**Abstract**

Plants are subjected to a variety of environmental stresses, which reduces and limits agricultural crop productivity. Environmental stresses that affect plants are of two types: biotic and abiotic stresses. Abiotic stress includes temperature, ultraviolet radiation, salinity, floods, drought, heavy metals, etc., which results in the loss of important crop plants globally, while biotic stress refers to damage caused by insects, herbivores, nematodes, fungi, bacteria, or weeds. Plants respond to all these environmental factors because the pants are fixed in a particular place. To cope with these stresses, a number of strategies have been developed by plants. They detect that the environmental stresses become activated and then generate the necessary cellular responses. Several investigations have been carried out to determine and understand plant assimilates partitioning and stress-tolerance plant genotype necessary for the understanding of the complexity of the response of a plant to biotic and abiotic stresses.

**Keywords:** biotic factors, environmental stresses, crop productivity, crop yield, tolerance mechanism


**11. Recommendations for future QOL after liver transplant studies**

Areas of future research related to QOL might help to settle long term problems associated with liver transplantation. There are a number of reliable and valid instruments, however none of them can be considered as the gold standard outcome to be used in all situations.

In future studies, more attention should be paid to the QOL outcomes in liver transplant recipients with alcoholic liver disease, hepatocellular carcinoma and those with hepatitis C. Some articles also suggest that female liver transplant recipients should receive special attention (Russell, 2008).

Others point out the importance of focusing on the recovering functions both during the initial period post-transplant and on the long term results. How the new available immunosuppressor drugs have to be adjusted will depend on further research incorporating HRQOL results. (Crone, 2006)

In relation to patients with previous addictions, there is no definitive biochemical test to identify alcohol relapse and the tests available have poor sensitivity and specificity, so emphasis on QOL is also important. More research is required in this field to detect alcohol ingestion before it has an effect on the new liver or when social and familiar behaviour could be difficult to manage. (Stilley, 2010)

More attention for caregivers, usually wives because men are more frequently affected by liver diseases, is required. In single patients, it is necessary to identify a circle of friends or other family members who are willing to support and take care of them. (Carver, 2005; Day, 2009; De Blesser, 2009).

Considering factors that could condition differences in the evolution, future researches should assess a larger number of cases than those done to date,and implement prospective studies.


*Article* **Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor**

**Yong Wang 1,\*, Di Wang 1, Yunhai Fu 2, Dengke Yao 1, Liangbo Xie 1 and Mu Zhou 1**

**Abstract:** With the development of human–computer interaction(s) (HCI), hand gestures are playing increasingly important roles in our daily lives. With hand gesture recognition (HGR), users can play virtual games together, control the smart equipment, etc. As a result, this paper presents a multi-hand gesture recognition system using automotive frequency modulated continuous wave (FMCW) radar. Specifically, we first constructed the range-Doppler map (RDM) and range-angle map (RAM), and then suppressed the spectral leakage, and dynamic and static interferences. Since the received echo signals with multi-hand gestures are mixed together, we propose a spatiotemporal path selection algorithm to separate the mixed multi-hand gestures. A dual 3D convolutional neural network-based feature fusion network is proposed for feature extraction and classification. We developed the FMCW radar-based platform to evaluate the performance of the proposed multi-hand gesture recognition method; the experimental results show that the proposed method can achieve an average recognition accuracy of 93.12% when eight gestures with two hands are performed simultaneously.

**Keywords:** frequency modulated continuous wave radar; gesture recognition; multi-hand; deep learning

#### **1. Introduction**

With the development of wireless sensing [1], human–computer interaction (HCI) [2] has widely been applied in daily life. The hand gesture recognition (HGR) technique, an important 'way' of HCI, is used in smart homes, robot control, virtual games, etc. For example, with HGR, users can control smart devices and play interactive virtual games. More importantly, with the development of intelligent vehicles, the application of gesture recognition in intelligent-assisted driving is particularly important. The driver can control various functions inside the car through gestures, such as adjusting the in car entertainment system or switching on or off the air conditioner, to help drivers concentrate and improve the driving safety. As a result, HGR is receiving a lot of attention; this paper focuses on HGR.

According to the acquisition method of hand gesture data, HGR can be divided into three types: (i) wearable sensor-based HGR [3], (ii) vision-based HGR [4], and radar-based HGR [5,6]. Based on the wearable sensors, the wearable-based HGR in [3] can acquire the motion information of hand gestures and achieve recognition accuracy as high as 99.3%. Since this method involves wearing sensors, wearable sensors usually bring uncomfortable and inconvenient experiences to users. On the other hand, vision-based HGR [4] applies cameras to capture RGB or depth images of hand gestures, in combination with image processing or computer vision for gesture recognition. Although the recognition accuracy is relatively high, vision-based HGR is usually invalid in case of poor lighting conditions and non-line-of-sight. Radar-based HGR applies the radar sensor to collect the hand gesture

**Citation:** Wang, Y.; Wang, D.; Fu, Y.; Yao, D.; Xie, L.; Zhou, M. Multi-Hand Gesture Recognition Using Automotive FMCW Radar Sensor. *Remote Sens.* **2022**, *14*, 2374. https://doi.org/10.3390/rs14102374

Academic Editors: Zhihuo Xu, Jianping Wang and Yongwei Zhang

Received: 20 April 2022 Accepted: 13 May 2022 Published: 14 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 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/).

motion in a device-free manner. It has a non-contact advantage, is not affect by light, and has attracted much attention in both academic and the industry [7,8].

In general, radar-based HGR mainly contains two stages: (i) signal preprocessing and (ii) gesture feature extraction and classification. In the first stage, the raw radar signal is collected and processed to obtain the motion parameters (such as range or Doppler) of hand gestures. In References [7,8], the authors used two-dimensional fast Fourier transform (2D-FFT) to estimate the range and Doppler map (RDM) of hand gestures. To improve the quality of RDM, Wang et al. in [9] adopted the gradient threshold to filter out the step peak in RDM, and then used the wavelet transform to further enhance RDM. To reduce the interferences, the authors in [10] adopted the neighbor threshold detection method instead of the constant false alarm rate (CFAR) [11] to filter out the interference targets and detect the hand gesture targets. After suppressing the interference signal by the first-order recursive filter, the authors in [12] used the lognormal CFAR to detect the hand gestures. Although there were some works focusing on interference suppression and hand gesture detection, they only considered the range and Doppler information of hand gestures. In fact, the angle information of hand gestures provided an additional dimension to describe the hand gesture motion. As a result, the motion parameters (range, Doppler, and angle) in combination with interference suppression should be fully utilized to explore the performance of HGR. In the stage of feature extraction and classification of HGR, a deep learning method, such as the convolutional neural network (CNN) [13,14] was proven to be an effective mean for HGR. The deep convolutional neural network (DCNN) was applied to extract the gesture features of micro-Doppler and achieve gesture recognition with 14 types of hand gestures [15]. However, the time features of the hand gesture motion were ignored, restricting the recognition accuracy of HGR. In References [16,17], 3D convolutional neural networks (3D-CNN) were presented to extract the motion features of continuous gestures. However, this 3D-CNN only focused on the local time features of hand gestures, and ignored the global time features of hand gestures. Zhang et al. [18] applied the long short term memory (LSTM) network [19] to learn the global time features. In [12], Yang et al. proposed a reused LSTM network to extract the trajectory features of the range, angle, and Doppler of hand gestures.

The aforementioned researchers conducted a lot of work and promoted the development of HGR. However, all of them focused on HGR with a single hand gesture. In many applications, such as virtual games and collaborative control, multi-hand gestures should be recognized simultaneously. When there are multiple dynamic hand gestures in front of the radar, the echo signals of multiple gestures mix together, and the multi-hand gesture recognition becomes more difficulty. Peng et al. [20] explored the multi-hand gesture recognition using the different ranges that corresponded to different hand gestures. Unfortunately, this method is not applicable when the multi-hand gestures have the same range. By using the range and angle information of multi-hand gestures with the beamforming technique, Wang et al. [21] extracted each gesture signal successively and then carried out a 2D-FFT operation to obtain the Doppler spectrum of each hand gesture. Although range and angle parameters are used for multi-hand gesture recognition, the Doppler information is missing. More importantly, when the multi-hand gestures have the same range and different hand gestures have similar Doppler characteristics, the recognition accuracies of the above two multi-hand gesture recognition methods are pessimistic. Therefore, this paper applies the automotive frequency modulated continuous wave (FMCW) radar, and tries to design a multi-hand gesture recognition system by making full use of the threedimensional parameters of range, Doppler, and angle, and designing a novel deep learning network. The main contributions of this paper are summarized as follows.

*Firstly*, we applied 3D-FFT on the fast-time domain, slow-time domain, and antenna domain to estimate the range, Doppler, and angle parameters. Specifically, the 2D-FFT was applied to construct the RDM, and the angle FFT was applied on the results of range FFT among multiple antennas to construct the range-angle map (RAM). We applied the Hanning window to suppress the spectral leakage, two-dimensional CA-CFAR (2D-CA-CFAR) to suppress the dynamic interference, and the average power of several continuous frames in feature maps to suppress the static interference in RDMs and RAMs of multi-hand gestures.

*Secondly*, we propose a spatiotemporal path selection algorithm to separate the multihand gestures in RDMs and RAMs. We designed a dual 3D-CNN-based feature fusion network (D-3D-CNN-FN) for feature extraction and classification of the separated hand gestures. Specifically, the dual 3D-CNN network was presented to extract the features of RDMs and RAMs, and the extracted features from multiple frames were then fused and input into LSTM. The output feature sequence is classified by softmax.

*Finally*, we designed a platform and used eight types of multi-hand gestures to validate the effectiveness of the proposed multi-hand gesture recognition system; the experimental results verify the superiority of the proposed method.

The rest of this paper is organized as follows. Section 2, introduces signal processing of FMCW radar. Section 3 details the proposed multi-hand gesture recognition system, including interference suppression, multi-hand gestures separation, and hand gesture recognition. Experiments are carried out in Section 4 and followed by a conclusion in Section 5.

#### **2. Radar Signal Processing**

In this section, we analyze the working principle of the automotive FMCW radar and construct the RDM and RAM maps of multi-hand gesture parameters in details.

#### *2.1. IF Signal of FMCW Radar*

The FMCW radar generates a linear frequency modulation continuous signal through a waveform generator and transmits it by the transmitters. The transmitted signal is reflected by multi-hand gestures, and is then received by the receiving antennas. The intermediate frequency (IF) signal is obtained (shown in Figure 1) by mixing the transmitted and received signals, filtering out the high frequency part.

