This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms. Due to the large volume of goods transported every day, modern hospitals have a great demand for logistics automation systems, to reduce the cost associated with manual deliveries, increase efficiency in material transport and improve service quality ,2. NowadPeople detection is an important functionality for robots that share workspaces with people as in a hospital environment. Laser range finders commonly mounted on mobile robots are an appropriate sensor for this task since they are less susceptible to ambient light and provide rapid and precise distance measurements over a wide field of view. However, the information contained in a single 2D range scan is considered not sufficient to reliably distinguish humans from other objects in cluttered environments. Thus, several approaches ,10,11 adTraditional approaches ,14,15,16Recently, Beyer et al. ,20 firstvideo). The key idea is to evaluate PointNet [In this paper, we present a deep learning-based detector for detecting humans using laser range finders mounted on a mobile robot. This is done by assigning a label (person or nonperson) to each laser point in a laser scan   using thThe observation of a laser scanner consists of a sequence of beams Since laser data have strong variations in point density, which naturally decrease quadratically with distance, the voxel-grid filter is employed to down-sample the points in high-density regions. The filter divides the 2D space into a regular grid of cells of a given resolution and projects laser points onto the grid, then it randomly selects only one point from each nonempty grid cell. Furthermore, the laser points which are 10 m away from the robot’s center are filtered out, since these points are too sparse to be used to reliably detect people.kD-tree to find all its neighbor points r. which are then centered around the current query point: K. In other words, if a neighborhood contains too many points, it is uniformly resampled to the size K. Conversely, if a sample contains too few points, zero paddings are applied. Note that the inputs are inherently translation-invariant due to the use of relative coordinates. Additionally, to increase the robustness against rotations, we augment the dataset with random rotation within The input of the network is the resampled neighborhood of a proposal point, as shown in The network architecture is illustrated in To decrease the number of points needed to be classified during inference time, the jump distance clustering method is applied to split a laser scan into groups of beams. Only the point nearest to the centroid of a group is chosen to be evaluated, other points in the same cluster are assumed to belong to the same object. After clustering, the number of points needed to be classified is decreased and the inference speed can be accelerated to about 5 × faster.The jump distance algorithm iterates over the range scan. If the difference of measurements of two adjacent beams It is a challenging and time-consuming task to manually annotate all laser scans in a large dataset. In this section, we describe the methods for dataset collection and automatic labeling.There are two types of datasets needed to be collected: the dynamic datasets obtained by placing the robot in a site with significant pedestrian traffic , and the static datasets collected by joy-sticking the robot in an environment devoid of people. All laser points in a static dataset collected from a moving robot are simply treated as static obstacles.S is projected onto an occupancy-grid X using Algorithm 2. The values of cells that contain any laser point are set to 1, otherwise, 0 (line 6 in Algorithm 2). The n all projected grid maps H (line 7 in Algorithm 1), of which each element represents the hit count of the corresponding cell. To get the static background, we calculate the mean hit ratio by dividing H by N, where N is the total number of laser scans. If a cell’s hit ratio is greater than a predefined kD-tree. Since the class distributions are highly imbalanced in a dataset, we resample the negative examples to have the same size as positive examples.Algorithm 1 Automated Annotation of Laser ScansRequire: set of laser scans Ensure: set of labels of laser scans 1:functionAnnotateDataset2: 3: 4:for all do 5:  6:  7:end for 8:returnM 9:end functionTo calculate the labels of the laser points in a dynamic dataset recorded from a stationary robot, we calculate the background of the dataset and subtract it to get the points belonging to people, other points falling on the background image belong to static obstacles. The background is obtained using Algorithm 1. First, each laser scan The local path planner generates candidate paths by sampling laterally and longitudinally in the Frenet coordinate frame. The parameters of the local path planner are summarized in J, which is the linearly weighted sum of three cost functions.To find the best path to maneuver around obstacles, all the candidate paths that run over obstacles will be trimmed, the rest are evaluated by the following objective function A heuristic rule, which has been widely used in many automated guided vehicles (AGVs), is applied here in order to reduce unnecessary avoidances and improve the efficiency of navigation in dynamic environments, shown in To track the reference path, a Pure Pursuit controller is employed to produce motion commands sent to the robot wheels. It runs at a frequency of 20 Hz and is used in the forward simulation to avoid collisions. The controller constantly simulates a virtual robot moving forward for a certain distance and checks whether the simulated trajectory is in collision with obstacles. If it is, the robot will stop and try to avoid the obstacle, otherwise, the robot will continue to travel along the path.L. The curvature of the circle is given by v, the angular velocity is The pure pursuit controller is illustrated in B is the length of the wheelbase, The velocity commands of the left and right wheels of a differential robot are given byIf the angle between the target point and the robot heading direction is above a certain threshold, the deviation between the shortest straight-line path and the arc is large. In this case, the robot first rotates to align its heading with the virtual target point and then moves forward.In this section, we compare the proposed method with the Dilated CNN, PointNet and the DROW detector on the dataset collected in crowded environments.We recorded 18 sequences at several places (such as teaching buildings and canteens) of our school. The recording time of a dynamic dataset ranges from 15 to 30 min. An example scene is shown in The radius of the neighborhood determines the size of the receptive field of the network. Experiments were conducted to study the relationship between the radius size and performance. In We compare the proposed method to three baselines: the dilated convolutional neural network (CNN), the distance robust wheelchair/walker (DROW) detector and PointNet. The evaluation metrics are the pixel accuracy  detector for the semantic segmentation task by removing the voting and nonmaximum suppression steps. We train the PointNet (github.com/fxia22/pointnet.pytorch) on our dataset with the default parameters.We implemented a dilated CNN which consists of 8 convolutional layers with dilation factors The pixel accuracy and mean IoU scores of different approaches are presented in ROS Navigation, which implements the A* and DWA algorithms. However, its behaviors are difficult to predict and not consistent in dynamic environments. In this section, we give a qualitative experimentation of the proposed algorithm.Comparison between different obstacle avoidance algorithms in real world is costly, and the source codes of most algorithms are not publicly available or tightly integrated into a big system. The most well known publicly available navigation package is video). In all the experiments, the robot follows the same predefined path from the start position to the goal position. The maximum linear velocity of the robot is 1 m/s, the maximum lookahead distance of the local planner is 2.5 m. The individual terms of the objective function are normalized between To verify the performance of the proposed obstacle avoidance algorithm, several experiments are carried out with a real robot in the corridor in front of our laboratory  to verify the performance of the proposed algorithm. This hospital has large traffic of people and a large volume of materials to be transported every day. According to statistics, more than In the experiments, the task of the robot is to transport drugs from the central pharmacy to the nurse station on the third floor. This paper focuses on the development of two important components  of a differential-drive robot for safe navigation in human-populated hospital environments.For people detection, we proposed to firstly classify each proposal point using the information of its local neighborhoods with PointNet, due to its invariance to the permutations of the input set. Secondly, in order to reduce the number of points to be evaluated during inference, we use jump distance clustering method to split the laser scan into groups and select the centers of each group as proposal points. Since neural networks need a large dataset to train, we proposed an approach to automatically label dataset collected in actual environments. This method also reduces the cost of deploying laser-based people detection algorithms in new scenarios. Experimental results show that the proposed method achieves comparable performance on par with the state-of-the-art approaches.For obstacle avoidance, we argued that the behaviors generated by the algorithm in human-populated environments should be legible and consistent. Pure reactive approaches can not be directly deployed in dynamic environments, since they have several drawbacks as mentioned above. To tackle these problems, we proposed to avoid obstacles by switching lanes generated by laterally sampling along the reference path. One advantage of the proposed method is that the candidate paths are consistent with the reference path and guaranteed to reach the goal position. Another one is that the robot’s behaviors can be intuitively understood by nearby pedestrians, this improves the safety of navigation. Moreover, we adopted a stop-wait-warn rule to reduce unnecessary deviations and improve the efficiency of navigating in dynamic environments. In addition, we proposed a new objective function which takes account of the distance to obstacles, the deviation from the global path and the social cost of passing on the right. Finally, experiments carried out in real-world environments confirm the effectiveness of the proposed method.In future work, we consider adding a BACKWARD behavior to the behavior states of the robot. Since in some narrow places, the robot may need to give way to let the other side go through. In this situation, the robot needs to predict the path that the people will take using the information obtained from the surrounding environment. We will also make the behavior of the robot a little smarter, for example, adjusting the waiting time according to the types of the front obstacle and choosing lanes according to orientations and speeds of nearby people.


