-deviations get stable over time as the filters reach their steady-state. On the right panel of the figure, the local-to-central standard-deviation ratios are also presented. In case of the vertical TECs νo,t, the ratios vary from 1.5 to 3. For the horizontal gradients νϕ,t and νψ,t, the ratios are about 2 and 2.5, respectively.

With the results of Figure 11, we observed that the central TEC solutions considerably outperform their local counterparts in the sense of delivering more precise outcomes, i.e. the local-to-central standard-deviation ratios are considerably larger than 1. We now employ the

Figure 11. Left: Standard-deviation of the central (green) and local (red) solutions of the TEC parameters νo,t (top), νϕ,t (middle) and νψ,t (bottom) as functions of time. Right: The corresponding local-to-central standard-deviation ratios.

13 p ≈ 3:6 times larger than those of the central TEC solutions (i.e.

Consensus-Based Distributed Filtering for GNSS http://dx.doi.org/10.5772/intechopen.71138 297

#### 5.2. Observational campaign

The network receivers i ¼ 1, …, n (n ¼ 13), shown in Figure 8, are assumed to track GPS satellites over 16 hours from 8:00 to 24:00 Perth local time, on 02-06-2016. The observation sampling rate is set to Δ ¼ 1 minute. Thus the number of observational epochs (time instances) is 960. As to the data sending rate δ (cf. 5), we assume three different sending rates δ ¼ 5, 10 and 15 seconds. Thus the number of iterative communications between the neighboring receivers takes the values kn ¼ 4, 6 and 12. The consensus protocol 3 (Table 1) is applied to the CKF of each receiver.

As the satellites revolve around the Earth, not all of which are simultaneously visible to the 'small-scale' network of Figure 8. Their visibility over time is shown in Figure 10 (left panel) in which the satellites with elevation angles smaller than 10 degrees are excluded. There are 31 GPS satellites (i.e. m ¼ 31), with PRN 4 absent (PRN refers to the satellite identifier). PRN 22 has the maximum duration of visibility, while PRN 21 has the minimum duration of visibility. Note also that PRNs 2, 6, 16, 17, 19, 26 and 32 disappear (set) and reappear (re-rise). That is why their visibility is shown via two separate time intervals. Figure 10 (right panel) shows the trajectories of the ionospheric pierce points on the ionospheric single layer that are made by receiver-to-satellite line-of-sight paths. It is the spatial distribution of these points that drives the coefficients a ps i,t; o, a ps i,t;ϕ, a ps i,t;ψ in Eq. (49).

In the following we present precision analyses on the measurement update solutions of xt in Eq. (52), given the network and satellite configurations shown in Figures 8 and 10, respectively. Throughout the text, PRN 10 is chosen as the pivot satellite p (cf. (49)). By the term 'standard-deviation', we mean the square-root of prediction errors' variance.

Figure 10. Left: GPS satellites visibility over time, viewed from Perth, Western Australia. Right: Trajectories of the corresponding IPPs made by receiver-to-satellite line-of-sight paths. The satellites are indicated by different colors.

#### 5.3. Central (network-based) versus local (single-receiver) solutions

Before discussing the precision performance of the CKF solutions, we first compare the networkbased (central) TEC solutions with the solutions that are obtained by the data of one singlereceiver only (referred to as the local solutions). At the filter initialization, the standard-deviations of the local TEC solutions are ffiffiffiffiffi 13 p ≈ 3:6 times larger than those of the central TEC solutions (i.e. square-root of the number of nodes). This is because of the fact that each of the 13 network receivers independently provides equally precise solutions. In that case, the central solution follows then by averaging all the 13 local solutions. Due to the common dynamic model Eq. (53) however, the local solutions become correlated over time. After the filter initialization, the central solution would therefore not follow the average of its local versions. The standard-deviation results, after one hour of the filter initialization, are presented in Figure 11. Only the results of the receiver 1 are shown as local solutions (in red). As shown, the standard-deviations get stable over time as the filters reach their steady-state. On the right panel of the figure, the local-to-central standard-deviation ratios are also presented. In case of the vertical TECs νo,t, the ratios vary from 1.5 to 3. For the horizontal gradients νϕ,t and νψ,t, the ratios are about 2 and 2.5, respectively.

#### 5.4. Role of CKF in improving local solutions

process noises are assumed to be mutually uncorrelated, having the standard-deviations

The network receivers i ¼ 1, …, n (n ¼ 13), shown in Figure 8, are assumed to track GPS satellites over 16 hours from 8:00 to 24:00 Perth local time, on 02-06-2016. The observation sampling rate is set to Δ ¼ 1 minute. Thus the number of observational epochs (time instances) is 960. As to the data sending rate δ (cf. 5), we assume three different sending rates δ ¼ 5, 10 and 15 seconds. Thus the number of iterative communications between the neighboring receivers takes the values kn ¼ 4, 6 and 12. The consensus protocol 3 (Table 1) is applied to the CKF of each receiver. As the satellites revolve around the Earth, not all of which are simultaneously visible to the 'small-scale' network of Figure 8. Their visibility over time is shown in Figure 10 (left panel) in which the satellites with elevation angles smaller than 10 degrees are excluded. There are 31 GPS satellites (i.e. m ¼ 31), with PRN 4 absent (PRN refers to the satellite identifier). PRN 22 has the maximum duration of visibility, while PRN 21 has the minimum duration of visibility. Note also that PRNs 2, 6, 16, 17, 19, 26 and 32 disappear (set) and reappear (re-rise). That is why their visibility is shown via two separate time intervals. Figure 10 (right panel) shows the trajectories of the ionospheric pierce points on the ionospheric single layer that are made by receiver-to-satellite line-of-sight paths. It is the spatial distribution of these points that drives

In the following we present precision analyses on the measurement update solutions of xt in Eq. (52), given the network and satellite configurations shown in Figures 8 and 10, respectively. Throughout the text, PRN 10 is chosen as the pivot satellite p (cf. (49)). By the term

Figure 10. Left: GPS satellites visibility over time, viewed from Perth, Western Australia. Right: Trajectories of the corresponding IPPs made by receiver-to-satellite line-of-sight paths. The satellites are indicated by different colors.

sec p [49].

sec p and σdϕ ¼ σdpsi ¼ 5 mm/rad/ ffiffiffiffiffiffi

i,t;ψ in Eq. (49).

'standard-deviation', we mean the square-root of prediction errors' variance.

σdo ¼ 1 mm/ ffiffiffiffiffiffi

the coefficients a

ps i,t; o, a ps i,t;ϕ, a ps

5.2. Observational campaign

296 Kalman Filters - Theory for Advanced Applications

With the results of Figure 11, we observed that the central TEC solutions considerably outperform their local counterparts in the sense of delivering more precise outcomes, i.e. the local-to-central standard-deviation ratios are considerably larger than 1. We now employ the

Figure 11. Left: Standard-deviation of the central (green) and local (red) solutions of the TEC parameters νo,t (top), νϕ,t (middle) and νψ,t (bottom) as functions of time. Right: The corresponding local-to-central standard-deviation ratios.

