Reliable vision in challenging illumination conditions is one of the crucial requirements of future autonomous automotive systems. In the last decade, thermal cameras have become more easily accessible to a larger number of researchers. This has resulted in numerous studies which confirmed the benefits of the thermal cameras in limited visibility conditions. In this paper, we propose a learning-based method for visible and thermal image fusion that focuses on generating fused images with high visual similarity to regular truecolor (red-green-blue or RGB) images, while introducing new informative details in pedestrian regions. The goal is to create natural, intuitive images that would be more informative than a regular RGB camera to a human driver in challenging visibility conditions. The main novelty of this paper is the idea to rely on two types of objective functions for optimization: a similarity metric between the RGB input and the fused output to achieve natural image appearance; and an auxiliary pedestrian detection error to help defining relevant features of the human appearance and blending them into the output. We train a convolutional neural network using image samples from variable conditions (day and night) so that the network learns the appearance of humans in the different modalities and creates more robust results applicable in realistic situations. Our experiments show that the visibility of pedestrians is noticeably improved especially in dark regions and at night. Compared to existing methods we can better learn context and define fusion rules that focus on the pedestrian appearance, while that is not guaranteed with methods that focus on low-level image quality metrics. Fully autonomous, artificial intelligence (AI)-based driving systems are currently one of the main promises for the future of smart transportation. A transition to fully autonomous vehicles, however, is likely to happen gradually, by increasing the degree of autonomy of current vehicles on the road. In the meantime, a human driver will remain present with important roles such as control takeover and situation monitoring, relying on advanced driver-assistance systems (ADAS).Currently, driver-assistance technology is already being integrated into commercial and passenger vehicles, to assist a human driver and improve road safety . BesidesSince the current commercially available sensors are limited in their spatial resolution and robustness for driving in various atmospheric conditions, diverse sensors are employed to capture more informative representation of the environment . In applA color camera performs well in good illumination conditions, providing rich color and detail information. However, poor light conditions compromise its effectiveness. At low light, the low signal-to-noise ratio (SNR) drastically degrades the image quality. To compensate for insufficient light the camera may increase the sensitivity of the sensor, the exposure duration, and/or the lens aperture. However, high sensor sensitivities amplify the noise, longer exposures increase the risk of motion blur and wide apertures narrow the depth of field resulting in unfocused regions. Moreover, while compensating for low light conditions, strong light from car headlights at night may cause glare. In addition to camera limitations, atmospheric conditions such as fog and mist diffuse the light and impair visibility.Conveniently, long wavelength infrared (LWIR) or thermal cameras are not affected by the reflected visible light since they are sensitive to the heat emitted by objects. Thermal information is particularly helpful in distinguishing humans from their surroundings, as they tend to be warmer than the environment. However, when the scene and the objects in it are of relatively homogeneous temperature (for example on sunny days), important targets such as humans may become poorly distinguishable. Therefore, visible light and thermal cameras are considered complementary for vision systems operating in variable conditions.In this paper, we propose a method for fusion of visible light- and thermal images, which produces a single blended image as an output, with the aim to preserve natural colors in the well-lit regions, and introduce artificial colors where objects are only visible to the thermal camera. Our goal is to increase the visibility of pedestrians by introducing information extracted from the thermal image, when thermal information is more reliable.We train a single image fusion network that will be used as a stand-alone tool which produces enhanced images in difficult visibility conditions and thereby will assist a human driver. During training we propose to pair the fusion network to a second neural network pre-trained for pedestrian detection, which will not be re-trained and will only serve to define the error functions for the fusion network. The main novelty is that instead of relying on quality metrics, the auxiliary detection network will serve as a proxy for the cognitive processes by the human visual system. The goal is to present the resulting fused stream to a driver on a display, and not to an automatic decision system.Presenting such enhanced video feed to a human driver on an in-car display allows the driver to rely on it in situations such as partial or complete lack of light when approaching a tunnel or poorly lit roads at night and make more informed decisions. The proposed method is conceptually different from the existing pixel-level image fusion techniques that use visible light- and thermal images in several aspects. In general, image fusion methods focus on maximizing the transfer of information from multiple input images into a single output, aiming to achieve good perceptual quality . Two keyBy relying on a learning-based framework and choosing a convolutional neural network (CNN) as a model, the proposed method performs automatic estimation of the optimal activity representation. CNNs are capable of modeling complex, nonlinear relationships and discovering new features of interest in the data, well suited for the application domain. Similarly, the actual fusion can be represented using more complex decision rules and learned jointly with the features. This provides CNNs with greater flexibility and performance advantage over conventional methods.Additional difficulty in the domain of visible and infrared fusion is the definition of a reference image, or ground truth for comparison. Essentially, there exists no ground truth reference based on which the algorithms may be objectively compared. Moreover, different methods focus on different quality aspects and evaluate their performance using a set of no-reference objective image quality metrics. However, there is no single criterion that can guarantee superior performance for all types of scenes. Main application domains for most of the existing methods are surveillance and military applications, typically requiring accurate detail preservation and thermal intensity transfer in the final output.The proposed method focuses on the quality aspect of visible and thermal fusion as well; however, our focus is directed towards maximizing the visibility of pedestrians in images of natural appearance for human drivers. To constrain the output to have natural appearance we optimize the fusion network with respect to the similarity between the output and the RGB input. The key difference compared to existing methods is that instead of relying only on low-level quality-based objective functions, we additionally use the detection performance of an auxiliary deep neural network for person detection as a training criterion. The auxiliary network is pre-trained for natural RGB images and fixed (non-trainable) during optimization. Our motivation for this is two-fold.First, not all existing quality metrics are coinciding with the visual perception, and optimizing one criterion may decrease the quality with respect to another. For example, the problem of preserving edges without reducing the contrast is still not successfully solved for fusion of infrared and visible images . This maSecond, when optimizing low-level, information-theoretic, or statistical metrics such as transfer of details or average gradient, important relationships related to the appearance of pedestrians are ignored. Using a pedestrian detector as an objective allows propagating the focus on high-level properties such as shape, color/intensity, typical location etc. to the feature level, as well as in the fusion rules.The first novelty is the idea to optimize a pixel fusion method using a criterion for enhancing the visibility of objects of interest in the scene. The proposed method ensures that all relevant information for automated person detection is contained in the fused RGB output. We conjecture that this same information also enhances the visibility of pedestrians to humans.We overcome the need for ground truth annotations at the pixel level by relying on an auxiliary detector network. We also overcome the need for ground truth detection annotations on multi-modal data by employing a regular detection network pre-trained on natural RGB images. This is important, since only a few annotated datasets exist for multi-modal pedestrian detection, without such annotations  available at the pixel level. While we focus on pedestrian detection, the approach can also be applied to enhance the visibility of any other object category, by replacing the auxiliary detector during training.The idea bears similarities to the concept of generative adversarial nets (GANs). The difference is that in the concept of GANs the generative network competes against a coupled discriminative network and both networks are optimized in alternation. In the proposed method we optimize only the generative (fusion) network, while the performance of the discriminator (detector) is a secondary goal. Another difference is that in the proposed method the discriminator is an object detector, while with GANs typically it provides a single decision on an image level, discriminating between natural or fake images.As a pre-processing step we compute visual saliency maps for both RGB and thermal input images and add them as input of the fusion network. In the proposed method the saliency maps serve as general low-resolution guiding information for the detail activity in the images, emphasizing the regions of good visibility. Additionally, saliency helps in reducing the importance of the thermal information of the scene background at night when no details are present in the RGB input.In the training process we aim to enforce the output to have high similarity to the RGB. Also, we want to retain the original image appearance in regions where pedestrians are clearly visible in the visible light domain, and only modify the result in regions of poor visibility. To achieve that, we modify the weight of the similarity-to-RGB loss function to have higher weight in pedestrian regions that are easy to detect in RGB.The main novelties of this paper listed below are related to the framework for training the fusion network:Advanced driver-assistance systems (ADAS) are control systems integrated in vehicles to assist the driving task. ADAS have different principles of operation and levels of assistance for drivers . DependiCurrent commercially available driver-assistance systems include Tesla Autopilot, Mobileye, and Openpilot that help in tasks such as collision avoidance, automatic lane centering, parking assistance etc. Similar to these solutions is the use of a thermal camera that is particularly beneficial for seeing in the dark. Systems such as Audi Night Vision Assistant, Mercedes-Benz Night View Assist, Toyota Night View and BMW Night Vision are some examples where thermal video is presented to the driver on a display in the car. However, relying only on the thermal vision at night is not sufficient due the lack of differentiating between colors which are significant for the road markings and traffic signs, and due to the low contrast between objects in the thermal range. In general, night vision assistance is actively being researched and the potential benefits of it are yet to be assessed .The proposed method relies on front-looking cameras (referred to as exteroceptive sensors) and