**Figure 1.** IF signal extraction.

The transmitted signals of the FMCW radar is

$$s\_{tx}(t) = A\_{tx} \cos(2\pi(f\_0 t + St^2/2 + \phi\_0(t))),\tag{1}$$

where *Atx* is the amplitude, *f*0 is the initial frequency, and *S* = *B*/*Tc* is the slope of transmitted signal. *B* is the bandwidth of the radar, *Tc* is the sweep period, and *φ*0(*t*) is the initial phase of the transmitted signal.

The received signal reflected by the *k*-th target (hand) can be expressed as

$$s\_{rx}(t,k) = A\_{rx} \cos(2\pi(f\_0(t-\tau\_k) + \zeta(t-\tau\_k)^2/2 + \phi\_0(t-\tau\_k))),\tag{2}$$

where *Arx* and *τk* = 2*Rk*/*c*, respectively, represent the amplitude and flight time of the received echo signal, *Rk* is the range from the *k*-th target (hand) to the radar, and *φ*0(*t* − *τk*) is the phase of the received signal.

The received signal and the transmitted signal are sent to the mixer and passed through a low power filter (LPF) to obtain an IF signal. The IF signal is expressed as

$$s\_{IF}(t) = \sum\_{k=1}^{K} A\_k \cos(2\pi(S\_l \tau\_k + f\_0 \tau\_k)) + N(t),\tag{3}$$

where *K* is the number of hand gesture targets, *Ak* is the amplitude of the IF signal of the hand gesture target, and *N*(*t*) is the white Gaussian noise.

#### *2.2. Theory of Parameters Estimation*

To achieve multi-hand gesture recognition, in this paper, the range, Doppler, and angle parameters of the FMCW radar are applied. In this subsection, we analyze the estimation theories of these three parameters.

#### 2.2.1. Range Estimation

If we obtain the delayed time of the echo signal, the range between the radar and hand gesture target is calculated, i.e., *Rk* = *τkc*/2. In fact, the delayed time cannot be obtained directly. Fortunately, according to the principle of the IF signal extraction in Figure 1, we find that the delayed time determines the frequency of the IF signal, and the relationship is as follows.

$$f\_{IF} = \mathbf{S} \cdot \boldsymbol{\tau}\_k = \frac{B}{T\_c} \cdot \boldsymbol{\tau}\_k. \tag{4}$$

Therefore, the delayed time is expressed as

$$
\pi\_k = \frac{T\_c}{B} f\_{IF} \,\tag{5}
$$

Then, the range can be computed

$$R(f\_{IF}) = \frac{cT\_c}{2B} f\_{IF} \,\tag{6}$$

Since the radar configuration parameters are predefined, the bandwidth *B* and the sweep period *Tc* are fixed. Then, different hand gesture targets in front of the radar result in different IF frequencies, and different ranges can be obtained. According to [22], the range resolution of the FMCW radar is *dres* = *c* 2*B* . The maximum detection range of the FMCW radar

$$R\_{\text{max}} = d\_{\text{res}} T\_{\text{c}} f\_{\text{s}}.\tag{7}$$

The range resolution is determined by the bandwidth, and the maximum detection range is affected by bandwidth, sweep period, as well as the sampling frequency. Therefore, to maintain the requirement with different ranges, these three parameters should be carefully designed.

#### 2.2.2. Doppler Estimation

To measure the Doppler of a moving hand gesture target, at least two chirps are required. Specifically, the phase difference of two continuous chirp signals is first calculated [23]

$$
\Delta\phi = \phi\_2 - \phi\_1 = \frac{4\pi\upsilon T\_c}{\lambda}.\tag{8}
$$

where *φ*1 and *φ*2 are the phases of the two chirp signals, *λ* is the wavelength, and *υ* is the Doppler of the moving target; it is expressed as *υ* = *λ* 4*πυTc* .

The Doppler resolution is *υres* = *λ* 2*MnTc* , where *Mn* is the chirp number in a frame. The maximum detected Doppler is *υmax* = *λ* 4*Tc* . The Doppler resolution is decided by the chirp number and sweep period, and the maximum detection Doppler is only determined by the sweep period.

#### 2.2.3. Angle Estimation

Since the FMCW radar has multiple transmitting and receiving antennas, we can estimate the angle of the hand gesture target using the phase differences of multiple receiving antennas. The multiple receiving antennas cause path differences of the echo signal from the same target, resulting in the phase difference. The phase difference between two adjacent receiving antennas is

$$
\Delta \varphi = 2\pi \Delta d / \lambda\_\prime \tag{9}
$$

where Δ*d* = *l* sin *θ* is the path difference, *l* is the range between the two continuous receiving antennas, and *θ* is the arrival angle of the signal.

Then, the arrival angle can be expressed as

$$\theta = \sin^{-1}(\frac{\lambda \Delta \varphi}{2\pi l}).\tag{10}$$

Then, we can estimate the arrival angle by searching the spectral peak.

#### *2.3. 3D-FFT-Based RDM and RAM Construction*

Based on the range, Doppler, and angle estimation analysis, this subsection gives the RDM and RAM construction process in detail. Assume that *M* frames are transmitted, and each frame contains *N* chirps and the number sampling points of each chirp is *Nadc*. Since the three parameters can be estimated by 3D-FFT [24], we performed 3D-FFT to construct the RDM and RAM. The process of 3D-FFT for range, Doppler, and angle estimation is shown in Figure 2.

**Figure 2.** 3D-FFT process.

To obtain the three parameters, FFT was firstly carried out on the sampling points of each chirp (such an operation is called range-FFT) to obtain the range of the multi-hand gestures. Based on the results of the first FFT, the FFT was further carried out over *N* chirps of a frame to obtain the Doppler frequency (called Doppler-FFT). By performing the 2D-FFT on different frames, we obtained the RDMs of a complete multi-hand gesture. RDMs can be obtained using a single transmitting and a single receiving antenna. With multiple transmitting and receiving antennas, the results of 2D-FFT (RDM) from different antennas were summed, resulting in less clutter and a higher SNR [25]. On the other hand, with multiple transmitting and receiving antennas, we can estimate the angle of the multi-hand gestures-based on the results of RDM. Generally, the third FFT is carried out on the results of RDM over different antennas. Then, we searched the spectral peak on the results of the third FFT to obtain the range-angle maps of multi-hand gestures.

#### **3. Proposed Multi-Hand Gesture Recognition System**

In this section, the proposed multi-hand gesture recognition system is presented, shown in Figure 3. Since there are a lot of interferences in the parameter maps (i.e., RDM and RAM), to achieve a satisfactory recognition accuracy, we have to suppress the interferences. The multi-hand gesture is then separated by the proposed spatiotemporal path selection algorithm. Finally, the separated hand gestures are trained and tested by the designed deep learning network.

**Figure 3.** Proposed multi-hand gesture recognition flowchart.

#### *3.1. Interference Suppression*

There are many interferences in the IF signal. To suppress the interference from the interfering radars, the authors in [26] designed novel orthogonal noise waveforms; the key idea is that the signal in the current transmission pulse is orthogonal to the next transmission signal. In [27], the authors proposed a tunable Q-factor wavelet transform to suppress the mutual interference between automotive FMCW radars. In [28], Wang utilized a onedimensional CFAR to detect interferences and the detection map was dilated to generate a mask for interference suppression. In case of radar self-motion, the authors in [29] investigated a range alignment processing approach for breathing estimation considering the radar self-motion and target motion. This work can be applied in the field of assistive devices for the disabled. In [30], Haggag et al. introduced a reliable and robust probabilistic method to estimate the radar self-motion. Different from the above cases, in our considered scenario, the interfering radar was not considered and the interferences in the IF signal

mainly came from the static and moving objectives in the test environment. In this paper, the interferences mainly came from the thermal noise inside the radar system, moving targets (such as torso and arms), and static targets (wall or static objects), etc. These interferences in RDMs and RAMs have significant impacts on the separation of multi-hand gestures and the recognition accuracy of hand gestures. Towards this end, we suppressed the interferences in RDMs and RAMs from the following three aspects.

#### 3.1.1. Spectral Leakage Cancellation

The spectral leakage mainly comes from the system noise and the aperiodicity of signal truncation of the ADC sampling. The system noise causes ghost targets near the radar, while aperiodicity of signal truncation spectrum tailing in the whole frequency band, resulting in pseudo peaks around the target. Therefore, we added the Hanning window [31] to the 'three-dimensional' of the data cube. Specifically, the Hanning window was added to the sampling points of each chirp, the same sampling points of multiple chirps and the sampling points from different antennas. The results in the next section show that such an operation reduces the spectral leakage.

#### 3.1.2. Dynamic Interference Suppression

Moving targets, such as the arms and torso, cause dynamic interferences in RDMs and RAMs. Therefore, we applied the 2D-CA-CFAR algorithm [32] to suppress these dynamic interferences in RDMs and RAMs. The 2D-CA-CFAR algorithm computes the average interference power in the referenced window in RDM (or RAM), and obtains an adaptive power threshold. The power in RDM (or RAM), higher than the threshold, is marked as hand gesture, otherwise it is marked as interference. Finally, we moved the referenced window along the range and Doppler domain (or range and angle domain) in turn to suppress the dynamic interference of RDM and extract the multi-hand target.

#### 3.1.3. Static Interference Suppression

Although the 2D-CA-CFAR algorithm suppresses the dynamic interferences from the torso or arms, there still exists static interferences caused by the wall or static objects. Obviously, the Doppler of the static target in RDM is zero, and the range and angle of static targets in RAM keep stable. Therefore, we computed the average power of RDMs (or RAMs) over several continuous frames and subtracted the average power from RDMs (or RAMs) obtained after dynamic interference suppression. In the experiments, the averaged power was computed using five continuous frames. The authors in [33] proposed a range and Doppler cell migration correction (RDCMC) algorithm to solve the range and Doppler cell migration problem. In the future, we will apply this algorithm in combination with our interference suppression scheme on RDM and RAM to obtain better feature maps of multi-hand gestures.

#### *3.2. Spatiotemporal Path Selection Algorithm*

The parameter maps of multi-hand gestures were obtained after map construction and interference suppression. Since one frame constructs one RDM and one RAM, we collected *M* frames to ensure that the RDMs and RAMs contained complete multi-hand gestures. Due to the different range, the Doppler, and angle of the multi-hand gesture, different hand gesture targets show different highlights in RDM and RAM. The same hand gesture needs to be matched in RDM and RAM over different frames. However, since the range, Doppler, and angle of the multi-hand gesture are changing, the strengths of the same hand gestures in RDM and RAM on different frames are different. As a result, we cannot simply apply the strength to match the same hand gesture over different frames. Thus, based on the continuous variation characteristics in space and time, we propose a spatiotemporal path selection algorithm to separate the multi-hand gesture.

