Much of the computational power of the mammalian brain arises from its extensive top-down projections. To enable neuron-specific information processing these projections have to be precisely targeted. How such a specific connectivity emerges and what functions it supports is still poorly understood. We addressed these questions in silico in the context of the profound structural plasticity of the olfactory system. At the core of this plasticity are the granule cells of the olfactory bulb, which integrate bottom-up sensory inputs and top-down inputs delivered by vast top-down projections from cortical and other brain areas. We developed a biophysically supported computational model for the rewiring of the top-down projections and the intra-bulbar network via adult neurogenesis. The model captures various previous physiological and behavioral observations and makes specific predictions for the cortico-bulbar network connectivity that is learned by odor exposure and environmental contexts. Specifically, it predicts that—after learning—the granule-cell receptive fields with respect to sensory and with respect to cortical inputs are highly correlated. This enables cortical cells that respond to a learned odor to enact disynaptic inhibitory control specifically of bulbar principal cells that respond to that odor. For this the reciprocal nature of the granule cell synapses with the principal cells is essential. Functionally, the model predicts context-enhanced stimulus discrimination in cluttered environments (‘olfactory cocktail parties’) and the ability of the system to adapt to its tasks by rapidly switching between different odor-processing modes. These predictions are experimentally testable. At the same time they provide guidance for future experiments aimed at unraveling the cortico-bulbar connectivity. In mammalian sensory processing, extensive top-down feedback from higher brain areas reshapes the feedforward, bottom-up information processing. The structure of the top-down connectivity, the mechanisms leading to its specificity, and the functions it supports are still poorly understood. Using computational modeling, we investigated these issues in the olfactory system. There, the granule cells of the olfactory bulb, which is the first brain area to receive sensory input from the nose, are the key players of extensive structural changes to the network through the addition and also the removal of granule cells as well as through the formation and removal of their connections. This structural plasticity allows the system to learn and to adapt its sensory processing to its odor environment. Crucially, the granule cells combine bottom-up sensory input from the nose with top-down input from higher brain areas, including cortex. Our biophysically supported computational model predicts that, after learning, the granule cells enable cortical neurons that respond to a learned odor to gain inhibitory control of principal neurons of the olfactory bulb, specifically of those that respond to the learned odor. Functionally, this allows top-down input to enhance odor discrimination in cluttered environments and to quickly switch between odor tasks. A key property of the mammalian brain that is essential for its vast computational power is the pervasiveness of centrifugal, top-down feedback from higher to lower brain areas –5. The tIn the olfactory bulb, which is the first brain area receiving sensory input from the nose, structural plasticity is not restricted to early development, but is also pronounced in adult animals. At that point its key players are the granule cells (GCs). They receive sensory input from the bulb’s principal neurons—mitral and tufted cells (MCs)—and are the target of massive top-down projections from the olfactory cortex –5. Not oFunctionally, adult neurogenesis is observed to enable and improve various aspects of learning and memory , 23. A pTaken together, the experiments suggest that sensory input together with non-olfactory information like context or valence may shape the connectivity within the olfactory bulb as well as that of the top-down projections onto the GCs. It is, however, poorly understood how the bottom-up and top-down inputs into the GCs jointly shape the network connectivity, what mechanisms are at work, what kind of connectivities arise, and what kind of functionalities these connectivities support. To address these issues we have employed computational modeling using a biophysically supported framework. This model makes a number of predictions that can be tested with physiological and behavioral experiments and that can guide future experiments aimed at unraveling the cortico-bulbar connectivity.completion [separation that is observed for the bulbar output [We have developed a computational network model for the olfactory bulb and its bidirectional communication with a cortical area, which is based on key features of adult neurogenesis as observed experimentally. Details of the model are given in Methods. Briefly, the bulbar component of the model network comprised the two main neuronal populations of the olfactory bulb, the mitral/tufted cells (MCs) and the granule cells (GCs). The MCs are known to project from the olfactory bulb to a host of other brain areas, including the anterior olfactory nucleus, the anterior and the posterior piriform cortex, the olfactory tubercle, the lateral entorhinal cortex, and the cortical amygdaloid nucleus –29. Eachmpletion –32 rather output –35. Thisr output –32. Thisr output .Motivated by the observation of cortical pattern completion we included a minimal model of an associational brain area through a third population of principal, excitatory cells (CCs), which were connected with each other through plastic excitatory connections as well as fixed inhibitory connections. The population of CCs was divided into a larger subpopulation that received olfactory inputs from the MCs and a smaller subpopulation that was driven by non-olfactory, contextual input. This allowed this area to learn the association of specific odors with specific contexts. To capture the experimentally observed excitation of GCs by non-olfactory context information associated with specific learned odors , we implIn All neurons were described using nonlinear firing rate models. For simplicity, the MCs and GCs had threshold-linear firing-rate functions. The MCs exhibited some spontaneous firing in the absence of odor input, while the GCs responded only when their combined input from MCs and CCs surpassed a positive threshold. The saturation of the CC-response needed for their associative property was implemented with a sigmoidal nonlinearity.Na- and K-channels, respectively [The key aspect, network restructuring by adult neurogenesis, was implemented by persistently adding and removing GCs, as sketched in ectively . This enectively .The network structure arising in this model for a range of suitable parameter values is illustrated in To wit, in Thus, the emerging network structure is characterized by subnetworks or network modules, each of which is associated with a learned odor and provides a bidirectional projection between the bulbar and cortical representation of that odor. This is idealized in The simulation experiments presented in the body of this paper are for one set of model parameters. Extensive further analysis has shown, however, that our results, particularly for the network structure, are robust with respect to parameter changes .excitatory component, which was independent of the odor stimuli. The impact of contexts on CCs was therefore opposite to that on MCs and reduced the discriminability of these CC-patterns  and the CCs that received the corresponding contextual input  the detection and discrimination of odors in a cluttered environment and ii) rapid switching between different odor tasks.We considered cluttered environments in which the presence of additional odors may or may not occlude (mask) the odors of interest, an olfactory analog of the ‘cocktail party’ problem . As an iIn these simulation experiments we associated the occluding odor during the training with context 1 . The odoFopt. HoweverwGC = 0) after the network had been established. Even in the absence of the context signal the CCs representing the familiar, occluding odor were excited, albeit less strongly. Blocking their input to the bulb therefore disinhibited the MCs representing the occluding odor and reduced the Fisher discriminant. Blocking the top-down input in the absence of the occluder avoided the excessive suppression of the target odor by the context, leading to a larger Fisher discriminant. However, the discrimination turned out not as good as in the presence of top-down input but without the context signal, since that top-down input also reduced the background activity of the MCs.The activity of the CCs and their inputs to the bulb play dual roles: they shape the cortical-bulbar connectivity during the network development and they provide—via that connectivity—input to the bulb during the task performance. To illustrate the impact of the established connections from the CCs to the GCs, we include in If the odors of interest are not occluded by any other odors in the environment, read-out cells can, in principle, adapt the weights of their input synapses so as to focus only on the relevant odors and ‘ignore’ the cluttered environment. However, this weight optimization is often not possible, since the animal may not know yet which odors are to be discriminated. This is, for instance, likely the case in the early phase of learning a new discrimination task . During MCs , neither the beneficial nor the detrimental impact of the two contexts arose.As in the case of discrimination via an optimal read-out  it was nflexible manner substantially enhanced olfactory processing.Thus, the ability of top-down inputs to induce specific inhibition in a The neurogenic evolution of the structure of the cortico-bulbar network occurs on a time scale of days, particularly since the apical reciprocal MC-GC synapses form only days after the proximal synapses of the top-down projections . SynaptiO1,2 and O3,4. The two odors in each pair were very similar, but the pairs were dissimilar from each other , it provided only little mutual inhibition between MCs that were activated by different mixture components: the number of connections between MCs representing odor O1 (MCs with index near 30 in O3 (MC index near 80) was small. However, these MCs are activated simultaneously in the mixtures and mutual inhibition of these MCs is needed to enhance the discrimination between the mixtures [O1 and those representing O3 can be effected by establishing associational excitatory connections between the CCs that disynaptically inhibit these two groups. To do so we exploited the Hebbian plasticity of the cortical synapses and trained the cortical network briefly on the mixture 0.5O1 + 0.5O3  in thosOn a behavioral level the model makes specific predictions for the learning of odor discrimination or detection in cluttered environments. Recent experiments have shown that the detection of an odor in a go/no-go task is particularly difficult if that