In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.  Medical image classification is one of the more basic and important tasks for computer-aided diagnosis (CAD). An efficient medical image classifier can help reduce the workload for the doctors and guide the inexperienced physicians. In recent years, deep learning (DL), especially the convolutional neural networks (CNNs)-based methods, have shown outstanding performance for image processing tasks. Therefore, some researchers have developed and applied lots of heavy-weighted CNNs for medical image classification . For exaFrom the perspective of loss function, the mentioned CNN models take the traditional cross entropy (CE) as the loss function for the training process. However, the CE function only measures the difference between the predicted probability distribution and the target distribution , which mGenerally, the DML-based CNNs use multi-inputs and produce multiple embedding vectors so that they can calculate a certain distance metric from the embedding vectors as the loss for training. By minimizing the loss, the CNNs can generate similar embedding vectors for images belonging to the same class, and dissimilar vectors for different categories of inputs . For exaDespite the fact that the DML-based CNNs can analyze the data distribution and improve the training process, they are designed for such few-shot recognition or image retrieval tasks as face recognition ,30. EvenTo more effectively utilize the DML loss to train the regular CNNs in the case of medical image classification applications, we have proposed a novel batch similarity-based triplet loss, denoted as “BSTriplet” loss for short. The proposed loss can facilitate assessing the similarities among a batch of input images instead of several pairs to evaluate the distribution of training data. As shown in We have introduced a novel batch similarity-based loss, which can be embedded into arbitrary CNN models to help the training process;A reasonable sample mining strategy is designed to help CNN models in the better estimation of the distribution of the training dataset;The proposed loss is combined with cross entropy to train several light-weighted CNN models, and its effectiveness has been demonstrated on different kinds of medical image datasets.In this study, our main contributions are as follows: The context of this paper is organized as follows. if. We use When training a CNN model fined as (1)LtripIn the BSTriplet, we aim at making full use of the input batch. Therefore, similarities between every set of two corresponding samples in the batch are measured to evaluate the data distribution. After the embedding vectors  product . 1∈ℝNb×NBased on the above discriminative similarity matrix As shown in To accomplish the classification and evaluate the classification capability, the softmax classifier and the CE loss Classify the original images into different categories according to the ground truth;Cluster the original images into several groups for each category;Count the number of images in each group, and calculate the ratio for each group;Randomly select samples from every group to construct an input batch according to the obtained ratios.For the regular triplet loss, each triplet must meet the requirement that every triplet input contains a sample The number of groups for each class in the dataset can be different because it depends on specific applications and data distribution. Notice that this strategy is only used in the training process. During the test phase of a trained CNN model, the model will ignore both the BSTriplet loss and the CE loss, and takes the output of the softmax function as the predicted results.Supposing the number of training datasets is The calculation process of the BSTriplet loss consists of basic mathematical operations. Therefore, it is computationally efficient and easy for implementation. Normalization for the embedding vectors requires www.python.org) with Keras 2.3.1 and TensorFlow 2.0.0. In addition, all the following experiments are conducted on a computer with Ubuntu 16.04, a CPU of Intel Xeon Gold 6129 and a GPU of Nvidia Tesla V100 with CUDA 10.0 for acceleration. In the following, some experiments have been performed to test the effectiveness of the proposed BSTriplet loss. We have tried to apply the proposed loss to such popular light-weighted networks as EfficientNet-B1, MobileNet-V3-Small, ShuffleNet-V2 and PeleeNet for testing its performance. Those chosen models have distinctive structures and state-of-the-art performance, and they have been widely used in a variety of image classification tasks. The reason for adopting EfficientNet-B1 rather than the other models in  is that During the training process of every network, the initial learning rate is set as 0.001 to let the training loss get smaller quickly. The training loss is monitored. Once there is no improvement for the training loss for 20 epochs, the learning rate is multiplied by 0.3 to help the models find the optimal solutions. The maximum number of epochs is set as 400, and the minimum learning rate is set as The number of images in one batch y images ,42 from y images  website.All the obtained To display the effect of the BSTriplet loss in an intuitive way, principal component analysis (PCA) was applied to reduce the dimension of the embedding vectors for visualization. We have compared two distributions of embedding vectors for the training images in To assess the effect of the data mining strategy, we trained MobileNet-V3-Small with several schemes. The training schemes utilize random selection (RS) or the proposed data mining (DM) strategy to construct input batches, and use CE or “CE+BST” as the loss function, where the latter means CE combined with the BSTriplet function. The loss curves of training and validation for each training scheme are given in From Moreover, we have compared the performances of several mentioned models with the BSTriplet loss to discover its applicability and effect. For each compared network, we have trained it with the CE loss and CE+BST loss. The training loss and validation loss are shown in To verify the effectiveness of the proposed combined loss, we have compared it with CE combined with triple loss  and CE cTo intuitively show the superiority of the proposed BSTriplet loss, the confusion matrixes of the four networks trained with CE loss and CE+BST loss are given in To further verify the consistency of the proposed BSTriplet loss, we have carried out comparative experiments on another dataset of chest X-ray images denoted as “Chest-2”. This dataset is also downloaded from the Kaggle  website.The results for the classification of Chest-2 are shown in To further validate the effectiveness of the proposed BSTriplet loss, some experiments have been conducted on a skin rash image dataset, which is used to distinguish the images containing Lyme disease from those with other disease . The comThe results for each evaluated model are given in The ROC curves for every test model on the rash image dataset are shown in To further verify the applicability of the BSTriplet loss to other medical image modalities, additional experiments have been done on an osteosarcoma histology image dataset ,49,50, wIn this paper, a novel batch similarity-based triple loss is proposed for light-weighted CNNs in the case of medical image classification. The proposed loss takes the similarities among all the samples in the input batch into account in order to gather samples of the same class and distinguish those of different classes. It can be easily assembled into existing CNNs, and assist cross entropy loss in training the CNNs by resolving the over-fitting problem. A reasonable data mining technique is also provided, which can help to build input batches according to the distribution of the training data. Several experiments have been implemented on such medical image datasets as chest X-ray images and rash images. The results show the superiority and consistency of the proposed loss combined with cross entropy loss compared to other combined losses in terms of sensitivity, specificity, accuracy and AUC. Our further work will be focused on the optimization of the computational efficiency of the proposed loss, its combination with more loss, and testing on other image datasets to ensure that the training process of CNNs will be more stable and the over-fitting problem can be addressed more effectively.


