**3. Tumour markers**

Screening for any disease is aimed at detection of premalignant conditions or early stage disease. Cervical cancer screening is a successful programme as the progression from premalignant to malignant disease is well understood. However, until recently, precursor lesions were not recognised for ovarian cancer. Now, there is evidence to suggest that some of the high-grade serous cancers start as premalignant lesions in the fimbrial end of the fallopian tube as serous tubal intraepithelial carcinoma (STIC) [3]. Strategies to detect these premalig-

Ultrasound has been used as a screening tool to detect early malignant lesions in the ovary and fallopian tube. Features such as presence of septa, papillary projections and solid areas are used to distinguish possible malignant lesions from benign ones. The use of colour flow Doppler to detect altered blood flow as a result of neo-vascularisation has also been explored

Transvaginal ultrasonography (TVS) has long been considered a useful modality for estimating morphological factors of carcinogenesis. Non-invasiveness and ease of implementation are amongst its benefits for screening, and women generally find TVS an acceptable modality for detection [4]. Factors often used for the assessment of ovarian masses include morphology and volume analysis, but more advanced methods such as Doppler and neuronal network

There are many challenges to overcome in the utilisation of TVS as a screening modality. Variation in operator competence is one such challenge. The United Kingdom Collaborative Trial for Ovarian Cancer Screening (UKCTOCS) trial overcame this challenge by providing standardised training regimes to all sonographers. Although this could be a viable solution, there will always be variation in competence based on operator experience. For example, more experienced sonographers may be better at detecting borderline cysts than less experienced sonographers. The lack of standardised terms to describe ovarian sonographic features is another issue. The International Ovarian Tumour Analysis (IOTA) Group have created a set of recommendations to address this by setting definitions for morphological features such as

Patient acceptability of screening modality is also an essential factor to consider. In a recent study, 72.7% of women (n = 651) reported no discomfort during TVS, 23.3% of women reported some pain or discomfort and 3.5% documented moderate to severe pain during the TVS procedure. Increasing pain was attributed to history of hysterectomy and a prolonged scanning time. Interestingly, those who experienced pain were noted less likely to return for

Visualisation of the ovary is a further quality assurance factor to overcome in ovarian cancer screening. Decreasing follicular activity and ovarian shrinkage in postmenopausal women makes visualisation problematic. In a study involving TVS of 43,867 women (median age 60.6 years), factors affecting visualisation of ovaries in postmenopausal women included

nant lesions are likely to change the approach to ovarian cancer screening.

**2. Role of pelvic ultrasound in ovarian cancer screening**

in diagnosing ovarian neoplasms.

216 Ovarian Cancer - From Pathogenesis to Treatment

analyses are being investigated for their efficacy.

'septum, solid, smooth, irregular', and so on. [5].

a subsequent scan 1 year later [4].

Tumour markers are substances, mostly proteins produced by the tumour cells, which can be detected in the blood and other bodily secretions of the affected individual. These markers can be produced by normal tissue as well but their levels are usually significantly elevated during a malignant process. Tumour markers are used for the early detection, to guide management and to assess treatment response in cancer.

sporadic of which 90% occur in postmenopausal women, and for this reason, screening trials in the general population have been aimed at this cohort. In the high-risk population, however, even premenopausal women are at increased risk and are therefore included in screening studies.

Screening for Ovarian Cancer

219

http://dx.doi.org/10.5772/intechopen.72726

Approximately, 5–10% of ovarian cancers are attributed to genetic mutations. Mutations in the BRCA1 and BRCA 2 genes increase the risk of developing both breast and ovarian cancer. The life time risk of developing ovarian cancer (up to the age of 70 years) is 40% (95% CI, 35–46%) for carriers of BRCA1 mutation and 18% (95% CI, 13–23%) for BRCA2 mutation carriers [27]. A strong family history of breast and ovarian cancers could be an indicator of the presence of mutations in BRCA genes given their high penetrance [28]. The age of onset of ovarian cancer tends to be younger in BRCA carriers when compared to the general population. Median age at diagnosis is 63 years in the general population [29], 51.2 years for BRCA 1

Lynch syndrome or hereditary nonpolyposis colorectal cancer (HNPCC) is a syndrome secondary to mutations in the mismatch repair genes (MMR)—MLH1, MSH2, MSH6 and PMS2, which not only increases the risk of developing colorectal cancer but also ovarian and endometrial cancer in female carriers. The estimated cumulative risks of ovarian cancer by age

Traditionally, testing for gene mutations has been undertaken in individuals with a strong family history of ovarian cancer. Earlier studies looking at family history alone have shown that women with a first degree relative with ovarian cancer have a 4–5% life time risk of developing ovarian cancer. With two affected close relatives, the risk increases to around 10% and

More recently there has been a different approach to screening for gene mutations. A randomised controlled trial looking at testing of the population regardless of family history in the Ashkenazi Jewish population reported a slightly higher incidence of BRCA mutations in the population screening group when compared with the family history group [33]. Such studies suggest that unselected testing of the population identifies 50% more carriers of genetic muta-

Other than genetic factors, risk of ovarian cancer has also been found to increase with nulliparity, early menarche and late menopause, hormone replacement therapy and endometriosis. Factors suppressing ovulation such as use of the oral contraceptive pill, multiparity,

Symptoms of ovarian cancer occur insidiously, with many patients presenting with non-gynaecological symptoms such as indigestion, abdominal bloating and early satiety,

70 years for women with Lynch Syndrome is around 10% (range 6–14%) [31].

can become higher with even more relatives affected by ovarian cancer [32].

tions than the traditional approach to screening based on family history alone.

longer periods of lactation have been associated with a decreased risk [34].

**6. Symptom-based screening**

**5. Genetic predisposition to ovarian cancer**

and 57.5 years for BRCA2 mutation carriers [30].

CA125 is the most commonly used tumour marker for the detection of ovarian cancer. In 1981, Bast et al. developed OC125, a murine monoclonal antibody, which was found to react with ovarian carcinoma cells [16]. An immunoassay was then developed to detect the antigen CA125 in the serum of patients affected by non-mucinous ovarian cancer. CA125 levels were found to be elevated in 82% of women affected by non-mucinous epithelial ovarian cancer, and it was useful in monitoring the treatment response [17].

Elevated CA125 levels are seen in 50% of stage I and >90% of stage II–IV serous ovarian cancers [18]. However, the levels are usually not elevated with mucinous and borderline ovarian tumours. CA125 is also not very specific to ovarian cancer as the levels are increased in other malignancies such that of the gastrointestinal tract, breast and lung; and in benign gynaecological (e.g. endometriosis, fibroids, adenomyosis, benign masses and pregnancy) [18] and non-gynaecological conditions (e.g. heart failure, pancreatitis, hepatitis) [19].

A cut-off value of 35 U/ml is accepted as the upper limit of normal [17]. This cut-off value is acceptable in postmenopausal women, whereas, in premenopausal women, the cut-off value tends to be significantly higher at 50 U/ml [20]. Other factors have also been found to affect the CA125 level. A study on CA125 levels in healthy postmenopausal women observed varying levels with race (highest in Caucasian and lowest in African women), lower levels with previous hysterectomy, regular smoking and caffeine intake, and, higher levels with a previous (non-ovarian) cancer diagnosis. Age of the individual, age at menarche and menopause and previous ovarian cysts were also predictive of baseline levels in postmenopausal women [21].

The CA125 level can be elevated for up to 5 years prior to the diagnosis of ovarian cancer. This finding has been crucial for its application in screening asymptomatic women [22]. Given its low sensitivity and specificity, interpretation of CA125 level using a cut-off value has not been very useful in screening. However, sequential measurements of CA125 as a first-line test and transvaginal ultrasound as a second-line test in multimodal screening have been found to significantly improve its sensitivity and specificity [23].

Human epididymis protein 4 or HE4 is another tumour marker which is elevated in ovarian cancer but not with benign ovarian masses. It can therefore be used to distinguish between the two [24]. In a study using an algorithm combining both HE4 and CA125, 93.8% of epithelial ovarian cancers were accurately classified as high risk [25]. Other markers that have been tested include prolactin, transthyretin, CA72-4 and CA15-3. Combining these markers with CA125 has not shown to improve its efficacy in screening for ovarian cancer [26].


**1. Introduction**

Advancements in modern technologies enabled field monitoring of some parameters of human health [1]; for example, heart monitoring is used for o ff-hospital monitoring and in fitness and professional sport activities. The most commonly measured value is the heart rate (HR), although advanced applications also use other values, e.g., pulse irregularity, as well as biometric identification or analysis of accurate electrical signals that cause heart contraction, i.e., electrocardiography (ECG) [2]. Accurate ECG requires connecting electrodes to the patient's body in several di fferent places, which is inconvenient for the patient, and it can be used only in certain situations. A much more convenient method is measuring the pulse on the wrist by using photoelectric methods. The skin of the wrist is irradiated with single or multicolor light, and then the reflected light is measured. The intensity of the reflected light depends on the absorption of the skin, which depends on the blood volume supplied to the tissues. In this way, the received signal contains information about the current blood supply to the vessels near the measuring device. This method, introduced by Hertzman [3], is known as photoplethysmography (PPG). Unfortunately, PPG signals obtained from a moving person's wrist are weak, distorted, and contain noise. The noise level is often higher than a usable PPG signal. Correct analysis of a low-quality PPG signal is a very challenging task and can consume significant processing time, energy, and resources.

