Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns. Computer systems are constantly threatened by a diversity of attackers  who intentionally compromise the integrity, availability of confidentiality of the protected assets. Driven by different motivations, their final goal is to take advantage of specific system vulnerabilities that will enable them to conduct cyber attacks ,2,3. AmoDefense countermeasures against Botnet threats entail detection as their primary objective. Different approaches to detect malware have been proposed over the past years, mostly underpinned on network mining techniques, machine learning, deep learning and, in a broader sense, data-driven intrusion detection methods . The litIn-depth literature review on malware recognition and the supervised learning methods most frequently used for botnet detection. Stressing their challenges on the current landscape as well.Understanding the behavior of botnets by analyzing the patterns involving the communication between bots and C&C server. Those expressed as network flow-based metrics.A comprehensive comparative methodology based on the selected supervised models by focusing the analysis on eight botnet families.Providing a detailed parameterization of the machine learning models, underpinning their optimal calibration and their testing and prediction results in the aftermath.An extended experimentation following the principles outlined above, along with a comprehensive discussion on the baseline accuracy achieved by similar proposals.Establishing a preliminary set of reference supervised algorithms with the potential of serving as benchmarking elements for further research.In order to provide a solid evaluation scheme aimed on proving the real effectiveness of common classifiers applied for botnet detection, in this research we perform a thorough comparison between a subset of supervised learning methods driven by a well-defined methodology. Our analytical approach examines the patterns disclosed by different botnet samples when exchanging network data from the zombie machine (bot) to the (C&C) server, defining six well-known supervised algorithms for further comparisons. Thereby, our object of study is the detection of botnets grounded on communication metrics measured at network flow level. To this outcome, we aim on developing robust machine learning models capable to deal with different botnet families for which network samples are gathered from real execution testbeds. On the other hand, performing classification with unbalanced data lead to unexpected behaviours unless a proper modeling is undergone. To tackle such limitations on reference data samples, we deepen into data splitting strategies and calibration procedures carefully examined to validate the real detection accuracy for specific botnet specimens. Our goal is also to generate well-elaborated models evidencing both their real modeling and testing accuracy and performance; for which a detailed calibration is provided. Furthermore, the Grid Search algorithm lead to optimize each of the model parameters based on the overall accuracy. It is worth stressing that our proposed methodology can also be extended to different classification algorithms or botnet families provided that the necessary network samples and flow metrics are collected. Bearing those considerations in mind, the main contributions of the presented research are listed as follows:The remaining of this document is structured as follows—An in-depth review of the literature evidences that most of the analytical techniques for malware detection are nowadays grounded on artificial intelligence. Among the different malware specimens, botnets are the object of study in this research and the analytical approaches for their detection can be broadly categorized as those based on graph mining, machine learning and deep learning. For each, the following subsections provide a summarized vision of relevant research works stressing their advantages and potential drawbacks per evaluation scenario.Wuchner et al.  proposedBou-Harb et al.  proposedOn the other hand, Sahu et al.  proposedWei et al.  proposedGadelrab et al.  proposedYerima and Sezer  developeZhou and Pezaros  proposedAlenazi et al.  proposedGonzalez-Cuautle et al.  proposedA multi-layered framework for botnet detection is proposed by Khan et al. , where iCakir and Dogdu  proposedThe work presented by Tran et. al.  exploredIntrusion Detection Systems (IDS) are pivotal defensive elements to address the detection of diverse cyber-threats in current network deployments. They are mainly categorized in the research literature are signature-based and anomaly-based systems. Signature-based IDS requires specific patterns of malicious samples to perform detection, which raises an issue against never seen threats. On the other hand, anomaly-based IDS profile the behaviour of network traffic in order to detect deviations when no traffic categorization can be performed . DiffereWahab et al. , a dual Li et al.  proposedThe four approaches reviewed in this research have exhibited a variety of applications of supervised learning to conduct botnet detection even when their modus operandi differ substantially. In particular, machine learning methods such as Decision Trees, have embraced a wide range of detection scenarios where their effectiveness has been demonstrated. However, it is to bear in mind the comparison challenges  stated iThis section describes the research methodology, the description of datasets, flow metric measurements, feature selection and machine learning model construction.The analysis of traffic-flow patterns targeted on observing, understanding and characterizing the behavior of botnets at the network level is the object of study in this research. To illustrate the communication scheme and the performed analysis of such network traffic-flow patterns, At this stage, traffic samples are obtained from different botnet specimens captured on the network, as well as normal traffic observations. These samples are commonly gathered with traffic monitoring tools , and the captured network packets are exported using standardized formats such as PCAP. Similarly, malware datasets of raw network traffic are represented; or in some cases aggregated metrics are already processed from the captured samples either at the packet or flow level. This stage of the methodology opens the possibility for deploying monitoring tools to obtain traffic captures in a controlled environment or collect them from available datasets. For experimental purposes, the latter consideration has been assumed in this research since our goal is to strengthen the comparison.Given a collection of network packets, relevant traffic-flow metrics are measured to analyze common traffic patterns that characterize the behavior of different botnet specimens. Network flow metrics typically represent quantitative relationships such as—the number of packets transmitted from the source IP address 192.168.50.31 to the destination 192.168.50.88 in the forward direction is 3, with one packet going backward. Flow-level metrics have been extensively used to model data-driven detection models to tackle with various network threats, having defensive solutions against DDoS as one of the most recurrent applications in the literature .In this phase the detection model is built with the selected classifiers. As a result of the literature review, some of the most prevalent methods for botnet detection have been chosen—Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors and Support Vector Machines (SVM).At this stage, the models are trained using a subset of the dataset. The training approaches and the tuning processes applied for each machine learning method are detailed later in this section.The results of each detection model are evaluated following a three-step process—(1) setting the training results as a baseline, (2) performing a cross-validation analysis on the training data set for each botnet family, and (3) assessing the botnet prediction accuracy using the test data set.Based on the detection capabilities previously acquitted, an in-depth analysis is carried out, putting into perspective the adequacy of the detection methods for each botnet type.In order to conduct the experimental validation of this proposal, two publicly available datasets have been used in this research:This dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and the Canadian Institute for Cybersecurity (CIC) . This daThis dataset is the result of a research work of Alenazi et al.  and diffThe CIC-AWS-2018 flow features are already represented in csv format, so no additional data transformation was required. However, as the ISOT HTTP Botnet Dataset is composed of five pcaps files, they were processed through the CICFlowMeter tool. This application allowed the generation of csv files with 84 flow-based metrics. For each report, the different types of exploits were labeled using the Wireshark tool and only those records referred to botnets were preserved. Upon the five obtained reports, it was decided to work with the fourth one as it contained samples of the 8 botnets families in a time frame elapsing from 1 June to 3 June 2017.The following considerations have been assumed for feature selection.From the total number of flow-based attributes obtained in the previous step, two groups of features were selected in accordance with previous research. The first group of eight characteristics was chosen following the methodology introduced by Sharafaldin et al. , where aTo get a general idea of the CIC-AWS-2018 distribution, a bar chart was plotted for each feature in contrast to the class attribute as shown in When the ISOT HTTP Botnet dataset was analyzed, it was found that the majority of flows were transmitted using the UDP protocol (17). Additionally, a comparative analysis between botnet types was carried out to find out the concentration of them across the dataset. The result of this analysis is shown in The main goal is to identify whether the information received in the flow belongs to benign or malicious traffic. This being the main target of supervised classification, the objective is to find the best classification model that fits both datasets. Since the proposal introduced a novel reference model for traffic-flow based botnet detection, they