CKF for each node (receiver) i ¼ 1, …, 13, to improve the local solutions' precision performance via consensus-based iterative communications between the receivers. In doing so, we make use of the three-step recursion given in Figure 7 to evaluate the error variance matrices Pi,t∣t (i ¼ 1, …, 13), thereby computing the CKF-to-central standard-deviation ratios. The stated ratios are presented in Figure 12 for two different data sending rates δ ¼ 15 seconds (left panel) and δ ¼ 5 seconds (right panel). In both cases, the CKF-to-central standard-deviation ratios are smaller than their local-to-central versions shown in Figure 11 (right panel), illustrating that employing the CKF does indeed improve the local solutions' precision. Since more iterative communications take place for δ ¼ 5, the corresponding ratios are very close to 1. In that case, the CKF of each receiver is expected to have a similar precision performance to that of the central (network-based) filter. For the case δ ¼ 15 however, the CKF performance of each receiver does very much depend on the number of the receiver's neighbors. This is because of the fact that only 4 iterative communications between the receivers take place (i.e. kn ¼ 4). The receivers with the minimum number of neighbors, i.e. receivers 1, 3 and 13 (Figure 8), have the worst precision performance as the corresponding ratios take largest values. On the other hand, the receivers with the maximum number of neighbors, i.e. receivers 4, 7, 9 and 8, have the best performance as the corresponding ratios are close to 1.

visible, the smaller the standard-deviation is expected. We now consider the required time to have between-satellite DCBs solutions with standard-deviation smaller than 0.5 nanoseconds. Because of the stated difference in the standard-deviations, each between-satellite DCB corresponds to a different required time. For the central filter, the minimum value of such required time is 7 minutes, with the 25th percentile as 12, median as 38, 75th percentile as 63 and the maximum as 84 minutes. Thus after 84 minutes of the filter initialization, all central DCB solutions have standard-deviations smaller than 0.5 nanoseconds. Such percentiles can be represented by a 'boxplot'. We compute the stated percentiles for all the CKF solutions and compare their boxplots with the central one in Figure 13. The results are presented for three

Consensus-Based Distributed Filtering for GNSS http://dx.doi.org/10.5772/intechopen.71138 299

Figure 13. Boxplots of the required time (minutes) to have between-satellite DCBs solutions with standard-deviation smaller than 0.5 nanoseconds. The performance of the CKF of each node (receiver) is compared to that of the central filter (Cen.). In each boxplot, the horizontal lines from bottom to top show the minimum (black), 25th percentile (blue), median (green), 75th percentile (blue) and maximum (black) of the stated required time. The data sending rate is set to Top: δ ¼ 15 seconds (i.E. 4 iterative communications), Middle: δ ¼ 10 seconds (i.E. 6 iterative communications), and Bottom: δ ¼ 5

seconds (i.E. 12 iterative communications).

Next to the solutions of the TEC parameters νo,t, νϕ,t and νψ,t, we also analyze CKF solutions of the between-satellite DCBs b ps t (s 6¼ p) in Eq. (52). Because of the difference in the satellites visibility over time (cf. Figure 10), the DCBs' standard-deviations are quite distinct and very much depend on the duration of the satellites visibility. The more a pair of satellites p � s are

Figure 12. Time-series of the CKF-to-central standard-deviation ratios corresponding to the TEC parameters νo,t (top), νϕ,t (middle) and νψ,t (bottom). Left: The data sending rate is set to δ ¼ 15 seconds (i.E. 4 iterative communications). Right: The data sending rate is set to δ ¼ 5 seconds (i.E. 12 iterative communications). The results of the nodes (receivers) are indicated by different colors.

visible, the smaller the standard-deviation is expected. We now consider the required time to have between-satellite DCBs solutions with standard-deviation smaller than 0.5 nanoseconds. Because of the stated difference in the standard-deviations, each between-satellite DCB corresponds to a different required time. For the central filter, the minimum value of such required time is 7 minutes, with the 25th percentile as 12, median as 38, 75th percentile as 63 and the maximum as 84 minutes. Thus after 84 minutes of the filter initialization, all central DCB solutions have standard-deviations smaller than 0.5 nanoseconds. Such percentiles can be represented by a 'boxplot'. We compute the stated percentiles for all the CKF solutions and compare their boxplots with the central one in Figure 13. The results are presented for three

CKF for each node (receiver) i ¼ 1, …, 13, to improve the local solutions' precision performance via consensus-based iterative communications between the receivers. In doing so, we make use of the three-step recursion given in Figure 7 to evaluate the error variance matrices Pi,t∣t (i ¼ 1, …, 13), thereby computing the CKF-to-central standard-deviation ratios. The stated ratios are presented in Figure 12 for two different data sending rates δ ¼ 15 seconds (left panel) and δ ¼ 5 seconds (right panel). In both cases, the CKF-to-central standard-deviation ratios are smaller than their local-to-central versions shown in Figure 11 (right panel), illustrating that employing the CKF does indeed improve the local solutions' precision. Since more iterative communications take place for δ ¼ 5, the corresponding ratios are very close to 1. In that case, the CKF of each receiver is expected to have a similar precision performance to that of the central (network-based) filter. For the case δ ¼ 15 however, the CKF performance of each receiver does very much depend on the number of the receiver's neighbors. This is because of the fact that only 4 iterative communications between the receivers take place (i.e. kn ¼ 4). The receivers with the minimum number of neighbors, i.e. receivers 1, 3 and 13 (Figure 8), have the worst precision performance as the corresponding ratios take largest values. On the other hand, the receivers with the maximum number of neighbors, i.e. receivers 4, 7, 9 and 8, have

Next to the solutions of the TEC parameters νo,t, νϕ,t and νψ,t, we also analyze CKF solutions of

visibility over time (cf. Figure 10), the DCBs' standard-deviations are quite distinct and very much depend on the duration of the satellites visibility. The more a pair of satellites p � s are

Figure 12. Time-series of the CKF-to-central standard-deviation ratios corresponding to the TEC parameters νo,t (top), νϕ,t (middle) and νψ,t (bottom). Left: The data sending rate is set to δ ¼ 15 seconds (i.E. 4 iterative communications). Right: The data sending rate is set to δ ¼ 5 seconds (i.E. 12 iterative communications). The results of the nodes (receivers) are

t (s 6¼ p) in Eq. (52). Because of the difference in the satellites

the best performance as the corresponding ratios are close to 1.

ps

the between-satellite DCBs b

298 Kalman Filters - Theory for Advanced Applications

indicated by different colors.

Figure 13. Boxplots of the required time (minutes) to have between-satellite DCBs solutions with standard-deviation smaller than 0.5 nanoseconds. The performance of the CKF of each node (receiver) is compared to that of the central filter (Cen.). In each boxplot, the horizontal lines from bottom to top show the minimum (black), 25th percentile (blue), median (green), 75th percentile (blue) and maximum (black) of the stated required time. The data sending rate is set to Top: δ ¼ 15 seconds (i.E. 4 iterative communications), Middle: δ ¼ 10 seconds (i.E. 6 iterative communications), and Bottom: δ ¼ 5 seconds (i.E. 12 iterative communications).

different data sending rates δ ¼ 15 seconds (top), δ ¼ 10 seconds (middle) and δ ¼ 5 seconds (bottom). As shown, the more the number of iterative communications, the more similar the boxplots becomes, i.e. the nodes (receivers) are reaching consensus. Similar to the TEC solutions, the DCB precision performance of the CKF corresponding to the receivers 4, 7, 9 and 8 is almost similar to that of the central one, irrespective of the number of iterative communications. This follows from the fact that the stated receivers have the maximum number of neighbors (Figure 8), thus efficiently approximating the averages Nt and rt in Eq. (28) after a few iterations. On the other hand, the receivers with the minimum number of neighbors require more number of iterative communications in order for their CKF precision performance to get similar to that of the central filter.