Heart rate estimation from wrist PPG is now a popular area of investigation, and many algorithms for such a task have been proposed [4]. Some of them require significant computing power and memory usage, blocking their application in small portable low-power devices. There are two main approaches used: time-domain sophisticated filtering and frequency-domain processing. Both are often accompanied by a movement sensor (accelerometer) for movement-based artefacts/spectrum removal.

A straightforward approach toward heart rate estimation is peak detection in a periodic signal. One of the simplest possibilities is to use threshold or auto-threshold values in the signal time window [5,6]. Another way is to use transforms such as continuous wavelet [7] or Hilbert [8]. In [9], a nonlinear filter bank was used, with variable cuto ff frequencies. In [10], the detection algorithm was based on a time-varying autoregressive model, with a Kalman filter used for autoregressive parameter estimations. Hidden Markov models were used in [11] for combining structural and statistical knowledge of the signal in a single parametric model. A neural network adaptive whitening filter to model the lower frequencies of the signal is presented in [12]. Much recent work is concentrated on methods using time-frequency spectra [13,14] and deep-learning approaches [15–17]. A review of the current state-of-the-art signal-processing techniques for HR estimation from a wrist PPG signal can be found, for example, in [4,18].

The proposed solution of time-domain heart rate measurement algorithm (TDHR) consists of three main blocks: signal conditioning, peak detection, and heart-rate-measuring blocks. In this work, the modified Automatic Multiscale-based Peak Detection (AMPD) algorithm from [19], together with a bandpass filter/limiter, was used for finding the HR from a wrist-based PPG signal. All of the signal processing was done while taking into account the need for low power, low resources, and computing power utilization, necessary for a self-su fficient mobile sensor. The main contributions of this paper are as follows:

This paper is organized as follows. In Section 2, the signal conditioning block is described, which is followed by the peak detection algorithm described in Section 3 and the heart rate calculation block presented in Section 4. The proposed algorithm was evaluated by using a multi-hour dataset; the results of the evaluation and the comparison to the other solutions from the literature are presented in Section 5. Section 6 contains the details of the implementation in a low-power wearable device. Finally, conclusions are provided in Section 7.

#### **2. Signal Conditioning**

PPG-signal measuring is usually done with an infrared (IR), red, or green light-emitting diode (LED) as the light source and a photodetector (PD) receiving the reflected or transmitted light. Often, instead of one, a few LEDs and/or PDs are used, providing the possibility to choose the best observed signal or to use additional preprocessing, such as, for example, averaging. Two measure modes can be used: reflectance and transmission. In the first case, the LED and PD are placed on the same skin surface, close to each other; the typical spacing between the PD and LED is in the range of 5–15 mm. The PD measures the light reflected from the tissue. In transmission mode, the LED and PD are placed on the opposite sides of a human body part, and the light reaching the PD goes through the whole body part. The commonly used places on a human body for such PPG devices are the fingertip, wrist, earlobe, forehead, torso, ankle, and nose [18,20]. The wrist is the most convenient place to mount the monitoring device for everyday life, but this is not optimal regarding the strength of the signal. Due to the relatively large thickness and the presence of bones, only the reflectance

method of measurement is practical on the wrist. In Figure 1, the waveforms of the signal received from the sensor in reflectance mode on the index finger and on the outer wrist, where a watch is usually worn, are presented. A useful signal carrying information about the pulse is present in the form of the peaks with the period of about 30–40 samples superimposed on the curve. As shown in Figure 1, the amplitude of these peaks for the signal measured at the finger is about 200, while the amplitude measured at the wrist is much lower, about 30–50, while the average signal level (baseline) is about 11,100.

**Figure 1.** Photodetector(PD) waveforms: raw signals obtained from photoplethysmography (PPG) sensor in reflective configuration placed on the index tip (dotted line) and on the wrist (continuous line).

As can be seen from the waveforms from Figure 1, the signal from the wrist is much weaker; moreover, each signal has a variable offset, and it contains noise and interference. To extract the heart rate in a time-domain, the signal must be preprocessed, so that the peak detection algorithm can find the peaks. The simplest approach is to use a band-pass filter that can help to reduce the noise and eliminate the constant component of the signal. Such a solution is in common use [4]. However, the signal at the input of the filter can have significant changes of the constant component, as well as signal fluctuations caused by wrist movement, resulting in large peaks and oscillations at the output of the band-pass filter, as can be seen in Figure 2a,b.

(**a**)

**Figure 2.** *Cont*.

**Figure 2.** Waveforms of the signals from the PPG detector: (**a**) the raw signal; (**b**) the raw signal from (**a**) filtered only by a band-pass filter; and (**c**) the raw signal from (**a**) filtered first with the limiting section described in the paper and then by the same biquadratic filter as used in (**b**).

The proposed approach for PPG signal acquisition and processing is presented in Figure 3. The raw signal obtained from the PD is fed, in the first step, to the band-pass biquad section, with an internal limiter. The output value of the standard biquad section using direct version I is given by Equation (1):

$$y\_i = \frac{1}{a\_0}(b\_0\mathbf{x}\_i + b\_1\mathbf{x}\_{i-1} + b\_2\mathbf{x}\_{i-2} - a\_1y\_{i-1} - a\_2y\_{i-2}),\tag{1}$$

where *yi* is *i*-th output sample; *xi* is *i*-th input sample; *a*0, *a*1, and *a*2 and *b*0, *b*1, and *b*2 are, respectively, the denominator and nominator coefficients of the biquad transfer function. The biquad section with the internal limiter works in two steps: First, it calculates candidate *yC,i* for the output value, according to Equation (1), and then it uses Equation (2) to update the actual output value:

$$\begin{array}{c} \text{if } y\_i = \begin{cases} \quad y\_{C,i} \text{ if } L\_L \le y\_{C,i} \le L\_H\\ \quad L\_L \text{ if } y\_{C,i} < L\_L\\ \quad L\_H \text{ if } y\_{C,i} > L\_H \end{cases} \end{array} \tag{2}$$

where *LL* and *LH* are the limiter's parameters. These parameters should be selected so that the PPG signal from the heart contractions will remain intact, while distortions resulting from hand movements should be cut off. Figure 2a shows the PPG signal containing distortions taken from a wrist. For this case, the limiter parameters should therefore cut off these high distortions, while the small sawtooth waveform should not be affected. The signal after the bandpass filter does not contain a constant component, as is presented in Figure 2b. In the case of the signal from Figure 2b, good choices for the *LL* and *LH* parameters' values could be −150 and 150, respectively. Figure 2c shows the results of preprocessing for such a limiter, together with the band-pass biquad section. In general, these values

for a given PPG system should be selected as about 100–150% of the negative and positive amplitude of the useful signal, respectively.

**Figure 3.** Block diagram of PPG signal acquisition, preprocessing, and final HR estimation by TDHR algorithm.

In the second stage of the proposed preprocessing block, a typical fourth-order band-pass filter built from two biquads was employed. The band-pass of both stages was set to 0.5–2.5 Hz.

The introduction of the limiting section in the form of a single biquad stage significantly reduces the rapid changes of the signal at the input of the band-pass filter and improves its recovery after a rapid change of the constant component in the input signal. An example of distorted signal, together with the accelerometer readouts, is presented later in this paper.

#### **3. Peak Detection**

The conditioned signal from the detector was used as an input to the block responsible for finding the peak values of the signal; the heart rate can then be easily calculated from the detected peaks. The detection of peaks is based on the AMPD algorithm [19]. The AMPD algorithm has the capability to work with noisy periodic and quasi-periodic signals. It needs the input signal to be linearly detrended, but the use of the input filter of band-pass characteristic with the limiter described in the previous section satisfies this requirement. The AMPD algorithm performs well for the filtered PPG signal, but it is computationally expensive, which can be unacceptable for wearable devices. The need to calculate a large matrix with real-valued elements, where moving windows are used, can be avoided due to the modifications of the algorithm proposed in further parts of this paper. This section starts with the detailed description of the AMPD algorithm, and then the proposed modifications are introduced. In this way, the authors wanted to make it easier for the reader to track changes that were applied to the original algorithm, without having to refer to the reference.