odor is masked or occluded by an odor . Our modEven if the cluttered odor environment does not occlude the task-relevant odors, it is expected that the learning speed in an odor-discrimination task  is reducAdult neurogenesis is a striking mechanism of structural plasticity that has the potential to rewire a network extensively. In mammals it arises predominantly in two brain areas. In the dentate gyrus it involves excitatory granule cells; their role in the network has been studied in detail , 53, alsWithout theoretical guidance, it is difficult in experiments to identify the functional structure of the cortico-bulbar connectivity, in particular, because odor representations in the olfactory system do not reflect detailed spatial maps like those of other sensory systems , 57. An Functionally, the model predicts, in particular, that the learned connectivity improves the detection and discrimination of novel odors in cluttered environments, if the occluding or distracting odors are familiar. This is achieved by a reorganization of the cortical-bulbar network so as to enhance the inhibition of familiar odors. Processing of odors in cluttered environments can also be enhanced by adaptation of sensory neurons or adaptation further downstream . For thoMoreover, the processing of cluttered environments can be enhanced by top-down input when the occluding or distracting odor activates cortical memory. This may indicate that a higher brain area has recognized parts of the odor scene and may allow something akin to the ‘explaining away’ of components of a complex odor mixture that is theoretically predicted for the optimal processing of stimuli –10, 16. Going beyond the purely olfactory aspect, the top-down input could encode task-related expectations or contextual information originating from other sensory modalities. Thus, it may implement a predictive coding in the bulb that reflects the context or task at hand . Our modThe formation of the bulbar and cortico-bulbar network via structural plasticity is a relatively slow process. However, our model predicts that the network structure emerging from it can be exploited by faster synaptic learning processes in cortex, which allow the system to switch relatively quickly between different discrimination tasks. This is reminiscent of the task-dependent switching of neuronal responses observed in V1 .Focusing on the slow evolution of the network structure our model is intentionally minimal with respect to the dynamics of the individual neurons. We describe the neurons in terms of their firing rate and focus on neuronal steady-state activities. So far, the knowledge about the biophysical mechanisms controlling GC survival and their dependence on neuronal activity is not sufficient to guide the development of a detailed model of that process, which would, e.g., connect GC spiking or calcium-levels with GC survival , 60, 61.To assess the discriminability of MC activity patterns we assumed that the firing rates result from an irregular firing in which the variance of the spike number is proportional, albeit not necessarily equal, to the mean spike number. Thus, we have not taken the widely observed rhythmic aspects of the bulbar activity into account , nor theNot much is known experimentally about the parameter values for the model. There have been recent efforts to constrain various coupling strengths in bulbar-cortical rate models based on experimental measurements of firing rate correlations . The twoWe have used a minimal model to focus on the dominant aspects associated with neurogenic network restructuring. Thus, in the bulb we have omitted the odor processing performed in the glomerular layer, which may also contribute to pattern separation and normalization , 71. OurThe olfactory system most likely gains additional richness through the nonlinear response properties of the GCs, which include local dendritic depolarization that can provide reciprocal inhibition to the MCs even without spiking , as wellAdult neurogenesis is not the only mechanism contributing to the plasticity of the olfactory bulb, which is, for instance, reflected in the temporal evolution of the receptive fields of adult-born GCs  and in cM), granule cells (G), and ‘cortical’ cells (C). Although the differences between mitral and tufted cells in terms of their properties and function are becoming increasingly known [Our model consisted of three populations of neurons, mitral/tufted cells Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.


Objectives: The role of the pharmacist has evolved and continues to evolve. The traditional role of the dispenser has been replaced with a patient-centered profession. This requires integration and application of pharmaceutical knowledge and skills to solve patient therapeutic problems and advance patient care. Therefore, having evidence-based teaching strategies for learning within pharmaceutical sciences is essential. New and maturing technologies enable traditional principles of pharmaceutical science to be visualized. We aimed to explore pharmacy students' performance before and after visual aids for learning are integrated within pharmaceutical science teaching. Student's opinions and views of the visual aids were determined.Methods: Students were taught about selected pharmaceutical science concepts at two time points; during the second teaching point, visual aids were introduced. Students' performance was compared before and after the implementation of visual aids using pre and post-quizzes. Alongside the post-quiz an evaluation was also completed by the students; a descriptive analysis was conducted for the Likert-type responses and an in-depth thematic analysis of the student's free-text questions was completed using an iterative process.Results: Significant differences were seen between pre and post-quiz sessions for total score and questions that mapped to the revised-Bloom's taxonomy lower and higher categories. Student evaluation of the visual aids were positive. Interesting themes and subthemes emerged regarding the perspectives of pharmacy students to these visual aids. Students indicated visual aids made it easier to understand, compared to written or verbal explanations, and helped with the application of pharmaceutical science concepts. However, a minority of students reported that the visual aids were irrelevant, or they did not understand them.Conclusion: Students had better performance after the introduction of, and favorable responses to, the visual aids. Visual aids were a beneficial tool in regards to understanding and application of complex concepts. Improvements can be made; tailoring accompanying descriptions and using more repetition. The role of the pharmacist has evolved and continues to evolve , before and after the implementation of the visual aids (pre and post quizzes). These quizzes centered on the concept of disintegration and associated formulations; specifically we wanted to look at (1) overall scores and distribution, in addition to (2) differentiating lower vs. higher categories of learning, according to the revised-Bloom's taxonomy  system-based module in the second semester.The foundation pharmaceutical science module in the first semester includes teaching on the concepts of dissolution, disintegration, and oral dosage forms. These concepts are taught in lectures with diagrammatic depictions of the concepts, where appropriate, but have no innovative visual aids  images, described in previous work  multi-choice questions, which were marked by the two members of the research team, one from the pharmaceutical science group and the second from the pharmacy practice group. The short-answer questions were also marked by the same two researchers, but then cross-marked for validatory means; any discrepancies in marks were discussed and resolved. When a question wasn't answered this was marked incorrect as it would be for any student assessment.Quantitative responses to the quiz and evaluation were recorded in Microsoft Excel 2010 (Redmond WA) and crosschecked for accuracy. Questions were grouped into the lower and higher categories of the revised-Bloom's taxonomy; lower scores encompassed questions targeting “remembering,” “understanding,” and “applying” and the higher categories included questions targeting “evaluating” and “creating”  respondents completed the pre-quiz and 94 students (90.4%) completed the post-quiz and evaluation.p ≤ 0.027; an adjusted value was used to account for multiple comparisons. Significant differences were seen between the groups in response all comparisons. The Levene's test was also conducted to see whether the assumption of the same distribution between groups was upheld. These results are all indicated in Table The Mann-Whitney non-parametric test was used to assess differences between the pre and post-quiz sessions for (i) total score (ii) lower-category questions (iii) higher category questions. Significance was taken at the False Discovery Rate (FDR)-adjusted Regarding the evaluation component, overall, the majority of students' agreed/strongly agreed (A/SA) with all Likert-type scale statements Table  regardinIn regards to the open ended questions, six key themes and sixteen sub-themes were identified from the students' responses, as outlined in Table The first theme to emerge was “learning” strategies. Many of the students described the visual representations of the formulations both within the anatomy and within the Youtube clips to enhance learning; over the free-text responses there was 43 separate comments regarding the effectiveness of visual learning. A sub-set of these students commented that they were “visual learners” and therefore this type of learning aid was a means to clarifying what was being taught within these topics. Students reported that these visual aids allowed for them to visually understand the concepts being taught rather than reading or listening to the concept described. A number of students indicated that this type of learning also committed the concepts to memory.The second theme to emerge was “application of learning.” The application of concepts was a strong theme that emerged from the free-text questions in this study. Participants reported that the visual aids allowed for the application of the concepts to be understood. Further to this, some made the connection with patient; students explained that they now could envisage the implications of the pharmaceutical science concepts to patients. The visual aids were also described as making concepts “real.”“Knowledge” was another theme that emerged. Subthemes of connecting with theory and deepening knowledge of formulation concepts emerged in students' answers. Some students said that the visual aids allowed them to connect to the theory that was being taught. Statements regarding students' development of knowledge through the aids were also made.The fourth emergent theme was “understanding.” Understanding was a key theme that had four distinct subthemes; deepening understanding, difficulty understanding, appropriate explanatory notes needed, and relevance to learning. Many students (22 comments) reported that the visual aids allowed deepening of understanding, allowing students to visually see the concepts and to understand complex topics. On the contrary, a smaller number of comments were made (3 comments) to state that the visual aids were difficult to understand, others remarked that they only understood the visual aids if they also listened to the corresponding audio explanation. Three students also could not see the relevance of these visual aids.