In this paper, we introduce an unsupervised method for the parcellation of the Corpus Callosum (CC) from MRI images. Since there are no visible landmarks within the structure that explicit its parcels, non-geometric CC parcellation is a challenging task especially that almost of proposed methods are geometric or data-based. In fact, in order to subdivide the CC from brain sagittal MRI scans, we adopt the probabilistic neural network as a clustering technique. Then, we use a cluster validity measure based on the maximum entropy (Vmep) to obtain the optimal number of classes. After that, we obtain the isolated CC that we parcel automatically using SLIC (Simple Linear Iterative Clustering) as superpixel segmentation technique. The obtained results on two challenging public datasets prove the performance of the proposed method against geometric methods from the state of the art. Indeed, as best as we know, it is the first work that investigates the validation of a CC parcellation method on ground-truth datasets using many objective metrics. Thanks to advances in magnetic resonance imaging, neuroscientists and clinicians can study in depth the Corpus Callosum (CC) and mainly the correlation between the CC’s dimensions and some neurological diseases. The CC, which is the largest white matter structure and the biggest fiber tract connecting corresponding regions of the cerebral cortex in the two cerebral hemispheres, integrates motor, sensory, and cognitive functions of the brain . AnatomiAs best as we know, we adopt for the first time the superpixel segmentation algorithm called Simple Linear Iterative Clustering (SLIC) for the CC parcellation . DespiteThe subdivision process of the proposed method is fully automatic and it is the second study that proposed a non-geometric analysis for the CC parcels, to the best of our knowledge . AlthougGenerally, the CC parcellation into callosal regions allows for a precise differentiation of motor connectivity and the structural integrity of these tracts in the CC . Thus, tThe rest of this paper is organized as follows. In Sect. Few CC parcellation methods were proposed. However, most of these methods have not surmounted all the challenges encountered. In fact, the CC parcellation is a challenging task given that a normal shape of the CC might not clearly highlight all parcels, what can increase the diagnosis complexity. In addition, many internal abnormalities might include bumps which are hard to detect. Existing CC parcellation methods can be divided into two main classes: geometric methods and non-geometric ones. On the one hand, since there are no real or visible boundaries allowing the CC parcellation, several geometrical methods were presented to perform this task. Among these methods, two particular ones are widely adopted. The first was proposed by Witelson and it is based on postmortem connectivity analysis in primates and humans . This meDifferently to existing methods, we propose a subdivision scheme that considers only the MRI data . Using tWe adopt herein our previous method  for the N and superpixels’ number K that is independent and user-defined. In our case, N and K are equal to 256 and 200, respectively. Secondly, a combination of color and spatial proximity is reached by a weighted distance measure that allows both controls over the size and compactness of the superpixels. Thus, each slice of the input MRI image is partitioned into different size regions. In fact, the initial grid size is defined as S . All images were acquired on the same scanner using the same sequences. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. Each MR image within this dataset is composed of 128 slices with a resolution of For the evaluation of the proposed parcellation method, we are the only study that used brain MRI scans from two public datasets. On the one hand, we used the Open Access Series of Imaging Studies (OASIS) dataset, which is freely available on For each subject, the proposed parcellation method gives an apparent variation in the positioning of the CC parcels. This is because this method is purely automatic and does not follow any atlas or any prior knowledge Fig. . The geoIn order to evaluate the performance of the proposed method, we used the following commonly used metrics: Dice, accuracy, sensitivity, specificity and precision.The Dice coefficient  is a staThe accuracy  is definThe sensitivity  is the pThe specificity  is the rThe precision  is the rmentclass2pt{minimCC is the biggest fiber tract within the human brain that allows the communication between the two cerebral hemispheres. The CC form and sub-regions might cause some diseases. The CC parcellation from MRI images can predict future cases of diseases or progress neurological patterns in the development of different diseases. This paper presented a fully automatic non-geometric CC parcellation based on the SLIC superpixel algorithm, with no parameter adjusting and instantaneous training. Since there is no gold standard used to evaluate the existing methods, we produced for the first time a ground-truth led to evaluate quantitatively CC parcellation methods. Extensive experiments and quantitative comparisons with relevant CC parcellation methods, proved the accuracy of the proposed method on two challenging standard datasets. Indeed, the proposed method achieves higher performance values for each parcel. As future work, we aim to propose a super voxel method based on the SLIC algorithm, from not only MRI scans but also from functional magnetic resonance imaging.


Directed energy deposition (DED) provides a promising additive manufacturing method to fabricate and repair large metallic parts. However, it may suffer from excessive heat accumulation due to a high build rate, particularly during a wire feeding-type DED process. The implementation of interpass time in between two depositions of beads plays an important process role to passively control the interpass temperature. In this study, a method to estimate the proper interpass time using regression analysis from heat transfer finite element analysis is proposed for maintaining the interpass temperature during a wire feeding-type DED deposition of a planar layer. The overlapping beads of a planar layer are estimated using a polygonal-shaped bead profile in the finite element model. From the estimated proper interpass time, a selected proper interpass time scheme (PITS) is suggested for practical implementation. The selected PITS is applied in a thermo-mechanical finite element model to evaluate the temperature distribution and its effects on the depth of the melt pool, the depth of the heat-affected zone (HAZ), displacement, and residual stresses. By comparing the predicted results with those using a constant interpass time scheme (CITS), the selected PITS shows better control in reducing the depths of the melt pool and HAZ without severely inducing large displacement and residual stresses. Many industrial products use metals to form components or structures to meet their functional and aesthetic requirements. In the era of the Industrial Revolution 4.0, metal additive manufacturing plays an important role to complement the conventional manufacturing techniques. Directed energy deposition (DED) is a metal additive manufacturing technology that uses a high-intensity heat source to melt and fuse metallic feedstock to substrate as they are being fed. It has shown promising applications for the production of large metallic components and the repair of the damaged region of an existing part ,2,3. In Nevertheless, industrial production using DED is considered premature. The identification of process parameters is crucial to fabricate a product with required quality . This chInvestigation of the influencing factors using numerical analyses has recently become prominent to significantly reduce the number of experimental trial-and-error run in the search for the optimum process parameters of a DED process ,11,12,13Interpass time is one of the important process strategies in the DED process to passively control the interpass temperature. Interpass time is defined as the time period between the end of the deposition of a bead and the start of the next bead . InterpaThe build rate is relatively higher for a wire feeding-type DED process than a powder feeding-type DED process. Hence, control of the temperature, particularly for a wire feeding-type DED process, is important to prevent excessive heat accumulation within the specimen ,31. HeatIn this paper, three-dimensional finite element (FE) models are developed for multi-bead planar layer deposition using a wire feeding-type DED process. A simple and practical methodology based on regression analysis is proposed to investigate the proper interpass time for maintaining interpass temperature during the DED process. From the estimated proper interpass time, a selected proper interpass time scheme (PITS) is suggested for practical implementation. The estimated depths of melt pool and heat-affected zones (HAZs) induced using a constant interpass time scheme (CITS) and selected PITS are compared and discussed. Finally, uncoupled thermo-mechanical FEAs are performed to further evaluate the influence of CITS and selected PITS on the displacement and thermal stress induced within the specimen.The wire feeding-type DED process applied in this paper is based on the system developed by Kim et al. . The DEDIn order to evaluate the distributions of temperatures, vertical displacements, and residual stresses, as well as the estimated depth of HAZs, a thermo-mechanical finite element (FE) model is required. The planar layer deposition is made using an alternative directional deposition pattern, such that the subsequent bead is deposited from the side where the previous bead deposition ends. The time between the end of a deposition and the start of the subsequent deposition is known as the interpass time . InterpaThe main intent of constructing a planar layer deposition in a DED process is to obtain a layer with uniform thickness to build upon. Therefore, a flat layer is desired as a result of the deposition process. However, the shape of the bead for a wire-feeding type DED process should be properly modeled due to its size being significantly larger as compared to those of a powder-feeding type DED process. Several overlapping models have been proposed by researchers ,41,42,43bg is predicted by Equation (1) to form a flat top planar layer profile in pA is the cross-sectional area of a polygonal-shaped bead in unit mm2, and bh is the height of the bead in unit mm.First, the cross-section profile of a single deposited bead is obtained from the experiment. The cross-section profile for a laser power of 1.5 kW and travel speed of 8 mm/s has been obtained and generated, as shown in . The meawg is determined by Equation (2):bw is the width of the bead in unit mm.The center distance-to-width ratio In this study, the optimum distance between beads and distance-to-width ratio are estimated as in Q with conical penetration depth pd [r is the radial distance from the center of the beam, Z is the z-coordinate relative to the frame of the beam, η is the efficiency, er is the effective beam radius at the surface of the bead, ir is the effective beam radius at distance pd from the surface of the bead, ez is the z-coordinate at the surface of the bead, iz is the z-coordinate at distance pd from the surface of the bead, and or is