are expected to serve as preliminarily benchmarking elements for further applications, thus needing to fulfill the following requirements—(1) they must be early-adopted solutions; (2) their pros/cons shall be well understood by the research community; and (3) there is a large variety of adaptations/modifications in the state-of-the-art solutions; and (4) to outperform them is viable, so they motivate their comparison against novel proposals. To this end, exhaustive tests are carried out with the most prevalent machine learning classification models for botnet detection documented in the research literature, as it is supported by Alenazi et al. , GonzaleThe maximum number of characteristics depends on the number of input variables in the dataset. The minimum amount of sample size required to divide the internal node equals 2 and to be in the leaf node equals 1. To define the maximum tree depth level, a fit chart was used for both datasets. For the ISOT HTTP Botnet dataset the model was created with a depth level of 51, and for the CIC-AWS-2018 dataset it was observed that a high accuracy rate was obtained with a depth level of 14.Given the simplicity of this method, the parameter that interprets the variance of all features was set to In this model, the number of trees built from 12 and the maximum number of features depends on the number of input variables in the dataset. The minimum number of the samples required to split is 2, and the minimum number of the samples to be at leaf node is 1. Upon this parameterization, the maximum tree depth is defined using a fit chart for both datasets. For the ISOT HTTP Botnet Dataset the model is built with a depth level of 35 and for the CIC-AWS 2018 dataset it is observed that with a depth level of 22 a high accuracy rate is obtained.For the implementation of this algorithm, it was decided to use the Euclidean distance and the leaf size is kept as 30 by default. To find the nearest K neighbor, Gonzalez-Cuautle et al.  in theirThe OneVsRestClassifier Multiclass was used to predict several classes. LinearSVC was used for the construction of the model because it better suits large datasets . It was Using the features already defined in In order to improve the performance of each of the models, the Grid Search algorithm was used. For this purpose, a range of possible values was defined for each parameter in the different ML models, which are indicated in The experimental validation was developed in Jupyter with Python 3.3.2. The Scipy, Scikit-learn  and PandThis section provides a detailed explanation of the experimental results obtained in this research.The CIC-AWS-2018 test dataset was evaluated with the following supervised learning classifiers—Random Forest, Decision Tree, Naive Bayes Gaussian, Support Vector Machine (SVM) and K-nearest neighbours. After analyzing the original CIC-AWS-2018 dataset, it was found that the Random Forest model turned out to be the most appropriate to correctly predict a botnet with 99.998% of precision over the rest of the models with a slight difference, with the exception of the Naive Bayes model (43.831% of precision). However, in spite of the average accuracy exceeding 94%, the results are biased, so it was necessary to balance the data. For this reason, the CIC-AWS-2018 dataset was filtered by TCP, being the predominant transport protocol as it is shown in The results of the ISOT HTTP Botnet Dataset are shown in As the Zyklon botnet contains fewer samples in the dataset, its analysis exhibited that Random Forest and Decision Tree models are the only ones capable of obtaining a high precision but a low percentage of recall. On the other hand, the Naive Bayes and SVM models obtained a low percentage of both precision and recall when analyzing the Zyklon and Blue specimens, thus failing to correctly classify those samples. Likewise, SVM were unable to classify the Liphyra botnet as well. Again, these results are explained due to the fact that there is a shortage of samples in of these specimens in the dataset as noted in Because all the evaluated models improved after implementing GS, in terms of accuracy, precision and recall, it was decided to divide the ISOT HTTP Botnet Dataset into minority and majority classes to balance the data. The results of this analysis are shown in A comparative analysis was performed between CIC-AWS-2018 and ISOT HTTP Botnet datasets in terms of accuracy as shown in The next evaluation criterion was the execution time measured for the different models, which are charted in A 5-fold cross-validation was considered to check the models’ accuracy. For both datasets, using the features already defined in Prediction tests were performed for the models before and after implementing the Grid Search algorithm on the CIC-AWS-2018 dataset, and the results obtained are shown in Prediction tests were also performed for the ISOT HTTP Botnet dataset as shown in Furthermore, the prediction results when analyzing the minority and majority classes separately are shown in Our experimental validation using GS has shown a substantial improvement in the prediction of the different botnet specimens even in highly unbalanced classes compared to the results shown by Alenazi et al.  and ZhouWhen analyzing the original CIC-AWS-2018 dataset, the results showed that the Random Forest, Decision Tree and K-Neighbors models yield a classification accuracy close to 100%, similar to the results exposed by Zhou and Pezaros , with thOn the other hand, a detailed evaluation was performed for each specimen of the ISOT HTTP Botnet dataset in terms of accuracy, precision and recall compared with the work of Alenazi et al. . AlthougAs shown by the results, the selection of the most relevant features played an important role. A decision grounded on previous research works where the behavioral metrics disclosed communication patterns for each botnet type in real testing environments. An important fact related to the creation of the CSE-CIC-IDS2018 dataset and the ISOT HTTP Botnet Dataset is that machine learning techniques had already been used to extract the most relevant malware behavioral features from the traffic captured in the network. For all the different types of attacks, the most relevant behavioral metrics for botnet detection have been considered grounded on the research works of Gonzalez-Cuautle et al.  and SharFinally, in the light of the obtained results, it can be concluded that Random Forest and Decision Tree models are generally more appropriate for detecting botnets by classifying large amounts of samples and still performing efficiently. The capabilities of such models demonstrate their suitability to develop defensive countermeasures against malware, a claim reasserted by this research after a deepen analysis. On the opposite, it was found that Naive Bayes showed mostly inaccurate results, a fact explained by the “Naïve assumption” where all the features included in the modeling process are independent from each other, thus improperly describing the behavior of botnets in the aftermath.Throughout this research, the state of the art on botnet detection has been thoroughly analyzed in order to understand the characterization of these network threats, their modus operandi, and special attention has been put on identifying the most relevant supervised detection methods. It was intended to set a comparative baseline both at evaluation methodology and classification accuracy, without overlooking any consideration regarding the models parameterization, training and validation of the detection models, without overlooking any evaluation criteria. For our analysis, it have been considered diverse machine-learning approaches to detect different botnet specimens. In order to set up an evaluation baseline, there have been selected two botnet reference datasets have been examined in detail—CIC-AWS-2018 and ISOT HTTP Botnet, both containing behavioral metrics that have led to perform traffic flow analysis. The former with two bot samples (Zeus and Ares) and the latter with eight botnet specimens. Feature selection has been guided by the contributions of similar research works which led to distinguishing two broader feature categories—descriptive and behavioral. Training and validation of the selected machine learning models have been addressed to benchmark the overall classification accuracy. TThe issue of unbalanced datasets has been considered, which led to differentiation of the analysis guided by majority and minority classes. In addition, the Grid Search algorithm was used to optimize its hyperparameters, which has introduced significant improvements in the classification by adjusting different supervised learning algorithms. As suggested by the proposed methodology, an in-depth evaluation was carried out stressing the comparative analysis of traffic flow patterns. It poses the main contribution of our research towards the state of the art in the area of botnet detection. Broadly speaking, it has been shown that Random Forest and Decision Tree models outperformed the rest of the machine learning models. In contrast, Naive Bayes showed the lowest performance based on the overall accuracy. Therefore, it is shown that it is possible to infer the detection of botnets from behavioral patterns. When measuring the execution time, it can be seen that the Support Vector Machines model still poses the main drawbacks in terms of resource consumption, which has been considerably higher than the rest of the classifiers.In the light of the extensive evaluation performed, the suitability of machine learning for botnet recognition has been proven in this paper. This research supports the outcomes of similar research works based on machine learning for detecting different botnet specimens. Although it has been clear that the proper calibration and training of the machine learning models directly influences their precision rates, their adequacy is yet to be validated with more botnet specimens. Future research outcomes will be focused on extending the evaluation methodology to make a more robust and exhaustive comparison with further supervised deep learning approaches. It is also intended to expand the range of botnet families to be analyzed. Furthermore, the methodology shall be extended to other botnet datasets as well as their real-time evaluation in network testbeds.