Nowadays, integrated joint modules are increasingly adopted in manipulators for their advantages of high integration, miniaturization and high repeatability positioning accuracy. The problem of generally low absolute positioning accuracy (namely angle measurement accuracy) must be solved before they can be introduced into the self-driven articulated arm coordinate measuring machine which is under study in our laboratory. In this study, the sources of joint module’s angle error were analyzed and the error model based on harmonic analysis was established. Two integrated joint modules were calibrated on the self-designed calibration platform and the model parameters were deduced, respectively. The angle error was then compensated in the experiments and the results demonstrated that the angle error of the joint modules was reduced by 82.03% on average. The established angle error model can be effectively applied into the self-driven articulated arm coordinated measuring machine. With the rapid development of industrial technology, industrial robots have gradually entered the industrial assembly line production chain and are replacing manual operations due to advantages such as speed, flexibility and low long-term investment cost. Industrial robots are widely used in automobile, mold, aerospace, shipbuilding and other mechanical processing industries; the six-axis multi-joint robot, namely the 6-DOF Manipulator, is among the most common and is used in handling, assembly, welding and surface treating processes . The motAs a result of the wide application of the manipulator, there have been problems with poor part generality caused by too many product models. It is extremely important to develop a modular manipulator with high integration, strong reliability and good versatility. Accordingly, research on modular manipulators has been conducted by Carnegie Mellon University , ToshibaAt the same time, the detection demand in the industry has derived the articulated arm coordinate measuring machine (AACMM), which has a structure similar to the manipulator. Compared with the traditional coordinate measuring machine (CMM), its high flexibility and portability gives AACMM a market advantage. Improving the measurement accuracy of this high-precision measuring equipment is particularly important. Many scholars have studied various aspects of improving the AACMM measurement accuracy. The measurement model of AACMM uses the joint angle measured by the grating sensor as a variable and includes the structural parameters of AACMM. A main research direction is improving the identification accuracy of the structural parameters of the AACMM system by studying the calibration technology ,21. In RIn Reference , we propThe integrated joint module is composed of motor, driver, encoder and harmonic reducer. The angle encoder is at the rear end of the motor shaft to sense motor in real time. The motor shift is connected with the wave generator of the harmonic reducer. The steel wheel of the harmonic reducer is fixed, and its flexible wheel outputs low rotational speeds. In this way, an error chain is formed between the encoder and the final output via harmonic reducer, so, the angle measurement error of the joint module is not only related to the encoder itself, but also influenced by the harmonic reducer. The absolute magnetic encoder is used in the joint module. Compared with the grating encoder, the magnetic encoder has the advantages of simple structure, fast response speed, strong resistance to environmental interference, easy integration in digital circuits and wide use in speed and position detection . Based oCompared with other mechanical transmissions, the harmonic reducer achieves greater transmission ratio, higher accuracy, lower noise and better working stability with fewer parts, smaller size and lighter weight . The harThe harmonic reducer error is classified based on its components, including the radial size error and radial run-out error of the wave generator, the geometric eccentricity error and the movement eccentricity error caused by processing and installing the rigid and flexible gears, the backlash error caused by gear backlash between the rigid and flexible gears. These transmission errors are periodic. In Reference , the traError compensation is divided into on-line error compensation and off-line error compensation, and the effect of on-line compensation is generally better. However, due to the integrated design of the joint module, this paper selected the harmonic analysis method based on the angle error characteristics of the joint module. Compared with on-line compensation, the cost is greatly reduced, and it is more convenient because it only relies on mathematical algorithms. The compensation process combines the amplitude and phase of the error harmonic to determine more harmonic orders, making the fitting curve much closer to the actual error.y = f(x) with a 2π period. If the Dirichlet condition is satisfied, any periodic signal can be expanded into Fourier series [i-th harmonic and i-th harmonic. The analysis method of expanding the periodic function of error into Fourier series as described above is called harmonic analysis.The angle error of the joint module is a function r series . The funn of the expansion must first be determined. According to the Shannon sampling theorem, when the sampling point N is even, the highest order n can only be calculated to N/2; when N is odd, it can only be calculated to (N-1)/2 [N is small due to insufficient information, harmonics higher than N/2 order cannot be separated but are instead mixed in harmonics lower than N/2 order. The mixing rules are shown below:N is 37, according to the Shannon sampling theorem, the highest order n can be calculated to 18, and the actual harmonic coefficients after harmonic mixing can be obtained from Equation (3) as follows:To construct a model based on the Fourier series, the highest order  (N-1)/2 . When thn as high as 17.Generally, harmonic coefficients are gradually attenuated as the order increases, therefore higher order coefficients have low influences. It is apparent from Equation (4) that in low-order harmonic coefficient analysis, the influence can be ignored due to the large order span of the unseparated high-order and theoretical harmonics. As the order gradually increases, the theoretical harmonic order gradually approaches the unseparated higher order; even at the 18th order, it is directly affected by the adjacent 19th order. As such, even if the harmonic coefficient is attenuated due to increased order, the mixing of the coefficients of adjacent orders with similar magnitudes will still greatly affect the analysis results. Moreover, there may be several high orders with larger coefficients in the actual harmonic, which will seriously affect the results when they are mixed into the adjacent harmonic coefficients. There are 37 sampling points in the experiment. To reduce the influence of harmonic mixing, it is appropriate to use Combined with Equation (2), the angle error can be expressed asTo correct the joint module angle error, a joint module calibration experiment platform was designed and built using the autocollimator and a 36-sided prism, as shown in During the experiment, the angle of the prism was first fine-tuned by making the cross image in the autocollimator eyepiece reflect back and align with the scale line in the eyepiece. In this process, the self-adjusting structure of the autocollimator can be used for adjustment. After the adjustment was completed, the position of the prism was fixed and recorded as the initial zero position; the current angle of the joint module was also recorded. Next, the PC software was used to control the joint module to rotate 10° and the position of the cross image in the eyepiece of the autocollimator was used to fine-tune the joint module angle until the cross image and the scale line coincided again. At this point, the joint module had rotated 10° and the joint module angle was read again. If the difference from the previous angle is not 10°, then the difference from 10° is the angle error. Similarly, the 36-sided prism was driven for one rotation and 37 joint module angles, corresponding to each standard 10° of the prism were obtained. The above experiments were repeated on two joint modules  of the same type every three days within three weeks. Finally, as shown in For the 6 groups of measured data, the first 5 groups were used to establish the error compensation model while the last group was used to verify the compensation model effect. First, for each group of measurement data, To fit an optimal harmonic error model in the actual fitting process while avoiding extra error caused by over fitting, only the harmonic components of finite order with prominent amplitude are considered, rather than harmonic components of all orders . The ampTheoretically, system errors such as eccentric error generally do not change with time and neither do the amplitudes and phases of the corresponding harmonic orders that they cause . TherefoTo reduce the impact of accidental error, after averaging the amplitude and phase of five groups of error data, the final error compensation models are as followsThe curve of the error model is shown in Before the compensation, the peak values of error of joint modules a and b were 149.53 and 101.35 arcsec, respectively. After applying the error model compensation, any joint module angle error between 0–360° can be corrected. The peak values were reduced by 82.20% and 81.85%, to 26.62 and 18.40 arcsec, respectively. The angle measurement accuracy was significantly improved for the joint module whose internal structure was a magnetic encoder with limited precision. In this paper, the integrated joint modules in the robot are used as the driving joints of the self-driven AACMM and the angle errors of the joint modules are corrected by establishing the angle error model. The angle measurement error of the joint module is not only related to the encoder itself, but also influenced by the harmonic reducer. The high frequency component of the angle error is larger than that of the general circular grating sensor. The established error compensation model uses the method of eliminating the weak harmonic orders and retaining the strong harmonic orders in the 17 harmonic components, rather than, simply obtaining the first three harmonic orders, to make the fitting curve much closer to the actual error. The fixing mechanism for the calibration experiment was designed and manufactured. The mechanism is easy to install and its coaxiality is adjustable. The experimental data used for modeling were obtained and the angle error was reduced by 82.03% on average after compensation using the error model. This research will be helpful to improve the measurement accuracy of the self-driven AACMM under study and would be helpful to improve the absolute positioning accuracy of robot.