belongs to the type night-vision enhancement systems, where information is presented on a screen for a visual support. Our current focus is on the support by enhanced visualization of the visible scene by fusing visible and thermal images, and not for processing by automatic, internal algorithms. In future development, the proposed framework could also be incorporated in automatic recognition by intelligent driving systems.Fusion of visible and thermal images to enhance the visibility of objects of interest has received little attention in the literature. Predominantly, existing methods focus on enhancing the quality and the amount of information in the output image, optimizing various global quality metrics. The core building blocks are effective information extraction and fusion rules with the goal to achieve improved visibility without introducing any artifacts.Paper  providesMore recent approaches for information extraction and definition of weights are mainly learning-based, where optimization is performed with respect to desired perceptual quality measures ,19,20,21Vast majority of the prior research efforts were focused on surveillance systems, while in recent years automotive applications are becoming dominant in the literature. Several application-oriented methods ,24,25,26In the literature, saliency estimation is a common building block introduced into the fusion process to improve the visual quality of the fused images ,20,21. SIn the proposed method, due to its simplicity in calculations, saliency is estimated using the method proposed by Hou et al.  and usedApart from direct low-level image fusion, some methods presented in the literature have been exploring fusion at a higher semantic level to improve object detection. The existing high-level fusion methods typically define fusion as a classification problem with multi-modal input ,29. In rIn contrast to existing methods, the proposed method focuses on improving the visibility of objects of interest, specifically pedestrians. In absence of ground-truth fused images or segmentation with semantic labels, our method relies on automatic pedestrian detection for extracting relevant visual information. Still, improved visualization to assist a human driver remains our main goal.Most existing pedestrian detectors are trained using regular RGB images, while in the proposed method they will be applied to fused images. This means that the auxiliary detector should have some robustness with respect to the appearance of pedestrians. The motivation for this approach is the broad availability of annotated RGB data, compared to the limited availability of thermal datasets required to robustly train a detector.Detectors that rely on contextual information in addition to pedestrian appearance are most suitable in this respect. Among the papers in the literature ,37 CNN-bIn , a labelAmong the deep learning techniques, discriminative models have achieved significant success in various classification tasks. Generative models, on the other hand, have had more challenges with model estimation, especially when ground truth information is not available. An important contribution for generative model training was proposed in . In geneIn terms of improving image quality, in  a no-refOur CNN-based image generator has a similar goal as the earlier learning-based techniques for improved visibility, while the model estimation process is closer to the GAN concepts. In the proposed method, the fusion network (generative) is trained to produce three-channel RGB images with high resemblance to the original RGB inputs to preserve natural appearance and intuitive results. At the same time, it is trained to modify the original RGB values in the pedestrian regions, so that the visibility of pedestrians is increased. This is achieved by coupling it to a pedestrian detector network (discriminative) as a measure of the degree of visibility of the pedestrians in the fused image. While keeping the detector fixed, the error gradients are propagated back to the fusion network to optimize the parameters of the latter.In the following sections we will explain into more details the proposed architecture of the image fusion network, as well as the novel framework for training it to combine RGB and thermal inputs into a new, more informative fused output.Deep CNNs have proven highly powerful in encoding spatial and spectral relationships and learning complex data associations between the input and the target output. Moreover, they do not require explicit definition of features, rather, they are able to learn the parameters of the convolution operations that define the features.residual between the input and the output of the residual block. This architecture has proven to facilitate the training and reduce the error, since it allows modeling more complex relationships in the data without having to model the input itself through the layers.Recently, the residual learning approach  has showThe proposed fusion function is modeled using a generative convolutional neural network, depicted in Motivated by the success of residual learning and its compatibility with our goal, we incorporate residual learning by forming residual blocks in the cascade of layers. Additionally, we include a copy-and-add operation which connects the original RGB input and the fusion output. This way the network inclines towards focusing on the content that contains additional information to the original RGB image to increase the visibility in regions with pedestrians, leaving all other areas unaffected.As shown in Computing saliency in the visible light images facilitates distinction of regions with good visibility and contrast. As a pre-processing step we apply the saliency detection method based on the so-called image signature, proposed by Hou et al . This meIn , the imaIn the proposed method, the saliency is calculated on a low resolution  or background, and proposes adapted bounding-box dimensions. The regions classified as positive (objects) are denoted as proposals. The output of the RPN is a set of 4-element vectors containing bounding-box coordinates and sizes of the positive samples, and a set of corresponding scalars that represent the decision scores.The classification sub-network of Faster R-CNN aggregates the shared features on the locations of the proposals and classifies them into multiple categories. This sub-network also outputs 4 coordinates and a classification score for each positively classified proposal, similarly to the RPN.In , to traiquations  and 3)..Lrpn forIn both equations the multi-task loss is a weighted average of two losses: log loss for the class scores i, respectively. Since the RPN is a binary classifier, In Equation , the argproposalsj with coordinates vectors with binary elements, with value 1 for the true object category and 0 for all others. The coordinates of the ground-truth bounding boxes corresponding to the positive proposals are Similarly, in the classifier loss Equation , pP={pjPIn Equations  and 3),,3), the The two multi-task loss functions in Faster R-CNN have different effects on the fusion output. Minimizing the region proposal error leads towards a solution with higher detail activity and larger set of proposals, but not exclusively related to the appearance of humans. Minimizing the classification error on the other hand leads towards a solution that makes the proposed pedestrians more distinguishable from other object categories. We have trained the network using the weighted mean of both errors, with an experimentally determined trade-off of 1:10, giving higher significance to the classification errors.During backpropagation, the error gradients originating from the RPN and classification parts are summed at the first (in reverse order) common convolution layer from the VGG-16 graph. An example of the gradient magnitudes from the region proposal and classification modules is shown in In the examples in Since there is no analytic solution to this problem, the minimum of the loss can be found by iterative methods such as gradient-based methods. Minimizing the error consists of finding the point in the multi-dimensional solution space where the loss function reaches a minimum. The location of the minimum is defined as a point in the solution space where the derivative of the loss with respect to the network parameters is zero. Gradient methods are iterative optimization algorithms that estimate the search direction towards the global minimum by relying on partial derivatives of the error function. A popular technique used in the deep learning literature that overcomes several issues typical for early gradient-based techniques is the Adaptive Moment Estimation (ADAM) . ADAM cofused RGB outputs.The Faster R-CNN model that we use as auxiliary loss was pre-trained using the Microsoft COCO (Common Objects in Context) dataset , which iThe KAIST Multi-spectral Pedestrian Detection Benchmark  providesRelying on the detection criteria from Faster R-CNN imposes difficulties in the model estimation due to several reasons. First, the problem is ill-posed as multiple solutions exist. In our specific case, we aim to achieve a natural image appearance and at the same time satisfy optimal detection criteria. Therefore, training simultaneously with the appearance loss will lead the solution towards a model that generates results similar to regular RGB images and at the same time optimizes the detection losses.Another aspect that we have considered is that the loss functions of Faster R-CNN penalize both the false positive and the false negative outcomes. Since the proposed method is mostly concerned with the information that needs to be extracted to increase the visibility of pedestrians in challenging conditions, we generally focus on improving the recall rate of the classifier. However, reducing false negatives is also important so that the network does not introduce artificial, human-like structures in non-person locations. We have experimentally chosen a trade-off for training. In the first 2 epochs we perform training based on the error computed only from the positive samples , later increasing the number to a limited set of around 5 ROIs (including positive and negative samples), gradually ending with approximately 20 ROIs per image.From the full KAIST training dataset, 14,300 images which contain at least one person were selected as a training set. The fusion network was trained image per image, iterating 10 epochs through the full training set. The initial learning rate was 0.001, later decreased to 0.0001 for the final 3 epochs.The main criteria for the evaluation are the visual properties of the fused images resulting from the learned fusion function. As previously explained, with the proposed training framework we rely on the auxiliary pedestrian detector to substitute a high-level cognitive perceptual metric correlated with the human vision. Since we do not have such a metric, the fusion network was trained to optimize features relevant for the detector and improve the performance of the detector. However, our main goal is improved visibility for interpretation by humans, and therefore we apply some post-processing operations to increase the contrast of the details generated by the network.We perform the proposed post-processing on the residual information generated by the fusion network. First, to suppress noise we perform guided filtering of the residual image The post-processing step amplifies the intensity of the details added to the RGB image, and creates a higher contrast with the background. It is evident that the fusion network has learned to make pedestrians visible in nighttime images by applying false colors, while at daytime in images with good visibility the pedestrians are less affected by false colors and more similar to the visible light image. The edges of the pedestrian silhouettes are accentuated by contrasting colors. This kind of output can be displayed on an in-car assistance system, so that a human driver can rely on it in challenging conditions for early risk prevention.The RGB images were recorded with a GoPro Hero 6 camera , while the thermal using a FLIR ThermiCam BPL 390 , mounted next to each other horizontally. The video sequences of both modalities were