We propose a novel wearable robotic glove or exo-glove design scalable to the variation of the hand kinematics. While most of the traditional robot hand is driven by rotating the joint directly with a rigid body, our exo-glove deforms a robotic finger's skin and, thus, the hand skeleton joints. Multiple tendons woven on the exo-glove's surface can make multi-DOF finger joint motions. We allocated tendons to mimic a hand's intrinsic and extrinsic muscles. Thus, a robotic hand actuated with the exo-glove can perform natural finger motions, including abduction/adduction and flexion/extension of finger joints. Moreover, additional tendons for the thumb enable power grips and the robotic hand's human-like motion. The proposed design approach places all the actuators on the surface without directly actuating any of the hand skeleton's joint. Therefore, a random hand skeleton can work as a robotic hand by putting the wearable robotic glove on it. Thus, the proposed model provides a high degree of freedom on choosing hand skeletons. We expect the aforementioned biomimetic features of our proposed method will benefit not only traditional robotic hands design but also the design of prosthetic hands and robot power-assisted hand glove. A robotic hand imposes significant constraints on the manipulability of a robotic system so that its ability often decides the type and the complexity of a task. In this context, an anthropomorphic robotic hand has been a popular research subject in robot manipulation area, considering the dexterity of the human hand. Most of the previously proposed anthropomorphic robotic hands have been developed to mimic the human hand's ability in terms of motion and tactile sensitivity  repeatability of motion, and (iii) gripping performance. The robotic hand's manipulability is assessed by posing the robotic hand for various object-gripping tasks, as suggested by Cutkosky  design worn on a robotic hand skeleton with the actuators mounted on the surface of a robot or human-operator arm. The exo-glove has two layers of tendons, which mimic the functionalities of a human hand's intrinsic and extrinsic muscles  move five fingers independently and (b) mimic the functionalities of human hand motion, considering its application as a prosthetic hand. Next, the actuators of the tendon-driven mechanism should (c) not increase the length of the overall robotic hand. Since the robotic hand is designed as a wearable robotic hand, the posture of the hand should be estimated with sensors installed on the surface. Finally, the robotic hand should have the (d) scalability in increasing/decreasing the motion degree-of-freedom and the ease of changing the actuator.In this section, we set several constraints on the design of the wearable robotic glove for a robot hand. First, (a) it needs to be able to We built the dual-layer exo-glove robotic hand by combining the actuator units to a glove with tendon-sheath, worn on the hand skeleton. The glove was made with multiple insulation glove pieces whose surface was nitrile foam coated, which made the glove's surface durable and elastic. The piecewise tendon sheaths were made by gluing inside Teflon tube to outside silicone tube, for the ease of attachment to the glove. We placed the tendon-sheath to mimic the extrinsic muscles on the hand so that each finger can flex and extend with a pair of tendons. We aimed to design our robotic exo-glove to have all actuators and sensors outside of the hand skeleton. Then, along with the tendon-driven actuation, a finger posture information is to be obtained with a flex sensor, whose relation to the joint rotation needs to be defined. Regarding this issue, previous research modeled a linear relation between the flex sensor reading and the rotation angle Saggio, . Also, tFor the control of the robotic hand posture, we adopted a synergistic control approach . In the last experiment, we evaluate the robot hand's grip in various aspects. A typical robotic gripper is designed to meet the constraints required for the task, such as a higher payload. One of the recent approaches to increase a robot gripper's payload is to introduce the variable stiffness of a joint. The method often contracts the tendon so that the stiffness of the joint is increased. Noting this, we test the possibility of increasing the gripping payload by increasing the stiffness of a finger joint, as well as measuring normal payload by increasing the gripping force.We chose the precision grip for the object grasping task, considering that it is one of the most common object grasping strategies. To mimic the human skin for the gripping task, we fabricated pieces of silicone skin with EcoFlex0030 . Prior to the test, the thumb and index fingertip were covered with the silicone skin, as shown in In this paper, we proposed a novel robot hand exo-glove that was designed to be combined with a robot hand skeleton to form a robotic hand. The design of our robot exo-glove platform can be applied to a prosthetic hand with more motion functionality and less dimension than previous works, e.g., Rudd et al. . The proThe results of the evaluation indicate that the glove-worn robot hand can strike all the poses proposed by Cutkosky . Also, tThe future work will extend the exo-glove to applications such as prosthetic hand and power assist glove. Especially, the application to the prosthetic hand will require the further implementation of the interface for intention cognition. We will conduct additional evaluations of the exo-glove in terms of device manipulability. We are also planning to apply lighter actuators to enhance the wearability of the exo-glove.All datasets generated for this study are included in the article/supplementary material.JP and WL conceived the research idea and analyzed the results JP and IH setup the system concept and implemented the hardware. JP prepared for the experiment. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study investigated the reflected spectral deformation mechanism of fiber Bragg grating (FBG) sensors with crack propagation. This analysis was performed based on the simulated FBG response by applying modified-transfer matrix modeling (TMM) with the strain states, which were extracted by the finite element method (FEM) analysis. Experimental data were obtained from FBG sensors bonded in an aluminum alloy structure and subjected to multiple crack lengths, and the strain values were obtained by digital image correlation (DIC) technology. Based on the simulations and the experimental full spectral response, we compared the performance of two damage features: The full width at half maximum (FWHM) and the spectral difference. In addition, results showed that the two features were insensitive to experimental noise and were highly sensitive to the complex strain field caused by crack propagation. Moreover, the damage features changes in the crack propagation process also provided a way for crack position measurement. Ultimately, the 10 mm grating lengths sensors showed better performance to the crack detection with longer sensitivity distance. According to the research in this paper, the crack position was quantitatively determined by evaluating different damage features of the reflected spectrum. Crack damage usually occurs in aircraft structures. However, it only can be detected by global sensors due to the changing of significant structural properties when the damage is at a high level . Hence, In the practical applications, damage sensitive features are required to be sensitive to spectral distortion caused by the uncertain damage state in materials. Additionally, the features are expected to have a low sensitivity to average strain components along the optical fiber, which is dominated by the magnitude of the loading applied to the structure. In addition, the measurements should be sensitive to the nonhomogeneous strain field loading along the optical fiber caused by the presence of damage. Finally, the choice of damage features should consider the central wavelength shifting caused by the temperature fluctuation effect. Consequently, a Bragg wavelength peak shifting is excluded from the damage features because the feature is converted into a measure of the uniform strain along the axis of the grating and the temperature changes . AccordiIn The proposed approach has four key advantages over the previous studies. First, an interpretation of reflected spectrum deformation mechanism with crack propagation were analyzed based on both simulations and experiments. Second, the performance of two potential damage features—FWHM and the spectral difference were proposed and evaluated. Third, the curve of damage features changing with crack propagation presented a significant break point, which corresponds to the moment of crack propagation toward FBG sensors. Fourth, the 10 mm FBG sensor may show superior performance compared to the 5 mm FBG sensor for crack position detection.The specimens for the target system were made of a 7075-T6 aluminum alloy, which is widely used in aircraft and the mechanical peripeties, shown in To stabilize the fatigue crack growth behavior during the loading with