The main part of the AMPD algorithm is the Local Maxima Scalogram (LMS) matrix M of elements, *mk,i*, which is calculated for the discrete uniformly sampled signal *x* = [*x*1, *x*2, ... , *xN*] in the analyzed window, where *N* is the constant number of samples, and scale *k* defines the moving window of varying length, *wk*, according to Equation (3):

$$m\_{k,i} = \begin{cases} 0 & \text{x}\_{i-1} > \text{x}\_{i-k-1} \land \text{x}\_{i-1} > \text{x}\_{i+k-1} \\ r+1 & \text{otherwise} \end{cases},\tag{3}$$

where *wk* = 2*k* | *k* = 1,2, ... , *L*, *k* is *k*-th scale of the signal, *L* = ceil(*N*/2) − 1, and *r* is a uniformly distributed random number of values [0,1]. The values of *mk,i* are calculated for every scale *k* and *i* = *k* + 2, *k* + 3, ... , *N* − *k* + 1.

JJJHaving calculated the LMS matrix, the next step in the AMPD algorithm is to calculate the scale-dependent distribution of zeros in the LMS, by calculating vector γ = [γ1, γ2, ... , γ*k*]:

$$\gamma\_k = \sum\_{i=1}^{N} m\_{k,i} \text{ for } k \in \{1, 2, \dots, L\} \tag{4}$$

and the global minimum of γ, λ = arg min(γ*k*). The value of γ is used to obtain the matrix Mr, which is the matrix M with deleted bottom rows for *k* > λ. The peaks are then found for indexes, *i*, for which the column-wise standard deviation, σ*i*, is equal to zero:

$$\sigma\_{i} = \frac{1}{\lambda - 1} \sum\_{k=1}^{\lambda} \left[ \left( m\_{k,i} - \frac{1}{\lambda} \sum\_{k=1}^{\lambda} m\_{k,i} \right)^{2} \right]^{\frac{1}{2}}. \tag{5}$$

Parameter λ enables the determination of how many rows of matrix M should be used for calculating the matrix Mr. The noisier the signal from the PPG sensor, the larger the value of λ.

In this paper, the authors propose modifying the AMPD algorithm toward a more e fficient implementation. From practical observation, it has been inferred that the signal is too noisy, and it is of no use for peak detection and heart rate calculation, when we have the following:

$$
\lambda > \lambda\_{\text{max}} \tag{6}
$$

The value of λmax = 17 was found empirically to be a good choice. Therefore, it is practical to assume a priori, that the signals for which the condition (6) is true are of no use and the full matrix M is not used. To save the memory, only the matrix Mr, instead of M, can be calculated and stored together with the vector γ.

Processing and storing floating point values, *mk,i*, requires storage space and processing resources. To simplify the processing, the authors propose replacing real-valued elements of matrix Mr with the matrix Mr' containing 1-bit binary values, *m*'*k,i*, as follows:

$$m'\_{k,i} = \begin{cases} 0 & \mathbf{x}\_{i-1} > \mathbf{x}\_{i-k-1} \land \mathbf{x}\_{i-1} > \mathbf{x}\_{i+k-1} \\ 1 & \text{otherwise} \end{cases} . \tag{7}$$

This saves a lot of the device's memory, requires only integer operations, and results in another simplification: Instead of calculating the column-wise standard deviation σ*i* from Equation (5), calculating the column-wise summation presented in Equation (8) can be used:

$$s\_i = \sum\_{k=1}^{A} m'\_{k,i}.\tag{8}$$

As the *m*'*k,i* are 1-bit binary values, *si* can be calculated with fast integer summation. The indices of peaks *pi* can be located by finding all indices, *i*, for which *si* = 0. The values of σ*i* and *si*, together with the values of γ*k* were shown in Figure 4b,c, for an exemplar input signal (Figure 4a).

The PPG signal from a wrist is weak, as can be seen in Figure 2c, and when sampled, the situation of a "flat" peak with two equal values for samples *ti*-1 and *ti*, as shown in an example in Figure 5, can sometimes occur. Such a peak would neither be detected by the AMPD algorithm nor its modified version proposed in this paper. To improve the peak detection in such a case, an additional rule was introduced: The peak is also detected at time *t*'*i*.

$$t'\_i = \frac{t\_{i-1} + t\_i}{2} \tag{9}$$

for which the following simple condition (10) holds for *si* from Equation (8):

$$s\_{i\cdot 2} > 1 \land s\_{i\cdot 1} = 1 \land s\_i = 1 \land s\_{i+1} > 1. \tag{10}$$

The proposed simplified peak detection algorithm was compared to the original AMPD algorithm presented in [19]. For this purpose, the PPG signals from the PPG-DaLia database [15] were used, and the detected peaks were compared. As the original AMPD peak detection algorithm uses a random variable to fill the LMS matrix, its results are slightly different from run to run, depending on the seed value of the random number generator. The results are very similar, while the modified version can be implemented more efficiently. An example is presented in Figure 6: the arrows indicate the missing peaks, which were exclusively detected by the other algorithm. It can be seen that only a few peaks are differently detected.

**Figure 4.** Sample of (**a**) filtered input PPG signal, (**b**) calculated and normalized values of σ*i* from Equation (5) and *si* from Equation (8), and (**c**) calculated values of γ*k* from Equation (4).

**Figure 5.** An example of special case of a "flat" peak consisting of two equal values at *ti*-1 and *ti*.

**Figure 6.** Comparison of the results of the peak detection algorithm proposed in this paper (**a**) with the AMPD peak detection algorithm (**b**) from [19] on the same waveform. The arrows indicate the peaks not detected by one of the algorithms but detected by the other one. All other peaks were detected at the same positions, by both methods.

The presented example uses an unfiltered PPG signal to present the robustness of the algorithm. The signal after filtration would be more regular; thus, the differences in the results between the two versions of the algorithm would be even smaller.

#### **4. Heart Rate Calculation**

In an ideal situation, where the patient is not moving, the detected peaks from the peak detection algorithm can be directly used to calculate the heart rate. However, the PPG signal can be seriously distorted by the movement of the patient. Each movement of the patient can cause a change in the saturation of the tissues with blood, and a sensor displacement on the wrist, which results in artefacts in the signal received by the optical detector. To mitigate this problem, the accelerometer is used for detecting the movement of the patient's hand. The movement data are aligned with the detected peaks, and the periods between the peaks which are affected by movements are discarded from the calculation of the pulse period. The example of the elimination of the patient's movement is presented in Figure 7.

**Figure 7.** Elimination of the patient's movement from pulse-signal detection: (**a**) the PPG signal distorted with the patient's movement; the areas in gray are excluded from pulse period calculation due to the detected movement; (**b**) acceleration values read from the accelerometer placed together with the heart rate detector on the wrist, (**c**) and the values of movement *di* from Equation (11); (**d**) signal *di* after threshold, as in Equation (12), and with the eliminated short-term peaks (the eliminated peak is shown with a dotted line).

To find the time periods that will be excluded from period calculations, first the acceleration values from the accelerometer are differenced to obtain the movement indicator, *di,* according to the following equation:

$$d\_i = \frac{1}{G} \sqrt{(g\_{x,i} - g\_{x,i-1})^2 + \left(g\_{y,i} - g\_{y,i-1}\right)^2 + \left(g\_{z,i} - g\_{z,i-1}\right)^2},\tag{11}$$

where *i* is the index of the sample; *gx,i*, *gy,i*, and *gz,i* are the acceleration values read from the accelerometer for axes X, Y, and Z, respectively; and *G* is a constant value used for normalization to obtain *di* ∈ [0,1] for all *i* and for the acceleration values in *gx,i*, *gy,i*, and *gz,i* in the accelerometer's full measuring range. The movement values, *di*, are then compared to the constant, *H*, to obtain a digital binary signal *Vi*:

$$V\_i = \begin{cases} 1 & d\_i > H \\ 0 & \text{otherwise} \end{cases} \tag{12}$$

Signal *Vi* is then filtered in time, to eliminate spikes longer than *Ts*. The values of *H* and *Ts* were experimentally set to *H* = 0.0025 and *Ts* = 500 ms for the accelerometer range ±2 g.

For the final result of the heart rate, the inverse of the median of the periods between the peaks not affected by the movement is calculated. For a valid result, a minimal number of peaks, *P*, is required, and it is calculated as shown in Equation (13):

$$IP = flow\left(\frac{N \cdot BPM\_{\text{min}}}{60f\_s}\right) \tag{13}$$

where *fs* is the sampling frequency, and *BPM*min is the minimal heart rate required to be measured by the system.

#### **5. Evaluation on Dataset**

For the purpose of evaluating the algorithm, the PPG-DaLia database [15], containing more than 35 h of data recorded from 15 persons, was used. The database contains signals collected from a PPG sensor, accelerometer, and ECG, where the ECG is used as ground truth. As described in [15], ground truth heart rate values were obtained from an ECG signal processed by R-peak detection algorithm [21]. Then, the detection results were manually inspected and corrected, mainly for a few cases where significant motion was observed. The ECG signal was segmented with a shifted window; ground truth heart rate was finally calculated as the mean heart rate within each window. The signals were collected during eight different types of typical daily life activities, under controlled but close-to-real-life conditions. This dataset is the longest one available to the authors. The accuracy of the algorithm was evaluated by using the mean absolute error (*MAE*) metric of beats per minute (bpm), calculated by using the sliding window approach of length 8 s, with a 2 s shift, according to the following equation:

$$MAE = \frac{1}{W} \sum\_{j=1}^{W} \left( BPM\_{est}(j) - BPM\_{ref}(j) \right). \tag{14}$$

where *W* is the total number of windows, *BPMest*(*w*) is the heart rate in bpm for window *j*, and *BPMref*(*j*) is the reference heart rate obtained from the ECG ground truth signal for the same window *j*. This evaluation method is commonly used in related work [14,15,22,23].