“Access to visual aids” was another theme to emerge. Many students reported that they would benefit from having continued access for revision purposes to these aids. More access to these types of examples encompasses two subthemes—within the course and across other topics. Within the course, more examples, longer examples and more integrated within the lectures.Lastly “broader implications for learning” was the final emergent theme. Students reported the benefits of the visual aids also provoked attention and made learning interesting, and through this, were able to understand concepts.The results of this study demonstrate three major findings; that, (1) overall, the visual aids used in this teaching improved total scores and both the lower and higher categories of questions within the quizzes, (2) that there was a significant change in distribution at both the overall scores and the higher category of learning, whereby more students performed better in the post-quiz and (3) the visual aids were perceived positively by pharmacy students as an addition to the pharmaceutical science teaching within the course, aiding with application of knowledge, with many students reporting that they would like to see more visual aids used across the degree programme. However, there was one question within the evaluation that was rated less favorably and there were a minority of students who found the visual aids irrelevant and had difficulty understanding them.p = 0.000). In the pre-quiz students found it difficult to use analysis and creative skills within the topic, providing rudimentary answers to the short-answer questions. However, by the second time point, at the post-quiz session the complexity and sophistication of their answers substantially increased.Significant differences were seen in the scores of the pharmacy students when pre and post-quiz sessions were compared. These significant differences were seen in the total scores and also when the scores were analyzed by grouping the questions within the quizzes by lower and higher categories within the revised-Bloom's taxonomy framework. This is an interesting finding; higher scores across all of these comparisons illustrates that the use of visual aids not only helps retention of concepts, but they also promote deeper understanding  of how this applies to the current lecture.It must be acknowledged that there could be other factors that contribute toward the increase in the depth and application of knowledge. As the BPharm curriculum at the University of Auckland is a spiral curricula, this could also be adding to the increase in depth of knowledge. The concept of spiral curriculum was introduced by Jerome Bruner in the mid-twentieth century, and has been documented in both medical and pharmacy education   all produced significant results, we do not believe that this has impacted the overall messages that this research reports.As the pre and post-quizzes were anonymous, we were unable to directly pair the results and perform a non-parametric version of the Visualization technologies can be applied to pharmaceutical science education. Pharmacy students had higher test scores post-quiz and favorable responses to the visual aids being used in pharmaceutical science teaching. Visual aids demonstrated clear and beneficial learning outcomes for the pharmacy students, especially in regards to understanding and application. Interestingly, after the use of visual aids there was a significant increase in test scores in the higher categories, alongside a significant change in the distribution of scores, indicating an increase depth of knowledge by more students. Improvements can always be made; tailoring the accompanying descriptions and using more repetition may improve the understanding for the students who found the visual aids difficult to interpret. In addition, integrating more aids, in this topic and across others, seems to be warranted.LC contributed to the initial concept, the design of the research, analysis, and interpretation of the data in addition to the writing and approval of the manuscript. ZW contributed to the design of the research, interpretation of the data in addition to the writing, and approval of the manuscript. DS contributed to the initial concept, the design of the research, interpretation of the data in addition to the writing, and approval of 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.


In order to obtain good quality radar terrain images using an aerial-based synthetic aperture radar, a motion compensation procedure must be applied. This procedure can use a precise navigation system in order to determine the aircraft’s position and velocity. A major challenge is to design a motion compensation procedure that can operate in real time, which is crucial to ensure convenient data for a human analyst. The article discusses a possibility of Inertial Measurement System (INS)/Global Positioning System (GPS) navigation system usage in such a radar imaging system. A Kalman filter algorithm designed for this system is described herein, and its modifications introduced by the authors allow the use of navigational data not aligned in time and captured with different frequencies. The presented navigation system was tested using measured data. Radar images obtained with the INS/GPS-based motion compensation system were compared to the INS-only results and images obtained without navigation corrections. The evaluation results presented in the paper show that the INS/GPS system allows for better reduction of geometric distortions in images compared to the INS-based approach, which makes it more suitable for typical applications. Obtaining high-quality terrain images using aerial synthetic aperture radar (SAR) requires several conditions to be met. Firstly, the aircraft that carries the radar should move along an assumed nominal trajectory of flight, which in most cases is rectilinear. Secondly, the vehicle’s velocity must be constant during imaging. Failure to meet these requirements significantly increases the computational complexity or even makes imaging not possible. Miniature unmanned aerial vehicles (UAV) have a small mass and a limited maximum speed; therefore, meeting these requirements is extremely difficult in practical implementations. As a result, during radar data processing, it is necessary to apply algorithms aimed to compensate the radar position and the velocity deviations, referred as the motion compensation (MOCO).The compensation can be achieved in two different ways. The first group of methods uses additional navigational equipment to determine UAV position and velocity deviations and to compute phase corrections for the received echo signals ,2,3. TheThe basic navigation system used to improve the quality of radar terrain images is the Inertial Measurement System (INS) ,11,12. EThe mentioned property of the INS prompted the authors to study and develop an integrated navigation system combining INS and Global Positioning System (GPS) to estimate the UAV position and velocity deviations. In the proposed system, the INS and the GPS data integration was implemented using a Kalman filter ,14,15,16The layout of the remainder of this paper is as follows. A mathematical description of the INS/GPS system is given in North-East-Down (NED) reference frame, also named n-frame . To determine the spatial resolution (SR) , the peaA graph of a normalized amplitude of image pixels lying on the mentioned line is shown in In the case of the INS/GPS-based MOCO image , the maiIn the MOCO using INS/GPS data, the high-level side lobes led to a significant deterioration  of the PSRL and the ISLR compared not only to the image obtained with the INS corrections  but also to the image obtained without the MOCO procedure . In the case of the INS/GPS method, the obtained results were: PSLR = −2.99 dB, ISLR = 1.52 dB. A comparison of these and the previously mentioned results is given in Similar experiments and analyses were conducted for various terrain scans. Results obtained for another flight are presented in In the case of the image obtained without MOCO , it was Considering the superiority of the INS/GPS-based approach in terms of its ability to reduce geometric distortions, it was necessary to investigate the reasons that led to the generally worse performance in terms of the quality parameters presented in The reasons for these phenomena were random errors associated with the GPS receiver. In the INS, the UAV position and the velocity changes were small in one measurement period (1 ms), while in the GPS-only data, abrupt changes occurred resulting from the GPS errors characteristics. These GPS random errors were subsequently transferred to the INS/GPS integrated solution. As an example, in In the case of the INS, the calculated position varied by a maximum of 2.9 mm between two consecutive samples. A similar trend was visible in the INS/GPS data (as a bold line). The points belonging to this line corresponded to the estimation results in which there was no INS error correction by a Kalman filter. The measurement update realized in the Kalman filter with a lower frequency than the calculation of INS solutions led to rapid changes of the estimated navigation parameters, which were visible as the points separated from the main trend line . These dThe article presents an integrated INS/GPS system with a dedicated Kalman filter algorithm, which considers a time difference between the IMU and the GPS updates and a frequency difference between the position and the velocity updates from the GPS receiver—problems usually neglected in available literature. The problem of time delay between the IMU and the GPS data was solved using an artificial IMU measurement determined for the GPS data time. A sequential Kalman filter algorithm was used to process the GPS position and the velocity data separately with their maximum available rates.The results of the experiments conducted by the authors show that the main advantage of the proposed INS/GPS approach is its ability to reduce image geometric distortions, which is more effective than in competitive solutions. This allows the user to conduct long-term observation sessions, because there is no time-limitation due to the INS error growth, which can be crucial in real-world scenarios. In such a system, the INS/GPS method can be used for current image analysis, whereas the INS-based approach can be triggered only occasionally and for short periods to evaluate small parts of the images in more detail. The analyses presented in the paper may be helpful for researchers and engineers in deciding when and how to apply an INS/GPS-based MOCO method in a SAR system and in properly assessing its influence on the quality of the obtained radar images.