the effective radius at arbitrary z-coordinate.The laser beam heat is represented by a volumetric top-hat distribution of heat flux depth dp  shown inFor the DED system used in this paper, the heat flux model has been calibrated according to the dimension of the heat-affected zone (HAZ) in . The calIn order to simulate a DED process that involves high temperature, the selected material Ti–6Al–4V for both substrate and beads are assumed to be temperature-dependent material properties in the numerical analysis for better accuracy . Figure 2·K during the deposition process. Natural convection is assumed for the side and lower surfaces of the substrate for entire deposition and cooling process. Coefficients of natural convection as a function of temperature at different substrate surfaces are calculated by estimating the average Nusselt numbers LNu from recommended correlations in Equations (5)–(7) [LRa and Pr are the Rayleigh number and the Prandtl number, respectively. A temperature-dependent emissivity of solid Ti–6Al–4V and Stefan–Boltzmann constant of 5.67 × 10−8 W/(m2·K4) is adopted to model the thermal heat loss via radiation [The ambient temperature is assigned as 20 ℃. The initial temperature for substrate and beads is set equal to the ambient temperature. An inert gas such as argon is applied directly to the part during the DED process to shield the process zone from oxidation. Based on the empirical correlation model for an impinging jet from a single round nozzle , the coe (5)–(7) :(5)NuL=0adiation .A simple and practical methodology based on regression analysis is proposed to investigate the proper interpass time from three results of heat transfer FEAs for maintaining desirable interpass temperature during the planar layer deposition of the DED process. First, three FEAs using interpass times of 1 s, 2 s, and 3 s, respectively, are performed on the FE model described earlier, as shown in ipT is recorded at a measurement node for each bead on the substrate, after the end of each interpass time. The location of the measurement node for a bead lies at the top surface of the substrate and coincides with the center of the laser beam at the start of deposition of the respective bead, as shown in From the temperature histories of FEAs, interpass temperatures can be evaluated. Interpass temperature is defined as the temperature of the deposition area between deposited passes . In ordeFrom the temperature histories, temperature rises and falls before it rises again to the highest temperature for each bead measurement node except for bead-1. The first rise in temperature is due to heat being transferred during the deposition of the preceding bead to the nearby measurement node. The substrate cools down during the period of the interpass time, which results in falling temperature. The interpass temperature for each bead measurement node is taken at the lowest temperature during the first fall in temperature. After the period of the interpass time ends, the temperature rises to the highest because the deposition of a new bead occurs directly on top of the respective bead measurement node.ipT for each bead are correlated with interpass times ipt by logarithmic regression, as shown in Equation (8):A and B are constants of the regression equation. tT is evaluated in Equation (9). The target interpass temperature for Ti–6Al–4V is assumed to be 400 °C in this study, as recommended [By solving Equation (8), the proper interpass time that maintains a target interpass temperature ommended ,27,28,29Using the proposed methodology, no integration of the algorithm between two types of commercial software is required. Furthermore, it can be easily visualized and adjusted by the researcher. The estimated proper interpass time curve in The influence of interpass time on temperature distributions of planar layer depositions is investigated using results of transient heat transfer FEAs. For selected PITS, the interpass time shown in A heat transfer FEA is performed using the selected proper interpass time scheme. From the result of heat transfer FEA, estimated interpass temperatures at the measurement node of each bead are recorded, as shown in In comparison to those curves in From the results of heat transfer FEAs, distributions of peak temperature throughout the DED planar layer deposition at the sectional plane across the middle of beads and substrate are obtained, as illustrated in The depth of the melt pool with respect to each deposition of bead, as plotted in In the case of planar layer deposition using selected PITS, the depth of the melt pool grows from bead-1 and reaches the maximum at bead-6. The depth of the melt pool is in the steady-state region from the deposition of bead-9 and onwards, when the interpass temperature is close to the target interpass temperature. The variation of depths of melt pools at the steady-state region is small and negligible, which is in the range of below 17 µm. This observation is consistent with the steady-state region of interpass temperatures. The estimated depth of the melt pool for all beads deposited using selected PITS is greater than the height of beads of 1.6 mm. This observation ensures that these beads and substrate are properly fused during planar layer deposition using the selected PITS. Thus, the control of interpass temperatures using the selected PITS is useful to control the formation of the melt pool while ensuring sufficient fusion between the beads and substrate.The maximum depth of HAZ is defined as the maximum vertical distance from the substrate to the lowest point of the HAZ region. During planar layer deposition using CITS, it is observed that the estimated HAZ region continuously deepens with increasing bead depositions. All the maximum depths of the HAZ for planar layer depositions using different CITS are identified near bead-16. In contrast, the maximum depth of the HAZ for planar layer deposition using the selected PITS is found near bead-6 and converged to a steady-state depth of HAZ for the further deposition of beads. This observation coincides with the highest estimated interpass temperatures at bead-6, as shown in In order to investigate the evolution of thermo-mechanical characteristics induced within deposited beads of planar layer deposition using different interpass time schemes, vertical displacement and residual stresses are evaluated from results of thermo-mechanical FEAs. Thermo-mechanical characteristics between a constant interpass time scheme (CITS) of 1 s and selected proper interpass time scheme (PITS) are compared to assess the suitability of the proposed selected PITS for the fabrication of a Ti–6Al–4V planar layer.In order to further investigate the influence of interpass time schemes on the surface profile across beads of the planar layer at the end of deposition of bead-16, vertical displacements on the top of each bead are measured at the mid-section of the planar layer, as shown in The maximum stress is estimated at the substrate near the corner of the planar layer of bead-1. It is noted that the stresses near the maximum stresses in both cases of interpass time schemes have changed tremendously from below 450 MPa to over 950 MPa. These sharp changes in residual stresses are due to the fact that there are singularities problems in FEAs caused by the sharp 90° corners between the substrate and the planar layer of the FEA models. Thus, these maximum residual stresses estimated in FEAs should be excluded from discussion. Nevertheless, the location of the maximum first principal stress indicates a region where cracking may start to occur. Overall, the planar layers are undergoing tension less than 450 MPa.In order to compare the influence of interpass time schemes on the formation of residual stresses within beads of the planar layer at the end of deposition of bead-16, the first principal stresses at the intersection points between the center of the beads and the surface of the substrate are measured at the mid-section of the planar layer, as shown in In the case of the selected PITS, the residual stress slowly increases from 142 MPa at the measured location of bead-1 to 210 MPa at the measured location of bead-11. This steady increment is because larger thermal expansion occurs at higher temperature and results in tensile residual stress. These residual stresses are not influenced by any further phase change when the temperature of the measured location at the interface between the beads and substrate of the mid-section is lower than the martensite finish temperature of 500 °C  during tMaximum tensile residual stresses are usually found at the interface between the beads and substrate, although the residual stresses vary at different locations of a multilayer three-dimensional printed part . TherefoFrom these viewpoints, the selected proper interpass time scheme should be applied because it provides better control of growth in the depth of the melt pool and HAZ without inducing excessive displacement and residual stress within a planar layer deposited using a wire feeding-type DED process.In this paper, a simple and practical methodology based on regression analysis was proposed to estimate the proper interpass time from several heat transfer finite element analyses to maintain the interpass temperature during a DED planar layer deposition. Control of the interpass temperature is crucial to prevent problems due to excessive heat accumulation. A three-dimensional finite element (FE) model was developed for multi-bead planar layer deposition by a wire feeding-type DED process. A polygonal-shaped cross-section profile of a single bead and overlapping model were implemented for the construction of the multi-bead planar layer in the FE model. From the estimated proper interpass time, criteria for practical implementation were proposed to select a proper interpass time scheme.As the result of implementing the proposed methodology, the interpass time was progressively increased with the number of beads. The interpass temperatures were successfully maintained within the range of −20 and 29 °C from the target interpass temperature of 400 °C. The proposed PITS reduces the overall process time when compared to a CITS of 9 s, which will converge slowly with large temperature variation to the same target interpass temperature. Thermo-mechanical analyses revealed that the depths of HAZ had peaked at 2.43 mm after the deposition of bead-6. The depths of the HAZs were kept within a steady-state region with no cracks being predicted at the interface between the beads and substrate. Hence, the proposed PITS based on regression analysis is effective for the mitigation of growing HAZ without severely inducing large displacement and residual stresses when a wide planar layer deposition is required.This methodology can be extended for a multi-layer deposition of the DED process. In order to create a multilayer three-dimensional part, the proposed selected PITS can be applied for the subsequent layer of deposition. The period of interpass time required to maintain a constant temperature is expected to increase when the number of layers is increased due to the heat accumulation in the preceding layer and reduced heat loss via conduction to substrate. However, the proposed PITS may not be suitable for a large or complex shape object, in which the thermal history of previous deposition has a minimal influence on the next deposition of a bead or a layer.


Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR , compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets. In recent years, convolutional neural networks have shown excellent performance in visual tasks. Starting from AlexNet , many suFor the past few years, the SPD (symmetric positive definite) matrix has drawn considerable attention because of its powerful representation ability. Based on non-Euclidean Riemannian geometric properties, the SPD matrix is more suitable for capturing the desired data distribution properties. The SPD matrix manifold is widely used in medical imaging , sonar sTransforming convolution features into SPD matrices is a problem of converting first-order data into second-order statistic information. Gaussian distribution and covariance matrix are widely used SPD matrix representations that transform first-order features into second-order statistical information ,21. HoweBased on the above empirical observations, we introduce a method to transform spectral convolution features into SPD matrices through an RBF kernel function, and then detect the target signal using a deep SPD matrix learning network. Two modules are included in this method. The first module uses a nonlinear function to transform convolution features of the spectrum into SPD matrices. All generated SPD matrices become processing objects for the second module. The second module utilizes SPDnet . It is aThe rest is in the following order: In The convolution features are derived from the convolutional neural network. It originates from the neocognitron proposed in 1980 , and theAlthough models of the convolutional neural network vary widely, they all follow some similar principles. When an image is fed to the convolutional neural network, it will be processed by a series of convolutional layers and pooling layers. The image is continuously reduced in dimension and finally vectorized into the full connection layers to get the decision result.An image is represented by a matrix of integers, and each pixel corresponds to each element value in the matrix. A monochromatic image is a two-dimensional matrix, while an RGB image is a three-dimensional matrix. In mathematics, convolution is an operation on two functions that produces a third function expressing how the shape of one is modified by the other. The convolution operation in the convolutional neural network adds each element of the image (or feature map) to its local neighbors, weighted by the convolution filter, to produce a new feature map. The computational relationship between the convolution filter and the image in The weighted sum between the convolution filter and the image enhances the effects of some local features of the image and suppresses the rest. The convolution features are enhancements of some features of the original image. Different convolution filters can extract different features, and these filters are learnable. Typically, the output feature maps are enhanced with specific features. The feature map will be used as the input of the next convolutional filter, to generate a more abstract feature map with a lower dimension.An image is represented as a three-dimensional matrix, with dimensions of height, width, and depth (color channels). So, the convolution filter has a depth dimension, and it covers the entire depth of its input. It is also important to note that multiple convolutions on input are performed, each using a different filter and resulting in a distinct feature map. The final output of the convolution layer is the superposition of all feature maps. The pooling layer also plays a role in the generation of convolution features. This section provides a brief introduction to the most commonly used pooling: max pooling.As shown in Although the covariance matrix is used to model the features and has acquired gratifying results, two issues still remain ineluctable. One is that the covariance matrix may be singular. This situation usually occurs when the dimension of local features is larger than the number of local features extracted from the image area. The other one is that the covariance matrix cannot evaluate the nonlinear correlation of features. It is not conducive to the modeling of convolution features. To settle the above two issues, we exploit the nonlinear kernel function to construct the SPD matrix.We set Elements in the SPD matrix In this paper, we exploit the RBF kernel function to construct SPD matrices. It is expressed as:The RBF kernel function can guarantee the positive definiteness of the matrix. The proof is as follows:To simplify the proof, we rewrite Formula (2) as:The Fourier transform convention of the Formula (3) is:Then, we prove that the quadratic form of the kernel matrix In Formula (5), The above is the whole process of the positive definiteness; proof of the RBF kernel function. The use of the RBF kernel function to construct the SPD matrix is essentially a correlation calculation between feature maps. Formula (2) reveals the nonlinear relationship between convolution features. It can be calculated faster by means of matrix operations through proper modification.Convolution features Three terms to the right of Formula y can be computed by matrix multiplication:We name In this paper, we use VGG19 and GoogLeNet-V3 to extract convolution features. For VGG19, we use feature maps for its pool1, pool2, and pool3 output. These three layers are located in layer 5, layer 10, and layer 19 of VGG19, and their depths are 64, 128, and 256, respectively. This also means that SPD matrices they build are 64 × 64, 128 × 128, 256 × 256, respectively. For GoogLeNet-V3, we use feature maps output by its Maxpool_5a_3 × 3 layer, which is located in layer 6 of GoogLeNet-V3 and has a depth of 192.SPDnet is characterized by embedding bilinear mapping (BiMap) and eigenvalue rectification (ReEig) into the algorithm, in the form of layers of the deep learning network. Their effect on SPD matrices is equivalent to the convolution layer and nonlinear computing layer of the convolution neural network, both of which aim to make objects to be processed more compact and discriminative.The computation expression for a BiMap layer to perform bilinear mapping isThe computation expression for a ReEig layer to perform eigenvalue rectification isExploiting singular value decomposition instead of eigenvalue decomposition to analyze the SPD matrix is an efficient approximation method. The computational speed of singular value decomposition is faster than that of eigenvalue decomposition. Similar to the eigenvalues, singular values are arranged from large to small in the diagonal matrix generated by the decomposition. In most cases, the sum of the first 10% singular values occupies around 99% of the sum of all singular values. Based on the square matrix property of the SPD matrix, the approximate reconstruction by the ReEig layer is reasonable.The LogEig layer is used to perform Riemannian calculation on SPD matrices generated after multiple bilinear mappings and eigenvalue correction. The log-Euclidean Riemannian metric endows the SPD matrices Riemannian manifold with a Lie group structure, and the SPD matrix manifold is reduced to a flat space. The log-Euclidean metric is described in detail below.The space of S isS along with T.The log-Euclidean metric of the SPD matrix manifold corresponds to the Euclidean metric in the SPD matrix logarithmic domain. Thus, the scalar product between The logarithmic and exponential maps related to the metric can be expressed asFrom Formulas (11) and (12), the geodesic distance obtained through log-Euclidean metric isFormula (13) is to the Euclidean distance in the logarithmic domain. Under the log-Euclidean metric, the distance between any two points on SPD matrix manifold is acquired through propagating by translation the scalar product in the tangent space at the identity matrix. The space of SPD matrices is reduced to a flat Riemannian space with the help of the log-Euclidean.The disadvantage of the log-Euclidean metric is that it is not an affine invariant metric, which leads to the fact that it cannot fully reflect the geodesic distance between two points on the Riemannian manifold. However, the form of the log-Euclidean metric has low computational complexity, and it is widely used in Riemannian manifold-based algorithms. For more details about the log-Euclidean metric, please refer to the literature .The computation expression for the LogEig layer isWe mainly exploit the following SPDnet model with 8 layers: In this part, we made a comparision with two signal detection methods on the basis of the deep neural network: deep network based on time-frequency spectrum for signal detection and deep SPD matrix learning network for signal detection based on spectrum convolution feature. The data originated from a simulated signal data set, and the simulated clutter based on K-distribution is used as interference. K-distribution is a widely accepted sea clutter simulation model. Compared with Rayleigh distribution, lognormal distribution and Weibull distribution, it considers the correlation between echo pulses, and better fits the amplitude distribution of sea clutter.The probability density of K-distribution follows the following formula:Target signal is generated by simulation. Its driving vector isThe definition of normalized Doppler frequency isThere are 11,000 samples with merely clutter and no target signal. The SCR of clutter interfered samples with the target signal is distributed from −5 dB to −20 dB at 1 dB intervals, with 4000 samples for each SCR. The time-frequency spectrum in the form of color map involved in this section is displayed in For the purpose of signal detection, SPDnet is exploited to process SPD matrices generated by convolution features, and GoogLeNet and VGG19 are exploited to process time-frequency spectra. Featuremaps of time-frequency spectra are generated and ourput by Tensorflow 1.8, and then they are processed by RBF kernel function to generate SPD matrices. We use transfer learning to adapt GoogLeNet and VGG19 to our signal detection problem, and retrain the SPDnet. The samples used for training all models are samples that only contain clutter and clutter interfered signal samples with SCR of −5 dB, −10 dB and −15 dB. Every training session, we use 1000 pure clutter samples and 1000 clutter disturbed signal samples, and the SCR of the latter is the same. The remaining samples are used to evaluate the detection performance of each model. Model training will have some preset parameters, which will not be updated with iteration, and we name them hyperparameters. The hyperparameters involved in this paper and their set values are as follows: the batch size, set as 20; the learning rate, set as 0.001; the weight decay, set as 0.0005; the epoch for SPDnet, set as 500; the epoch for GoogLeNet and VGG19, set as 800. All models have been fully trained. We trained SPDnet on an i7-8565U CPU, while GoogLeNet and VGG19 were trained on an Nvidia GeForce GTX 1080 Ti GPU. The detection probability of the above models is shown in The learning effect of deep neural networks is influenced by hyperparameters. We explore the impact of the learning rate, the output layer of convolution features and the dimension of the SPD matrix before entering the LogEig layer, on the performance of the spectral convolution feature-based SPD matrix learning method, with deep learning for signal detection. In order to simplify the work, we merely use the SPD matrix network based on pool2 convolution features of VGG19. The signal samples interfered by clutter used by the network models involved in The detection probability of the SPD matrix learning network trained by SPD matrices based on pool2 convolution features of VGG19 under different learning rates is shown in In this paper, “dimension” refers to the size of the SPD matrix before entering the LogEig layer. The SPD matrix will be more compact and discriminative through bilinear mapping and eigenvalue rectification. However, the reduction of matrix size is bound to cause some features to be lost theoretically. The results shown in From On the one hand, the deeper the layer is, the more abstract the convolution feature is extracted, which may lead to a loss of information for signal detection. The convolution features extracted at the shallow level retain more information, but in this case, there are fewer feature maps, and the generated SPD matrix dimension is smaller. On the other hand, although the convolution features extracted at a deeper level are more abstract, there are more feature maps and more comprehensive descriptions of samples, which can build the SPD matrix with a larger dimension.The false alarm probability of above models when changing learning rate, dimension and the output layer of convolution features is shown in In this section, we select and use part of the IPIX (Ice Multiparameter Imaging X-Band) lake radar echo data as the clutter exerting interference . Table 5The false alarm probability of different models is shown in The detection property of the SPD matrix learning networks for signal detection and the convolutional neural networks is shown in In this section, we analyze the complexity of the involved models from two aspects: training time and size of space occupied by models. The comparison of training time and size of space occupied is shown in We present a deep SPD matrix method for signal detection on the basis of spectral convolution features. Utilizing the SPD matrix obtained by transforming convolution features through the nonlinear kernel function and integrating with the deep SPD matrix learning network, a target signal under low SCR can be detected by the present method. The superiorities of this approach are that it combines the convolution features with the SPD matrix, converts original samples to the SPD matrix space, exploits its Riemannian manifold property to enhance the discriminability between different samples. Synchronously, the use of deep learning ameliorates the capacity of signal detection. A simulation date set with K-distribution clutter as interference and a semi-physical simulation data set with real sea clutter as interference are used by us to evaluate the performance of our method. The final results show that this method is effective, and it can obtain a gain of 0.5–2 dB on the simulation data set, and can obtain a gain of 0.5–1 dB on the semi-physical simulation data set. We explored the impacts of the hyperparameters associated with our method. It is inevitable that, due to the non-uniformity of interference in the real environment, our method may have poor detection ability. Our next goal is to enhance the anti-interference ability of our method and reduce the possibility of false alarms.