Author details

Amir Khodabandeh1

References

\*, Peter J.G. Teunissen1,2 and Safoora Zaminpardaz1

Consensus-Based Distributed Filtering for GNSS http://dx.doi.org/10.5772/intechopen.71138 301

[1] Cattivelli FS, Sayed AH. Diffusion strategies for distributed Kalman filtering and smooth-

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lite Systems. Switzerland: Springer; 2017

\*Address all correspondence to<EMAIL_ADDRESS>
1 Curtin University of Technology, Australia

2 Delft University of Technology, The Netherlands

### 6. Concluding remarks and future outlook

In this contribution we reviewed Kalman filtering in its information form and showed how the additive measurement update (28) can be realized by employing average-consensus rules, even when not all nodes are directly connected, thus allowing the sensor nodes to develop their own distributed filters. The nodes are assumed linked to each other at least through a 'path' so that information can flow from each node to all other nodes. Under this assumption, average-consensus protocols can deliver consensus states ½ � Ni,tð Þ kn ;ri,tð Þ kn as an approximation of the averages Nt;rt in Eq. (28) at every time instance t ¼ 1, 2, …, thus allowing one to establish a CKF recursion at every node i ¼ 1, …, n. To improve the stated approximation, the neighboring nodes have to establish a number of iterative data communications to transfer and receive their consensus states. This makes the CKF implementation applicable only for the applications in which the state-vectors change rather slowly over time (i.e. the sampling rate Δ can take large values) and/or for the cases where the sensor nodes transfer their data rather quickly (i.e. the sending rate δ can take small values).

We developed a three-step recursion of the CKF error variance matrix (Figure 7). This recursion conveys useful information about the precision performance of the local filters i ¼ 1, …, n, thereby enabling one to a-priori design and analyze sensor networks with different numbers of iterative communications. As an illustrative example, we applied the stated recursion to a small-scale network of GNSS receivers and showed the role taken by the CKF in improving the precision of the solutions at each single receiver. In near future the proliferation of low-cost receivers will give rise to an increase in the number of GNSS users. Employing the CKF or other distributed filtering techniques, GNSS users can therefore potentially deliver highprecision parameter solutions without the need of having a computing center.

### Acknowledgements

The second author is the recipient of an Australian Research Council Federation Fellowship (project number FF0883188). This support is gratefully acknowledged.

### Author details

different data sending rates δ ¼ 15 seconds (top), δ ¼ 10 seconds (middle) and δ ¼ 5 seconds (bottom). As shown, the more the number of iterative communications, the more similar the boxplots becomes, i.e. the nodes (receivers) are reaching consensus. Similar to the TEC solutions, the DCB precision performance of the CKF corresponding to the receivers 4, 7, 9 and 8 is almost similar to that of the central one, irrespective of the number of iterative communications. This follows from the fact that the stated receivers have the maximum number of neighbors (Figure 8), thus efficiently approximating the averages Nt and rt in Eq. (28) after a few iterations. On the other hand, the receivers with the minimum number of neighbors require more number of iterative communications in order for their CKF precision performance to get

In this contribution we reviewed Kalman filtering in its information form and showed how the additive measurement update (28) can be realized by employing average-consensus rules, even when not all nodes are directly connected, thus allowing the sensor nodes to develop their own distributed filters. The nodes are assumed linked to each other at least through a 'path' so that information can flow from each node to all other nodes. Under this assumption, average-consensus protocols can deliver consensus states ½ � Ni,tð Þ kn ;ri,tð Þ kn as an approxima-

establish a CKF recursion at every node i ¼ 1, …, n. To improve the stated approximation, the neighboring nodes have to establish a number of iterative data communications to transfer and receive their consensus states. This makes the CKF implementation applicable only for the applications in which the state-vectors change rather slowly over time (i.e. the sampling rate Δ can take large values) and/or for the cases where the sensor nodes transfer their data rather

We developed a three-step recursion of the CKF error variance matrix (Figure 7). This recursion conveys useful information about the precision performance of the local filters i ¼ 1, …, n, thereby enabling one to a-priori design and analyze sensor networks with different numbers of iterative communications. As an illustrative example, we applied the stated recursion to a small-scale network of GNSS receivers and showed the role taken by the CKF in improving the precision of the solutions at each single receiver. In near future the proliferation of low-cost receivers will give rise to an increase in the number of GNSS users. Employing the CKF or other distributed filtering techniques, GNSS users can therefore potentially deliver high-

The second author is the recipient of an Australian Research Council Federation Fellowship

precision parameter solutions without the need of having a computing center.

(project number FF0883188). This support is gratefully acknowledged.

in Eq. (28) at every time instance t ¼ 1, 2, …, thus allowing one to

similar to that of the central filter.

300 Kalman Filters - Theory for Advanced Applications

tion of the averages Nt;rt

Acknowledgements

6. Concluding remarks and future outlook

quickly (i.e. the sending rate δ can take small values).

Amir Khodabandeh1 \*, Peter J.G. Teunissen1,2 and Safoora Zaminpardaz1

\*Address all correspondence to<EMAIL_ADDRESS>

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304 Kalman Filters - Theory for Advanced Applications


*3.1. Spectral Local Linearization Scheme*

Let us having a system of differential equations *G* = *g*1(ξ), *g*1(ξ), ... , *gp*(ξ)satisfying the system:

$$\mathcal{L}\_{\text{j}} + \overline{\mathcal{N}}\_{\text{j}} = \mathcal{H}\_{\text{j}}, \text{ j} = 1, 2, \dots, p,\tag{18}$$

where *p* describes the number of differential equations, each H*j* is a function of ξ[*A*, *B*] and L*j*, N *j* are the linear and nonlinear components in the system, respectively.

Usually, the SLLM is an iterative approach to solve the differential equations, starts from an initial approximation *g*0, and then implements the SLLM successively, yielding the new approximations *g*1, *g*2, ..., where *Gt* = *g*1,*t*, *g*2,*t*, ... , *gp*,*t* for each *t* = 0, 1, 2. When once linearized, the nonlinear components are N 0 *j*.