To show the separation procedure, we took RDM as an example (RAM follows the same procedure). Firstly, we found the hand gesture target that had the maximum amplitude in the first frame of RDM. We denoted by *Y*1(*r*1, *d*1) the maximum amplitude of the first hand gesture, and (*r*1, *d*1) the corresponding coordinate. Then, we calculated the cost function from (*r*1, *d*1) in the first frame to all the points in the second frame. The cost function is defined as

$$
\omega\_{ij}^1 = -|\mathcal{Y}\_1(r^1, d^1)| - |\mathcal{Y}\_2(r\_{ij\prime}^2 d\_{ij}^2)| + \omega\_{ij\prime}^1 \tag{11}
$$

where *Y*2(*r*2 *ij*, *d*2 *ij*) is the amplitude of the hand gestures in the second frame, *i* = 1, ··· , *Nadc* and *j* = 1, ··· , *N*, *r*2 *ij* and *d*2 *ij* are, respectively, the coordinates of the range and Doppler at the second frame, *ωij* is the weight function between (*r*1, *d*1) and (*r*2 *ij*, *d*2 *ij*) . The weight function is defined as

$$\|\omega\_{ij}^1 = a\_i^1\|r^1 - r\_{ij}^2\|\mathbf{2} + a\_j^1\|d^1 - d\_{ij}^2\|\mathbf{2}\_{\prime} \tag{12}$$

where *α*1 *i* and *α*1 *j* are the weight factors of the range and Doppler domain, respectively, and ·2 is the Euclidean norm.

We found the minimum value of the cost function by searching all of the computed cost values. Following the search procedure, we can obtain the coordinates of the first hand gesture target in all the *M* RDMs. Since the hand gesture target occupied several consecutive coordinates, we used a rectangular window to select the hand gesture target based on the obtained coordinates. To extract the rest of the hand gesture targets, we subtracted the obtained hand gesture targets from RDMs until all the hand gestures were separated. The detailed separation process is concluded in Algorithm 1.

**Algorithm 1** Spatiotemporal path selection algorithm for multi-hand gesture separation.

5: Compute the maximum amplitude *Yf*(*rf* , *df*) and the corresponding coordinate at the *f*-th frame (*rf* , *df*), and record the cost function *C*.

*Yf*(*rf* , *df*) at the *f* + 1-th frame.

12: Construct a window centered on (*rf*+1, *df*+1) , and obtain the RDM of the *Nn*-th hand gesture at the *f* + 1-th frame.

15: Subtract the RDM of the *Nn*-th hand gesture target from the RDM of multi-hand gesture, and update *Nn* = *Nn* − 1. 16: **end for**

The input of Algorithm 1 is the *M* RDMs of multi-hand gestures and the output is the separated *M* RDMs of the *Nn* single hand gestures. The hand gesture target in the first RDM was selected randomly, and the selected hand gesture was then subtracted. Then, by applying Algorithm 1, we obtained the *M* RDMs of each hand gesture. Similarly, the RAMs of each hand gesture can be obtained.

#### *3.3. D-3D-CNN-FN for HGR*

After the multi-hand gestures were separated by the proposed spatiotemporal path selection algorithm, we input the separated hand gesture dataset of RDMs and RAMs into the designed deep learning network for HGR. A total of 32 frames contained a single complete multi-hand gesture motion. To realize real-time applications, we had to segment the continuous data flow into a single complete gesture motion by finding the start and end times of the continuous multi-hand gesture motion. Since the motion features of each hand gesture are described by multiple continuous RDMs and RAMs, the features and continuity of each hand gesture should be carefully considered. Therefore, we propose a dual 3D-CNN-based feature fusion network for HGR, shown in Figure 3. The dual 3D-CNN is applied to extract the features of RDMs and RAMs, and the LSTM network is further applied to extract the continuous features of the gesture motion. The detailed procedure is described as follows.

#### 3.3.1. 3D-CNN-Based Feature Extraction

Since each hand gesture has *M* RDMs and *M* RAMs, we applied two 3D-CNNs to respectively extract the feature of RDMs and RAMs. Then, the extracted feature sequences of range-Doppler and range-angle were fused. The detailed architecture of the dual 3D-CNN is shown in Figure 4.

The sizes of RDM and RAM were 64 × 64 and 64 × 128, respectively, and the sizes were determined by the sampling points of each chirp and the chirp number in each frame. To extract the features of 32 RDMs, the designed network contained five 3D convolution and pooling layers, and one full connection layer. We carried out one 3D convolution and one maximum pooling operation in each layer of the first three 3D convolution and pooling layers. In the fourth and fifth layers, we carried out two 3D convolutions and one maximum pooling operation. To reduce the network parameters and improve the generalization ability of the network, all the convolution operations adopted a 3 × 3 × 3 convolution kernel, followed by the linear activation function of ReLu to reduce the interdependence

between parameters and alleviate the overfitting phenomenon of the network. To fulfill the characteristics of RAMs, the 3D-CNN for feature extraction of RAMs contained six 3D convolution and pooling layers; each layer applied maximum pooling. Since the size of RAM is 64 × 128, we used 1 × 1 × 2 and 1 × 2 × 2 in the first two pooling layers to maintain the size of RDM. To extract subtle local features of hand gestures, we carried out two 3D convolution operations in the final 3D convolution layer.

#### 3.3.2. LSTM-Based Time Sequential Feature Extraction

The dual 3D-CNN extracts the dynamic hand gesture features of RDMs and RAMs over *M* frames, and obtains range-Doppler and range–angle features with two 1 × 1024 feature sequences. To obtain the time sequential features of the complete dynamic hand gesture, the LSTM network was applied. Since the two time sequential features were fused sequentially to obtain the feature sequence with 2 × 1024, the step of the applied LSTM network is 2. Then, the feature with size 2 × 1 is sent to each LSTM, and the time sequential feature with 1 × 1024 is the output. The LSTM for the time sequential feature extraction is shown in Figure 5. In Figure 5, *ft* the forgetting gate, *it* is the input gate, *ct* is the storage unit, and *ht* is the hidden layer state. At each time step, *it*, *ft*, *ct*, *ot*, and *ht* are expressed as

$$\begin{cases} \begin{aligned} \dot{\boldsymbol{t}}\_{t} &= \sigma(\boldsymbol{W}\_{\boldsymbol{m}\boldsymbol{i}} \cdot \mathbf{TF}\_{t} + \boldsymbol{W}\_{\boldsymbol{h}\boldsymbol{i}} \cdot \boldsymbol{h}\_{t-1} + \boldsymbol{W}\_{\boldsymbol{c}\boldsymbol{1}} \boldsymbol{c}\_{t-1} + \boldsymbol{b}\_{t}) \\ \boldsymbol{f}\_{t} &= \sigma\Big(\boldsymbol{W}\_{\boldsymbol{m}\boldsymbol{f}} \cdot \mathbf{TF}\_{t} + \boldsymbol{W}\_{\boldsymbol{h}\boldsymbol{f}} \cdot \boldsymbol{h}\_{t-1} + \boldsymbol{W}\_{\boldsymbol{c}\boldsymbol{f}} \boldsymbol{c}\_{t-1} + \boldsymbol{b}\_{f} \Big) \\ \boldsymbol{\tilde{c}}\_{t} &= \sigma\boldsymbol{f}\_{t} \odot \boldsymbol{i} + \boldsymbol{i}\_{t} \odot \tanh(\boldsymbol{W}\_{\boldsymbol{m}\boldsymbol{c}} \cdot \mathbf{TF}\_{t} + \boldsymbol{W}\_{\boldsymbol{h}\boldsymbol{c}} \cdot \boldsymbol{h}\_{t-1} + \boldsymbol{b}\_{c}) \\ \boldsymbol{c}\_{t} &= \boldsymbol{f}\_{t} \cdot \boldsymbol{\tilde{c}}\_{t-1} + \boldsymbol{i}\_{t} \cdot \boldsymbol{\tilde{c}}\_{t} \\ \boldsymbol{o}\_{t} &= \tanh(\boldsymbol{W}\_{\boldsymbol{m}\boldsymbol{o}} \cdot \mathbf{TF}\_{t} + \boldsymbol{W}\_{\boldsymbol{h}\boldsymbol{o}} \cdot \boldsymbol{h}\_{t-1} + \boldsymbol{W}\_{\boldsymbol{c}\boldsymbol{o}} \boldsymbol{c}\_{t-1} + \boldsymbol{b}\_{o}) \\ \boldsymbol{h}\_{t} &= \boldsymbol{o}\_{l} \cdot \tanh(\boldsymbol{c}\_{t}) \end{aligned} \tag{13}$$

where *σ*(·) is the sigmoid function, tanh(·) is the tanh function, *σ*(*x*) = 1 1+*e*−*x* , *Wmi*, *Whi*, *Wci*, *Wm f* , *Wh f* , *Wc f* , *Wmc*, *Whc*, *Whc*, *Wmo*, *Who*, and *Wco* are the weights in the LSTM unit, and *bf* , *bi*, and *bc* are the corresponding biases.

**Figure 5.** LSTM network-based time series feature extraction.

#### 3.3.3. Gesture Classification

The output of the LSTM network is the time sequential feature of the hand gesture with 1 × 1024. To classify the hand gesture using the time sequential features, we first normalized the sequences and input them into the full connection layer. The feature sequences were then input into the following normalized exponential function

$$softmax\_{v\_i} = \frac{\exp(w\_i^T v\_i)}{\sum\_{j=1}^k \exp(w\_j^T v\_j)}\,\tag{14}$$

where *i* is the *i*-th hand gesture, *vi* is the *i*-th element of the feature sequence, *wi* is the weight corresponding to *vi*, and *k* is the types of hand gestures.

#### **4. Experiments and Analysis**

#### *4.1. Experimental Setup*

In this paper, we designed and built a multi-hand gesture recognition platform using the automotive FMCW radar and data collection card provided by Texas Instruments (TI). The FMCW radar sensor is AWR1642 [34], and the data collection card is DCA1000 [35], shown in Figure 6. The collected data were processed by a personal computer (PC); the recognition results were displayed with the graphical user interface (GUI). The radar had two transmitting and four receiving antennas; the start frequency was 77 GHz and the bandwidth was 4 GHz. We collected 32 frames to acquire the complete multi-hand gestures; the detailed radar parameters are configured in Table 1. In the experiment, the Intel-6700K processor and NVIDIA-GTX1080 graphics card were used.