The quality of the signal and the possibility of the measurement are checked in the TDHR algorithm. When the measurement is not possible due to not satisfying Equation (6), i.e., λ > λ*max* or due to movement detected by the accelerometer affecting all of the periods between the detected peaks, the measurement result is indicated as invalid. The evaluation of the algorithm is performed in two ways: (i) When the result is not available, the last valid result is used; (ii) only valid results are used to calculate the performance metric; and then the percentage of the valid samples is also given. The results of the evaluation, together with the performance of the SpaMa [14], SpaMaPlus [15], Schaeck2017 [23], CNN average, and CNN ensemble [15] algorithms are presented in Table 1. The TDHR algorithm was evaluated for several lengths of the sliding window: *N* = 1024 (32 s), *N* = 512 (16 s), *N* = 256 (8 s), and *N* = 128 (4 s).

**Table 1.** Comparison of the accuracy of the presented algorithm to the algorithms from the literature on the large PPG-DaLia dataset as MAE (bpm). The proposed TDHR algorithm was tested in several versions, with various numbers of analyzed samples *N* (for all other algorithms *N* = 256), sampled with a frequency of 32 Hz, with a *BPMmin* = 40 bpm. The measurements were done every 2 s. For the TDHR algorithm, the accuracy was also calculated for valid measurements, and then the percentage of valid measurements was also given.

Table 2 presents the comparison of the computational cost of several algorithms. For the TDHR algorithm, the number of operations per second was estimated from a manual analysis of the C code as the number of arithmetic operations needed to obtain a single heart-rate result. The TDHR algorithm requires only a few parameters, as opposed to the CNN algorithms, but it needs storage memory for calculating the LMS array; the size of this memory depends on the window length, *N*.

**Table 2.** Comparison of the performance versus computational cost of the TDHR algorithm and the algorithms from the literature. The computational cost of the TDHR algorithm was calculated as the number of arithmetical operations and the number of memory bytes needed for algorithm realization in a microcontroller.

As can be seen from Tables 1 and 2, the performance of the proposed TDHR algorithm is similar to *SpaMa*, *SpaMaPlus*, and *Schaeck2017*, while it is worse than *CNN average*, *CNN ensemble*, and *CNN constrained*. The computational costs of *SpaMa*, *SpaMaPlus*, and *Schaeck2017* are not known, but they can be high, as those algorithms require the calculation of the power spectral density and the analysis of the PPG spectrum. The CNN-based algorithms, even *CNN constrained*, require larger computational costs than most versions of the TDHR algorithm. The proposed algorithm uses the mechanism of removing the time periods with body motion registered by the accelerometer, so there can be gaps between valid measurements in the case of long-lasting motions. The presented results in Tables 1 and 2 can help to select *N* to achieve a compromise between the accuracy and computational cost.


*4.2. Collective Bands and γ Vibration in* 166*Er*

The features of the collective motion in 166Er have been studied by the MCSM similarly well (see Figure 15a). Among rotational nuclei, 166Er is characterized by particularly lowlying 2+ 2 state and the *γ* band built on it. Aage Bohr stressed that this 2+ 2 state was a *γ* vibration from the prolate ground state [9–11,13]. The relatively strong 2+ 2 → 0+ 1 E2 transition (B(E2)∼5 W.u., see Figure 15a, was ascribed to the annihilation of one *γ* phonon in the 2+ 2 state. This was one of the major points of the Nobel lecture by Aage Bohr and has been a common sense as stated in many textbooks of nuclear physics. We now challenge

this traditional belief, by utilizing the recent MCSM calculation. It is reminded that no firm experimental evidence to uniquely pin down the *γ*-vibration nature of 166Er has been reported and also that in a systematic calculation of many heavy nuclei [78], the excitation energies of the 2+ 2 states in the *γ* band appeared to be about twice higher than the observed values, despite much better description of those of the 2+ 1 state in the ground band.

**Figure 15.** Experimental and calculated properties of the lowest states of 166Er. (**a**) Energy levels and electromagnetic transitions (W.u.) [42] as well as spectroscopic electric quadrupole moments (eb) [79]. (**b**) Three-dimensional PES and its cut surface for *β*2 = 0.3. (**c**–**e**) T-plots for the 0+ 1 and 2+ 2 states and for the monopole-frozen 0+ 1 state at spherical shape. Based on Figure 4 of [71].

Figure 15b shows the calculated PES, which shows the minimum not at *γ* = 0◦ but around *γ* = 9◦ (see also [80]). The T-plot is shown for the 0+ 1 and 2+ 2 states in Figure 15c and Figure 15d, respectively. The patterns of the T-plot circles are nearly identical between these two panels. This is consistent with a (rigid) triaxial interpretation, and indeed E2 transition strengths follow the predictions of the Davydov triaxial model [81,82] with *γ* = 9◦. Certainly, a pure rigid triaxiality is not the correct picture, and there are quantum fluctuations, as evident from Figure 15c,d [80]. After all, the displacement from the *γ* = 0◦ is obvious. The triaxiality of 166Er is also suggested by the triaxial projected shell model, although the rigid-triaxiality is not an outcome but an assumption [83,84].

The experimentally known *Jπ* = 4+ state around 2 MeV excitation energy provides a long-standing puzzle [85,86]: the observed relatively strong E2 transition from this state to the 2+ 2 state looks like a sign that the 2+ 2 state and this *Jπ* = 4+ states are the singleand double-phonon states in the *γ* vibration picture (à la A. Bohr [9,10]), respectively, but the excitation energy of this *Jπ* = 4+ state is too high for a double-phonon excitation. The present calculation, on the other hand, reproduces both the excitation energy and the E2 transition strength, and this *Jπ* = 4+ state appears as the *Kπ* = 4+ member of the triaxial states including the 0+ 1 and 2+ 1,2 states (see Figure 15c,d) [71,80]. Thus, the triaxiality is shown to be one of the key aspects for understanding/predicting the shapes of heavy nuclei.

The monopole-frozen analysis referring to the spherical CHF state shows that the ground state moves to *γ* = 0◦, confirming the important role of the monopole interaction activated. The triaxial ground states are now shown to appear in a large number of nuclei in the nuclear chart, besides the known triaxial domain [87].


*3.1. Data*

For the analysis, historical temperature, precipitation, and river discharge data recorded from about 1960 to 1979 at gauging stations installed within the Kunduz River Basins are analyzed. Between 1979 and 2009, or even later, there are no data records due to political turmoil in the country available. The recent data from 2009 to 2019 are, with some exceptions, available. The data for available years are listed in Table 1. Only meteorological and river discharge stations with over 20 years of data records have been used in the study and stations with less data available neglected in order to have a minimum of confidence in the time series (discussed in more detail in Section 5.1). The hydrological and meteorological data were provided by the Ministry of Energy and Water and Afghanistan Meteorological Department. The river discharge data are also provided by the same ministry [30].

**Table 1.** Overview of gauges and meteorological stations in the Kunduz River basin, including drainage area, elevation, and the period for which data are available.

For topographical and hydrological mapping, remote-sensing data and satellite images from sources, including National Geographic and Esri, were accessed and processed using ArcGIS software (https://www.arcgis.com/home/item.html?id=b9b1b422198944fbbd5250b3241691b6). For the LULC classification, Landsat 5 Thematic Mapper (TM) scenes and Landsat 8 Operational Land Imager (OLI) have been used [31].


**3. Reproductive management**

*Animal Reproduction in Veterinary Medicine*

in most of the farms [21].

*Lidia bull growth estimation [9].*

**Figure 1.**

therefore, the show itself.

aptitude: the behavior in the ring.

so on. Later we will address this pathology more widely.

The use of long feeders is common, especially in southern Spain, compared to the classic individual and small feeder used in farms located on the center of Spain (**Figure 2**). The distribution of food is done during the morning and the afternoon

There is a critical point in the strategy of feeding management, due to the overfeeding carried out during the last year, prior to the fight, which causes an overload of weight in the bone structure, added to the state of obesity that causes a lack of strength and mobility of the animal that limits its behavior in the arena and,

The problem lies in the overfeeding to which it is subjected in the final phase of its growth during variable periods of time (from 8 to 12 months) that generates a series of pathologies and inconveniences that negatively influence its productive

Several studies have been carried out on the effect of intensive bait on rumen physiology of Lidia cattle [16, 20, 22–31], and all of them point to ruminal acidosis, a primary pathology that predisposes the appearance of secondary lesions such as liver abscesses, gastrointestinal ulcers, ruminal parakeratosis, laminitis, anthill, and

**114**

**Figure 2.**

*Long feeder and single feeders.*

Lidia females reach puberty at approximately 12 months of age but must reach the two-third of adult body weight before becoming pregnant [7] at approximately 2 years of age, and the productive lifespan time lasts for 8–10 years with a calvinggestation interval of 2–4 months [32].

