**1. Introduction**

Radar modulation signal (RMS) recognition is the basis of radar electronic countermeasures and electronic jamming, and is a necessary problem in electronic warfare [1]. With the use of various multi-band and full-coverage communication equipment, electronic interference and signal aliasing have appeared in space. This makes the electromagnetic environment more complicated [2,3], which brings difficulties to the recognition of RMS in low-SNR environments. Because RMS has good performance in complex spaces, it is important to improve the probability of the successful recognition of RMS in low-SNR environments.

For the traditional recognition methods of RMS, those methods based on signal characteristic parameter matching proposed by [4] and the judgment method based on the expert system proposed by [5] are used to identify RMS. Both of them are disturbed by human factors and are not stable. In [6], the principal component analysis (PCA) was used to extract radar signal features. In [7], the support vector machine (SVM) and T–F distribution images of signals were used to identify RMS. These methods, based on traditional machine learning, tend to select the characteristics of RMS manually. The methods require much a priori knowledge and struggle to meet the recognition requirements for the new radar systems and the variety of modulated signals [8]. With the development of artificial intelligence, deep learning has also been applied to the recognition of radar signals. Classification and recognition based on deep learning have many advantages. Without human assumptions and intervention about the features to be extracted, the deep neural network can effectively learn the features of the signals [9]. Deep learning can better

**Citation:** Mao, Y.; Ren, W.; Yang, Z. Radar Signal Modulation Recognition Based on Sep-ResNet. *Sensors* **2021**, *21*, 7474. https://doi.org/10.3390/ s21227474

Academic Editors: Moulay A. Akhloufi and Mozhdeh Shahbazi

Received: 28 September 2021 Accepted: 7 November 2021 Published: 10 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

resist the interference of noise in the extraction of signal features, thereby improving the generalization ability and accuracy in the identification of RMS [10]. In [11], the input of the classification network is a one-dimensional RMS sequence based on the time domain, frequency domain, and autocorrelation domain. By combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a deep neural network (DNN), the recognition of RMS was completed. However, this method completed the signal recognition on a one-dimensional basis. The method had no denoising processing in the case of one-dimensional signal sequences. The result was not ideal in a low-SNR environment of ≤−10 dB. In [12], the improved AlexNet and Choi–Williams T–F distribution is used to complete the recognition of RMS based on two-dimensional images. This method did not denoise the T–F images, and the classification network was designed simply, resulting in weak anti-noise ability. This method cannot extract the features of T–F images well, and the PSR is low. In [13], the Cohen T–F transform method was used to convert the RMS to obtain T–F images, and the surface features of the image were extracted by CNN, and then the recurrent neural network was used to classify the T–F images of RMS. Because the images obtained by the Cohen transform had strong cross-terms, the characteristics of the signal experienced interference by the cross-terms, and the network could not extract the T–F image features well in low-SNR environments, resulting the PSR of the radar signal not being ideal when the SNR was −8 dB.

In this paper, in response to the difficulty in recognizing the RMS in low-SNR environments, we propose a novel method that combines the CMWT and the Sep-ResNet to accurately identify the RMS in a low-SNR environment of −13 dB. Through the enhancement T–F images by CMWT and the classification network of Sep-ResNet, the method in this paper has a strong anti-noise interference ability. This method can identify seven types of RMS, including normal signal (NS), linear frequency modulation (LFM), non-linear frequency modulation (NLFM), two-frequency shift keying (2FSK), two-phase shift keying (2PSK), four-frequency shift keying (4FSK) and four-phase shift keying (4PSK). The overall PSR of our method for seven types of RMS could reach 96.57% from 8 dB to −13 dB. In the case of SNR ≥ 2 dB, the PSR was 100%. In the case of low-SNR environments of −10 dB and −13 dB, the PSR was 93.44% and 88.24%, respectively.

Our major contributions are summarized as follows:

The remainder of this paper is organized as follows. Section 2 introduces the related work in the field of radar-modulation signal recognition. Section 3 introduces the recognition system framework and the proposed method, including the T–F analysis of CMWT, the T–F image-enhancing algorithm, and the improved classification network of Sep-ResNet. Section 4 shows the experimental data and results, and discusses the effectiveness of our method. Finally, Section 5 includes the conclusion of the whole work.

#### **2. Related Work**

In the past, many scholars have devoted themselves to exploring the automatic recognition system of RMS in applications. They have proposed several practicable approaches, making the system more intelligent, more robust, and less artificial. These achievements have pushed forward the development of the field of RMS recognition.

The recognition of RMS includes the extraction and classification of characteristics. In [7], the T–F analysis was used to extract signal characteristics. The SVM and auto encoder

were used to classify the signal. This method introduced a slack variable to consider a non-linearly separable problem to find the best hyperplane so that the classification result had widths of the maximum margin. The method solved the problem of highdimensional classification by selecting a suitable kernel function. The method identified RMS successfully, and the PSR was 82% in an SNR environment of −6 dB. The authors of [11] proposed a network combining CNN, LSTM, and DNN. They successfully identified six types of RMS when the SNR was from −14 to 20 dB. This method extracted the characteristics of the signal as the original one-dimensional sequence in the time domain, the fast Fourier transform sequence in the frequency domain, and the result of signal autocorrelation in the autocorrelation domain. A CNN was used to extract the surface features of the signal in different domains, and the features extracted by the CNN were used as the input of the LSTM, and the DNN was used to classify the characteristics of the signal. The length of the signal sequence extracted by this method needed to be set in advance, and the sequence length was different in the time domain, frequency domain, and autocorrelation domain. Under the preset optimal sequence length, the PSR was about 90% when the SNR was −6 dB. This method required preprocessing to obtain the optimal sequence length of a signal for a specific domain, which had limitations. Moreover, the features of one-dimensional sequence were not as rich as that of two-dimensional T–F images, and the recognition accuracy was not as high as that obtained in [14]. When signal features were extracted by the one-dimensional sequence, some feature parameters needed to be manually selected for the data. The T–F analysis method can overcome the shortcomings of the Fourier transform and reflect the signal characteristics in the twodimensional space of T–F. By obtaining the T–F images, the order of appearance of each frequency component can be well distinguished. The T–F analysis method can adequately extract the characteristics of non-stationary signals, such as RMS. In [12], the Choi–Williams distribution (CWD) was a method of T–F analysis that was used to extract the features of a modulated signal *x*(*t*). The expression is as follows:

$$\mathbb{C}\_{x}(t,\omega) = \frac{1}{4\pi^{2}} \iiint\_{\infty} \phi(\tau,\nu) e^{-j(\tau t + \omega \tau)} \mathbf{x} \left(\mu + \frac{\tau}{2}\right) \mathbf{x}^{\*} \left(\mu - \frac{\tau}{2}\right) e^{j\nu u} d\nu d\tau d\mu \tag{1}$$

where *t* and *ω* are the time and frequency coordinates, respectively. *x*∗(*t*) is the conjugate expression of *x*(*t*). *φ*(*τ*, *v*) and *σ* are the kernel function and filter bandwidth, respectively. In [12], the kernel function was a Gaussian kernel *φ*(*τ*, *v*) = *exp* −(*τv*) 2 *σ* ! and *σ* = 1. The larger *σ*, the better aggregation of signal on the T–F images. However, a large *σ* will bring more serious cross-terms by *x*(*t*) times *x*∗(*t*). Cross-terms will reduce the quality of T–F images. In the improved AlexNet, the dropout is added to reduce overfitting, the size of the convolution kernel is modified, the receptive field of convolution is increased, and the fully connected layer is reduced to decrease the weight parameters. When the SNR is −2 dB, the recognition rate can reach 80%. In [15], the signal characteristics were extracted through the improved phase difference short-time Fourier transform (STFT). The STFT of the signal is expressed as follows:

$$STFT(t,f) = \int\_{-\infty}^{+\infty} x(\tau)w(\tau - t) \exp(-j2\pi f \tau)d\tau\tag{2}$$

where *w*(*t*) and *x*(*t*) are fixed-width window functions and signals to be analyzed, respectively. Through the continuous sliding of the *w*(*t*) window, the Fourier transform was performed in the window to extract the signal characteristics. It reduced the influence of noise by increasing the order of the phase difference. However, an increase in the phase difference order would increase the complexity of the algorithm. In addition, the STFT used a fixed-width window function to extract signal characteristics, resulting in low T–F resolution. When the SNR was −6 dB, the PSR of recognition result was 93%. In [14], by improving the kernel function of CWD, the original Gaussian kernel function was changed

to *φ*(*τ*, *v*) = *exp* −*ατ*2+*βv*2 *σ* , which solved the problem of *φ*(*τ*, *v*) = 0 when *τ* = 0 or *v* = 0. To a certain extent, it was able to reduce the interference of the cross-terms in the T–F distribution images. Furthermore, the T–F images were denoised by 2D-Wiener filtering. This method improved the PSR to 96% when the SNR was −6 dB, and the PSR was found to be 78% when the SNR was −8 dB. Due to the existence of cross-terms, this method does not perform well in low-SNR environments. In [16], the wavelet transform T–F analysis method was used to extract the characteristics of the signal. The wavelet transform represents an improvement upon STFT; the signal to be analyzed is decomposed into a series of superpositions of wavelet functions. The wavelet functions are obtained from the mother wavelet through translation and scaling transformation. The mother wavelet is stretched at low frequencies and compressed at high frequencies, and has the characteristics of multi-scale refinement. In order to restore the characteristics of the signal on the time and frequency scale, the wavelet transform method continually approximates the signal that is to be analyzed.

A large number of RMS recognition systems have been designed, but the identification of a variety of RMS with high accuracy in low-SNR environments of ≤−10 dB remains a challenging problem.

#### **3. System Framework and Method**

This paper designs an intelligent method to identify RMS with high accuracy in low-SNR environments. The first step is to transform the RMS into two-dimensional T–F images using CMWT. In the second step, the T–F images are grayed out, and the T–F images are denoised and enhanced through adaptive filtering and morphological processing to reduce the interference of noise on signal characteristics. In the last step, the enhanced T–F images are fed into the Sep-ResNet to train the model, and the trained model is used to accurately predict the type of RMS. The system framework is shown in Figure 1.