**Figure 6.** The adopted radar sensor and data collection card.

We designed eight types of multi-hand gestures with two-hands (the method presented in this paper is also feasible to the case of multiple hands (more than two); we will leave the test for future work. Since the FMCW radar can distinguish the two hands from the range and angle, the presented method can be applied to recognize the gestures of the driver and passenger in the front row of the car), shown in Figure 7a. The arrows indicate the movement directions of the hand gestures. Although there were eight types of multi-hand gestures, each hand performed four types of hand gestures. After multi-hand gesture separation, there were eight hand gestures in total, namely left hand slides to left (LSL), left hand slides to right (LSR), left hand slides to up (LSU), left hand slides to down (LSD), and right hand slides to left (RSL), right hand slides to right (RSR), right hand slides to up (RSU), right hand slides to down (RSD), shown in Figure 7a. Since the left hand and right hand perform similar actions, these similar features increased the recognition difficulty. To improve the robustness of the design deep learning network, we collected multi-hand gestures of three men and two women, and each person collected 100 multi-hand gesture data. As a result, each multi-hand gesture had 500 data; there were 4000 data for the eight types of multi-hand gestures in total.

**Figure 7.** Hand gesture types.

#### *4.2. Results and Analysis*

4.2.1. Effect of Interference Suppression

To show the effect of interference suppression, we used the RDM of the multi-hand gesture G1 in Figure 7 at the 10-th frame, and the results are shown in Figure 8a–d. It can be seen from Figure 8 that the spectral leakage in the original RDM was suppressed. Moreover, the dynamic interferences (caused by the torso and arms as well as micro-motion in the environment) in Figure 8b labeled by black boxes were suppressed. The static interferences caused by the wall or static objects were also suppressed in Figure 8d.

**Figure 8.** Effect of interference suppression for RDM. (**a**) Original. (**b**) With the added Hanning Window. (**c**) Dynamic interference suppression. (**d**) Static interference suppression.

We also used the RAM of the same hand gesture, as in Figure 8a–d to show the effect of interference suppression, shown in Figure 9a,b. We can see from Figure 9a that the two hand gesture targets are located at −30◦ and 40◦. Due to the spectral leakage and interferences, there were interference targets in the original RAM, marked by black and white boxes. Figure 9b shows that the two hand gesture targets are clearly after interference suppression.

**Figure 9.** Effect of interference suppression for RAM. (**a**) Original. (**b**) After interference suppression.

#### 4.2.2. Impact of Training Dataset Size

In this HGR experiment, we first analyzed the influence of the ratios of the training to the testing dataset of 2:8, 5:5, 7:3 and 8:2 on the results, shown in Figure 10. It can be seen that when the ratio is 2:8, the generalization ability and recognition result of the D-3D-CNN-FN network is very poor, which is mainly because of the poor fitting ability of the small training dataset. By increasing the ratio, there are more experimental samples in the training set, which makes the recognition accuracy of the proposed network higher. When the ratio is 7:3, the D-3D-CNN-FN network has the best performance, and a ratio of 7:3 in the following experiments.

**Figure 10.** Accuracy under different dataset ratios.

4.2.3. Impact of Learning Rate

Since the learning rate adjusts the weight during network training using the loss function, Figure 11 compares the recognition performance of the network under different learning rates. It can be seen that with a large learning rate, such as 0.001 and 0.005, the D-3D-CNN-FN network fails to converge or falls into a local optimum. If the weight is too small ( such as 0.00001), the D-3D-CNN-FN network will converge very slowly. As a result, we set the learning rate to 0.00008 in the following experiments.

**Figure 11.** Accuracy under different learning rates.

4.2.4. Recognition Accuracy Comparison

To verify the effectiveness of the D-3D-CNN-FN, we took 3D-CNN [17] with RDMs or RAMs and dual 3D-CNN with RDMs and RAMs for comparison. The comparison methods are marked by 3D-CNN+RDM, 3D-CNN+RAM and D-3D-CNN+RDM and RAM, respectively. The dual 3D-CNN (D-3D-CNN) applied two 3D-CNN networks to extract features of RDMs and RAMs, directly followed by the softmax classifier. The ratio of the training dataset to the test data set was 7:3, the initial learning rate was set to 0.0008, and the iteration number used for training was 10,000.

The training accuracy comparison on the test dataset is shown in Figure 12. Since the proposed D-3D-CNN-FN applied dual 3D-CNN for local feature extraction of both RDMs and RAMs and used LSTM for global time feature extraction, the proposed D-3D-CNN-FN has higher recognition accuracy than D-3D-CNN. Moreover, 3D-CNN with RDMs or RAMs have poor recognition accuracy mainly because of the limited motion parameters.

**Figure 12.** Training process comparison of each network.

The recognition accuracies of each type of gesture are summarized in Table 2. We used 'Ave'. to represent the average recognition accuracy in Table 2. Compared to 3D-CNN with only RDM or RAM, D-3D-CNN uses two network branches to respectively learn the local features of RDM and RAM, and then fuses the features and input into a full connection layer for classification. As a result, the average recognition accuracy (86.95%) of dual 3D-CNN is improved by 4.2 compared to the single branch 3D-CNN. With fused feature input into the LSTM for global feature extraction, the average recognition accuracy of the proposed D-3D-CNN-FN is about 6.2 higher than that of the D-3D-CNN network.

**Table 2.** Recognition accuracy comparison (%).

#### **5. Conclusions**

In this paper, we proposed a multi-hand gesture recognition method using an automotive FMCW radar sensor. The 3D-FFT was applied to construct the maps of RDM and RAM. Then, the interference was suppressed and the multi-hand gestures were separated by the proposed spatiotemporal path selection algorithm. The dual 3D-CNNs were proposed to extract the features of RDM and RAM, and the fused features were input into the LSTM network. The performance of the presented system was validated on the self-built dataset. The results showed that the averaged recognition accuracy of the proposed method was 93.12%, which was improved by 6.2 compared with the state-of-the-art. In the future, we will design an efficient and robust interference suppression scheme, as well as a deep learning network with the consideration of radar self-motion. Moreover, we will carry out real-time tests with more hands and more multi-hand gesture types. The multi-hand gesture recognition system applies the radar to recognize the multi-hand gestures in a device-free manner. It has potential application in intelligent driving, industry 4.0, HVAC systems, etc.

**Author Contributions:** Y.W. conceived the original idea and wrote the paper; D.W. and D.Y. collected and tested the multi-hand gesture recognition system; Y.F., L.X. and M.Z. analyzed the data and revised the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by the National Natural Science Foundation of China under grant 61901076; in part by the National Science Foundation of Chongqing under grant cstc2020jcyjmsxmX0865; in part by the China Postdoctoral Science Foundation under grant 2021M693773; and in part by the Science and Technology Research Program of Chongqing Education Commission, under grant KJQN201900603.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank the reviewers and editor for their valuable comments and suggestions.

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

#### **References**


1. Introduction

The concept of electrical energy propagation with physical interconnection eradication spearheaded by Nikola Tesla has been adapted into diverse low- and high-power wireless energy transfer (WET) applications ranging from wireless charging for consumer electronics to electric vehicles. The massive landscape of consumer electronics has expanded toward healthcare, wellness, and fitness apart from the indispensable smartphones that have become such an integral part of one's daily life. Enhancement on quality of life reaches beyond patients who are dependent on implanted medical devices with healthcare monitoring devices for elderly and disabled communities. Keeping track with one's physical location, activity, and fitness regime is empowered by smart wearables [1]. However, data accumulated from monitoring of heart pacing or metabolic activities require appropriate transmission for further analysis or backup and storage. As such, simultaneous power and data transfer without any physical interconnections such as cables turn out to be an alternative option made possible with the forthcoming fifth generation (5G) technology. Ubiquitous integration of hardware compatibility to support wireless charging for all smart devices is foreseeable.

Provision of more than one frequency band is enabled by resonator which is intended for several functions either concurrent power and data transfer or concurrent wireless charging at multiple standards. At present, there are two principal standards for wireless charging of consumer electronics specifically smartphones. The Wireless Power Consortium, otherwise known as Qi, is one of the leading wireless charging standards operating in low-frequency (LF) band, 110–205 kHz [2]. AirFuel Alliance is the merger between Power Matters Alliance (PMA) and the Alliance for Wireless Power (A4WP, also known as Rezence) [3]. A4WP employs magnetic resonance coupling technique operating at 6.78 MHz 15 kHz [4], while both Qi and PMA engage in inductive charging technique. The range of PMA's operating frequency is from 277 to 357 kHz [5]. Nonetheless, unlicensed industrial, scientific and medical (ISM) radio bands of up to 13.56 MHz are commonly selected to be the operating frequency for both inductive and magnetic resonant techniques in the current research community [6].

This chapter commences with challenges in relation to dual-band near-field WET design and the corresponding key performance metrics followed by design approaches and rectification techniques currently employed to alleviate the adverse effects on performance metrics with the intention of providing insights for designers in deciding and further enhancing current rectification options available in the design of dual-band near-field resonators. This framework, however, solely pertains to front-end resonator designs excluding end-to-end scope of near-field WET systems.


**3. Limitations in the approach to risk and vulnerability assessment**

Risk matrices approaches are widely used by public administration as a basis for risk management decisions, with applications [5] from terrorism risk analysis, to highway construction project management, to dam and levee safety and climate change risk management. Risk matrices are widely used for risk reporting, risk prioritization, and risk monitoring [6].

On one hand, they are easy to use and intuitive, and they are often defended as a practical way to flood-risk management, especially when quantitative information is scarce or nonexistent [7]. On the other hand, their theoretical basis is superficial and their employ in decision making is hard, especially when different technical solutions for flood-risk management have to be compared to each other.

At present state, it is sustained that "*the flood directive asks for vulnerability parameters only. The risk as such is not explicitly requested, but implicitly the notion of risk is an integrated part*" (*Handbook on good practices for flood mapping in Europ*e [8]). In other terms, the EFD does not require a quantitative estimation of risk. The cited handbook addresses the map of assets at risk (i.e., the distribution of population, vulnerable groups, and buildings at risk) more than to "*risk maps.*"

The same *Floods Directive Reporting Schemas* [9], which should be followed by countries in reporting to European Commission, allow different methods for reporting, and quantitative estimation is optional. Actually, according to the reporting schema, damage can be indicated (i) as a range, (ii) as a percentage of the total GDP for the flood event, (iii) by classes (Insignificant, Low, Medium, High, Very high), or (iv) by means of other numerical measure indicative of the degree of (potentially) adverse consequences, leaving a wide choice for implementation.

Therefore, the report of quantitative damage is not compulsory, qualitative estimations are widespread, even if the risk formula, which implies the quantification of risk, is often reported in official documents (see [9], p.23).

There are several practical problems with matrix approach, either with respect to the guidelines of the EFD or theoretical.