force control, the NASGRO software was applied to calculate the reasonable stress magnitude. Rice  concludeng loads . TherefoBased on the crack tip strain distributions, which were calculated by the extended finite element method (XFEM), the singular strain field near a crack tip was an important factor adopted to optimize FBG sensor placement. The C3D8R hexahedron-8-node reduced integration elements were used to construct the crack-tip mesh. The refined mesh size around the crack region was 0.05 mm and the remaining mesh size was 1 mm. As a result, the width and the length of the primary plastic zone at the tip of the crack were approximated to be 0.44 and 1.6 mm, respectively, as shown in According to previous studies , due to yy plots, the strain filed corresponded with the pre-crack tip, shown in yy, which exist as a significant portion of the strain composition in the crack wake. According to previous studies on the mechanisms for fatigue crack closure and the mechanisms under plane strain  shows that the wavelength range of the smoothed reflected spectral X1 is the signal located in the wavelength range  and the length approximates to N.Step 2: A similar approach to the real-time damage signal S1 and the smoothing data 2X satisfies the wavelength range M.Step 3: The smoothing spectrum can be expressed by a sequence of feature vectors.Step 4: To clarify the wavelength length differences between these two spectral patterns, an n − m plane is considered, for which the spectral patterns n-axis and the m-axis with the same category. The wavelength differences between patterns can be described by a series of points c = :n = m when there is no wavelength difference between these patterns. However, as the wavelength difference grows, it deviates further from the diagonal line. As a distance is employed between the two feature vectors This sequence presents an approximate warping function from the wavelength axis of pattern w(k) is introduced to keep the E(F) measurement flexible. The data point number on the warping function F is defined as K.The summation of the distances on weighted warping functions F becomesStep 5: By optimally adjusting the wavelength difference, the minimum value of the weighted warping function F is considered to be the spectral difference between spectral patterns Since the criterion function in (5) is a rational expression, its maximization is an unwieldy problem. If the denominator in (5)F, it can be simplified in the equation as follows: is independent of warping function This simplified problem can be effectively solved by use of the dynamic programing technique and the typical weighting coefficient definitions enable this simplification. Additionally, it is knowing that in the FBG signal, the The weight coefficient definitions can be definition as follows.ThenStep 6: The spectral difference ε is equal to the value of minimum cumulative cost function at the optimal weight warping.As expected, based on the simulated data, the calculated spectral difference varies with different crack lengths as shown in This study used a hole-edge crack damage detection fatigue experiment platform. The experiment platform contained a fatigue crack measurement system, an optical sensing system, a DIC measurement system and a fatigue load-cycling system, as shown in Therefore, a LEICA microsystem 8-bit intensity resolution CCD camera was used as an optical microscope and fixed to monitor the fatigue crack growth during the loading process. The strain field located perpendicular to the crack propagation direction at each millimeter step was collected and serviced as the current and reference image. In The DIC experimental measurements of the strain fields in the FBG sensor location were focused on the high-resolution strain fields at several crack growth lengths. To investigate the strain loading along the grating during a growing fatigue crack, the DIC method uses image processing techniques to obtain a one-to-one correspondence between small subsets in the initial unformed picture and subsequent deformed images. The respective locations and the strain information in the current configuration were obtained through a matching subset location transformation. Additionally, a spacing parameter (8 pix) was used to reduce the computational costs. In the end, a grid contained the strain information with respect to the reference configuration, also referred to as Lagrangian strains. The strain fields can then either be reduced or interpolated to form a continuous strain field. Then, the strain fields around the FBG1 sensor at crack lengths of 5.48 and 5.56 mm are shown in The DIC technology was analyzed in MATLAB environment, and the code was provided by Václav Nežerka . In FiguThe experimental data of FBG1 sensor, were subject to the fatigue loading impact. These reflected spectra were affected by multiple complex strain fields and noise disturbances. The reflected spectra of FBG1 are presented in The experimental reflected spectra are used to evaluate whether the proposed two damage parameters can detect crack damage even under the experimental complex strain field and noise interference. The different grating lengths sensors were located with different distances to the crack. Then, the signals of three FBGs were used to evaluate the crack detection capabilities and distinguish the influence of two differences (grating lengths and crack location).The graphs of The behavior of the spectral difference for the two FBG sensors is presented in In the practice, the proposed crack detection method based on damage features is important for the hole-crack detection monitoring and aircraft structural behavior evaluation. However, as the structural analysis becomes even more complex, it may consider the loads factors, materials factors, human factors, and the exposed environmental factors of structures. In order to eliminate the interference of above factors, the different types of sensors are distributed near the FBG crack sensors under a free-strain condition and applied as comparison sensors, such as the FBG temperature sensor and FBG curvature sensor. Then, a typical health monitoring system is composed by a network of sensors that measure a series of parameters relative to the structure and to its environment. What is more, is the ground FBG signal database that will bring a deeper knowledge of how this structure behaves. Additionally, the crack damage procedure is obtained by analyzing the features difference of real-time signal and the healthy basic signal. The stability of the methods has been tested under the full-scale fatigue and residual strength test of XX-aircraft.The crack damage location detection was carried out using FBG sensors by analyzing the changing of damage features. Compared to the other research, the advantage of this technique was concluded in the following four aspects. Firstly, the study highlighted the reflected spectrum deformation mechanism with crack propagation by analyzing strain field variation along grating based on both the simulation and experiment method. The simulated FBG reflected spectra were measured by the modified-TMM method applied with strain states, which were extracted from FEM. The experimental strain along the grating was obtained by DIC technology, which is a kind of noncontact measurement method. In this connection, a useful viewpoint was introduced to explain the relationship between the FBG spectral deformation level and the strain state caused by crack propagation. Secondly, the performance of two potential damage features—FWHM and the spectral difference were evaluated. At the same time, the performance of two damage features were compared under simulations and experiments environment. The experimental results indicated that the two damage features excellently indicate the presence of damage with the characteristics that insensitivity and strong robustness to noise. Both damage features were sensitive to the spectral deformation due to the strain gradient and complex strain profile along the FBG axis caused by crack damage. The application of two sensitivity features would provide a robust measure of damage location within a material. Thirdly, the experimental trends of the two damage features from the bonded FBG sensors presents a significant break point corresponding to the moment of crack propagation toward to the damage detection region. The break point phenomenon was due to crack variations in the surrounding material that affect the damage accumulation loading near the FBG. Such models provide simple measures to detect the crack location according to these feature responses. At the end of this paper, due to the variability in response between the two different grating lengths sensors employed at the same damage conditions, the detection sensitivity results of the FBG sensors under different damage states show that a 10 mm FBG sensor may show superior monitoring distance performance compared to a 5 mm FBG sensor for crack position detection. In the future, the stability of the methods will be tested under a real experiment, such as the full-scale fatigue and residual strength test of XX-aircraft.