For this intention, the *j*-th differential Equation (18) after the first *t* + 1 iterations can be express as

$$\left. \mathcal{L}\_{\dot{\jmath}} \right|\_{t+1} + \left. \widetilde{\mathcal{N}}\_{\dot{\jmath}} \right|\_{t+1} = \mathcal{H}\_{\dot{\jmath}}.\tag{19}$$

0

The nonlinear components can be linearized by using Taylor series

$$\left.\overline{\mathbf{N}}\_{\dot{l}}\right|\_{t+1} = \left.\overline{\mathbf{N}}\_{\dot{l}}\right|\_{t} + \nabla \overline{\mathbf{N}}\_{\dot{l}}\Big|\_{t} [V\_{t+1} - V\_{t}]\_{\prime} \tag{20}$$

where *Vt* is an n-tuple of *Gj*,*t* and its differentials. Now using Equations (19) and (20) in Equation (18), it becomes

$$\left. \mathcal{L}\_{\dot{j}} \right|\_{t+1} + \nabla \widetilde{\mathbf{N}}\_{\dot{j}} \Big|\_{t} V\_{t+1} = \left. \mathcal{H}\_{\dot{j}} + \nabla \widetilde{\mathbf{N}}\_{\dot{j}} \right|\_{t} V\_{t} - \widetilde{\mathbf{N}}\_{\dot{j}} \Big|\_{t}. \tag{21}$$


Image Sharpness-Based System Design for Touchless Palmprint Recognition

*Xu Liang, Zhaoqun Li, Jinyang Yang and David Zhang*

### **Abstract**

Currently, many palmprint acquisition devices have been proposed, but how to design the systems are seldom studied, such as how to choose the imaging sensor, the lens, and the working distance. This chapter aims to find the relationship between image sharpness and recognition performance and then utilize this information to direct the system design. In this chapter, firstly, we introduce the development of recent palmprint acquisition systems and abstract their basic frameworks to propose the key problems needed to be solved when designing new systems. Secondly, the relationship between the palm distance in the field of view (FOV) and image pixels per inch (PPI) is studied based on the imaging model. Suggestions about how to select the imaging sensor and camera lens are provided. Thirdly, image blur and depth of focus (DOF) are taken into consideration; the recognition performances of the image layers in the Gaussian scale space are analyzed. Based on this, an image sharpness range is determined for optimal imaging. The experiment results are obtained using different algorithms on various touchless palmprint databases collected using different kinds of devices. They could be references for new system design.

**Keywords:** palmprint recognition, system design, image sharpness assessment, scale space, field of view, depth of focus

#### **1. Introduction**

Biometric identification has been widely applied in modern society, such as electronic payment, entrance control, and forensic identification. As a reliable solution for identity authentication, biological characteristics refer to the inherent physiological or behavioral characteristics of the human body, including the iris, pattern, retina, palmprint, fingerprint, face and also voiceprint, gait, signature, key strength, etc. In the last decade, we have witnessed the successful employment of recognition systems using fingerprint, iris, and face. With the development of image capture devices and recognition algorithms, palmprint recognition receives more and more attention recently. Palmprint image contains principal lines, wrinkles, ridges, and texture that are regarded as useful features for palmprint representation and can be captured with a low-resolution image [1]. Palmprint recognition has several advantages compared with other biometrics: (1) the line features and texture features in a palmprint are discriminative and robust, which

can be easily fused with other hand features (dorsal hand vein, fingerprint, finger knuckle); (2) the pattern of palmprint is mainly controlled by genetic genes, when combined with palm vein information it can achieve high antispoof capability; (3) palmprint image acquisition is convenient and low-cost, and a relative lowresolution camera and a light source are sufficient to acquire the images; (4) the palmprint acquisition is hygienic and user friendly in the real applications. Based on

the custom acquisition devices, more information can be retrieved in a multispectral image or 3D palmprint image. A 2D gray scale palmprint example with feature definitions is shown in **Figure 1**. The purpose of this chapter is to review recent research on palmprint acquisition systems to trace the development of palmprint recognition-based biometric systems. In this chapter, we coarsely divide the devices into three types by acquisition mode: touch-based devices, touchless devices, and portable devices. Touch-based devices usually have pegs to constrain the hand pose and position, which can capture the details of palmprint to the most extent. The illuminating environment is also stable during capturing process. These constrains ensure the captured palmprint images to be high quality. For touchless devices, users can freely place their palms in front of the camera while the hand pose is generally required to spread out the fingers. The environment during the capturing process becomes more complicated, especially the illumination. There are also datasets composed of palmprint images captured in a relatively free fashion. These images may be collected on the Internet which we will not discuss here. Otherwise, collectors use digital cameras or phone cameras to capture palmprint image, and usually, there are no strict conditions forced on the user. In the rest of this chapter, first, we will introduce the representative palmprint acquisition devices, and then study the relationship between the palm distance, image sharpness, hardware parameters, and the final recognition performance. **Table 1** summarizes the

*Image Sharpness-Based System Design for Touchless Palmprint Recognition*

*DOI: http://dx.doi.org/10.5772/intechopen.92828*

Reference [1] is a pioneer work for palmprint acquisition and recognition that builds the first large-scale public palmprint dataset. The captured palmprint images are low-resolution with 75 pixels per inch (PPI), so that the whole process can be completed in 1 s, which achieves real-time palmprint identification. The palmprint capture device includes a ring light source, charge-coupled device (CCD) camera, a frame grabber, and an analog-to-digital (AD) converter. Six pegs are serving as control points that constrain the user's hands. To guarantee the image quality, during palmprint image capturing, the device environment is semiclosed, and the ring source provides uniform lighting conditions. After capturing the palmprint, the AD converter directly transmits the captured images by the CCD camera to a computer. The well-designed acquisition system can capture high-quality images, which boosts the performance of the identification algorithm. The experiment result also demonstrates that low-resolution palmprint can achieve efficient person identification. Our palms are not pure planes, and many personal characteristics lie on the palm surface. From this view, 2D palmprint recognition has some inherent drawbacks. On one hand, much 3D depth information is neglected in 2D imaging. The main features in 2D palmprint are line features including principal lines and wrinkles, which is not robust to the illumination variations and contamination influence. On the other hand, the 2D palmprint image is easy to be counterfeited so that the anti-forgery ability of 2D palmprint needs improvement. For capturing depth information in palmprint, [4, 14] explores a 3D palmprint acquisition system that leverages the structured light imaging technique. Compared to 2D palmprint images, several unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted in 3D images. Many studies have proposed different algorithms that encode the line features on the palm surface; however, the discriminative and antispoof capability of palm code needs to be further improved for

palmprint acquisition devices.

**2.1 Touch-based devices**

**69**

**2. The current palmprint recognition devices**

**Figure 1.** *Palmprint images and feature definitions.*


*Systematic*  **A Review on the Bioactivity of Cannabinoids on Zebrafish Models: Emphasis on Neurodevelopment**

**Rosario Licitra 1, Maria Marchese 1,\*, Valentina Naef 1, Asahi Ogi 1, Marco Martinelli 2, Claudia Kiferle 2, Baldassare Fronte 3 and Filippo Maria Santorelli 1,\***

**Abstract:** For centuries, the cannabis plant has been used as a source of food, fiber, and medicine. Recently, scientific interest in cannabis has increased considerably, as its bioactive compounds have shown promising potential in the treatment of numerous musculoskeletal and neurological diseases in humans. However, the mechanisms that underlie its possible effects on neurodevelopment and nervous-system functioning remain poorly understood and need to be further investigated. Although the bulk of research on cannabis and cannabinoids is based on in vitro or rodent models, the zebrafish has now emerged as a powerful in vivo model for drug-screening studies and translational research. We here review the available literature on the use of cannabis/cannabinoids in zebrafish, and particularly in zebrafish models of neurological disorders. A critical analysis suggests that zebrafish could serve as an experimental tool for testing the bioactivity of cannabinoids, and they could thus provide important insights into the safety and efficacy of different cannabis-extractbased products. The review showed that zebrafish exhibit similar behaviors to rodents following cannabinoid exposure. The authors stress the importance of analyzing the full spectrum of naturally occurring cannabinoids, rather than just the main ones, THC and CBD, and they offer some pointers on performing behavioral analysis in zebrafish.