captured with a frame rate of 30 frames per second. They were then synchronized and calibrated by applying a pre-computed, global geometric transformation to match the viewpoints as closely as possible.With this experiment we aimed to visually evaluate the results in a more realistic scenario, and on completely different data than the training dataset. This collection has not been annotated with object labels; however, it is of higher quality than KAIST in terms of noise and image resolution. Moreover, we present a situation in which both the visible and thermal information are crucial for safe driving at the same time.While the RGB contains more rich details of the environment, the thermal frames capture well the humans in the dark scene. From the results presented in the third column in both In terms of quantitative evaluation, using standard quality metrics, we have compared the results of our algorithm to ten recent fusion methods with publicly available codes: ,12,13,14Most of the reference methods to which we compare the proposed work were designed to fuse grayscale visible light and thermal images. Since our experiments involve three-color visible light inputs plus a single-channel thermal image and three-color fused outputs, we first convert the RGB inputs to a grayscale representation. We transform the input RGB colors to the CIE Lab color space and use only the luminance channel to represent the grayscale version of the visible light images. The grayscale images are used as the inputs for the compared methods from literature. Since our method uses three-color visible light images, for consistent comparison we convert the three-channel output of our network to a grayscale representation and calculate the quality metrics.In the image fusion literature, there is no standard quality metric used for comparison between methods. Therefore, all methods are typically evaluated using a set of multiple metrics. We have evaluated our fusion method using several popular objective quality metrics . The metopy (IE) ,51, AverThe results are presented in Due to differences with the existing methods in the definition of the fusion goal, the estimated quality of the proposed method varies in these metrics. For example, our method achieves very high MI score due a high similarity between the fused images and the input RGB images. On the other hand, preservation of all gradients from the thermal images was not our primary goal and therefore, mainly the pedestrian regions are enriched with details. Nevertheless, the proposed method still ranks relatively high in all columns, indicating that it does not degrade the image quality and transfers information from both input modalities.In the absence of a reliable human perception model, as additional experiment we have evaluated the fusion results with respect to automatic person detection. We assume that enhancing the information relevant for automatic detection at the same time enhances the visibility of pedestrians to humans observing the image. In line with this assumption, in the last two columns of The fusion network was conditioned by the auxiliary Faster R-CNN detector and is biased towards that specific model. To evaluate whether the network truly generates robust and relevant pedestrian details, we have tested the performance of another pedestrian detector using our fused results  is not a universal indicator of the fusion success, and that the improved low-level image properties do not necessarily improve other higher-level vision tasks performed on the high-quality reconstructed images. Our intended application, using the fused images to warn a human driver of pedestrian presence on the street, is also a high-level recognition task. Focusing on context-based image features in addition to low-level-based ones helps in optimizing the visualization of specific objects of interest. For more realistic method evaluation, quality metrics related to driving applications that closely simulate high-level cognitive perception are needed.The proposed method was implemented using the MatConvNet toolbox , which iIn terms of the processing speed, the trained fusion network fuses a single-precision pair of RGB and LWIR images of size 750 × 600 at about 1.3 frames per second on the Nvidia GeForce GTX 1070 GPU.In this paper, we have proposed a novel learning-based method for fusion of visible and thermal images with the goal to create intuitive images with improved pedestrian visibility. The proposed method is conceptually different from most recent visible light and thermal image fusion techniques. Our goal was to develop a general image fusion method to enhance object visibility, with the main application in driver-assistance applications for reliable visualization in challenging conditions. The main novelty of this paper is the training framework for the neural network that performs image fusion. The idea is that the training relies both on low-level (similarity) error and a high-level (pedestrian detection ) error to learn to extract relevant features from the inputs and combine them into a more informative output.The experimental results indicate that using an auxiliary detection performance score as an objective metric can effectively guide the extraction of information from the inputs that is relevant for a specific goal, such as pedestrian visualization in natural images. Moreover, the experiments show that for high-level tasks such as object detection, the optimal input is not necessarily an image which satisfies low-level perceptual metrics. Our approach allows automatic exploration of the best pixel-wise combinations of images from different modalities that contributes towards improved details in pedestrian regions.As deep learning-based methods are becoming more popular, exhaustively trained models are increasingly more available. Re-using them as auxiliary means to optimize problems from other domains may further extend the applicability of frameworks such as the proposed.In future work we will experiment with multi-scale generative networks for stronger context modeling. Opportunities for further development lie in the direction of distinguishing between modalities which are suitable for each visibility condition, while still retaining a unified, general, and robust framework. Moreover, we expect that the investigation of solutions that more accurately follow cognitive models of the human visual system will prove beneficial for visualization and improved safety of the ADAS systems. Future work will also involve investigation of the potentials to extend the applicability of the proposed method in different automotive vision tasks, not limited to object detection and visualization.


Design of underwater acoustic (UWA) modems for compact-sized, underwater platforms such as autonomous underwater vehicles (AUVs) is challenging because of the practical requirement to keep an engineering balance between the performance and the system overhead. Considering this type of mobile communication scenario, Doppler spread as well as the multipath draws substantial attention in implementing the system’s design and engineering. Specifically, for a small AUV, the large computational complexity of real-time resampling for the classic Doppler correction poses significant difficulty for the limited capability of the low-cost processor. In this paper, by adopting an adjustable AD  sampling rate, a Doppler compensation approach is proposed to enable low-complexity hardware implementation. Based on this, a direct sequence spread spectrum (DSSS) acoustic modem is designed for a low-cost, small-sized AUV. Meanwhile, the performance evaluation of this acoustic modem is conducted in terms of the robustness upon varying Doppler as well as AUV integration. Finally, experimental results performed on a commercial, small-sized AUV under different speeds are reported to verify the effectiveness of the proposed acoustic modem. With the rapid development in underwater information acquisition and transmission, the small underwater autonomous mobile platform is drawing more and more attention from the fields of marine environmental monitoring, ocean sampling, and oceanic engineering because of its low-cost, convenience, and capability for dense deployment. Underwater acoustic communication is urgently required for many autonomous underwater vehicle (AUV) applications to allow transmission of data and control among each other . HoweverFor the specific application in small AUVs, because of the relatively short distance as well as low data rate requirement, the problems of low signal noise ratio (SNR) or channel multipath can be mitigated by adopting a suitable modulation manner such as spread spectrum. Thus, in many cases the Doppler spread generated by AUV motion becomes the most severe issue  to be adA significant amount of research has been conducted on Doppler estimation and correction. Johnson et al.  proposedAmong other areas of progress, He et al.  proposedCompared with extensive and active research in different Doppler estimation strategies, there is a lack of investigation on the theoretical principle of Doppler correction, as it is straightforward that Doppler compensation can be effectively achieved by resampling after estimating Doppler with various approaches. While it is easy and convenient to conduct offline resampling in software, from the viewpoint of hardware implementation, real-time resampling still poses significant difficulties to the engineering application of an acoustic modem, mainly because of the computational complexity involved. For small AUVs where the power supply and processor calculation capability are strictly limited, reconstructing and resampling the frame signal, thus, poses challenges to the onboard electronics.In this paper, a hardware Doppler mitigation approach is proposed in the modem design. By directly tuning the analog-to-digital (AD) sampling rate according to the Doppler estimate, our approach significantly reduces the computational load for its implementation on small AUVs. The proposed modem also utilizes a spread spectrum modulation scheme to enable robust, low data rate communication. Each signal frame begins with a synchronization signal that also serves as the Doppler probe. Once the synchronization is in lock, Doppler can be estimated to calculate the corresponding sampling rate to correct the Doppler spread. Then, the AD sampling rate is adjusted accordingly to directly compensate for the Doppler effects. Our approach enables the adoption of a low-cost and relatively simple processor for modem design; thus, it is well suited to small AUVs. A sea trial as well as a lake trial has been conducted to verify the effectiveness of the proposed acoustic modem.As the principle of spread spectrum (SS) technology is well known, they are briefly reviewed here from an engineering viewpoint of the modem design. By directly modulating the information to be transmitted into a broad frequency band with a pseudorandom sequence and then recovering the transmitted information sequence through demodulation and de-spreading processes, a direct sequence spread spectrum (DSSS) is adopted to yield a strong anti-interference ability, a strong anti-multipath capability, and multiple user access capabilities and robustness under low SNR . MoreovetpT is set to be ten times the length of the LFM signal synT. Meanwhile, a protection interval was inserted between the second LFM signal and the following data packet to avoid interference, the length of which guardT equals the length of the LFM signal. The specific duration of each component of the frame structure is provided in The frame structure of the system is displayed in As the estimation of Doppler is not the novel contribution of this work, a conventional and simple time- domain estimation approach is directly utilized to facilitate practical implementation. With two chirp signals located with a fixed interval xpansion , the DopGenerally, after estimating the Doppler, a resampling calculation can be directly used to compensate for it. As mentioned above, while it is pretty convenient to perform resampling in an offline manner by means of computation software such as Matlab, from the viewpoint of practical modem design the implementation of