Lesion studies have shown that the right temporal lobe is crucial for recognition of facial expressions, particularly fear expressions. However, in previous studies, premorbid abilities of the patients were unknown and the effects of epileptic discharge could not be excluded. Herein, we report a case of a patient who underwent assessments of facial recognition before and after brain surgery and exhibited biased recognition of facial expressions. The patient was a 29-year-old right-handed male who underwent an awake craniotomy. Compared with the preoperative assessment, after the surgery, he showed biased recognition of surprised facial expressions, and his ability to recognize other facial expressions either improved or remained unchanged. These findings support the idea that the right temporal lobe is crucial for the recognition of facial expressions of surprise and that functional connectivity between various brain regions plays an important role in the ability to recognize facial expressions. Functional imaging studies e.g.,  and obseThe patient was a 29-year-old, right-handed male. Following graduation from graduate school, he worked as an office worker for 5 years. After experiencing olfactory hallucinations, he visited a local hospital and was diagnosed with a suspected brain tumor. To diagnose and treat the tumor, he was hospitalized at Tokyo Metropolitan Komagome Hospital. Magnetic resonance imaging (MRI) showed a right temporal lobe lesion see . NeuropsThe patient completed a recognition test of emotional facial expressions before and after surgery to examine his abilities in this regard. The pre-operative session was performed 3 days prior to the surgery and the post-operative session was performed 4 days after the surgery; therefore, the period between the pre- and post-operative assessments was 7 days. For reference, control subjects were also administered the facial recognition test. The control subjects were recruited from graduate and undergraduate courses and consisted of 23 healthy male volunteers (mean age: 28.6 ± 2.10 years) with no history of neurological disorder. Before the examination, all of the control subjects were informed of the aims of the test, and the authors obtained their written informed consent. The tests were administered at two timepoints with an interval of 1 week, which corresponded with the patient’s interval between tests. The facial expression stimuli included six emotion groups and a neutral group, yielding a total of 54 pictures. The presented emotions and the corresponding numbers of pictures for each emotion were digitalized from the pictures used by t-tests were used to compare our case with control subjects .t-tests revealed that the patient’s subtraction score for surprise was significantly larger than that of the control subjects , which indicates that the score improved after surgery. On the other hand there were no significant differences for the other stimuli . Taken together, these results indicate that the patient’s recognition of the facial expressions of surprise improved after surgery beyond a learning effect  whereas after surgery his score was within 1 SD . On the other hand, after surgery the patient responded to fear stimuli as would be expected to a surprise target much more than control subjects; however, the difference was not statistically significant . These results imply that his facial recognition ability was impaired in that his recognition of surprised facial expressions was biased. We found no significant differences for other facial expressions (p > 0.10).Furthermore, to evaluate the accuracy of the patient’s performances between pre- and post-surgery, a z-score of the partial credit score was calculated based on the average of two time points in control subjects see . The z-sWe examined the role of the right temporal lobe in the recognition of facial expressions before and after an awake craniotomy. Following the surgery, it was confirmed that the extent of the operation affected the amygdala of the patient and that he showed biased recognition of surprised facial expressions. Compared to previous research, this study has a number of advantages. First, because the patient’s preoperative status was known, we were able to compare the recognition ability before and after surgery. Second, the surgical procedure used was awake craniotomy. Compared to surgery under general anesthesia, awake craniotomy for brain tumors improves safety in terms of preserving neurological function . In addiIt is easy to confuse a surprised facial expression with a fearful one and this phenomenon is related to activity in the right amygdala region . As showThe present findings are similar to those of The right amygdala, right posterior central gyrus, and left insula are crucial for the recognition of surprised facial expressions . ChangesAlternatively, several other factors may explain the changes observed in the present study. At his preoperative assessment, the patient exhibited deterioration in the ability to recognize facial expression of surprise. Imaging studies of the amygdala region have revealed activation during the identification of not only fearful facial expressions but also surprised facial expressions . TherefoThere are several limitations to the present study that must be noted. First, the present patient was diagnosed with a brain tumor in the temporal region that may have influenced his cognitive abilities. Prior to surgery, the patient exhibited poor recognition of surprised facial expressions as well as poor performance on a delayed recall task. The right amygdala region is related to the recognition of surprised faces , and thuIn conclusion, the present study identified discrepancies in the ability of the patient to recognize facial expressions before versus after the awake craniotomy. Compared to before the surgery, the patient exhibited changes in the ability to recognize surprise. It is our opinion that these results were not due to changes in independent regional function but to disturbances in functional connectivity between the damaged right temporal lobe and the preserved left temporal lobe and among other regions.The datasets generated for this study are available on request to the corresponding author.This study was approved by the Ethics Committee of Tokyo Metropolitan Komagome Hospital (reference number: 1736).AM designed the study, conducted the research, analyzed the data, and wrote the manuscript. SS and CI contributed to the research in their clinical capacity and helped with manuscript preparation. KH contributed to the research on normal subjects. RO contributed to the data analyses and manuscript preparation. RY and NS contributed to the manuscript preparation.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.