**Keywords:** zebrafish; cannabis; cannabinoids; phytocannabinoids; Δ9-tetrahydrocannabinol; THC; cannabidiol; CBD


**5. Conclusions**

In conclusion, our study (the first study in Poland that has used reliable, internationally recognized questionnaires (PPAQ-PL and WHOQoL-Bref) makes an important contribution to the knowledge concerning the correlations between PA and QoL in women during different periods of pregnancy. The study showed that *total activity* and *total activity of light intensity and above* did not differentiate between women in the second and third trimesters of pregnancy. However, it indicated higher values of *moderate activity* in women in the second trimester of pregnancy and higher values of *sedentary activity* and *inactivity* in women in the third trimester. Our findings concerning the relationships between physical activity and quality of life should be approached with caution, due to the low values of correlation coefficients. The low MET values for sport/exercise recorded in both groups of women can indicate the need for improving the prenatal care, especially in terms of promotion of physical

activity programs for pregnant women and encouraging women to participate in these programs. In terms of intervention activities, special attention should be given to barriers existing at the level of the provider, the patient, and practice [57]. Researchers [58] have specified concrete actions that should be taken by prenatal care providers in order to promote prenatal PA, suggesting, among other things, providing information by healthcare providers about both guidelines and contraindications for the involvement in physical activity during pregnancy. Most guidelines from around the world, gathered by Evenson et al. [59], promoted moderate-intensity physical activity during pregnancy and defined its frequency and duration/time. The latest physical activity guidelines published by the U.S. Department of Health and Human Services USDHHS in 2018 [60], indicate 150 min (2 h and 30 min) of moderate-intensity aerobic activity a week during pregnancy and the postpartum period. Recommending aerobic activity should be spread throughout the week.

Undoubtedly, our study does not exhaust the problems discussed but its findings emphasize the need for raising awareness of the importance of physical activity during pregnancy. We believe that further research on a larger sample with the consideration of socio-economic factors and a comprehensive inventory of pregnancy-related symptoms, along with a mechanism for assessing their effect on function [61] is needed to provide deeper understanding and identify correlations between PA and determinants of QoL in pregnant women.

**Author Contributions:** J.K. prepared the study design, searched the literature, wrote the background and discussion of the study, gathered the necessary data, prepared it for analysis, described the results of the study and prepared the manuscript. D.S. searched the literature, refined the data, performed the statistical analysis, and described its results. E.B. searched the literature, corrected and improved the manuscript of the study for the final version. All authors have read and approved the final manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to thank all the women who participated in the study and Katarzyna Sempolska, who contributed to data acquisition.

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


**3. Translocations in insects**

The translocation of insects and other invertebrates has received considerably less attention than vertebrates; thus, not many examples of insects were translocated. However, ex situ conservation has become recognized as a more important technique for conservation for insects. With small body size, high reproductive rates, and short generation times, the insects have high potential to breed in mass captivity involving lower maintenance costs. Pearce-Kelly et al. suggested that the easy-breeding species with large captive populations have high potential for successful reintroduction programs [50]. The summary of 134 terrestrial insect translocations demonstrated that the proportion of success (52%) was higher than other animals while failed translocation programs were lower, 31% [51]. Thus, insects are the group most frequently considered in future translocations [52].

The objectives of insect translocation were classified into two groups, for conservation of the rare species and for socio-economic benefits of the flagship species. Examples of the rare insect translocation are two vulnerable crickets, *Gryllus campestris* and *Decticus verrucivorus*, in England [53–54], the threatened tiger beetle *Cicindela dorsalis dorsalis* [55], a rare damselfly *Ischnura gemina* [56], Quino checkerspot butterfly *Euphydryas editha quino* [57] and the Genji firefly *Luciola cruciata* [58] (**Table 1**). With several iterations of releasing, the released insects could establish over a period of time and produced subsequent self-sustaining populations. The failure of translocation cases were caused by small released populations, disease infection, high dispersal stage used for releasing, low quality of habitat and weather conditions when releasing. The previous study [59] analyzed the documentations of 50 reintroduction activities of butterfly species and concluded that the successful projects had a higher number of attempts (per species) (11.1 ± 11.3 times for successful and 3.5 ± 3.2 times for unsuccessful programs). Successful programs introduced at least 292 individuals per reintroduction and continued for three years. Significantly, captive breeding was recommended for reintroduction programs for almost 50% of butterfly species.

As a dominant invertebrate flagship, the translocation of butterflies could be effectively used to build public awareness using live exhibits of butterfly farms. Many exotic butterflies were large-scale bred and imported across countries and regions for exhibition. If the butterflies come from similar environmental conditions and habitats, they might have high potential to establish in the new habitats. Consequently, the unintentional translocation might happen and cause ecological

#### **Table 1.**

*Comparison of factors in some examples of rare insect translocation programs.*

#### *Firefly Translocation: A Case Study of Genetic and Behavioral Evaluation in Thailand DOI: http://dx.doi.org/10.5772/intechopen.97455*

impact [60]. The opposite effect also may result, that captive bred populations lose the ability to live in natural habitats. After breeding in captivity for 100–150 generations, the large white butterfly have developed adaptive characters to captive conditions, i.e., heavier, higher ovary mass, higher numbers of laid eggs, and smaller wings that could decrease the butterflies' ability to re-establish in the wild [61].

The firefly is also a potential flagship to stimulate conservation awareness and action to support habitats for fireflies and other sympatric invertebrates. Apparently, firefly populations have declined or become extinct in many areas due to the impact of anthropogenic activities (i.e., habitat destruction, fragmentation, pollution and urbanization). Fireflies can be used to help promote public awareness and concern for biological diversity conservation.

The history of firefly translocation probably began in Japan [58]. The famous case happened in Tatsuno, Nagano prefecture where several thousand of the non-native Genji fireflies from Shiga prefecture were released as a tourist attraction. Subsequently the variation in flashing behavior and population genetics were investigated. Although the population of Genji fireflies in Tatsuno could self-establish over 70 years in the translocated area and bring more than 100,000 tourists a year, the native populations might be destroyed or lose genetic diversity. That is the risk under environmental change in the upcoming global crisis. Later, the scientists raised awareness of the firefly conservation issue and recommended the approach of using habitat preservation instead of artificial habitat creation for tourism. The fireflies were commonly labeled as an indicator species for environmental conservation. The translocation of captive fireflies in recovering polluted environments received more attention and resulted in appearance of 540 firefly events throughout Japan.