Lidia bulls begin to show sexual activity from 6 months of age reaching puberty at 10–12 months, having been necessary to separate them from females before 1-year olds [7]. At 2 or 3 years, the selected sires are tested with a small group of females, but they are not profusely used until their female offspring are tested, and the quality of their genetic is proved, once this happens, they could be 15 years contributing its genetic flow in natural mating to the cattle ranch [32].

At present, in the majority of Lidia farms, the reproductive handling is very traditional with natural mating of one sire and 30–40 cows during several months. The outstanding difference with the past management is that now the fertility is greater due to a better cow's body condition that allows them to perform a successful gestation and lactation every year [33] (**Figures 3** and **4**).

In Europe, the duration of the mating period in Lidia cattle is similar to that of other extensive bovine breeds, being able to reach up to 8 months (autumn–spring) in livestock farms with longer periods, but its duration is often shorter, from the end of winter to the beginning of summer (March–July), since at this time, the best results are obtained in heat of the cows and fertility, due to both photoperiod and feeding reasons. In countries as Colombia without reproductive stations, the cycle is continuous.

There are relevant anatomical differences in the reproductive system of the female of the Lidia cattle: the cervix is longer in length than other bovine breeds; they present a uterine body very short, and it seems as nonexistent during transrectal palpation [34]. It is similar to the bipartite uterus in rodents, and the ovary has a very small size compared to other breeds of similar size presenting at the oviduct level the largest infundibulum that surrounds much of the ovary [35].

At the same time, there are hormonal differences because the Lidia cow reaches puberty earlier and has a shorter gestation period than other breeds: 286 days [36]. The natural mating should last long enough to guarantee a good fertility rate, with a minimum recommended period of 3–4 months (each cow has at least three opportunities to get pregnant), but there are farmers who extend it more, and

**Figure 3.** *Live weight variation of a Lidia cow [23].*

#### **Figure 4.**

*Calving and natural mating management based on grazing availability in seasonal countries.*

there are even systems with continuous natural mating, more common in tropical countries like Colombia. Short mating periods have the advantage of being able to concentrate the calving with better control of herd management and feeding. It is done more in larger herds, in large areas, where lactation is adapted to times of pasture abundance.

The utilization of techniques for semen collection and conservation for artificial insemination (AI) began to be used three decades ago in Lidia bulls. Later, embryo-transfer from high genetic merit Lidia cows to dairy cattle and cloning of one sire to preserve the excellent genetic quality was achieved. These reproductive methods, used to improve the productive characteristic of dairy and beef cattle, could be useful future tools to increase the genetic progress in Lidia cattle behavior selection [37].

There are immense advantages in using cryopreservation, due to semen dilution and conservation during medium and large periods, increasing the possibilities to use it for decades through AI when the behavioral results of their offspring are well known. There is also the possibility to extract *post-mortem* semen from the epididymis after the fight in the bulls of extraordinary behavior [38]. In this way, each farmer begins to have his own semen bank of his own sires and bulls. In turn, this would allow the exchange of semen between breeders, to refresh the blood of their livestock, being easy to transport to farms located in the countries of America. Among the advantages of this technique are avoid risks of contagions of potential pathologies, allowing the reproduction of animals of different sizes because natural mating is not necessary, and also is not necessary to move the male, allows the collection of semen in extreme situations, and, above all, enables the possibility to use some improving individuals of a contrasted character in a large number of females [39].

The biggest problems are due to the handling difficulties of these animals due to the untidy nature of this breed. Insemination implies added high-risk management both for animals and for people that seriously conditions, from a technical and economic point of view, its generalization in the Lidia cattle [37]. The introduction of other reproductive techniques such as early pregnancy diagnosis allows to discover and treat uterine pathologies, helping to detect nonpregnant cows that could be resynchronized or intended for natural mating and reducing calving pregnancy intervals. Reproduction control does not necessarily imply the hormonal treatment

**117**

livestock farm.

*The Lidia Breed: Management and Medicine DOI: http://dx.doi.org/10.5772/intechopen.92008*

in danger of extinction [35].

proper and strict selection pressure is not applied.

[33] its ontogenesis or sequential development.

males to assess their bravery [32].

2

1 *encastes* = specific genetic lines in Lidia cattle breed.

practiced in the *tienta*,

to test its offspring to see if it is able to convey his characters.

*encastes*1

**4. Selection**

of all animals nor their subsequent insemination because it is possible to use mixed models in which the natural mating and AI are used in a complementary way [40]. Once the AI technique will be established, the next step will be to adapt an embryo transfer program to this type of cattle. Currently, it is used to preserve the valuable genetic material of small farms and to increase the reproductive efficiency of some females. In recent years, this technique has contributed to the formation of germplasm banks as genetic reserve in cases of farms with severe health problems or

At the same time, the use sexed semen to obtain a greater number of males than females could create an opportunity, considering the superior economic value of those. However, its use could jeopardize the process of selection and breeding of the farms due to the fact that reducing the numbers of females could be a risk if the

Regarding cloning, there are many questions about its efficacy in general and in Lidia cattle in particular. It is not known, for example, if a cloned animal can develop and interact normally with its peers in a highly hierarchical and of great rivalry environment. A cloned individual may have a poor development of the immune or cardiovascular system, and it is not known whether the libido and fertility of a future cloned breeder will be normal. At the moment, it is known that it ages quicker and has a shorter productive lifespan [41]. A cloned bull must also be tested, and in the event that his quality would be acceptable, it will be also necessary

The cloning of a sire, with the aim of collect semen, may be important in the case of some farms with few breeding males or if it is an individual of outstanding genetic merit and advanced age [42]. In any case, a clone might not have the same ethological characteristics as the animal from which it proceeds, since the behavior is the consequence of its genetic background, the environment in which it develops

Traditionally, three types of selection are made: genealogical, morphological, and functional [32]. In relation to the first, the farmer systematically records information, in his own books, the lines, or families that form the basis of the genetic heritage of his livestock, as well as the results of the offspring of each generation. This information is used to choose future breeding animals. In addition, each farm defines its morphological preferences, depending on the type it belongs to or the priorities of the owner. The selection criteria are usually higher for males than those used for females. They focus, fundamentally, on aspects related to external appearance, neck musculature conformation, bone structure and development, and so on [43]. And finally, the functional selection consists in measuring the brave character of each animal, although each farmer understands the meaning of this term in a very subjective way. A series of tests are carried out on both females and

In the case of females, animals of 1, 2, or 3 years are evaluated. The test is

of professional bullfighters, trying to discover the functional performance of each animal. The behavior of each individual in each phase of the test is assessed using

2 *tienta* = selection test applied mainly to female Lidia animals where a bullfighting show is played on

under the direction of the farmer and with the participation

#### *The Lidia Breed: Management and Medicine DOI: http://dx.doi.org/10.5772/intechopen.92008*

*Animal Reproduction in Veterinary Medicine*

pasture abundance.

**Figure 4.**

selection [37].

females [39].

there are even systems with continuous natural mating, more common in tropical countries like Colombia. Short mating periods have the advantage of being able to concentrate the calving with better control of herd management and feeding. It is done more in larger herds, in large areas, where lactation is adapted to times of

*Calving and natural mating management based on grazing availability in seasonal countries.*

The utilization of techniques for semen collection and conservation for artificial insemination (AI) began to be used three decades ago in Lidia bulls. Later, embryo-transfer from high genetic merit Lidia cows to dairy cattle and cloning of one sire to preserve the excellent genetic quality was achieved. These reproductive methods, used to improve the productive characteristic of dairy and beef cattle, could be useful future tools to increase the genetic progress in Lidia cattle behavior

There are immense advantages in using cryopreservation, due to semen dilution and conservation during medium and large periods, increasing the possibilities to use it for decades through AI when the behavioral results of their offspring are well known. There is also the possibility to extract *post-mortem* semen from the epididymis after the fight in the bulls of extraordinary behavior [38]. In this way, each farmer begins to have his own semen bank of his own sires and bulls. In turn, this would allow the exchange of semen between breeders, to refresh the blood of their livestock, being easy to transport to farms located in the countries of America. Among the advantages of this technique are avoid risks of contagions of potential pathologies, allowing the reproduction of animals of different sizes because natural mating is not necessary, and also is not necessary to move the male, allows the collection of semen in extreme situations, and, above all, enables the possibility to use some improving individuals of a contrasted character in a large number of

The biggest problems are due to the handling difficulties of these animals due to the untidy nature of this breed. Insemination implies added high-risk management both for animals and for people that seriously conditions, from a technical and economic point of view, its generalization in the Lidia cattle [37]. The introduction of other reproductive techniques such as early pregnancy diagnosis allows to discover and treat uterine pathologies, helping to detect nonpregnant cows that could be resynchronized or intended for natural mating and reducing calving pregnancy intervals. Reproduction control does not necessarily imply the hormonal treatment

**116**

of all animals nor their subsequent insemination because it is possible to use mixed models in which the natural mating and AI are used in a complementary way [40].