In the recent past, the field of optofluidics has thrived from the immense efforts of researchers from diverse communities. The concept of optofluidics combines optics and microfluidics to exploit novel properties and functionalities. In the very beginning, the unique properties of liquid, such as mobility, fungibility and deformability, initiated the motivation to develop optical elements or functions using fluid interfaces. Later on, the advancements of microelectromechanical system (MEMS) and microfluidic technologies enabled the realization of optofluidic components through the precise manipulation of fluids at microscale thus making it possible to streamline complex fabrication processes. The optofluidic system aims to fully integrate optical functions on a single chip instead of using external bulky optics, which can consequently lower the cost of system, downsize the system and make it promising for point-of-care diagnosis. This perspective gives an overview of the recent developments in the field of optofluidics. Firstly, the fundamental optofluidic components will be discussed and are categorized according to their basic working mechanisms, followed by the discussions on the functional instrumentations of the optofluidic components, as well as the current commercialization aspects of optofluidics. The paper concludes with the critical challenges that might hamper the transformation of optofluidic technologies from lab-based procedures to practical usages and commercialization.  Optofluidics is referred to as the sciences and technologies which explore novel functions and the related physics through the marriage between light and fluid . OptofluFrom a physical point of view, fluids can offer unique properties to realize novel optical functions: (1) replacing a liquid with another liquid inside an optical cavity can easily tune optical performance or function of the optical cavity; (2) refractive index (RI) gradients can be created by the control of diffusion between two miscible fluids; (3) an optically smooth interface can be formed between two immiscible liquids; and (4) light and sample can transfer along the same channel which allows long light–matter interaction length. These novel properties show promise to equip adaptive optical systems with more flexibilities and functionalities. In 2006, the review paper by Psaltis et al. summarized and categorized the early development of reconfigurable optical systems synergistically using optics and fluidics, and conceptualized the field of optofluidics . Since tA good number of review papers have been published with emphasis on a specific area dealing with applications using optofluidic technologies, such as whispering gallery mode sensor ,7, labelThe function of most solid optical components is permanently fixed. Recently, the infiltration or injection of liquid into hollow structures has brought unique light–liquid interactions and excellent tunability of the optical components. Examples are infiltrated photonic crystal devices, such as photonic crystal fibers or planar photonic crystals. Thermal actuation , manual Another example of replacing liquid inside an optical component is the optical switch based on the phenomenon of total internal reflection . A mirroWaveguide is one of the key elements in optical systems for confinement and transmission of light, equivalent to channel in terms of fluid transportation . The intLlobera et al. reported a hollow prism built in a microfluidic system for optical sensing ,36. The Instead of replacing the liquid inside an optical component, tuning refractive index of liquid by subjecting it to a flow field, temperature field, electrical field, acoustic field or mechanical strain are the alternative methods to manipulate the optical properties of light propagating in the optical material such as the optical path, phase change and polarization . The genThe refractive index gradient can be controlled through the variation in concentration of a solution, which could be well defined by diffusion of microfluidic laminar flows. Based on this motivation, two-dimensional (in-plane)  and thredn/dT of most liquids is relatively small. Thus, a large temperature contrast is required to enable an effective waveguide function. Another drawback is the decay of temperature contrast along the flow direction due to dissipation of heat, which leads to unevenly distributed refractive index along the optical axis. This phenomenon causes a lot of optical energy loss during the propagation of light. To improve these drawbacks, Chen et al. proposed to integrate two heat sources into the lens-development chamber, and built up a high temperature gradient, which enabled strongly bending of light to realize lensing effect [Another mechanism to produce the refractive index gradient is by placing the liquid in a temperature gradient field . In thisg effect .Acoustics as an alternative method to enable liquid GRIN lens can offer faster response than the temperature and diffusion driven mechanisms. It was found through theoretical studies and experimentations that acoustic standing wave created inside a cylindrical cavity by a circular piezoelectric transducer can generate a refractive index gradient and consequently result in lensing effect  interfacial intension based tuning; and (b) hydrodynamic tuning. The balance between the applied pressure and the interfacial tension can be used to tune the curvature of the liquid lens interface. Lien et al. fabricated a spherical micro-lens by injecting liquid-phase PDMS into the microfluidic channel . One draHydrodynamic tuning is a more flexible method to manipulate the liquid/liquid interface and realize different functional structures, such as waveguides, lenses, prisms, etc. In such configurations, multiple flows propagate in the channels with specific geometries. In a straight-line channel, a core flow with higher RI sandwiched by two cladding flows with lower RI can form a typical waveguide structure . The widThe categorization of optofluidic components is illustrated in Based on the abovementioned optofluidic components, a variety of detection and analytical tools have been developed, which have applications in biomedical research, biochemistry, pharmaceuticals, healthcare, and environmental monitoring. The integration of optofluidic components on a microchip can greatly reduce the fabrication cost and significantly downsize the entire systems. It is promising and possible to make the system portable for field test even in remote regions that cannot afford high-cost equipment. Fundamentally, light carries the information encoded with its basic properties, such as phase, electromagnetic frequency, intensity or polarization. The interaction of light with the inspected sample can modulate its properties, which need to be decoded by specific tools such as interferometer, spectrometer, photometer and polarization analyzer . This seThanks to the realization of on-chip waveguide, the Mach–Zehnder interferometer (MZI) can be easily developed with both sensing and reference arms aligned on an optofluidic chip a. The se3N4 on a silicon substrate to configure the two waveguides arms, and successfully measure the concentration of biomolecules [The first on-chip MZI was realized by growing Siolecules ,88,89. Folecules . It has olecules . To improlecules . Howeverolecules ,94. The olecules . Anotherolecules ,97,98. TSpectroscopy, as an important optical tool, can measure analytes at very low concentration level based on the absorption, fluorescence or Raman characteristics. The main limitation to integrate the spectroscopic function on a micro-chip is the short light–matter interaction length due to the inherently small size of the chip. However, the invention of the optofluidic waveguide enables an enhanced light–matter interaction to achieve maximum sensitivity by offering a way to transmit the light and analytes along the same guide b.In the work of Yin et al. , an antiA traditional flow cytometer employs bulky optics to inspect the flowing particles of micro-size or biological cells aligned in a single line. The scattered light or fluorescence signal can be collected and analyzed to reveal the physical and chemical properties of the particles or cells. Since both delivery and collection of light involve external bulky lenses as well as the alignment of optics, the system can hardly be used for field test.Recently, many efforts have been dedicated to downsize the cytometry system and make it portable. The microfluidic platform offers an efficient way to align the particles along a single streamline due to the laminar flow characteristic. The most common implementation is to utilize a flow carrying particles sandwiched by two sheath flows. The hydrodynamic focusing effect allows the particles following a line to pass through the inspection window  Figure c. The inThe manipulation of micro-object involves trapping, sorting, and separation, which can find their applications in the fields of chemistry, biology and colloidal science. The optical method, which is referred to as optical tweezers ,115, hasCombining laminar flow characteristics in a microfluidic channel with optical trapping, sorting of particles or cells can also be achieved. The absence of turbulence in a microchannel gives the particle or cells a predictable drag force along flow direction. The optical trap will exert a gradient force along a defined direction, which combined with drag force results in a net force that can redirect particles from their original tracks into the designated fluidic branch ,117. SimInstead of using external optical instruments to manipulate the light, the evanescent field trapping is another type of optical method to manipulate particles which can employ on-chip waveguide to generate an intensity gradient field of light. Schmidt et al. investigated the forces exerted by the evanescent field, which can attract the particles to the surface of the waveguide and at the same time propel the particles along the propagation direction of light . ParticlX, Y and Z axis by selectively controlling the electrodes, which can find its application in ophthalmology. Other actuation methods, such as using deformable diaphragm and electro-active polymer, were also used in the production of tunable liquid lenses by Optotune [Leveraging the superior reconfigurable properties, optofluidic systems can be dynamically tuned without any moving mechanical parts, which can be utilized to develop faster and more compact optical systems. Thus far, several concepts have been commercialized with their applications in a variety of fields. Due to the compatibility with conventional optical systems, the out-of-plane optofluidic configurations, especially lenses, were the forerunners during the development of optofluidic products. An acoustics-driven GRIN lens  is avaiOptotune .For the lab-on-chip applications, the in-plane waveguides are commonly used to develop fluid sensor and particle