As far as the Po basin District Authority ([4], p. 17) is concerned, the risk is assessed by the combination of the damage and hazard classes, through a matrix approach. The rows show the damage classes and the columns the hazard levels, that is, the probability of flood occurrence. The implementation of this matrix allowed associating a risk class to each exposed

**Figure 1.** The matrices adopted in PGRA, by Po basin District Authority. Hazard classes P1, P2, and P3 refer to the return

To distinguish the different impacts in terms of human life and anthropic activities risk, three different matrices were used, each for a different flooding process: (i) first matrix (a) in **Figure 1** refers to flooding in main rivers, (ii) second matrix (b) to lake flooding and Apennines rivers,

On the basis of hazard and flood-risk maps, the District Authorities prearrange the flood-risk

Risk matrices approaches are widely used by public administration as a basis for risk management decisions, with applications [5] from terrorism risk analysis, to highway construction project management, to dam and levee safety and climate change risk management. Risk matrices are widely used for risk reporting, risk prioritization, and risk monitoring [6].

On one hand, they are easy to use and intuitive, and they are often defended as a practical way to flood-risk management, especially when quantitative information is scarce or nonexistent [7]. On the other hand, their theoretical basis is superficial and their employ in decision making is hard, especially when different technical solutions for flood-risk management have

At present state, it is sustained that "*the flood directive asks for vulnerability parameters only. The risk as such is not explicitly requested, but implicitly the notion of risk is an integrated part*" (*Handbook on good practices for flood mapping in Europ*e [8]). In other terms, the EFD does not

management plans (PGRA), coordinated at the level of river basin district.

**3. Limitations in the approach to risk and vulnerability assessment**

and (iii) third matrix (c) refers to plane secondary rivers.

period indicated in EFD (P1 for T = 20–50; P2 for T = 100–200; P3 for T = 500).

20 Natural Hazards - Risk Assessment and Vulnerability Reduction

element (receptor).

to be compared to each other.

First, the scales shown in the matrix are ordinal (i.e., rank-ordered); consequently, mathematical operations with ordinal scales are meaningless.

Second, the risk matrix generally shows only probability and consequence, and rank risks by that pair of measures; vulnerability is implicitly considered in the matrix or set equal to a constant value.

Third, communication to people may reveal to be captious; for example, the damage in D4 class in **Table 1** is not necessarily twice the damage of D2 class, in spite of the damage class indication.

Fourth, it is questionable if risk matrices actually improve decision making. Cox has been particularly critical of risk matrix and hazard ranking systems, concluding that "*Applying portfolio optimization methods instead of risk prioritization ranking, rating, or scoring methods can achieve greater risk-reduction value for resources spent"* [10].

The present limitations of risk mapping approach adopted in the implementation of EFD can be reviewed and in the next step of "*…reconsideration and updating…*" in the flood directive.

Actually, the *management plans for flood risk (PGRA)* should be periodically reviewed, and if necessary updated, taking into account "*…the likely impacts of climate change on the occurrence of floods*" (EFD, art.14). Italian legislation provides the time limits to review the preliminary flood-risk assessment (before 09/22/2018 and at a later stage, every 6 years), the hazard maps and flood risk (before 22.9.2019 and thereafter every 6 years) as well as the Management Plans (before 22.09.2021 and thereafter every 6 years).

To this aim, ISPRA edited "*Methodological proposal for updating hazard and risk maps for risk mapping*" [11]. The document aims at proposing some approaches, taken from scientific literature, to be implemented in the revision and updating of the flood-risk maps.

On the basis of the proposal of the ISPRA document, a model (named IRP model) has been proposed by public administration in Piemonte region to quantify and evaluate risks, in view of the revision of the flood-risk maps. The model can also be a usable instrument to fulfill the requirements of the Re.N.Di.S. (National Repository for Soil defense) platform, described in the following paragraph.


**8.2 Potential mechanisms of amylin-Aβ-induced toxicity in neurons and pancreatic β cells**

Several studies have showed that amyloid aggregates have been found to be associated with disruption of several cellular functions, including mitochondrial activity [83, 84], oxidative stress [85], receptor mediated functions [86, 87], disruption of Ca2+ homeostasis [88], and membrane depolarization and disorder [89]. Possibly there is a toxic interaction between Aβ and tau that together with insulin resistance participate in the progression of AD. Similarly, the accumulation of amylin in the brain and its ability to induce neurotoxicity and form "cross-seeding" aggregates with Aβ provide a role for this pancreatic amyloidogenic protein in neurodegeneration [89].

**221**

amyloid deposits in the brain.

*resistances, and finally cell death.*

**9. Conclusions**

**Figure 5.**

to prevent neurodegeneration [90, 95, 96].

more studies about this issue are needed.

*Diabetes Mellitus and Amyloid Beta Protein Pathology in Dementia*

On the other hand, it is likely that there is also a synergistic interaction between

Both in the animal models and in the clinical trials of AD, those drugs related to production of insulin have been observed or have focused on improving the mechanisms of insulin and improving the condition of patients with cognitive impairment [93]. Analogues of amylin, for example, pramlintide, have been used as adjunctive therapy with insulin for diabetes [94] and are also being evaluated for their ability

The advantages of studying therapies for T2D in diseases that occur with an insane process are evident. However, no one has questioned whether targeted therapies for dementias could be useful in the treatment of T2D. Immunotherapy targeting Aβ has been shown to improve blood glucose by increasing sensitivity to insulin [97, 98]. Thus, we believe that amyloid deposits as therapeutic targets could be key in the treatment of dementias and alterations in glucose metabolism. But

the accumulation of amylin in the pancreas and the insulin secretion decrease, which will lead to abnormalities in glucose metabolism, promoting the development of neurodegenerative diseases (**Figure 5**). It is suggested that amylin mediates neurotoxicity by crossing the blood-brain barrier and binding to its receptors [90–92]. This leads to the hyperamulinemia of insulin resistance and to the accumulation of

*Association between AD and T2D. Both AD and T2D present cellular loss and abnormal deposition of Aβ, tau, and amylin. These aggregates have the ability to promote the accumulation of amyloid by cross-seeding in neurons and pancreatic cells. The aggregation of amyloid deposits is favored by the presence of aggregates of tau and amylin, which in turn leads to oxidative stress, mitochondrial dysfunction, inflammation, insulin*

*DOI: http://dx.doi.org/10.5772/intechopen.84473*

*Diabetes Mellitus and Amyloid Beta Protein Pathology in Dementia DOI: http://dx.doi.org/10.5772/intechopen.84473*

**Figure 5.**

*Amyloid Diseases*

ity of the PHF [72].

upon the fragment of the minimal filament (92–95 amino acids culminating in Glu391). The process of proteolysis in the asp-421 of tau favors in the beginning its polymerization, and the truncation in glu391 favors the stability and insolubil-

The incidence of both AD and T2D is increasing at an alarming rate at present and has become a major public health concern in many industrialized countries [73]. Many epidemiological studies have shown that diabetic individuals have a significantly higher risk of developing AD [74]. Recently, it has become increasingly recognized that there is an overlap between the pathology of AD and vascular dementia and cerebrovascular dysfunction plays a role not only in vascular dementia but also in AD [75]. Nevertheless, clinical observations suggest that the association is independent of vascular factors [76], which raises the possibility that diabetic conditions such as insulin resistance and hyperglycemia may affect the fundamental pathogenesis of AD. Many neuronal functions are affected by changes in the insulin signaling pathway; therefore diabetes mellitus may have an important

Another possible mechanism that has been involved is the **amyloid deposition** in islets composed primarily of islet amyloid polypeptide (IAPP or amylin) that is a common feature in T2D. IAPP amyloid deposition has been correlated with disease severity, reduced β-cell mass, the development of hyperglycemia, and islet inflammation. Similarly, Aβ plays a central role in synaptic dysfunction and in the cognitive deficiencies associated with AD pathogenesis [78]. Evidences from clinical and animal studies associate the pancreatic amyloid, amylin in mediating neuronal loss in AD, suggesting its role as a potential link between AD and T2D pathogenesis [79–81]. The presence of amyloid deposits in pancreas and brain has been demonstrated in patients with T2D, which can serve as seed to increase the aggregation of these deposits. This suggests that pancreatic IAPP can potentiate amyloid beta misfolding in patients with AD [81]. Previously, de la Monte and colleges, 2008, reported that IAPP enters the brain, augments Aβ misfolding, and associates with Aβ plaques, and plasma levels correlate with AD diagnosis [82]. Interestingly, amylin has been identified in human cerebrospinal fluid and brains of diabetic patients with vascular dementia or AD and nondiabetic patients with AD. Furthermore, co-localization of amylin and Aβ deposits was also observed in postmortem human brains [81]. Likewise, amylin deposits were observed in the temporal lobe gray matter in diabetic patients [79]. Therefore, the co-existence of Aβ and amylin in the brain suggests the potential ability of amylin to infiltrate the brain and induce

**8.2 Potential mechanisms of amylin-Aβ-induced toxicity in neurons and**

Several studies have showed that amyloid aggregates have been found to be associated with disruption of several cellular functions, including mitochondrial activity [83, 84], oxidative stress [85], receptor mediated functions [86, 87], disruption of Ca2+ homeostasis [88], and membrane depolarization and disorder [89]. Possibly there is a toxic interaction between Aβ and tau that together with insulin resistance participate in the progression of AD. Similarly, the accumulation of amylin in the brain and its ability to induce neurotoxicity and form "cross-seeding" aggregates with Aβ provide a role for this pancreatic amyloidogenic protein in

**8.1 Amyloid formation is the pathological hallmark of T2D and AD**

role in the progression of AD ([77]; see **Figure 2**).

amyloid deposition in the brain [81].

**pancreatic β cells**

neurodegeneration [89].

**220**

*Association between AD and T2D. Both AD and T2D present cellular loss and abnormal deposition of Aβ, tau, and amylin. These aggregates have the ability to promote the accumulation of amyloid by cross-seeding in neurons and pancreatic cells. The aggregation of amyloid deposits is favored by the presence of aggregates of tau and amylin, which in turn leads to oxidative stress, mitochondrial dysfunction, inflammation, insulin resistances, and finally cell death.*

On the other hand, it is likely that there is also a synergistic interaction between the accumulation of amylin in the pancreas and the insulin secretion decrease, which will lead to abnormalities in glucose metabolism, promoting the development of neurodegenerative diseases (**Figure 5**). It is suggested that amylin mediates neurotoxicity by crossing the blood-brain barrier and binding to its receptors [90–92]. This leads to the hyperamulinemia of insulin resistance and to the accumulation of amyloid deposits in the brain.