resampling poses significant difficulties for the microprocessor because of the real-time computational complexity involved. Considering that adjusting the AD sampling rate is equivalent to resampling the received signal, a novel Doppler correction approach, by directly tuning the AD sampling rate, is proposed to reduce the real-time computational complexity of the microprocessor and thus facilitate hardware implementation.sf = 75 K sps. With the traditional principle of software resampling, the original signal sequence needs to first be up-sampled by an integer factor of I, and then the output sequence is calculated by a linear interpolation algorithm according to the following equation [Oy of the STM32F4 processor is directly used under the timer interruption mode; thus, the instant sampling rate Specifically, implementation of the adjustable AD sampling rate is achieved by software to set the timer parameter of the processor according to the calculated Doppler. As there is a protection interval between the second LFM signal and the data segment, the calculation of Doppler as well as the adjustment of the AD sampling rate can be conducted during this period to ensure real-time implementation.As the software design of the transmitter is relatively straightforward for the community, we briefly introduce the software design of the AD sampling rate adjustable receiver to further illustrate the main contribution of this paper. The software flowchart of the receiver is presented in At the beginning of the flowchart, successful detection of the first LFM signal indicates the capturing of synchronization and then triggers the detection of the second LFM signal. Afterward, the real interval To ensure real-time implementation of synchronization capturing and Doppler estimation, a dual-buffer manner is adopted by dividing the memory space into a ‘ping’ buffer and a ‘pong’ buffer. With one buffer receiving data via ADC, the other buffer performs the cross-correlation calculation to detect and measure the arrival times of two LFM signals. The role of the two buffers will switch between ‘ping’ and ‘pong’ once the receiving buffer is full to guarantee real-time implementation.Moreover, as the internal correlation function provided in the STM32 Digital Signal Processing function library will automatically save the end part of the previous correlation result to assemble with the current correlation result, continuity of the cross-correlation result can be ensured, even when the LFM signal is more or less equally spread over both buffers.The flow chart of the transmitter and receiver is provided in Because the bandwidth of the proposed MODEM (Modulator and Demodulator) is within the audio frequency range, the general audio interface integrated circuit chip WM8978 is directly adopted for the DAC of the emission signal, which offers convenient control registers for the processor. The piezoelectric transducer is adopted in our design, the bandwidth of which is 13–18 kHz with omni-directivity in the horizon to facilitate the communication application. As most of the piezoelectric transducer exhibits capacitive characteristics, an inductance circuit is used as an electrical impedance matching network to improve the transmitting efficiency.In the receiver, the low-noise AD603 integrated circuit chip is selected as the preamplifier because the gain of which can be controlled by extra voltage to provide accurate, pin-selectable gains of −11 to +31 dB with a bandwidth of 90 MHz, or +9 to +51 dB with a bandwidth of 9 MHz. For the band-pass filter, the continuous-time active filter MAX274 integrated circuit chip is employed as it provides four second-order sections to enable the implementation of an eighth-order band-pass filter.The experimental field of the first trial was the sea near Xiamen harbor, Xiamen, Fujian province, the configuration of which is shown in The time-varying response of the sea trial channel in the first experiment is shown in In order to evaluate system performance, a total of ten frames of communication signals were collected for analysis between the software and hardware demodulation. For the software receiver, Matlab software was adopted for offline demodulation, while the proposed STM32 microprocessor hardware was used for real-time hardware demodulation with Doppler compensation implemented by tuning the resampling rate. The values of Doppler estimated by software and hardware are shown in Shown in In addition, The trial field of the second experiment was Xingan River, Zhejiang province, China. To evaluate the performance of the proposed design in terms of engineering AUV integration, the acoustic modem was integrated onboard a small-sized TH-050B AUV. With a length of 1500 mm and a diameter of 180 mm, the adopted AUV is a typical low-cost commercial AUV with the purpose of being used for extensive marine missions. With the help of it, the effectiveness of the proposed modem onboard AUV under different velocities was evaluated. Shown in With the receiver at an anchored vessel, the AUV was controlled to transmit while moving at different speeds ranging from 1 to 3 knots under a depth of 3 m. The distance between the AUV and the vessel was about 200 m with a water depth of 25 m. The signal parameters of the modem were the same as those in the first trial.In −2 without compensation. Meanwhile, the Doppler compensation can reduce the BER to zero under the Doppler of about 2 Hz, indicating error-free communication under a relatively small AUV speed.The Doppler estimated by the modem is provided in Furthermore, under the Doppler of 10 Hz, while the BER approached 40% without Doppler correction, the BER performance of the proposed modem improved by 7% after Doppler correction and was further promoted to 3% after Doppler correction and channel encoding. Thus, the experimental results obtained by the modem verify the effectiveness of the proposed modem in Doppler mitigation when the AUV experiences a relatively fast movement.This paper presents the design and performance evaluation of a compact modem for a small AUV, which adopts a hardware Doppler compensation method by directly tuning the AD sampling rate to greatly reduce implementation complexity. Meanwhile, a low data rate DSSS modulation scheme is adopted to further improve the performance under noise generated by the AUV itself. Experimental results of a mobile communication sea trial as well as a trial onboard the moving AUV verified the effectiveness of the proposed scheme, which has the potential of being used for small-sized and low-cost underwater vehicles.


We investigate the effect of changing the chemical equilibria by modifying the dissociation constant D.We investigate the chemical equilibria of weak polyelectrolyte nanogels with reaction ensemble Monte Carlo simulations. With this method, the chemical identity of the nanogel monomers can change between neutral or charged following the acid-base equilibrium reaction HA ⇌ A Micro- and nano-gels have recently gained increasing attention as intelligent materials ,3,4,5,6.Polyacids typically become negatively charged in solution via the deprotonation of dissociable groups. The monomer charge is sensitive to the pH. In this work, a generic polyelectrolyte nanogel having a tunable acid dissociation constant is considered.The electrophoretic mobility of colloidal particles with grafted weak polyelectrolytes have been theoretically treated on a mean-field level ,11. ThisTheoretical and computational work on weak polyelectrolytes has so far mainly focused on static equilibrium properties. Starting from the 1990s, computational methods were developed and employed to investigate the properties of weak polyelectrolytes. Using the constant pH method by Reed and Reed ,22,23,24+ ions in the system. This yields the  concentration dependence of the dissociation, which is in contrast to the constant pH method [In a simulation, the definition of the pH is not obvious . In the H method . A more H method .For linear weak polyelectrolytes, the mean monomer charge along the backbone is generally not homogenous. The simple reason is that the monomers near the ends are, on average, at greater distances to the remaining monomers. As a consequence, they can increase their charge with a lower electrostatic energy penalty compared to monomers near the center of the chain. As such, weak polyelectrolytes generally are more charged at the ends. This effect has been predicted by theoretical arguments  and MontIn general, the polymer topology can affect the dissociation equilibrium of the individual monomers quite severely. For example, the central node connecting the arms of a weak star-polymer will ‘resist’ charging due to its relative proximity to the remaining monomers , and in In high salt conditions, the electrophoretic mobility is highly sensitive to the charge near the ends of a linear polyelectrolyte ,37. TherAlthough the charges of all monomers contribute to the total polymer mobility, their individual contributions are not equally weighted. Constraints imposed by the polymer configuration  affect the given weight of a monomer. For a weak polyelectrolyte, this picture is further complicated by that fact that the mean monomer charge itself is sensitive to a similar distance metric.E. The free solution electrophoretic mobility v scaled with the magnitude of the driving field Since dynamical properties of weakly-charged nanogels have not been addressed theoretically, we will investigate in this work for the first time the electrophoretic mobility and the diffusion constant of the nanogel. The charged monomers of the ionized nanogel can be driven by the application of an external field Electrostatic attraction causes counter-ions in solution to preferentially crowd near the charged monomers. The mean field description of the counter-ion concentration around a charged monomer has an exponential decrease over the so-called Debye length E creates on both ion species forces in opposite directions. These forces cause a hydrodynamic shear over the length scale of Since the counter-ions have an opposite charge as the backbone monomers of the gel, the electric field This effect is modulated by the distribution of the ionic cloud near the monomers. This distribution is affected by the local charge density of the monomers, which is a direct expression of the mean dissociation state. Thus, the amount hydrodynamic screening effect between the monomers can vary inside the nanogel. We therefore need to turn to explicit simulations in order to investigate the electrophoretic mobility.Although the main dynamical property we investigate is the electrophoretic mobility, we also investigate how diffusion is affected by salt concentration and dissociation constant. We utilize the components of the nanogel trajectories orthogonal to the applied field to obtain the nanogel diffusion coefficients.Our nanogel consists of a total of milar to . The nanmilar to ,41.We employ two distinct simulation approaches to evaluate the properties of the nanogel. The annealed charge distribution of the weak polyelectrolyte nanogel is investigated with the reaction ensemble method. This method generates an ensemble of nanogel states that have different conformations (monomer positions), as well as different chemical configurations (protonated/deprotonated monomer states). For each parameter combination considered, we generate an ensemble having a minimum of 3000 states. These are then successively used to obtain static quantities like the mean nanogel size or the mean nanogel charge.However, the reaction ensemble method is ill-suited to investigate dynamical quantities since it employs Monte Carlo moves, which do not follow a physical kinetic path. Therefore, the dynamical quantities are investigated with separate runs of pure MD simulations that perform an explicit time integration. Conversely, these NVT simulations are not designed to included changes in the charge state of our chemical species.i-th monomer As a consequence of these limitations, we thus confine our dynamic investigations to “quenched” weak polyelectrolytes. Since a change in the chemical species of the monomers only affects the charge , we can perform a charge pre-average of the Other simulation studies have