**Figure 1.** RMS recognition system framework.

The received RMS model is as follows:

$$x(t) = s(t) + n(t) \tag{3}$$

where *x*(*t*) represents the received RMS, and *t* is the time variable; *s*(*t*) and *n*(*t*) represent the transmitted RMS and the received noise, respectively. The transmitted RMS is as follows:

$$s(t) = A \text{rect}\left(\frac{t}{T}\right) e^{-j(2\pi f\_c t + \phi(t) + \phi\_0)}\tag{4}$$

$$\text{rect}\left(\frac{t}{T}\right) = \begin{cases} \ 1, \left|t/T\right| \le 1/2\\ \ 0, \left|t/T\right| \ge 1/2 \end{cases} \tag{5}$$

where *A* represents the signal amplitude, and *A* = 1 during simulation. The *rect*(•) is the rectangular threshold function as shown in Equation (5), *T* is the pulse width of the signal,

*fc* is the signal carrier frequency, *φ*0 is the initial phase of the signal, and *φ*(*t*) is the different phase function, which determines the different signal modulation modes.

To simulate the received noise in the real channel of signals, Gaussian noise, white noise, narrow-band Gaussian noise, and carrier frequency random disturbance are added [17]. Gaussian noise and white noise are some of the most common noises in actual channels. The probability density function of Gaussian noise obeys a normal distribution, with a mean value of 0 and a variance of 1. The power spectral density of white noise is a constant over the entire bandwidth and obeys a uniform distribution, *S*() = *N*0/2, ∈ (−∞, +∞). When adding white noise, it only needs to be added within the actual signal bandwidth. Often, in a real radar communication system, a band-pass filter of the target signal bandwidth is added at the receiving end. Because the communication frequency of the radar signal is high, much larger than the bandwidth of the band-pass filter, the generation of Gaussian narrow-band noise occurs. Narrow-band Gaussian noise obeys the Rayleigh distribution on the random envelope and obeys the uniform distribution on the phase. It is generally a stationary random process. Its mathematical model is *n*(*t*) = *nc*(*t*)*cosct* − *ns*(*t*)*sinct*, where *n*(*t*) represents the average power of narrow-band Gaussian noise, *nc*(*t*) is the co-directional component of *n*(*t*), and *ns*(*t*) is the orthogonal component of *n*(*t*). The mean value of each component is *E n*(*t*)]= *E*[*nc*(*t*)]= *E*[*ns*(*t*) = 0, and the variance is *σ*2 *n*=1. The jitter component of the carrier frequency is set to a random number in the range of 0 to 0.05 multiplied by the carrier frequency to simulate the error of the actual transmission carrier frequency and the interference received during transmission.

The T–F analysis method can transform one-dimensional signal sequences into twodimensional T–F images to obtain RMS characteristics.  Complex Morlet Wavelet Transform*

Wavelet transform (WT) is a T–F analysis method. WT decomposes the original signal into a series of superpositions of wavelet functions through mother wavelet translation and scaling transformation, which solves the problem that the fixed-width window function does not change with frequency in the STFT [15]. WT does not involve the conjugate multiplication of the signal itself, which avoids the occurrence of cross-terms in [12,15]. WT can obtain high T–F resolution. The mathematical model is as follows:

$$\text{CWT}\_{\mathbf{x}}(a,b) = \langle \mathbf{x}(t), \phi\_{a,b}(t) \rangle = \int\_{-\infty}^{+\infty} \mathbf{x}(t) \phi^\*\_{\
u,b}(t) dt = \frac{1}{\sqrt{a}} \int\_{-\infty}^{+\infty} \mathbf{x}(t) \phi^\* \left(\frac{t-b}{a}\right) dt \tag{6}$$

where *x*(*t*) is the signal to be analyzed, and *φ*∗ *a*,*b*(*t*) is a series of wavelet functions after the mother wavelet is translated and stretched. The transformation of the mother wavelet is as follows:

$$
\phi^\*\_{\
u,b}(t) = \frac{1}{\sqrt{a}} \phi\left(\frac{t-b}{a}\right) \tag{7}
$$

where *a* is the scale expansion factor and *a* = 0. When *a* increases, *φa*,*b*(*t*) will widen and the amplitude will decrease, showing that the wavelet is caused by the compression of the amplitude and the stretching of the width, corresponding to the analysis of low-frequency signals. When *a* decreases, the wavelet becomes narrower and the amplitude increases, corresponding to the analysis of high-frequency signals. *b* is the time shift factor, which changes the center position of the wavelet. WT has adaptive capabilities. By selecting the appropriate mother wavelet (symmetry, orthogonality, and similarity), more detailed features can be obtained in the T–F resolution.

In the WT of our method, the complex Morlet function was chosen as the mother wavelet. CMWT is a complex exponential sinusoidal Gaussian wavelet, which has symmetry and non-orthogonality. CMWT has a good ability to extract the local characteristics

of signals in the T–F domains and improves the resolution of T–F images. The complex Morlet mother wavelet model is given in Equation (8):

$$\phi(t) = \exp\left(\frac{-t^2}{2}\right) \exp(j\omega\_0 t) \tag{8}$$

$$\Phi(\omega) = \sqrt{2\pi} \exp\left(\frac{-\left(\omega - \omega\_0\right)^2}{2}\right) \tag{9}$$

Equation (9) is the Fourier transform of complex Morlet wavelet, where *ω*0 represents the center frequency. The complex Morlet mother wavelet *φ*(*t*) is divided into two parts: the real part and the imaginary part. A series of wavelet functions *φ*∗ *a*,*b*(*t*) can be obtained after the translation and scaling transformation using Equation (7). Incorporating Equation (8) into Equation (6), *CWTx*(*a*, *b*) = *CWTR* + *jCWTi*. By undertaking T–F analysis, the added imaginary part of the complex Morlet wavelet can express more changeable phase information on the original signal [18]. The complex Morlet wavelet has non-orthogonality and Gaussian adjustment, which make it possible to obtain T–F images of high time and frequency resolution through a series of variable scale wavelet functions. The CMWT avoids the interference of cross-terms in signal characteristics in low-SNR environments and improves the quality of T–F images. CMWT is suitable for the T–F analysis of RMS and can obtain clear T–F images. Figure 2 shows the T–F images of seven types of radar modulation signals of CMWT without adding noise.

**Figure 2.** The T–F images of seven types of radar modulation signals. The images from (**a**)–(**g**) are the T–F images of NS, LFM, NLFM, 2PSK, 2FSK, 4PSK, and 4FSK without noise. The abscissa of the image is the number of sampling points. The ordinate is the normalized frequency, which is the signal frequency divide by the sampling frequency. According to the Nyquist sampling theorem, the sampling frequency must be greater than twice the signal frequency to avoid signal aliasing. The normalization frequency is between 0 and 0.5.

In the absence of noise, the T–F images obtained by CMWT can clearly obtain the characteristics of different RMSs. The T–F images have no cross-terms interference, the signal characteristics will not be distorted, and the T–F resolution is high. Generally, the actual received radar signal will contain a lot of noise. The SNR will seriously affect the performance of the signal characteristics on the T–F images. The SNR is defined as follows:

$$SNR\_{dB} = 10 \log\_{10} (P\_s / P\_n)$$

$$P\_s = \frac{1}{N} \sum\_{t=0}^{N-1} |s(t)|^2 \tag{10}$$

$$P\_n = \frac{1}{N} \sum\_{t=0}^{N-1} |n(t)|^2.$$

where *s*(*t*) is the modulated radar signal, *n*(*t*) is the noise signal, *Ps* is the power of the signal, *Pn* is the noise power, and *N* is the signal length. The lower SNR, the greater the noise power, and the characteristics of signal are submerged by noise on the T–F images. Figure 3 shows T–F images of the noise with different SNR added to LFM signal by CMWT.

**Figure 3.** The T–F images of LFM signal in different SNR. The images from (**a**)–(**f**) are LFM T–F images under 10 dB, 5 dB, 0 dB, −5 dB, −10 dB and −15 dB noise, respectively.

In Figure 3, the SNR values are 10, 5, 0, −5, −10 and −15 dB, respectively. As the SNR decreases, although the characteristics of the LFM signal are still preserved, the quality of the T–F images deteriorates, and the signal characteristics are overwhelmed by noise. The difficulty of identifying the RMS will increase. Therefore, in this paper, the T–F images of the signal are properly denoised and enhanced before feeding into the CNN. T–F image enhancement can reduce the interference of noise, while better retaining the original characteristics of the signal. In addition, the enhancement algorithm improves the recognition rate of the RMS.

#### *3.2. T–F Image Enhancement*

The T–F analysis method is usually used to extract RMS characteristics and obtain T–F images. Before recognition, it is necessary to denoise and enhance the T–F images, which includes the following steps: image cropping and gray-scale, adaptive filtering [19], morphology processing [20], and normalization. Normalization involves the down-sampling of T–F images to reshape the images into a 64 × 64-pixel form. The enhancement of the T–F images will affect the Sep-ResNet extraction features and make possible the identification of the RMS. Algorithm 1 shows the enhancement algorithm for the adaptive filtering and morphological processing of T–F images.

The eroding operation is *A S*1 = {*z*|(*S*1)*z* ⊆ *A*}, and S1 is the structural element for eroding. The shape of S1 is designed to be round-like, which can better eliminate round-like noise points generated on the T–F images. The dilating operation is *A* ⊕ *S*2 = {*z*|(*S*2)*z* ∩ *A* = ∅}, and S2 is used as the structural element of dilating, which can enhance the characteristics of the RMS on the T–F images. The Opening Operation is first eroding and then dilating, as in formula: (*A S*1) ⊕ *S*2. The enhancement algorithm for T–F images is shown in Algorithm 1.