Once the AI technique will be established, the next step will be to adapt an embryo transfer program to this type of cattle. Currently, it is used to preserve the valuable genetic material of small farms and to increase the reproductive efficiency of some females. In recent years, this technique has contributed to the formation of germplasm banks as genetic reserve in cases of farms with severe health problems or *encastes*1 in danger of extinction [35].

At the same time, the use sexed semen to obtain a greater number of males than females could create an opportunity, considering the superior economic value of those. However, its use could jeopardize the process of selection and breeding of the farms due to the fact that reducing the numbers of females could be a risk if the proper and strict selection pressure is not applied.

Regarding cloning, there are many questions about its efficacy in general and in Lidia cattle in particular. It is not known, for example, if a cloned animal can develop and interact normally with its peers in a highly hierarchical and of great rivalry environment. A cloned individual may have a poor development of the immune or cardiovascular system, and it is not known whether the libido and fertility of a future cloned breeder will be normal. At the moment, it is known that it ages quicker and has a shorter productive lifespan [41]. A cloned bull must also be tested, and in the event that his quality would be acceptable, it will be also necessary to test its offspring to see if it is able to convey his characters.

The cloning of a sire, with the aim of collect semen, may be important in the case of some farms with few breeding males or if it is an individual of outstanding genetic merit and advanced age [42]. In any case, a clone might not have the same ethological characteristics as the animal from which it proceeds, since the behavior is the consequence of its genetic background, the environment in which it develops [33] its ontogenesis or sequential development.


**2.3. Idea of physical safety**

This section mainly describes an example of optical intensity modulation that is generally used in optical communication. A semiconductor laser diode used in general optical communication uses high-output continuous light (coherent light). The generation probability of each photon that forms this coherent light in the phase and optical power directions is unstable during measurement due to quantum phenomenon, and the value of the probability is not uniquely determined. The distribution of this undefined value is handled as a part of shot noise that is generated during the photoelectric conversion in optical communication, which is an element that degrades the receiving sensitivity. However, effects of other classical noise (including thermal noise of receiving amplifier) generated in the receiver become dominant to the receiving sensitivity of optical communication using the normal optical intensity modulation system. Normal receivers of the devised optical modulation system in Y-00 can maintain little quantum noise effect as in the case of conventional optical communication system. Also Y-00 produces an effect of significantly degrading the receiving sensitivity (to an unreceivable level) for eavesdroppers. In other words, is established safety by great difference of Signal-to-Noiseratio (S/N) between at normal receiver and eavesdropper. In this case, safety will be improved by as close as possible to 0.5 the error rate of eavesdroppers. Measured value of quantum noise that affects optical communication varies at completely random against the phase value and power value of light as described in section 2.2. Therefore, the phase modulation system and intensity modulation system used in the existing optical communication can be applied to the Y-00 encryption system that uses this quantum noise. The following describes basic idea to establish safety.

1. Multi-value modulation

6 Optical Communication

complexity.

**Figure 1.** Image of the safety of Y-00

**2.3. Idea of physical safety**

to get the initial key from acquired sample data like fast correlation attack that makes exhaustive search. In the exhaustive search, phenomena cannot be copied correctly due to effects of physical phenomena. It is disabling parallel processing [19]. Therefore, eavesdropper must be stacked in serial to find correlation of data. In addition, the sample data volume required for decryption is an order of 1E20~1E30 bytes or more by implementing the safety enhancing measures described later. That memory capacity to store the sample data are 1E20~1E30 bytes or more and the sample data acquisition time is several tens of millions to several hundred millions of years (at 10Gbps transmission) even if effects of physical phenomena cannot be diffused over the signal area as described above. This is limited and that is considered to be indecipherable safety. Because it does not depend on computer's performance unlike the present cryptography that pursues mathematical

This section mainly describes an example of optical intensity modulation that is generally used in optical communication. A semiconductor laser diode used in general optical communication uses high-output continuous light (coherent light). The generation probability of each photon that forms this coherent light in the phase and optical power directions is unstable during measurement due to quantum phenomenon, and the value of the probability is not uniquely determined. The distribution of this undefined value is handled as a part of shot noise that is generated during the photoelectric conversion in optical communication, which is an element that degrades the receiving sensitivity. However, effects of other classical noise (including thermal noise of receiving amplifier) generated in the receiver become dominant to the receiving sensitivity of optical The normal optical communication performs a multiple value transfer to increase transmission capacity. However in Y-00, only a single bit out of multiplexed values is used for transmitting information and other values are dummy information for eavesdroppers. It is important for multi-values to establish safety that the quantum noise distribution sufficiently overlaps between adjacent levels (both in optical intensity and phase). The number of values becomes several thousands or more depending on conditions.

2. Encryption and decryption

In the encryption by a transmitter, a combination of binary data (1-bit "1" or "0" level value) is selected for each bit of transmit data from multiple signal values created under conditions (1) using the initial key by the multi-value selection information. This selected binary combination is called base in the same way as phase modulation. The amplitude of this base (between two values) determines the receiving sensitivity of normal receivers in the case of intensity modulation. Therefore, 1/2 (180 degrees for phase modulation) of the maximum signal amplitude is the best value for normal receivers to obtain the optimum receiving sensitivity (Figure2). Decryption process of receiver is essential to distinguish the base that varies in each bit by the best threshold value at signal reception. The amplitude of "1" and "0" levels during reception is estimated based on the multi-value selection information generated by the initial key shared with the transmitter and the threshold value is momentarily moved to the best point to distinguish "1" level and "0" level of the signal. Synchronization of multi-level selection information is critical at this time between the transmitter and receiver. This information is changed at random in each bit between transmitter and receiver (Figure2) [17,18,20-22].

3. Tapping

People other than those who are engaged in optical communication believe in many cases that optical fiber does not be able to tapping unlike electric wires. The principle of optical fiber transmission is well known. Light travels in an optical fiber while repeating reflection using the refraction of light generated by junction of the core and clad in the optical fiber.

**Figure 2.** Signal basis and quantum noise

This reflectance varies by bending the optical fiber. If the optical fiber is bent at a sharp angle in particular, the refractive index of the core and clad extremely changes. Therefore optical signal not be able to total reflection. Part of the optical signal will leak out for that. Signal monitoring equipment that uses this principle has been commercialized and used as a measuring instrument. Tapping data from optical fibers has become relatively easy at present due to the technical advance (including high-speed, high-sensitivity detector and low-noise optical amplifier) in optical communication as shown in this example. Measures for improving safety to independently protect transmission paths have become imperative for background mentioned above (Figure3) [22,23].

**Figure 3.** Eavesdropping from optical fiber


**Author details**

Yonggang Zhou1 , Mingzhou Yu1 \* and Zhandong Shi2

1 Laboratory of Aerosol Science and Technology, China Jiliang University, Hangzhou, Zhejiang

2 Zhengzhou Tobacco Research of CNTC, Zhengzhou, China

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Author details**

Mohd Nazip Suratman Faculty of Applied Sciences and Institute for Biodiversity and Sustainable Development, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Introductory Chapter: Tropical Biodiversity Research in Protected Area of Pahang National Park… DOI: http://dx.doi.org/10.5772/intechopen.102616*


**3. Conclusion**

A key role in the algorithm is represented by the parallel data processing combined with the use of a pipeline structure, which allows managing more input data at the same time, thus relaxing the speed specifications.

The choice of an FPGA device turns out to be the best among those available, since it allows many advantages as follows:

Examining how the performance of FPGA devices has evolved over the years, by taking into consideration factors such as the data storage capacity and maximum operating fre‐ quency, one can note that these are strongly linked to the progressive miniaturization of the technology with which they are made.

They follow the technological scaling, which led to the advent of FinFET technology to reduce the size of the channel length to less than 16 nm. The effect of technological scaling leads to different types of advantages as follows:

The algorithm executed with MATLAB performs slightly better than VHDL, for example, for a rate of fake peaks of 1%, one has an efficiency of 76% in the case of MATLAB and an effi‐

A further test was performed by implementing the algorithm in MATLAB on the signals previously analyzed without the noise contribution. The results obtained are shown in

By comparing **Figures 9** and **11**, it can be seen how the presence of noise induces a reduction in efficiency and an increase of false peaks, indicating that a part of the errors of the algorithm

This problem can be mitigated somehow by trying to increase the signal‐to noise‐ratio by

The use of the algorithm described results in data reduction factors of more than one order of magnitude. It is executed for each readout channel and, considering the high number of I/O FPGA pins, it is possible to process multiple channels corresponding to different drift cham‐

A key role in the algorithm is represented by the parallel data processing combined with the use of a pipeline structure, which allows managing more input data at the same time, thus

may actually be due to the characteristics of the signal at input.

ciency of 69% for VHDL.