combined the reaction ensemble method with MD as a means to sample dynamical properties ,43,44. TThe paper is organized in the following way. In the next section, we present the static results obtained from the reaction ensemble Monte-Carlo method, followed by a dynamical results obtained from molecular dynamics simulations. We discuss the results in the their respective sections.In the section that follows, we describe the methods we apply, namely the reaction ensemble Montel-Carlo and the molecular dynamics method. Lastly, we present a summary of the main results and our concluding remarks.The reaction ensemble Monte Carlo simulations provide all the static properties shown in this section.The titration curve shown in i-th monomer, Ka will yield an almost completely charged nanogel.We define the latter as:Ka. Thus, the curves in Adding salt ions into the solution screens the electrostatic interactions, effectively reducing the electrostatic potential energy between two charged monomers. This screening results in an increased number samples where the monomers are dissociated for a given value of Ka in There are two important length scales in play: the nanogel size The plot shows how the charges are distributed for weak polyelectrolyte of increasing mean dissociation state In the low mean dissociation state, the nanogel topology does not stand out. The monomers are only weakly charged on average, and thermal fluctuations cause internal conformational changes, which “wash-out” the internal polymer structure. There is a slight increase in dissociation for monomers found far from the center of mass. This is to be expected since the electrostatic potential is smallest near the nanogel surface.The swelling caused by the higher monomer charges in the moderate dissociation state allows for the internal connectivity to be manifested. Lastly, the nearly fully-dissociated polyelectrolyte case contains striking features of the internal structure. The polymer chains between the crosslinks are essentially fully charged, except for the crosslinker monomer itself and one neighboring monomer. Again, there is a noticeable increase in monomer charge near the surface of the nanogel.As discussed above, the dissociated monomers carry a negative unit charge. An increase in the electrostatic repulsion between charged monomers causes an increased radius of gyration. This is shown in Our results of One can consider the extreme of infinitely small Debye length with “perfect” screening. This will eliminate the effect of the electrostatic swelling and increase the amount of dissociated monomers. Thus, the majority of monomers will become charged (depending on the dissociation constant) without an associated increase in the polymer size. In this situation, the polymer charge increases with On the other extreme with zero screening (infinite dilution of the monomer’s counter ions), having two charged monomers on the chain comes at a high energetic cost. Increasing the value of To explore this scenario, results with different salt concentrations are also plotted in Consider now a moderately-charged nanogel  from solution are attracted to the negatively-charged nanogel. Counterion condensation along the polymer backbone is typically considered to take place when the polymer linear charge density meets the Manning criterion. For an infinitely long rod-like polyelectrolyte having a uniform linear charge density, the criterion is met when the linear charge density is such that the mean distance between unit charges along the backbone is less than the Bjerrum length, We demonstrated in The dimensionless Manning parameter thus takes the form of y axes for these data to optimize the visual representation of their features since the scales are quite different.We select two nanogel cases on opposite sides of the threshold and plot the distribution of monomers and mobile ions in x-axis shows an obvious swelling for the higher charged case; see the corresponding data points in In both cases shown in Although the monomer profile shows a simple electrostatic swelling, the ions near the nanogel exhibit quite different behavior. Indeed, crossing the manning threshold radically changes the way in which the ions are distributed.In the case of In contrast to this, the ionic density shown in Since there is no obvious method to count the number of condensed counter ions, some authors can choose to simply tabulate the counter ions inside some arbitrary cutoff distance to the monomers ,48. In oFurthermore, the shape of the We now turn to combining the monomeric and ionic contributions to the nanogel charge. Using this value of In For the case with no added salt D and its electrophoretic mobility We now turn to dynamical properties of the nanogel, mainly its diffusion coefficient Due to the electrohydrodynamics at play, the Nernst–Einstein relation igration ,49. ThisHowever, these physical effects are uncoupled: the diffusion in the direction orthogonal to the driving field follows the Zimm prediction. We can thus extract these independent metrics from the same simulation trajectory; provided the driving field does not significantly deform the nanogel ,51.y and z direction. The MSD is fitted with a relation of the form:D is used as a fitting parameter. The fitted results are shown in Ka. In both subplots, the y-axis is rescaled with the diffusion value measured from the first point, i.e., Ka ≈ −2.The nanogel diffusion is measured using the mean-squared-displacement (MSD) in the Ka reveals signs of the size crossover observed in Ka ≈ 2. The diffusion data fitted from the MSD are unfortunately more noisy, and the apparent crossover is not as convincing as from the static data.The diffusion coefficient decreases with the nanogel charge. This qualitative behavior is consistent with the observed swelling shown in x direction for the duration Ka.The nanogel velocity is measured by considering the total displacement in the Ka, the mobility rises accordingly and ultimately shows signs of leveling off.At low charge, the mean nanogel position does not significantly drift in the direction of the applied field. As the nanogel becomes charged by increasing pH-pKa, some degree of leveling off is to be expected since the effect of changing the dissociation constant should saturate to the state where all the monomers are charged. However, recall that the parameter space has been reduced due to the computational cost of these simulations; the simulation data terminate before the saturation point is reached.At high values of pH-pWhen plotted as a function of The conventional interpretation of a constant velocity is to consider a force balance between the effective charge and the effective friction. Although these loose notions of effective need to be defined for a specific context, the idea here would be that since the charge grows, some kind of effective friction grows in the same manner to balance out the forces.+ ions, these effects can come into play in a nontrivial manner. We are currently exploring the physical mechanisms behind the observed plateau in the mobility.Ions that enter the nanogel can partially compensate for the nanogel charge, as shown in − and a free mobile ion H+. In general, the reaction follows:z chemical species of type k to l in terms of an individual reaction step K is the dimensionless dissociation constant; V is the simulation box volume; K is related to the dissociation constant Reaction ensemble Monte Carlo (RxMC) simulations provide a means to numerically consider chemical equilibrium ,27. The ients νi . The adde system . Therefoe system .Molecular dynamics is used in two distinct areas in this work; (i) inside the RxMC as a means of exploring conformational phase space and (ii) as pure MD of the quenched nanogel.r is the distance between two particles and the cutoff distance Excluded-volume interactions are applied between all particles in the system: monomers; cations; anions and counterions. They are imposed via a truncated Lennard–Jones interaction of the form:epulsive . The nomMonomers are bonded with finitely extensible nonlinear elastic (FENE) springs that have a potential of the form:crossing .i and j are implemented via a potential of the form:Coulombic interactions between particles For typical conditions in water, this is about When used in the RxMC simulations, the particle positions are integrated following Langevin dynamics . These cKa . In this work, we employ the naive definition of pH as + ions released in the simulation volume by the nanogel, as well as pKa = Ka between −2 and 5 that covers the ionization limits of the nanogel. In the simulations in this paper, this is achieved via sweeping the dissociation constant K  in Equation . In orde version ,59,60, uractions ,62 and tractions  with corractions .All nanogels are simulated in a 100 × 100 × 100 cubic simulation box to minimize finite-size effects. Depending on system parameters, this results in the approximate range of (430–2860) for the total number of MD particles. The D3Q19 lattice Boltzmann implementation employs the parameters of grid size 1.0, a viscosity 1.0, thermal energy 1.0, particle density 1.0, time step 0.01 and coupling friction coefficient of 20.0. Unless specified, the results are all expressed in reduced MD units.Reaction ensemble Monte Carlo simulations were used to study the ionization profile of a weak nanogel. Static nanogel properties were computed from these simulation data.Ka value. However, this increase is modulated near the crosslinks where the average dissociation state is considerably lowered. These results are consistent with the idea that monomeric charges on a weak polyelectrolyte tend to be found on monomers that are statistically more distant from all other monomers. On the nanogel scale, this is expressed as charges accumulating near the nanogel surface. On the polymeric scale, this is expressed by having less charge near the crosslinks.We find that the average dissociation state of the monomers in the nanogel increases towards the outside of the gel with increasing pH-pKa. This is due to two competing effects arising from screening: increasing the screening will increase the mean nanogel charge, but will also decrease the electrostatic repulsion between charges.We find that salt concentrations affect the nanogel size in different ways depending on the value of pH-pKa, all of the curves for the simulated salt concentrations cross at the same critical value of pH-pKa. More work is needed in order to ascertain whether this is a coincidence or if there exists a physical property to the nanogel that leads to the observed critical pH-pKa. When the nanogel size is plotted as a function of the dissociation fraction  the curves corresponding to different salt concentrations no longer cross, suggesting that the mean monomer charge is the relevant physical parameter in determining the size.Interestingly, when the nanogel size is plotted as a function of pH-pThe total charge enclosed inside the nanogel is assessed by considering both monomeric and ionic contributions. We find that unsurprisingly, the internal charge increases with the monomeric charge and that increasing the salt concentration contributes to the reduction of the internal charge.With the chemical equilibrium information computed using RxMC simulations (mainly the mean monomer charge), we use the pure MD with hydrodynamic interactions to investigate dynamical properties like diffusion and mobility.The diffusion coefficient is expected to be related to the nanogel size: bigger nanogels have a lower diffusion coefficient. We find that the diffusion decreases with the mean charge fraction, which is consistent with the associated swelling observed from the reaction ensemble simulations.The measured values for the electrophoretic mobility are leveling off surprisingly fast for increasing dissociation state. Beyond a dissociation state The addition of salt ions decreases the Debye length Our simulations provide qualitative insights into the dynamics of weak polyelectrolytes gels. We outlined possible challenges in treating the behavior of weak polyelectrolytes under the influence of an external electric field. Our results can serve as a stepping stone for upcoming models of weak polyelectrolyte dynamics.