**Figure 4.** The morphological structural elements.

#### *3.3. Classification Network of the Sep-ResNet*

After the RMS is transformed by CMWT and enhanced, these T–F images were fed into the Sep-ResNet for the extraction of features and the classification of images to complete the recognition of RMS. At this point, the traditional neural networks will encounter some problems such as feature information loss, gradient vanishing, and gradient exploding as the network depth increases. It is hard to design a deeper network to extract the deep features of the images [21]. However, this paper uses the idea of residual learning, and introduces residual blocks and shortcut channels [22,23]. The classification network Sep-ResNet was designed, which solves problems of the loss of feature information, gradient vanishing, and gradient exploding. The Sep-ResNet can be designed to extract richer image features with greater depth. The Sep-ResNet structure was designed as shown in Figure 5.

In Figure 5, the Pre Conv uses three 3 × 3 convolution kernels to convolve the input image. The first one uses the convolution kernel step size s = 2 to conduct down sampling. The remaining two convolutions have the same receptive field as the original 7 × 7 convolution kernel, but the number of parameters is reduced by 45%. Furthermore, the features extracted by the smaller convolution kernel are more refined. Stage 1 includes two parts: the Down Sampling and the Residual Block. The Down Sampling part first

adjusts the number of channels in Path A by using a 1 × 1 convolution kernel and then uses a kernel of s = 2 at the 3 × 3 convolution. Reference [22] uses s = 2 for convolution at the 1 × 1 convolution kernel, which will completely lose 50% of the information of the feature map. At Path B, the convolution operation with a size of 1 × 1 and s = 2 is also replaced with an average pooling with a size of 2 × 2 and s = 2. The above adjustment can ensure that the information of the feature map will not be lost when conducting the Down Sampling. The output of Down Sampling involves the width and height of the feature map being reduced by half, and the number of output channels is increased. Another part of the improved Residual Block is shown in Figure 6.

**Figure 5.** The Sep-ResNet structure developed in this paper.

In Figure 6, the three parameters for convolution in the rectangle represent the input channel, kernel size, and output channel. The input channel of the residual block is M feature maps. The first layer of convolutional kernel size is 1 × 1 for convolution, and all of the four out channels are obtained M/16 feature maps. The obtained feature maps use the activation function of Leaky ReLu and conduct convolution with the kernel size of 3, 5, 7 and 9, respectively. The different sizes of convolutional kernels make it possible to obtain different receptive fields and extractions of the multi-scale features. The M/16 feature maps of four channels are stacked in a concatenated manner on the channel to obtain M/4 feature maps. This allows the obtaining of fused feature maps. The obtained feature maps use kernels size is 1 × 1 for convolution and mapping to obtain M feature maps. Finally, the obtained M feature maps and the original M feature maps before convolution are correspondingly added to each channel to obtain a residual block. This method has a larger receptive field than the original residual structure, and the extracted features are more abundant. Although the increase in residual block parameters is caused by increasing the receptive field of the kernels, there is no increase in the number of convolutions. The residual block only separates the channels instead of increasing the number of channels. The residual block output of Sep-ResNet is *Xn*+1, as follows:

$$X\_{n+1} = X\_n + \sum\_{i=1}^{4} F\_i(X\_{n\prime}, W\_n) \tag{11}$$

where *Fi*(*Xn*, *Wn*) is the output of *Xn* after convolution in the *i*-th channel. The improved residual block can extract the features of T–F images with multiple scales and multiple receptive fields. The Sep-ResNet extracted features are more abundant in low-SNR environments, which increases the recognition accuracy of the RMS. Stage 1 repeats the residual

block 3 times, and the output of Stage 1 is the input of Stage 2. Stage 2 repeats the above residual block 4 times, and the output of Stage 2 is the input of Stage 3, and so on. The structure of Sep-ResNet has a total of 53 layers of the network, including 51 convolutional layers, one auto average-pool layer, and one fully connected layer. In the Batch Normalization (BN) layer, the data of a mini batch are normalized to uniformly distributed data with a mean of 0 and a variance of 1, which can better prevent the problems of overfitting and vanishing of gradients [24]. The activation function is Leaky ReLu: *x* = *max*{0.01*x*, *x*}.The initial learning rate (LR) of the training model is LR = 0.001, and the learning rate is adjusted to LR = LR \* 0.5 every 20 epochs. As the number of iterations increases, the learning rate decreases, for a total of 100 epochs. The auto average pool makes the feature maps of any width and height become size = 1 × 1 feature maps and then maps them to seven types of radar modulation signals through a full connection. The final Softmax layer maps the output probabilities of each type to between 0 and 1.

**Figure 6.** Comparison of the residual block. (**a**) is the original residual block of ResNet50 in [22]; (**b**) is the improved residual block in this paper. The residual block of (**b**) has multi-channel feature fusion and the multiple receptive fields.

The loss function in this experiment is cross-entropy, and label smoothing [25] is introduced to reduce the over-fitting of the model, as shown in Equation (12).

$$y' = (1 - \varepsilon) \times y + \frac{\varepsilon}{K} \tag{12}$$

$$Loss = -\sum\_{i=0}^{n} \left[ y' \times \log p + (1 - y') \times \log(1 - p) \right] \tag{13}$$

where *y* is the original label (named the hard label) and *y* is the smoothing label (named the soft label), the allowable error rate *ε* is 0.1, the number of categories *K* is 7, *p* is the prediction result, and *loss* is the error between the prediction result and the given truth label. The hard label only has values of 0 and 1. If there is a label error, the model will learn the features of the image in the wrong direction, resulting in poor model generalization and easy overfitting. The soft label allows a certain error tolerance rate, which can alleviate the overfitting of the model. Considering that the signal characteristics are not obvious and are severely polluted by noise when the SNR is low, the signals can easily be misclassified in low-SNR environments. The addition of label smoothing also causes the model have a certain anti-noise ability, which alleviates the problem of the loss function of cross-entropy being easily overfitted. Finally, back propagation is used to update the weight parameters of each layer to complete the training of the RMS recognition model.

#### **4. Experimental Results and Discussion**

In this part, the experimental dataset and the results are given. According to the framework of Figure 1, the method processes radar signals to obtain T–F images, and the separable Sep-ResNet channels classify the enhanced T–F images. Our method accurately identified seven radar modulation signals in low-SNR environments of ≤−10 dB.

#### *4.1. Experimental Dataset*

The experimental environment used for the generation of the simulation signals and T–F images was MATLAB2018a. The deep learning framework for training and predicting model was Pytorch1.5.

The experimental data comprised the radar signal of seven modulation modes generated by simulation, namely NS, LFM, NLFM, 2FSK, 2PSK, 4FSK, and 4PSK. The frequency of the modulation signal was the normalized frequency, which was the signal frequency divided by the sampling frequency. Table 1 shows the specific modulation method, carrier frequency, bandwidth and Baker codes of RMS.

**Table 1.** The specific simulation parameters of RMS.

Note: The pulse width for each type signal is 0.5 μs and sampling frequency is 2 GHZ.

In Table 1, the simulated RMS parameters are the dynamic range [9]. To simulate the actual received signal, the modulated signal had the range of a certain parameter, and the noise was added. The SNR of radar modulation signals was −13, −10, −7, −4, −1, 2, 5 and 8 dB, respectively. An SNR point was taken every 3 dB, for a total of eight SNR points. A total of 400 T–F images were taken for each SNR point, and each radar modulation signal contained 3200 T–F images. There were a total of 22,400 T–F images in this dataset. Overall, 60% of the dataset was used as the training set—a total of 13,440 T–F images; 40% was used as the test set—a total of 8960 T–F images.

The T–F images shown in Figure 7 could obtain the characteristics of the NLFM signal well, but the characteristics of the T–F images were gradually overwhelmed by noise as the SNR decreased. When the SNR was ≥−1 dB, the signal characteristics were clear in the T–F images. When the SNR was ≤−4 dB, the characteristics became vague. When the SNR was −10 dB, the noise seriously interfered with the signal characteristics. Furthermore, when the SRN was −13 dB, the characteristics were completely overwhelmed by noise.

**Figure 7.** T–F images of the NLFM signal in different SNR from −13 dB to 8 dB in the training set. The images from (**a**)–(**h**) are NLFM grayscale T–F images under 8 dB, 5 dB, 2 dB, −1 dB, −4 dB, −7 dB, −10 dB and −13 dB noise, respectively.

#### *4.2. Experimental Results*

The T–F images of the RMS in the training set were fed into the CNN after denoising and enhancement to the train models for the recognition of the RMS.

#### 4.2.1. Verification of the Effectiveness of CMWT

In this paper, the RMS used three T–F analysis methods, including STFT [15], CWD with an improved kernel function [14], and CMWT, to obtain the T–F images. The T–F images were fed into AlexNet, the improved AlexNet [12], and ResNet50 [22], and our Sep-ResNet for the classification of T–F images, which used an enhancement algorithm, is shown in Algorithm 1. The probability of the successful recognition (PSR) of different T–F analysis methods were compared, as shown in Figure 8:

**Figure 8.** The PSR of three T–F analysis methods in different classification networks.

 The T–F analysis method of CMWT had the highest PSR of the four networks, while the CWD method with an improved kernel function had the middle PSR, and the STFT method had the lowest PSR. Because CMWT had the advantage of containing wavelets with transformable scales, there were no cross-terms and phase information in the imaginary part. It showed anti-noise ability and effectively extracted the RMS characteristics. It was found that the improved T–F analysis method can improve the PSR of the RMS. For the four CNNs, the overall PSR values of the three T–F analysis methods of the CMWT, CWD, and STFT are shown in Table 2.

In Table 2, it can be seen that the T–F analysis method of the CMWT and the Sep-ResNet classification network had the highest PSR of the seven radar signals. The overall PSR of this method was 96.57%.

#### 4.2.2. Verification of the Effectiveness of Sep-ResNet

To verify the effectiveness of the classification network of the proposed Sep-ResNet, the enhanced T–F images obtained via the CMWT were fed into the Sep-ResNet for training of the recognition model. The validation dataset was composed of 20% of the test dataset. Because the CMWT was able to better extract the RMS characteristics to obtain clear and distinguishable T–F images, the loss function successfully converged as the number of epochs increased, as shown Figure 9.

**Figure 9.** (**a**,**b**) are the curves of the validation accuracy and loss function value, respectively, obtained during the training of Sep-ResNet. The abscissa is the number of epochs. An epoch represents all samples in the training set being trained once. The ordinate of (**b**) is the specific loss function value under the current epoch.