**3. Mitochondrial dysfunction can help us to predict AD?**

activity is increased [113], suggesting an important role of the mPTP in mitochondrial injury

**Figure 2. Improving mitochondrial health as a valid therapy for AD**. (**A**) **Mitochondrial morphology therapy**. Ma‐ nipulating the processes of mitochondrial dynamics has a considerable potential for treating neurodegenerative diseas‐ es. Therapies that increase mitochondrial biogenesis and fission/fusion cycle may improve mitochondrial function and decreased oxidative stress. Mitophagy is a selective autophagy process that removes dysfunctional mitochondria and maintains adequate mitochondria quality control. Increasing PINK1‐mediated mitophagy improves mitochondrial in‐ tegrity and function. (**B**) **Mitochondrial bioenergetics therapy**. Several agents that boost bioenergetics could have effi‐ cacy in improving mitochondrial function. These compounds show neuroprotective effects, which may be a useful target for treating neurodegenerative diseases. Treatment with CoQ10, MitoQ and MitoE prevented oxidative stress; cyclosporine A, a substance that blocks the opening of mPTP, prevented mitochondrial depolarization, blocks cyto‐ chrome c release and increased superoxide dismutase activity. Drugs that mediate the activation of Nrf2 induce the

In that context, several groups have found that some compounds not only improve one aspect of mitochondrial damage but also improve several alterations at once by the activation of several pathways like nuclear factor E2‐related factor 2 (Nrf2) [10, 101, 102]. The Nfr2 and the Nrf2‐Are pathways have been studied in mitochondrial dysfunction and neurodegeneration [10, 115]. In response to oxidative stress, the Nrf2 translocate from the cytoplasm into the nucleus and activates the expression of several antioxidant genes [116]. Nrf2 is the principal regulator of the antioxidant cellular response and seems to be a promising target in the treatment of age‐related neurodegenerative diseases [10, 101, 102, 117]. Nrf2 activation induces changes in mitochondrial structure and function, which is of particular importance under conditions of oxidative stress [10, 102, 118]. In primary murine cortical cultures, neurons lacking Nrf2 are more susceptible to oxidative stress induced by H2O2 and glutamate [116, 119] and overexpression of Nrf2, totally prevented these changes [116]. Furthermore, overex‐ pression of Nrf2 can rescue neurons from mitochondrial complex II inhibition and ischemic

expression of antioxidant enzymes and improve mitochondrial function and biogenesis.

insult in animal models of Huntington disease and stroke [116, 120, 121].

in AD [113, 114].

358 Update on Dementia

In 2011, the National Institute of Aging (NIA) and the Alzheimer's Association proposed a revised criteria and new guidelines for diagnosing Alzheimer's disease [123]. They proposed three stages of progression of AD, preclinical AD, mild cognitive impairment (MCI) due to AD and dementia due to AD. Also, they incorporated the use of biomarker tests to corroborate the presence or absence of AD or the risk to develop it [124]. Biomarker tests will be essential to identify which individuals are in the early stages of the disease and if they should receive some disease‐modifying treatment. They are also critical for monitoring the effects of treatment against AD [123, 124].

AD mainly affects memory and cognitive functions and to this date, there is no early biomarker that shows the reliability and accuracy needed to diagnose the disease [125]. Currently, AD can be diagnosed with over 90% of confidence but with invasive and expensive tools based on cerebrospinal fluid (CSF) analysis and neuroimaging with positron emission tomography, with Pittsburgh compound‐B radiotracer (PET/PiB) [126]. For this reason, the diagnosis is based on neuropsychological surveys and in the exclusion of other age‐related dementias only when there is an advanced cognitive impairment [127]. The conclusive diagnosis of AD is only possible in autopsy with the presence of characteristic pathological brain lesions [125, 127].

Despite that AD early treatment can slow down the progression of the disease, the ability to diagnose AD at early stages is currently limited. In the search for potential biomarkers for early diagnosis of AD, several studies have shown that a significant number of peripheral tissues, both in animal models and patients, showed from early stages of the disease an abnormal presence of markers normally associated with nerve tissue [128].

For example, deposits of Aβ have been reported in skin, blood vessels, glandular structures and fibroblasts in human tissue [129–131], and the presence of total and phosphorylated tau protein were detected in plasma of AD and healthy patients [132, 133]. These facts suggest that the use of peripheral tissues as a source of inexpensive and minimally invasive samples is taking force in the diagnosis of AD. Interestingly, several studies have shown that there is an important relationship between the peripheral tissue in patients and animal models that develop AD and mitochondrial damage. Here, we show that AD peripheral tissues present different mitochondrial alterations that include mitochondrial defects in morphology, dynamics and bioenergetics.

#### **3.1. Evidence for mitochondrial dynamics defects in AD peripheral tissues.**

Mitochondrial dynamics is a complex cellular process that controls the shape, localization, turnover and function of mitochondria. As we previously discussed, several findings in patients and animal models of AD suggest that the deregulation of mitochondrial dynamics is a common feature in the disease, but may vary from case to case [134]. In the case of peripheral tissue of patients with AD, different studies indicated an altered mitochondrial morphology that could be related with changes in mitochondrial dynamics [21, 140, 141].

Several studies had proposed that the platelets could be a promising peripheral surrogate to detect AD [135], which is because these cell fragments express high levels of APP [136], tau protein [137, 138] and they have an increased GSK3β activity, a kinase responsible for tau hyperphosphorylation [139]. More important, in studies with cytoplasmic hybrid (cybrid) cells created from human neuroblastoma cells repopulated with mitochondria from platelets obtained from sporadic AD and control donors, it was shown that cybrid cells from AD patients contained a significantly increased percentage of enlarged or swollen mitochondria, and they also present a reduced mitochondrial membrane potential [140].

Using another blood cell component, the analysis of peripheral blood lymphocytes from AD patients showed an increase in SNO‐Drp1 and Fis1 and reduced Drp1 levels compared with healthy controls, PD patients and vascular dementia patients [141]. The protein expression pattern observed here suggests the presence of morphological alterations of mitochondria [141].

On the other hand, in a study with fibroblasts of sporadic AD patients, an abnormal mito‐ chondrial distribution characterized by elongated mitochondria that are accumulated in perinuclear areas with a significant decreased in Drp1 levels was found [21]. These findings are very relevant because several publications suggest that the basic pathogenic mechanism of amyloidogenesis is similar in brain and skin fibroblasts, with an increase in the production and depositions of Aβ [128, 142]. Therefore, a mitochondrial deregulation in the fibroblasts of AD patients could be indicative of the neurological progression of the disease [143, 144].

#### **3.2. Mitochondrial bioenergetics is altered in AD peripheral tissues.**

Evidence of a primary role for mitochondrial damage in AD development has also been provided through post‐mortem examination of AD brains, revealing oxidative stress, mito‐ chondrial DNA damage and bioenergetic deficiencies in MCI and AD patients [145–147]. In contrast, studies on peripheral tissues of AD patients have generated inconsistent findings [135, 140, 148–174].