When we reach to pick up an object, our actions are effortlessly informed by the object’s spatial information, the position of our limbs, stored knowledge of the object’s material properties, and what we want to do with the object. A substantial body of evidence suggests that grasps are under the control of “automatic, unconscious” sensorimotor modules housed in the “dorsal stream” of the posterior parietal cortex. Visual online feedback has a strong effect on the hand’s in-flight grasp aperture. Previous work of ours exploited this effect to show that grasps are refractory to cued expectations for visual feedback. Nonetheless, when we reach out to pretend to grasp an object (pantomime grasp), our actions are performed with greater cognitive effort and they engage structures outside of the dorsal stream, including the ventral stream. Here we ask whether our previous finding would extend to cued expectations for haptic feedback. Our method involved a mirror apparatus that allowed participants to see a “virtual” target cylinder as a reflection in the mirror at the start of all trials. On “haptic feedback” trials, participants reached behind the mirror to grasp a size-matched cylinder, spatially coincident with the virtual one. On “no-haptic feedback” trials, participants reached behind the mirror and grasped into “thin air” because no cylinder was present. To manipulate haptic expectation, we organized the haptic conditions into blocked, alternating, and randomized schedules with and without verbal cues about the availability of haptic feedback. Replicating earlier work, we found the strongest haptic effects with the blocked schedules and the weakest effects in the randomized uncued schedule. Crucially, the haptic effects in the cued randomized schedule was intermediate. An analysis of the influence of the upcoming and immediately preceding haptic feedback condition in the cued and uncued random schedules showed that cuing the upcoming haptic condition shifted the haptic influence on grip aperture from the immediately preceding trial to the upcoming trial. These findings indicate that, unlike cues to the availability of visual feedback, participants take advantage of cues to the availability of haptic feedback, flexibly engaging pantomime, and natural modes of grasping to optimize the movement. Goal-directed grasping is multisensory and integrative in nature. The muscle extensions and contractions that are engaged when we reach out to pick a goal object are specified by motor cortex and rely on computations performed on real-time visual and somatosensory information, stored information about the object’s function, and the agent’s intention . StudiesSome of the most compelling evidence for this claim comes from individuals with deficits in visual shape perception  rather than on the details of the unfolding movement in real-time. Conversely, for grasps in which parameter specification is under direct cognitive control, cognitive supervision is focused on the unfolding movement in real-time, rather than on the overarching goal. Pantomime grasps and grasps made iteratively without haptic feedback exhibit signs of cognitive control: Relative to natural grasps, they take longer to initiate, the movement is slower, the hand’s in-flight aperture is typically smaller, and the aperture’s scaling to the size of the target is more variable, i.e., some participants use their hand aperture to grossly exaggerate the differences among target sizes, whereas others hardly bother to differentiate the target sizes at all  occurs with minimal awareness and minimal cognitive control, as is clearly the case for DF and in cases of action blindsight, in which the person cannot reliably perceive the geometry and spatial disposition of the goal object. Cognitive supervision under these more natural circumstances maintains focus on the overarching goal of the movement ; in a third set of trials the haptic conditions alternated predictably from one trial to the next ; and in a fourth and fifth set of trials, the haptic conditions were randomly intermixed with identical trial orders (randomized). Crucially, in one of these two randomized schedules, the availability of haptic feedback was made known to the participant in advance (random cued vs. random uncued). The two randomized schedules possess the same sequence of haptic conditions and are, therefore, controlled for trial-to-trial haptic-calibration based influences.We based our current experiment primarily on the design of without haptic feedback and more natural-like grasps on upcoming trials with haptic feedback. Thus, support for the role of cues and cognitive control should be reflected in a larger effect of removing haptic feedback in the randomized trial set with cues than in the one without cues. Second, we tested the unique influences of (1) the pending haptic condition on the current trial (t) on grip aperture and (2) the haptic condition on the immediately preceding trial (t − 1). If cues about the availability of haptic feedback can flexibly switch the response-mode between natural and pantomime grasps, as cognitive control (cognitive penetrability) would predict, then we should observe an influence of the pending, cued haptic condition on grip aperture and little or no influence of the immediately preceding haptic condition. Furthermore, in the absence of cues, we should observe a greater influence of the immediately preceding haptic condition on grip aperture and no influence of the pending and unknown (to the participant) haptic condition on grip aperture.This design affords two approaches to data analysis: First, we performed targeted tests for the influence of the cues on block-level performance. This analysis addresses the hypothesis that reliable expectations regarding haptic feedback can promote pantomime-like grasps on upcoming trials M = 22.4, SD = 7.99). Participants provided written and informed consent prior to participating in the study. Ethical procedures were approved by the local ethics committee. Participants were compensated $10 for their time, and they were naïve to the purpose of the study.Thirty right-handed participants with normal or corrected-to-normal vision were recruited at the University of Western Ontario . The lenses of the goggles can switch between the two states (translucent and transparent) in < 6 ms. The lenses of the goggles assumed a default translucent state that blocks the wearer’s view. The lenses remained translucent between trials but were cleared at the start of each trial to reveal the workspace, which included the virtual target cylinder. When participants released the microswitch at movement initiation, the lenses switched back to their default translucent state. Thus, participants did not receive visual input about the target or their hand throughout their reach; actions performed under these circumstances are referred to as being performed in visual “open loop.”The principal reason for using open loop stemmed from the fact that the mirror obstructs the view of the participant’s hand throughout most of the reach. Using an open loop procedure in this context means that the participant does not have the experience of seeing their hand disappear behind the mirror and, for the subset of movements in which their hand makes contact with an object, the experience of lifting an object behind the mirror while it remains visibly static in the mirror. In other words, the visual open loop condition was believed to minimize a reduction in immersion.The set of target objects were three pairs of wooden cylinders painted matte black. Each pair differed only in diameter . On any given trial, any one of the three different objects was positioned in front of the mirror, so that the participant would see its reflection in the mirror , while its partner  could be positioned behind the mirror such that this hidden cylinder was spatially coincident with the virtual cylinder. Thus, the cylinders behind the mirror were located mirror-opposite of the cylinders in front. The cylinders in front of the mirror could be located at any one of two possible locations and the distance between them was 10 cm. To ensure a consistent and accurate placement, the cylinders each had holes drilled into the bottom, so that each cylinder could be placed onto the peg of small square wooden plates, painted matte white, that were fixed to the table surface. This was done for the cylinder locations behind the mirror as well. The distance between the start position and the closest cylinder location was ∼17 cm. The start position was allowed to vary by small amounts to suit each participant’s preference for comfortability provided it did not block the participants ability to see their hand, because (1) target distance was not a variable of theoretical interest and (2) the manipulations of theoretical interest were within-subjects.TM 3020  with a positional measurement error of < ± 21 mm.The positions of three infrared emitting diodes (IREDs) affixed to the inner nails of the index finger and thumb, and the wrist of the right hand, was recorded with submillimeter precision at sampling rate of 200 Hz by an active optoelectronic motion capturing system, OPTOTRAKThe subject was seated comfortably at the table in front of the mirror and oriented so that (1) the occluding board blocked a direct view of the cylinder at each one of the two locations, but (2) did not block a binocular view of the virtual cylinder at each location. The experimenter explained the task, using demonstrations, after which the three IREDs were attached with medical tape to the participant’s right hand: one at the corner of the proximal base of the thumb nail, one at the corner of the proximal base of the index finger nail, and one at the proximal end of the index finger on the dorsal face. The medical tape was used to secure the IREDs and to ensure that the pads of the index finger and thumb were uncovered and would receive typical tactile feedback from touching objects. Note that the distance between the IREDs was non-zero and, therefore, the computed distance between the IREDs will include this additional amount.Participants started each trial with the tips of their index finger pinched together and depressing the microswitch. They were instructed to use visibility of the workspace as the imperative to locate the