Both in the animal models and in the clinical trials of AD, those drugs related to production of insulin have been observed or have focused on improving the mechanisms of insulin and improving the condition of patients with cognitive impairment [93]. Analogues of amylin, for example, pramlintide, have been used as adjunctive therapy with insulin for diabetes [94] and are also being evaluated for their ability to prevent neurodegeneration [90, 95, 96].

### **9. Conclusions**

The advantages of studying therapies for T2D in diseases that occur with an insane process are evident. However, no one has questioned whether targeted therapies for dementias could be useful in the treatment of T2D. Immunotherapy targeting Aβ has been shown to improve blood glucose by increasing sensitivity to insulin [97, 98]. Thus, we believe that amyloid deposits as therapeutic targets could be key in the treatment of dementias and alterations in glucose metabolism. But more studies about this issue are needed.


**2.3 Microstructural characterization**

This section focuses on all characterization techniques of ECAPed magnesium alloys including all forms of microscopy and analysis especially microanalysis and surface analytical techniques. The methodology of microstructure analysis and phase identification are also discussed.

*Effect of ECAE Die Angle on Microstructure Mechanical Properties and Corrosion Behavior… DOI: http://dx.doi.org/10.5772/intechopen.94150*

## *2.3.1 Optical microscope*

Specimens for microstructure inspection were prepared by mechanical polishing with silicon carbide abrasive papers (grades of 400, 800, 1000, 1200, 1500, 2000) followed by cloth polishing using diamond paste and kerosene for obtaining mirror finish surface and finally cleaned with acetone. Further, Etching was carried on the polished surface for approximately 3 to 5 s, in a solution of 4.2 g picric acid, 10 ml acetic acid, 10 ml distilled water and 70 ml ethanol for 3–5 s [8–12]. So that sample turns light brown and washed with running distilled water and dried. Microstructures and elements distributions were observed and analyzed using optical microscopy by image analyzer facilitated BIOVIS material plus software, and average grain sizes were measured by linear intercepts method according to ASTM E-112. Same samples were observed under a scanning electron microscope.


*2.1. Materials*

Anhydrous ethanol (CH3CH2OH, 99.7%) was obtained from Fuyu Chemical Co., Ltd. (Tianjin, China). Sodium borohydride (NaBH4, 97.0%), potassium nitrate (KNO3, 99.0%), potassium persulfate (K2S2O8, 99.0%), ammonium chloride (NH4Cl, 99.8%), and sodium nitrite (NaNO2, 99.0%) were acquired from Kermel Chemical Reagent (Tianjin, China). Ferrous sulfate heptahydrate (FeSO4·7 H2O, 99.0%) was purchased from Sheng Ao Chemical Reagent (Tianjin, China). The raw pine sawdust was collected from Weinan, Shaanxi Province, China. The sawdust was rinsed three times with deionized (DI) water and then dried overnight in an oven at 80 ◦C. nZVI used for comparison were purchased from Xiangtian Nanomaterials Co., LTD., Shanghai, China.


Preface

Thermoplastic elastomers (TPEs), sometimes referred to as thermoplastic rubbers, are a class of co‐ polymers or a physical mix of polymers (usually a plastic part and a rubbery part) which consist of materials with both thermoplastic and elastomeric properties. A TPE is a rubbery material with prop‐ erties and functional performance similar to those of conventional vulcanized rubber at ambient tem‐ perature; yet it can be processed in a molten state as a thermoplastic polymer at an elevated temperature. Most of the elastomers are thermosets; thermoplastics are in contrast relatively easy to manufacture, for example, by injection molding. A TPE shows advantages typical of both rubbery materials and plastic materials. The principal difference between thermoset elastomers and thermo‐ plastic elastomers is in the type of the crosslinking bond in their structures. In fact, crosslinking is a critical structural factor which contributes to imparting high elastic properties. TPEs are a class of polymers bridging between the service properties of elastomers and the processing properties of thermoplastics. Thus, TPEs combine the conventional rubbers and thermoplastics. They provide the simplest way of achieving outstanding properties by simply varying the blend compositions and the viscosity of the components and compounding ingredients at a low cost.

A thermoplastic elastomer must fulfill the following three essential characteristics:

TPE materials have the potential to be recyclable since they can be molded, extruded, and reused like plastics, but they have the typical elastic properties of rubbers which are not recyclable owing to their thermosetting characteristics. TPEs also require little or no compounding and do not require the ad‐ dition of reinforcing agents, stabilizers, or cure systems. Hence, batch-to-batch variations in weight‐ ing and metering components are absent, leading to improved consistency in both raw materials and fabricated articles. TPEs can be easily colored by most types of dyes, and more economical control of product quality is possible.

The applications of TPEs are listed below:

Gaskets, Seals, Stoppers, Housings, Rollers, Valves, Bumpers, Wheels, Pads, Casters Strain Reliefs, Cable Jacketing, Switch Contact Points, Bellows, Fuel Line Covers, Housings Shoe Soles/Heels, Wrist Straps, Cushions, Airbag Doors Keycaps, Cosmetic Cases, Handles, Grips, Push Buttons, Knobs

> **Professor Chapal Kumar Das** Materials Science Centre IIT Kharagpur, India


**Author details**

Max F.K. Happel1,2\* and Renato Frischknecht3\*

\*Address all correspondence to<EMAIL_ADDRESS>and rfrischk@lin-magde‐ burg.de

1 Department Systems Physiology of Learning, Leibniz-Institute for Neurobiology, Magde‐ burg, Germany

2 Institute for Biology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

3 AG Perisynaptic Extrazellular Matrix, Department Neurochemistry and Molecular Biolo‐ gy, Leibniz-Institute for Neurobiology, Magdeburg, Germany


**2. Basics of MRI**

Before reviewing the work that has been done with MR, a brief overview of the theory be‐ hind MR should be covered. MR physics can essentially be understood using principles of classical physics, however for a more comprehensive understanding the reader is directed to an excellent review by Plewes and Kucharzck [4,10]. Briefly, MR takes advantage of the be‐ havior of a system of protons in the presence of a magnetic field and how this behavior changes based on the micro- and macroscopic environment. Magnetic strength is generally reported in units of Tesla (T), and MRI scanners have very high field strengths. In human research, 1.5T, 3T, and 7T scanners are commonly used, though 1.5T and 3T scanners pre‐ dominate the clinical setting. The magnet's strength and direction is represented by the vec‐ tor B0 (see Figure 1), and lies along the Z-axis (generally from foot to head).

Due to the large amount of water that constitutes tissue (~80-99% depending on tissue type), most MR is specifically focused on the protons on water molecules. Protons have an intrinsic spin that in nature is oriented randomly. In the presence of a magnetic field however, these spins align themselves on average parallel or antiparallel to the axis of the field (Figure 1a). The number of protons aligned parallel to the field is very slightly larger than the number of protons aligned antiparallel, and it is this difference that produces the net magnetization vector in a voxel. When a radiofrequency (RF) pulse is applied at the proper frequency (Lar‐ mor frequency), the longitudinal (z) component of the magnetization vector is tipped away from the axis of the main magnetic field, but continues to spin around the longitudinal axis or "precess" (Figure 1b). When the pulse is removed, the longitudinal component of the magnetization vector will realign itself with the field with a unique time constant that varies with the local environment. commonly used, though 1.5T and 3T scanners predominate the clinical setting. The magnet's strength and direction is represented by the vector B0 (see Figure 1), and lies along the Z-axis (generally from foot to head). Due to the large amount of water that constitutes tissue (~80-99% depending on tissue type), most MR is specifically focused on the protons on water molecules. Protons have an intrinsic spin that in nature is oriented randomly. In the presence of a magnetic field

ing a difficult task, performing MR work in advanced AD cases is quite challenging and as such, most studies are limited to mild and very mild cases. While these studies are typically performed at a time when pathology is irreversible, the results of this work point to changes that may be apparent before cognitive decline has become clinically ap‐ parent. For this reason, studies that examine differences between people who will even‐ tually develop AD and people who will not develop AD provide insight into both the

It is impossible to predict with certainty who will develop AD, but there are several fac‐ tors that increase the risk. These at-risk populations include individuals in the prodromal stage of AD, termed amnestic mild cognitive impairment (aMCI), and people at a genetic risk for developing AD. A diagnosis of aMCI indicates that there is more memory de‐ cline than would be expected based on the person's age and education level, however memory impairment is not interfering with daily activities. It is estimated that 10-20% of people 65 and older have aMCI, and out of those 10-15% will progress to develop AD in 3-4 years. [8,9] Because approximately 30% of people diagnosed with aMCI will remain stable or improve over time, it is important to find biomarkers that will identify those

This chapter will focus on the use of MR in the early detection of AD. Major advances have been made in structural imaging of both gray and white matter using proton density, T1 and T2- weighted imaging, and Diffusion Tensor Imaging (DTI). Functional imaging in AD will also be reviewed, and Blood Oxygenation Level-Dependent (BOLD) functional Magnet‐ ic Resonance Imaging (fMRI) will be broken down into its primary contributors: Cerebral Blood Flow (CBF), Cerebral Blood Volume (CBV), and the Cerebral Metabolic Rate of Oxy‐ gen (CMRO2). Finally, hemodynamic fMRI contrast can be complemented using measures of neurochemistry, including measuring the balance between excitatory (glutamatergic) and inhibitory (γ-aminobutyric acid; GABAergic) neurotransmission. This can be achieved with new single-voxel chemical imaging techniques such as Magnetic Resonance Spectroscopy

Before reviewing the work that has been done with MR, a brief overview of the theory be‐ hind MR should be covered. MR physics can essentially be understood using principles of classical physics, however for a more comprehensive understanding the reader is directed to an excellent review by Plewes and Kucharzck [4,10]. Briefly, MR takes advantage of the be‐ havior of a system of protons in the presence of a magnetic field and how this behavior changes based on the micro- and macroscopic environment. Magnetic strength is generally reported in units of Tesla (T), and MRI scanners have very high field strengths. In human research, 1.5T, 3T, and 7T scanners are commonly used, though 1.5T and 3T scanners pre‐ dominate the clinical setting. The magnet's strength and direction is represented by the vec‐

tor B0 (see Figure 1), and lies along the Z-axis (generally from foot to head).

(MRS), or more recently using multi-voxel MRS imaging (MRSi)

cause and the physiology of the disease.

226 Understanding Alzheimer's Disease

most likely to progress to AD.