Different studies reviewed by Cervellati's group have reported changes in the hydroperoxide levels, a biomarker of oxidative stress, in plasma and serum of AD patients [148]. In addition, these studies revealed that the levels of the oxidant damage markers, MDA and 4‐HNE, were increased in plasma and serum of AD and MCI patients compared to controls [148]. Comple‐ mentary, in blood samples of individuals with mild cognitive impairment and AD, there are evidence of mitochondrial dysfunction with decreased expression of respiratory complex genes, TOMM40, and subunits of the core mitochondrial ribosome complex [149, 150]. In addition, in human peripheral blood mononuclear cells was found an increase in oxidative stress and phosphorylated levels of Nrf2 [151].

important relationship between the peripheral tissue in patients and animal models that develop AD and mitochondrial damage. Here, we show that AD peripheral tissues present different mitochondrial alterations that include mitochondrial defects in morphology,

Mitochondrial dynamics is a complex cellular process that controls the shape, localization, turnover and function of mitochondria. As we previously discussed, several findings in patients and animal models of AD suggest that the deregulation of mitochondrial dynamics is a common feature in the disease, but may vary from case to case [134]. In the case of peripheral tissue of patients with AD, different studies indicated an altered mitochondrial morphology that could be related with changes in mitochondrial dynamics [21, 140, 141]. Several studies had proposed that the platelets could be a promising peripheral surrogate to detect AD [135], which is because these cell fragments express high levels of APP [136], tau protein [137, 138] and they have an increased GSK3β activity, a kinase responsible for tau hyperphosphorylation [139]. More important, in studies with cytoplasmic hybrid (cybrid) cells created from human neuroblastoma cells repopulated with mitochondria from platelets obtained from sporadic AD and control donors, it was shown that cybrid cells from AD patients contained a significantly increased percentage of enlarged or swollen mitochondria, and they

Using another blood cell component, the analysis of peripheral blood lymphocytes from AD patients showed an increase in SNO‐Drp1 and Fis1 and reduced Drp1 levels compared with healthy controls, PD patients and vascular dementia patients [141]. The protein expression pattern observed here suggests the presence of morphological alterations of mitochondria

On the other hand, in a study with fibroblasts of sporadic AD patients, an abnormal mito‐ chondrial distribution characterized by elongated mitochondria that are accumulated in perinuclear areas with a significant decreased in Drp1 levels was found [21]. These findings are very relevant because several publications suggest that the basic pathogenic mechanism of amyloidogenesis is similar in brain and skin fibroblasts, with an increase in the production and depositions of Aβ [128, 142]. Therefore, a mitochondrial deregulation in the fibroblasts of AD patients could be indicative of the neurological progression of the disease [143, 144].

Evidence of a primary role for mitochondrial damage in AD development has also been provided through post‐mortem examination of AD brains, revealing oxidative stress, mito‐ chondrial DNA damage and bioenergetic deficiencies in MCI and AD patients [145–147]. In contrast, studies on peripheral tissues of AD patients have generated inconsistent findings

Different studies reviewed by Cervellati's group have reported changes in the hydroperoxide levels, a biomarker of oxidative stress, in plasma and serum of AD patients [148]. In addition,

**3.1. Evidence for mitochondrial dynamics defects in AD peripheral tissues.**

also present a reduced mitochondrial membrane potential [140].

**3.2. Mitochondrial bioenergetics is altered in AD peripheral tissues.**

dynamics and bioenergetics.

360 Update on Dementia

[141].

[135, 140, 148–174].

On the other hand, several studies had shown that blood platelets from AD patients also present an increase in markers related to mitochondrial bioenergetics damage [135]. Platelets presented intracellular calcium deregulation [152, 153], an increase in oxidative damage [152, 153], a decrease in CoxIV and ATP synthase activities [154–158], and as we previously mentioned, a reduced mitochondrial potential in the cybrid condition [140]. Interestingly, in a study with cognitively normal individuals with maternal history of late onset of AD was found a reduced activity of platelet CoxIV compared to those with paternal or negative family history [159]. These findings suggest not only a possible mitochondrial peripheral biomarker but also an exclusively maternally inherited marker in humans [159].

Mitochondria isolated from AD lymphocytes showed an increase in several markers of oxidative stress [160, 161], increased susceptibility to oxidative death [162, 163], and the extent of this oxidative damage inversely correlated with dementia severity [161, 162, 164]. Also, this cell type presented alterations in proteins levels of mitochondrial‐related factors categorized as energetic, structural and antioxidants such as glyceraldehyde 3‐phosphate dehydrogenase (GAPDH), lactate dehydrogenase B chain and ATP synthase [164]. Furthermore, analysis of mitochondrial function in lymphocytes of AD patients showed a reduction in basal respiration and a lower ATP turnover that could finally lead to accumulate mutations in mitochondrial DNA [161].

Furthermore, a recent study determined that mitochondrial population, ATP production and respiratory function are altered in fibroblasts of patients with genetic type of AD [165]. While genetic forms of the disease do not account for the majority of cases, these observations marks an important precedent that directly links mitochondrial dysfunction in peripheral tissue of AD patients [165]. Also, in this cell type, mitochondrial dysfunction is associated with high levels of ROS and oxidative damage [166–168]. This alterations could be explained because of the lower levels of antioxidant defences observed in AD patients [169], and more interesting is the fact that these fibroblasts exhibit an alteration of the calcium buffering capacity compared to control cells [170, 171].

Based on that, recent studies have shown that fibroblasts of sporadic and familial AD present an enhanced link between the endoplasmic reticulum (ER) and mitochondria, through the mitochondria‐associated ER membranes structures (MAMs) [172]. This alteration in the communication between these organelles could affect the mitochondrial dynamics and function, calcium homoeostasis and production of ROS [172]. This is an interesting observa‐ tion, since a recent study showed that nanomolar concentrations of oligomeric Aβ regulated MAM and mitochondrial calcium in neuronal cells of human AD cortical tissue, as well as in AD mouse models [173]. These findings suggest that these subcellular structures are affected in AD and this would not be considered an isolated effect of fibroblasts culture.

**Figure 3. Mitochondrial impairment as a potential biomarker for early diagnosis of AD**. Diagram shows that possi‐ ble markers of mitochondrial damage could be present in blood plasma, blood cells and skin fibroblasts from AD pa‐ tients. The compressive evaluation of mitochondrial health in these tissues could early detect neurodegenerative changes reported in AD.

Interestingly, a recent study with fibroblasts from an AD patient demonstrated that is possible to induce the differentiation of dermal fibroblasts into neuronal cells [174]. This study demonstrated that those neurons derived from fibroblasts expressed significant levels of phosphorylated tau and presented significant changes in the expression of genes associated with AD [174]. These studies indicate that the fibroblasts of patients could be a reliable tool for obtaining physiological information that reflects the neurological state of the patients.

Peripheral biomarkers with effective action in the early detection of Alzheimer's pathology are currently unknown, but the evidence of possible markers of mitochondrial damage in blood plasma, blood cells and skin fibroblasts represents an important step in the search for an AD biomarker (**Figure 3**). Although the fact that these tissues may provide less invasive and inexpensive sources to investigate AD progression, the finding of a new biomarker would not only be important for early diagnosis but also be an opportunity to prove direct and person‐ alized therapies in patients with AD. Future research should focus not only in search for therapies of the disease but also in the search for a good and safe model to test the effectiveness of these pathways proposed.


**Sustainable Composite Construction Materials**

Editors

**Krishanu Roy G. Beulah Gnana Ananthi**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editors* Krishanu Roy The University of Waikato Hamilton New Zealand

G. Beulah Gnana Ananthi Anna University Chennai India

*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 *Journal of Composites Science* (ISSN 2504-477X) (available at: https://www.mdpi.com/journal/jcs/ special issues/sustainable construction composite materials).