virtual cylinder and then reach out behind the mirror to grasp it. Furthermore, the participants were asked to reach out naturally, neither speeded nor labored. On trials in which a cylinder was positioned behind the mirror , participants were to grasp the cylinder, lift it, and move it to the right side of the table, and then return to the starting position (the microswitch). The participants were also asked to simulate grasping, lifting, and moving the virtual cylinder on trials in which no cylinder was positioned behind the mirror , before returning to the starting position. The participants were asked to simulate holding the object so that the fingers would not go through it e.g., .Trials were organized into separate sets that differed with respect to their schedule of haptic and no-haptic feedback conditions . On all trials, the experimenter positioned an object first in front of the mirror and then behind the mirror. If that trial called for no-haptic feedback, after positioning the object behind the mirror, the experimenter removed it as they withdrew their hand. This was done so that the same sequence of experimenter events and timing occurred for both the haptic and no-haptic feedback trials. Before the trial was initiated and depending on the trial set, the experimenter verbally cued the participant about whether an object was behind the mirror or not.Between each set, the participant was invited to remove the goggles and to rest for up to 5 min. During this time, the experimenter familiarized the participant with the nature of the haptic- and no-haptic trial expectancies for the next set of trials.blocked ; alternating haptic and no-haptic feedback; randomized haptic- and no-haptic feedback without reliable cues (randomized uncued); and randomized haptic- and no-haptic feedback with cues (randomized cued).Each participant was tested across five sets trial schedules. The five trial sets were dubbed In a given set of trials, each cylinder was presented three times at each of the two locations in pseudorandom order for a total of 18 trials for each of the haptic and no-haptic feedback conditions (if present). Target position was manipulated to reduce the contribution of memory to the responses and to keep the task more engaging for the participants. Thus, the blocked haptic and no-haptic trial sets each consisted of 18 trials, and the alternating and randomized haptic- and no-haptic feedback trial-sets each consisted of 36 trials (18 for the haptic feedback condition and 18 for the no-haptic feedback condition). In total, each participant was administered 144 trials. The order in which the five trial sets were administered was pseudo-randomized from one participant to the next and counterbalanced across all participants. Notably, the presence or absence of an object hidden behind the mirror was always discovered by the participant at the end of their reach, regardless of presence or absence of the verbal cue (or what the participant believed would be the case).Positional data was Butterworth lowpass filtered at 20 Hz after which the 3D derivatives corresponding to speed and acceleration were computed for each sample frame for each IRED. Grip aperture was computed at each sample frame as the 3D distance between the IREDs positioned on the index-finger and thumb and the first derivative of this variable was computed (grip aperture velocity). Our principal dependent measure was PGA, which was isolated on each trial by operationally defining the forward reach component of the response. The first sample frame in which the velocity of the forward reach exceeded 5 cm/s for 100 ms was defined as the point at which the forward reach was initiated, and the time from trial start to the initiation of the forward reach was operationally defined as the movement preparation time (MPT). The forward reach was operationally terminated on the first sample frame in which the velocity of the wrist IRED fell below 10 cm/s. The length, in ms, of this time window comprised the movement time (MT). PGA was the largest grip aperture observed throughout the forward reach. Peak hand velocity (PHV) was the fastest velocity achieved by the wrist IRED throughout the forward reach time. In order to define the final grip aperture (FGA) for grasps made with haptic feedback and the pantomimed ones made without haptic feedback, we used the grip aperture velocity and defined FGA as the first sample frame in which the grip aperture velocity remained within a range ±1 cm/s following the sample frame in which PGA was achieved. We performed linear regression analysis to derive the slope relating target size to PGA and to FGA. Finally, the time it took from movement start to achieve PGA (tPGA) was also computed. Each trial was visually inspected to ensure the algorithm selected sensible sample frames.haptic feedback (available or not available) and the schedule in which these two conditions were administered . An expanded ANOVA that included target size as an additional factor was of no theoretical interest for the dependent measures other than PGA and FGA. Furthermore, for those measures the slopes relating target size to PGA and to FGA, by definition, capture the linear influence of target size on PGA. Type I error rate  was defined as 0.05 for each rmANOVA effect. Violations of sphericity were addressed using Greenhouse-Geisser adjustments to the degrees of freedom. Note that, as mentioned in section “Apparatus and Experimental Setup,” target distance was of no theoretical interest, and so the means for target size, target distance, haptic feedback, and haptic trial schedule were weighted accordingly.For a given participant, the dependent measures from each trial were grouped by their corresponding unique conditions, which were the unique combinations of target size, target distance, haptic feedback, and haptic trial schedule. The means of these groupings served as the basic unit of analysis for each participant for repeated measures ANOVA (rmANOVA). The factors of interest for the rmANOVA were t-tests. Relative to pair-wise alternatives, orthogonal tests reduce the number of comparisons made, which reduces the correction for multiple comparisons and increase statistical power, and they analyze unique, rather than redundant, variance. For the interactions, the first orthogonal contrast involved the haptic trial schedules that were most similar: the alternating trials and the randomly interleaved ones with reliable haptic cues. These condition pairs were tested, and, if non-significant, were averaged together and contrasted against either the blocked or randomized haptic condition schedule without reliable haptic cues. A non-significant difference at this stage led us to average together the three tested conditions into one to test the remaining haptic trial schedule. The alpha error rate was defined per family of tests, and the Holms step-down variant of the Bonferonni correction was used to control it at 0.05  were analyzed using sets of orthogonal paired-samples  at 0.05 .t-test to contrast mean PGA on and the mean slopes from the two randomly interleaved variants for a targeted check on the influence of reliable haptic cues on PGA and the slopes; and (2) we applied multiple linear regression to predict the average PGA on a given trial as a function of the haptic feedback condition of that trial and of the immediately preceding trial. We included the size and distance of the target on the given trial as covariates. For each participant, this regression was run separately for each of the two randomly interleaved haptic schedules. Four means were computed using the regression coefficients relating the availability of haptic feedback on the current trial to PGA and the availability of haptic feedback on the immediately preceding trial to PGA for the two randomly interleaved schedules. If inducing reliable expectations about the availability of haptic feedback can flexibly switch the response mode between natural and pantomime variants, then we should observe an influence of the haptic condition on the current trial and little influence of the haptic condition on the previous trial. Furthermore, in the absence of reliable haptic expectations, we should observe a larger influence of haptic feedback on the previous trial and no influence of haptic feedback on the current trial.Given our interest in PGA and the influence that preceding haptic availability exerts on it, we (1) performed a single paired-samples The results of the rmANOVAs for each measure are presented in All measures showed a main effect of removing haptic feedback, which can be viewed in the leftmost panels of The pattern of results shown here for haptic feedback are, at first blush, strikingly similar to the results for visual feedback c.f., . For exaFor each measure, we found a null difference between the alternating haptic schedule and the randomly interleaved haptic schedule with reliable cues. The only difference between these two schedules was the opportunity, in the randomly interleaved schedule, to encounter small runs of trials with or without haptic feedback. Note too that in both of these haptic trial schedules, participants were given cues concerning haptic feedback.The left panels of bPGA).bPGA. The middle panels show that these haptic effects were strongest in the blocked schedule, intermediate in the cued mixed schedules, and, in the uncued randomized schedule, negligible for the PGA but, interestingly, reversed for the bPGA. For PGA, the haptic effect was greater in the blocked schedule than in the randomized uncued schedule , and significantly greater in the randomized cued schedule than in the randomized uncued schedule . For PGA, the right panel shows that the mixed schedules increase PGA in both haptic conditions, but more so in the no haptic feedback condition. Furthermore, the PGA was greatest in the uncued randomized schedule. For bPGA, the right panel shows that, relative to the blocked schedules, the mixed cued schedules reduced bPGA on no-haptic feedback trials but increased bPGA on haptic feedback trials. This pattern was the most extreme in the uncued randomized schedule in which haptic effect reversed.The leftmost panels of bFGA).bFGA. The middle panels show that, for the FGA, the haptic effect was equivalent across the schedules in which cues were available. The