In the experiment, we conducted a classification comparison of AlexNet, ResNet50, VGGNet16, Inception-v3, and the backbone of U-Net models. The AlexNet structure was consistent with that reported in [12]. The residual structure of ResNet50 [22] is shown in Figure 6. VGGNet16 [26] increases the number of the output channels and uses max pooling to reduce the size of the feature map. VGGNet16 has a total of 16 convolutional layers, which is deeper than AlexNet. By decomposing large convolution filters such as 5 × 5 into two 3 × 3 filters, Inception-v3 [25] has improved performance on VGGNet. The

parameters are reduced by 28%. Furthermore, Inception-v3 uses an asymmetric method to decompose the spatial convolution filter. The n × n size filter is decomposed into 1 × n and n × 1 filters to further reduce the parameters and improve performance. The main idea of U-Net is to use a feature pyramid network for feature fusion [27]. The U-Net described in this paper used a 3 × 3 size filter to convolve a T–F image of (1, 64, 64) size to obtain a feature map of (64, 32, 32) size, where the dimensions were the output channel, image width, and image height. These features were deconvolved again to obtain a feature map of size 128, 16, 16, the feature map was copied, and it was named as *m*1. Then, the convolution was continued to obtain the feature maps of size (256, 8, 8) and (512, 4, 4), which were named *m*2 and *m*3, respectively. *m*3 was upsampled to obtain feature maps of 512, 8, 8 size, and concatenated with *m*2 on the channel to obtain sizes of 768, 8, 8. Then, up-sampling allowed the feature maps of 768, 16, 16 size to be obtained, and these were added to *m*1 in order to obtain the feature maps of 896, 16, 16 size. Finally, four 3 × 3 size convolution filters and fully connected layers were mapped to seven classification nodes, corresponding to seven types of radar signals. The key to U-Net is the integration of deep and shallow features to better express image features.

During the experiment, the data processing method was consistent with the use of CMWT and the enhanced algorithm in Algorithm 1. The T–F images were fed to AlexNet, improved AlexNet, VGGNet16, Inception-v3, U-Net, ResNet50, and our Sep-ResNet, respectively. The PSR values of seven CNNs are shown in Figure 10.

**Figure 10.** The PSR of seven CNNs.

The proposed Sep-ResNet had the highest PSR for the above seven radar modulation signals. Under SNR = −13 dB, the PSR of the Sep-ResNet was still 88.24%. The PSR of the AlexNet, improved AlexNet, VGGNet16, Inception-v3, ResNet50 and U-Net were 58.36%, 62.31%, 81.62%, 83.17, 85.92, and 86.81%, respectively. Because the residual network was able to design the deep enough network structure to extract T–F image features, we observed that Sep-ResNet has a multi-scale receptive field and multi-channel feature fusion, which can extract richer features of T–F images. It was found that Sep-ResNet has the best classification effect in low-SNR environments, improves the PSR, and has the anti-noise ability. Furthermore, the Sep-ResNet model showed a recognition rate of 100% for the above seven radar signals when the SNR was ≥2 dB.

Figure 11 shows the PSR of the Sep-ResNet model for seven radar modulation signals at different SNRs. Among the seven modulation signals, NLFM, 2FSK, 4FSK, and LFM had higher recognition rates. When the SNR was −13 dB, their average PSR was able to reach 93.45%, and when the SNR was ≥−4 dB, the PSR could reach 100%. The average PSR of the remaining signals—NS, 2PSK, and 4PSK—was 81.36% when the SNR was −13 dB. Their PSR was lower because the signal characteristics were relatively similar on the T–F images. Furthermore, with the decrease in SNR, the signal characteristics were overwhelmed by noise, resulting in low recognition accuracy (see Figure 12).

**Figure 11.** The PSR of seven RMSs in Sep-ResNet.

**Figure 12.** The T–F images of NS, 2PSK, and 4PSK when the SNR was −1 dB and −10 dB, respectively. The (**a**,**d**) are the grayscale T–F images of the signal NS under −1 dB and −10 dB noise, respectively. The (**b**,**e**) are the grayscale T–F images of the signal 2PSK under −1 dB and −10 dB noise, respectively. The (**c**,**f**) are the grayscale T–F images of the signal 4PSK under −1 dB and −10 dB noise, respectively.

The input of the confusion matrix was 100 random enhanced T–F images from the test dataset by CMWT when the SNR was −4 dB. The output on the diagonal was the recognition recall rate of each radar modulation signal. The number of identification errors was divided on the diagonal. From Table 3, we can conclude that the Sep-ResNet model had identification errors for NS, 2PSK, and 4PSK. The PSR values of NS, 2PSK, and 4PSK were 92%, 94%, and 95%, respectively. These values could be identified with 100% accuracy for the remaining four types of signals—the NS, 2PSK, and 4FSK identify errors—because their T–F images were very similar. The strong noise interfered with the characteristics of the signal, which led to errors in the Sep-ResNet classification. The LFM, NLFM, 2FSK, and 4FSK had high recognition accuracy because the features of their T–F images were distinguishable. Therefore, it can be concluded that an effective T–F analysis method is very important for the identification of radar signals. The CMWT we proposed can

obtain clear T–F images, and there are no cross-terms. Our Sep-ResNet also has good classification performance.

**Table 3.** The confusion matrix of Sep-ResNet model for signal recognition when the SNR was −4 dB.

4.2.3. Verification of the Effectiveness of T–F Image Enhancement

To verify the effectiveness of the proposed enhancement algorithm for T–F images, the T–F images obtained by CMWT were enhanced using the algorithm in Algorithm 1, which also shows the image-enhancement processing results.

Figure 13b shows the grayscale T–F image of LFM signal after adaptive filtering and morphological processing. The noisy T–F images had better reductions in their noise interference, which was caused by their enhancement, and they retained the characteristics of the signal. The seven types of pre-enhanced and post-enhanced T–F images were fed into the Sep-ResNet model for recognition.

**Figure 13.** The LFM signal enhancement process at SNR = 8 dB. The (**a**) is the image before enhancement. The (**b**) is the image enhanced by the algorithm in this paper.

In Figure 14, it can be seen that after the T–F image enhancement algorithm was implemented, the overall PSR was improved by 2.35%, especially in the low-SNR −13, −10, −7, and −4 dB interval, by an average of 4.21%. When the SNR was −13 dB, the PSR was increased by 7.08%. The proposed enhancement algorithm could improve the PSR of radar-modulated signals in low-SNR environments. In general, a good image denoising and enhancement algorithm can reduce the interference of noise while retaining the characteristics of the signal, thereby improving the quality of T–F images and improving the system PSR of radar-modulated signals in low-SNR environments.

**Figure 14.** The PSR of the pre-enhancement and post-enhancement.

#### **5. Conclusions**

In response to the difficulty in identifying radar modulation signals in low-SNR environments, this paper proposed a method for combining the T–F analysis methods of CMWT and Sep-ResNet to intelligently identify radar modulation signals. In this paper, the T–F analysis of CMWT was used to extract the two-dimensional feature of the signal to obtain T–F images, and the images were enhanced through adaptive filtering and morphological processing. The enhanced T–F images were used as the input of Sep-ResNet for classification to intelligently and accurately achieve the recognition of radarmodulated signals in low-SNR environments. The experiments show that the T–F analysis of CMWT was better than the STFT and the improved CWD model. The classification performance of the proposed Sep-ResNet was better than AlexNet, the improved AlexNet, VGGNet16, Inception-v3, ResNet50, and the backbone of U-Net. Furthermore, the proposed enhancement algorithm was effective in filtering out the noise on T–F images. The method proposed successfully identified seven types of radar modulation signals (NS, LFM, NLFM, 2FSK, 2PSK, 4FSK, and 4PSK) in low-SNR environments. With SNR values ranging from −13 dB to 8 dB, the overall recognition rate was 96.57%, which was sufficient to effectively identify radar-modulated signals. Therefore, this method has the ability to resist noise interference, and can still maintain high PSR in low-SNR environments of ≤−10 dB, thereby avoiding the difficulty and instability involved in the manually identification of radar modulation signals.

**Author Contributions:** Conceptualization, W.R., Z.Y. and Y.M.; investigation and analysis, W.R. and Z.Y.; software, Y.M.; validation, Y.M. and W.R.; writing—original draft preparation, Y.M. and W.R.; writing—review and editing, Y.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant No. 61725105.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. We certify that the submission is original work and is not under review at any other publication. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**

The following abbreviations were used in this manuscript:

#### **References**

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2.1.4. Graphene

Graphene is a two-dimensional (2D) material of honeycomb structures formed by single-layer carbon atoms arranged in hexagonal lattices. Graphene has complex conductivity that supports the propagation of plasmonic modes at THz frequencies [68]. The surface conductivity can be efficiently controlled through a perpendicular-bias electric field that induces charge carriers to shift the graphene chemical potential (Fermi level) away from the Dirac point. Compared with conventional semiconductors, graphene has the attractive advantages of high electron mobility, considerable thermal conductivity, and strong mechanical ductility [69]. Hence, graphene has considerable potential to be applied in the THz frequency for dynamic wave control.

A graphene-based electroabsorption modulator was demonstrated by placing atomically thin graphene layer on top of a dielectric substrate with a reflective metal back gate (Figure 5a) [70,71]. The substrate thickness was designed to be an odd multiple of a quarter-wavelength of the incident wave to enhance the modulation depth. With a biasing voltage of −10 V, the Fermi level in graphene was tuned at the Dirac point, realizing the maximal reflectance. The carrier concentration increased with increased voltage, resulting in enhanced absorption. The graphene layer was patterned using O2 plasma to demonstrate a 4 × 4 pixelated reflectance modulator (Figure 5b) [71]. An alternative approach using electrolyte gating, producing higher charge densities to build a spatial phase modulator, was presented in [72] (Figure 5c). Large electric fields were generated at the graphene–electrolyte interface, giving rise to charge accumulation over a large area without an electrical short circuit and removing the thickness control. In order to build the 16 × 16 pixelated modulator, graphene was grown by chemical-vapor deposition (CVD) on a large area. Selective control of a single pixel was achieved by voltage biasing through the corresponding column and row (Figure 5d).