Nano-domains are sub-light-diffraction-sized heterogeneous areas in the plasma membrane of cells, which are involved in cell signalling and membrane trafficking. Throughout the last thirty years, these nano-domains have been researched extensively and have been the subject of multiple theories and models: the lipid raft theory, the fence model, and the protein oligomerization theory. Strong evidence exists for all of these, and consequently they were combined into a hierarchal model. Measurements of protein and lipid diffusion coefficients and patterns have been instrumental in plasma membrane research and by extension in nano-domain research. This has led to the development of multiple methodologies that can measure diffusion and confinement parameters including single particle tracking, fluorescence correlation spectroscopy, image correlation spectroscopy and fluorescence recovery after photobleaching. Here we review the performance and strengths of these methods in the context of their use in identification and characterization of plasma membrane nano-domains. Since the development of the Fluid Mosaic Model , multiplWith the implementation of higher resolution analytical methods to measure diffusion e.g., single-particle tracking , local rNano-domains have been shown to affect multiple mechanisms involved in cell metabolism and function. There is strong evidence for a role of nano-domains in membrane signalling processes ,17,18 anOver the time period that models of membrane nano-domains have been developed and refined, multiple analytical methodologies have been introduced to define them through measurements. Many different methodologies are used in current research of nano-domains, including the detergent method, model membranes, cholesterol manipulation, membrane sensitive probing and diffusion methods. The focus of this paper is exclusively to summarize diffusion measurement methods to identify both the size and function of membrane nano-domains. The other methods are well reviewed elsewhere ,23. The Single particle tracking (SPT) was implemented for the first time in the 1980s using gold nanoparticles as labels for biomolecules which could be imaged and tracked individually via video microscopy . HoweverSPT follows  the movement of imaged labels attached to single particles or molecules, and the resulting trajectories may be analyzed in order to measure the molecular motion. In practice, a molecule is tagged with a fluorophore or alternatively with a more photostable quantum dot nanoparticle, since long tracking periods improve the signal-to-noise ratio for the method . The samindividual molecules or particles. Consequently, if each trajectory is sufficiently long, no population averaging is needed, and the method may deliver single-molecule results based on single-molecule measurements. This is desirable in the common case where the population of molecules under study shows heterogeneous behaviour in terms of mobility. In order to take advantage of the single-molecule resolution, each trajectory must be analysed individually. Initially, one may identify individual molecules that are immobilized but appear to be moving due to localization errors [One key advantage of SPT is that it tracks the motion of n errors  in the mn errors . If retan errors , as illun errors . An alten errors ,39. In tn errors ,39. Thern errors .One should be wary when using 2D SPT analysis, as it might introduce a bias due to the assumption that the topography plasma membrane is completely flat in the imaged region of the membrane. It has however been shown that this is not completely true, and might actually compromise the diffusion coefficient measurement significantly . It has δI is the amplitude of fluorescence fluctuation and <I> is the average fluorescence intensity from the time series, and τ is the time lag or shift parameter of the autocorrelation function. The calculated autocorrelation function can be fitted with a decay model and the characteristic fluctuation time is extracted as a fitting parameter if the focal spot size is known. The characteristic fluctuation time is effectively the average residency time for the fluorescent molecule to be present within the illumination spot [Fluorescence correlation spectroscopy (FCS) was originally developed as a tool to identify binding of a fluorescent marker (ethidium bromide) to DNA molecules in a solution . In pracion spot .(1)G(τ)Shortly after the development of FCS, it was applied with the aim of detecting and measuring the mobility of molecules in cell membranes. Unfortunately, instrumental limitations did not allow such measurements at the time, and needed the technological development provided by scanning FCS (sFCS) in the mid-1980s  and confA significant extension of FCS is the ImFCS technique , which uThis tool has been utilized to identify and describe nano-domains in regard to both diffusion coefficient and lateral distribution .Another method that has become quite popular in the plasma membrane field is the spot variation FCS (svFCS) method . ShortlyBinned-imaging FCS (bimFCS) is conceptually similar to svFCS . The difk-space and time) which made it possible to extend the range of parameters that could be analysed by ICS analysis.Image correlation spectroscopy (ICS) is a technique developed as an extension to the scanning FCS technique . ICS extICS is thoroughly described in  and is ak-space image correlation spectroscopy (kICS) [Since its inception in the early 1990′s, multiple variants of the ICS were developed to optimize different types of measurements. These extensions included temporal image correlation spectroscopy (TICS) , image cy (kICS) , raster-y (kICS) ,66, party (kICS)  and photy (kICS) . Real-spy (kICS)  (this doy (kICS) ):(2)rab and in time (t) where t is the time collection point for each frame in the image series. The fluorescence intensity fluctuation, i> is the mean intensity average over space for the image or ROI.The intensity, The angular brackets in the numerator of Equation (2) indicate the calculation of a spatial correlation function averaged over pairs of images separated by time (frame) lags through the image time series and this is normalized by mean intensities as shown. The calculation is repeated for each sequentially increasing discrete integer step from 0 to the maximum time lag (N image frames). The overall space-time correlation is a function of pixel shift  variables and frame shift  variable.Spatio-temporal image correlation spectroscopy (STICS) is an extension of the original ICS methods that analyses imaged fluorescence fluctuations in space and time by calculating the full spatio-temporal correlation function represented by Equation (2) for a single imaging collection channel . STICS has proven to be very useful for biomolecule flow measurements in static and motile cells for identifying directed movement of various labelled molecules . STICS hThe basic STICS method has been extended to mapping co-transport of two different biomolecules labelled with fluorophores of different emission wavelengths and imaged in two different detection channels. This derivative is termed spatio-temporal image cross-correlation spectroscopy (STICCS) and the analysis involves the calculation of the full spatio-temporal correlation function represented by Equation (2) for two imaging collection channels  from the dual color image times series collected for the two different labelled molecules.More recently, an extension has been developed for STICS called velocity landscape correlation (VLC), which is able to extract multiple flow velocities from the extension of the basic STICS analysis . Such exSTICS can be used for diffusion measurements and is even able to identify diffusion behaviour in a similar manner to SPT . CompareAn extension to the STICS method called imaging mean square displacement (iMSD) has also been introduced recently. The iMSD method measures the time-dependent spreading of the width of the space-time correlation function in order to quantify an ensemble averaged MSD as a function of time from the imaged region. The iMSD technique can detect mean displacements between 25 and 100 nm without any a priori assumptions on the diffusion properties of the molecules  and can The particle image correlation spectroscopy (PICS) method combines the advantages of SPT and ICS , resultiPICS has been shown to be able to discriminate and measure two diffusion coefficients, which is a great advantage, when working on analysing plasma membrane organization including nano-domains. The PICS model may not be valid if there are protein-protein interactions that do not follow the mean-field approximation . This cok-space time correlation function is represented symbolically as follows:k-space time correlation function is radially averaged and then the natural logarithm is applied. If the photophysics of the fluorophore is only time dependent , there is only diffusion (not flow) and there is only one diffusing fluorescently labelled species, then we obtain equation 5 which is used for kICS analysis:N is the number of particles in the image, q is the quantum yield, I0 is the peak central intensity of the focused laser, ω0 is the e−2 laser beam radius, θ is a photophysics variable describing if the particle is fluorescing (1 if yes and 0 if no), and τD is the diffusion coefficient.The k-space image correlation spectroscopy (kICS) is a reciprocal space variant of ICS and was developed to allow measurements of diffusion and/or flow in the presence of complicating on/off emission blinking of the probes as was exhibited by quantum dot nanoparticles . With kIk-space correlation function versus y-intercept with depend on the emission photophysics correlations, instrumentation factors and the molecular density. The plot is fitted from its linear decay regime through to a noise floor using a three-knotted spline of first order. The initial linearly decreasing part of the spline spans k-vectors where molecular transport is still correlated for a given time-lag. The slope of the linear correlation decays is collected for each time lag and plotted against the time lag resulting in another linear plot to which linear regression is applied and the slope is the diffusion coefficient [A plot of the natural logarithm of the radially averaged fficient ,64. An eAn extension of kICS analysis was applied to study nano-domains in cell membranes by a two-component model that could detect and measure the motion and fractional distribution of e.g., a protein inside and outside domains smaller than the PSF focus ,79.Fluorescent recovery after photobleaching (FRAP) was a technique developed in the 1970s ,81 and iThe method works by focusing a laser on a small area of a fluorescently labelled cell, and after a low intensity initial fluorescence baseline measurement in time, the laser is increased to high intensity for a brief time period. The high intensity perturbation photobleaches the fluorophores and, consequently, the illumination area becomes dark as the emitting fluorophores are depleted. The