tweezers due to the convenient fabrication and control. World Precision Instruments commercialized a device using liquid core waveguide for the measurement of light absorbance of analytes . A NanoTRecently, several ways have been explored to manipulate the fluid interface at microscale with integrated control units in the field of droplet-based microfluidics. Thermal heating generated by integrated microheater has been demonstrated to effectively change the interfacial tension between two immiscible fluids ,128. MaxThis paper gives an overview of the developments of fundamental optical elements using fluid referred as optofluidic components. Driven by the demands of lab-on-chip applications, many efforts have been devoted to realize the integration of optofluidic components into microfluidic systems. With the advancements of MEMS technologies, a variety of optofluidic components can be miniaturized and printed on the same planar structure with other assay modules. Those optofluidic components can be categorized based on their working mechanisms: (1) liquid replacing; (2) RI gradient control; and (3) liquid/fluid interface deformation. Owing to the reconfigurable features, these optofluidic components possess a high level of tuneability to make the optical system highly adaptive. Adaptability as well as the miniaturization can significantly downsize the entire system and make the system portable for field test. Instead of using bulk optics in free space, the integrated optics can greatly reduce the cost of system and thus hold a promise for affordable point-of-care diagnosis even in those regions suffering from poverty.Based on the developments of fundamental optofluidic components, many of the optical tools for biochemical analysis and treatment can be realized with miniaturized and simplified architectures. The invention of optofluidic waveguide enables an efficient way to print the optical arms of an interferometer on a planar structure with the sensing arm directly interacting with the analytes; the liquid core waveguide allows the transfer of sample fluid and light along the same guide. The enhanced interaction between light and fluid enables integration of spectroscopy into microfluidic platforms; the developments of optofluidic lenses enable enhanced detection of micro-particles or even single fluorescent molecule without using external bulky optics. It is envisioned that an all-optofluidic system can be practically realized with all optical functions integrated into a single chip in the near future.To achieve this goal, several critical challenges need to be addressed carefully. Firstly, many of the optofluidic components consume functioning fluids even when working at steady state. An effective way needs to be figured out to recycle and reuse the fluid, which can further lower the operational cost of the system and make the system more practical. The stability of fluid pumping is another critical concern. Any discontinuity in pumping might introduce disturbance to the functioning of optofluidic components, which can result in inaccuracy of the analysis or malfunction of the optical treatment. Besides the discontinuity in pumping, the vibrational noise from the environment may also introduce perturbation to the optofluidic system. The environmental noise can be isolated for the free-space optics by mounting the optical elements on an anti-vibration table, but it is not practical to do the same for the optofluidic systems since they are born to be used for field test and point-of-diagnosis. Hence, cost-effective housing of on-chip optofluidic systems with effective anti-noise techniques has to be developed for the practical use and their further commercialization. Different from the manipulation of light in free space using glass-based optical elements, it is difficult to tightly focus a light beam with a high numerical aperture using optofluidic components, since most structural materials for microfluidic system have very high refractive index around 1.4 and the RI of liquids lies between 1.3 and 1.6 RIU. This drawback of optofluidic components may hamper the developments of optical tweezing with high strength and high-resolution imaging. Efforts need to be dedicated to exploit novel fabrication methods or materials for the light manipulation using on-chip elements.


Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals. At present, inflammable and explosive resources such as oil and natural gas are mainly transported through pipelines, which is convenient and fast. Meanwhile, problems such as resource waste and environmental pollution caused by pipeline leakage also exist. Therefore, real-time monitoring for pipelines’ status is essential ,2,3. In When we choose OFPS based on Φ-OTDR to recognize fiber vibration signals, how to improve the recognition rate of different vibration signals and noisy vibration signals is a challenging task. On the one hand, some scholars studied from the aspect of traditional feature extraction ,12,13,14The recognition methods of vibration signals based on neural network is studied by many scholars, because it is simple to operate and does not require too much preprocessing of vibration signals. At the same time, the vibration signal recognition methods based on neural network is also a challenging research field. With the improvement of computing power, neural networks or artificial intelligence will be widely used in the field of optical fiber vibration signal recognition.The main contribution of this paper is combining original SCN with bootstrap sampling of Bagging and AdaBoost, and proposing three improved methods: Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN for noisy fiber vibration signal recognition in the case of small sample set. Likewise, the robustness of the proposed methods is tested by using noisy vibration signals. In the experimental part, we first pretreat collected signals and superimpose various types of noise. Then, we send noisy signal sets to five methods  network) for training. At last, we compare the results of five methods.The article is divided into the following parts: the second section describe the details of related works; the third section briefly introduces original SCN, proposed methods, and methods for generating various types of noisy signals; the fourth section makes a presentation of the optical fiber pre-warning system, original data of fiber vibration signals, and signal pretreating process; the fifth section shows the experiments and analysis; the sixth section is the discussion of experimental results; and the last section concludes this study.Scholars have done in-depth studies on the recognition technology of optical fiber signals, including instrument improvement, optical fiber signal processing, and classification. Traditional analysis methods are based on time and frequency domain ,8,9,10. It is also necessary to recognize fiber vibration signals in the case of small sample set. Because not only the acquisition of a large number of fiber vibration signals is difficult, but also the sample calibration work is heavy. Once there is a problem in the calibration of samples, it will directly affect the training of classifier. If a deep network is used for training in the case of small sample set, the network could not be trained effectively. In the field of small networks, Wang et al.  proposedAt the same time, since OFPS usually works in a complex environment and is susceptible to various disturbances, the acquired fiber vibration signals contain various noises. During the propagation of backscattered light, the remaining types of scattered light generated in fiber may become the optical noise of the system. Photoelectric detectors based on avalanche photo diode module (APD) generate random noise during the fiber conversion and amplification process. All these noises overwhelm the fiber signals of the system . Sun et For the recognition task of noisy fiber signals, common methods are to use synchronization overlapping average algorithm to suppress the white noise of the system. The increase of the number of accumulations can effectively promote the signal–noise ratio (SNR) and improve the performance of the system . Since tIn the recognition of noisy fiber vibration signals with a small sample set, the problem of low classification accuracy still occurs in the use of SCN, and the recognition rate is sometimes even lower than 50%. This shows that the SCN after training is less robust in this case. Combining models with ensemble learning methods is a common choice in solving the problem of low robustness ,27. In tSCNs are a class of randomized neural networks proposed by Wang and Li in , which oN is the number of training samples, n is the dimension of sample’s input, m is the dimension of sample’s output. Meanwhile, we define the input weight matrix as L is the number of hidden layer nodes. Then, we calculate the output of hidden layer nodes of network according to the input of network, input weight matrix and bias vector. Herein, the output of the Lth hidden layer node is expressed asFrom The output weight matrix is Lth hidden layer node, the constraint condition is expressed asr is a sequence greater than 0 and less than 1, and varies in the process of finding parameters, When adding the Lth hidden layer node, u is the number of candidate nodes. Then, one set The training process of SCN is as follows. When adding the It can be seen from the above description that the difference between SCN and other neural networks lies in its unique training mode. It gives up the traditional iterative method of updating network parameters, and uses the pattern of adding hidden layer nodes under constraints, which makes the scale of network flexible and controllable, and greatly reduces the loss of computing resources.The robustness of the model refers to the ability to resist or overcome adverse conditions. In these experiments, the stronger models’ ability to overcome noisy environment, the better the robustness of models. In order to strengthen the robustness of original SCN, we combine the original SCN with two ensemble learning methods. We also strengthen the robustness of model by combining two ensemble learning methods based on original SCN.Bagging is a parallel ensemble learning method, its process is as follows. First, we randomly sample a number of different training subsets with the same number of training set samples from training set, and this step is called bootstrap sampling. Then, we use these different training subsets to train corresponding base classifiers with the same type of classifier models. Finally, these base classifiers are combined by simple voting or other methods to form the final classifier. Here, bootstrap sampling is the core step of bagging.By using bootstrap sampling in the case of small training set, on the one hand, these base classifiers will have a larger difference between each other according to training subsets; On the other hand, it