A reflect array combined with graphene for dynamic beam steering was first proposed using a square graphene patch (Figure 5e) [68]. Due to the slow wave propagation in the plasmonic mode of graphene, a patch was designed with much smaller dimensions (*λ*/10, compared with conventional conductors of *λ*/2) for a resonance response. This reduced interelement spacing allows for more efficient wavefront manipulation. By tuning the chemical potential from 0 to 0.52 eV, a phase coverage of 300◦ was obtained at 1.3 THz with a fixed patch dimension. To have full 360◦ phase coverage for dynamic-gradient phase control, graphene was designed with resonant structures (Figure 5f) [73,74] or combined with resonant metallic patterns [75,76]. A graphene-based coding metasurface for beam splitting was proposed by controlling the Fermi level (Figure 5g) [77–81]. Such coding metasurfaces also have great potential to be applied to security systems for message transmission [81]. Spatially selective column-level tuning was presented with graphene embedded in SRR, resulting in four deflection angles (5◦, 11◦, 17◦, and 23◦) at 1.05 THz (Figure 5h) [82]. Such graphene-based beam steering metasurfaces had only been tested in simulations. The first experimentally demonstrated design was illustrated in [83] (Figure 5i). The graphene strip was placed at the gap of a bowtie structure for field concentration. Each column was individually controlled, resulting in a maximum beam steering of ±25◦ at 1 THz (Figure 5j).

**Figure 5.** Graphene-based reconfigurable metasurface. (**a**,**b**)A4 × 4 reflection modulator. (**a**) A schematic of the graphene-SiO2-Si structures. The substrate has an optical thickness of an odd quarter wavelength. (**b**) Optical image of the graphene-enabled reflection modulator. Reprinted from Ref. [71]. (**c**,**d**) A 256-pixel spatial light modulator. (**c**) Photo of the modulator (left). The enlargement (right) shows the graphene–electrolyte–graphene unit-cell structure. (**d**) A THz transmission image at 0.1 THz with two rows and columns biased at +1.0 and −1.0 V, respectively. Reprinted from Ref. [72]. (**e**) Unit cell consisting of a square graphene patch for a tunable reflective metasurface at 1.3 THz. The cell has dimensions of a = b = 14 μm, ap = bp = 10 μm. Reproduced with permission from [68]. (**f**) Beam steering with graphene patterned in SRRs. Reprinted from Ref. [73]. (**g**) Digital metasurface using a graphene–insulator–graphene stack for beam steering. Reprinted from Ref. [81]. (**h**) Columnlevel controlled beam steering with graphene embedded with SRR structures. Reprinted from Ref. [82]. (**i**–**j**) Experimentally demonstrated beam-steering metasurface with graphene embedded with a bowtie structure. (**i**) Cross section of the experimentally demonstrated beam-steering metasurface (left) and its unit-cell structure (right). (**j**) Schematic of the metasurface with the individually biased column. Reprinted from Ref. [83].


Gender-Based Violence

**45**

**Chapter 4**

**Abstract**

**1. Introduction**

Freeing Rural Girls and Women

Violence to Have Quality of Life

*Livingstone Malinge Gqeba and Nokuzola Gloria Gqeba*

**Keywords:** rurality, poverty, globalisation, self-actualisation, empowerment

Different researchers have, over the years, offered different definitions of what globalisation is. Beaglehole and Yach [1] define globalisation as "the increasing interconnectedness of countries and the openness of borders to ideas, people, commerce, and financial capital-has beneficial and harmful effects on the health of populations." Important to note about the above definition is that it not only focuses on the commercial and financial aspects but also on ideas. The reference to ideas is critical insofar as it relates to knowledge production and knowledge sharing. This is of pivotal importance in attempts to deal with such psychosomatic health issues as gender-based violence and HIV/AIDS. Furthermore, Turken and Rudmin [2] posit that globalisation is a process where global shifts in international politics have resulted in interconnectedness of people across the globe and it has removed physical as well social borders. The above two definitions differ greatly with how Castles and Davidson in Chryssochoou [3] describe globalisation, merely in economic terms as an economic integration that involves the opening and crossing of borders. This is a myopic view that does not enhance the developmental agenda, particularly of third-world

within the Global Village

from the Bondages of Gender-Based

In this article, the authors look at how rurality and poverty deprive young people from the rural parts of South Africa from benefiting from globalisation. Beyond explaining, what globalisation is and the benefits of being an active participant in the global village, the article explores the challenges faced by young women trapped in the rural areas. These include gender-based violence, diseases, and poor access to education. Through desktop analysis done through textual analysis of scholarly articles and books, the authors argue that, in spite of the positive aspects of globalisation, rural women still struggle to make ends meet, unlike their counterparts in the global space. Feminism in the African context is used as the theory to ground the study. The authors posit that the country, South Africa, can leverage on globalisation to deal with the negative social conditions associated with rurality. The authors posit that governments and metropolitan cities should act as motivational forces for improving the quality of life of the rural women through partnerships with other social partners.

#### **Chapter 4**

## Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality of Life within the Global Village

*Livingstone Malinge Gqeba and Nokuzola Gloria Gqeba*

#### **Abstract**

In this article, the authors look at how rurality and poverty deprive young people from the rural parts of South Africa from benefiting from globalisation. Beyond explaining, what globalisation is and the benefits of being an active participant in the global village, the article explores the challenges faced by young women trapped in the rural areas. These include gender-based violence, diseases, and poor access to education. Through desktop analysis done through textual analysis of scholarly articles and books, the authors argue that, in spite of the positive aspects of globalisation, rural women still struggle to make ends meet, unlike their counterparts in the global space. Feminism in the African context is used as the theory to ground the study. The authors posit that the country, South Africa, can leverage on globalisation to deal with the negative social conditions associated with rurality. The authors posit that governments and metropolitan cities should act as motivational forces for improving the quality of life of the rural women through partnerships with other social partners.

**Keywords:** rurality, poverty, globalisation, self-actualisation, empowerment

#### **1. Introduction**

Different researchers have, over the years, offered different definitions of what globalisation is. Beaglehole and Yach [1] define globalisation as "the increasing interconnectedness of countries and the openness of borders to ideas, people, commerce, and financial capital-has beneficial and harmful effects on the health of populations." Important to note about the above definition is that it not only focuses on the commercial and financial aspects but also on ideas. The reference to ideas is critical insofar as it relates to knowledge production and knowledge sharing. This is of pivotal importance in attempts to deal with such psychosomatic health issues as gender-based violence and HIV/AIDS. Furthermore, Turken and Rudmin [2] posit that globalisation is a process where global shifts in international politics have resulted in interconnectedness of people across the globe and it has removed physical as well social borders.

The above two definitions differ greatly with how Castles and Davidson in Chryssochoou [3] describe globalisation, merely in economic terms as an economic integration that involves the opening and crossing of borders. This is a myopic view that does not enhance the developmental agenda, particularly of third-world

countries, also known as the global south. It is this narrow view, amongst others, that is used as the basis of criticism against globalisation.

#### **1.1 Criticism against globalisation**

Critics of globalisation view globalisation as some form of coloniality, hence the term global coloniality, meaning "the heightened marginalisation and suppression of knowledge and culture of subaltern" [4].

This school of thought views globalisation as modernisation or the western informed form of development. The critics view globalisation as having failed to provide solutions for today's current problems. To the critics modernity is a perpetuation of coloniality through continued conquest of being, knowledge, and power of the third-world countries and their peoples. This brings about the ideological tension and dichotomy that exist between social regulation (which is a core function of modernity) and social emancipation (which is the cornerstone of the struggles of the peoples of third-world countries). It is in this context that Davies' J-Curve comes into play as a fitting explanation of the phenomenon described. The J-Curve is an illustration of the disjuncture that happens over time between the expectations of the people and the reality of their lived experiences as a result of modernisation (Cohen, 1975: 193; [5]: 6).

Below are definitions of industrialisation and modernisation as the two concepts that are central in any definition of globalisation and the definition of these concepts gives context to the concept of globalisation.

Industrialisation denotes "the wide transformation of society during which manufacturing and industrial activity became primary forms of social production."

Modernisation refers to the "broader spectrum of changes in which society becomes complex, urbanised and differentiated, and in which production and social organisation is increasingly based on science".

Sporer [6] posits "Globalisation is the latest stage in the permanent process of social change that started as industrialisation and modernisation in Europe but now is spreading globally."

**47**

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality…*

the expectations of people and reality widens over time (**Figure 1**).

**2. Globalisation and its impact on South African rural women**

Globalisation generally has its positive aspects that have contributed constructively to the lives of women. These include the global initiatives against child marriages, equality of genders, and eradication of all forms of discrimination. This has happened through global integration of ideas, improved communication technologies, and internationalisation of discourses and culture. In most cases, all these developments have not significantly changed the plight of women. This is particularly obvious in areas "including education, employment, health and civil rights [7]. Even though the UN has tried to ensure worldwide gender parity through the UN's Millennium Development Goals, the adverse conditions against women

Another global initiative towards improving the lives of women in general and rural women in particular is the consortium "Girls Not Brides" and the African Union Campaign to End Child Marriage. These initiatives have not stopped the practice where young girls are forcefully married off to older men under the guise of a cultural practice called *ukuthwala*. Mwambene [8] describes *ukuthwala* as "the mock abduction of an unmarried woman or girl for the purpose of a customary marriage." The practice is rife amongst the IsiXhosa speakers of the rural parts of the Eastern Cape and amongst IsiZulu speakers of KwaZulu-Natal [8–10] *Ukuthwala* is a form of gender-based violence against women and girls. In spite of globalisation, such practices as *ukuthwala* perpetuate the stereotype of male

Furthermore, even though labour relations have been liberalised globally, women in rural areas continue to suffer in the workplace. Women who face sexual harassment at work are faced with difficult choices. The women either decide to continue to work and endure the pain and humiliation of continued sexual harassment or they can opt to leave work and face the possibility of starving or dependence on their partners. Dependence on their partners brings problems of its own as it can lead to harassment and abuse of different kinds. Rural women are more likely to be illiterate or drop out of school early as no emphasis and importance are usually attached to educating the girl child in rural areas. Their illiteracy opens them up to abuse and exploitation in the workplace. With limited access to education, women get deprived of an opportunity to act as competitive and equal partners in the global

Rural women also face poverty of the worst kind as compared to their urban counterparts. According to Casale and Posel [11] "poverty rates are highest amongst rural women: in 2008, 70% of all African women living in rural areas were poor, compared to 55% of rural men, 42% of urban women and 30% of urban men." This renders rural women the most vulnerable marginalised group in the country. Bower [12] posits that in South Africa "to be a child … is to walk a fragile path to adulthood and to be a woman is, for far too many, to be poor, disempowered, and vulnerable to

In addition, poverty is further exacerbated by unemployment as a result of globalisation [12]. A reflection of poverty is the poor access to basic services such as

running water, electricity for lighting, and telecommunications [13–15].