illumination laser is switched back to its lower observation intensity and the fluorescence intensity from the bleached region is monitored over time. The fluorescence signal in the photobleached area will recover over time due to labelled molecules diffusing (or flowing) back into and photobleached molecules diffusing (or flowing) out of the FRAP observation area. After a certain time, the maximal recovery will be reached as a plateau in the intensity recovery curve. If this does not match the initial pre-bleach intensity, it indicates the presence of an immobile fraction of labelled molecules. The recovery curve can be fit with an appropriate model to obtain the diffusion coefficient for the mobile fraction. Various derivatives of FRAP have been developed to improve the technique. Line FRAP is an important extension which can be used in research on membrane nano-domains as it allows for faster and more localized measurements of diffusion coefficients due to a smaller region being bleached .FRAP has been widely applied for studies on membrane nano-domains, by measuring the diffusion coefficients and immobile fractions of nano-domain related proteins. This has been achieved by manipulating the cholesterol levels in the cellular plasma membrane using methyl-β-cyclodextrin (MBCD) and then observing the changes in the diffusion coefficients via FRAP . An incrThere are many techniques which are able to measure the diffusion coefficients of proteins and lipids in plasma membranes and all of them are widely used in the nano-domain research field. All of these methods have both strengths and weaknesses and the strengths are amplified and weaknesses compensated for when multiple techniques are used to address the same system. The greatest strength of SPT is that it is possible to get trajectories from individual molecules, which makes it possible to visually define the transport behavior of each labelled molecule. However, SPT is much more time consuming as it requires many replicates to obtain statistically meaningful results for the diffusion coefficient and SPT begins to fail when the labelled molecule density is too high. These weaknesses of the SPT can be solved by applying either the FCS or ICS techniques. Among the correlation-based techniques, FCS is the most developed for the identification of nano-domains and in particular the svFCS and bimFCS methods have advantages, as they also provide some information about confinement-induced deviations from free diffusion behavior along with the diffusion coefficients. They are, however, limited to only detecting three kinds of anomalous diffusion behavior, whereas SPT trajectories can reveal all diffusion and transport patterns. The original FCS and imFCS techniques work well for measuring diffusion coefficients and detecting changes in the diffusion, however they do not have the ability to clearly define transport modes that deviate from free diffusion apart from assessing anomalous diffusion. This is also the case for most of the ICS techniques. The development of STICS to work in a similar fashion as SPT but for higher densities (ensembles) of labelled molecules has certain advantages in cell expression systems  and might be able to be developed further in combination with super-resolution microscopy for the identification and characterization of nano-domains. PICS have also shown some great promise in the identification of nano-domains but the complication of protein-protein interactions might present a significant challenge when investigating the interaction between proteins and nano-domains. The FRAP technique has some of the same strength as FCS and ICS, but when using FRAP it is difficult to sample large representative areas for analysis. However, it still remains an excellent tool to define the diffusion coefficient and also to identify the immobile fraction of the molecule of interest in the plasma membrane.All the mentioned techniques have added to the current knowledge of plasma membrane organization and nano-domains. This is exemplified by how SPT has helped in identifying different diffusion patterns ,31,32,33


We performed the functional head impulse test (fHIT), which measured the percentage of correct responses by asking the child to identify an optotype briefly presented during passive head impulse in each direction of each semicircular canal plane. We observed significantly lower correct answers in children with NDDs compared with those with TD (p < 0.0001). Surprisingly, there was no significant difference between the three groups of children with NDDs. Our study fostered preliminary evidence suggesting altered efficiency of vestibular system in children with NDDs. VOR abnormalities estimated using the fHIT could be used as a proxy of NDD impairments in children, and represent a potential biomarker.Several studies in children with neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASDs), reading impairment, or attention deficit/hyperactive disorder (ADHD) pointed toward a potential dysfunction of the vestibular system, specifically in its complex relationship with the cerebellum. The aim of the present study was to test the functional vestibulo-ocular reflex (VOR) responses in children with NDDs to measure functional performance of the vestibular system. The VOR is specifically involved in this stabilization of the image on the retina during rapid movements of the head. To perform this study, four groups of children with ASD, ADHD, reading impairment, and with neurotypical development (TD) were enrolled ( Highlights The vestibulo-ocular reflex (VOR), which is specifically involved in the stabilization of the image on the retina during rapid movements of the head, can be explored using the functional head impulse test (fHIT). The number of appropriate responses during the fHIT was significantly lower in children with neurodevelopmental disorders (NDDs) than in children with neurotypical development. Surprisingly, we did not report any significant difference when exploring the VOR functioning between all the three subgroups of children with NDDs. The VOR abnormalities we observed in children with NDDs brought further evidence of the strong relationship between a loop involving the vestibular system, the ocular system and the cerebellum, and atypical brain development.Vision plays a key role in the knowledge of our environment during each moment of our daily life. It is essential for the coordination of all our movements in natural space, for the precision to perform a task, to use a tool, as well as for reading or writing, and is fundamental in relationships and social communication with other humans. Eighty percent of the information coming from the world goes through our eyes and is immediately processed by the brain. The stabilization of an image on the retina mainly depends on the activity of the vestibular and visual systems . The actWhen the head rotates rapidly in the horizontal or in the anterior or posterior planes, the two semi-circular canals of each pair participate in the estimation of the speed of the head movement. The ipsilateral canal at rotation gives the main excitatory information—via the vestibular nerve fibers—to which the weaker and inhibitory information from the contra-lateral channel is added. In response, the oculomotor system will induce an eye movement equal in amplitude, but in an opposite direction to the movement of the head. This reflex, called the vestibulo-ocular reflex (VOR), allows the stabilization of the image on the retina during rapid movements of the head and is measurable during a test known as the head impulse test (HIT) .The VOR is monitored at the central level  and, speFor instance, Kheradmand & Zee , combiniFor several years, the cerebellum has been considered a key brain actor in neurodevelopmental disorders (NDDs) ,7. Some In the present study, our goal was to further explore the VOR in the specific context of children with NDDs. The fHIT was developed to estimate the VOR during passive head impulses. In other words, the VOR is tested by asking the subject to identify an optotype briefly presented during passive head impulses ,16,17. CBased on previous findings ,17, we hGroup 1 included twenty children with reading impairments; Group 2 enrolled twenty children with ADHD; Group 3 included twenty children with autism spectrum disorder (ASD), but without intellectual deficiency (ID); while Group 4 had twenty children with typical neurodevelopment (TD). Subjects from Groups 1, 2, and 3 were enrolled in the study at the Child and Adolescent Psychiatry Department, Robert Debré Hospital . To be included, they should have a neurological exam in the normal range and should be naïve of psychotropic treatment. Children with reading impairment were recruited from the Centre for Language and Learning Disorders, to which they had been referred for a complete evaluation of their difficulties, including an extensive examination of their phonological capabilities. For each child, the time required to read a text passage, text comprehension, and the ability to read words and pseudo-words using the L2MA battery  the presence of any binocular visual deficit, such as strabismus or high phoria ; and (ii) the presence of any vestibular impairment as vertigo, dizziness, or evident balance deficit.Four different groups of sex-, intelligence quotient (IQ)-, and age-matched children participated in the study : Group 1ttention ) were mettention  and carrttention ). ADHD sttention ,22. Chilttention , the ADOttention ), and exttention , for allThe clinical characteristics of all four groups of children are summarized in The investigation followed the principles of the Declaration of Helsinki; the study was approved by the Institutional Human Experimentation Committee in France at the Hotel-Dieu hospital . Written informed consent was obtained from children and their parents after the nature of the procedure was explained.www.beonsolutions.it), is based on the ability of a subject to read an optotype briefly presented during impulsive head rotations at varying angular accelerations [This test, commercialized by BeOnSolutions society . A trained operator performed subsequent head impulses, consisting of brief, small rotatory movements executed with both hands on the head of the child in the plane of each semi-circular canals pair and, at the same time, the child was asked to recognize the Landolt C optotype that was presented on a black PC screen for 80 ms in eight possible orientations A. Note t2 wide bins based on the peak head angular acceleration reached during each head impulse that triggered the display of the Landolt C. Thresholds ranged from 2000 to 7000 deg/s2 and the data were thus organized in six bins for each subject. The fHIT software automatically separated the trials  according to the acceleration bins defined above and the semi-circular canal stimulated. As established by the fHIT procedure, the performance of a subject was assessed estimating the proportion of appropriated answers, that is, when the child recognized the