also avoids the problem that training subsets are too small which caused by dividing the completely different subsets, and finally the base classifiers are not trained well.Combined with the training process of SCN, we propose Bootstrap-SCN method. This method generates a training subset by using bootstrap sampling before adding hidden layer nodes to SCN. This training subset is used for the generation of new hidden layer node of SCN. After that, we repeat this step to generate more hidden layer nodes until the end of training. The flow of this method is shown in Algorithm 1: Pseudo code of Bootstrap-SCN method.L and maxL, training error Given training set Initialize While1: 2:   Generate 3:   Train 4:   End while5: Return:When we train network with small sample sets, it is very likely that the network training is not in place and the robustness of network is poor. AdaBoost can be used to reduce the deviation and improve the effect of network on the vibration signal recognition . MeanwhiIn the process of SCN training, we consider the hidden layer nodes generated by each training round as different classifiers. When SCN is in the initial state, the weights of samples are the same. When training the network, the samples’ weights are adjusted according to the training error of SCN and the recognition results of samples after each time the hidden layer node is added, and are used for the calculation of training error when the hidden layer node is added next time. Then, we repeat this step to generate more hidden layer nodes until the end of training. The flow of this method is shown in Algorithm 2: The pseudo code of AdaBoost-SCN method.L and maxL, training error, expected error tolerance Given training set Initialize While1: 2:   3:   4:   if 5:   6:   End while7: ReturnAccording to The way we combine two ensemble learning methods with SCN is as follows. During the process of training SCN, we need to update the weights of the samples according to the training error and samples’ recognition results of current network before adding new hidden layer node. Then we use bootstrap sampling to generate a training subset and choose the corresponding weights of samples in the training subset. At last, we use this training subset and the corresponding weights to train SCN and obtain a new hidden layer node. We repeat this step to generate more hidden layer nodes until the end of training. The flow of this method is shown in Algorithm 3: The pseudo code of AdaBoost-Bootstrap-SCN method.Initialize While1: 2:   Generate 3:   Generate 4:   5:   6:   if 7:   8:   9:   Update End while10: ReturnDue to the unique nature of OFPS, it needs to work in complex environments for long periods of time. The various noises also cause some interference to the recognition of the vibration signals of system. In order to eliminate the influence of noises, we need to improve the robustness of classifiers. In this paper, we mainly use four common noises for simulation and verify the effectiveness of the proposed methods.White Gaussian noise is a kind of noise whose instantaneous value obeys Gaussian distribution and power spectral density obeys uniform distribution. It is commonly used to simulate ambient noise because its probability distribution is normally distributed. In order to analyze the influence of noise on signal recognition performance, we combine pretreated fiber vibration signals with white Gaussian noise to obtain noisy vibration signalsIn order to accurately understand the interference of different noise intensities on signal recognition, we use the variable White uniform noise refers to the noise whose power spectral density is constant in the whole frequency domain. In this paper, the vibration signal superimposed white uniform noise is defined as In this paper, the parameters of white uniform noise are set to: Rayleigh distributed noise refers to the noise whose instantaneous value obeys Rayleigh distribution. In this paper, the vibration signal superimposed Raleigh distributed noise is defined as In this paper, the parameter of Raleigh distributed noise is set to: Exponentially distributed noise refers to the noise whose instantaneous value obeys exponential distribution. In this paper, the vibration signal superimposed exponentially distributed noise is defined as In this paper, the parameter of exponentially distributed noise is set to: The acquisition of fiber vibration signals was completed by the OFPS developed by the authors’ laboratory, and the schematic diagram of the OFPS is shown in The fiber vibration signals used in this experiment was collected from the experimental site in the suburbs of Beijing. These signals include pickaxe, electric drill, and shovel. When we perform pretreating process on these signals, we first remove the direct current (DC) part of the acquired signals. Three types of vibration samples after removing DC part are shown in Meanwhile, the dashed boxes in Then we randomly divide these three kinds of signals into a training set and a testing set in a 4:1 ratio. The training set is a set of samples used for learning, which is to fit the parameters of the classifier. The testing set is used only to assess the performance of a fully specified classifier. The data is shown in In our experiments, we assume that the vibration signals used are relatively pure and simulate the noisy signal recognition task by superimposing different artificial noises. It can be seen from After preparing fiber vibration signals and the signal sets superimposed by four different noises, we use these noisy signal sets to train SCNs which combine with different ensemble learning methods. The recognition effects of different methods on the noisy signal sets is obtained and compared. In order to eliminate the influence of the random generation of hidden layer nodes on the experimental results, we repeat each experiment and average the experimental results. Here, we set network to stop training when the hidden layer nodes of network exceed 100. Meanwhile, the maximum number of hidden layer candidate nodes is set to 100 (one of these candidate nodes is selected when adding a hidden layer node). During the training process, each time a hidden layer node is added, the accuracy of training and testing is recorded.Here, we have four classifiers including original SCN, Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN and different multiples of white Gaussian noise, white uniform noise, Rayleigh distributed noise and exponentially distributed noise. First, we train four different types of classifiers with the signal sets superimposed by different types and intensities of noises. In order to eliminate the influence of randomness on experimental results, each sample set is performed 10 times of training and testing on each classifier. Then, the experimental results are averaged to obtain the actual classification effect of classifier in current sample set. Afterwards, we find the position with the highest testing accuracy in the case of each intensity of noises, and use the highest testing accuracy to form the testing accuracy curve. In addition, we use this training method to obtain the results for other signal sets superimposed by different types and intensities of noises. According to the calculation method mentioned above, we first use original SCN to recognize the vibration signals superimposed by four kinds of noises. The results are shown in From In this section, we combine bootstrap sampling with SCN to recognize noisy vibration signals. The comparison of the recognition accuracy between original SCN and Bootstrap-SCN is shown in The above results show that Bootstrap-SCN and original SCN have the same recognition effect when there is no superimposed noise. In the case of superimposing four kinds of noises, the recognition rate of Bootstrap-SCN is slightly higher than that of original SCN, but the effect is not obvious.In this part, we use another ensemble learning method to improve the recognition rate of SCN. We combine AdaBoost with SCN to recognize four kinds of noisy signals. The recognition results of noisy signals by original SCN and AdaBoost-SCN are shown in After the above experiments of two combination methods, we carried out experimental verification on the proposed AdaBoost-Bootstrap-SCN. The recognition rates of noisy signals by original SCN and AdaBoost-Bootstrap-SCN are shown in Because the structure of RVFL network is similar to SCN, we use RVFL network for comparison. RVFL network have three layers. Its input layer has 128 nodes, which is the same as the dimension of vibration samples. The number of output layer nodes is the same as the dimension of sample’s label. As the number of hidden layer nodes is a hyper-parameter, which need be set before network training, we need to record the experimental results of RVFL network with different numbers of hidden nodes. In order to eliminate the influence of the randomness in network training, we repeat each experiment 10 times and average experimental results. The condition of stopping training is set as: when testing error is less than 0.01. The experimental results are as follows.The recognition results of four kinds of noisy signals with four classifiers are shown in It can be seen from the above discussion that: (1) after combining bootstrap sampling method with original SCN, the robustness of SCN can be improved slightly; (2) by combining AdaBoost method with SCN, the recognition rate of SCN can be improved over the entire noise range; (3) when bootstrap sampling method and AdaBoost method are combined with SCN simultaneously, AdaBoost-Bootstrap-SCN can improve the robustness of classifier on the basis of AdaBoost-SCN and achieve better recognition results.In the case of small sample sets, this paper proposes three improved methods  to improve the recognition rate of noisy optical fiber vibration signals. These methods enhance the robustness of SCN model by combining two ensemble method (Bootstrap and AdaBoost) when original SCN adds hidden layer nodes. These three improved methods have been trained and tested with noiseless and noisy optical fiber vibration signals. Compared with the existing research results, the prediction results of these methods are improved. In the noiseless vibration signal recognition task, the recognition accuracy is improved from 0.8612 of original SCN to 0.9955 of AdaBoost-Bootstrap-SCN. In the noisy vibration signal recognition task, the recognition accuracy is improved from 0.5582 (average) of original SCN to 0.7735 (average) of AdaBoost-Bootstrap-SCN. By combining SCN with ensemble learning methods, it can not only effectively reduce the impact of different noises on vibration signals, but also enhance the robustness of original SCN, which allows classifier to adapt to more complex and varied scenarios. Future efforts are aimed towards reducing the training time of AdaBoost-Bootstrap-SCN and improving the recognition effect of noisy optical fiber vibration signals.