This clearly expresses what the critics of globalisation hold against globalisation as a Eurocentric process of industrialisation; some argue that it leads to the phenomenon called the J-Curve. The J-Curve is an occurrence where the gap between

*DOI: http://dx.doi.org/10.5772/intechopen.91301*

have not fundamentally changed.

superiority.

village [7].

appalling high levels of sexual violence."

**Figure 1.** *Davies: J-curve (1962) 27: 5-19.*

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality… DOI: http://dx.doi.org/10.5772/intechopen.91301*

This clearly expresses what the critics of globalisation hold against globalisation as a Eurocentric process of industrialisation; some argue that it leads to the phenomenon called the J-Curve. The J-Curve is an occurrence where the gap between the expectations of people and reality widens over time (**Figure 1**).

#### **2. Globalisation and its impact on South African rural women**

Globalisation generally has its positive aspects that have contributed constructively to the lives of women. These include the global initiatives against child marriages, equality of genders, and eradication of all forms of discrimination. This has happened through global integration of ideas, improved communication technologies, and internationalisation of discourses and culture. In most cases, all these developments have not significantly changed the plight of women. This is particularly obvious in areas "including education, employment, health and civil rights [7]. Even though the UN has tried to ensure worldwide gender parity through the UN's Millennium Development Goals, the adverse conditions against women have not fundamentally changed.

Another global initiative towards improving the lives of women in general and rural women in particular is the consortium "Girls Not Brides" and the African Union Campaign to End Child Marriage. These initiatives have not stopped the practice where young girls are forcefully married off to older men under the guise of a cultural practice called *ukuthwala*. Mwambene [8] describes *ukuthwala* as "the mock abduction of an unmarried woman or girl for the purpose of a customary marriage." The practice is rife amongst the IsiXhosa speakers of the rural parts of the Eastern Cape and amongst IsiZulu speakers of KwaZulu-Natal [8–10] *Ukuthwala* is a form of gender-based violence against women and girls. In spite of globalisation, such practices as *ukuthwala* perpetuate the stereotype of male superiority.

Furthermore, even though labour relations have been liberalised globally, women in rural areas continue to suffer in the workplace. Women who face sexual harassment at work are faced with difficult choices. The women either decide to continue to work and endure the pain and humiliation of continued sexual harassment or they can opt to leave work and face the possibility of starving or dependence on their partners. Dependence on their partners brings problems of its own as it can lead to harassment and abuse of different kinds. Rural women are more likely to be illiterate or drop out of school early as no emphasis and importance are usually attached to educating the girl child in rural areas. Their illiteracy opens them up to abuse and exploitation in the workplace. With limited access to education, women get deprived of an opportunity to act as competitive and equal partners in the global village [7].

Rural women also face poverty of the worst kind as compared to their urban counterparts. According to Casale and Posel [11] "poverty rates are highest amongst rural women: in 2008, 70% of all African women living in rural areas were poor, compared to 55% of rural men, 42% of urban women and 30% of urban men." This renders rural women the most vulnerable marginalised group in the country. Bower [12] posits that in South Africa "to be a child … is to walk a fragile path to adulthood and to be a woman is, for far too many, to be poor, disempowered, and vulnerable to appalling high levels of sexual violence."

In addition, poverty is further exacerbated by unemployment as a result of globalisation [12]. A reflection of poverty is the poor access to basic services such as running water, electricity for lighting, and telecommunications [13–15].

*Counseling and Therapy*

**1.1 Criticism against globalisation**

of knowledge and culture of subaltern" [4].

modernisation (Cohen, 1975: 193; [5]: 6).

is spreading globally."

cepts gives context to the concept of globalisation.

social organisation is increasingly based on science".

countries, also known as the global south. It is this narrow view, amongst others,

Critics of globalisation view globalisation as some form of coloniality, hence the term global coloniality, meaning "the heightened marginalisation and suppression

Below are definitions of industrialisation and modernisation as the two concepts that are central in any definition of globalisation and the definition of these con-

Industrialisation denotes "the wide transformation of society during which manufacturing and industrial activity became primary forms of social production." Modernisation refers to the "broader spectrum of changes in which society becomes complex, urbanised and differentiated, and in which production and

Sporer [6] posits "Globalisation is the latest stage in the permanent process of social change that started as industrialisation and modernisation in Europe but now

This school of thought views globalisation as modernisation or the western informed form of development. The critics view globalisation as having failed to provide solutions for today's current problems. To the critics modernity is a perpetuation of coloniality through continued conquest of being, knowledge, and power of the third-world countries and their peoples. This brings about the ideological tension and dichotomy that exist between social regulation (which is a core function of modernity) and social emancipation (which is the cornerstone of the struggles of the peoples of third-world countries). It is in this context that Davies' J-Curve comes into play as a fitting explanation of the phenomenon described. The J-Curve is an illustration of the disjuncture that happens over time between the expectations of the people and the reality of their lived experiences as a result of

that is used as the basis of criticism against globalisation.

**46**

**Figure 1.**

*Davies: J-curve (1962) 27: 5-19.*

One of the features of globalisation in the workplace is the casualization of the workforce where full-time jobs are replaced by casual jobs. This unfortunately affects women more than men. This change in labour patterns has also introduced the employment of workers through labour brokers, even on farms. Employment of workers on seasonal contracts is more cost-effective for employers (though exploitative for employees). Through casualization of labour, farmers can rely on off-farm employees, thus avoiding such costs as granting security tenure to workers living on farms [16] Casualisation has also led to the proliferation of informal settlements that are unserviced. Casualisation together with farm evictions is the main cause of the development of sprawling, underserviced informal settlements. Women are the hardest hit under these conditions from having to make ends meet for their families to being attacked and raped. Women also fall victim to diseases as a result of the poor living conditions and lack of immediate access to medical care.

This adds to the vulnerability of rural workers, particularly women who have to depend on men for security (sometimes on complete strangers). This opens women up to such practices as gender-based violence. There are many different forms of gender-based violence that rural women get exposed to.

#### **3. Forms of gender-based violence**

#### **3.1 Physical violence**

Physical violence is the most severe form of gender-based violence that sometimes leads to death. Substance abuse and alcohol in particular is the most prevalent cause of physical violence. Seilberg (2014) posits that physical violence is "an act that may result in pain, injury, impairment and can even lead to death." Physical violence can take many forms such as beating, shaking, tripping, punching, burning, pulling of hair, slapping, gripping, pushing, pinching, kicking and the use of physical restraints. The other prevalent form is sexual violence.

#### **3.2 Sexual violence**

Sexual violence includes such violations as rape even by marriage partners and/or lovers. This is rooted in the false belief that men own women, which as a result of the patriarchal system on which rural life is centred.  It also includes using coercion of any person regardless of their relationship to the victim. To demonstrate the extent of sexual violence in South Africa, Turmen (2003) found that 30% of girls said their first intercourse was forced, 71% had experienced sex against their will, and 11% had been raped. If these statistics reflect the experiences of women in general, the situation is worse in the rural areas. What most people do not consider as gender-based violence is emotional violence.

#### **3.3 Emotional violence**

Emotional violence can take different forms as it can happen overtly or covertly. Iwaniel (2006) argues that emotional violence projects victims as worthless, flawed, unloved, unwanted, and endangered. This form of violence includes spurning,

**49**

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality…*

terrorising, isolation, exploitation, and denying emotional responsiveness, and it

This is another common form of violence in the rural areas where women are mostly economically dependent on men. This is because of discriminatory employment practices and casualization of workers, particularly women, as noted above. Mark and Melville (2011) posit that economic violence may involve a situation where the perpetrator creates dependence by the victim through withholding money for basic necessities, e.g. food and sanitary towels. It is ironic that in South Africa condoms are provided for free yet sanitary towels are still a scarce and expensive necessity for women, particularly in the rural areas [17]. This is worse in South Africa in a country where women constitute less than a quarter of land beneficiaries. Women still carry the cost of oppression and marginalisation by the land reform programme. Even land inheritance is done on a gender basis in favour of males. In this case, legislation ignores the fact that women are in majority in rural areas and as part of the labour force in commercial farms [18]. This is also in spite of the fact that women are the primary users of agricultural land in rural countries.

Rural parts of the Western Cape are known for practising the "dop system" which was a system where farm workers were paid some of their salaries in the form of alcohol [19]. This invariably led to a number of children being born with foetal alcohol syndrome. Children born of parents who abuse alcohol and with siblings and partners abusing alcohol live in an environment where alcohol abuse and consumption become the modelled behaviour. The reality is that this behaviour gets perpetuated, as there are hardly any health awareness programmes in these areas

Alcohol advertising has also worsened the situation, particularly amongst rural adolescents. Young women are now the target of marketing by alcohol companies. This has led to a situation where alcohol is a major contributor to the global burden

Globalisation has created the illusion that the global markets are able to attract everybody for employment. Whilst this is true for certain professions, disciplines, and industries, it is not true for everybody. The possibilities are slim for illiterate, unskilled women. The illusion of work for all in global markets has opened a loophole for criminal elements. Young women, mostly from rural areas and desperate for employment, are lured into human trafficking. Criminals have taken to establishing recruitment agencies posing as labour brokers. Visas and air tickets are made available to job seekers only to find themselves in serious cases of sexual assault and physical abuse [21]. The UN defines "trafficking in persons" as the purpose of exploitation by means of the threat or use of power or of a position of vulnerability or of the giving or receiving of payments of benefits to achieve consent of a person having control over another person for the purpose of exploitation [21]. Traffickers mainly target the poor, and in worse-case scenarios, some parents sell their children to traffickers.