correct orientation of the Landolt C [Data were divided into 1000 deg/sandolt C .p-value was below 0.05.Statistical analysis was performed with the Statistica software  using the GLM (Advanced Linear Models) with the four groups of children as inter-subject factors, and the percentage of the correct responses for the distinct semi-circular canals stimulated in the two directions (left and right) as within-subject factors. In the case of significant effects, we conducted post-hoc comparisons using Bonferroni correction for multiple comparisons. The effect of a factor was considered significant when the corrected F = 67.48, p < 0.0001). Bonferroni post hoc test showed that the performance of the fHIT of children with TD was significantly better than those of the three groups of children with NDDs .We first explored the percentage of correct answers in the left- and rightward direction for each vestibular canal  for each group of children . We obseF = 24.11, p < 0.0001) on the performance. Bonferroni post hoc analysis revealed that the percentage of correct responses of the horizontal vestibular canal was higher than the percentage of responses reported after posterior and anterior stimulations of the vestibular canals (both p < 0.01).We also estimated the effect of the type of vestibular canal tested = 10.89, p < 0.0001 and vestibular canal × group, F = 2.21, p < 0.04, respectively).Finally, the analysis of variance (ANOVA) exhibited two significant interactions .The Bonferroni post hoc analysis showed that the number of appropriated responses for both directions (left and right) of the horizontal vestibular canal was significantly higher with respect to the two directions of the posterior and anterior canals .The performances measured for the posterior and anterior canals in the children with NDDs were significantly lower than those recorded in the horizontal canal  that children with neurodevelopmental disorders (NDDs) showed lower correct responses during the fHIT when compared with children with neurotypical development (we failed to report any significant difference between the three subgroups of children with NDDs); (ii) that all children reported higher correct responses for the horizontal semi-circular canal with respect to the other two semi-circular canals (posterior and anterior).The aim of the present study was to explore the VOR using the fHIT in children with NDDs in order to develop a new technique and objective tool to improve NDD diagnosis. We reported the following: We compared the performance on this test to estimate a potential functional defect of the loop involved in the VOR, i.e., the vestibular and ocular systems, and the cerebellum . As hypoThis finding is in line with previous studies suggesting deficiencies in vestibular capabilities in cognitive dysfunction and psychiatric disorders  and in cInterestingly, Van Hecke and colleagues  pointed Finally, we have to point out that the fHIT testing the VOR abilities confirms our hypothesis of poor cerebellar abilities in children with NDDs, even if imaging studies combined with eye movement recordings and quantitative vestibular tests will need to be done in such child populations to confirm our hypothesis.Taken together, all these findings underlined the importance of systematically introducing the assessment of vestibular abilities (even indirectly tested) in children with neurodevelopmental disorders together with other clinical examinations of sensory and motor capabilities ,51 in orFinally, we could suggest that vestibular/cerebellar rehabilitation could help children with NDDs to develop compensatory mechanisms in order to override/decrease their pathologies. This hypothesis will be tested in future studies.This study reported a “qualitative” VOR deficit in children with NDDs. Note, however, that some NDD children had a correct response similar to TD children. Consequently, further studies with video-HIT could be necessary to explore the “quantitative” VOR capabilities in a larger population of NDD children. In the future, we could test together dynamic visual acuity and gaze stabilization capabilities in children with NDD in order to better understand the relationship between visual and vestibular abilities in these children. Finally, it will be also interesting to explore, in a larger number of NDD children, the eventual correlations between the fHIT response and clinical evaluation in order to more precisely define biomarkers in NDDs.VOR abnormalities estimated using the fHIT could be used as a proxy for NDD impairments in children. Additional efforts will be required to better understand the intimate links between vestibular abnormalities and brain developmental disorders. Despite considerable efforts that have been made to identify biomarkers in NDDs, the results continue to be contrasting. The machine-assisted approach of neuroimaging/electrophysiological data may provide new directions to NDD research and leads to the identification of patterns of clinical, cognitive, and biological features, helpful for diagnosis and treatment. Qualitative-VOR abnormalities, which are easily measurable in patients with NDDs, could be integrated into these statistical models. In the future, we could explore the eventual benefits of vestibular rehabilitation in these kinds of patients.


We present a convenient way to obtain an optical power limiting behavior in a quantum dot molecule system, induced by an interdot tunneling. Also, the effect of system parameters on the limiting performance is investigated; interestingly, the tunneling rate can affect the limiting performance of the system so that the threshold of the limiting behavior can be a function of the input voltage, allowing the optimization of the limiting action. Furthermore, by investigating the absorption of the probe field, it is demonstrated that the optical limiting is due to a reverse saturable absorption mechanism; indeed, analytical results show that this mechanism is based on a cross-Kerr optical nonlinearity induced by the tunneling. Additionally, the limiting properties of the system are studied by using a Z-scan technique. Optical power limiting occurs when the absolute transmittance of a material decreases as the intensity of the input laser field increases beyond a critical value; indeed, an optical power limiter exhibits a high and linear transmission  has been a topic of great scientific and practical interest12. One of the mechanisms that may lead to a nonlinear absorption effect is reverse saturable absorption (RSA), also referred to as excited-state or two-photon absorption, in which the absorption cross section from excited-state energy levels is significantly higher than the ground-state absorption cross section13; in fact, RSA, being one of the major OL mechanism, has been reported for applications in efficient OLs20, with compounds such as organic and inorganic materials22, liquid crystal23, nanocomposites24, nanoparticles25 and thin film26.Due to the widespread use of intense lasers in various fields such as the scientific, industrial, medical therapy and military sectors, the power limiting becomes an important research branch in nonlinear optical processes; materials possessing large optical nonlinearities have been potentially employed in a number of applications in limiting devices and extensive theoretical and experimental investigations on power limiting, based on optical nonlinear processes, have been reported28. It is well-known that the transitions between different electronic states in QD nanostructures can be optically excited by laser fields. In this context, studying nonlinear optical process, including RSA and saturable absorption (SA) have been pursued by several researchers32; for example, the intensity-dependent nonlinear absorption and nonlinear refraction of QDs were experimentally investigated in Ref.29. When two or more closely-spaced individual QDs- with significant interdot coupling- are combined, a quantum dot molecule (QDM) is formed by the tunneling interaction among QDs, in which an electron can pass through the potential barrier between quantum dots via the interdot tunneling33. Such molecules are applicable volunteers for quantum coherence-based studies, because of ease of integration, controlling size and energy level spacing as well as their large bandwidth34. In this regard, it has been shown that the optical properties of such systems can depend on the interdot tunneling effect, which has been used for controlling optical bistability35, entanglement37, light propagation39 and orbital angular momentum transfer40. Notice that the QDMs cannot exhibit vibrational or rotational features and they indeed display somewhat different properties with respect to usual molecules.Quantum dots (QDs)-semiconductor nanostructures that confine the motion of conduction band electrons in all three spatial directions so that electrons and holes can occupy only the set of discrete energies-have attracted tremendous attention from scientists over the past four decades; because of the size-tunable atomic-like properties, along with their high nonlinear optical coefficients and great flexibilities in device design by choosing materials and structure dimensions, QDs have been intensively investigated for their unique structural and optical behaviors41, has been used for characterizing the nonlinear optical properties of materials 43. The Z-scan technique simply involves the measurement of the sign and magnitude of real and imaginary parts of nonlinear susceptibility, via investigating the intensity-dependent variation of optical properties of the sample. Briefly, in a typical Z-scan experimental setup, a laser beam with a transverse Gaussian profile is focused using a lens. The sample is then moving along the propagation direction of the beam, and the transmittance (transmitted energy divided by the input energy) of the sample is determined during its translation; at the focal point, the intensity of the laser beam increases substantially and the sample experiences maximum pump intensity, which gradually decreases in both directions from the focus. Interaction between such a laser beam and the nonlinear material may result in the increase or decrease of the transmittance. Noting that the nonlinear absorption is classified into two types: SA and RSA; in the SA mechanism, the nonlinear absorption decreases by increasing the intensity, leading to a peak in transmission near the focus. Whereas, the nonlinear absorption in the latter increases and therefore a dip in the vicinity of the focal point occurs.On the other hand, the Z-scan technique is a sensitive and simple method for the determination of intensity-dependent optical properties of materials, and therefore, can determine the limiting properties of systems; in fact, this technique, which was first introduced by Sheik-Bahae et al.In this paper, we present a convenient way to generate an optical power limiting in a four-level QDM system being induced by the interdot tunneling which can be simply controlled by applying a gate