A barrier to cardiopulmonary resuscitation (CPR) training in low-income countries is limited resources. Our goal was to build a CPR training model of simple design that would provide a good feedback system.We developed a low-cost, Basic Life Support training manikin made entirely of natural rubber. Our in-house manikin provides feedback when performing correct chest compression and rescue breathing. The properties of the manikin were tested using simulated chest compression in a laboratory and compared with a commercial manikin. Forty healthy nurse volunteers with CPR experience performed CPR in both types of manikins and responded to questionnaires.A tensile test in a laboratory demonstrated that both types of manikins had acceptable ranges of properties for real-situation CPR in cardiac arrest patients. There were no differences in aesthetic properties, and the manikins felt to the volunteers like a real patient when they were performing chest compression. The feedback response was clear when chest compressions and rescue breathing were performed correctly, and the overall satisfaction with the manikin was good. In addition, the mean scores in terms of the manikin feeling like a real patient when performing rescue breathing and the positive feedback from the rubber manikin were statistically higher than those for the commercial manikin (p=0.001 vs. p=0.023).The in-house developed CPR manikin employing real-time feedback by simple mechanics is effective compared with a commercial manikin. The advantage of our manikin is that it is easy to build and costs substantially less than a commercial manikin. The use of an in-house developed manikin could make effective CPR training more available in limited-resource areas. Cardiopulmonary resuscitation (CPR) is a lifesaving skill used in many situations such as drowning and acute cardiac arrest due to heart disease. Basic CPR, which is performed with chest compressions and rescue breathing, increases the chance of survival.The key to success of CPR is proper training, which provides the learner with Basic Life Support (BLS) knowledge and the opportunity to practice the needed skills. Skill development is achieved by practicing CPR on manikins that simulate the human body. Over the past 50 years, the number of CPR training sessions for the general public has increased.We developed an in-house, low-cost, BLS training manikin made entirely of natural rubber. The manikin provides feedback when the user performs chest compressions and rescue breathing correctly. We are making this [patent-pending] intellectual property available at no charge for non-profit proposes to those who request it from us.A durable, natural rubber film sheathes the outside of the manikin, which is the same size as an adult body from head to waist. The inside of the manikin is made from natural rubber foam that consists of two types of material: 1) the first material, in the center of the chest, is a very elastic, high-density rubber that simulates the heart;; and 2) the remainder of the manikin is made of low-density natural rubber foam to form the head, neck, and body .Inside the rubber that forms the heart, a twin air-space mechanism reacts to chest compressions. As chest compression is performed, the upper air space collapses. At a chest compression depth of 1–2 inches, the bottom air space collapses and releases air through a tube that leads to the outside of the manikin, and a whistle at the opening makes a sound .The mouth of the manikin is open. Its back and the back of the head are flat. The neck is curved and concave with a tilted occiput. In the innermost part of the manikin’s oral cavity, a tube connects the mouth to a space at the back of the manikin’s head where a whistle is installed. The whistle makes a sound while the user is blowing into the mouth of the manikin . The detWhat do we already know about this issue?Cardiopulmonary resuscitation (CPR) skill development is achieved by practicing CPR on manikins that simulate the human body.What was the research question?Our goal was to build a low-cost CPR training model of simple design that would provide a good feedback system.What was the major finding of the study?We designed an in-house developed manikin for CPR training that provides real-time feedback using simple mechanics and has a low manufacturing cost.How does this improve population health?The use of our in-house developed manikin could make effective CPR training more available in limited-resource areas.This study was approved by the Ethics Committee and the Institutional Review Board of the Faculty of Medicine, Prince of Songkla University, Thailand. The natural rubber properties of the CPR manikin were tested in a laboratory using a simulated chest compression, universal testing machine . To evaluate the tensile strength of the materials, chest compression was performed on both a commercial manikin  and the natural rubber manikin that we developed in-house. The testing demonstrated that when both manikins  were comForty healthy nurses, aged 20–50 years, volunteered for the CPR test. All of them had real-life CPR experience and no morbidities that would have limited their performance of CPR on the manikins. The volunteers were randomized into two groups by opaque envelopes containing a computer-generated sequence. The first group of 20 volunteers performed CPR on the rubber manikin using both chest compression and rescue breathing at a ratio of 30:2 (5 cycles), and then did exactly the same on the commercial manikin. The second group of 20 volunteers performed CPR following the same steps as the first group but performed CPR on the commercial manikin first and then on the natural rubber manikin. We collected data from the volunteers using a self-reported questionnaire on which they responded to questions about the appearance, response, and feedback of the two manikins, as well as the volunteers’ overall satisfaction with the manikins. The questionnaire for each manikin was completed immediately after CPR on each manikin.t-test, we analyzed the continuous data of the rubber manikin and the imported commercial manikin to detect differences. Statistical significance was assumed if p<0.05.We performed the statistical analysis using the R software version 3.1.0 . Using Student’s The feedback data of the volunteers are shown in Effective CPR increases the chance of survival in cardiac arrest patients by two- to three-fold if CPR is done immediately after cardiac arrest.Many manikins on the market have electronic systems to provide real-time feedback when users apply the correct chest compression force. The feedback systems include a computer monitor screen or a light that indicates correct compression. In rescue breathing, some manikins use chest expansion as a feedback response. In our study, we developed a low-cost CPR manikin of simple design. It provides real-time feedback responses via whistling sounds when the trainee performs chest compression and rescue breathing correctly. The sounds are caused simply by air passing through a whistle, a simple design that substantially reduces the cost of manufacturing. We believe that the development of this low-cost CPR manikin can expand or make CPR training more readily available in areas or countries with limited resources.The cost breakdown of the rubber manikin can be categorized into the price of the raw materials, the fiberglass mold, and the fabrication of the rubber foam. The raw material is latex, which is not expensive and is readily available in many countries. Moreover, the fiberglass mold is simple and cheap. Lastly, the fabrication of rubber foam is not complicated; it does not require a lot of technical knowhow or technologically advanced equipment and/or facilities. Furthermore, the technology required for the production of this manikin can be easily transferred to small and medium-size enterprises. In our setting, the cost of the low-fidelity natural rubber CPR manikin under study was about 100 U.S. dollars. Meanwhile, the cost of the commercial product used for comparison in this study was about 400 U.S. dollars.Some limitations of this study need to be acknowledged. First, we performed the comparison with only one type of commercial CPR manikin that was available in our hospital. The costs of other commercial products in Thailand are shown in the appendix. Second, this study could not blind the volunteers to the manikins tested .We designed a low-cost manikin for CPR training that provides real-time feedback using simple mechanics and has a low manufacturing cost. We believe that, based on this model for creating a low-cost manikin, this concept could be expanded on for other training venues.