haptic effect on FGA was greatest in the uncued random schedule. As the rightmost FGA panel shows, this was primarily driven by the much smaller mean FGAs observed when haptic feedback was not available in this schedule. The middle panels also show that, for bFGA, the haptic effect was weakest in the cued schedules, and greatest in the uncued randomized schedule. The rightmost panel for the bFGA show that mean bFGA approached unitary in all of the haptic feedback conditions, regardless of schedule, which would be expected given that participants were most likely to be holding the target at that point. The haptic effect on bFGA was greatest in the uncued randomized schedule. This was driven by the no-haptic feedback condition in which participants exhibited a much smaller mean slope.The leftmost panels of As described in section “Materials and Methods,” we used multiple regression to predict, for each participant, PGA as a function of the current haptic feedback condition and the immediately preceding one for the cued and uncued versions of the randomized schedules. If cues to haptic feedback are used predictively, then the main haptic contribution to PGA should stem from reliable expectation for haptic feedback on the current trial, rather than on the availability of haptic feedback on the immediately preceding trial. In the absence of reliable cues, the biggest haptic contribution to PGA should stem from the availability of haptic feedback on the immediately preceding trial.This study examined the influences of both trial-to-trial calibration and of expectations about the availability of haptic feedback on reaching to grasp virtual objects. From the outset, we expected that the sensorimotor system responsible for natural goal-directed grasps would exploit the haptic experience on previous grasps to reduce error on subsequent grasps e.g., . We alsoWe implemented a task in which participants reached behind a mirror to pick up visible “virtual” objects, which were sometimes available for the hand to make contact with (haptic feedback), and at other times were not (no haptic feedback). In all haptic trial schedules except one, we provided verbal cues to the availability of haptic feedback. We reasoned that cognitively accessible operations should be capable of predictively exploiting cues about the availability of haptic feedback at the end of the reach, particularly in otherwise randomized haptic-feedback schedules. Our results demonstrate that cognitive supervisory processes can exploit verbal cues about the availability of haptic feedback to plan grip aperture for future events.We manipulated expectations about the availability of haptic feedback by varying the availability of verbal cues to the participant before the start of the trial. In the blocked trial schedule in which an object was always available for the hand to make contact with at the end of the reach, participants’ grasps were free to be influenced both by the recent history of haptic-experience  and the expectation that the hand would make contact with the object at the end of the reach. In the blocked trial schedule in which an object was not present behind the mirror and so the hand grasped “thin-air,” the expectation for no haptic feedback was available but no haptic information was available to calibrate subsequent grasps. The blocked schedules led to the greatest differences in haptic effect across all measures tested, in line with previous work drawing similar comparisons e.g., .In three additional haptic condition schedules, the object was available on some trials and not on others, permitting intermittent opportunities for haptic calibration to occur. One set consisted of a strict schedule of alternation between haptic feedback and no feedback, allowing participant expectations about feedback to potentially be based on the regular pattern and/or their awareness of the alternating schedule. The two remaining sets consisted of randomized haptic schedules. Crucially, a verbal cue about the availability of haptic feedback on the upcoming trial was provided in only one of them. If grasps are dominated by dorsal stream operations that are inaccessible to cognitive supervisory processes, then the grasps should not dissociate as a function of the reliability of haptic expectations (as indexed by the reliability of the cues). Alternatively, if an absence of haptic feedback induces a switch to a pantomime mode of responding, recruiting structures outside the dorsal stream that are accessible to cognitive supervisory processing, then reliable haptic expectations in the cued randomized schedule should dissociate grasps made with and without haptic feedback, such that the former should look more like natural grasps while the latter should look more like pantomimed ones. Our findings support the alternative proposal. The haptic effects in the mixed schedules were reduced, relative to the effects observed in the blocked schedules, in-line with the influence of intermittent haptic calibration. Crucially, however, for all measures except for MT, reliable haptic expectations led to larger haptic effects relative to when haptic expectations were unreliable, indicating that cognitive supervisory processing can flexibly intervene to assume limited control over the response.but not the upcoming haptic condition. Specifically, PGA was smaller if haptic feedback was experienced on the immediately preceding trial than if it was not. There are two explanations for this second result. One interpretation is that without reliable cues, participants attempt to predict whether or not their hand would encounter an object on the upcoming trial, anticipating the opposite of their experience on the previous trial. Notably, this strategy would necessarily fail to differentiate the response to haptic and no-haptic feedback conditions across the trial set, because the haptic conditions are randomized. Furthermore, this explanation does not seem plausible, because participants were aware of the random nature of the haptic condition. A competing interpretation uses these considerations to suggest that participants simply adopt a wider “fail safe” margin in their PGA, resulting in the largest PGAs in this schedule . Grip scaling has been shown to be exaggerated more for pantomime grasps than for natural ones e.g., . Our finOur findings are consistent with those from a previous investigation that tested memory-guided pantomime grasps directed at the remembered location of a previewed object with and without haptic feedback . In the Our findings are also consistent with previous work using a similar virtual setup to test the effects of visual-haptic mismatches in target size on grasping . In one It will be important for future studies to examine more closely the ability of the dorsal stream to use calibration and to form expectations when it is dissociated from the ventral stream. A recent study has shown that an individual, MC, with large bilateral lesions to occipital cortex cannot only point to visual targets but is also susceptible to the effects of lateral shifts in the visual field that can be introduced with prism goggles . Over thAs we reviewed in the introduction, patient DF’s ventral stream is compromised and yet, when instructed to direct her pantomimed grasps beside a nearby visible target, her grasp aperture covaried reliably with target size. How is the dorsal stream using tactile feedback in DF’s case to automatically scale her grip aperture to the size of the target? The present results point to a range of possibilities. At the simplest extreme, the dorsal stream may only be able to benefit from calibration based on the most recent experience of tactile feedback. At an intermediate level, it may be able to harness memory for simple scheduling patterns such as an alternating sequence. At the most sophisticated level, it may be able to take full advantage of learned associations between reliable cues and their predictive relations to haptic feedback. We speculate, based on the literature we have reviewed, and of the role played by expectations in the present study, that the dorsal stream is only capable of calibration. But this question deserves a closer look in patients without functioning ventral streams and/or in studies using transcranial magnetic stimulation to temporarily disrupt the ventral stream contribution to a grasping task.1.These findings also have important practical implications. One ready application is in virtual reality environments, where the aim is to enhance the user’s immersive experience by simulating manual interactions with virtual objects as realistically as possible. Our findings reinforce a broad observation that participants tend to adopt pantomime styled grasps in these settings that consist of stereotypically different kinematic characteristics than do natural grasps. When combined with the virtual environment, motion tracking equipment sensitive enough to resolve fine movements of the fingers that occur during reach to grasp actions may benefit from algorithms that interpret the resultant kinematic data in a way that matches the agent’s actual movements to virtual ones that more closely resemble natural grasps. Recent work has shown that although naïve individuals are poor at discriminating videos of pantomimed and natural grasps, magicians perform at above-chance levels, suggesting that expertise in sleight of hand and pantomime enhances discrimination of fine-grained differences between pantomimed and natural grasps . AlthougFuture work should examine the computational basis for inferring an agent’s intended goal when they perform pantomime reaching and grasping in more realistic virtual environments with cluttered spaces and multiple possible goal objects within reach. Existing research has shown that an individual’s intentions for a given grasp  are available in the visible kinematics . StudiesThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The studies involving human participants were reviewed and approved by the Behavioural Research Ethics Board Western University. The patients/participants provided their written informed consent to participate in this study.RW designed the experiment. NK administered the experiment. RW and NK analyzed the data. All authors contributed to writing 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.