voltage. Also, by solving density-matrix and field-propagation equations, we theoretically investigate the effect of various parameters, including the detunings and the tunneling rate on the limiting effect; by choosing appropriate parameters, the system reveals good OL properties in a certain intensity region which can be attributed to a RSA mechanism. Also, it has been shown how the interdot tunneling can affect the optical limiting performance of the system and the threshold of the limiting behavior can be lowered by increasing the tunneling coupling; indeed, threshold would be a function of the input voltage, allowing the optimization of the OL behavior. In addition, this paper deals with further characterization of the optical power limiting, using a Z-scan technique. Such a controllable limiting behavior, associated to the high linear transmittance at low intensities and the low-threshold OL behavior, may make these molecules promising for OL device development.44. The average lateral size of each QD is almost 9 nm and the interdot barrier thickness is assumed to be few nanometers to occur significant interdot electron tunneling45. Noting that applying an external electric field along the molecular (coupling) axis via simple Schottky contacts can control the interdot coupling .Let us consider a couple of lateral self-assembled As/GaAs quantum dots with different band structures; the self-assembled lateral QDMs can be produced by the combination of molecular beam epitaxy and atomic layer precise in situ etching on GaAs(001) substrates, which can provide a low density about 48. Then, we assume that a coupling laser field of frequency 50.In this paper, we consider a four-level cascade configuration for QDM, which is shown in Fig. i = 2, 4 and j = 1, 3). Also, Using rotating-wave and dipole approximations, the Hamiltonian is written asV being as the fraction of the optical power guided in the QDs and the effective mode volume of the single QD, respectively. Noting that the probe transition coherence ; it means that the output intensity can be limited by the system. As mentioned previously, power limiting behavior can be found in RSA materials since they become more opaque on exposure to high input intensities. It is worth noting, however, that once RSA action has been started, it does not necessarily continue to all higher intensitiesFigure Figure Here, we are going to explain a physical interpretation of tunneling-induced OL on the basis of the RSA; Fig. entclass1pt{minimaNow, we proceed to study the effect of the interdot tunneling on the population distribution of levels in order to gain a simple physical mechanism for such limiting behavior via the population transfer and their redistribution among the energy levels. Figure entclass1pt{minimaThe Rabi frequency of the coupling field is another useful parameter for controlling the OL properties of the system. The variation of the transmittance as a function of the input intensity are shown in in Fig. It seems that the decay rate of states can affect OL properties of the system. We are going to study the effect of the decay rate of the levels on the limiting threshold and limiting amplitude of the system. Figure 53. Due to nonuniformity of QDs, laser fields experience various detunings during interacting with different QDs55. Then, the effect of size distribution on the OLs can be understood via Fig. It is worth mentioning that, we ignore the size distribution and indeed consider a uniform QD molecule ensemble. Production of an ensemble of QDs with highest possible uniformity has a major role in generation of the limiting behavior. Several techniques have been introduced to reduce the inhomogeneous distribution of dot sizez-direction is focused to a narrow waist by using lens. The sample is moved along this direction and the transmittance of the tightly focused laser beam(s) is measured as a function of the z position; the sample experiences the maximum intensity at the focus, which gradually decreases in either direction from the focal point. An increase in the transmittance at the focus is assigned to the RSA mechanism, while the presence of SA action indicates a decrease in the transmittance near the focal point. Here, we have performed a simulation using a Gaussian laser pulse, propagating along +z, with an intensity ofz , In previous figures, the OL properties in the system are determined by plotting the transmittance versus the input intensity. Here, we proceed to discuss the features of the optical power limiting through a Z-scan technique with a laser field at 1.36 z = 0), giving rise to a dip in transmission near the focus; while, an SA action indicates a peak with respect to the focus in its accompanying transmission curve. Here, we present the numerical results for the Z-scan transmittance for different input intensities, with the same parameters of Fig. As noted previously, in the case of a typical RSA, the sample would experience the maximum absorption in the vicinity of the focal point As/GaAs QDs with the radius of 9 nm and low intensity about In conclusion, a convenient way to generate the tunneling-induced optical power limiting behavior in a four-level QDM system has been proposed. Also, the effect of various parameters on the limiting effect has been investigated and it has been found that the system can reveal good OL properties in a certain intensity region. More interestingly, the threshold of the limiting behavior would be a function of the input voltage, allowing the optimization of the OL behavior; in fact, such a convenient control of the limiting action, along with the high linear transmittance at low intensities and the low-threshold OL behavior, may render these molecules promising for OL device development. Moreover, by investigating the absorption of the probe transition, it has been demonstrated that the tunneling-induced limiting can be attributed to the RSA mechanism; furthermore, analytical results show that the RSA is established via the cross-Kerr nonlinearity which is induced by the inerdot tunneling. Finally, a theoretical investigation of the Z-scan technique is presented to further characterization of the optical power limiting.


The past decade has seen a significant shift in the number of centers performing intracranial electroencephalography from subdural grids and strips to stereoelectroencephalography (SEEG). Unlike grid and strip insertion or other stereotactic procedures in which the cortical surface is visualized, SEEG involves insertion of an electrode through a bolt anchored into the skull. Due to the multidisciplinary nature of SEEG trajectory planning, it often is time-consuming and iterative. Computer-assisted planning improves time taken and efficacy of SEEG trajectory planning. This article provides an overview of the considerations, controversies, and practicalities of implementing an automated computer-assisted planning solution for SEEG planning. The planning of SEEG trajectories is a time-consuming process, and potential target structures are identified from the multidisciplinary evaluation of the clinical history, seizure semiology, scalp electroencephalography (EEG), and structural and functional imaging as well as neuropsychological and neuropsychiatric assessments. In clinical practice, once the target structures are defined (implantation strategy), surgeons follow several heuristics during SEEG trajectory planning Box 1SterIn order to automate SEEG planning, parameter ranges for each of these factors must be identified and applied in a systematic and hierarchical manner to find the optimal solution for all electrodes in the implantation and not on an individual electrode basis. To be prospectively integrated within the SEEG pathway, the image acquisitions and preprocessing should be standardized, performed in advance of the SEEG implantation, and automated as far as possible to maximize efficiency and consistency. The selection of the anatomic structures for sampling should be available for input in a user-friendly manner and the output of the algorithm be returned within a clinically acceptable timeframe that allows the neurosurgeon and multidisciplinary team to review and modify the trajectories as needed. Finally, a method for seamlessly transferring the plan to the operating room for implantation is required. An example of one such software that has been tailor-made for this purpose is EpiNav. This is an automated stereotactic trajectory planning platform developed at University College London and has been validated for use in SEEG, laser interstitial thermal therapy, and brain biopsy planning. EpiNav is now used prospectively to plan all SEEG cases at the National Hospital for Neurology and Neurosurgery  and has replaced manual SEEG trajectory planning since 2017. This article provides an overview of the considerations, controversies, and practicalities of implementing an automated computer-assisted planning solution for SEEG planning.,,The minimum requirements for performing computer-assisted planning for SEEG are a volumetric T1 magnetic resonance imaging (MRI) of the head, with and without gadolinium (Gad)-enhancement and an appropriate vascular image. The Gad-enhanced (T1+Gad) acquisition serves as the reference image and defines the planning coordinate space to which all other images are registered. The nonenhanced T1 sequence should allow differentiation between the gray and white matter, such as magnetization-prepared rapid acquisition with gradient-echo acquisitions, so that whole-brain parcellations can be derived.Trajectory distance and drilling angle metrics are measured from the external surface of the skull model. Scalp models are derived directly from the T1 MRI, whereas skull models require either pseudo–computed tomography (CT) to be generated from the T1 MRI or from a CT scan, which may have been acquired as part of previous investigations, such as positron emission tomography (PET) or single-photon emission CT (SPECT). The user is able to select entry and target points for automated planning based on the labeled structures provided by the whole-brain parcellation. The extent to which anatomic structures are subdivided, therefore, is crucial, because this defines the precision with which the computer-assisted plans conform to the required implantation strategy. In the example outlined in The greatest risk associated with SEEG trajectory planning is intracranial hemorrhage, and meta-analytic data of the published literature suggests one haemorrhage for every 29 patients implanted, with the most common type being intracerebral followed by subdural and extradural hemorrhages.,,The optimal vascular imaging modality for SEEG is still controversial.Due to the low incidence of hemorrhage during SEEG, it is unlikely a comparative study between vessel imaging modalities will be performed, because prohibitively large sample sizes would be required .15 Anoth,Another controversial topic is the diameter of vessels that are considered to be significant for SEEG. DSA allows vessels less than 1 mm in