More precisely, the research findings support the hypothesis that the reduced neural activity of brain regions associated with the dlPFC can predict sales at the PoS, emphasising another crucial neural signature to predict shoppers’ purchase behaviour, next to the frequently cited reward association system. The research findings offer an innovative perspective on how to design and evaluate PoS merchandising elements, indicating fruitful theoretical and practical implications.The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns in order to foster sales. Predicting the success of these merchandising elements at the PoS on sales is of interest to research and practice, as the misinvestments that are based on the fragmented PoS literature are tremendous. Likewise, the predictive power of neuropsychological methods has been demonstrated in various research work. Nevertheless, the practical application of these neuropsychological methods is still limited. In order to foster the application of neuropsychological methods in research and practice, the current research work aims to explore, whether mobile functional near-infrared spectroscopy (fNIRS) – as a portable neuroimaging method – has the potential to predict the success of PoS merchandising elements by rendering significant neural signatures of brain regions of the dorsolateral prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers’ behaviour aka sales at the PoS. Building on previous research findings, the results of the given research work indicate that the neural signal of brain regions of the dlPFC, measured with mobile fNIRS, is able to predict actual sales associated with PoS merchandising elements, relying on the  The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns, given that effective PoS merchandising elements have been shown to significantly increase sales of advertised products . PredictThe selection of merchandising elements is frequently grounded on insights that are received from exploring the consumers’ perceptions of the – advertised – product or service-associated attributes. In order to measure the consumers’ perceptions of these attributes, self-report measurements are often used, asking consumers directly about their subjective opinions in regard to a product or service. Although self-report measurements have been indicated to be beneficial in some marketing studies, social psychology suggests that self-reports, when used in isolation, are unreliable to accurately predict the consumers’ preferences . This isreward associations in order to predict sales with the utilisation of neuropsychological methods , the ventral striatum, the orbitofrontal cortex (OFC) and the ventromedial prefrontal cortex (vmPFC). These brain areas can only be measured with stationary neuroimaging methods, such as functional magnetic resonance imaging (fMRI), whose application is quite costly and time-consuming. However, although just recently a study conducted by cortical relief effect.The application of neuropsychological methods, using neural brain activity data to forecast products and marketing campaigns success, has been indicated to offer a promising approach to gain further knowledge about the consumers’ perception processes . Plassma methods . Therebyreward evaluation system; only a few studies considered the dlPFC in their prediction models. Consequently, this research work is one of the first to evaluate whether the reduced neural dlPFC activity, as a neural signature, can predict PoS sales, building on insights of the cortical relief effect.In conclusion, preferred choice options seem to be easier to process, which makes it easier to choose for the favoured product during a decision-making process that seem to be less cognitively controlled and assumed to elicit a reduced activity in brain regions of the dlPFC . Less secortical relief signatures of the dlPFC.Having this in mind, the current research work aims to explore the predictive power of the cortical brain regions of the dlPFC to forecast the success of PoS merchandising elements. By doing so, the given research work overcomes the limitations of stationary neuroimaging methods by utilising mobile fNIRS as a portable applicable neuropsychological method for the research field of shopper neuroscience, demonstrating its potential application in ecological valid setting, such as the PoS . AgainstA special case in the analyses of PoS merchandising elements is the product ‘duplo’ by Ferrero . ‘Duplo’reward system  and the deactivation of the dlPFC (Brodmann area 9 and 46). Furthermore, explicit subjective ratings of the ‘duplo’ merchandising elements were evaluated. In order to measure the actual sales – defined as the revenue generated by the different merchandising elements – the merchandising elements were tested at the PoS in a field experiment in parallel to the fMRI study psychological methods, the fMRI data and the IAT data, seem to outperform self-report shoppers’ ratings of the merchandising elements. A high brand-fit score as indicated by l relief . Buildinl relief , the invThe neural signatures of the dlPFC during the perception of merchandising elements measured with mobile fNIRS are able to predict the sales associated with the PoS merchandising elements.N = 45), who indicated that they were mainly responsible for the grocery shopping in their household, were recruited to participate in this study. Female participants were recruited because women are more frequently responsible for the household’s grocery shopping . All participants were right-handed and had no history of major psychological or neurological disorders.In line with previous research  only heashopping . Due to After participants were welcomed, they were informed verbally and in written form about the aim of the study, the task and the utilised mobile fNIRS device. Once participants fully understood the task, a written informed consent was signed in accordance with the Declaration of Helsinki. Thereafter, participants were seated in front of a computer screen and the mobile fNIRS headband was attached on the participants forehead. In order to increase consistency between the participants measured brain regions, the mobile fNIRS headband was locally standardised on the vertical axis using the craniometric point of the nasion as an orientation point and the middle of the two preauricular points for positioning on the horizontal axis, covering the prefrontal cortex. Before starting the experimental task, data quality was checked and, if necessary, signal quality was improved by shifting the hair away from the detectors, making direct skin contact. In addition, the fNIRS headband was covered with an light-protecting cap to control for external light sources. Once the preparation was finished, participants were instructed to look at the computer screen while the task was performed.The task was designed analogous to the paradigm developed by The continuous-wave fNIRSport-System  was used for data collection . In geneThe valid application of mobile fNIRS in the field of consumer and shopper neuroscience has been demonstrated in several studies ,b. Most In order to analyse the collected data, data was pre-processed using the NIRx Software Package . In order to increase signal quality, channels exhibiting discontinuous shifts during the measurement were removed. Furthermore, fNIRS data time series were smoothed, applying a band-pass filter (high and low frequency filter)  with thet-contrast was executed for each merchandising element against the implicit baseline, using the t-values in the further analysis. Given that significant activation differences are not of interest, the contrast analysis was used as a procedure to standardise the neural activations, which made a multiple comparison correction redundant. To test the hypothesis, fNIRS-derived sales prediction values were calculated from the standardised activation values of the t-contrasts for every merchandising element, respectively  that is hypothesised to translate to sales at the PoS. Consequently, the resulting rank order based on the reduced dlPFC signal values should coincides with the rank order of the actual sales data. In order to evaluate the predictive success of the fNIRS-derived sales prediction values rankings with the original sales data, the results were compared qualitatively and based on Spearman rho correlation coefficients for the ordinal rank orders as well as on Pearson correlation for the quantifiable sales prediction values and actual sales data at a significance threshold of p < 0.05.A general linear model (GLM) was set up for every participant and convolved with the haemodynamic response function, including six regressors with one for each merchandising element and an additional 12 regressors for the product stimuli (six before and six after each merchandising element). The GLM was first calculated on a single subject individual level (within-subjects level), and subsequently, a second-level group contrasts analysis was carried out to calculate neural activations across subjects (between-subjects level). In order to extract standardised activation values, a ectively . The resConclusively, based on the neural data analysis two different types of dlPFC fNIRS-derived sales prediction values were extracted and rank ordered, according to their degree of the reduced dlPFC activity. First, the fNIRS-derived sales prediction values of Brodmann area 9; and second of Brodmann area 46, calculated from the contrasts of each merchandising element against the implicit baseline, have been evaluated. The participants’ explicit subjective rating of the merchandising elements was also evaluated, whereby the total number of 1st rank positions for each merchandising element was taken as an indicator. Finally, and in order to estimate the predictive power of the different data types, the actual sales associated with the merchandising elements – defined as the revenue generated by the different merchandising elements – were adopted from rs = 0.943, n = 6, p = 0.005) and a positive significant Pearson correlation on the sales prediction values and actual sales   . For the= 0.025) , this ra= 0.025) .p < 0.05 . For the explicit subjective ranking no matched rank positions could be identified qualitatively . The t-values on each channel and scatterplots on the non-significant predictors are available in the Similarly, the neural results reveal that the first rank position based on the calculated Brodmann area 9 fNIRS-derived sales prediction value of the merchandising contrast  correspotatively , confirmcortical relief effect. More precisely, the integration of mobile fNIRS in the field of shopper neuroscience has been used to investigate six PoS merchandising elements, which have been examined with marketing methods in earlier studies, while overcoming the limitations associated with stationary neuroimaging methods  cortex, leading to different cognitive processes. Whereas in the past the neural activity of the cortical) relief effect for congruent brand-merchandising PoS elements or vice versa result in an increased neural activity effect in the dlPFC, when the product and merchandising element are perceived as incongruent. Both effects can, consequently, be measured in brain regions of the dlPFC, indicating its specificity to predict sales at the PoS. Consequently, next to the reward association system, brain regions of the dlPFC might also function as a process variable to predict sales in a PoS setting. The utilisation of mobile fNIRS with its technical capabilities to measure cortical brain regions might, therefore, provide an innovative and fruitful method for future research.Supposing that 70% of the purchases at the PoS are spontaneous and given that an act of purchase takes approximately about 60 s , it is sThe research findings provide several implications for marketing theory and practice. First, from a theoretical perspective, the research findings suggest that the shopper behaviour at the PoS is not only driven by reward associations offered by brands, but is also influenced by the perceived (in-)congruency and the level of conflicts or cortical relief experienced between the shoppers’ brand image and the experienced PoS merchandising element. While earlier neuropsychological studies investigated mainly medial and subcortical located brain regions of the reward evaluation system to forecast population success; only a few studies considered the dlPFC to predict shoppers’ behaviour. Consequently, this research work is one of the first that evaluates the predictive power of brain regions ascribed to the dlPFC neural deactivation, providing an innovative approach to interpret consumer responses to merchandising elements at the PoS.Second, as a methodological contribution, the validation of a mobile and in its application fast-growing methodology of mobile fNIRS demonstrates its potential to predict success in real-world settings such as the PoS. Due to its mobile application it provides a great variety of application options for research and practice to measure shoppers’ neural responses directly in complex