Conventional eddy current testing (ECT) using a pickup coil probe is widely employed for the detection of structural cracks. However, the inspection of conventional ECT for steel structures is difficult because of the magnetic noise caused by the nonuniform permeability of steel. To combat this challenge, we have developed a small magnetic sensor probe with a dual-channel tunneling magnetoresistance sensor that is capable of reducing magnetic noise. Applying this probe to a complicated component of steel structures—such as the welds joining a U-shaped rib and deck plate together—requires the reduction of signal fluctuation caused by the distance (liftoff) variations between the sensor probe and the subject. In this study, the fundamental crack signal and the liftoff signal were investigated with the dual-channel sensor. The results showed that the liftoff signals could be reduced and differentiated from the crack signals by the differential parameters of the dual-channel sensor. In addition, we proposed an extraction technique for the crack signal using the Lissajous curve of the differential parameters. The extraction technique could be applied to the inspections not only for flat plates but also for welded angles to detect cracks without the influence of the liftoff signal. Eddy current testing (ECT) using a pickup coil probe is a widely used technique employed to detect cracks in various conductive structures ,2,3. HowRecently, nondestructive testing methods using magnetic sensors instead of a pickup coil have been reported ,5,6. TheHowever, few methods are suitable for inspection of steel complicated structures because the size and shape of typical magnetic probes do not fit the complicated components of structures. For example, the welds joining U-shaped ribs and deck plates together are difficult to inspect where fatigue cracks often appear by the vibration of vehicles. To inspect these structures, a small sensor probe of a gradiometer using dual channel magnetic sensors and an analysis method based on differential parameters are required. For this reason, we have developed a small ECT sensor probe using two tunneling magnetoresistance (TMR) sensors. In addition, the fundamental characteristics of magnetic response and data analysis for crack detection in flat steel plates have been reported . HoweverIn order to show the measured signal, conventional ECT typically uses a Lissajous curve that displays the real and imaginary parts, which are produced from the change of the crack-induced signal from a single sensor output . The LisIn this paper, the distributions of eddy current on the flat plate and magnetic field inside the induction coil were simulated to confirm that the crack signal can be detected with liftoff which will occur in the probing of the complicated structure. Then, the fundamental characteristics of the crack signal and the influence of liftoff variations were analyzed with a vertical and tilted sensor probe. As a result, an extraction technique of the crack signal using the Lissajous curve of the differential parameters obtained from a dual-channel magnetic sensor probe was proposed. In addition, the frequency dependence of the magnetic signal was also investigated to discuss the optimum frequency with which to extract the crack signal. Using the developed extraction method of the Lissajous curve, the complicated structure was measured.2. The system consisted of a voltage source, a function generator, a sensor probe with two TMRs, lock-in amplifiers, a personal computer, and an XYZ stage. The TMR sensors were driven by a voltage source applying 5 V, and the induction coil was operated by a function generator applying a sine wave voltage of 2 Vp-p at frequencies between 200 Hz and 5 kHz. The sensor probe was line-scanned vertical to the sample surface for the 7 mm deep slit at 0.1 mm intervals, with different liftoff between 0.1 and 0.9 mm to evaluate the liftoff dependence. It is expected that the real structures are covered by a thin paint which is uneven and cracked. In addition, for the complicated part of the structure, manual probing is required. For these reasons, we assumed the liftoff change to be between 0.1 and 0.9 mm under this condition. The sample was also line-scanned with the sensor probe tilted by 5 degrees from the vertical line. The lock-in amplifiers obtained the TMR sensor output signals as magnetic vectors with real and imaginary components, and the signals were analyzed using a computer.A flat steel plate with artificial cracks and a U-shaped rib with artificial cracks in its welded part were prepared to evaluate the liftoff dependence, respectively. The test sample of the flat plate was a 7 mm thick SS400 with slit-like artificial cracks of different depths ranging from 0.5 to 7.0 mm (through hole). p-p at frequencies between 1 kHz and 5 kHz was applied to the sensor probe, which was then manually moved along the scanning path for each crack and non-crack area.p-p at a frequency of 1 kHz was applied to the induction coil with different liftoff between 0.0 and 1.0 mm.To evaluate the fundamental magnetic response and to estimate the intensity of the crack signal with different liftoff, the distributions of eddy current on the surface of a flat steel plate (SS400) and the magnetic field in an induction coil were simulated using the commercial software package JMAG (JSOL Corporation). The induction coil with the air area inside the coil was positioned at the center of the flat steel plate with a trough-hole slit of 7 mm depth, as shown in The eddy current was assumed to be induced along and under the induction coil. However, the eddy current flow was interrupted by the slit. Thus, the simulated eddy current was divided into two parts and the two parts of the eddy current flowed along the opposite of the slit edge . The eddn (mm)) was able to be obtained by lock-in amplifiers using the outputs from two independent TMR sensors. The slit was located at the position of 10 mm. The magnetic vector The differential vector escribed c,d. The Although the influence of the liftoff variation may be reduced by the differential parameters, there is a case for not ignoring the influence of liftoff. When the sensor probe is not vertical to the surface, the baseline shifts of the two sensors are considered to be not the same because the liftoff of each sensor is different and the differential parameters are affected by the liftoff variations. In order to calculate the direction of the liftoff signal, As well as In this subsection we investigated a signal extraction method to emphasize the crack-induced signal change that is not influenced by the liftoff variations caused by the probe tilt. We observed that the differential parameters of the two sensors can reduce the magnetic noise and influence of the liftoff. In addition, even when the differential parameters are affected by the liftoff, the Lissajous curves of the crack and non-crack signal are assumed to show differences. The peak-to-peak values of the differential parameters at frequencies between 200 Hz and 5 kHz with the vertical and tilted sensor probe are shown in However, also of consideration is that the gradient of the liftoff signal also increases as the frequency increases, which makes it difficult to extract the crack signal. To confirm these effects, the subtracted gradient In this work, an extraction method for the detection of cracks in complicated structures was developed. It was shown by simulation and measurement that crack signals could be obtained with a small sensor probe using a dual-channel TMR sensor and that the differential parameters reduced the influence of the liftoff signal. The Lissajous curves of the differential parameters clearly differentiated between the crack signal and liftoff signal, which occur when probing the complicated structure. Hence, the extraction technique can be applied to inspections not only for welds but also for other complicated steel structures to detect cracks reliably.