diameter to be reliably segmented, whereas MR-based modalities are reported to be between 2 mm and 4 mm in diameter.Based on the vascular imaging method provided, the automated planning software then must calculate an optimal trajectory accounting for the location and distance from the vasculature. This is undertaken through the calculation of the minimum distance (millimeters) from the vasculature at any point along the trajectory and the risk score, which is an approximation of the size of the avascular corridor along the entire trajectory. Several different risk scores have been applied27,Anatomic dissections of the brain reveal that vessels are present within sulci, even if they cannot be visualized with the vascular imaging method used, and data from deep brain stimulation (DBS) electrode implantation suggest that crossing such pial and ependymal boundaries increases the intracranial hemorrhage rates more than 10-fold.To date, only 2 studies have analyzed the effect of implementing sulcal models on computer-generated trajectories. In the first study,Seizures arise within the gray matter. It, therefore, is an essential requirement of efficient SEEG sampling that the maximum possible number of electrode contacts record from gray matter. The gray matter  can be extracted directly from the whole-brain parcellation by segmentation of all cortical labels. Computer-assisted planning algorithms, therefore, calculate the proportion of the electrode that lies within the gray matter model. With prior knowledge of the electrode specification from the manufacturer, the optimal electrode can be assigned automatically to each trajectory to maximize sampling efficiency, based on the active length and contact spacing. An example of this is shown in The skull is most reliably segmented from a plain CT scan of the head. In practice, this usually is already acquired as part of other investigations, such as PET, SPECT, and DSA imaging, and does not require additional radiation exposure. In cases where CT imaging is not available, a pseudoCT can be generated from a T1 MRI sequence. The skull model then can be can extracted from either the CT or pseudoCT image by thresholding. The skull model is used to measure the angle of electrode insertion through the skull because this determines the drilling angle. Drilling angles perpendicular to the bone are considered more accurate because they prevent the drill from sliding along the bone, a phenomenon known as skiving. As much as possible, drilling angles are limited to less than 30° to the orthogonal, although improved drill bit designs and robotic drilling platforms with increased stability likely overcome this limitation.The total intracerebral length of the electrode is minimized to provide the most direct trajectory to the target structures and also minimize cerebral transgression. In practice, this also prevents the algorithm from considering contralateral entry points and returning bilateral target sampling in cases of paramedian structures, such as the cingulate cortices or the supplementary motor areas. The intracerebral length is measured from the surface of the cortical model to the target point.In addition to vasculature, other critical structures that must be considered as part of automated SEEG trajectory planning include the orbit, face, skull base, posterior fossa, ears, ventricles, and basal ganglia. The skull or scalp models can be modified, either manually or by using a predefined mask, to remove the face, orbit, ears, posterior fossa, and contralateral hemisphere in cases of unilateral implantations see F and 4. Unlike DBS implantations, where usually only 1 electrode is implanted into a single hemisphere, SEEG typically requires consideration of multiple trajectories . To prevent electrode conflict within the brain or clashing of the bolts on the scalp surface, it is common for algorithms to implement a minimum distance constraint between the trajectories. Based on user preference, a distance of 10 mm usually is employed. This adds another layer of computational complexity, because the optimal electrode for a single target point, therefore, may not be the optimal solution for the entire implantation strategy. A slightly higher risk score for a single trajectory may therefore ultimately translate to a lower overall risk score for the entire implantation. A potential advantage of this is that if an electrode trajectory is then modified or added at a later time by the user, the remaining trajectories then can be automatically recalculated and optimized without the need to laboriously manually adjust each of the other electrodes.After SEEG implantation, patients usually have CT imaging to check for any immediate postimplantation complications, such as hemorrhage, and to segment the electrodes. An MRI scan also may be performed to provide accurate data regarding the anatomic regions that have been sampled, although the artifact from the electrode contacts may be significant. From the postoperative implantation data, automated systems are available to automatically segment the individual contacts and assign them to the corresponding electrode.There is a lack of uniformity in the published literature regarding how postoperative SEEG accuracy is measured and reported.,The first reported clinical use of computer-assisted planning software was to aid the calculation of frame-based coordinates for pediatric brain biopsy.,41The NeuroPlanner software allowed image coregistration, integration of multiple brain atlases, 3-D model generation, manual stereotactic trajectory planning, and simulation of therapeutic lesioning and stimulation,,,,Early studies reporting automated SEEG planning software incorporated many of the planning features that had been developed for single DBS planning. These systems additionally integrated 3-D multimodal imaging that allowed coregistration of different imaging modalities, such as fluid-attenuated inversion recovery, PET, and SPECT to aid the inference of the epileptogenic zone.Unlike DBS procedures, where the target points are stereotyped, in SEEG the target points vary significantly between electrodes and between patients. If the manually selected target points were close to a blood vessel or critical structure, the number of potential trajectory options subsequently was restricted.,,The next level of complexity introduced to automated SEEG trajectory planning was the consideration of multiple trajectoriesA critical additional feature of automated trajectory planning is that the user then should be able to review the individual trajectories and modify these as required. Although manually changing the proposed entry or target point is possible, a more efficient method is to iterate through the automated trajectories in a risk-stratified manner based on the planning preference of the surgeon. In this case, a Next Entry or Next Target function allows the user to iterate through the computer-generated trajectory options until satisfied with the electrode trajectory. This involves the computation of several different putative entry and target points within the region of interest that the surgeon can evaluate. Given that these Next trajectories are derived using the multitrajectory planning algorithm, they are cognizant of other trajectories in the plan. A clinical validation study utilizing this function also was used to determine the external feasibility of the algorithm in an additional 13 consecutive patients (116 electrodes).To date, only a single study has reported on the prospective integration and validation of such software within a clinical workflow.A major limitation of computer-assisted planning algorithms is that they are dependent on the quality of the imaging provided and user-defined planning parameters and reflect the planning practices at the institution from which they are developed. This significantly limits the generalizability of the software and its applicability to other SEEG programs. One potential method to overcome this is the use of machine learning to modify the planning parameters and practices to that of the individual user. One such example is the use of spatial priors. The current computer-assisted planning systems are all, to some extent, restricted by the whole-brain parcellation and the subsegmentation of individual structures. If the hippocampus, for example, is not subdivided into head, body, and tail, the user, therefore, cannot choose from these options when specifying the trajectory targets. It is common practice to place SEEG electrodes within both the hippocampal head and body during an investigation of a patient with presumed temporal lobe–onset seizures. Furthermore, it is preferred to place the targets within the medial aspect of the structures to ensure as many contacts are within the target structure when approaching from a lateral neocortical entry zone. The experience gained from previous manual implantations, therefore, can be used to inform the preferred entry and target regions of common trajectories and be used prospectively to guide future implantations. It has been proposed, therefore, that the whole-brain parcellation initially acts as default, and machine learning algorithms then are able to modify the regions based on the surgeons planning preference. Recent preliminary data also have suggested that it may be possible to cluster implantation types into a discrete number set of implantation subtypes.Given sufficient data, it also has been shown that machine learning techniques can be applied to electrode implantations and accurately predict the degree of electrode bending within the brain. Given that current automated planning systems are based on linear trajectories, knowledge of the potential bending may allow for compensatory corrections to be incorporated at the planning stage.The ultimate goal of SEEG is to identify the seizure-onset zone, map the ictal and interictal networks, and identify eloquent functions in order to guide tailored resections. Understanding the exact anatomic location of the electrode contacts within the brain, therefore, is paramount when proposing resective surgery from SEEG data. Future iterations of computer-assisted planning software will integrate SEEG data with the 3-D models and attempt to solve the inverse solutions from the ictal and interictal electrophysiologic patterns. With the integration of structural and functional connectivity data, computer-assisted algorithms then may be able to propose potential resection or ablation volumes.Computer-assisted SEEG trajectory planning has advanced significantly in the past decade due to increased computing power and collaboration between clinicians and engineers. The rise in the number of commercially available robotic trajectory guidance systems will aid in the translation and implementation of computer-assisted planning. Together with robotic devices, the hope is that computer-assisted planning will be integrated into clinical workflows to standardize SEEG trajectories and overcome early learning curves associated with this technique.