Micromirrors based on micro-electro-mechanical systems (MEMS) technology are widely employed in different areas, such as optical switching and medical scan imaging. As the key component of MEMS LiDAR, electromagnetic MEMS torsional micromirrors have the advantages of small size, a simple structure, and low energy consumption. However, MEMS micromirrors face severe disturbances due to vehicular vibrations in realistic use situations. The paper deals with the precise motion control of MEMS micromirrors, considering external vibration. A dynamic model of MEMS micromirrors, considering the coupling between vibration and torsion, is proposed. The coefficients in the dynamic model were identified using the experimental method. A feedforward sliding mode control method (FSMC) is proposed in this paper. By establishing the dynamic coupling model of electromagnetic MEMS torsional micromirrors, the proposed FSMC is evaluated considering external vibrations, and compared with conventional proportion-integral-derivative (PID) controls in terms of robustness and accuracy. The simulation experiment results indicate that the FSMC controller has certain advantages over a PID controller. This paper revealed the coupling dynamic of MEMS micromirrors, which could be used for a dynamic analysis and a control algorithm design for MEMS micromirrors. With the rapid development of unmanned driving and intelligent assisted driving technology, the vehicle-mounted laser LiDAR, which serves as an important sensor for vehicle intelligent perception systems, is generating increasing interest. Vehicle-mounted LiDAR lasers have historically been split between three camps: the solid-state LiDAR, the mechanical rotating LiDAR, and MEMS-based torsional LiDAR. Solid-state LiDAR does not require rotating mechanical parts and has the advantage of being maintenance free and high resolution. However, its high price prevents solid-state LiDAR from being widely used. Tiny micromirrors are used to reflect the laser from the laser source in MEMS-based LiDAR; omni-directional scanning can be realized through the motion of the micromirror. MEMS-based LiDAR has numerous advantages, including small volume and low cost. As the main components of MEMS-based LiDAR, based on different actuation methods, MEMS micromirrors can be divided into four main types: electromagnetic , piezoelJi  designedThough the electromagnetic MEMS torsional micromirror can solve the problems of LiDAR miniaturization and cost, other problems still need to be solved urgently. As a vehicle-mounted instrument, the MEMS micromirror is seriously impacted by vibration originating from the vehicle body due to the rolling road surface, and the resonance of the micromirror can disturb the predefined motion trajectory of MEMS micromirror itself.Iskiman et al.  presenteTo further improve the performance of the electromagnetic MEMS torsional micromirror, many scholars have studied the control method of the MEMS micromirror, which is essential for a satisfactory performance. In 2000, Pannue  successfAfter investigating the literature, we found that the control research of electromagnetic MEMS micromirrors has been performed under the static state of the micromirror itself; however, due to the lack of research on dynamic modeling of the MEMS micromirror with external vibration, the performance of control methods under realistic disturbance conditions has not been evaluated.In this paper, we proposed a dynamic coupling model of the MEMS micromirror considering external vibration, and an experimental method is used to identify the coefficients in the dynamic coupling model. The structure of this paper is as follows. In The structure of the electromagnetic MEMS torsional micromirror is shown in When the electromagnetic MEMS torsional micromirror is deflected by electromagnetic force, the micromirror twists around the torsion beam but does not deform itself; the deformation mainly occurs in the torsion. The dynamic deflection model can be described by the second-order vibration system of the mass-spring-damper, as follows :(1)Jmθ··According to the working mode of the fast axis and the slow axis, the electromagnetic MEMS torsion micromirror can be divided into the resonant type and non-resonant type. In this manuscript, the fast axis of the electromagnetic MEMS torsional micromirror worked at the resonant frequency, and the slow axis worked at the free frequency . When thEquation (4) is the static model of the MEMS micromirror. To explore the dynamic coupling model of the MEMS micromirror considering external vibration, it is necessary to clarify whether the system model parameters will be perturbed under external vibration. Therefore, in the physical experiment, to explore the coupling response of the electromagnetic MEMS torsional micromirror and the possible change of the model parameters, external vibrations of different accelerations were loaded through an electric vibration table to simulate the vibration interference caused by an uneven road surface, which unmanned vehicles may encounter on actual road. In the research of this article, we only considered road interference with unmanned vehicles at low frequencies of 200 Hz and below.The experimental framework used in the experiment is shown in When the electromagnetic MEMS torsional micromirror is used with LiDAR, to ensure a high resolution the actual driving signal of the slow axis of the micromirror is generally a 20 Hz drive signal. Therefore, the driving signal of the micromirror in this experiment was a 20 Hz sine wave signal. The sampling frequency was set to 20 kHz, and the sampling times were 1 s. In the open-loop test, the driving signal of the input slow axis was a 20 Hz sine wave signal of 0.1 V.The acceleration of the electric vibration table was set to 0.1 g when it was used to sweep the frequency of the micromirror. For the frequency sweeping result to be closer to the real mode of the micromirror, the frequency of the electric vibration table during the frequency sweep was at a superimposed speed of 1 Hz. When the unmanned vehicle is driving on the actual road, only low-frequency vibration interference below 200 Hz is considered; therefore, the frequency was only swept to 200 Hz in the test. Since the rigid fixing structure for fixing the micromirror itself has resonance characteristics, in the actual frequency sweep and standing frequency experiments, to ensure the safety of the micromirror, the electric vibration table of acceleration was set to 0.8 g, 1.0 g, and 1.2 g, which can prevent the micromirror chip from being broken by the vibrations. When performing a standing frequency vibration experiment on the micromirror, an open-loop drive signal was applied to the micromirror at the same time through the host computer. The open-loop drive signal information is shown in 2. During the frequency sweep of the micromirror, the laser reflected by the micromirror can assist in viewing the resonance of the micromirror. When the laser light reflected by the micromirror is the longest, the corresponding frequency at this time is the resonance frequency of the micromirror chip.The value of the parameter g in the acceleration of the electric vibration table was 9.18 m/sBefore carrying out the physical experiment, the modal analysis of the micromirror was carried out by using COMSOL Multiphysics 5.4 software . A main vibration mode within 200 Hz was obtained, which is shown in The experimental results in Combined with the above spectrum analysis information, the influence of vibrations on the open-loop drive of the micromirror under different accelerations is shown in When the electromagnetic MEMS torsional micromirror works under the interference of external vibrations, the coupling vibration presents a strict linear superposition relationship. Therefore, when the external vibration is considered, the coupling dynamic model of the electromagnetic MEMS torsional micromirror can be expressed as a linear superposition of the two. This can be represented by Equation (6):Sliding mode variable structure control theory first appeared in the 1950s. As a special nonlinear control method, it has been widely used because of its strong robustness and insensitivity to external disturbances. It has been successfully applied to many control fields, especially the field of MEMS-based control; the sliding mode control (SMC) method has proven to be effective ,22,23,24In the sliding mode controller section, the sliding mode surface has the following form:By using the second method of Lyapunov, we differentiate When the Lyapunov condition To ensure In the function of It has been proven that the controlled system is stable. Although the conventional sliding mode controller can achieve a satisfactory control effect, to eliminate large external interferences, the conventional sliding mode control often requires a large switching gain. At the same time, it will inevitably increase the chattering of the system. In order to improve the control performance of the system, we added a feedforward compensation link to the designed sliding mode control. By adding feedforward compensation for dynamic interference, the gain of the switching item in the sliding mode controller and the chattering of the system are reduced. The feedforward compensation controller has the following form:As can be seen from For validating the FSMC controller and comparing the anti-interference performance of the PID and FSMC control methods, the simulation test was performed by using MATLAB/Simulink. In the simulation experiment, we used a step disturbance signal and uniform random disturbance signal as the interference signal source. There are two types of reference track signals: the step signal and triangular wave signal. The step signal is used to compare the anti-interference ability of the controller, as the signal of the MEMS LiDAR driving the electromagnetic MEMS torsional micromirror in practical applications—the 20 Hz triangular wave signal—was mainly used to test the influence of external interference on the controller’s tracking signal ability in the simulation experiment. The step signal value given in the simulation experiment was 0.2 V.(1)PID controller: (2)SMC controller: The Disturbance signal value was 30 V and started at 0.02 s, the uniform random disturbance signal value ranged from –30 V to 30 V, and the triangular wave signal output value was—0.1 V to 0.1 V. In this paper, the controller parameter tuning method used in the simulation experiment was the empirical method. For a fair comparison, we aimed to minimize overshoot and regulate time, and adjusted the PID controller and SMC controller parameters to make the dynamic process of the two control systems similar. The two controller parameters used in the simulation experiment are as follows:Firstly, comparative simulations were carried out for the step response of the PID and FSMC controllers in the presence of a step disturbance. In this paper, the dynamic system model of the electromagnetic MEMS torsional micromirror when there is external low-frequency vibration interference below 200 Hz is analyzed, and the coupling dynamic model of the electromagnetic MEMS torsional micromirror is established. An integrated sliding mode controller with feedforward compensation was designed, and we proposed a comparison method based on the electromagnetic MEMS torsional micromirror resonant point control. Through the experimental analysis of the performance comparison experiment of the control method and the modal analysis of the micromirror, some conclusions can be drawn. When the micromirror is disturbed by low-frequency vibrations below 200 Hz, the slow axis of the micromirror will be significantly twisted, and the coupling dynamic model of the electromagnetic MEMS torsional micromirror presents a strict linear superposition relationship at this time. The proposed FSMC control algorithm can effectively suppress the external vibrations, and has certain advantages over the PID control algorithm. The innovation of this research lies in the establishment of the coupling dynamic model of the electromagnetic MEMS micromirror under external low-frequency vibration interference below 200 Hz, and the corresponding anti-disturbance method we designed.