settings such as the PoS, increasing the ecological validity of research results.From a practical point of view, the research results offer an innovative perspective on how to design, evaluate or forecast the success of PoS merchandising elements in combination with the to-be-advertised products – including all kind of merchandising elements such as lighting, furnishing, display screens, price tags and information displays. Cortical relief disrupting conflicts can arise on all levels of the customer journey, beginning with the perception of a stimulus and ending in cognitive overload effects elicited by, for example, the overwhelming assortment in the shelves. To carefully match the shoppers’ brand image with PoS merchandising elements in order to reduce conflicts and cognitive dissonance might, consequently, be of high value for producers and retailers. The integration of the idea to investigate the (in-)congruency and potential conflicts as well as its repercussions enables the analysis of the shoppers’ PoS journey by evaluating different merchandising elements, with its aim to reduce or at best avoid conflicts in the perception of the product specific attributes  and the PoS merchandising elements to be used. A comprehensive investigation of all cues that appear at the PoS during a customer journey, to explore all potential reactions of the shoppers’ brain during a shopping trip, to identify cues that potentially reduce the overall net-incongruence at the PoS, might be beneficial. The neuropsychological neuroimaging method of fNIRS may, therefore, be of particular interest as it enables the investigation of the hypothesised effect directly at the PoS because of its mobile, ecological valid usability. Following from this, the research results might be used to explore different PoS merchandising elements to quantify the cognitive engagement represented by the neural activity of the dlPFC evoked by a shopping trip, measured with the use of mobile fNIRS. The ultimate goal would be a measurement of all rewarding and conflicting cues during an average shopping trip, possibly enhanced by the identification of additional motivating cues, to generate a deeper understanding of the shoppers’ behaviour at the PoS.cortical relief effect is measured. However, it might be that the merchandising elements have been seen in a TV or PoS campaign before, leading to the measurement of a familiarity effect. This effect might be evoked because the familiar merchandising element might require less cognitive effort to be processed, resulting in a reduced neural activity of the dlPFC. In order to cope with this potential limitation, future studies might replicate the given study with only novel PoS merchandising elements that vary in the degree of their brand fit.One aim of the research work is to indicate the usefulness of mobile fNIRS to predict shopper behaviour at the PoS. The current study provides a first step to actually measure shoppers’ neural activity, when confronted with PoS merchandising elements and products at the PoS, using mobile fNIRS. Nevertheless, this research work investigates the neural signatures on basis of a laboratory setting with an experimental paradigm performed in front of a computer screen. The next logical step for future studies should be to explore whether the research findings received under laboratory settings remain also valid in a naturalistic environment measurement at the PoS, utilising mobile fNIRS in realistic PoS settings. Furthermore, mobile fNIRS is a relative innovative neuroimaging method, at least for the research field of shopper neuroscience, indicating the need to consider the continuous development of its technical capabilities. Future research might, thus, use other more advanced mobile fNIRS devices to improve data quality and reduce the application costs. Finally, whilst interpreting the neural activity and the neural reactions associated with PoS merchandising elements, it is implicitly assumed that the reward association system and its associated subcortical brain regions to predict sales, utilising stationary neuroscientific methods (e.g., Whereas previous research work mainly focused on the ds e.g., , the resThe datasets presented in this article are not readily available because it was ensured to the participants that their data is not available for third parties and it was guaranteed that participants can request the complete deletion of their datasets at any time. Requests to access the datasets should be directed to nadine.gier@hhu.de.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.NG conducted the study, performed the data analysis and wrote the manuscript. NG and CK contributed to the conception and design of the study. NG, ES, and CK were involved in the data collection. ES and CK substantiated sections of the manuscript. All the authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that this study received funding from Ferrero Deutschland GmbH. The funder was not involved in the study design, data collection and analysis, interpretation of data, the writing of this article or the decision to submit it for publication. This research work was conducted in cooperation with Ferrero Deutschland GmbH to which ES is directly associated. The authors ensure that the cooperation has in no possible way influenced the research results nor the development of the manuscript.


It is demonstrated that the heart-rate can be sensed capacitively on a touch screen panel (TSP) together with touch signals. The existing heart-rate sensing systems measure blood pulses by tracing the amount of light reflected from blood vessels during a number of cardiac cycles. This type of sensing system requires a considerable amount of power and space to be implemented in multi-functional mobile devices such as smart phones. It is found that the variation of the effective dielectric constant of finger stemming from the difference of systolic and diastolic blood flows can be measured with laterally interspaced top electrodes of TSP. The spacing between a pair of non-adjacent top electrodes turns out to be wide enough to distinguish heart-rate signals from noises. With the aid of fast Fourier transform, the heart-rate can be extracted reliably, which matches with the one obtained by actually counting heart beats on the wrist. There has been a great deal of interest in integrating bionic sensors into mobile devices including fingerprint ,2,3, skiX) is absorbed by the finger. Then, the mutual capacitance between the upper electrode and the lower (or bottom) electrode acting as receiver (RX) is reduced so that the finger touch can be sensed [X and RX, the oscillatory change of measured capacitance reflecting the modulation of finger dielectric constant is as small as comparable to the typical noises occurring during measurements. Even in this case, the capacitance oscillation caused by cardiac cycles can still be observed. However, the measured capacitance is affected mostly by the medium between top and bottom electrodes and thus the heart-rate extracted from the capacitance oscillation will be non-reliable [X and RX, the capacitance oscillation associated with the modulation of finger dielectric constant can be magnified much more. It is observed that the capacitance oscillation can be enhanced enough to distinguish the heart beat signals reliably from the noises with a proper separation of two interspaced electrodes. Quite interestingly, a convenient method to achieve the proper separation while keeping the high spatial resolution for touch sensing is found to be utilizing non-adjacent interspaced electrodes as TX and RX. This simple method will be quite efficient for resolving the issue of the capacitance oscillation being reduced with a protective layer positioned on top of the top electrodes, which is a mandatory component in a commercial TSP.In this work, it is demonstrated experimentally that heart-rate can be sensed by analyzing the time-dependent capacitive signals when a particular portion of finger touches the TSP surface. While the finger touches the surface of the TSP constructed with two vertically-spaced layers of crossing electrodes, some amount of the electric field lines starting from the upper (or top) electrode acting as transmitter  dissolved in deionized (DI) water will be adhered selectively only on the converted (hydrophilic) area of PDMS surface so that AgNW electrode patterns can be formed without any lithography processes. On the PDMS surface treated with O2 plasma, an AgNW solution film is spin-coated at 300 rpm for 15 sec and baked on a hot plate at 105 °C for 10 min to dry off water and form the bottom electrode lines. On top of the AgNW bottom electrode lines, another PDMS film is spin-coated by following the procedures identical to those utilized for the PDMS substrate. This PDMS film takes a role of an insulating layer separating top and bottom electrodes. As performed previously for the PDMS substrate, the surface of PDMS insulating layer is exposed to O2 plasma while being covered with the shadow mask to induce line-shape hydrophilic regions. Then, the top electrode lines are formed on the hydrophilic regions by spin-coating an AgNW solution film and baking it in the same way as for the bottom electrode lines. Finally, a protective layer, either glass or PDMS film, can be attached for preventing potential physical damages given to the AgNW top electrode during repeating finger touch.The non-lithographic fabrication procedures of capacitive TSP ,26 are sfT (CfR) represents the additional capacitance between finger and transmitter (receiver) introduced by the finger touch. blood ≈ 104) is quite large, more than a hundred times that of water (εwater ≈ 80) at the operating frequency of commercial TSP, 200 kHz [The ulnar artery is a blood vessel distributed in the palms and fingers. For a period of heart beat, the volume flow rate of blood in the ulnar artery increases about 3.5 times for the systolic phase compared with that of the diastolic phase. The dielectric constant of blood .When operating the fabricated TSP for touch sensing, the top and bottom electrodes were used as TX, RX, or floating line. In order to perform the heart-rate measurement properly, it is needed to know where the finger has touched. Therefore, all the top electrodes should function as TX lines with the bottom electrodes acting as RX lines for touch sensing in the first place. Once the location of finger touch is identified, a pair of interspaced top electrodes in the finger-touched region are used for sensing heart-rate. In this case, one of the top electrode pair should be used as TX line and the other as RX line while all the other top electrodes are floated electrically. Hence, it is essential to change the configuration of signal transmission and reception through the top electrodes as needed for the on-panel heart-rate measurement with TSP. In case that the touching finger covers several pairs of top and bottom electrodes and multiple touch signals are detected, a pair of top and bottom electrodes giving the largest touch signal will be identified to locate the touch center. Once the touch center is identified, two interspaced top electrodes at the touch center will be chosen to measure the heart-rate.One thing to note here is that a switch circuit shown in the dotted square of X and RX in the heart-rate sensing mode  top electrodes can hardly reach the blood vessels. One straightforward way to resolve this penetration depth issue is to enlarge the gap between TX and RX. Here, the measured capacitance was found to decrease overall with finger touch, differently from the adjacent TX and RX case with no protective layer (X and RX is already pretty large. Hence, the relative increment of effective dielectric constant due to the finger will be smaller than the case of no protective layer. In this case, the electric field loss to the finger can be more dominating and lead to the capacitance decrease. Nonetheless, the clear oscillation in the measured capacitance was still observed, indicating the cyclic modulation of the dielectric constant of finger synchronized with heart beats. As done previously, the 0.5–3.0 Hz of band pass filtering (ve layer a. Since iltering d and FFTiltering e were peIn summary, we demonstrated experimentally that the heart-rate can be sensed on a conventional capacitive type TSP by tracing the time-dependent capacitance between two laterally interspaced top electrodes with a proper spacing. The cyclic change of the effective dielectric constant of finger due to the periodic blood flow synchronized with cardiac cycles was found to produce the regular oscillatory pattern in the measured capacitance. With the aid of band pass filtering and FFT, the heart-rate can be extracted reliably, matching with the one obtained by actually counting heart beats on the wrist. Our work can provide a readily-applicable way for implementing the on-panel heart-rate sensing functionality into the conventional TSP without deteriorating the spatial resolution of touch sensing, which extends the usage of TSP to the real-time health monitoring.