Pipe integrity is a central concern regarding technical safety, availability, and environmental compliance of industrial plants and pipelines. A condition monitoring system that detects and localizes threats in pipes prior to occurrence of actual structural failure, e.g., leakages, especially needs to target transient events such as impacts on the pipe wall or pressure waves travelling through the medium. In the present work, it is shown that fiber-optic distributed acoustic sensing (DAS) in conjunction with a suitable application geometry of the optical fiber sensor allows to track propagating acoustic waves in the pipeline wall on a fast time-scale. Therefore, short impacts on the pipe may be localized with high fidelity. Moreover, different acoustic modes are identified, and their respective group velocities are in good agreement with theoretical predications. In another set of experiments modeling realistic damage scenarios, we demonstrate that pressure waves following explosions of different gas mixtures in pipes can be observed. Velocities are verified by local piezoelectric pressure transducers. Due to the fully distributed nature of the fiber-optic sensing system, it is possible to record accelerated motions in detail. Therefore, in addition to detection and localization of threatening events for infrastructure monitoring, DAS may provide a powerful tool to study the development of gas explosions in pipes, e.g., investigation of deflagration-to-detonation-transitions (DDT). In recent years, the integrity of pipelines and piping systems in industrial plants has moved into the focus of public concern as the global piping infrastructure grows and ages. Moreover, to meet increasingly higher standards in technical safety, environmental compliance and failsafe performance the demand for a holistic pipe monitoring system are expected to rise even further in the future. Such a system should ideally be able to detect, classify, and localize a wide range of different threatening pipe conditions prior to the occurrence of leakages over the whole length of the pipeline and in real time. In the long run it should replace time-consuming and costly routine inspections, which are currently taking place at predetermined time-points .Since piping systems in industrial plants typically are highly branched and stretch over long distances the use of conventional point sensors is usually not practicably feasible to realize a monitoring system without gap. Therefore, the principle of distributed fiber optic sensing (DFOS) ,2 has coPresent DFOS techniques established in pipeline monitoring are widely based on distributed temperature sensing (DTS) and focus on leak detection ,4,5. DTSBesides the established methods to measure temperature and strain, distributed acoustic or vibration sensing (DAS/DVS) has lately received considerable attention as a means to detect and localize third party threats to pipelines, i.e., approach of vehicles, digging, and mechanical manipulation on the pipe ,6,7,8. TThe principle of distributed acoustic/vibration sensing relies on the detection of Rayleigh backscatter caused by naturally occurring inhomogeneities along an optical fiber by employing the measuring scheme of coherent optical time domain reflectometry (C-OTDR) ,22. BasiAlthough, strictly speaking, the term distributed acoustic sensing would imply detection of the full acoustic field including the phase, in many application fields it has become a convention to collect all C-OTDR schemes under the term DAS. For brevity, we will therefore employ this designation in the following.Undamaged pipe: incidents causing (dynamic) circumferential changes of the pipe or initiating propagation of acoustic waves in the pipe wall .Damaged pipe: changes inside the pipeline causing altered flow generated noise .Developing damage of the pipe: crack formation and propagation in the wall with accompanying acoustic emissions.In the sense of a holistic monitoring system described above, the so far not utilized potential of DAS as a means for continuous condition monitoring of pipes by detecting and localizing acoustic signals that point to certain damage scenarios, has been investigated in the interdisciplinary research project AGIFAMOR (Ageing Infrastructures—Fibre Optic Monitoring of Pipes) at BAM from 2015 to 2018 . The feaWhile the results for scenarios 2  and 3 2 are descSince the better part of the events falling into the first category is highly transient, e.g., impacts on the pipe wall or pressure waves travelling through the pipeline, it is necessary to develop a scheme for data analysis that makes do without any temporal averaging. Especially for steel pipes, which are commonly used, localization is additionally aggravated since each event causes acoustic waves propagating inside the pipe wall with the very high speed of sound around 5000 m/s and very little dampening. This means that dynamic strain changes are transmitted to the sensor fiber along many meters along the pipe within milliseconds, which could significantly reduce the spatial resolution of the measuring system. Therefore, tracking of propagating waves on a very fast time-scale is necessary to reveal the signal onset and the location of the initial source of disturbance.In any implementation of a (distributed) fiber-optic sensing system the application of the optical sensor fiber to the structure to be monitored is crucial. This is especially true for cases, where (dynamic) strain has to be transmitted to the fiber. Nevertheless, in most of the established applications of DFOS for pipeline monitoring, standard optical telecommunication cables are employed which are usually installed in the ground with some spatial gap to the pipeline. This very cost-efficient sensor installation is suitable for detection of temperature changes caused by leakages (DTS) and the above-mentioned detection of third-party intrusion by recognizing vibration patterns transmitted via the ground (DAS). However, to enable a true condition monitoring system that is sensitive to small changes in the piping system itself and amongst others needs to pick up circumferential changes of the pipe, installation of the optical fiber sensor directly on the pipe is a prerequisite.The optimization of the application method was one of the main aims of the project AGIFAMOR and in a first investigation comparing different ways of linear fiber application along a pipe the importance of a high quality fiber installation was underlined . AdditioAnother way to provide close contact is helical wrapping of the fiber sensor around the pipe. This type of application has already been employed when carrying out DSS measurements on pipes ,30 and hOn the down side, helical application of the optical fiber sensor to the pipe reduces the overall accessible length of pipe and is therefore expected to be of interest for short to medium pipe lengths. Additionally, it is noted that the bandwidth of the system is reduced in comparison to a linear fiber application on the same pipe length as the maximum pulse repetition rate depends inversely on the length of the fiber under test.However, in the sense of a feasibility study, we focus on optimal conditions with respect to spatial resolution and sensitivity. Therefore, in the work presented here, a bare fiber is used wrapped helically around the pipe under test without any additional glue to exclude influences arising from the adhesive. Two helical configurations are investigated in the following. The first corresponds to a fully distributed sensor system, where the fiber is continuously wrapped around the pipe with a defined pitch, while the second constitutes a quasi-distributed system, where the fiber is applied in spaced segments of densely wound fiber. The latter resembles the deployment of point sensors—as was for example investigated by Cui et al.  for acouFor a fundamental investigation of the capability of DAS in conjunction with a suitable sensor application geometry to detect and localize threatening pipe conditions in an undamaged pipe, as summarized under scenario 1, the present study was carried out in two sets of experiments.In the first set of experiments presented in this paper, we compare the fully distributed and quasi-distributed sensor configuration with respect to their capability to localize transient acoustic events occurring on a pipe. As a showcase application mechanical impacts on the outside of the pipe wall are analyzed. Different procedures for data evaluation and automatic impact detection and localization are introduced. Consequently, localization accuracy and precision are analyzed for different sensor configurations, data evaluation procedures and experimental settings of the DAS system.In the second part an example for monitoring critical conditions occurring inside the pipe, i.e., propagation of pressure waves, is presented. Data evaluation procedures introduced in the first part are employed to detect and localized such events. Additionally, we focus on separation of different acoustic signatures occurring at the same time and capturing of non-linear propagation.In all experiments a sensing system was used consisting of a standard single mode optical fiber  without jacketing and a commercial direct detection C-OTDR-interrogator . This system is specified for a maximum range of 40 km with optical pulse lengths between 10 ns and 1 µs and has a maximum pulse repetition rate of 100 kHz. The settings chosen in this study are specified for each experiment in the following paragraphs. For optimal signal transduction, the optical fiber was pre-strained and directly applied onto the surface of the pipe under investigation by helical wrapping using an inhouse designed wrapping device. Uniform and defined pre-strain was achieved by employing a loosely hanging fiber spool with an attached weight onto the sensor fiber during the wrapping process.Tp used to interrogate the fiber were optimized for each experiment. Longer Tp entail higher sensitivity but lower spatial resolution c is the speed of light in vacuum and neff is the effective refractive index of the optical fiber mode. Pulse lengths were chosen between 30 ns and 100 ns, corresponding to an expected spatial resolution of approximately 3 m to 10 m along a straight fiber stretch. With respect to localization along the pipe, spatial resolution is additionally increased by a factor given by the pitch of the helically wrapping of the fiber sensor and the outer diameter of the pipe. To capture transient processes on a short time scale with high precision the pulse repetition frequency fp was set to 80 kHz—lose to the maximum possible setting of the interrogator—for experiments targeting acoustic waves in the material and to 40 kHz for experiments targeting pressure waves in the medium. According to Nyquist’s theorem, this allows for registering of signals up to 40 kHz or 20 kHz, respectively. It is noted that the maximum monitoring distance Lm along the optical fiber inversely depends on the repetition frequency given by fp may be adjusted to lower values depending on the specific monitoring task and required bandwidth.Optical pulse lengths Position along the fiber is usually given in bins, where one bin corresponds to the portion of fiber from which the interference pattern is analyzed within the interrogator. With the system used one bin represents either 0.57 m or 0.68 m of fiber depending on the digital sampling settings (180 or 150 mega samples per second) for the two sets of experiments, respectively.Raw data recorded by the C-OTDR system was further processed using a home-written Python program. Transient events propagating along the pipe appear as edges within the three-dimensional plot of signal intensity versus time and position along the fiber. For optimal detection of these edges, we employ edge filtering by numerical differentiation of raw data along the time axis. Step width for calculating estimated derivatives was optimized for each experiment.After preparing data in this way, edges were automatically detected by thresholding the signal intensity within a certain bin or summed up intensity within a range of bins with respect to the noise level. The threshold level was set as a multiple of the standard deviation of the noise. From the signal onsets detected in this way the propagation velocity and/or localization of events was deduced by either slope fitting in the case of a fully distributed setup, or by calculation of the runtime difference for the quasi-distributed setup. The whole process of data analysis is summarized in For sensor ranges that can be approximated as localized sensors, data processing was carried out based on the procedure of random quadrature demodulation (RQD) proposed in  for thisNumerical simulation of the propagation of acoustic waves in cylindrical waveguides was carried out using the MATLAB toolbox PC disp (Pochhammer–Chree dispersion) . Pipe geFor laboratory-scale tests a stainless-steel pipe of 6.25 m length, inner diameter of 40.7 mm and outer diameter of 48.3 mm was simultaneously equipped with two separate sensor configurations. The pipe was open on both ends. Each configuration could be connected to the C-OTDR system independently. They are schematically depicted in Configuration 1 represents a fully distributed system, where the fiber is helically wrapped around the pipe on the