*DOI: http://dx.doi.org/10.5772/intechopen.91301*

**4. Alcohol abuse amongst rural women**

compared to elsewhere in the global village.

of disease [20].

**5. Human trafficking**

**3.4 Economic violence**

includes verbal and non-verbal demeaning communication.

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality… DOI: http://dx.doi.org/10.5772/intechopen.91301*

terrorising, isolation, exploitation, and denying emotional responsiveness, and it includes verbal and non-verbal demeaning communication.

#### **3.4 Economic violence**

*Counseling and Therapy*

lack of immediate access to medical care.

**3. Forms of gender-based violence**

**3.1 Physical violence**

**3.2 Sexual violence**

emotional violence.

**3.3 Emotional violence**

gender-based violence that rural women get exposed to.

physical restraints. The other prevalent form is sexual violence.

One of the features of globalisation in the workplace is the casualization of the workforce where full-time jobs are replaced by casual jobs. This unfortunately affects women more than men. This change in labour patterns has also introduced the employment of workers through labour brokers, even on farms. Employment of workers on seasonal contracts is more cost-effective for employers (though exploitative for employees). Through casualization of labour, farmers can rely on off-farm employees, thus avoiding such costs as granting security tenure to workers living on farms [16] Casualisation has also led to the proliferation of informal settlements that are unserviced. Casualisation together with farm evictions is the main cause of the development of sprawling, underserviced informal settlements. Women are the hardest hit under these conditions from having to make ends meet for their families to being attacked and raped. Women also fall victim to diseases as a result of the poor living conditions and

This adds to the vulnerability of rural workers, particularly women who have to depend on men for security (sometimes on complete strangers). This opens women up to such practices as gender-based violence. There are many different forms of

Physical violence is the most severe form of gender-based violence that sometimes leads to death. Substance abuse and alcohol in particular is the most prevalent cause of physical violence. Seilberg (2014) posits that physical violence is "an act that may result in pain, injury, impairment and can even lead to death." Physical violence can take many forms such as beating, shaking, tripping, punching, burning, pulling of hair, slapping, gripping, pushing, pinching, kicking and the use of

Sexual violence includes such violations as rape even by marriage partners and/or lovers. This is rooted in the false belief that men own women, which as a result of the patriarchal system on which rural life is centred.  It also includes using coercion of any person regardless of their relationship to the victim. To demonstrate the extent of sexual violence in South Africa, Turmen (2003) found that 30% of girls said their first intercourse was forced, 71% had experienced sex against their will, and 11% had been raped. If these statistics reflect the experiences of women in general, the situation is worse in the rural areas. What most people do not consider as gender-based violence is

Emotional violence can take different forms as it can happen overtly or covertly. Iwaniel (2006) argues that emotional violence projects victims as worthless, flawed, unloved, unwanted, and endangered. This form of violence includes spurning,

**48**

This is another common form of violence in the rural areas where women are mostly economically dependent on men. This is because of discriminatory employment practices and casualization of workers, particularly women, as noted above. Mark and Melville (2011) posit that economic violence may involve a situation where the perpetrator creates dependence by the victim through withholding money for basic necessities, e.g. food and sanitary towels. It is ironic that in South Africa condoms are provided for free yet sanitary towels are still a scarce and expensive necessity for women, particularly in the rural areas [17]. This is worse in South Africa in a country where women constitute less than a quarter of land beneficiaries. Women still carry the cost of oppression and marginalisation by the land reform programme. Even land inheritance is done on a gender basis in favour of males. In this case, legislation ignores the fact that women are in majority in rural areas and as part of the labour force in commercial farms [18]. This is also in spite of the fact that women are the primary users of agricultural land in rural countries.

#### **4. Alcohol abuse amongst rural women**

Rural parts of the Western Cape are known for practising the "dop system" which was a system where farm workers were paid some of their salaries in the form of alcohol [19]. This invariably led to a number of children being born with foetal alcohol syndrome. Children born of parents who abuse alcohol and with siblings and partners abusing alcohol live in an environment where alcohol abuse and consumption become the modelled behaviour. The reality is that this behaviour gets perpetuated, as there are hardly any health awareness programmes in these areas compared to elsewhere in the global village.

Alcohol advertising has also worsened the situation, particularly amongst rural adolescents. Young women are now the target of marketing by alcohol companies. This has led to a situation where alcohol is a major contributor to the global burden of disease [20].

#### **5. Human trafficking**

Globalisation has created the illusion that the global markets are able to attract everybody for employment. Whilst this is true for certain professions, disciplines, and industries, it is not true for everybody. The possibilities are slim for illiterate, unskilled women. The illusion of work for all in global markets has opened a loophole for criminal elements. Young women, mostly from rural areas and desperate for employment, are lured into human trafficking. Criminals have taken to establishing recruitment agencies posing as labour brokers. Visas and air tickets are made available to job seekers only to find themselves in serious cases of sexual assault and physical abuse [21]. The UN defines "trafficking in persons" as the purpose of exploitation by means of the threat or use of power or of a position of vulnerability or of the giving or receiving of payments of benefits to achieve consent of a person having control over another person for the purpose of exploitation [21]. Traffickers mainly target the poor, and in worse-case scenarios, some parents sell their children to traffickers.

#### **6. Rural women through a feminist lens**

The authors have used Ntantala [22] and Maathai [23] as feminists and humanists to ground the study. In her autobiography, Ntantala reflected on the patriarchal society that has made women properties of men. She cites the one-family one-lot system of land tenure that pertained in the segregated South Africa. Under this system, polygamous men could acquire vast tracks of land based on the one-lot per family system. In this case, women were treated as resources owned by men and used to justify acquisition of more land.

Ntantala [22] also reflects on the migrant labour system that led rural women to live like widows, as their husbands had to leave their rural homes to sell their labour in the cities. Women became victims of capitalism sometimes more so than men. The women had to remain behind in the rural areas and look after their families in a system that undermined them as people. The migrant labour system opened up women whose husbands left for long periods to unscrupulous men with resources. These men would support these women in exchange for sexual favours and in some instances even impregnate them. This is the point also stressed by Maathai [23] where she reflects on the double exploitation of women in terms of gender and as part of the oppressed population. She makes an argument for the mobilisation and conscientising of rural women. Both Ntantala and Maathai make it clear that there cannot be freedom when women are still oppressed. This is captured in a quotation by Ntantala [22]: "Though I seemed free, there could be no freedom where others were not free and that in fact, nobody in South Africa, or any other country, was free while others were not."

#### **7. Potential counselling and therapy strategies**

The multiplicity of abuses experienced by rural girls and women inevitably leads to psychological distress. These posttraumatic stress disorders manifest themselves in a range of secondary problems particularly mental problems. Unless these are addressed, the situation worsens with no end in sight. The situation requires timely psychological interventions particularly on children and adolescents to mitigate the risk of chronic mental disorders playing out later in their adult lives. These chronic mental disorders may include suicidal behaviour and/or self-harming behaviour. Part of the interventions to deal with posttraumatic disorders may include the training of lay health workers to work with abused women.

Amongst the suggested therapies particularly for rural girls and women is cognitive behavioural therapy (CBT). Salient aspects of cognitive behavioural therapy are joint efforts between the therapist and the client, and the therapy is based on an educational model. The therapy is premised on the understanding that feelings and behaviours are informed by thoughts and not external things (people, situations, and events). The ultimate benefit of the therapy is that victims can change the way they think to feel and act better even if the situation does not change. Compared to other therapies like psychoanalysis, CBT brings about rapid results. CBT requires an average of 16 sessions, whilst psychoanalysis takes years. It is not an open-ended, never-ending process. It is a process where the ultimate end of formal therapy is arrived at between the therapist and client [24].

#### **8. Conclusion**

Globalisation has not proven to be helpful for rural South African women. Instead of improving their lives for the better, it has contributed to the misery and

**51**

**Author details**

Livingstone Malinge Gqeba and Nokuzola Gloria Gqeba\*

© 2020 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,

\*Address all correspondence to<EMAIL_ADDRESS>
University of Fort Hare, South Africa

provided the original work is properly cited.

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality…*

hardships. Globalisation has merely exposed them to a life they cannot obtain. Whilst other parts of South Africa are reaping the fruits of globalisation, rural women have not really moved out of the colonial and traditional power relations

The problems faced by rural women point to the urgent need for a directed government intervention. It is an accepted fact that the government alone cannot surmount the challenges faced by rural women. Any meaningful intervention would necessitate multiple-partner collaborations between government and other social partners. There also needs to be focussed programmes to deal with socioeconomic problems that compromise the quality of life of rural women. The authors suggest that the social partners to be identified by government must be multifaceted to include organisations that have a voice in the rural communities. These include but are not limited to faith-based organisations, cultural organisations, and community-based organisations. Even though the study is located in rural South Africa, the findings from it have a bearing on and meaning to most rural women in Africa

*DOI: http://dx.doi.org/10.5772/intechopen.91301*

and lifestyles.

in general.

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality… DOI: http://dx.doi.org/10.5772/intechopen.91301*

hardships. Globalisation has merely exposed them to a life they cannot obtain. Whilst other parts of South Africa are reaping the fruits of globalisation, rural women have not really moved out of the colonial and traditional power relations and lifestyles.

The problems faced by rural women point to the urgent need for a directed government intervention. It is an accepted fact that the government alone cannot surmount the challenges faced by rural women. Any meaningful intervention would necessitate multiple-partner collaborations between government and other social partners. There also needs to be focussed programmes to deal with socioeconomic problems that compromise the quality of life of rural women. The authors suggest that the social partners to be identified by government must be multifaceted to include organisations that have a voice in the rural communities. These include but are not limited to faith-based organisations, cultural organisations, and community-based organisations. Even though the study is located in rural South Africa, the findings from it have a bearing on and meaning to most rural women in Africa in general.