Collision avoidance and autonomous control of vehicles have become essential needs for providing a high-quality and safe life. This paper introduces a new generic scheme for a virtual antenna array (VAA) and its application in a train collision-avoidance system (TCAS). The proposed TCAS shall have the capability of identifying the range and angle of an object in front of a moving train and provide the required alerts. Thereby, a new virtual array distribution for both the transmitting and the receiving antenna arrays is introduced to get a long-range object detection and high-resolution multi-input multi-output (MIMO) system. This can be accomplished because the VAA radiation pattern is the multiplication of the radiation patterns for both the transmitting and receiving antenna arrays, which is different than each one of them alone. In this work, the VAA is utilized in radar systems in which the radar range depends on the multiplication of the gain of the transmitting and receiving antennas. So, we introduce a new scheme for the general design of VAA-based radars. A prototype for the antenna system was fixed on a of Texas Instruments platform for the cascading radar. One of the main problems of the VAA is the loss of radiated power in undesired directions, which affects the maximum detection range in beamforming systems and degrades the diversity gain in MIMO applications. These issues have been solved by the introduction of the practical implementation of a proposed high-gain, low side lobe level VAA system for automotive radar that is based on the integration of four AWR1243 RF chips operating in a frequency range of 76 GHz to 81 GHz. It was implemented using low-power 45 nm (TI) RFCMOS technology. The measured gain of the realized VAA was 47.2 dBi, which was 1.815 times higher than that of the Texas instrumentation linear frequency modulated continuous wave (TI’ LFMCW) radar, which was 26 dBi. The proposed VAA saved 45% of the required implementation area compared to the TI’ LFMCW antenna array. The VAA system was fabricated and tested in an anechoic chamber, and it was found that the simulated and measured patterns of the proposed VAA were highly matched in terms of half-power beamwidth and side lobe level. Recently, radars and multi-input multi-output (MIMO) radars have been extensively used as contactless sensors in critical applications such as vital-sign detection and human localization , imagingects. In , the autIn recent years, 77 GHz automotive radars have become popular and have drawn the interest of many researchers around the world, particularly to achieve a longer detection range and better range resolution. For this purpose, the authors in  introducthors in ,6 introdOn the other hand, the virtual antenna array (VAA) concept has emerged as a promising beamforming technique in high-performance radar systems that are equipped with separate transmit and receive antenna arrays. VAA allows the proper utilization of the transmitter array (TA) and the receiver array (RA) for the synthesis of wide varieties of highly efficient and cost-effective radiation patterns that are fully dependent on the TA and RA radiation patterns. However, the synthesized patterns may have distinct characteristics compared to that of the individual patterns of the transmitter and receiver arrays. The implementation of a VAA is not restricted to a specific design scheme. Recently, many VAA beamforming schemes have emerged for performance enhancement of traditional radar systems, multi-input multi-output (MIMO) radars, and automotive radars ,19,20,21In , a virtuIn , an effeIn , a VAA bFor MIMO synthetic aperture radars (SAR), several VAA configurations have been introduced to improve the performance of remote-sensing systems. Taking into consideration the phase differences arising from the transmitting antenna elements with the phase differences caused by the receiving antenna elements, new sets of virtual antenna array steering vectors can be generated by optimizing the designed antenna array positions .The VAA concept is not confined to linear and rectangular array configurations only, but also has been achieved for hexagonal array configurations. In this context, a 49-element virtual hexagonal antenna array was formed from two aligned 7-element hexagonal transmitting and 7-element hexagonal receiving arrays, as introduced in . The simIn this paper, the drawbacks of the VAA are mitigated to a large extent while retaining the main merits of low cost and optimum number of resources. In our case, we considered a monostatic radar equipped with two separate LAAs. The contribution of this research focuses on introducing a new generic scheme for synthesizing a radar total transceiver beam in a flexible and cost-effective procedure. The transmitter and the receiver antennas may be distributed in different configurations, keeping the resulting VAA beams with nearly similar beams. This allows flexibility in synthesizing the required VAA beam according to the available transmitter sources, the number of receivers, and the available space for the transmitting and the receiving antennas. Along these lines, a different number of VAA configurations with different radiation characteristics can be realized based on the design of the utilized LAAs and the relative alignment between them. Furthermore, practical implementation and testing of a radar transceiver antenna to be applicable in a low-speed train collision-avoidance system are introduced. The proposed radar system is based on the integration of four AWR1243 RF chips operating in a frequency range of 76 GHz to 81 GHz. It was implemented using low-power 45 nm (TI) RFCMOS technology. The fabricated VAA system achieved a 47.2 dBi gain, which was 1.815 times higher than the gain of the TI’ LFMCW radar, which was 26 dBi. Furthermore, the proposed VAA saved 45% of the required implementation area compared to the TI’ LFMCW antenna array. In addition, the fabricated VAA had a fast-decaying side lobe level (SLL) with In radar systems, one or two separate antenna arrays are used for transmitting and receiving functions, as illustrated in The advantages of such radar systems are: (i) If the transmitter and receiver arrays have the same side lobe levels (SLLs), the SLL of the resulting pattern is the square of the individual SLL value, which can be calculated as twice the negative value of the individual SLL in decibels. Nevertheless, if the transmitter and receiver arrays have different SLLs, the resultant SLL is sum of the SLLs of both the transmitter and receiver in decibels. (ii) The radiated power is concentrated in the direction of the main beam, while the received power is enhanced by the maximum gain value of the receiving antenna array.These features are useful for military radars where the interference with any neighboring source represents a danger to the radar operations. Nevertheless, in automotive radars, the interference is not the main concern, since the vehicles are moving so that most interferences may occur momentarily. Therefore, the interest of designers in automotive radars focuses on the pattern beamwidth with acceptable SLL. Furthermore, the cost of the radar depends mainly on the number of transmitters, voltage-controlled phase shifters, and voltage-controlled amplifiers or attenuators. From these points, the concept of VAAs appears where one can achieve a cost-effective radar with a minimum number of resources that achieve the required beamwidth as the traditional radars. In this research, we introduce a proposed scheme for the transmitter array to enhance the SLL to large extent, but the immunity to interference depends mainly on the receiving array, which is kept to its standard value.Taking the proposed general scenario shown in M-elements sub-ULA  to Equation (6) into Equation (7) and by some mathematical manipulations, the virtual array factor is calculated as the following:Accordingly, Equation (8) reveals that the synthesized VAA is equivalent to an -element VAA pattern can be synthesized following the general array design and alignment criteria shown in As an extension of the first test case, a nonoverlapping In this case, a high-gain VAA pattern is synthesized following the array design and alignment criteria shown in In this case, the outcome of the proposed beamforming technique is a high-gain VAA with fast-decaying side lobes. The array radiation patterns of the new configuration are plotted in The subarray was chosen to have the same number of elements as the receiving array. This allowed the nulls of the subarray to occur at the locations of the transmitter grating lobes, as illustrated in A. Problem formulationThe implementation of the proposed automotive radar system was based on the integration of four AWR1243 RF chips. The AWR1243 is a single-chip standalone frequency-modulated continuous-wave (FMCW) transceiver system that implements the automotive radar sensors in the range of 76 GHz to 81 GHz. It was implemented using low-power 45 nm Texas Instruments (TI) RFCMOS technology. Each chip had three transmitters and four receivers. Therefore, when using four AWR1243 chips, we had only B. Proposed VAA design considerationsTo mitigate the problem of the limited number of transmitter sources of the four AWR1243 integrated chips, the number of elements of the For the sake of brevity, the design steps for the transmitter subarray elements and the scattering (S) parameters of the single antenna were omitted. ECC), the diversity gain (DG), and the total active reflection coefficient (TARC).There are several parameters that measure MIMO parameters, such as the envelop correlation coefficient , which can be expressed as Equation (13) ,27.