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

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

**ISBN 978-3-0365-9754-6 (Hbk) ISBN 978-3-0365-9755-3 (PDF) doi.org/10.3390/books978-3-0365-9755-3**

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**5. Acknowledgements**

This review was supported by the National Institutes of Health grant numbers NIH-FIC (1T90-HG004151-01) for postdoctoral training in Genomics and Hemoglobinopathies, NIH/FIC/NINDS R21 and NIH-RCMI (RR033062).


**3.1 Cerebral vasculature**

Cerebral vessels play a critical role in mediating between the whole body and the brain by transporting molecules between the blood and brain [5].  One is the internal carotid artery system. This system is responsible for approximately 70% of the total CBF. The other system is the vertebral artery system, which is responsible for approximately 30% of the total CBF. These two major blood systems converge at Willis' circle, which allows communication between the left and right brain hemispheres, and branch out into the whole brain through cerebral arteries [66]. The most common structural feature of cerebral vessels is the blood–brain barrier (BBB), which comprises tight junctions and adherens junctions [5]. These tight junctions exist between endothelial cells, the basal membrane, pericytes, and the astrocyte end feet [67].

The BBB is important because it prevents harmful molecules from entering the brain tissue from the systemic circulation. Accordingly, a malfunction in BBB permeability has been reported in neurodegenerative disorders and cognitive decline that leads to dementia [68–70]. CBF is regulated in response to blood pressure through cerebral autoregulation. It was demonstrated that there were minor differences between CBF and blood pressure (within 10 mmHg of blood pressure) among healthy humans in the plateau region [71]. The report also suggested that hypertension and a higher pulsatile rate might disrupt cerebral autoregulation, which make subjects prone to neurodegenerative diseases because the aging brain is subject to hypoperfusion [71].


**1. Introduction**

Project Management is the discipline to manage, monitor and control the uncertainty inherent to projects. Project management processes are used to monitor and control the progress of projects in order to reduce the uncertainty, and each such process requires effort from the project manager and her team. The academic literature has been overwhelmed by research studies in project management and control, and many of them focus on the construction of the project baseline schedule to assess the project risk and to monitor the performance of a project in progress. The combination of these three dimensions—schedule, risk and control—is often referred to in the literature as *dynamic scheduling* [1,2] or *integrated project management and control* [3].

This paper starts with the observation that the relation between managerial effort and the ability to reduce the project uncertainty lies at the heart of many research studies, although this relation is

often not explicitly mentioned. Especially in some research papers that rely on the concept of *entropy* as a way to express that projects have the natural tendency to move to a state of disorder, authors have referred to the relation between entropy (uncertainty) and energy (effort). They have proposed different entropy measures to enable the project manager to better predict the project uncertainty and eventually reduce it by taking better actions. This concept of entropy is—to the best of the authors' knowledge—not widely used in the previously mentioned dynamic scheduling studies; however, it is believed that it sheds an interesting light on the project management domain and opens ways to look at the dynamic scheduling literature (schedule/risk/control) in a fundamentally different way.

The current study reviews the research on entropy in project management and proposes a new way to accurately estimate project uncertainty to improve project forecasting and decision-making. This paper first elaborates on the link between the traditional dynamic scheduling literature and the much less investigated concept of entropy in project management and argues that entropy is an ideal concept to measure project uncertainty. Then, it will be shown that, in order to reduce a project's entropy, forecasting and estimates are crucial for a project manager and her team to make well-informed decisions. Then, finally, a new so-called *calibration method* is proposed that should help project managers to better quantify the project uncertainty by providing better estimates for the activity durations. Such calibration procedures are relatively new in the literature, since they rely on a combination of statistical data analysis and the correction for human biases.

The paper is organized in the following sections. Section 2 reviews the most important studies on entropy for managing projects that have been used as an inspiration for the current research study. Based on this, the section also explains the basic idea of calibrating project data to better estimate project uncertainty, which constitutes the main theme of our study. In Section 3, two currently known data calibration methods from literature are then briefly reviewed, as they will be used as foundations for a new third calibration method taking the shortcomings of the existing methods into account. This new so-called *statistical partitioning heuristic* is discussed in Section 4. Section 5 presents the results of a computational experiment on a set of 83 empirical projects (mainly construction projects) from a known database. The section shows that the statistical partitioning heuristic outperforms the two other procedures, but also discusses some limitations that can be used as guidelines for future research. Finally, Section 6 draws conclusions and highlights some potentially promising future research avenues.


**Acknowledgements**

This work has received funding from the European's Union Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). This paper reflects only the author's views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein.


*2.1. Fabrication of the Sensor*

Figure 2 illustrates the fabrication process of the sensor. In the whole process, no specific semiconductor fabrication equipment was required. Ecoflex was chosen as the substrate material since it is flexible, highly stretchable and biocompatible [19–21]. Ecoflex mixture of part A and part B (Ecoflex 00-30, Smooth-On Inc., Easton, PA, USA) was prepared at a ratio of 1:1. The 15 g of mixture was poured into a petri dish to make a 1 mm thick substrate and left for 10 min at room temperature to remove air bubbles and make the surface even. The substrate was then partially cured in an oven at 60 ◦C for 3 min. Partially cured substrates enabled copper wires to be easily embedded in them (Figure 2a). Prior to the transfer step, the wire pattern was prepared using a custom-made winding mold. A copper wire with a diameter of 160 μm was wounded manually on the pillars printed by a 3D printer (Projet 3500, 3D systems, Rock Hill, SC, USA) to make a serpentine pattern (Figure 2b). The coil pattern was transferred to the top of the partially cured substrate and then 15 g of silicone rubber mixture was poured again onto the substrate with the serpentine pattern transferred (Figure 2c). It was then cured completely in the 60 ◦C oven for 10 min. The resulted coil had a single turn in a serpentine pattern embedded in silicone rubber in a thickness of 2 mm (Figure 2d). The outer dimension of the fabricated coil was 35 mm × 35 mm, the radius and angle of a single arc were 1.57 mm and 216◦, respectively, taken from the previous study to maximize the stretchability [22] (Figure 2e). Figure 3 shows the fabricated sensor in non-stretched and stretched states. When the sensor was stretched by 100% in one direction, the coil pattern was stretched by 63% in the same direction and shrunk by 30% in the orthogonal direction. The resistance and the inductance of the sensor was 1.45 ± 0.04 Ω and 192 ± 3 nH, respectively. The Young's modulus of the sensor was measured to be 40.5 ± 1.1 kPa.

**Figure 2.** Fabrication process of the wireless strain sensor: (**a**) curing the substrate partially, (**b**) patterning the copper wire in serpentine shape, (**c**) transferring the pattern and (**d**) curing the device completely. (**e**) Detailed design of the serpentine pattern of the coil.

**Figure 3.** Photographs of the wireless strain sensor in (**a**) non-stretched and (**b**) 100% stretched states.

To make the sensor resonate at a specific frequency, additional capacitors were connected in parallel to the sensor. Ceramic capacitors were used as they have a low parasitic inductance [23]. 13.56 MHz was selected as the resonating frequency of the sensor since it belongs to the industrial, scientific and medical (ISM) bands. The capacitance value to tune the sensor to resonate at 13. 56 MHz was 708 pF on average.