172 Field - Programmable Gate Array

filtering the input signal to the ADC.

**Figure 11.** Algorithm results with MATLAB without noise.

ber signals with a single device.

relaxing the speed specifications.

**3. Conclusion**

**Figure 11**.

The adoption of an FPGA, as the main block of data management and communication between various devices, appears to be winning, not only relying on the existing technology and on the various application tools created by the different manufacturers, but also thinking about the future, that is, how much they can be improved in terms of performance with improved technologies and how fast they can be improved.


**Abstract**

Electromyography (EMG) is routinely used in diagnostics of root syndromes in the lower extremity. By studying signs of axonal damage of different root levels in the corresponding myotomes of the lower extremity and back muscles with needle EMG reveals, which of the motor roots are injured in patients with suspected root compression. But by EMG study only injuries of the anterior motor roots are diagnosed. Routine electroneuromyography does not disclose specific injury of the afferent sensory posterior roots. However, the integrity of some the posterior roots is readily studied with myotatic reflexes. We have routinely measured a proprioceptive reflex, the H-reflex of the soleus muscle with stimulation of the posterior tibial nerve, and found it to be useful in the diagnostics of the S1 root syndrome. It seems to be possible to record H-reflex of the peroneus longus muscle at the L5 level. We discuss the serious problems with volume conduction, when trials to measure proprioceptive reflexes of the L4 and L5 levels are performed. It may also be useful to record the medium latency reflexes in the area of the posterior tibial nerve, which seems to have a different reflex arch (II-afferents – β-efferents) from H-reflex (Ia afferents – α efferents). These measurements are non-invasive and not time consuming, and we hope to be able to add them for the routine ENMG diagnostics, when appropriate.

**Keywords:** proprioception, Ia afferent, II afferent, alpha motor neuron, beta motor neuron, electromyography, H-reflex, muscle reflex, root compression


**1. Introduction**

Piezoelectric materials are capable of undergoing reversible phase transitions as a result of voltage and pressure. Due to these special material properties, piezoelectric materials have become increasingly popular in sensors and actuators. Piezoelectric ceramics actuators (PCAs) have been employed in precision positioning systems for their large force generation, high stiffness, high resolution and fast response. However, they commonly exhibit strong hysteresis behaviors, which greatly degrade the overall positioning accuracy.

According to whether the rate of the input is considered or not, hysteresis behaviors can be divided into rate-dependent and rate-independent hysteresis behaviors. The corresponding hysteresis models can be classified into rate-dependent and rate-independent hysteresis models. Over the past few decades, great efforts have been devoted to developing hysteresis models such as the Prandtl–Ishlinskii model [1–3], Preisach model [4–6], Maxwell-Slip model [7,8], Duhem model [9,10], Polynomial-based hysteresis model [11,12], and Bouc–Wen model [13,14]. For modeling of rate-independent hysteresis, it mainly focuses on the nonlinear relationship between the amplitude of input voltage and output displacement at low input frequency or rate. However, for modeling of rate-dependent hysteresis, it needs an analysis of the nonlinear relationship between the rate of input voltage and output displacement at high input frequency or rate. Comparisons of rate-dependent and rate-independent hysteresis models reveal that the rate-independent model is just a special kind of rate-dependent model when the rate of input is low enough. Therefore, it is more difficult to develop the rate-dependent

model relatively. Overall, most related literatures focused on developing rate-independent hysteresis models and few literatures paid attention to modeling of rate-dependent hysteresis.

Due to its differential equations and ability to capture an analytical form, the Bouc–Wen model has been widely applied in hysteresis modeling and compensation for piezoelectric ceramics actuators. Based on the classical Bouc–Wen (CB–W) model, Zhu and Wang [15] added a non-symmetrical formula to describe non-symmetrical hysteresis and the corresponding experiments demonstrated its validity. Fujii et al. [16] proposed an extended Bouc–Wen model by introducing a velocity sign sensitivity. To eliminate the influence of nonlinear hysteresis, Li et al. [17] presented an adaptive sliding mode control with perturbation estimation (SMCPE) based on the classical Bouc–Wen model. In addition, Liu et al. [18] proposed an adaptive neural output-feedback control based on a modified Bouc–Wen model. Lin and Yang [19] used a Bouc–Wen model to describe the hysteresis behavior and designed a hysteresis-observer based control to compensate for the piezoelectric actuator.

It should be noted that the piezoelectric actuator possesses a non-symmetrical hysteresis according to a lot of experimental research [20,21]. When the input rate is high, the non-symmetrical characteristic of the piezoelectric actuator is more serious. However, the classical Bouc–Wen model is used to describe a symmetrical hysteresis. When the input frequency or rate is low, the modeling error of the classical Bouc–Wen model is not large. However, when the input frequency or rate is high, its modeling error is large. Therefore, it can be found that the classical Bouc–Wen model is mainly used to characterize the rate-independent hysteresis behavior and modeling, but cannot characterize the rate-dependent hysteresis behavior precisely though it is a rate-dependent hysteresis model according to traditional classifications. The modeling accuracies of published hysteresis models are not high enough. Furthermore, due to the existence of many parameters and differential equations, it is a hard task to identify the parameters of hysteresis models.

In our previous work [22], we have developed an enhanced Bouc–Wen model by introducing input frequency. But there is a limitation that the developed model cannot be applied when the input frequency is unknown. To solve the problems above, this paper proposed a generalized Bouc–Wen (GB–W) model by introducing relaxation functions in the classical Bouc–Wen model, which can characterize both rate-independent and rate-dependent hysteresis behavior for piezoelectric ceramics actuators precisely. A lot of experiments are conducted in advance to characterize hysteresis behaviors and subsequently the relaxation functions are determined cautiously based on these experimental characteristics. The generalized Bouc–Wen (GB–W) model doesn't have the aforementioned limitation and can be widely applied. In addition, the nonlinear least squares method through MATLAB/Simulink is used to identify the corresponding parameters of hysteresis models. Both simulations and experiments finally demonstrate the validity of the developed model. Therein, the classical Bouc–Wen model is set as a comparison. The rest of this paper is arranged as follows: Section 2 gives the descriptions of the classical Bouc–Wen model. In Section 3, the generalized Bouc–Wen model is presented. Section 4 gives the experimental validation of results and discussion. Finally, conclusions are drawn in Section 5.

#### **2. Classical Bouc–Wen Model**

.

The Bouc–Wen model was initially applied for nonlinear vibrational mechanics. With the rapid development of smart actuators, it was gradually used to describe nonlinear hysteresis for piezoelectric actuators. The hysteresis curve can be considered as the superposition of a linear component *X*(*t*) and a hysteretic component *h*(*t*). The classical hysteretic Bouc–Wen model as a nonlinear system is described as follows:

$$y(t) = X(t) + h(t) = k \cdot u(t) + h(t) \tag{1}$$

$$\dot{h}(t) = a\dot{u}(t) - \beta \dot{u}(t)|h(t)|^n - \gamma |\dot{u}(t)| |h(t)|^{n-1} h(t) \tag{2}$$

where *u*(*t*) is the input voltage and *y*(*t*) is the output displacement. *k*, *α*, *β*, *γ* and *n* are the model parameters, which decide the shape of hysteresis curves. In order to simplify the model, *n* is usually set as 1 and the hysteretic components is expressed by

$$h(t) = a\dot{u}(t) - \beta \dot{u}(t)|h(t)| - \gamma \left|\dot{u}(t)\right| h(t) \tag{3}$$

#### **3. Generalized Bouc–Wen Model**

To analyze the performance of the classical Bouc–Wen model in detail, some efforts were devoted to research on the characteristics of its parameters. The variations of its parameters *k* and *α* at different frequencies of the input are shown in Figures 1 and 2, respectively. Table 1 gives the detailed values of parameters of the classical Bouc–Wen model at different frequencies. The parameters were identified by the nonlinear least squares method through MATLAB/Simulink, which will be introduced in detail in the next part. The identified results based on experimental data clearly reveal that the parameters *k* and *α* both decrease with the increase in frequency. Such frequency dependence of the parameters *k* and *α* cannot be described by the classical Bouc–Wen model. The parameters of the classical Bouc–Wen model are fixed constants, which cannot characterize their change trend with the increase in frequency. To some degree, thus, it can be concluded that the classical Bouc–Wen model cannot describe rate-dependent hysteresis behaviors.

**Figure 1.** Variations of *k* under *u*(*t*) = 5 sin(2*π f t*) + 5 at different frequencies.

**Figure 2.** Variations of *α* under *u*(*t*) = 5 sin(2*π f t*) + 5 at different frequencies.

**Table 1.** Identified parameters of the classical Bouc–Wen (CB–W) model at different frequencies.

#### *3.1. Formulation of the Generalized Bouc–Wen Model*

.