(13)TARC. TARC means the ratio of the square root of the total reflected power divided by the square root of the full incident power [TARC means the return loss of the overall MIMO antenna elements. TARC is calculated using the two antennas’ scattering parameters, as in Equation (14) [TARC, it utilizes the random phase-excitation vectors. The MIMO channel is expected to be Gaussian with a multipath spread in the propagation channel [TARC is considered at each random phase between 0 and 180. TARC of the MIMO antennas. It is noted that the simulated TARC ranged between −10 dB and −50 dB within the operating band, which were considered accepted values.The third parameter that shows the coupling among ports is nt power . TARC meion (14) :(14)TARC channel . TARC isUsing the proposed array distribution and the proposed antenna element shown in A comparison between the synthesized VAA pattern and the pattern of the traditional TI’ LFMCW radar is shown in C. Experimental resultsIn order to test the proposed high-gain and low-SLL VAA configuration, the proposed VAA was integrated in a working radar system, as shown in The setup for the measurement of the VAA radiation pattern is illustrated in A novel flexible scheme for synthesizing a VAA system for automotive radar systems was introduced. The proposed scheme aimed to reduce the size, minimize the SLL, and increase the gain in a cost-effective procedure. The scheme allowed for different VAA configurations that took into consideration the available area, number of transmitters, and number of receivers. The scheme was applied to the design of a practical automotive radar with 12 transmitters and 16 receivers. The proposed VAA distribution saved the required implementation area by


This case report is aimed at describing a flapless, minimally invasive clinical crown lengthening with an osteotomy performed using a piezoelectric ultrasound. A female patient complained about the amount of gum that was exposed when she smiled, which caused aesthetic discomfort. After a clinical examination, it was confirmed that the patient had excessive gum exposure in the upper arch of the dental region for teeth 14 to 24 when she smiled. The tomographic exam showed that bone tissue was at the level of the enamel-cementum junction, and gingival tissue covered a part of the anatomic crown. Virtual analysis using digital smile design (DSD) demonstrated that enlarging the clinical crowns would provide better aesthetics. The excess gingival tissue was removed from the gingival margin region with the aid of a mockup without interference to the interdental papillae. Then, osteotomy was performed using piezoelectric ultrasound until there was a 2.5 mm distance from the top of the bone crest to the new gingival margin. In the postoperative period, good repositioning of the gingival margin, absence of postoperative complications, and rapid healing of the gingival tissue were verified. After 6 months, a good aesthetic outcome was observed with stability in the level of the periodontal tissues obtained via the crown-lengthening technique. It can be concluded that the minimally invasive clinical crown-lengthening technique was effective in repositioning the gingival margin with no postoperative complications. The use of minimally invasive surgical techniques has been a trend in medicine and dentistry because they result in good clinical outcomes with minimal complications during the postoperative phase , 2. UsinSpecifically, in the dentistry field, efforts have been made to develop minimally invasive surgical techniques, such as the use of a surgical microscope , computeOne of the possible indications for the use of minimally invasive surgery is anterior crown lengthening indicated due to passive eruption disturbances. In this condition, the top of the bony crest is close to the cementum-enamel junction, and simply removing the gingival tissue is not sufficient to predictably expose the clinical crown over long follow-up periods , 13. IndThus, the objective of this case report was to present a minimally invasive crown-lengthening technique with papilla preservation and an osteotomy performed by piezoelectric ultrasound.A 22-year-old female patient complained about the amount of gingiva that was exposed when she smiled, which caused aesthetic discomfort for the patient. In the anamnesis, the patient reported not having any systemic problems, not continuously using any medication, and not being a smoker. A clinical examination confirmed the presence of an excessive exposure of the gingiva in the upper arch of teeth 14 to 24 . The perThe tomography showed that the bone tissue was at the level of the enamel-cementum junction and that the gingival tissue covered part of the anatomic crown . VirtualSurgical planning was performed by DSD with the patient's smile photos and crown measurements using an 80% width to length ratio. After design of the optimal teeth, the distance from the cementum-enamel junction to the top of the bone crest and the position of the gingival margin in relation to the enamel-cementum junction was measured by tomographic analysis . These mWith the mockup in position, the projection of what the gingival condition would look like after the surgical procedure was shown to the patient. After patient approval, the clinical crown-lengthening procedure was performed; this procedure consisted of marking the soft tissue height to be removed  and a suAfter removing the gingival collar, the need for bone tissue removal due to the proximity of the new gingival margin was detected, since the bone tissue was practically at the level of the enamel-cementum junction . A flaplThen, the osteotomy height was confirmed by probing , with thThis case report shows a flapless, minimally invasive clinical crown-lengthening technique and osteotomy performed with piezoelectric ultrasound with a good clinical outcome; it was possible to correct the altered passive eruption with no complications in the postoperative period.Altered passive eruptions are one of the main indications for clinical crown-lengthening procedures . In thesThe conventional surgical procedure in these cases would be to remove excess gingival margin tissue with a flap detachment and osteotomy until the establishment of biological distances; however, because it is sometimes necessary to open the flaps and because the osteotomy is a more intense dental procedure, undesirable side effects, such as bleeding, inflammation, and postoperative pain, are expected , 13. ThuThe surgical technique applied in this study was based on DSD planning that allowed the production of a mockup that guided the amount of soft tissue to be removed in the primary incision . For thiβ [Another important technical detail of the surgical technique was that the osteotomy procedure was performed with piezoelectric ultrasound. Hand and rotary instruments are traditionally used to perform osteotomies , 14, 16;β , which mβ , 18; howIt can be concluded that using a minimally invasive clinical crown-lengthening technique was effective in repositioning the gingival margin when used with an osteotomy and presented no postoperative complications. However, randomized controlled clinical trials, with longer periods of follow-up, should be conducted to compare the effectiveness of this minimally invasive technique compared to those traditionally used for clinical crown-lengthening procedures in altered passive eruption disorders.