**2. Basics of MRI**

Manipulating the timing of the RF pulses controls the magnetization and creates the desired contrast. The most fundamental timing parameters of relevance are repetition time (TR), echo time (TE), and in some cases inversion time (TI). TR is the time between consecutive acquisitions, and TE is the time from the onset of the excitation pulse that is used for prepar‐ ing the signal for detection to the signal refocusing and in most cases acquisition. In an in‐ version recovery pulse sequence, TI refers to the time between the inversion pulse and the excitation pulse. Importantly, simply by manipulating the timing of the above parameters a range of MR contrasts can be obtained with varying sensitivity to different tissue types. A simple pulse sequence indicating RF and gradient timing is illustrated in Figure 1c. however, these spins align themselves on average parallel or antiparallel to the axis of the field (Figure 1a). The number of protons aligned parallel to the field is very slightly larger than the number of protons aligned antiparallel, and it is this difference that produces the net magnetization vector in a voxel. When a radiofrequency (RF) pulse is applied at the proper frequency (Larmor frequency), the longitudinal (z) component of the magnetization vector is tipped away from the axis of the main magnetic field, but continues to spin around the longitudinal axis or "precess" (Figure 1b). When the pulse is removed, the longitudinal component of the magnetization vector will realign itself with the field with a unique time constant that varies with the local environment. Manipulating the timing of the RF pulses controls the magnetization and creates the desired contrast. The most fundamental timing parameters of relevance are repetition time (TR), echo time (TE), and in some cases inversion time (TI). TR is the time between consecutive acquisitions, and TE is the time from the onset of the excitation pulse that is used for preparing the signal for detection to the signal refocusing and in most cases acquisition. In an inversion recovery pulse sequence, TI refers to the time between the inversion pulse and the excitation pulse. Importantly, simply by manipulating the timing of the above parameters a range of MR contrasts can be obtained with varying sensitivity to different tissue types. A simple pulse sequence indicating RF and

gradient timing is illustrated in Figure 1c.

(1) ܯ ή ܯήܥןܵ

Figure 1. Physics underlying magnetic resonance. Hydrogen atoms align parallel and antiparallel to a strong magnetic field, producing a net magnification vector (a). When a radio frequency pulse is applied at the Larmor frequency, the net magnetization vector is tipped away from the main magnetic field (b). Example of a simple pulse sequence showing timing parameters of the application of radio frequency pulse (RF), the onset of gradients in the Z direction (Gz), and the timing of signal acquisition (Signal) (c). In most cases, the detectible signal (S) that is measured in MRI is a combination of three primary factors: water proton density (C; **Figure 1.** Physics underlying magnetic resonance. Hydrogen atoms align parallel and antiparallel to a strong magnetic field, producing a net magnification vector (a). When a radio frequency pulse is applied at the Larmor frequency, the net magnetization vector is tipped away from the main magnetic field (b). Example of a simple pulse sequence show‐ ing timing parameters of the application of radio frequency pulse (RF), the onset of gradients in the Z direction (Gz), and the timing of signal acquisition (Signal) (c).

ml tissue /100 ml water ), magnetization in the longitudinal plane (MZ) and magnetization in the transverse plane (MXY):

The two major contrasts or "weighting" that are used for generating contrast are T1 and T2. T1 and T2 are independent measures and reflect different properties of the tissue of interest, with T1 governing the Mz term and T2 the Mxy term in Eq. 1 above. The time it takes for the magnetization to realign itself longitudinally is measured using T1 weighting (Figure 2a), and is achieved with a short TR and a short TE sequence. T1 is a constant that is unique for each tissue type and is equal to the point when 63% of longitudinal magnetization is recovered (Figure 2b). At the times selected for T1 imaging, there is a high amount of contrast between gray and white matter and therefore T1 weighted imaging is useful for viewing structural changes in the brain (Figure 2c).

In most cases, the detectible signal (S) that is measured in MRI is a combination of three pri‐ mary factors: water proton density (C; ml tissue /100 ml water ), magnetization in the longi‐ tudinal plane (MZ) and magnetization in the transverse plane (MXY):

$$S \propto \mathbb{C} \bullet M\_Z \bullet M\_{XY} \tag{1}$$

Note that in the absence of a prepulse (α=0), the TR determines the T1-weighting. When the RF pulse is applied, individual protons will also precess in synchrony in the transverse plane. When the pulse is removed, the protons will lose that synchrony or dephase, which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue (Figure 3b). Unlike T1, T2 weighting is achieved with a

(Figure 3b). Unlike T1, T2 weighting is achieved with a long TE and long TR.

is a constant that is unique to each tissue type (b). Example of T2 weighted images (c).

takes into account both the T1 and the T2 properties of the tissue:

*S* ∝*C* ∙(1 - *αe*

**Figure 3.** T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony is a constant that is unique to each tissue type (b). Example of T2 weighted images (c).

The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue

The three equations can be combined to form one overall equation for the MR signal that

T1 and T2 components are each present whenever a proton is flipped out of alignment, but by manipulating the pulse sequences one can contribute to the signal more than the other. This is referred to as weighting. If neither the T1 nor the T2 signal contributes strongly to the signal, only the C component is left. These images are referred to as proton density images.

following a pre-pulse with flip angle, , is given by:

భ ሻ (2)

భ ݁షೃ

for each tissue type based on the magnetic field strength that is applied (b). Example T1 weighted images (c).

Figure 2. T1 weighted imaging. After removal of an RF pulse the magnetization vector recovers longitudinally (a). The recovery time is a constant

http://dx.doi.org/10.5772/54445

229

Using Magnetic Resonance Imaging in the Early Detection of Alzheimer's Disease

The MZ component of the magnetization vector is based on pulse timing as well as the T1 of tissue, and for magnetization

Note that in the absence of a prepulse (=0), the TR determines the T1-weighting. When the RF pulse is applied, individual protons will also precess in synchrony in the transverse plane. When the pulse is removed, the protons will lose that synchrony or dephase, which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue

Figure 3. T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony

The three equations can be combined to form one overall equation for the MR signal that takes into account both the T1 and the T2

*MXY* =*e*-*TE*/*T* 2 (3)

*T* 1 ) ∙*e*-*TE*/*T* 2 (4)

The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue area, and can be written:

long TE and long TR.

ܯ ൌ ሺͳ െ ߙ݁ష

ܯ ൌ ݁ି்ாȀ்ଶ (3)

area, and can be written:

properties of the tissue:

The two major methods or "weighting" that are used for generating contrast are T1 and T2. T1 and T2 are independent measures and reflect different properties of the tissue of interest, with T1 governing the Mz term and T2 the Mxy term in Eq. 1 above. The time it takes for the magnetization to realign itself longitudinally is measured using T1 weighting (Figure 2a), and is achieved with a short TR and a short TE sequence. T1 is a constant that is unique for each tissue type and is equal to the point when 63% of longitudinal magnetization is recov‐ ered (Figure 2b). At the times selected for T1 imaging, there is a high amount of contrast be‐ tween gray and white matter and therefore T1 weighted imaging is useful for viewing structural changes in the brain (Figure 2c).

Figure 2. T1 weighted imaging. After removal of an RF pulse the magnetization vector recovers longitudinally (a). The recovery time is a constant for each tissue type based on the magnetic field strength that is applied (b). Example T1 weighted images (c). The MZ component of the magnetization vector is based on pulse timing as well as the T1 of tissue, and for magnetization **Figure 2.** T1 weighted imaging. After removal of an RF pulse the magnetization vector recovers longitudinally (a). The recovery time is a constant for each tissue type based on the magnetic field strength that is applied (b). Example T1 weighted images (c).

following a pre-pulse with flip angle, , is given by: ܯ ൌ ሺͳ െ ߙ݁ష భ ݁షೃ భ ሻ (2) The MZ component of the magnetization vector is based on pulse timing as well as the T1 of tissue, and for magnetization following a pre-pulse with flip angle, α, is given by:

(Figure 3b). Unlike T1, T2 weighting is achieved with a long TE and long TR.

is a constant that is unique to each tissue type (b). Example of T2 weighted images (c).

ܯ ൌ ݁ି்ாȀ்ଶ (3)

properties of the tissue:

$$M\_Z = \begin{pmatrix} 1 \ -ae \ \stackrel{\cdot n}{\dashrightarrow} + e^{\frac{\cdot n}{\dashrightarrow}} \end{pmatrix} \tag{2}$$

which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue

Figure 3. T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony

The three equations can be combined to form one overall equation for the MR signal that takes into account both the T1 and the T2

The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue area, and can be written:

Note that in the absence of a prepulse (α=0), the TR determines the T1-weighting. When the RF pulse is applied, individual protons will also precess in synchrony in the transverse plane. When the pulse is removed, the protons will lose that synchrony or dephase, which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue (Figure 3b). Unlike T1, T2 weighting is achieved with a long TE and long TR. The MZ component of the magnetization vector is based on pulse timing as well as the T1 of tissue, and for magnetization following a pre-pulse with flip angle, , is given by: ܯ ൌ ሺͳ െ ߙ݁ష భ ݁షೃ భ ሻ (2) Note that in the absence of a prepulse (=0), the TR determines the T1-weighting. When the RF pulse is applied, individual protons will also precess in synchrony in the transverse plane. When the pulse is removed, the protons will lose that synchrony or dephase, which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue

(Figure 3b). Unlike T1, T2 weighting is achieved with a long TE and long TR.

In most cases, the detectible signal (S) that is measured in MRI is a combination of three pri‐ mary factors: water proton density (C; ml tissue /100 ml water ), magnetization in the longi‐

The two major methods or "weighting" that are used for generating contrast are T1 and T2. T1 and T2 are independent measures and reflect different properties of the tissue of interest, with T1 governing the Mz term and T2 the Mxy term in Eq. 1 above. The time it takes for the magnetization to realign itself longitudinally is measured using T1 weighting (Figure 2a), and is achieved with a short TR and a short TE sequence. T1 is a constant that is unique for each tissue type and is equal to the point when 63% of longitudinal magnetization is recov‐ ered (Figure 2b). At the times selected for T1 imaging, there is a high amount of contrast be‐ tween gray and white matter and therefore T1 weighted imaging is useful for viewing

for each tissue type based on the magnetic field strength that is applied (b). Example T1 weighted images (c).

(Figure 3b). Unlike T1, T2 weighting is achieved with a long TE and long TR.

**Figure 2.** T1 weighted imaging. After removal of an RF pulse the magnetization vector recovers longitudinally (a). The recovery time is a constant for each tissue type based on the magnetic field strength that is applied (b). Example T1

The MZ component of the magnetization vector is based on pulse timing as well as the T1 of

is a constant that is unique to each tissue type (b). Example of T2 weighted images (c).

following a pre-pulse with flip angle, , is given by:

*MZ* =(1 - *αe*

tissue, and for magnetization following a pre-pulse with flip angle, α, is given by:

భ ݁షೃ భ ሻ (2)

The MZ component of the magnetization vector is based on pulse timing as well as the T1 of tissue, and for magnetization

which results in a reduced MXY (Figure 3a). This is referred to as T2 decay. Like T1, the T2 time constant is unique for each tissue

*T* 1 ) (2)

Figure 3. T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony

The three equations can be combined to form one overall equation for the MR signal that takes into account both the T1 and the T2

The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue area, and can be written:

*S* ∝*C* ∙*MZ* ∙*MXY* (1)

tudinal plane (MZ) and magnetization in the transverse plane (MXY):

structural changes in the brain (Figure 2c).