In this work, we perform  In other cases, understanding the effect of oxygen vacancies is pursued to improve their positive consequences, such as for applications in catalysis, or solid oxide fuel cells3.It is well known that oxygen vacancies in perovskite oxides have a great impact on the functional properties of these materials, modifying the interplay among lattice, charge, spin, and orbital degrees of freedom. In mixed-valence manganites, the coexistence of ferromagnetism and metallic conduction has been traditionally described via a combination of a strong electron-phonon interaction, and the double-exchange mechanism in which the Mn 3d electrons hop through the O 2p orbitals. An oxygen vacancy disrupts this mechanism, inducing structural, electronic and magnetic changes that could have dramatic consequences. A considerable number of studies, both experimental and theoretical works, have tried to identify these effects, mitigating the negative consequences that they could pose for applications, for example in spintronics4. It can be argued that the oxygen vacancy acts as electron donor, and the excess electron is localized in the proximity of the defect, giving rise to such 7. The partial occupation of this level results in the lost of half-metallicity and has been linked to the degradation of the magnetic properties6. However, calculations show that the position of the defect level is very sensitive to structural relaxations7 and it is usually located above the Fermi level, close to the bottom of the conduction band, hence decreasing the minority spin-gap. Ultraviolet photoemission measurements also reveal a significant reduction of the work function in samples where the 8, evidencing that the extra electrons left behind by the oxygen vacancy play an important role on the physical properties of the material.X-ray photoemission (XPS) and photoabsorption spectroscopies (XAS) on oxygen or manganese core-levels have been used to identify the presence of oxygen vacancies in samples grown under different oxygen environments, or subject to annealing treatments. Although O-1s photoemission and O-K edge XAS spectra show features that have been correlated to oxygen vacancy states, probably the most clear evidence for their presence comes from the Mn-L edge XAS spectra. In particular, a peak or shoulder at 9 corresponding to a decrease in the formal valence of Mn ions. However, the opposite trend was found in samples annealed in UHV for an extended period of time (where oxygen vacancies are expected to develop), and not exposed to air5. It can be argued that under these conditions, the 10, and that the XPS spectra reveals an increase of the bonding covalency. Interestingly, depopulation of the defect level in previous DFT calculations seems to be related to a weakening of the Mn bonding covalency and an increase of the Mn-Mn distance, which correlates nicely with a slight expansion of the lattice observed in X-ray diffraction experiments11.The splitting of the Mn 3s XPS spectra is commonly used to estimate the oxidation state (and magnetic moment) of Mn atoms. In samples with significant presence of oxygen vacancies, an increase in the splitting was reported12. To study the influence of both global and local Sr doping, we work in the composition range In any case, these contradictory results might indicate that under certain circumstances the electronic defect level could be occupied, and under other conditions, the level is empty. The aim of this paper is to determine under what conditions could the defect state be occupied and to clarify what are the consequences in the materials properties. Using first principles calculations, we provide a description at the atomic level of the electronic, structural and magnetic properties of bulk By analysing the character of the vacancy electronic level along with the effect of structural parameters and the magnetic interactions, we connect our results with experimental evidences published in the literature. Our main findings can be summarized as: (i) oxygen vacancies give rise to a new electronic defect level that has its main contribution mostly from neighbouring Mn d orbitals pointing towards the vacant site; (ii) this defect level acts as a scattering center that might be partially responsible for the diminished transport properties observed in reduced LSMO samples; (iii) the occupation of this bonding level reduces the interatomic distance of the surrounding Mn leading to a local shrinking of the cell volume; (iv) conversely, depopulation of this defect level results in an increased interatomic distance leading to an overall volume expansion; (v) oxygen vacancies produce a weakening of the ferromagnetic interaction, as revealed by the substantial reduction of the exchange energy for the Mn ions close to the vacancy, regardless of the defect state occupation, and in agreement with the observed degradation of the magnetic properties of reduced LSMO.13, which presents equal counterphase rotations of the octahedra around the three pseudocubic crystallographic axes. All the stoichiometric (pristine) supercells used in this study display this distortion pattern, with minimal dispersion of the octahedra rotations due to the direct substitution of the La cations for Sr dopant. Details can be found in the 14. For the minority spin (down channel), there is a pseudo-bandgap that decreases when increasing the Sr doping , there is a rigid shift of the levels due to the Fermi energy moving downwards, but the half-metallic character of the system remains. Addition of a Hubbard term shifts the 18 which is restricted in our case for neutral defects, because our system is metallic and the electronic reservoir is determined by the position of the Fermi level.In this section we focus our study on the reduced host, namely mentclass2pt{minimThe lack of an oxygen atom bridging two neighbors Mn ions reduces the electronic hopping behind the double-exchange ferromagnetic mechanism, and favors superexchange interactions that can result in anti-parallel magnetic configurations for the two Mn atoms linked to the vacancy. To explore this hypothesis, in the following we analyze two different magnetic states illustrated in Fig. To explore the limit corresponding to a diluted vacancy concentration, where vacancies are considered as isolated defects within the bulk of LSMO, we take a When an oxygen vacancy is formed, the two electrons left behind are available for the bulk states. Consequently, there are visible modifications in the electronic structure, particularly in the levels of the Mn atoms surrounding the defect. This can be seen in Fig. There is, however, a feature that stands out when the vacancy is formed: a defect level in the minority spin channel. We argue that the position of this level relative to the Fermi energy critically determines the behaviour of the system. This defect state, can be either above or below the Fermi energy, thus changing its occupation depending on the Sr doping (i.e. the amount of holes added into the system). As a result, a slope change is observed in the formation energy as a function of Sr doping  the defect level is occupied (OL) and the FM configuration is the ground state, as can be seen in the central panel of Fig. 20, the reduced Mn-Mn distance goes in the opposite direction to a lattice expansion reported for vacancy-rich samples21.At mentclass2pt{minimhigh doping concentration, the increased number of holes introduced by the Sr dopant lowers the Fermi energy and the defect level becomes depopulated (UL). The electron that was localized in this state, is now distributed in the available electronic levels, which in this case are the At entclass1pt{minima11. To elucidate this issue, we explore spin-flip (SF) configurations for the neighbouring Mn atoms  calculation, by forcing a constrain over the total magnetic moment of the simulation cell. Taking the pristine LSMO magnetic moment as a reference, we impose a To tune the occupation of the defect level, we perform a mentclass2pt{minimSr clustering in the system. To address how different local Sr environments influence the electronic and magnetic properties of bulk LSMO in the presence of oxygen vacancies, we take low doping, the defect level is always occupied and the local environment introduces a high doping, the defect level is always unoccupied, with slightly larger dispersion in the energies depending on the local Sr environment , depending on the number of available electrons in the system. Although the presence of this defect level has been reported in previous When the number of oxygen vacancies increases, the interaction among the defect states can give rise to a dispersive band which can have an impact on the magnetic properties of the system. We explore this high vacancy concentration limit using a pseudocubic 27. In particular, Guo et al.7 ascribed this expansion to the repulsion between the The removal of a single oxygen atom in this supercell corresponds to a mentclass2pt{minimThe formation energy for oxygen vacancies in the concentrated limit is reported in Table Mulliken population analysis reinforces our interpretation of charge distribution throughout the studied cases. Table 7, we show that the creation of oxygen vacancies pushes the 4; (ii) a degradation of the average magnetization around the defect; (iii) an overall weakening of ferromagnetic interactions in the system and (iv) a scattering center that can be responsible for the degradation of the electronic conductivity in reduced systems.In this work, first-principles calculations have been used to study the influence of oxygen vacancies in LSMO within the most typical range of compositions. In agreement with previous works28. When the Hubbard term is included in the Hamiltonian  when the defect level is empty, both in the GGA high doping limit and in GGA + U calculations. On the other hand, the bonding character of the defect state gives a reduced Mn-Mn distance when the level is occupied, leading to a small volume contraction, in opposition with the above-mentioned experimental findings. Although this might seem contradictory with our low doping bare GGA results, the DOS spectral features  obtained with GGA are in better agreement with experimental measurements compared to GGA + U. Furthermore, LSMO thin films grown under low oxygen pressure . Non local Troulliers-Martins pseudopotentials34 are employed, which conserve the norm and are factorized by the Kleinman-Bylander method35. All pseudopotentials and bases used in this work were developed and optimized in Ref.25. The pseudopotential of manganese was generated using the atomic configuration 36, for the structural optimization. K-points mesh is reduced accordingly to 38 or hybrid functionals39  has been shown not necessary by previous studies in the composition range analyzed of pristine LSMO, characterized by a metallic state and Jahn-Teller dynamic distortions45. Regarding reduced LSMO and its defect state, it is fair to assume that bare DFT might underestimate the position of that level. Although a more complete description of the electronic interactions  might be needed to precisely determine the energy position of the defect level, these calculations are computationally challenging and exceed the scope of this work.To perform total energy calculations we employ the SIESTA codeSupplementary Information.