#### **Author details**

*Counseling and Therapy*

**6. Rural women through a feminist lens**

used to justify acquisition of more land.

**7. Potential counselling and therapy strategies**

ing of lay health workers to work with abused women.

arrived at between the therapist and client [24].

The authors have used Ntantala [22] and Maathai [23] as feminists and humanists to ground the study. In her autobiography, Ntantala reflected on the patriarchal society that has made women properties of men. She cites the one-family one-lot system of land tenure that pertained in the segregated South Africa. Under this system, polygamous men could acquire vast tracks of land based on the one-lot per family system. In this case, women were treated as resources owned by men and

Ntantala [22] also reflects on the migrant labour system that led rural women to live like widows, as their husbands had to leave their rural homes to sell their labour in the cities. Women became victims of capitalism sometimes more so than men. The women had to remain behind in the rural areas and look after their families in a system that undermined them as people. The migrant labour system opened up women whose husbands left for long periods to unscrupulous men with resources. These men would support these women in exchange for sexual favours and in some instances even impregnate them. This is the point also stressed by Maathai [23] where she reflects on the double exploitation of women in terms of gender and as part of the oppressed population. She makes an argument for the mobilisation and conscientising of rural women. Both Ntantala and Maathai make it clear that there cannot be freedom when women are still oppressed. This is captured in a quotation by Ntantala [22]: "Though I seemed free, there could be no freedom where others were not free and that in fact, nobody in South Africa, or any other country, was free while others were not."

The multiplicity of abuses experienced by rural girls and women inevitably leads to psychological distress. These posttraumatic stress disorders manifest themselves in a range of secondary problems particularly mental problems. Unless these are addressed, the situation worsens with no end in sight. The situation requires timely psychological interventions particularly on children and adolescents to mitigate the risk of chronic mental disorders playing out later in their adult lives. These chronic mental disorders may include suicidal behaviour and/or self-harming behaviour. Part of the interventions to deal with posttraumatic disorders may include the train-

Amongst the suggested therapies particularly for rural girls and women is cognitive behavioural therapy (CBT). Salient aspects of cognitive behavioural therapy are joint efforts between the therapist and the client, and the therapy is based on an educational model. The therapy is premised on the understanding that feelings and behaviours are informed by thoughts and not external things (people, situations, and events). The ultimate benefit of the therapy is that victims can change the way they think to feel and act better even if the situation does not change. Compared to other therapies like psychoanalysis, CBT brings about rapid results. CBT requires an average of 16 sessions, whilst psychoanalysis takes years. It is not an open-ended, never-ending process. It is a process where the ultimate end of formal therapy is

Globalisation has not proven to be helpful for rural South African women. Instead of improving their lives for the better, it has contributed to the misery and

**50**

**8. Conclusion**

Livingstone Malinge Gqeba and Nokuzola Gloria Gqeba\* University of Fort Hare, South Africa

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 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.

#### **References**

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[2] Turken S, Rudmin WF. On psychological effects of globalisation: Development of a scale of a scale of global identity. Psychology & Society. 2013;**5**(2):63-89

[3] Chryssochoou X. Cultural Diversity: Its Social Psychology. Oxford: Blackwell; 2005

[4] Escobar A. Imperial globality, global coloniality and anti-globalisation. Third World Quarterly. 2004;**25**(1):207-230

[5] Davies JC. Toward a theory of revolution. American Sociological Review. 1962;**27**:5-19

[6] Sporer Z. Controversies of Globalisation-Semantic Scholar. Revija za sociologiju. 2000;**xxxi**(3-4):165-181

[7] Kaur P. Impact of globalization on women. Global Journal of Commerce & Management Perspective. 2018;**7**(2):41-44

[8] Mwambene L. Recent legal responses to child marriage in Southern Africa: The case of Zimbabwe, South Africa and Malawi. African Human Rights Law Journal. 2018;**18**(2):527-550

[9] Kheswa JG, Hoho VN. "Ukuthwala" the sexual-cultural practice with negative effects on the personality of adolescent females in Africa. Mediterranean Journal of Social Sciences. 2014;**5**(20):2808

[10] Nkosi M. Bride abduction in KwaZulu-Natal schools and its effects on education. Agenda. 2009;**23**(80):109-114. DOI: 10.1080/10130950.2009.9676246 [11] Casale D, Posel D. Investigating the well-being of the rural women in South Africa. Agenda: Empowering Women for Gender Equity. Gender & Rurality. 2010;**84**:46-52

[12] Bower C. The plight of women and children: Advancing South Africa's least privileged. The Annals of the American Academy of Political and Social Science. 2014;**642**:106-126. DOI: 10.1177/00027162113512086

[13] Diale T. Rural women ease plight in communities, but more can be done. Business Report. 2018

[14] Orr L. Women's work and globalisation trends: The South African picture. In: Agenda: Empowering Women for Gender Equity. Vol. 48. Globalisation, Challenging Dominant Discourses. 2001. pp. 31-37

[15] Van Vuuren JU. South Africa cannot continue to ignore the plight of young girls and women. 2018. Available from: https://www.one.org

[16] Visser M, Ferrer S. Farm worker's living and working conditions in South Africa: Key trends, emergent issues, and underlying and structural problems. The Pretoria Office of the International Labour Organisation. 2015

[17] Magcaba B. Gender equality—A real-challenge. SABC-News. 2018

[18] Mzwakali S. Land reform has failed the women of South Africa. Daily Maverick. 2019

[19] Ojo OA, Louwagie G, Morojele N, Rendall-Mkosi K, London L, Olorunju S, et al. Factors associated with female high-risk drinking in rural areas and urban South Africa site. South African Medical Journal. 2010;**100**(3):180-181

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*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality…*

*DOI: http://dx.doi.org/10.5772/intechopen.91301*

[20] The Conversation. How easy access to alcohol, and adverts, affect women in

[21] Mercy International Association. Root Causes of Human Trafficking and Slavery in Kenya (The Congregation). Catherine Spiritual Global Action. 2018

[22] Ntantala P. A Life's Mosaic: The Autobiography of Phyllis Ntantala. University of California Press; 1993

[23] Maathai W. Unbowed: A Memoir.

[24] UN Women. Emotional care and support. Virtual knowledge centre to end violence against women and girls.

New York: Knopf; 2006

2011

South Africa. 2019

*Freeing Rural Girls and Women from the Bondages of Gender-Based Violence to Have Quality… DOI: http://dx.doi.org/10.5772/intechopen.91301*

[20] The Conversation. How easy access to alcohol, and adverts, affect women in South Africa. 2019

[21] Mercy International Association. Root Causes of Human Trafficking and Slavery in Kenya (The Congregation). Catherine Spiritual Global Action. 2018

[22] Ntantala P. A Life's Mosaic: The Autobiography of Phyllis Ntantala. University of California Press; 1993

[23] Maathai W. Unbowed: A Memoir. New York: Knopf; 2006

[24] UN Women. Emotional care and support. Virtual knowledge centre to end violence against women and girls. 2011

**52**

*Counseling and Therapy*

**References**

2003;**362**(9387):903-908

2013;**5**(2):63-89

2005

[2] Turken S, Rudmin WF. On

[1] Beaglehole R, Yach D. Globalisation and the prevention and control of noncommunicable disease: The neglected chronic disease of adults. The Lancet.

[11] Casale D, Posel D. Investigating the well-being of the rural women in South Africa. Agenda: Empowering Women for Gender Equity. Gender & Rurality.

[12] Bower C. The plight of women and children: Advancing South Africa's least privileged. The Annals of the American Academy of Political and Social Science. 2014;**642**:106-126. DOI:

[13] Diale T. Rural women ease plight in communities, but more can be done.

globalisation trends: The South African picture. In: Agenda: Empowering Women for Gender Equity. Vol. 48. Globalisation, Challenging Dominant

[15] Van Vuuren JU. South Africa cannot continue to ignore the plight of young girls and women. 2018. Available from:

[16] Visser M, Ferrer S. Farm worker's living and working conditions in South Africa: Key trends, emergent issues, and underlying and structural problems. The Pretoria Office of the International Labour Organisation.

[17] Magcaba B. Gender equality—A real-challenge. SABC-News. 2018

[18] Mzwakali S. Land reform has failed the women of South Africa. Daily

Morojele N, Rendall-Mkosi K, London L, Olorunju S, et al. Factors associated with female high-risk drinking in rural areas and urban South Africa site. South African Medical Journal.

10.1177/00027162113512086

[14] Orr L. Women's work and

Discourses. 2001. pp. 31-37

https://www.one.org

2015

Maverick. 2019

[19] Ojo OA, Louwagie G,

2010;**100**(3):180-181

Business Report. 2018

2010;**84**:46-52

psychological effects of globalisation: Development of a scale of a scale of global identity. Psychology & Society.

[3] Chryssochoou X. Cultural Diversity: Its Social Psychology. Oxford: Blackwell;

[4] Escobar A. Imperial globality, global coloniality and anti-globalisation. Third World Quarterly. 2004;**25**(1):207-230

[5] Davies JC. Toward a theory of revolution. American Sociological

[6] Sporer Z. Controversies of

& Management Perspective.

Journal. 2018;**18**(2):527-550

2018;**7**(2):41-44

Globalisation-Semantic Scholar. Revija za sociologiju. 2000;**xxxi**(3-4):165-181

[7] Kaur P. Impact of globalization on women. Global Journal of Commerce

[8] Mwambene L. Recent legal responses to child marriage in Southern Africa: The case of Zimbabwe, South Africa and Malawi. African Human Rights Law

[9] Kheswa JG, Hoho VN. "Ukuthwala" the sexual-cultural practice with negative effects on the personality of adolescent females in Africa. Mediterranean Journal of Social Sciences. 2014;**5**(20):2808

[10] Nkosi M. Bride abduction in KwaZulu-Natal schools and its effects on education. Agenda. 2009;**23**(80):109-114. DOI: 10.1080/10130950.2009.9676246

Review. 1962;**27**:5-19

**55**

Section 4

Self-Efficacy and

Self-Esteem

Section 4