228 Understanding Alzheimer's Disease

ܯ ൌ ሺͳ െ ߙ݁ష

weighted images (c).

ܯ ൌ ݁ି்ாȀ்ଶ (3)

properties of the tissue:

Figure 3. T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony is a constant that is unique to each tissue type (b). Example of T2 weighted images (c). **Figure 3.** T2 weighted imaging. Protons lose synchrony after removal of an RF pulse (a). The amount of time it takes for protons to lose synchrony is a constant that is unique to each tissue type (b). Example of T2 weighted images (c).

The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue area, and can be written: ܯ ൌ ݁ି்ாȀ்ଶ (3) The MXY component of MRI is based on pulse timing as well as the T2 constant of the tissue area, and can be written:

$$M\_{XY} \triangleq e^{-TE/T\,2} \tag{3}$$

Figure 2. T1 weighted imaging. After removal of an RF pulse the magnetization vector recovers longitudinally (a). The recovery time is a constant The three equations can be combined to form one overall equation for the MR signal that takes into account both the T1 and the T2 properties of the tissue:

properties of the tissue:

$$S \propto C \bullet \begin{pmatrix} 1 \ -ae^{\frac{\cdot \cdot \cdot}{T \cdot 1}} + e^{\frac{\cdot \cdot \cdot \cdot}{T \cdot 1}} \end{pmatrix} \bullet e^{-TE/T2} \tag{4}$$

Note that in the absence of a prepulse (=0), the TR determines the T1-weighting. When the RF pulse is applied, individual protons will also precess in synchrony in the transverse plane. When the pulse is removed, the protons will lose that synchrony or dephase, T1 and T2 components are each present whenever a proton is flipped out of alignment, but by manipulating the pulse sequences one can contribute to the signal more than the other. This is referred to as weighting. If neither the T1 nor the T2 signal contributes strongly to the signal, only the C component is left. These images are referred to as proton density images.


**7. Program management implications of PAAs**

A number of considerations must be made when applying PAAs in program management. Good prediction can be achieved only if there are good data such as past records, which can be used to predict future outcomes of a process or an activity. For instance, prediction of sales of an organization in the next six months is subject to the availability of historical data that, when analyzed, provide a better understanding of the trend of changes in sales [22]. Before data analysis is conducted, they must be organized to reduce redundancy and unnecessary fields must be discarded. In order to deploy the insights from predictive analysis into the systems, it is recommended that software applications should be used to integrate them into predicting performances of businesses [23]. Some of the software that can be used includes API calls, predictive markup language, and web services. The reliability of PAAs algorithms is subject to the use of original data that have been prepared effectively through calculation of aggregate fields, identifying missing data, and merging a number of sources. Each component of data analysis should be analyzed independently. In case of advanced requirements, more advanced algorithms may be required [24].


*Edited by Ahmed M.A. Nahhas and Akaehomen O. Akii Ibhadode*

This book provides an overview of recent progress in renewable energy materials and devices. Various forms of renewable energy, such as solar, water, and wind energy, have garnered significant attention in research domains due to their potential applications. Solar cells have become particularly intriguing for harnessing solar energy, while the distinctive characteristics of wind energy have drawn the focus of numerous researchers. Renewable energy offers several advantages and applications in contrast to conventional energy sources. The book comprehensively addresses recent advancements in diverse aspects of renewable energy, encompassing solar, water, and wind energy resources.

ISBN 978-1-80356-527-9

Renewable Energy - Recent Advances

Published in London, UK © 2023 IntechOpen © hernan4429 / iStock


**4. Lipid peroxidation products: A possible biomarker of HIV disease progression**

Lipid peroxidation is a free radical reaction. Any species that has sufficient reactivity to abstract a hydrogen atom from a polyunsaturated fatty acid side chain in membrane lipids may initiate this process [38]. This occurs when hydroxyl radicals and possibly oxygen react with the unsaturated lipids of bio-membrane, resulting in the generation of lipid peroxide radical (R000 ), lipid hydroperoxide (R00H) and fragmentation products such as hydroxyoctadecadie‐ noic acid from linoleates, F2-isoprostanes from arachidonates and neuroprostanes from docosahexaenoates [39], 4-hydroxy-hexanal, 4-hydroxy-2-nonenal and malondialdehyde (MDA) [40] that gives the OS condition. MDA seems to be the most potent biomarker of lipid peroxidation although 4-hydroxy-hexanal and 4-hydroxy-2-nonenal are also specific, sensitive and quantifiable markers (noninvasive) measurable in urine for OS overview [41]. Lipid peroxidation in biological membranes generally proceeds through a complex process involv‐ ing rearrangement and destruction of the double bonds in polyunsaturated fatty acid. This occurs in three steps, viz. initiation, propagation and termination phases. Peroxidation of membrane lipids can have several outcomes, such as increased membrane rigidity, decreased activity of membrane–bound enzymes, altered activity of membrane receptors (e.g. sodium pumps), altered permeability (increased permissibility), formation of hydrophobic centres that approaches the external phase, alteration of protein structure, mutagenicity (resulting from DNA/RNA damage or binding) and inhibition of growth and protein synthesis [24]. This is caused by the high nucleophilic properties of decomposed aldehyde products formed during lipid peroxidation, which enables them to react with electrophilic sites such as amino and thiol groups [42]. Biomarkers are characteristics that are specific, reliable and can be measured objectively upon evaluation as indicators of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention.

It is now established that HIV infection is associated with formation of lipid peroxidation products. The importance of evaluating these products is to determine or rather to have a fair idea of the degree of damage the infection has caused the cell. Numerous research works have implicated lipid peroxides as a marker of disease prognosis, drug development and evaluation of efficiency of drugs [43, 44]. A number of scientists have suggested the use of OS marker MDA as a potent additional tool for the assessment and surveillance of HIV/AIDS disease [25,39] and a possible predicator of HIV/AIDS disease progression [45].This is not unlikely knowing that different levels of OS measures as MDA is associated with stages of HIV/AIDS infection classification (Fig. 1).


*Article* **Sound Coding Color to Improve Artwork Appreciation by People with Visual Impairments**

**Jun Dong Cho 1,2,\*, Jaeho Jeong 3, Ji Hye Kim 4 and Hoonsuk Lee 2**

Received: 8 October 2020; Accepted: 19 November 2020; Published: 23 November 2020

**Abstract:** The recent development of color coding in tactile pictograms helps people with visual impairments (PVI) appreciate the visual arts. The auditory sense, in conjunction with (or possibly as an alternative to) the tactile sense, would allow PVI to perceive colors in a way that would be di fficult to achieve with just a tactile stimulus. Sound coding colors (SCCs) can replicate three characteristics of colors, i.e., hue, chroma, and value, by matching them with three characteristics of sound, i.e., timbre, intensity, and pitch. This paper examines relationships between sound (melody) and color mediated by tactile pattern color coding and provides sound coding for hue, chroma, and value to help PVI deepen their relationship with visual art. Our two proposed SCC sets use melody to improve upon most SCC sets currently in use by adding more colors (18 colors in 6 hues). User experience and identification tests were conducted with 12 visually impaired and 8 sighted adults, and the results suggest that the SCC sets were helpful for the participants.

**Keywords:** user experience; visually impaired; color sound coding; accessibility; art appreciation


**Acknowledgements**

The authors would like to acknowledge the great help and support of STDF for the continuous support and financial funds for STDF grants within the framework of the German Egyptian Research Fund (GERF), Project ID: 33601, "A novel innovative Approach for the Optimization of Phytopharmaceuticals: Modulation of Medicinal plants Constituents by Elicitation", Principal Investigator: Farid A. Badria.


**5. Conclusion**

This paper discusses the current state of the art regarding the use of multirotor UAVs for spraying tasks in precise agriculture. The possibilities of application of the proposed autonomous aerial robotic system consisting of a mobile base station and a multirotor type of UAV were demonstrated. The purpose of the presented system was to autonomously perform spraying tasks on different ranges of surfaces, including large crops parcels. In such a system, special emphasis was placed on the functions of

the mobile base station, which had to provide support for autonomous spraying and be a user interface. By selecting the correct components and parameters of the aircraft system, satisfactory spraying coverage, flight performance, and flight duration were achieved. In the future work, it is planned in the first phase to prototype the aircraft and then extensive testing of the control module. In the second phase, it is planned to design custom aircraft equipment and a mobile base station.


*3.4. Human Capital and Human Capability*

Human capital theory is relevant to higher education access as it posits that by enabling more people to access higher education and to achieve post-secondary qualifications, we will generate a greater economic contribution and therefore create more opportunities for all.

Melanie Walker [57] has written extensively on the limitations of a human capital approach to education in the South African and UK contexts. She argues that it assumes labour markets are efficient at placing people in work suitable to their skills and that opportunities are shared equally. Walker [57] remarks that developing widening access to higher education is primarily useful in building human capital and is a persuasive and verifiable, market-aligned model, but it offers an impoverished model for education as it does not prioritise well-being, human agency or the transformative potential of education. She concludes that the human capital model is not sufficiently expansive to address social justice, given that what is really at stake in the model is economic growth.

Taking a capability approach, the dimension for measuring equality would be each person's capabilities. Walker [58] advocates that we ask the question "What is each person however able to be and to do?" rather than "What resources do they have?" Policy, Sen argues, should not just aim to increase income and educational qualifications but to increase access to the resources that enable these freedoms [59].

From this perspective, for instance, it may be that some students within schools with low progression rates do not choose higher education because they are "low achievers" or have "low aspirations" but instead they have "different aspirations". The freedom to choose higher education must include the freedom to reject it [60]; however, it is important to consider the context within which decisions are made and whether or not adequate information, guidance and support have been available. The capability approach invites a range of more searching questions about equality than just a focus on desire satisfaction. It is a challenge not just to evaluate resources and inputs, but also to consider whether learners are able to convert resources into capabilities and thereafter potentially into functionings. This section has provided an overview of both of the programs within the CCS and the materials and methods used to compare their impact in Ireland and the UK. It also explained the strengths and purpose of applying a "capability approach" lens to qualitative findings in both programs. The following section elaborates on the outcomes of this capability approach exploration and compares other key outcomes from both programs.