full length. Pitch of the fiber was 2 cm, so that 7.9 m of fiber correspond to 1 m on the pipe. Altogether 39.5 m of fiber were applied to the pipe in this manner.Configuration 2 corresponds to a quasi-distributed setup with two localized sensors regions at the beginning and at the end of the pipe. Each of the two localized sensors sections consists of approximately 30 m of fiber densely wrapped around the pipe covering a region of 3.7 cm to 3.8 cm. They are separated by two loose coils of fiber (15 m each), which are decoupled from the pipe, to ensure separation of signals originating from the two sensor regions. The distance between the centers of the two sensor regions amounts to 5.98 m.In each setup, a coil of 200 m of fiber was introduced between the measuring system and the applied fiber stretch under test.Short mechanical impacts were produced by manually hitting the pipe with a metallic device either on the front face (A) or on the side (B) as indicated in Propagation of pressure waves within the medium inside a pipe was caused by gas explosions in a laboratory-scale gas-filled pipe. The setup for these experiments was placed in an underground bunker dedicated to pipe tests involving explosive gas mixtures. The pipe equipped with sensors and the ignition device was set up in a separate room whereas control of gas filling and ignition as well as data acquisition was carried out remotely. The pipe used for explosion tests was a stainless-steel pipe of 5.54 m length, inner diameter of 30 mm and outer diameter of 38 mm, which was sealed on both ends and contained two sensor penetrations.After evacuation of the pipe different gas mixtures were introduced into the pipe to the desired pressure level (0 to 10 bar overpressure). Consequently, ignition was carried out at one end of the pipe employing a melting wire. Gas mixtures used were 4.2% vol. propane in air and 28.5% vol. hydrogen in air.Overall guide values of pipe temperature were monitored using a conventional thermocouple placed close to the sensor penetrations on the surface of the pipe. Maximum temperatures throughout the course of the test series amounted to around 50 °C.As a first test of the ability of the system to localize short mechanical impacts on the pipe, the pipe was hit on the front face  m/s in agreement with the above value within the scope of the measurement uncertainties. The observed greater uncertainty when hitting the side of the pipe may be a result of less effective excitation of the present acoustic modes than for excitation on the front face.For shorter pulse lengths  and impacts in position B the signal-to-noise ratio is not sufficient for automatic edge detection and derivation of velocities. For these cases, acoustic propagation velocities are estimated manually from visual recognition of the edges. The derived values are approximately 5100 m/s for the main mode and 3300 m/s for the second mode using 50 ns pulses and 5100 m/s for the main mode using 30 ns pulses. All of these are in good agreement with the ones derived for position A.Localization of the source is readily obtained in all cases, as indicated in Localization of short impacts on a pipe equipped with a quasi-distributed fiber-optic sensor configuration as described in In each sensor region, the signal is detected simultaneously over all bins and is recurring periodically as the acoustic wave is reflected on both ends of the pipe. Therefore, the two sensor regions indeed act as point sensors with respect to the propagating acoustic waves, and signal processing using summation over the range of each localized sensor  is a valid approach. The potential of such an approach is illustrated in Additionally, the recurring nature of the signal may be used for data evaluation by correlation of the two time traces presented in If excitation takes place on the side of the pipe  and are discarded for further processing of the data. A linear fit of the remaining points yields a velocity of the pressure wave of (380 ± 10) m/s. The average velocity determined by the two conventional pressure sensors is 406 m/s and is compatible with valued derived by DAS measurements.For ambient pressure, i.e., 0 bar initial overpressure, similar signal features are observed as in the experiments using propane in air as seen when comparing At 2 bar initial overpressure a more complex signal shape is observed b. The auFirst, a higher acoustic intensity is noticed leading to much more pronounced signal features of the structure-borne waves (marked in yellow) in the filtered data so that automatic signal evaluation is possible—albeit with a rather high uncertainty. The deduced velocity amounts to (4850 ± 1360) m/s in agreement with former results.Moreover, propagation of the pressure wave along the observed length of pipe is non-linear and may be roughly divided into two linear regions with different slopes. In the first region as seen from the place of ignition  and shows an overall agreement with the gradient of the onset points.For comparison of the propagation velocities of structure-borne acoustic waves inside the pipe material determined by distributed acoustic sensing to theoretical predictions, a numerical simulation of the propagation of acoustic waves was carried out for the chosen pipe geometry. The calculated dispersion curves for the different acoustic modes expected in a cylindrical waveguide are presented in Interestingly, the (frequency independent) group speed of the torsional mode T  is 3130 m/s indicating this as the second mode observed in some of the experiments with velocities in a range of 3000 m/s to 3300 m/s.2 leakage from transportation pipes. Propagation velocities of acoustic waves traveling in the pipe material were determined in a laboratory scale experiment on a steel pipe of comparable dimensions as the ones used here, i.e., 6 m length, 50 mm outer diameter, and 46 mm inner diameter, employing two acoustic emission sensors and Nielsen-Hsu pencil lead break tests as excitation source. Two main modes with velocities of 5070 m/s und 3268 m/s were derived in agreement with the findings of the present study.Recently, a study  was presIn the first set of experiments, which were carried out with a fully distributed sensor configuration, the determined position of the disturbance and the localization precision can be directly read from a spatial-temporal representation of raw or adequately filtered data. Localization accuracy is basically determined by the accuracy of the mapping of bins to actual positions along the pipe. This can be done in a calibration procedure after installation of the fiber where the fiber is tapped in crucial places and the observed signals are assigned to the position. This procedure may be carried out with the shortest pulse length available yielding an expected accuracy determined by the spatial resolution of the system. Here, this amounts to When employing a quasi-distributed sensor configuration knowledge of the propagation velocity in the material is a prerequisite to map the observed events to a position on the pipe based on the time difference of signal onset. In practice, this value could be either taken from a theoretical consideration or from a calibration procedure with localized signals in known positions.For the sake of analyzing localization accuracy here, the relative deviation of the experimentally determined speed values from the theoretical value confirmed by numerical simulation is used as a measure for the deviation from the actual point of impact. The localization accuracy is depicted in A summary of the deduced localization uncertainty for different sensor configurations and evaluation procedures is given in However, the positions of impact determined for the quasi distributed configuration by evaluation of signal onsets hints at an additional systematic deviation to one side of the points of impact corresponding to systematically underestimated time differences between the two localized sensors. This is also evident in the trend to somewhat higher speed values  deduced Overall, the quasi distributed sensor configuration tends to produce smaller localization uncertainties, especially at longer pulse lengths than the fully distributed one for the setup with a short pipe segment used here. This is attributed to the possibility of signal averaging over a certain bin range and resulting higher signal-to-noise levels. However, for longer pipes it is expected that signal intensities and localization precision will deteriorate if the distance between disturbance and the localized sensors increases. This effect was also observed by Cui et al.  for locaFor selection of an optimal application geometry in an industrial environment these advantages and disadvantages must be considered alongside issues concerning accessibility of pipes, overall range to be monitored and the required spatial resolution.Moreover, for practical pipes, which often have a larger diameter than the ones inspected here, the localization precision and accuracy would increase when employing similar fiber pitch as in the experiments shown here. However, in this case the monitoring distance along the pipe would be decreased and/or the bandwidth restricted further. Therefore, it is expected to be favorable to adjust the fiber pitch to larger values in order to cover longer stretches of pipeline, as in many practical cases lower spatial resolution than the one reached here should be sufficient.In the set of experiments carried out to detect and localize propagating pressure waves in pipes it was found that the fully distributed fiber-optic sensor configuration is indeed able to track such waves continuously along the pipe. Besides the intended use in condition monitoring this opens new perspectives in the fundamental study of gas explosions.In general, gas explosions can be subdivided into two groups with tremendous differences in their consequences on pipeline structure. While deflagrations are characterized by velocities below the speed of sound in the medium and explosion pressures on the order of 10 bar above initial pressure, detonations propagate with supersonic velocities and pressures an order of magnitude larger usually causing catastrophic damage. Due to the complex interplay between fluid dynamics, heat transfer and turbulences depending on the specific gas mixture and pipeline configuration, determination of occurrence and location of deflagration-to-detonation transitions (DDT) is still a topic of current research ,38.pex being the explosion and p0 the initial overpressure, this is characteristic of a deflagration. A similar situation was found in the experiment with a gas mixture of hydrogen in air ignited at ambient pressure.Here, both measurement techniques, DAS and piezoelectric pressure transducers, confirmed a (constant) velocity in the range of the acoustic speed inside the medium after ignition of a gas mixture of propane in air. Together with the observed pressure values of The advantage of using a fully distributed sensor as represented by the distributed acoustic sensing system, emerges in cases where a non-linear propagation takes place. This is illustrated in the experiment where a gas mixture of hydrogen in air was ignited at 2 bar initial overpressure. The supersonic propagation velocity and pressure values of Therefore, DAS, together with an appropriate sensor configuration, may be used as tool for fundamental studies on the behavior of gas explosions in pipes. As shown in the first part of this paper, the present helical sensor configuration may allow to localize such events with a spatial resolution down to ~20 cm. This would correspond to installing 30 piezoelectric pressure transducers on a pipe of 6 m length to achieve the same resolution.In the present study, the capability of DAS to detect and localize transient events on pipes was investigated. It was found, that in conjunction with a fitting sensor configuration of the optical fiber, mechanical impacts can be identified and localized along the pipe. Depending on the favored application, a fully distributed configuration, represented by continuous helical wrapping of the sensor fiber around the pipe or a quasi-distributed configuration consisting of short local sections of densely wound fiber, may be favorable. The localization accuracy and precision in general depends on the chosen fiber configuration, experimental settings  and the procedure of data evaluation. For the parameters tested here, values down to 20 cm were achieved. In summary, the methods presented in this study may be understood as a toolbox for practical implementation of DAS on pipes.In a second set of experiments the technique was applied for spatially continuous tracking of pressure waves in pipelines following gas explosions. It was demonstrated that the employment of DAS with a fully distributed sensor configuration not only allows for observation of propagation with a constant velocity but also of accelerated motion. In contrast to conventional testing of gas explosions with local pressure sensors, the proposed technique may provide a means to identify the point of deflagration-to-detonation transition without gap and without a priori knowledge of its location.The approach is therefore of interest not only in condition monitoring of pipes but also in investigations of the behavior of gas explosions in closed pipe systems.