The results above show that the classical Bouc–Wen model cannot describe rate-dependent hysteresis behaviors precisely. It is resulted by a non-symmetrical hysteresis of piezoelectric actuators while the classical Bouc–Wen model is a symmetrical model. Therefore, the classical Bouc–Wen model should be redefined to solve this problem. However, it should be noted that the redefined model should possess the capacity to describe both rate-independent and rate-dependent hysteresis, which is the purpose of this work.

How to formulate a generalized hysteresis model of piezoelectric actuators is still a hard task and some researchers have made some contributions, such as, Al Janaideh et al. [23] presented a generalized P-I model with relaxation functions to characterize rate-dependent hysteresis behaviors. Mayergoyz [24] proposed a generalized Preisach model of hysteresis by introducing a generalized density function. Based on the experimental dynamic characteristics and researchers' experiences above, the generalized Bouc–Wen model is thus formulated upon integrating relaxation functions *k*(*v*(*t*), .*v*(*t*)) and *α*(*v*(*t*), .*v*(*t*)), such as

$$y(t) = X(t) + h(t) = k(u(t), \dot{u}(t)) \cdot \dot{u}(t) + h(t) \tag{4}$$

$$\dot{h}(t) = a(u(t), \dot{u}(t)) \cdot \dot{u}(t) - \beta \dot{u}(t)|h(t)| - \gamma |\dot{u}(t)|h(t) \tag{5}$$

.

where *k*(*u*(*t*), .*u*(*t*)) and *α*(*u*(*t*), .*u*(*t*)) are relaxation functions of current input *u*(*t*) and its rate of .*u*(*t*) and *k*(*u*(*t*), .*u*(*t*)) is a positive function. It should be noted that the generalized Bouc–Wen model could describe rate-independent hysteresis behaviors at low input frequency. Therefore, *k*(*u*(*t*), .*u*(*t*)) and *α*(*u*(*t*), .*u*(*t*)) should converge to fixed constants when the input frequency is low. Based on the characteristics above, the relaxation functions can be defined by inducing the exponential function, such as

$$k(u(t), \dot{u}(t)) = p e^{-q\dot{\hat{u}}(t)} \tag{6}$$

$$\kappa(\dot{u}(t), \dot{u}(t)) = \varepsilon e^{\delta |\dot{\hat{u}}(t)|} \tag{7}$$

where *p* ≥ 0, *q* ≥ 0, *ε*, *δ*, *β* and *γ* are constants. From the expressions of the relaxation functions, it can be found that when the input frequency is very low, such that .*u*(*t*) ∼= 0, the relaxation functions converge to fixed constants, such that *k*(*u*(*t*), .*u*(*t*)) ∼= *p* and *α*(*u*(*t*), .*u*(*t*)) ∼= *ε*. Thus, it can describe the rate-independent hysteresis model the same as the classical Bouc–Wen model.

Compared with the classical Bouc–Wen model, the parameters *k*(*u*(*t*), .*u*(*t*)) and *α*(*u*(*t*), .*u*(*t*)) of the proposed model values vary with the rate of input, which are not fixed constants any more. Furthermore, the parameters *k*(*u*(*t*), .*u*(*t*)) in the rising hysteresis curves at the same input are different from that in the decreasing hysteresis curves. The characteristics above form the non-symmetrical hysteresis of the proposed model.

#### *3.2. Properties of the Generalized Bouc–Wen Model*

This section will focus on analyses of the properties of the generalized Bouc–Wen model. First, the characteristics of the hysteretic component *h*(*t*) should be analyzed based on simulations. Figure 3 shows the relationship between *h*(*t*) and input frequency *f* under input voltage *u*(*t*) = 5 sin(2*πf t*) + 5. The corresponding parameters of the generalized Bouc–Wen model are set as *p* = 0.2107, *q* = 1.189 × 10−5, *ε*= −0.1331, *δ* = 5.2622 × 10−4, *β* = 5.3743 and *γ* = 6.4698. The results clearly reveal that the width of the hysteretic component *h*(*t*) increases monotonically with the increase in the rate of input.

**Figure 3.** Variations of the component *h*(*t*) under *u*(*t*) = 5 sin(2*π f t*) + 5: (**a**) *f* = 10 Hz; (**b**) *f* = 40 Hz; (**c**) *f* = 80 Hz.

Figure 4 shows the relationship between the component *X*(*t*) and input frequency *f* . The results reveal that the component *X*(*t*) is still nearly linear with input voltages at low frequencies. But the curves of the component *X*(*t*) have a hysteresis loop with the increase in frequencies, which shows non-symmetrical characteristics.

**Figure 4.** Variations of the component *X*(*t*) under *u*(*t*) = 5 sin(2*π f t*) + 5: (**a**) *f* = 10 Hz; (**b**) *f* = 40 Hz; (**c**) *f* = 80 Hz.


*Edited by Maguid Hassan*

Recent advances in relevant technologies have led to innovative solutions that can achieve reliable, resilient, and sustainable designs. Such characteristics are now considered essential for any newly designed engineering system. Such innovative solutions include applications of artificial intelligence in the design of integrated smart systems. This book discusses the implications of new concepts such as sustainability, resilience, health monitoring, and artificial intelligence on the reliability of designed systems.

Published in London, UK © 2023 IntechOpen © NiseriN / iStock

Avantgarde Reliability Implications in Civil Engineering

Avantgarde Reliability

Implications in Civil

Engineering

*Edited by Maguid Hassan*


**4.4 Brain natriuretic peptide**

Zyng et al. reported that high brain natriuretic peptide (BNP) levels were associated with AF recurrence after catheter ablation [28]. In contrast, we studied postprocedural ablation predictors of AF recurrence after univariate and multivariate analyses in 82 patients, which found no relationship between BNP level and AF recurrence. This might be attributed to the policy of the institute, in which AF ablation is performed after adequate medical treatment for heart failure is administered. Thus, the relationship between BNP level and AF recurrence after catheter ablation requires further investigation.


2.5.2. T-Cell-Mediated Rejection Diagnosis

The previously mentioned study by Tinel et al. also provided separate outcome analysis for CXCL9 and CXCL10, with the best performance for TCMR diagnosis with a NPV of 98% and a very good AUC of 0.81 [16]. Also of note, CCL2, at a threshold level of 198 pg/mL, yielded very good performance (AUC 0.81) for TCMR identification among a population of 300 normal and dysfunctional grafts in the study by Raza et al. [43]. Urinary exosome proteins were investigated in two case-control studies for the diagnosis of TCMR. Lim et al. found significantly higher urinary tetraspanin-1 (TSPAN1) and hemopexin (HPX) expression levels in TCMR patients with good diagnostic performance (AUC 0.74) [28], while Park et al. reported the initial results of an optimized integrated kidney exosome analysis (iKEA) able to distinguish TCMR from normal histology patients, with a very good performance (AUC 0.84) in a small validation cohort [33].


*Limitations of the Study*

This study provides very novel information about a series of indicators of mental health in a student population that had been poorly studied to date in a pandemic situation, thereby updating the knowledge on this topic to contribute to future research lines that address this problem. However, this study presents some limitations that must be pointed out. One of such limitations is that, despite the large sample size, most of the population who responded to the questionnaire were Spanish, thus the data cannot be extrapolated to the international scope. Similarly, participation in this study was voluntary, which can pose a sample selection bias.

Further research in this line is required, with larger populations and greater representation of international origin, in order to extrapolate the results. Moreover, randomized sampling must be applied to avoid selection bias.

#### **5. Conclusions**

In sum, the final-year nursing students who participated in this study suffered negative psychological consequences during the pandemic. These participants showed a high level of psychological discomfort. The differences in the latter were related to sociodemographic variables such as age, sex, university of origin, and feeling ready for the world of work.

The COVID-19 pandemic has caused serious repercussions. Therefore, in these periods of health crisis, it is necessary to develop research lines focused on studying the phenomenon and its impact with other variables, in order to know its effect on future healthcare professionals. This could help to prevent and address the negative personal aspects that can affect students' professional integration.

**Author Contributions:** Conceptualization, J.S.-P. and F.E.-L.; methodology, E.M.-S.; software, F.-J.G.- V.; validation, V.S.-N.; M.-d.-l.-Á.M.-G. and J.S.-P.; formal analysis, F.E.-L.; investigation, E.M.-S.; resources, F.-J.G.-V.; data curation, V.S.-N.; writing—original draft preparation, M.-d.-l.-Á.M.-G.; writing—review and editing, M.-d.-l.-Á.M.-G.; visualization, F.E.-L.; supervision, E.M.-S.; project administration, F.-J.G.-V.; funding acquisition, V.S.-N. All authors have read and agreed to the published version of the manuscript.

**Funding:** University of Huelva.

**Institutional Review Board Statement:** The formal aspects of the declaration of Helsinki were considered at all times. The study was approved by the Research Ethics Committee of Huelva (code: MG-COV-2021-02; internal code: 0742-N-21).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

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